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The Internet's widespread adoption and recent technological developments have provided researchers with the opportunity to capture new data types (e.g., images, passively collected data), which offer several advantages over conventional survey data. Nevertheless, the limited participation rate in projects requiring to share such data may limit their benefits, and even their feasibility. To estimate the willingness to participate in such projects in advance, researchers frequently rely on surveys. However, the way researchers describe what data should be shared and how, and the phrasing of questions asked to participants to obtain their agreement may result in different estimates. This study presents the results of a conjoint experiment that estimates the hypothetical willingness of participants to engage in two research activities that require sharing geolocation data. Three different descriptions of the activities were presented to participants: a neutral one, one emphasizing the necessity to commit to participate, and one emphasizing safe handling of shared data. The three descriptions elicited different levels of willingness, with a maximum effect of 7.0 percentage points (statistically significant). Additionally, the importance given by participants to the attributes of the activities (e.g., project duration or incentives) differed depending on the description. Notably, the description that emphasized data safety produced lower levels of willingness compared to the neutral one. This suggests that researchers should avoid overly emphasizing safety assurances when requesting the sharing of sensitive data, beyond the necessary information required for obtaining informed consent from participants. [ABSTRACT FROM AUTHOR]

• Understanding plastic litter in cities is crucial to preventing marine pollution. • Citizen science is suitable for long-term, extensive data collection. • Despite human error, ∼30 % of data could be used to visualize and quantify litter. • Litter in cities was studied using Pirika and deep learning-based image processing. • Trained models exhibited high precision and recall in classifying litter types. Cities are a major source of litter pollution. Determination of the abundance and composition of plastic litter in cities is imperative for effective pollution management, environmental protection, and sustainable urban development. Therefore, here, a multidisciplinary approach to quantify and classify the abundance of litter in urban environments is proposed. In the present study, litter data collection was integrated via the Pirika smartphone application and conducted image analysis based on deep learning. Pirika was launched in May 2018 and, to date, has collected approximately one million images. Visual classification revealed that the most common types of litter were cans, plastic bags, plastic bottles, cigarette butts, cigarette boxes, and sanitary masks, in that order. The top six categories accounted for approximately 80 % of the total, whereas the top three categories accounted for more than 60 % of the total imaged litter. A deep-learning image processing algorithm was developed to automatically identify the top six litter categories. Both precision and recall derived from the model were higher than 75 %, enabling proper litter categorization. The quantity of litter derived from automated image processing was also plotted on a map using location data acquired concurrently with the images by the smartphone application. Conclusively, this study demonstrates that citizen science supported by smartphone applications and deep learning-based image processing can enable the visualization, quantification, and characterization of street litter in cities. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Various methods such as k-anonymity and differential privacy have been proposed to safeguard users' private information in the publication of location service data. However, these typically employ a rigid "all-or-nothing" privacy standard that fails to accommodate users' more nuanced and multi-level privacy-related needs. Data is irrecoverable once anonymized, leading to a permanent reduction in location data quality, in turn significantly diminishing data utility. In the paper, a novel, bidirectional and multi-layered location privacy protection method based on attribute encryption is proposed. This method offers layered, reversible, and fine-grained privacy safeguards. A hierarchical privacy protection scheme incorporates various layers of dummy information, using an access structure tree to encrypt identifiers for these dummies. Multi-level location privacy protection is achieved after adding varying amounts of dummy information at different hierarchical levels N. This allows for precise control over the de-anonymization process, where users may adjust the granularity of anonymized data based on their own trust levels for multi-level location privacy protection. This method includes an access policy which functions via an attribute encryption-based access control system, generating decryption keys for data identifiers according to user attributes, facilitating a reversible transformation between data anonymity and de-anonymity. The complexities associated with key generation, distribution, and management are thus markedly reduced. Experimental comparisons with existing methods demonstrate that the proposed method effectively balances service quality and location privacy, providing users with multi-level and reversible privacy protection services. [ABSTRACT FROM AUTHOR]

Misidentification and misreporting of hate crimes by victims and law enforcement are significant barriers to accurate data collection of hate crimes, and their consequent study and prevention. The use of machine learning in crime detection can improve the accuracy and speed at which reported incidents with bias elements are identified. This study develops a machine learning classifier that categorizes police reports as either events with bias elements or events with no bias elements. We use incident/offense reports from the Seattle Police Department to train a Natural Language Processing classification algorithm. We collect narratives, location data, and victim and suspect demographics to use as features. We evaluate the performance of logistic regression, random forest, and XGBoost algorithms, as well as several text embedding techniques. Despite substantial class imbalance, our model achieves a macro F1-score of 0.79, demonstrating the benefits of applied machine learning in accurately detecting and reporting hate crimes. [ABSTRACT FROM AUTHOR]

This article discusses a study on the breeding and movement patterns of the Hawaiian Short-Eared Owl, also known as pueo. The study used GPS tracking devices to monitor the movements of a tagged pueo and found that it nested in two different locations during the breeding season. The research provides valuable information for future conservation efforts and highlights the need for further studies to better understand the pueo's ecology and conservation needs. The study also revealed that the pueo spent most of its time in wooded areas during the day and agricultural fields and urban areas at night. [Extracted from the article]

