Global Positioning System Research Articles

Addressing out-of-hospital cardiac arrest with current technology advances: Breaking the deadlock with a mobile network.

Out-of-hospital cardiac arrest (OHCA) is a global public health problem, with survival rates remaining low at around 10% or less despite widespread cardiopulmonary resuscitation (CPR) training and availability of automated external defibrillators (AEDs). This is partly due to the challenges of knowing when and where a sudden OHCA occurs and where the nearest AED is located. In response, countries around the world have begun to use network technology-based smartphone applications. These applications are activated by emergency medical service dispatchers and alert preregistered volunteer first responders (VFRs) to nearby OHCAs using Global Positioning System localization. Accumulating evidence, although mostly from observational studies, shows their effectiveness in increasing the rate of bystander CPR, defibrillation, and patient survival. Current guidelines recommend the use of these VFR alerting systems, and the results of ongoing randomized trials are awaited for further dissemination. This article also proposed the concept of a life-saving mobile network (LMN), which uses opportunistic network and wireless sensor network technologies to create a dynamic mesh network of potential victims, rescuers, and defibrillators. The LMN works by detecting a fatal arrhythmia with a wearable sensor device, localizing the victim and the nearest AED with nearby smartphones, and notifying VFRs through peer-to-peer communication. While there are challenges and limitations to implementing the LMN in society, this innovative network technology would reduce the tragedy of sudden cardiac death from OHCA.

Assessing the Predictive Power of GPS-Based Ground Deformation Data for Aftershock Forecasting

Abstract We present a machine learning approach for aftershock forecasting of the Japanese earthquakes catalog. Our method takes as sole input the ground surface deformation as measured by Global Positioning System (GPS) stations on the day of the mainshock to predict aftershock location. The quality of data heavily relies on the density of GPS stations: the predictive power is lost when the mainshocks occur far from measurement stations, as in offshore regions. Despite this fact and the small number of samples and the large number of parameters, we are able to limit overfitting, which shows that this new approach is very promising.

Present‐Day Three‐Dimensional Crustal Deformation Velocity of the Tibetan Plateau Due to Multi‐Component Land Water Loading

AbstractQuantitative understanding of the land water loading is a prerequisite to the construction of reliable tectonic deformation velocity field in the Tibetan Plateau (TP). Here, for the first time, we image the three‐dimensional crustal loading deformation velocity field of each land water component in the TP. Our results reveal that the loading signal strength of the six land water components ranks from largest to smallest as groundwater, glacier, lake, soil water, permafrost, and snow, with the maximum vertical velocity close to ±1.60 mm/yr and the maximum horizontal velocity exceeding 0.40 mm/yr. All land water components can achieve a strong enough vertical loading velocity exceeding the present‐day Global Positioning System (GPS) velocity at some sites. But for horizontal loading, apparent impacts are only from glacier, lake and groundwater, however, are very limited, with the absolute ratio of loading velocity to GPS velocity being smaller than 5% at almost all the sites.

More Than Aging in Place: “Aging in Networks” in Singapore

Aging in place has become a popularly accepted logic and practice in both the academic and policy domains. Older adults’ preferred facilities and social networks, however, might not always be situated in the residential neighborhoods where their homes are located. While recognizing the value of aging in place, this article argues that geographical proximity should not be conflated with aging well, nor should a spatially delimited place be the only locus of “community.” We propose the concept of aging in networks to capture a range of social networks in the life-worlds of older adults, including those that extend beyond their neighborhoods. We combine qualitative and geographic information systems (GIS) methods (i.e., qualitative GIS) to analyze three types of activity spaces observed among older adults in Singapore. Our analysis elicits the reasons why older adults use facilities and cultivate social networks both within and beyond their neighborhoods. This approach highlights the importance of studying and supporting both the local and extralocal ties of older adults. The article draws on qualitative GIS research (interviews and Global Positioning System tracking) with fifty older adults in two neighborhoods in Singapore (from a wider survey with 1,199 older adults).

