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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]

The editorial "Geospatial big data: theory, methods, and applications" discusses the emergence of geospatial big data, which encompasses large, diverse datasets with spatial components collected from various sources. The article highlights the challenges and opportunities in defining, collecting, analyzing, and visualizing geospatial big data, emphasizing the need for interdisciplinary exploration and ethical considerations. The special issue within the editorial presents seven articles focusing on novel methods to collect, process, and analyze geospatial big data for applications such as human activities, social interactions, disasters, health crises, and environmental predictions. The editorial concludes by addressing challenges related to geo-privacy, multimodal GeoAI, and scale issues in geospatial big data analysis, encouraging further research and innovation in the field. [Extracted from the article]

This study examines the development and preliminary validation of a protocol for fully automated production analysis in forest harvesting operations, utilizing onboard computer data. By integrating ignition status, motion status, and machine location data from FPDat II data loggers with LiDAR forest inventory data, this research aims to accurately predict key production metrics such as productive time, area covered, volume harvested, and overall productivity for individual machines. The efficacy of this fully automated data collection and analysis approach is scrutinized using a direct comparison with traditional in-field data collection methods, with a focus on feller buncher operations. Findings indicate minimal discrepancies in productive time recordings (0.9%) and area covered by machines (-1.9%), with slightly larger discrepancies observed in volume harvested (-4.4%) and productivity (-5.3%). More significant disparities in area coverage estimations were noted during individual shifts, particularly when multiple machines operated simultaneously or when there was incomplete coverage of machine tracking by FPDat II data loggers. This study is a crucial step towards understanding the capabilities and limitations of onboard computer data for remote production analysis in forest operations. Through comprehensive analysis, it contributes to the digital transformation of forestry, underscoring both the challenges and opportunities of automated tools in enhancing harvesting efficiency. [ABSTRACT FROM AUTHOR]

This research estimated models for long-distance domestic passenger trips before and after the introduction of autonomous vehicles (AVs) and their application to a 10% synthetic US population. The authors synthesized 12.1M households and 28.1M individuals across 73,056 US census tracts. To generate disaggregated passenger trips, travel demand models, including trip frequency, season, purpose, party size, mode choice, and destination choice, and vehicle ownership models were estimated. Different datasets, including a 2021 long-distance AV survey, 2016/17 National Household Travel Survey (NHTS) survey, EPA Smart Location data, and FHWA rJourney dataset were used for model estimation. The model applications indicated AV ownership to be 0.33 per capita after the introduction of AVs within the marketplace with a $3500 AV technology cost premium in the year 2040. Total person-miles traveled per capita in long-distance trips was also estimated to rise 35% (from 280 to 379 miles per month). [ABSTRACT FROM AUTHOR]

An uncontrolled fire poses severe threats to both humans and the environment, making firefighting a perilous and complex task. Traditional fire suppression methods are inefficient, costly, and without thorough testing, leading to delays in gaining control over fire outbreaks. This paper presents a novel firefighting drone aimed at mitigating risks to firefighters by extinguishing fires and providing real-time imaging, gas concentration and fire location data monitoring. The proposed intelligent quadcopter utilizes the Pixhawk PX4 microcontroller for precise control and the Pixhawk Telemetry system for data processing. The proposed device is constructed from an ultra-strength S500 Quadcopter frame, NodeMCU, Arduino Nano, various gas sensors, a servo motor to extinguish the fire and a camera to detect fire events in real time. Equipped with an FPV camera and a video transmitter, it transmits live video feed to the ground, enabling efficient navigation using the Flysky I6X controller. The intended position and height of the drone are controlled using an adaptive optimization technique known as fuzzy-based backstepping control. This article demonstrates the effectiveness of the device by collecting and analyzing gas emissions data from controlled burns of various materials. The drone successfully measured concentrations of CO, CO2, O3, SO2, and NO2 in affected areas, providing valuable insights for firefighting operations. Different levels of gases have been measured depending on the concentration from burning alcohol, clothes, plastic materials, paper, leaves, and so on. The novelty of this work lies in the development and comprehensive analysis of an IoT-based firefighting drone conducting extensive real-time experiments. [ABSTRACT FROM AUTHOR]

The decision of the Federal Court of Justice (BGH) on January 10, 2024, 2 StR 171/23, deals with difficulties in the interpretation and application of §§ 100a et seq. of the Code of Criminal Procedure. It is discussed that the legal provisions for the covert collection of data are inadequate following constitutional court rulings. A specific example concerns the retrograde collection of location data within the framework of a cell tower query according to § 100g para. 3 sentence 1 StPO, where the legal regulation is unclear. The decision of the BGH emphasizes the necessity of a uniform reform of the regulations on data surveillance. [Extracted from the article]

ABSTRACT In this study, an ANN‐based Global Navigation Satellite System (GNSS) location data reduction approach was proposed. As GNSS location data becomes more common, efficient data reduction techniques are needed to reduce transmission, storage, and processing costs. This involves selecting key points from the original trajectory to maintain integrity, eliminating redundancy, and lowering transmission and storage expenses. In this study, we proposed a new method for reducing GNSS location data in both online and offline settings, utilizing an ANN trained with a mathematically generated dataset. ANN has not been used in data reduction in the literature. The approach involves training the ANN with a window size of 3 and a threshold value of 10°, followed by using the trained model for data reduction. Experimental results show that the ANN achieves a reduction rate of around 59.18% compared to the original trajectory. Notably, the ANN yields a significantly lower RMSE compared to a mathematical method, particularly in areas requiring precision. Despite the slightly slower computation times, the ANN remains suitable for real‐time applications, demonstrating its efficacy for GNSS location data reduction. Our study highlights its online capability, reasonable reduction rates, and low RMSE values, distinguishing it from existing literature. This method shows potential for scenarios where balancing reduction rates with data quality is crucial. [ABSTRACT FROM AUTHOR]