The European Climate Law sets a legally binding target of net zero greenhouse gas emissions by 2050. In this regard, in 2023 the EU Environmental Council decided that from 2035 onwards only CO2‐neutral new cars will be licensed in the European Union. Simultaneously, since the energy crisis caused by the Russian invasion of Ukraine, fuel prices increased in Germany. This has made more and more households think about purchasing an e‐car. However, not every household has the opportunity to install their own wallbox, and when travelling long distances, e‐car users are dependent on the availability of ample public charging stations. Against this background, with a special focus on rural areas, this paper considers the questions whether and where spatial inequalities in the accessibility of public charging stations might currently exist. To approach these questions, based on an empirical analysis of the 2023 official location data of public charging points in Germany, we examine the nationwide distribution, accessibility and available capacity of public charging stations in Germany at small‐scale from the point of view of the 'household' as well as those who travel by e‐car within Germany. In order to evaluate the location data of public charging stations we mainly use raster‐based accessibility analysis methods as well as isotropic kernel intensity estimates. We found that, in contrast to the common belief, the network of public charging stations is quite dense. However, taking a closer look reveals that the charging infrastructure in Germany has so far been tailored to the needs of intra‐regional travelers (commuting, shopping, leisure) rather than to those of the residential population with no access to their own wallbox (daily recharging in close proximity to one's home) or the mid‐ to long‐distance travelers. [ABSTRACT FROM AUTHOR]

Background: Situational awareness is an essential component of wildland firefighter safety. In the US, crew lookouts provide situational awareness by proxy from ground-level locations with visibility of both fire and crew members. Aims: To use machine learning to predict potential lookout locations based on incident data, mapped visibility, topography, vegetation, and roads. Methods: Lidar-derived topographic and fuel structural variables were used to generate maps of visibility across 30 study areas that possessed lookout location data. Visibility at multiple viewing distances, distance to roads, topographic position index, canopy height, and canopy cover served as predictors in presence-only maximum entropy modelling to predict lookout suitability based on 66 known lookout locations from recent fires. Key results and conclusions: The model yielded a receiver-operating characteristic area under the curve of 0.929 with 67% of lookouts correctly identified by the model using a 0.5 probability threshold. Spatially explicit model prediction resulted in a map of the probability a location would be suitable for a lookout; when combined with a map of dominant view direction these tools could provide meaningful support to fire crews. Implications: This approach could be applied to produce maps summarising potential lookout suitability and dominant view direction across wildland environments for use in pre-fire planning. We use machine learning to predict the probability an area is suitable for a wildland firefighter lookout based on incident data, roads, and lidar-derived visibility, terrain, and vegetation information. This approach may aid pre-fire planning and enhance situational awareness by providing maps of potential lookout locations. [ABSTRACT FROM AUTHOR]

Due to the scarcity of subnational interregional input–output (IRIO), various approaches to their estimation are actively under investigation in the literature. This paper focuses on the application of spatial econometric method. It determines intra- and interregional coefficients through a joint procedure which successfully avoids the direct recycling of estimates for other geographies and granularities. Instead, the use of Bayesian methods is proposed, which formally integrate limited evidence from existing regional tables (Finland) with a set of sectoral data on value added for 73 NUTS-3 regions in Poland, the latter being dominant. An empirical test of replicating the Korean survey-based IRIO table demonstrates that the accuracy of this approach slightly outperforms an alternative IRIOLQ procedure. The incorporation of time-based distance measurement has only modest effects on empirical fit, and the use of big geolocation dataset to account for commuting relocates 18.9% of the induced effect from a city to its periphery. [ABSTRACT FROM AUTHOR]

Location data is used in many aspects of journalism today. For example, journalists can identify the number of COVID-19 cases by neighborhood, map those, and then share hot spots with their audiences. They can break down political allegiances, block by block, and predict elections. They can tag location data on reported events and filter those to provide a neighborhood-specific newsletter, and so on. But how is this location data acquired and attended to? This study examines how location data is being used by journalists and the ethical concerns that can arise with such data. Recent location data breaches with third-party technology companies have raised questions about how this kind of data is collected, stored, maintained, and shared on a wider scale. This study used the Delphi method of progressive interviews with a panel of subject matter experts and consisted of three rounds of discussions with U.S. journalists and journalism ethicists. The findings identify several themes of how location data poses new storytelling opportunities and significant ethical issues related to privacy, transparency, and validity. Implications for future uses of location data in journalism are discussed, including placing such data into contexts both in the profession and in the academy. [ABSTRACT FROM AUTHOR]

AbstractArtificial 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. [ABSTRACT FROM AUTHOR]

Infectious diseases usually originate from a specific location within a city. Due to the heterogenous distribution of population and public facilities, and the structural heterogeneity of human mobility network embedded in space, infectious diseases break out at different locations would cause different transmission risk and control difficulty. This study aims to investigate the impact of initial outbreak locations on the risk of spatiotemporal transmission and reveal the driving force behind high-risk outbreak locations. First, we built a SLIR (susceptible-latent-infectious-removed)-based age-stratified meta-population model, integrating mobile phone location data, to simulate the spreading process of an infectious disease across fine-grained intra-urban regions (i.e., 649 communities of Shenzhen City, China). Based on the simulation model, we evaluated the transmission risk caused by different initial outbreak locations by proposing three indexes including the number of infected cases (CaseNum), the number of affected regions (RegionNum), and the spatial diffusion range (SpatialRange). Finally, we investigated the contribution of different influential factors to the transmission risk via machine learning models. Results indicate that different initial outbreak locations would cause similar CaseNum but different RegionNum and SpatialRange. To avoid the epidemic spread quickly to more regions, it is necessary to prevent epidemic breaking out in locations with high population-mobility flow density. While to avoid epidemic spread to larger spatial range, remote regions with long daily trip distance of residents need attention. Those findings can help understand the transmission risk and driving force of initial outbreak locations within cities and make precise prevention and control strategies in advance. [ABSTRACT FROM AUTHOR]