Microstrip GPS Circular Antenna for Terrestrial Transportation Systems

In this paper, the design, implementation, and evaluation of a microstrip circular antenna is presented and analyzed. This antenna has two resonant frequencies, which are used in the Global Positioning System (GPS). The antenna was built in a low-cost Printed Circuit Board (PCB) with a FR-4 substrate. The top layer of the PCB includes two slots in the main circular patch, which normally is used in a single carrier reception. The radiation pattern of the antenna shows a wide main lobe in the vertical axis, intending to obtain line-of-sight communication links with five or more GPS satellites.

Monitoring Within-Individual Dose-Response Relationships in Professional Soccer Players: The Importance of Fitness Level.

To (1)examine within-individual player dose-response associations between selected training-load measures and changes in aerobic fitness level via submaximal exercise heart rate (HRex%) and (2)measure the relationships between these dose-response associations with basal HRex% (to study the influence of fitness level on dose-response relationship). During an in-season phase, selected training-load measures including total minutes, total distance, mechanical work (the sum number of accelerations and decelerations > 3m2), high metabolic load distance, and Edwards' training impulse were collected via Global Positioning System and heart-rate sensors for analyzing accumulated load. A submaximal warm-up test was used repeatedly before and after 9 phases to elicit HRex% and track fitness changes at an individual level. Negative to positive extensive ranges of within-individual associations were found among players for different metrics (r = -.84 to .89). The relationship between pooled HRex% (basal fitness) and dose-response correlations showed inverse very large (r = -.71) and large (r = -.65) values for accumulated weekly minutes and distance. However, moderate values were found for all other measures (r = -.35 to -.42). Individual players show extensive different ranges of dose-response associations with training measures. The dose-response association is influenced by players' fitness level, and players with lower fitness levels show stronger inverse relationships with accumulated minutes and total distance.

Pole‐To‐Pole Ionospheric Disturbances Due To Solar Flares, During Low Solar Activity

AbstractThere are growing concerns about the effect of solar flares on the ionosphere, mainly due to possible deterioration or damage to our communication and navigation satellite systems. On 3 July 2021, and 28 October 2021, there were solar flares (SFs) classified as X1.59 and X1.0, respectively. These two SFs were the only ones of X‐class that occurred during the last low solar activity (LSA:2018–2021). Data from magnetometers and Global Positioning System (GPS)—Total Electron Content (TEC) are used to investigate the spatial‐temporal electrodynamics of the ionosphere from pole‐to‐pole in the American sector. Employing ∆H and vertical TEC, along with the ROT (rate of change of VTEC) parameter. Rapidly ∆H disturbances closely follow the X‐ray variation and the ∆H valleys and peaks are well‐synchronized during the SFs, indicating that they are linked. Major disturbances in the ∆H are noticed in the mid‐low‐equatorial latitudes. However, minor disturbances were seen at high latitudes. Also, |ROT| is a good indicator of the electron density changes during the SFs, especially when the X‐ray intensity rises to the peak.

Interactions between MSTIDs and Ionospheric Irregularities in the Equatorial Region Observed on 13–14 May 2013

We investigate the interactions between medium-scale traveling ionospheric disturbances (MSTIDs) and the equatorial ionization anomaly (EIA) as well as between MSTIDs and equatorial plasma bubbles (EPBs) on the night of 13–14 May 2013, based on observations from multiple instruments (an all-sky imager, digisonde, and global positioning system (GPS)). Two dark bands (the low plasma density region) for the MSTIDs were observed moving toward each other, encountering and interacting with the EIA, and subsequently interacting again with the EIA before eventually dissipating. Then, a new dark band of MSTIDs moved in the southwest direction, drifted into the all-sky imager’s field of view (FOV), and interacted with the EIA. Following this interaction, a new dark band split off from the original dark band, slowly moved in the northeast direction, and eventually faded away in a short time. Subsequently, the original southwestward-propagating dark band of the MSTIDs encountered eastward-moving EPBs, leading to an interaction between the MSTIDs and the EPBs. Then, the dark band of the MSTIDs faded away, while the EPBs grew larger with a pronounced westward tilt. The results from various observational instruments indicate the pivotal role played by the high-density region of the EIA in the occurrence of various interaction processes. In addition, this study also revealed that MSTIDs propagating into the equatorial region can significantly impact the morphology and evolution characteristics of EPBs.