Fast and robust vehicle recognition from remote sensing images (RSIs) has excellent economic analysis, emergency management, and traffic surveillance applications. Additionally, vehicle density and location data are vital for intelligent transportation systems. However, correct and robust vehicle recognition in RSIs has been complex. Conventional vehicle recognition approaches depend on handcrafted extracted features in sliding windows with distinct scales. Recently, the convolutional neural network can be executed for aerial image object recognition, and it has accomplished promising outcomes. This research projects an automatic vehicle detection and classification utilizing an imperialist competitive algorithm with a deep convolutional neural network (VDC-ICADCNN) technique. The primary purpose of the VDC-ICADCNN technique is to develop the RSI and apply deep learning (DL) models for the recognition and identification of vehicles. Three main procedures were involved in the presented VDC-ICADCNN technique. At the primary stage, the VDC-ICADCNN technique employs the EfficientNetB7 approach for the feature extractor. Then, the hyperparameter fine-tuning of the EfficientNet approach takes place utilizing ICA, which aids in attaining improved performance in the classification process. The VDC-ICADCNN technique utilizes a variational autoencoder (VAE) model for the vehicle recognition method. Extensive experiments can be implemented to establish the superior solution of the VDC-ICADCNN technique. The obtained outcomes of the VDC-ICADCNN technique highlighted a superior accuracy value of 96.77% and 98.59% with other DL approaches under Vehicle Detection in Aerial Imagery (VEDAI) and Potsdam datasets. [ABSTRACT FROM AUTHOR]

Background: In April 2019, Mozambique was hit by Cyclone Idai leaving substantial damage to infrastructure and nearly two million people in need of humanitarian assistance. Malaria risk has been associated with living in a rural setting, vicinity to water, and household structure, all factors which are impacted by severe storms. This study quantified the association between damage to infrastructure (health care facilities and schools) following Cyclone Idai and malaria outcomes: malaria incidence, severe malaria, and administration of intermittent preventative treatment in pregnancy (IPTp) in Sofala Province, Mozambique. Methods: To quantify infrastructure damage, historical satellite imagery was assessed visually using Google Earth Pro®. Damage to 66 school and 105 health centres was assessed for severity and duration. Location data was imported to QGIS and joined with district level data on malaria outcomes and environmental covariates. Multivariable Poisson regression models were used to quantify the associations between facility damage and malaria incidence and severe malaria. Generalized linear models were used to determine the association between facility damage and changes in malaria incidence and administration of IPTp from 2018 to 2019. Results: An increase in facility damage was associated with an increase in malaria incidence and severe malaria. An increase in proportion of facilities damaged by 10% was associated with a 7.3% (95% CI 6.8–7.8%) increase in malaria incidence. Risk of severe malaria was 2.16 (95% CI 1.42–3.32) times higher in districts with any damage compared to those with no damage. However, proportional facility damage was associated with decreased malaria incidence and increased access to IPTp from 2018 to 2019, but these were not statistically significant. Conclusion: An increase in proportion of facilities damaged following Cyclone Idai was associated with increased risk of malaria and severe malaria at the district level. This may be a function of decreased access to healthcare and an increased exposure to risk factors for malaria. A non-statistically significant decrease in malaria incidence was also found at the district level from 2018 to 2019, indicating that malaria may have been underreported in 2019, likely again a function of decreased access to healthcare. [ABSTRACT FROM AUTHOR]

ABSTRACT Access to information pertaining to land facilitates strategic planning for individuals, businesses, and governmental entities and allows them to anticipate and address contemporary challenges such as climate change, housing requirements, and economic prosperity. Improvements in information technologies contribute to data creation, analysis, and dissemination processes that enable gaining knowledge about land‐related events. Greater use of location data within property technologies gives rise to an efficient and sustainable land administration system. GeoAI can contribute greatly to the solution of spatial problems by extracting information from complex data through geospatial intelligence. Property valuation requires a spatially explicit model to evaluate locational factors with spatial analyses. In this study, a Land Administration Domain Model (LADM)‐based mass valuation model is developed for Great Britain by integrating GeoAI techniques. First, the LADM Valuation Information package is matched with the country's current organizational structure and it is extended according to the needs to create a country profile. After developing the conceptual model, a database schema is created and automatic conversion to the physical model is conducted. Loading different data sources into the database, a mass valuation application is carried out through spatial analysis and Random Forest regression analysis. As a result, a highly accurate and holistic valuation model is implemented based on the international land administration standards. [ABSTRACT FROM AUTHOR]