Mobile sensing-based depression severity assessment could complement the subjective questionnaires-based assessment currently used in practice. However, previous studies on mobile sensing for depression severity assessment were conducted on homogeneous mental health condition participants; evaluation of possible generalization across heterogeneous groups has been limited. Similarly, previous studies have not investigated the potential of free-living audio data for depression severity assessment. Audio recordings from free-living could provide rich sociability features to characterize depressive states. We conducted a study with 11 healthy individuals, 13 individuals with major depressive disorder, and eight individuals with schizoaffective disorders. Communication logs and location data from the participants' smartphones and continuous audio recordings of free-living from a wearable audioband were obtained over a week for each participant. The depression severity prediction model trained using communication log and location data features had a root mean squared error (rmse) of 6.80. Audio-based sociability features further reduced the rmse to 6.07 (normalized rmse of 0.22). Audio-based sociability features also improved the F1 score in the five-class depression category classification model from 0.34 to 0.46. Thus, free-living audio-based sociability features complement the commonly used mobile sensing features to improve depression severity assessment. The prediction results obtained with mobile sensing-based features are better than the rmse of 9.83 (normalized rmse of 0.36) and the F1 score of 0.25 obtained with a baseline model. Additionally, the predicted depression severity had a significant correlation with reported depression severity (correlation coefficient of 0.76, p < 0.001). Thus, our work shows that mobile sensing could model depression severity across participants with heterogeneous mental health conditions, potentially offering a screening tool for depressive symptoms monitoring in the broader population. [ABSTRACT FROM AUTHOR]

By selecting appropriate microhabitats, animals gain various benefits. For amphibians, which are ectothermic, appropriate microhabitat selection is directly related to their survival. This study aims to characterize the microhabitat type selected by Bufo gargarizans in South Korea, which has not yet been examined, using radiotelemetry. We analyzed the percent cover of different microhabitat features at sites where B. gargarizans was found. The microhabitat was categorized into eight types: stacked dry leaves litter or hay, grass, naked field, stones/pebbles, fallen branches, alive or dead trees/shrubs, wetland, and other. We collected a total of 60 location data, all of which had closed cover. From these, nine hibernation sites and six post-breeding dormancy sites were identified. Dry leaf litter was found to be the most frequently selected microhabitat type. We also found significant differences in microhabitat type between the hibernation period and the post-breeding dormancy period. We considered that these differences can be explained by the ecological characteristics of B. gargarizans. [ABSTRACT FROM AUTHOR]

In recent years, ecological research has become increasingly synthetic, relying on revolutionary changes in data availability and accessibility. In spite of their strengths, these approaches may cause us to overlook natural history knowledge that is not part of the digitized English-language scientific record. Here, we combine historic and modern documents to quantify species-specific nesting habitat associations of bumblebees (Bombus spp. Latreille, 1802 Apidae). We compiled nest location data from 316 documents, of which 81 were non-English and 93 were published before 1950. We tested whether nesting traits show phylogenetic signal, examined relationships between habitat associations at different scales, and compared methodologies used to locate nests. We found no clear phylogenetic signals, but we found that nesting habitat associations were somewhat generalizable within subgenera. Landcover associations were related to nesting substrate associations; for example, surface-nesting species also tended to be associated with grasslands. Methodology was associated with nest locations; community scientists were most likely and researchers using nest boxes were least likely to report nests in human-dominated environments. These patterns were not apparent in past syntheses based only on the modern digital record. Our findings highlight the tremendous value of historic accounts for quantifying species' traits and other basic biological knowledge needed to interpret global-scale patterns. [ABSTRACT FROM AUTHOR]

As a central topic in Behavioral Ecology, animal space use involves dynamic responses to social and ecological factors. We collared 22 rhesus macaques (Macaca mulatta) from six groups on Neilingding Island, China, and collected 80,625 hourly fixes over a year. Using this high‐resolution location data set, we quantified the macaques' space use at the individual level and tested the ecological constraints model while considering various environmental and human interfering factors. As predicted by the ecological constraints model, macaques in larger groups had longer daily path lengths (DPLs) and larger home ranges. We found an inverted U‐shape relationship between mean daily temperatures and DPLs, indicating that macaques traveled farther on mild temperature days, while they decreased DPLs when temperatures were too high or too low. Anthropogenic food subsidies were positively correlated to DPLs, while the effect of rainfall was negative. Macaques decreased their DPLs and core areas when more flowers and less leaves were available, suggesting that macaques shifted their space use patterns to adapt to the seasonal differences in food resources. By applying GPS collars on a large number of individuals living on a small island, we gained valuable insights into within‐group exploitation competition in wild rhesus macaques. Highlight: By applying GPS collars, we assessed home range quality and space utilization for 22 rhesus macaques in six groups.Our results offered supportive evidence for the ecological constraints model, indicating that within‐group exploitation feeding competition is the predominant factor influencing macaques' space use on Neilingding Island. [ABSTRACT FROM AUTHOR]