Performance evaluation of multiclass classification models for ToN-IoT network device datasets

Internet of things (IoT) technology has empowered tangible objects to establish internet connections, facilitating data exchange with computational capabilities. With significant potential across sectors like healthcare, environmental monitoring, and industrial control, IoT represents a promising technological advancement. This study explores datasets from ToN-IoT’s IoT devices, focusing on multi-class classification, including normal and attack classes, with an additional aim of identifying potential attack sub-classes. Datasets comprise various IoT devices, such as refrigerators, garage doors, global positioning systems (GPS) sensors, motion lights, modbus devices, thermostats, and weather sensors. Comparative analysis is conducted between two prominent multiclass classification models, extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM), utilizing accuracy and computational time metrics as evaluation criteria. Research findings highlight that the LightGBM model achieves superior accuracy at 78%, surpassing XGBoost 74.31%. However, XGBoost demonstrates an advantage with a shorter computational time of 1.23 seconds, compared to LightGBM 6.79 seconds. This study not only provides insights into multiclass classification model selection but also underscores the crucial consideration of the trade-off between accuracy and computational efficiency in decision-making. Research contributes to advancing our understanding of IoT security through effective classification methodologies. The findings offer valuable information for researchers and practitioners, emphasizing the nuanced decisions needed when selecting models based on specific priorities like accuracy and computational efficiency.

Hit and run: Forensic vehicle event reconstruction through driver-based cloud data from Progressive's snapshot application

Driving Insurance Applications (DIAs) have emerged as a valuable resource in the ever-evolving digital landscape. Automobile owners are storing extensive data on driving behaviors and patterns. This study pioneers the forensic analysis of Progressive's Snapshot application, focusing on the extraction and potential forensic use of data that remains inaccessible through the mobile application's interface. In our approach we focused on four research questions: How accurate is location and speed data collected by Progressive Snapshot?, What forensically relevant data can we extract from the Progressive Cloud that is unavailable to the user from the mobile application interface?, Can we employ anti-forensics techniques, specifically fake location data, to create false trip details?, Can we reconstruct a hit-and-run scenario from trip event details? To answer these questions, we developed PyShot, a Python-based open-source tool, to extract data from the Progressive cloud. Our tests confirmed Snapshot's accuracy in recording speed and location. Despite efforts to fake the Global Positioning System (GPS) location, the cloud still maintained accurate records. PyShot revealed more detailed driving data, like dangerous maneuvers and distracted driving, compared to the mobile application. This study also explores the forensic reconstruction of hit-and-run incidents, using a mannequin and focusing on Progressive's server data. Analyzing event categories, geographical coordinates, and timestamps provides insights into the capabilities and constraints of this application in forensic investigations. The findings offer valuable insights into the forensic capability of data retained by DIAs, contributing to their potential use in forensic investigations.

The impact of air pollution on the facial skin of Caucasian women using real-life pollutant exposure measurements.