ABSTRACT The COVID‐19 pandemic highlighted and worsened social inequalities in the United States. This study uses mobile phone location data at the Census Block Group level and explainable machine learning methods to examine the relationships between various factors and human mobility in ten Metropolitan Statistical Areas to uncover how different factors influenced mobility disparities. Our key findings show significant reductions in all mobility indices during the summer of 2020 compared to 2019, mainly due to stay‐at‐home orders, business closures, and health concerns. Median household income was a consistent positive driver of mobility, while the minority rate negatively impacted movement, exacerbating existing inequalities. Remote work significantly affected full‐time and part‐time job mobility. These findings highlight the need for fair and resilient mobility policies that consider decentralized commuting, flexible work models, and sustainable transportation. These insights can help policymakers and urban planners address inequalities, support economic recovery, and build inclusive urban environments. [ABSTRACT FROM AUTHOR]

Knowledge of the habitat requirements for migratory species throughout their full annual cycle is necessary for comprehensive species protection plans. By describing seasonal shifts of space‐use patterns in a key nonbreeding area, the Senegal Delta (Mauritania, Senegal), this study addresses a significant knowledge gap in the annual cycle of the rapidly declining continental Black‐tailed Godwit Limosa limosa limosa. We fitted continuous‐time stochastic‐process movement models with GPS location data to describe the core areas used by 22 GPS‐tagged godwits over the 2022–2023 nonbreeding period. We mapped key habitat types, such as floodplain wetlands and rice fields, via supervised classification of satellite imagery. Godwits in the Senegal Delta show a distinct shift in habitat use over the nonbreeding period. The core areas of godwits in the early stages of the nonbreeding period (the wet season) were primarily in natural wetlands and fields with newly planted rice. As the rice crop matured and became too dense, godwits moved towards more recently sown rice fields. Later, as floodwaters receded and rice fields dried out, godwits abandoned rice fields and shifted towards natural wetlands with fewer invasive plants, particularly within the marshes and shallow floodplains of nature‐protected areas in the lower Delta. Synthesis and applications: Our findings illustrate the shifting importance of natural and agricultural wetlands for godwits at different stages of the nonbreeding season. Protected areas in the Senegal Delta, particularly the Djoudj National Bird Sanctuary (Senegal) and Diawling National Park (Mauritania), are crucial habitats during the dry season as godwits prepare for their northward migration, whilst rice fields take a key role during the wet season. Conservation efforts should prioritize eradicating invasive plants from the Djoudj and Diawling, as well as promote agroecological management in specific rice production complexes indicated in this study. [ABSTRACT FROM AUTHOR]

Managers attempt to minimize spatial use conflicts in siting of offshore wind developments, but they must rely on available data to balance biological, commercial, and recreational needs. Marine spatial planning products are only as good as the data they are built upon and fishing data present major challenges due to their confidentiality and the difficulty in isolating true fishing activity. We present a methodology to increase the spatiotemporal resolution of fishing effort and exposure estimates for Southern New England scallop fishing activity using random decision forests to perform supervised classification on AIS data, with fallback to lower resolution datasets for vessels without AIS coverage. Final predictive accuracy of the tuned random forest AIS model was 97.9%, offering improvements of 24.7, 48.6, and 50% over VTR fishing footprints, and AIS and VMS speed cutoff methods, respectively, to predict whether vessel locations correspond to fishing activity. Comparison of the AIS model with VMS and VTR fallback to the VTR fishing footprints data product demonstrated that the increased precision of the AIS point data delineated as fishing dramatically changed how fishing effort, and therefore exposure in the form of fishery landings values, is distributed spatially in Southern New England wind energy areas. This is due to how the probability of fishing is distributed across location data points in the various products, which has implications for marine spatial planning and mitigation decision-making. Therefore, multiple data products should be considered when evaluating management options, as exposure estimates may differ depending on what inputs are used. The higher resolution AIS product may offer enhanced value in understanding exposure and impacts to individual vessels, especially once wind farms are under construction or operational. [ABSTRACT FROM AUTHOR]

The European Court of Justice issued a judgment on the processing of data concerning health in the context of online marketing of pharmacy-only medicinal products. The case involved a dispute between two pharmacy operators regarding the processing of customer data on an online platform. The Court clarified that personal data entered by customers when ordering pharmacy-only medicinal products online constitutes data concerning health, even if the products do not require a prescription. The judgment emphasized the broad interpretation of data concerning health to ensure a high level of protection for individuals' fundamental rights and freedoms. [Extracted from the article]

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]

Introduction: Dementia comprise a broad spectrum of cognitive declines affecting 47 million people worldwide, with numbers projected to reach 131 million by 2050. Predominantly associated with older adults, dementia can also impact younger individuals, having a significant impact on daily functioning of the affected patients, relatives, caregivers and the socioeconomic system. Recent research underscores the utility of social media, particularly X (previously designed as Twitter), in understanding public perceptions and sentiments related to neurological disorders. Despite some initial studies have explored social perceptions of dementia in X, broader and deeper analysis of this condition is still warranted. Materials and methods: In this retrospective study, we collected and examined all tweets posted in English or Spanish from 2007 to 2023 that mentioned dementia and compare the information with other highly representative neurological disorders like migraines, epilepsy, multiple sclerosis, spinal cord injury, or Parkinson's disease. We developed a codebook to analyze tweets, classifying them by themes such as trivialization, treatment perceptions, and etiopathogenesis. Manually categorized tweets trained machine learning models, BERTWEET for English and BETO for Spanish, which then classified larger datasets with high accuracy. Statistical analysis, including ANOVA, Kruskal-Wallis, and chi-square tests, was conducted to explore linguistic and cultural differences in perceptions of neurological disorders, with results visualized. Results: Our study reveals that dementia is by far the most frequently discussed neurological disorder on X. Likewise, this condition appears to be the most trivialized neurological disorder in Spanish tweets and the second most trivialized in English tweets, with notable differences in geolocation data. Additionally, we found significant differences in perceptions of dementia treatment and associated sentiments between Spanish and English tweets. Furthermore, our study identified varying perceptions of medical content (etiology) and non-medical content (positive/negative experiences and aid requests) related to dementia and other neurological disorders, unveiling a complex landscape of these topics on X. Conclusions: This study explores the importance of X as a social platform for addressing various critical issues related to dementia, comparing it with other neurological disorders in English and Spanish tweets. Future research could further investigate the valuable role of social media in understanding public perceptions and needs regarding dementia and neurological disorders among X users. [ABSTRACT FROM AUTHOR]