The spatial ecology of feral horses (Equus ferus f. caballus) in Greece has never been studied before, including home range size and habitat selection. We tracked two mares fitted with global positioning system collars between September 2020 and August 2021 in a portion of Mount Menoikio in Central Macedonia, Northern Greece. We used K-select analysis to assess habitat selection for the study period by combining location data with several environmental variables. The mean home ranges for horses varied from 26.72 km2 (95% Minimum Convex Polygon; SE = 0.442) to 27.84 km2 (95% Kernel Density; SE = 1.83). Both horses selected areas with flat and smooth topography near natural grasslands with high green productivity. Conversely, they avoided areas near broadleaved forests and pastures, as well as at north-facing aspects. Overall, suitable habitats corresponded to a small portion (15.1%) of the available habitat. Our findings could assist land managers in mapping primary horses' habitat in the wider region and implement management regimes that will aid in preserving natural grasslands. [ABSTRACT FROM AUTHOR]

As public transportation faces increasing ridership demand, metrics such as the number of passengers denied boarding become important for measuring the service quality of transit systems. Many studies in the past have used automated fare collection (AFC) (also known as smart card data) and automated vehicle location data to infer the probability distributions for commuters that experience unable-to-board (UTB) events in metro systems, but few have studied UTB events for buses. In this paper, we demonstrate that the probability distribution of UTB commuters inferred from AFC data can be modelled by a truncated binomial distribution under certain assumptions. This model is then validated against synthetic UTB events generated using simulations and against actual UTB events recorded from ground surveys. Finally, we apply our model on real AFC data of commuters in the Singapore bus network to serve as a case study. Our method enables transport planners and operators to identify bus stops and time intervals where overcrowding and UTB events is prevalent, so that appropriate measures can be taken to mitigate such occurrences. [ABSTRACT FROM AUTHOR]

Spatial mapping of food demand is essential for understanding and addressing disparities in food accessibility, which significantly impact public health and nutrition. This research presents an innovative geospatial framework designed to map food demand, integrating individual dietary behaviors with advanced spatial analysis techniques. This study analyzes the spatial distribution of eating habits across Lithuania using a geospatial approach. The methodology involves dividing Lithuania into 60,000 points and interpolating survey data with Shepard's operator, which relies on a weighted average of values at data points. This flexible approach allows for adjusting the number of points based on spatial resolution and sample size, enhancing the reliability and applicability of the generated maps. The procedure includes generating a structured grid system, incorporating measurements into the grid, and applying Shepard's operator for interpolation, resulting in precise representations of food demand. This framework provides a comprehensive understanding of dietary behaviors, informing targeted policy interventions to improve food accessibility and nutrition. Traditional food spatial mapping approaches are often limited to specific polygons and lack the flexibility to achieve high granular detail. By applying advanced interpolation techniques and ensuring respondent location data without breaching privacy concerns, this study creates high-resolution maps that accurately represent regional differences in eating habits. The methodology's flexibility allows for adjustments in spatial resolution and sample size, enhancing the maps' validity and applicability. This novel approach facilitates the creation of detailed food demand maps at any granular level, providing valuable insights for policymakers and stakeholders. These insights enable the development of targeted strategies to improve food accessibility and nutrition. Additionally, the obtained information can be used for computer simulations to further analyze and predict food demand scenarios. By leveraging spatial data integration, this study contributes to a deeper understanding of the complex dynamics of food demand, identifying critical areas such as food deserts and swamps, and paving the way for more effective public health interventions and policies aimed at achieving equitable food distribution and better nutritional outcomes. [ABSTRACT FROM AUTHOR]

This paper introduces a novel efficient hybrid reconfigurable intelligent surface (RIS) approach designed to significantly reduce power loss and enhance coverage area in 6G networks. The core innovation of this approach lies in an advanced iterative algorithm introduced as the Hybrid reconfigurable intelligent surface decision-making algorithm (HRIS-DMA) that integrates precise user location data into the RIS configuration process. By dynamically adjusting RIS elements to reflect and direct signals based on real-time user positions, this method minimises signal attenuation and optimises signal propagation. The mechanism driving the performance gains includes precise beamforming and intelligent reflection, continuously refined through iterative updates. This technique ensures robust signal strength and expanded coverage, addressing the challenges of dense and diverse deployment scenarios in 6G networks. The proposed scheme's application in 6G networks demonstrates substantial improvements in signal quality and network reliability, paving the way for enhanced user experiences and efficient communication infrastructures. This novel approach was tested using MATLAB R2023a, and its performance was evaluated using three downlink scenarios: zero to few, few to moderate, and moderate to many obstacles. The three scenarios show higher coverages than conventional simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) and base station (BS) handover. Based on the evaluation metrics, the analysis results of the novel HRIS-DMA show 70% less signal power loss, 0.17 μ s less system delay, 25 dB and 12 dB channel gain compared with the conventional STAR-RIS and BS handover, respectively, and 95% improvement in the overall system's efficiency compared to STAR-RIS and 13% compared to BS-BS handover. [ABSTRACT FROM AUTHOR]