To date, studies examining the effect of air pollution on skin characteristics have relied on regional pollution estimates obtained from fixed monitoring sites. Hence, there remains a need to characterize the impact of air pollution in vivo in real-time conditions. We conducted an initial investigation under real-life conditions, with the purpose of characterizing the in vivo impact of various pollutants on the facial skin condition of women living in Paris over a 6-month period. A smartphone application linked to the Breezometer platform was used to collect participants' individual exposures to pollutants through the recovery of global positioning system (GPS) data over a 6-month period. Daily exposure to fine particulate matter (PM 2.5µm and PM 10µm), pollen, and air quality was measured. Facial skin color, roughness, pore, hydration, elasticity, and wrinkle measurements were taken at the end of the 6-month period. Participants' cumulated pollutant exposure over 6 months was calculated. Data were stratified into two groups (lower vs. higher pollutant exposure) for each pollutant. 156 women (20-60 years-old) were recruited, with 124 women completing the study. Higher PM 2.5µm exposure was associated with altered skin color and increased roughness under the eye. Higher PM 10µm exposure with increased wrinkles and roughness under the eye, increased pore appearance, and decreased skin hydration. Exposure to poorer air quality was linked with increased forehead wrinkles and decreased skin elasticity, while higher pollen exposure increased skin roughness and crow's feet. This study suggests a potential correlation between air pollution and facial skin in real-life conditions. Prolonged exposure to PM, gases, and pollen may be linked to clinical signs of skin ageing. This study highlights the importance of longer monitoring over time in real conditions to characterize the effect of pollution on the skin.

Predicting Injuries in Elite Female Football Players With Global-Positioning-System and Multiomics Data.

Injury prevention is a crucial aspect of sports, particularly in high-performance settings such as elite female football. This study aimed to develop an injury prediction model that incorporates clinical, Global-Positioning-System (GPS), and multiomics (genomics and metabolomics) data to better understand the factors associated with injury in elite female football players. We designed a prospective cohort study over 2 seasons (2019-20 and 2021-22) of noncontact injuries in 24 elite female players in the Spanish Premiership competition. We used GPS data to determine external workload, genomic data to capture genetic susceptibility, and metabolomic data to measure internal workload. Forty noncontact injuries were recorded, the most frequent of which were muscle (63%) and ligament (20%) injuries. The baseline risk model included fat mass and the random effect of the player. Six genetic polymorphisms located at the DCN, ADAMTS5, ESRRB, VEGFA, and MMP1 genes were associated with injuries after adjusting for player load (P < .05). The genetic score created with these 6 variants determined groups of players with different profile risks (P = 3.1 × 10-4). Three metabolites (alanine, serotonin, and 5-hydroxy-tryptophan) correlated with injuries. The model comprising baseline variables, genetic score, and player load showed the best prediction capacity (C-index: .74). Our model could allow efficient, personalized interventions based on an athlete's vulnerability. However, we emphasize the necessity for further research in female athletes with an emphasis on validation studies involving other teams and individuals. By expanding the scope of our research and incorporating diverse populations, we can bolster the generalizability and robustness of our proposed model.

Research on cycling power estimation and prototype of a GPS bike computer based on system-on-chip platform

A GPS (Global Positioning System) bike computer is a specialized electronic device that allows for recording the traveled trajectory and training parameters of cycling activities. Current GPS bike computer devices on the market often do not provide cycling power parameter to support the riders. This paper presents a method to efficiently estimate cycling power based on the ground speed and altitude variation of the bike. The suggested method can be applied in the design and implementation of bike computers on embedded systems. The paper also presents the design and implementation of a GPS bike computer prototype based on a System-on-Chip platform.

Geospatial Assessment of Impacts of Sand Mining Activities in Zanzibar

This study assessed the impacts of sand mining activities in Zanzibar by using geospatial technology, focusing on three villages in the North 'B' district of Unguja: Donge-Muwanda, Mchangani, and Misufini. Geospatial technology was employed, utilizing two distinct datasets to analyse the spatial extent of environmental degradation due to sand mining. The first dataset comprised of geographic coordinates of sampled mining sites, acquired through field survey by using the Global Positioning System (GPS). The second dataset involved satellite imagery obtained from the Google Earth application. These datasets were utilized to find the area, volume, and depth of mining sites and create three-dimensional (3D) models for each sand mining site by using ArcMap software and Surfer 19 software. The Google Earth images from 2020, 2021, 2022, and 2023 were utilized to determine changes in vegetation cover before and after the mining activities in the study area. The key finding of the study indicates that there is a high extent of environmental degradation caused by intensive sand mining activities, impacting both the community and environment in surrounding sand mining areas. The study suggests that the results should be used to emphasize the importance of existing policies, guidelines, and laws related to sand mining.