The global energy demand is growing, and the world is shifting towards using more renewable energy, like increased onshore wind power development. We used Global Positioning System (GPS) and Very High Frequency (VHF) location data from adult, territorial wolves Canis lupus in Scandinavia (Sweden and Norway; 1999–2021), to examine the potential for wind power development to affect wolf behavioural ecology. We examined the spatial overlap of areas proposed for wind power development with wolf territory activity centres prior to construction, to test to what extent overlap varies with season, time of day and social status (breeding versus non‐breeding wolves). Measures of overlap were the distance between wolf activity centre points and nearest proposed wind turbine, the probability of proposed wind turbines being within the activity centre, and the density of proposed wind turbines within the activity centre. The wolf activity centre points were closer to sites of proposed turbines in early summer than in late winter and the density of proposed turbines in the activity centre was higher in early summer than in late winter. These findings probably result from an altitudinal shift in wolf area use between summer and winter. We also found that the probability for proposed turbines to be within the activity centre was higher for non‐breeding than for breeding wolves during early summer, whereas it was higher for breeding compared to non‐breeding wolves during late winter. This difference might be an effect of that breeding wolves have a restricted area use during the early summer season (denning period), resulting in a lower probability of turbines being inside their activity centre as compared to late winter. There was no clear pattern for other seasonal and social status differences. The results should be viewed as a starting point for further research and supplemented with before‐after studies. [ABSTRACT FROM AUTHOR]

The Asian black bear (Ursus thibetanus) shows seasonal changes in body weight, activity level, and range use. However, there is little information available on these factors in individuals in the wild. We documented the body weights of free-ranging Asian black bears from spring to autumn, 2017 to 2019, in the Nikko-Ashio Mountains, Tochigi Prefecture, Japan, by using a newly noninvasive body weighing system (NIBWS) that we developed. We also fitted Asian black bears with global positioning system (GPS) collars with built-in activity sensors to evaluate the relationship between body weight, behavioral factor, and activity level. Bears visited the NIBWS 88 times in 2017, 176 times in 2018, and 321 times in 2019. We recorded the body weights of 4 bears in 2017, 15 bears in 2018, and 11 bears in 2019. Specifically, we consecutively recorded the body weight of an adult female from 2017 to 2019 and collected GPS location data from 2017 to 2018. The adult female was solitary in 2017, and she was accompanied by 2 cubs in 2018 and the yearlings of those cubs in 2019. Her body weight was lowest in 2018, likely because she had spent a large amount of her energy expenditure on nursing her cubs, compared with the year when she was solitary or nursing her yearlings. The GPS data showed that no significant relationship was found between body weight and any behavioral factors. Although her activity level decreased drastically in late August, her body weight did not decrease accordingly. In contrast, a subadult female that we tracked in 2019 increased her body weight during May to August, likely because she was solitary and required high intake of energy and nutrition for growth. Although our results provide only a snapshot, we established NIBWS and this is the first record of body weight changes over multiple years in free-ranging Asian black bears to our knowledge. [ABSTRACT FROM AUTHOR]

In the era of the Internet of Things (IoT), the demand for accurate positioning services has become increasingly critical, as location-based services (LBSs) depend on users' location data to deliver contextual functionalities. While the Global Positioning System (GPS) is widely regarded as the standard for outdoor localization due to its reliability and comprehensive coverage, its effectiveness in indoor positioning systems (IPSs) is limited by the inherent complexity of indoor environments. This paper examines the various measurement techniques and technological solutions that address the unique challenges posed by indoor environments. We specifically focus on three key aspects: (i) a comparative analysis of the different wireless technologies proposed for IPSs based on various methodologies, (ii) the challenges of IPSs, and (iii) forward-looking strategies for future research. In particular, we provide an in-depth evaluation of current IPSs, assessing them through multidimensional matrices that capture diverse architectural and design considerations, as well as evaluation metrics established in the literature. We further examine the challenges that impede the widespread deployment of IPSs and highlight the potential risk that these systems may not be recognized with a single, universally accepted standard method, unlike GPS for outdoor localization, which serves as the golden standard for positioning. Moreover, we outline several promising approaches that could address the existing challenges of IPSs. These include the application of transfer learning, feature engineering, data fusion, multisensory technologies, hybrid techniques, and ensemble learning methods, all of which hold the potential to significantly enhance the accuracy and reliability of IPSs. By leveraging these advanced methodologies, we aim to improve the overall performance of IPSs, thus paving the way for more robust and dependable LBSs in indoor environments. [ABSTRACT FROM AUTHOR]

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]