We analyze low‐altitude DEMETER spacecraft measurements obtained between 2006 and 2010, complemented by WWLLN lightning location data, to investigate the importance of lightning‐generated whistlers for the energetic electron precipitation from the Van Allen radiation belts. We focus, in particular, on the United States region, where a significant seasonal variation in the occurrence of lightning has been observed. We show that both the precipitating electron fluxes and very low frequency wave intensities correlate well with the total lightning occurrence in the region. We further demonstrate that lightning‐induced electron precipitation is more significant during periods of low geomagnetic activity compared to periods of high geomagnetic activity and during the nighttime than during the daytime. The energies of precipitating energetic electrons extend up to about 700 keV, roughly in agreement with the cyclotron resonance theory. Key Points: Precipitating electron fluxes are correlated with the total lightning occurrence in the regionLightning‐induced electron precipitation is more pronounced during the nighttime and during periods of low geomagnetic activityThe precipitated electron energies range up to a few hundred keV, roughly in agreement with the cyclotron resonance theory [ABSTRACT FROM AUTHOR]

The social media data provides great source of information about global and local events, with millions of users. More precisely, the fact that brief messages are practical and are highly popular. Many recent studies have been motivated to estimate the location of the events identified by tracking posts in social media text messages. It might be difficult to extract location data and estimate the location of an event while maintaining a sufficient level of situation awareness, particularly in disaster situations like fires or traffic accidents. In this presented work we proposed an approach to identify geo-references in the text messages. We used bi-directional long short term memory (LSTM) neural networks to extract location information in the text messages. The results show that applying Bi-LSTM on dataset gives high level accuracy after fine-tuning (up to 10 epochs). The testing results show that accuracy achieved is 0.98 and 0.076 loss value. This proves that the proposed methodology is better than the previous conditional random field (CRF)-based approaches. [ABSTRACT FROM AUTHOR]

This study introduces an innovative approach to predicting signal strength degradation in urban areas by leveraging a data-driven machine learning methodology. Focusing on the issue of signal variation within diverse urban infrastructures, we introduce EzziSignal, a real-time signal data collection method through a mobile application. Traditional methods, such as manual drive tests, are labor-intensive and costly. In response, our research offers a novel and efficient alternative utilizing data mining and machine learning techniques. The study area was subdivided into multiple zones and sectors, independent of existing broadcast station locations. A custom mobile application was developed to systematically collect signal strength and location data across these zones, harnessing the collective power of over ten users' smartphones. This extensive dataset was then analyzed using the gradient-boosted algorithm, a sophisticated machine-learning technique. Its accuracy reached approximately 96.96%. The model exhibited promising results in predicting signal strength degradation, providing valuable insights into the dynamics of urban signal behavior. This research carries significant practical implications for telecommunication companies, offering an intelligent framework to optimize resource allocation and base station installation. By considering the deterioration of signal, strength determines the relationship between infrastructure and base station installation regarding the quality of networking. Ultimately, this optimization contributes to enhanced network coverage and improved service quality. [ABSTRACT FROM AUTHOR]

Background: Adolescents and young adults account for over 21% of new HIV infections in the U.S. with most new cases among young men. As an important information source for this group, social media can uniquely reveal the perspectives and communicative patterns of this key population. We identified 6,439 young male Twitter users (ages 13–24) in the U.S. using an NLP pipeline with geolocations. From their Twitter timelines, we collected 24,600 HIV-related tweets, among which the most retweeted and favorited tweets (n = 472) were analyzed through a content analysis. Results: Three themes arose in this online viral discourse around HIV among young men: (i) othering, (ii) politics and activism, (iii) risk and wellness. Othering tweets contained stigmatizing jokes and insults alienating individuals who identify as lesbian, gay, bisexual, transgender, queer or questioning, intersex, asexual, or being elsewhere on the gender and sexuality spectrum (LGBTQIA +), and people with HIV. Politics and activism tweets discussed awareness, stigma, HIV criminalization, violence, LGBTQIA + , and women's rights. Risk and wellness tweets discussed risk behaviors for sexually transmitted infections (STIs) (e.g., condomless sex, transactional sex, multiple sexual partners), or safer sex and preventive practices (e.g., pre-exposure prophylaxis [PrEP], condom use, achieving undetectable viral load, medication adherence, and STI testing). Conclusion: The social acceptability of high-risk sex behaviors is high among young male Twitter users. Given the double-edged nature of social media—health-promoting (e.g., awareness, health activism) as well as risk-promoting (e.g., risky behavior endorsement, identity attacks)— this population may benefit from targeted health communication intervention. Future HIV prevention efforts should counter the stigma, misinformation, and risk-promoting viral messages prevalent on social media. [ABSTRACT FROM AUTHOR]

To effectively solve the reverse logistics distribution problem caused by the increasing number of scrapped parts in the automotive market, this study constructs a multi-trip green vehicle routing problem model with time windows by comprehensively considering the coordination between carbon dioxide emissions and cost efficiency. A hybrid adaptive genetic algorithm is proposed to solve this problem, featuring innovative improvements in the nearest neighbor rule based on minimum cost, adaptive strategies, bin packing algorithm based on the transfer-of-state equation, and large-scale neighborhood search. Additionally, to efficiently obtain location data for supplier factory sites in the distribution network, a coordinate extraction method based on image recognition technology is proposed. Finally, the scientific validity of this study is verified based on the actual case data, and the robust optimization ability of the algorithm is verified by numerical calculations of different examples. This research not only enriches the study of green vehicle routing problems but also provides valuable insights for the industry to achieve cost reduction, efficiency enhancement, and sustainable development in reverse logistics. [ABSTRACT FROM AUTHOR]