Pengembangan Aplikasi Pencarian Lokasi Pabrik Tahu di Kota Depok Berbasis Android Menggunakan Metode Location Based Service (LBS) dengan Android Studio

In this modern era, technology is advancing rapidly, allowing for the search of tofu factories using smartphones. This study aims to develop an application to locate tofu factories in Depok City utilizing GIS (Geographic Information System) technology and GPS (Global Positioning System) integrated with the Android Google Maps platform. The application is designed to facilitate the public in finding the nearest tofu factory locations. This Android-based tofu factory search application employs Location Based Service (LBS) methods to provide real-time location information. The primary objective of this research is to simplify the process for the community in Depok to find tofu factories, thereby improving efficiency and saving time. Additionally, this application is expected to assist tofu factory owners in attracting more customers by providing accurate and accessible location information. The methodology includes literature review, direct observation, and application development using Java programming language in Android Studio. The results show that the application significantly simplifies the process for the community in Depok to find tofu factories, improving efficiency and saving time. Furthermore, the application is expected to assist tofu factory owners in attracting more customers by providing accurate and accessible location information

Assessment of Roof Tilt and Building Azimuth for Off-Grid Photovoltaic Power for Buildings in Metropolitan Lagos, Nigeria

The study determined optimum roof tilt and building azimuth for rooftop-based distributed photovoltaic (PV) energy generation in Ikeja, Lagos State, Nigeria as strategic technological input to alternative power generation and new housing development in Metropolitan Lagos. The energy planning &amp; foresight analysis methodology was used. The data were obtained via literature, Global Positioning System (GPS) systems, energy models and site visits; and comprised manual and satellite imagery, dimensions, roof angles and coordinates of the building structures in the study area (Rows between 0 – 60o, Columns between 0 – 180o; System sizes: 533 kW, 110 kW and 0.547 kW). The results showed solar array yield varied from 768,944 kWh per annum for a 500 kVA system to 780 kWh per annum for a 500 VA system. A five tier rooftop generation template of 245 MWp was consequently developed. Optimal roof tilt and building azimuth angles for the systems were estimated to be 5o and 180o respectively though combinations of azimuth and tilt angles not exceeding 30o tilt and 90o azimuth gave acceptable yields. The study concluded that the optimum roof tilt and building azimuth for rooftop-based distributed photovoltaic (PV) energy generation in the study area were strategic policy intelligence inputs for the renewable power generation and new housing development programmes of the Federal and Lagos State Governments.

Analysis of Availability and Size Distribution of Giant Shrimp (Macrobranchium sp) in River Togafo, Ternate City, North Mollucas Province

Giant prawns (Macrobrachium sp) are one of the freshwater aquaculture commodities that have high economic value (Murtidjo, 1992). The demand for giant prawns in Indonesia has only been met by 40% of all existing demand (Tambunan, 2009. The aim of the research was to analyze the abundance of giant prawns and the size distribution of giant prawns in Togafo Village, West Ternate District, Ternate City. The purposive sampling method was used in this research. Then the sampling points that had been determined using purposive sampling were marked with coordinates with the help of the Global Positioning System (GPS). For the research location, it was divided into 3 observation stations. First, the shrimp body length and weight were measured using calipers and analytical scales. Abundance relative = individual/trip and Distribution of length measurements and distribution of shrimp weight measurements can be grouped using the equation (Effendie, 1979) as follows: K = 1 + 3.3 log n. Note: K = Number of Interval Classes, n = Number of Observation Data, log = Logarithm. Based on the results, the highest abundance of giant prawns (Macrobrachium sp) is at station 3 with a value of 0.575 ind., station 1 is 0.336, the lowest is at station 2 with a value of 0.088 ind. Size Distribution The smallest ranges between 40.17-52.19 mm and the largest ranges between 105.25 – 117.27 mm.