In Saudi Arabia, drones are increasingly used in different sensitive domains like military, health, and agriculture to name a few. Typically, drone cameras capture aerial images of objects and convert them into crucial data, alongside collecting data from distributed sensors supplemented by location data. The interception of the data sent from the drone to the station can lead to substantial threats. To address this issue, highly confidential protection methods must be employed. This paper introduces a novel steganography approach called the Shuffling Steganography Approach (SSA). SSA encompasses five fundamental stages and three proposed algorithms, designed to enhance security through strategic encryption and data hiding techniques. Notably, this method introduces advanced resistance to brute force attacks by employing predefined patterns across a wide array of images, complicating unauthorized access. The initial stage involves encryption, dividing, and disassembling the encrypted data. A small portion of the encrypted data is concealed within the text (Algorithm 1) in the third stage. Subsequently, the parts are merged and mixed (Algorithm 2), and finally, the composed text is hidden within an image (Algorithm 3). Through meticulous investigation and comparative analysis with existing methodologies, the proposed approach demonstrates superiority across various pertinent criteria, including robustness, secret message size capacity, resistance to multiple attacks, and multilingual support. [ABSTRACT FROM AUTHOR]

This paper evaluates the role of 'temporality' in defining functional regions. Functional regions are viewed as relatively closed in terms of selected population flows (or more generally concerning spatial interactions). They are usually defined by the daily commuting to work and are therefore commonly referred to as local labour market areas or travel-to-work areas. Using mobile phone location data, however, it is possible to work with population flows in a broader temporal and spatial context. Then we can talk about the temporality alternatives of functional regions depending on whether we base them on regular daily population flows, irregular daily population flows (which, according to the data analysis, are irregular from an individual's point of view but regular from a spatial unit's point of view between which they take place) or weekend population flows. Thus, several functional region's versions can be defined for a single regional system, where the different population movement's rhythm lengths movements limit their length and also determine their hierarchy. All functional regions' temporal alternatives according to mobile phone location data are defined based on data from the Czech Republic. [ABSTRACT FROM AUTHOR]

Last mile delivery is a significant component in the growth of e-commerce, especially in urban areas. The deliveries in dense cities cause several serious issues such as air/noise pollution and congestion. In this study, we compare the financial costs and CO2 emissions from different last mile delivery strategies of grocery bins in a scenario in Paris with four types of delivery vehicles (diesel/electric vans, cargo bikes, and autonomous delivery vehicles). We use costs associated with each vehicle type, location data with constraints for establishments in Paris, and delivery scenarios obtained by solving the vehicle routing problem for an overall cost comparison. We consider an economic analysis of the daily delivery to optimise the costs and estimate the CO2 emissions. The results highlight the competitiveness among last mile delivery and reflect the transition knowing the transport expenses since 2018: electric van costs become competitive with diesel ones, it concluded by some perspectives with cargo bikes and autonomous vehicles with their current limits. The strategies show a great impact on overall cost and emissions, which are vital in the development of sustainable urban last-mile delivery. [ABSTRACT FROM AUTHOR]

ABSTRACT The positioning uncertainty of mobile phone location (MPL) data greatly influences location services and crowd behavior analysis. Although many achievements have been made in controlling its main sources (signal drift and ping‐pong effects), several problems, such as single‐oscillation patterns, insufficient position optimization, and a lack of effective evaluation, remain. In this study, a set of MPL data quality optimization methods are proposed. First, the characteristics of drift records and the oscillation patterns of ping‐pong records are discussed. The quality of the MPL data is subsequently controlled with the proposed feature‐based drift‐record detection method, complex oscillation pattern‐based ping‐pong‐record detection method, and cumulative duration weighting‐based ping‐pong‐record optimization method. These methods are applied to the MPL dataset of a major operator in Nanjing city, and the optimization effect is evaluated with GPS data collected synchronously. The results show that the proposed detection and optimization methods can effectively improve the accuracy of MPL data. [ABSTRACT FROM AUTHOR]

Mobile phone location datasets offer opportunities to conduct spatial analysis at very granular levels. In most jurisdictions their collection and use are subject to legal requirements to protect the privacy of the individuals generating the data and to prevent its disclosure. However, it can be the case that geographically referenced datasets, which meet the legal and statistical requirements of non-disclosure, reveal much more when they are plotted on a map. This paper provides an overview of the particular ethical challenges posed to mapmakers by mobile phone location data, whilst keeping in mind the potential benefits of harnessing these data to inform research and public policy. We identify the principal ethical concern pertaining to the use of such location datasets: preventing disclosure and protecting privacy, but also considering the spatial and socio-political contexts that the data are collected and produced from. We then discuss case studies that highlight the context-specific considerations necessary to create ethical maps from sensitive location datasets, and offer experience-based recommendations for users considering the ethical implications of using mobile phone location data in research. [ABSTRACT FROM AUTHOR]

Given trajectories with gaps (i.e., missing data), we investigate algorithms to identify abnormal gaps in trajectories which occur when a given moving object did not report its location, but other moving objects in the same geographic region periodically did. The problem is important due to its societal applications, such as improving maritime safety and regulatory enforcement for global security concerns, such as illegal fishing, illegal oil transfers, and trans-shipments. The problem is challenging due to the difficulty of bounding the possible locations of the moving object during a trajectory gap, and the very high computational cost of detecting gaps in such a large volume of location data. The current literature on anomalous trajectory detection assumes linear interpolation within gaps, which may not be able to detect abnormal gaps since objects within a given region may have traveled away from their shortest path. In preliminary work, we introduced an abnormal gap measure that uses a classical space-time prism model to bound an object's possible movement during the trajectory gap and provided a scalable memoized gap detection algorithm (Memo-AGD). In this article, we propose a space time-aware gap detection (STAGD) approach to leverage space-time indexing and merging of trajectory gaps. We also incorporate a dynamic region merge-based (DRM) approach to efficiently compute gap abnormality scores. We provide theoretical proofs that both algorithms are correct and complete and also provide analysis of asymptotic time complexity. Experimental results on synthetic and real-world maritime trajectory data show that the proposed approach substantially improves computation time over the baseline technique. [ABSTRACT FROM AUTHOR]