Summary Location‐based underwater communication applications such as strategic surveillance, disaster prevention, marine research, and mine detection have given the field of underwater wireless sensor networks (UWSN) a head start. Node localization is a prerequisite for accurate data collection, target monitoring, and network management in UWSNs. However, the unique characteristics of the underwater environment, such as signal attenuation, multipath propagation, and variable acoustic properties, pose a major challenge to effective node localization. Accurate sensor node location data is essential for successful underwater data collection, but difficult to achieve as the GPS system cannot be used in an underwater environment. In this paper, existing node localization techniques such as ALS, SLUM, MASL, SLMP, UDB, USP, etc., and recent advances such as the fusion of range‐based and range‐free techniques, the fusion of RSSI and AoA to improve localization accuracy by using directional information in addition to signal strength, and the use of optimization techniques such as PSO, COA, and WOA algorithms to improve the accuracy of the applied node localization algorithm, e.g., TP‐TSFLA, and challenges related to UWSN are discussed. Also, different localization algorithms that affect the accuracy of UWSN localization techniques have been evaluated and compared with NS2 in terms of localization error, localization coverage, energy consumption, and average communication cost metrics. In addition, this paper also provides an up‐to‐date investigation of localization techniques. Finally, the tools available for simulation are presented, followed by open research questions that need to be addressed in the localization of nodes. [ABSTRACT FROM AUTHOR]

The study of animal movement provides insights into underlying ecological processes and informs analyses of behaviour and resource use, which have implications for species management and conservation. The tools used to study animal movement have evolved over the past decades, allowing for data collection from a variety of species, including those living in remote environments. Satellite-linked radio and GPS collars have been used to study polar bear (Ursus maritimus) ecology and movements throughout the circumpolar Arctic for over 50 years. However, due to morphology and growth constraints, only adult female polar bears can be reliably collared. Collars have proven to be safe, but there has been opposition to their use, resulting in a deficiency in data across much of the species' range. To bolster knowledge of movement characteristics and behaviours for polar bears other than adult females, while also providing an alternative to collars, we tested the use of fur- and ear-mounted telemetry tags that can be affixed to polar bears of any sex and age. We tested three fur tag designs (SeaTrkr, tribrush and pentagon tags), which we affixed to 15 adult and 1 subadult male polar bears along the coast of Hudson Bay during August–September 2021–2022. Fur tags were compared with ear tags deployed on 42 subadult and adult male polar bears captured on the coast or the sea ice between 2016 and 2022. We used data from the tags to quantify the amount of time subadult and adult males spent resting versus traveling while on land. Our results show the three fur tag designs remained functional for shorter mean durations (SeaTrkr = 58 days; tribrush = 47 days; pentagon = 22 days) than ear tags (121 days), but positional error estimates were comparable among the Argos-equipped tags. The GPS/Iridium-equipped SeaTrkr fur tags provided higher resolution and more frequent location data. Combined, the tags provided sufficient data to model different behavioural states. Furthermore, as hypothesized, subadult and adult male polar bears spent the majority of their time resting while on land, increasing time spent traveling as temperatures cooled. Fur tags show promise as a short-term means of collecting movement data from free-ranging polar bears. [ABSTRACT FROM AUTHOR]

An automatic identification system (AIS) is often installed on merchant ships and fishing boats to prevent collisions and ensure safe navigation. The location information of ships transmitted from AIS equipment can help maritime traffic control prevent accidents. The southern coast of Korea comprises a complex coastline with numerous fishing boats and transit vessels. In particular, the Tongyeong and Geoje Islands include high-altitude mountains and islands, resulting in several radio-shaded areas where AIS signals cannot be received, owing to geographical effects. However, only a few studies have explored this region and performed practical experiments on the reception status of AIS locations in radio-shaded areas. In this study, we performed an experiment in the Geoje Island Sea on the southern coast to analyze the impact of high terrain on the reception rate and status of automatic identification devices. Two identical pieces of AIS equipment were installed to generate multiple radio waves, and the location data transmitted via different antennae were compared. The experimental analysis forms the basis for identifying the exact location of ships in the event of maritime accidents, facilitating rapid rescue. Moreover, the accuracy of the location transmitted by the AIS equipment can aid in detecting the cause of accidents. [ABSTRACT FROM AUTHOR]

This work presents a comprehensive study and methodology to cope the problem of multi-object tracking. The methodology proposed here aims to simplify the problem space by using RNN as a regression network for predicting current object tracker information from past data and using Siamese network for re-identification. The number of re-identification is reduced by doing suboptimal perfect matching using a tree data structure for geometric location data of objects in view port. [ABSTRACT FROM AUTHOR]