An Adaptive Fast Incremental Smoothing Approach to INS/GPS/VO Factor Graph Inference

In response to asynchronous and delayed sensors within multi-sensor integrated navigation systems, the computational complexity of joint optimization navigation solutions persistently rises. This paper introduces an adaptive fast integrated navigation algorithm for INS/GPS/VO based on factor graph. The factor graph model for INS/GPS/VO is developed subsequent to individual modeling of the Inertial Navigation System (INS), Global Positioning System (GPS), and Visual Odometer (VO) using the factor graph model approach. Additionally, an Adaptive Fast Incremental Smoothing (AFIS) factor graph optimization algorithm is proposed. The simulation results demonstrate that the factor-graph-based integrated navigation algorithm consistently yields high-precision navigation outcomes even amidst dynamic changes in sensor validity and the presence of asynchronous and delayed sensor measurements. Notably, the AFIS factor graph optimization algorithm significantly enhances real-time performance compared to traditional Incremental Smoothing (IF) algorithms, while maintaining comparable real-time accuracy.

Snowmelt Onset and Caribou (Rangifer tarandus) Spring Migration

Caribou (Rangifer tarandus) undergo exceptionally large, annual synchronized migrations of thousands of kilometers, triggered by their shared environmental stimuli. The proximate triggers of those migrations remain mysterious, though snow characteristics play an important role due to their influence on the mechanics of locomotion. We investigate whether the snow melt–refreeze status relates to caribou movement, using previously collected Global Positioning System (GPS) caribou collar data. We analyzed 117 individual female caribou with &gt;30,000 observations between 2007 and 2016 from the Bathurst herd in Northern Canada. We used a hierarchical model to estimate the beginning, duration, and end of spring migration and compared these statistics against snow pack melt characteristics derived from 37 GHz vertically polarized (37V GHz) Calibrated Enhanced-Resolution Brightness Temperatures (CETB) at 3.125 km resolution. The timing of migration for Bathurst caribou generally tracked the snowmelt onset. The start of migration was closely linked to the main melt onset in the wintering areas, occurring on average 2.6 days later (range −1.9 to 8.4, se 0.28, n = 10). The weighted linear regression was also highly significant (p-value = 0.002, R2=0.717). The relationship between migration arrival times and the main melt onset on the calving grounds (R2 = 0.688, p-value = 0.003), however, had a considerably more variable lag (mean 13.3 d, se 0.67, range 3.1–20.4). No migrations ended before the main melt onset at the calving grounds. Thawing conditions may provide a trigger for migration or favorable conditions that increase animal mobility, and suggest that the snow properties are more important than snow presence. Further work is needed to understand how widespread this is and why there is such a relationship.

Digitalization in Indonesia’s Agrifood Sector in the Wake of the COVID-19 Pandemic

COVID-19 has disrupted Indonesia’s agricultural food supply chain, leading to the massive mintage and exertion of digitalization in the food and agriculture (agrifood) sector. This study systematically mapped the landscape of agrifood digital technology studies and startups in Indonesia and its relation to the COVID-19 pandemic. A systematic evidence evaluation was harnessed for this study to obtain data, which were translated into thematic and interactive maps. The study shows that COVID-19 has hampered some agrifood activities but has positively accelerated the development of digital technologies in the sector. The Government of Indonesia has issued national initiatives and policies that support implementing digital technologies in the agrifood sector. The digital technologies studied and utilized in Indonesia’s agrifood industry are websites, the Internet of Things (IoT), global positioning system (GPS) and geographic information system (GIS) technology, artificial intelligence (AI), big data, and robotics. About 22.8 percent of the reviewed literature discusses the ripple effects of COVID-19 on the digitalized agrifood sector in the country. Most startups are in the form of farmers’ advisory, mechanization platforms, digital marketplace, e-commerce, traceability, food delivery, and peer-to-peer lending. Both the studies and applications are primarily concentrated on Java island and have benefited from digital technologies, such as IoT, blockchain, AI, smartphone or Android, mobile apps, GPS/GIS, and drones. Startup companies have applied strategic measures to cope with the implications of the pandemic, such as suspending some of their activities.