Optimal foraging theory (OFT) and the energy maximization hypothesis (EMH) have long been essential when examining wildlife habitat selection. At high latitudes and altitudes, animals in winter face greater limitations in availability and accessibility of forage. Here we explore the foraging behavior of wood bison (Bison bison athabascae) during winter within the Ronald Lake bison herd in northeastern Alberta, Canada, and examine the trade‐offs they face due to limitations in forage abundance and availability (snow conditions), as well as the need to minimize predation risk. We used Global Positioning System (GPS) location data collected from 70 female wood bison to identify winter foraging sites and craters selected by bison to access forage beneath the snow. Within wetlands used by bison we selected 190 pairs of used (foraged) and random (available) sites to test eight a priori hypotheses explaining how bison traded‐off between forage availability, accessibility, and minimizing predation risk. We found with matched‐paired logistic regression that Carex atherodes was 1.21‐times more likely to be selected per unit increase in ground cover, compared to 1.17‐times per unit ground cover for C. aquatilis and C. utriculata. However, all Carex species showed an increase in selection when cover was > 50% cover within individual craters. While the importance of Carex was clear, forage site selection was still inversely related to snow depth. There is also a neutralizing combined effect of snow depth and Carex species ground cover which suggests that bison maximized their energy return by avoiding areas with deep snow (> 30 cm) that demanded intensive cratering, even when highly selected forage was accessible beneath. Avoidance of forage areas with deep snow demonstrates that wood bison employed a foraging strategy that considers both forage availability and environmental conditions, with snow depth being a limiting factor. We highlight the relationship between optimal foraging based on food availability and the trade‐offs within an energy restrictive winter season, furthering the understanding of how large herbivores forage strategically to maximize energy intake in northern environments. [ABSTRACT FROM AUTHOR]

The Sechura fox (Lycalopex sechurae) is a little-studied species despite being common in northwestern Peru and listed as Near Threatened internationally. This study presents the home range, habitat preference, and activity patterns of L. sechurae in two locations of the Sechura Desert: The Mangroves of San Pedro de Vice (MSPV) and the Illescas National Reserve (RNI). Using GPS collars, the minimum convex polygon method, and the Kernel estimator (KE), the home range of four female individuals (two from each site) was calculated. Additionally, the GPS location data were used to estimate activity patterns and habitat preferences for each individual. The home range of females from MSPV was 0.6 and 1 km² (95% KE), whereas, for the RNI, it was 3.5 and 25.1 km² (95% KE). Females from MSPV preferred dry forest (54.8% and 76.1%), while those from RNI favored desert scrub (64.3% and 64.5%). All individuals exhibited cathemeral behavior, with increased nocturnal activity. Our results demonstrate variability in home range sizes, similarity in activity patterns compared to other studies, and highlight the importance of dry forest and desert scrub as resource-rich habitats for Lycalopex sechurae. [ABSTRACT FROM AUTHOR]

Atmospheric pollution affects air quality and can pose a serious risk to public health. Traditional PM2.5 monitoring is limited by the uneven distribution of ground stations, which makes it difficult to obtain spatially continuous and accurate PM2.5 concentrations information. Using aerosol optical depth (AOD) retrieved from satellite remote sensing to study the temporal and spatial variation characteristics of PM2.5 can accurately predict the PM2.5 concentrations in a wide range and provide a basis for atmospheric pollution prevention and control. Considering the spatio-temporal correlations among the data, this study uses AOD data, introduces geographic location data, temporal data, meteorological data, and elevation data to construct an Optimized XGBoost (O-XGBoost) model with spatial and temporal characteristics to estimate PM2.5 concentrations in the Guanzhong region from 2019 to 2021 and analyse its spatial and temporal distribution characteristics. The results show that compared with the random forest (RF) and XGBoost models, the O-XGBoost model has higher estimation accuracy, with $$ {R^2}$$ R 2 , $$ RMSE$$ RMSE , and $$MAE$$ MAE of 0.873, 11.460 $$\mu g\cdot{m^{ - 3}}$$ μg ⋅ m − 3 , and 8.061 $$\mu g\cdot{m^{ - 3}}$$ μg ⋅ m − 3 , respectively. The estimation of PM2.5 concentrations based on the O-XGBoost model makes up for the uneven distribution of ground monitoring stations and improves the estimation accuracy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The proliferation of GPS-enabled devices and advances in positioning technologies have greatly facilitated the collection of user location data, making them valuable across various domains. One of the most common and practical uses of these location datasets is to recommend the most probable route between two locations to users. Traditional algorithms for route recommendation rely on true trajectory data collected from users, which raises significant privacy concerns due to the personal information often contained in location data. Therefore, in this paper, we propose a novel framework for computing optimal routes using location data collected through differential privacy (DP)-based privacy-preserving methods. The proposed framework introduces a method for accurately extracting transitional probabilities from perturbed trajectory datasets, addressing the challenge of low data utility caused by DP-based methods. Specifically, to effectively compute transitional probabilities, we present a density-adjusted sampling method that enables the collection of representative data across all areas. In addition, we introduce an effective scheme to approximately estimate transitional probabilities based on sampled datasets. Experimental results on real-world data demonstrate the practical applicability and effectiveness of our framework in computing optimal routes while preserving user privacy. [ABSTRACT FROM AUTHOR]