With the increasing prevalence and social impacts of Alzheimer’s disease (AD), innovative approaches to our understanding of its etiology, progression, and potential interventions are critical. Geospatial applications are a promising tool in addressing the complex spatial and temporal dynamics of AD. A substantial knowledge gap remains, however, regarding the comprehensive utilization and impact of geospatial applications in AD research. We comprehensively consider the current status of geospatial-information application in AD based on 159 research articles on geospatial applications published between 1991 and 2022 from the Web of Science and PubMed databases. Four main research themes are identified: geographic environmental factors, geographic disparities, activity behaviors, and care services. Geospatial information has been extensively applied using techniques such as spatial clustering and hotspot analysis. We investigate the influence of location and spatial factors on AD risk, the activity behavior of patients with AD using location data and spatial analysis, and medical-resource proximity via geospatial modeling and predictive analysis. Additionally, we identify three knowledge gaps: geographic environment measurement, spatiotemporal contrastive analysis of disease prevalence, and multidisciplinary integration. The review underscores the value of geospatial information in AD-related research and public health, offering researchers insights into prospective directions and collaborative opportunities with geographers. [ABSTRACT FROM AUTHOR]

Simple Summary: The global trade of pet birds has grown significantly, leading to more non-native birds being introduced into the wild. Our study examined the potential habitats and environmental factors that might allow pet birds lost and sighted in South Africa to survive outside captivity. Using geographic location data of pet birds, we predicted which areas might be suitable for various species such as the African grey parrot, Budgerigar, Cockatiel, Green-cheeked conure, Monk parakeet, and Rose-ringed parakeet. We found that a significant portion of South Africa could support these species, with some areas being highly suitable. The study highlighted how pet birds could escape or be released into the wild due to human activities, and that urban areas are particularly at risk. This research emphasizes the need for careful monitoring to prevent these birds from becoming invasive, which could impact local ecosystems and biodiversity. The global trade of non-native pet birds has increased in recent decades, and this has accelerated the introduction of invasive birds in the wild. This study employed ensemble species distribution modelling (eSDM) to assess potential habitat suitability and environmental predictor variables influencing the potential distribution of non-native pet bird species reported lost and sighted in South Africa. We used data and information on lost and found pet birds from previous studies to establish and describe scenarios of how pet birds may transition from captivity to the wild. Our study revealed that models fitted and performed well in predicting the suitability for African grey (Psittacus erithacus), Budgerigar (Melopsittacus undulatus), Cockatiel (Nymphicus hollandicus), Green-cheeked conure (Pyrrhura molinae), Monk parakeet (Myiopsitta monachus), and Rose-ringed parakeet (Psittacula krameri), with the mean weighted AUC and TSS values greater than 0.765. The predicted habitat suitability differed among species, with the suitability threshold indicating that between 61% and 87% of areas were predicted as suitable. Species with greater suitability included the African grey, Cockatiel, and Rose-ringed parakeet, which demonstrated significant overlap between their habitat suitability and reported lost cases. Human footprint, bioclimatic variables, and vegetation indices largely influenced predictive habitat suitability. The pathway scenario showed the key mechanisms driving the transition of pet birds from captivity to the wild, including the role of pet owners, animal rescues, adoption practices, and environmental suitability. Our study found that urban landscapes, which are heavily populated, are at high risk of potential invasion by pet birds. Thus, implementing a thorough surveillance survey is crucial for monitoring and evaluating the establishment potential of pet species not yet reported in the wild. [ABSTRACT FROM AUTHOR]

In the field of distributed medical data storage, relayed data is usually kept centrally on the central node, which may lead to an increased risk of a single point of failure and security challenges from malicious tampering. While introducing backup nodes can partially alleviate these issues, the efficiency of synchronization and switching between nodes remains constrained. In addition, nodes storing relayed data can reach a consensus to modify the forwarded data, which lacks trustworthiness. Given the problems faced by traditional distributed storage in the field of medical data, combined with the characteristics of blockchain technology, we propose a distributed medical data storage model based on blockchain technology, named DDS (Decentralized Distributed Storage), which is intended to be applied in medical data management. In the DDS model, we embed relay data into blocks for storage and ensure the integrity and security of relay data for medical data by introducing redundant blockchain storage and collaborative verification mechanisms. The model is divided into two key phases: the storage phase of relay data of medical data and the verification phase. In the storage phase, the doctor's signature and the associated copy location data are sent to multiple validation nodes, after which the relay data block of medical data is generated and written to the blockchain. In the validation phase, the node first verifies whether the local relay data blockchain state is consistent with the global state. If it is not, a state synchronization operation is performed. Next, the validation node retrieves the local relay data blockchain to verify the integrity of the medical data relay data. From the theoretical analysis and experimental results, the DDS model can ensure the traceability and integrity of relay medical data and has good concurrent processing performance and less impact on the efficiency of medical data storage. [ABSTRACT FROM AUTHOR]

In military applications, a highly accurate and precise navigation system is necessary for some ground-based combat system applications to navigate the vehicle and mark the location of the fuses and pickets laid by the mobile equipment. Few companies across the globe have developed expertise in manufacturing Global Navigation Satellite System (GNSS) receivers with Real Time Kinematics (RTK) capability along with integrated INS. On mobile equipment, it is not always possible to precisely place the Differential GNSS (DGNSS) antenna and GNSS receiver at the point for which the data needs to be marked. For such applications, lever arm calculation needs to be implemented. Also, due to uneven terrain conditions and slopes, the vehicle undulates up to 10o about its longitudinal and transverse axis. This dynamic condition induces considerable errors in the actual data. During the fuse laying process, there is also a continuous requirement for real-time location data in the GNSS-denied environment for a short period. INS was integrated with the GNSS receiver. We discuss test methodology for GNSS receiver performance evaluation. Experimentations were performed, taking into consideration these requirements. Data collected are also analyzed and discussed. Test results confirmed that the module is accurate with an accuracy of a few centimeters. [ABSTRACT FROM AUTHOR]