Identifying small, overlapping wheat ears in UAV images continues to be a difficult task. This paper proposes SCP-YOLO, a novel detection model that addresses this limitation. Initially, the dataset comprises remote sensing images of wheat kernels captured at two periods and three altitudes. Using the YOLOv8n network as a baseline, SCP-YOLO processes the network's low-resolution feature layer using the space-to-depth (SPD) approach. At the stage of feature fusion, the Context-Aggregation structure is executed to facilitate the aggregation and interaction of data on the feature map on a global scale. The PConv method ingeniously implements the lightweight detection head structure. On top of that, a new detection scale that integrates more superficial information with location data is positioned. The experimental outcomes demonstrate that the proposed method outperformed several established state-of-the-art detection models by achieving a detection speed of 90 frames per second and an AP@50 value of 96.3%. Compared with the baseline network, the AP@0.5, and AP@0.5:95 exhibited respective increases of 2.5% and 6.3%, respectively. Experimental results indicate that the methodology demonstrates exceptional robustness for six scenario datasets. About counting, it is exact and capable of quantifying wheat ears in images acquired through remote sensing. [ABSTRACT FROM AUTHOR]

This manuscript presents a novel geofencing method in behavioral research. Geofencing, built upon geolocation technology, constitutes virtual fences around specific locations. Every time a participant crosses the virtual border around the geofenced area, an event can be triggered on a smartphone, e.g., the participant may be asked to complete a survey. The geofencing method can alleviate the problems of constant location tracking, such as recording sensitive geolocation information and battery drain. In scenarios where locations for geofencing are determined by participants (e.g., home, workplace), no location data need to be transferred to the researcher, so this method can ensure privacy and anonymity. Given the widespread use of smartphones and mobile Internet, geofencing has become a feasible tool in studying human behavior and cognition outside of the laboratory. The method can help advance theoretical and applied psychological science at a new frontier of context-aware research. At the same time, there is a lack of guidance on how and when geofencing can be applied in research. This manuscript aims to fill the gap and ease the adoption of the geofencing method. We describe the current challenges and implementations in geofencing and present three empirical studies in which we evaluated the geofencing method using the Samply application, a tool for mobile experience sampling research. The studies show that sensitivity and precision of geofencing were affected by the type of event, location radius, environment, operating system, and user behavior. Potential implications and recommendations for behavioral research are discussed. [ABSTRACT FROM AUTHOR]

Policymakers and scholars have long proposed that willingness to participate in armed conflict is influenced by citizens' income-earning opportunities. Testing this opportunity cost mechanism has led to mixed results. One reason for this might be the fact that current proxies can also serve as indicators to test grievance-based theories. In this study, we construct a more suitable measure. We use crop calendars and crop location data to build an index of agricultural idle time for first administration units on the African continent from 1990 to 2017. We test the explanatory power of this measure by examining its relationship with armed conflict. Our results show that agricultural idle time increases the probability of observing armed conflict by more than 20 percent. [ABSTRACT FROM AUTHOR]

Understanding species' critical habitat requirements is crucial for effective conservation and management. However, such information can be challenging to obtain, particularly for highly mobile, wide-ranging species such as cetaceans. In the absence of systematic surveys, alternative economically viable methods are needed, such as the use of data collected from platforms of opportunity, and modelling techniques to predict species distribution in un-surveyed areas. The present study used data collected by ecotourism and other vessels of opportunity to investigate important habitats of a small, poorly studied population of bottlenose dolphins in Port Phillip Bay, south-eastern Australia. Using 16 years of dolphin sighting location data, an ensemble habitat suitability model was built from which physical factors influencing dolphin distribution were identified. Results indicated that important habitats were those areas close to shipping channels and coastlines with these factors primarily influencing the variation in the likelihood of dolphin presence. The relatively good performance of the ensemble model suggests that simple presence-background data may be sufficient for predicting the species distribution where sighting data are limited. However, additional data from the center of Port Phillip Bay is required to further support this contention. Important habitat features identified in the study are likely to relate to favorable foraging conditions for dolphins as they are known to provide feeding, breeding, and spawning habitat for a diverse range of fish and cephalopod prey species. The results of the present study highlight the importance of affordable community-based data collection, such as ecotourism vessels, for obtaining information critical for effective management. [ABSTRACT FROM AUTHOR]

This article provides a study on the foraging behavior and habitat selection of harbor seals in the archipelago of Saint-Pierre-and-Miquelon in the Northwest Atlantic. The study used telemetry data from ten harbor seals to analyze their diving behavior, spatio-temporal trends at sea, and habitat selection. The results indicate that harbor seals primarily feed on benthic prey but can occasionally forage on pelagic prey. They exhibit nocturnal foraging behavior, with most foraging dives occurring at night. The study also found that harbor seals spend a significant amount of time at sea and tend to select habitats within 100 km of their haul-out sites, with a preference for shallow waters. This information is valuable for wildlife managers in understanding the foraging areas and rhythms of harbor seals in this region. [Extracted from the article]