While the quality and quantity of data directly impact the estimation accuracy of deep learning-based localization technology, collecting empirical location data in large-scale indoor environments consumes significant time and resources. This paper proposes a novel training and data collection method for constructing a deep learning-based localization model for indoor vehicle localization using multi-directional beacons. We first develop different deep learning-based localization models and then define a directed graph to analyze the model applicability. We deploy the large number of multi-directional beacons to evaluate the proposed scheme and run extensive set of experiments to obtain the training data in the indoor testbed. The performance evaluation shows that the proposed deep learning-based localization model provides fairly good estimation accuracy. Furthermore, the directed graph demonstrates that the location labeling data of beacons can be significantly reduced while providing good estimation accuracy for the indoor scenario. [ABSTRACT FROM AUTHOR]

Although digital twins have been established in manufacturing for a long time, they are only more recently making their way into the urban environment and present a relatively new concept for the construction industry. The concept of a digital twin—a model of the physical environment that has a real-time two-way link between the physical and the digital, with the virtual model changing over time to reflect changes in the real world—lends itself well to the continually changing environment of a construction project. Predictive capabilities built into a twin also have great potential for construction planning—including in supply chain management and waste disposal as well as in the construction process itself. Underpinning this opportunity is location data, which model where something is happening and when and can be used to solve a wide range of problems. In particular, location (the power of where) can integrate diverse data sources and types into a single system, overcoming interoperability challenges that are known to be a barrier to twin implementation. This paper demonstrates the power of location-enabled digital twins in the context of a highway construction project, documenting and addressing data engineering tasks and functionality development to explore the potential of digital twins in the context of two case studies—health and safety and construction monitoring. We develop two demonstrators using data from an existing construction project (building on data and requirements from industry partner Skanska) to build twins that make use of the powers of 4D data presentation offered by the Unreal Gaming Engine and CesiumJS mapping, while software development expertise is sometimes available to construction firms, we specifically explore to what extent the no-code approach available within Unreal can be deployed in this context. Our findings provide evidence to construction companies as to the benefits of digital twins, as well as an understanding of the data engineering and technical skills required to achieve these benefits. The overall results demonstrate the potential for digital twins to unlock and democratise construction data, taking them beyond the niche use of experts and into the boardroom. [ABSTRACT FROM AUTHOR]

Headway variability has a negative impact on the public transport passengers' perception of service quality. However, most of the existing methods aimed at improving the headway regularity operate in real time and require precise vehicle location data, making it difficult to implement them in practice. On the other hand, planning-level methods can be used to increase the resilience of public passenger transport (PPT) to the accumulation of headway disturbances. As this is typically done from the operator's perspective, the passengers' perspective tends to be overlooked, motivating the current work. In this article, an optimisation procedure for evaluating the viability of diametrical line splitting in terms of passenger travel time and headway regularity is proposed. The aim is to increase the robustness/resistance of the PPT system to the propagation of headway disturbances without reducing the service quality. The developed optimisation procedure was validated by applying it to real data pertaining to an urban PPT line. The results show that there is a positive correlation between the transport demand and the effects of the optimisation procedure, whereby an increase in the primary headway disturbance increases the sensitivity of the optimisation procedure to the transport demand. [ABSTRACT FROM AUTHOR]

The article discusses the issue of automotive data privacy and the concerns surrounding the collection and use of data from vehicles. It highlights the increasing amount of data that can be gathered from modern vehicles and the potential privacy implications. The article also mentions the lack of control that consumers have over their data and the need for greater transparency and regulation in the industry. It concludes by suggesting that the auto industry may face legal challenges in the future as consumers become more aware of and concerned about their privacy rights. [Extracted from the article]

Reliability is a key factor in public transportation systems, significantly influencing passenger satisfaction and their perceptions of service quality. Therefore, measures of Travel Time Reliability (TTR), which help quantify unexpected delays, are essential for effectively planning and managing travel times. This research aims to provide a prediction model for standard deviation of travel time, as an indicator of TTR, on an urban bus route using Automatic Vehicle Location data as well as evaluating and comparing the results of ARIMA and LSTM models. Our research contributes to the existing body Ofliterature by considering the effect of a wide range of predictor variables which were categorized by route characteristics, weather condition, congestion, and temporal feature, on bus TTR, which received less attention in previous studies. The results show that LSTM is an efficient and accurate TTR predictive model than ARIMA, with an accuracy of about 87%. In addition, the LSTM model exhibits lower mean absolute percentage error (40%), mean square error (52.08%), and root mean square error (39.70%) compared to the corresponding values in the ARIMA model. Also, the congestion, weather condition, and holidays are the key variables in increasing the accuracy of LSTM model, respectively. Our findings provide insights to facilitate the decision-making of managers and planners in public transportation planning sector to improve transit reliability and passengers' satisfaction level. [ABSTRACT FROM AUTHOR]