One of the most pressing issues in livestock farming is the protection of economically valuable livestock. The prevention and the treatment of infectious diseases are directly related to maintaining stable livestock output. Vermin is a primary source of livestock infection, resulting in the occurrence and expansion of epidemic diseases. To protect livestock against infections caused by epidemic diseases, this study proposes a vermin repulsion system called the Miyazaki Vermin Repulsion Robot (MiVeReR). Different from existing vermin repulsion systems, the development objective of MiVeReR is to repel vermin rather than kill them. In particular, MiVeReR generates changeable acousto-optic signals as repulsion signals for wild animals. Furthermore, MiVeReR employs image data to monitor the invasion of wild animals and their location data to track them, and accurately focuses the generated signals on them. These acousto-optic stimuli can be changed based on the reactions of the intruder through the feedback of the image data to ensure the effectiveness of the repulsion motions for vermin. Details on the hardware configuration of MiVeReR and its control scheme are explained. As a first step to develop MiVeReR, we attempted to repel vermin such as mice and wild cats from farm environments. Extensive experiments were conducted to verify the effectiveness of MiVeReR and the proposed control solution. Through experiments in wild environments, the feasibility of MiVeReR was inspected. The results of this study are concretely described. [ABSTRACT FROM AUTHOR]

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Prey must balance resource acquisition with predator avoidance for survival and reproduction. To reduce risk of predation, prey may avoid areas with high predator use, but if they are unable to due to resource acquisition requirements, they may instead change their habitat use or movement speed to mitigate predation risk. Prey risk response may depend on spatially or temporally varying forage availability as well as seasonal variation in prey vulnerability and availability of alternate foods for predators. To quantify how prey respond to spatial and temporal variation in risk of brown bear predation, we examined Roosevelt elk (Cervus canadensis roosevelti) spatiotemporal behavior responses to brown bear (Ursus arctos) habitat use on Afognak and Raspberry islands, Alaska, using Global Positioning System location data during elk parturition (20 May–15 June), summer (16 June–20 September), and autumn (21 September–10 November). During parturition and summer, elk used forest and shrub landcover in areas of higher brown bear probability of use. During parturition, elk used areas with lower forage productivity in areas of higher bear probability of use, and movement speed decreased with higher bear probability of use, especially in shrub landcover. During summer, elk used areas with higher forage productivity in areas of higher brown bear probability of use. During autumn, elk were less likely to use areas with higher bear habitat probability of use across landcover categories and forage productivity. During summer and autumn, elk movement speed increased with higher brown bear probability of use. Elk behavioral response to risk of brown bear predation could increase energy expenditure and decrease their ability to acquire forage, therefore negatively impacting survival and reproduction with spatiotemporal variation in risk response potentially amplifying these impacts. [ABSTRACT FROM AUTHOR]

Seeking to fill a void in the existing scholarship since no scholar has yet to examine the Islamic State Sahel Province's threat to China, this study utilizes a unique approach to examine two questions, namely does the Islamic State Sahel Province pose a threat to Chinese interests in the Central Sahel, and is it likely to impact Beijing's relations with Mali, Burkina Faso, and Niger? Part one of this study utilizes data from the Armed Conflict and Event Data Location Data (ACLED) and the Global Terrorism Database (GTD) to construct the group's operational profile. The operational profile covers political violence and strategic developments in each country and across the subregion; the expanse of and shifts in areas of operations; and tactics and targets in each terrorism incident by country and the subregion. Findings from the data analysis are utilized in the second part in conjunction with additional data collected on the economy and China's economic relations to assess whether the group poses a threat to Chinese interests. The study concludes that in the short-term, the Islamic State Sahel Province does not pose a threat to those interests and recognizes the potential change of the threat in long-term. [ABSTRACT FROM AUTHOR]

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. [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]

The impact of sporting events on local economies and their spatial distribution is a topic of active policy debate. This study adds to the discussion by examining granular cellphone location data to assess the spillover effects of Major League Baseball (MLB) games in a major US city. Focusing on the 2019 season, we explore granular geospatial patterns in mobility and consumer spending on game days versus non-game days in the Saint Louis region. Through density-based clustering and hotspot analysis, we uncover distinct spatiotemporal signatures and variations in visitor affluence across different teams. This study uses features like game day characteristics, location data (latitude and longitude), business types, and spending data. A significant finding is that specific spatial clusters of economic activity are formed around the stadium, particularly on game days, with multiple clusters identified. These clusters reveal a marked increase in spending at businesses such as restaurants, bars, and liquor stores, with revenue surges of up to 38% in certain areas. We identified a significant change in spending patterns in the local economy during games, with results varying greatly across teams. Notably, the XGBoost model performs best, achieving a test R2 of 0.80. The framework presented enhances the literature at the intersection of urban economics, sports analytics, and spatial modeling while providing data-driven actionable insights for businesses and policymakers. [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]

The effective management of solar photovoltaic (PV) arrays is vital to maximising energy generation and ensuring long-term performance reliability. The presence of faults, or partial shading, can give rise to hotspots in PV arrays, making their detection critical for timely maintenance and avoiding further impacts on their performance. This paper describes a new approach to the hotspot detection issue in thermal images of solar PV arrays by leveraging deep learning. This study employs the YOLOv10 (You Only Look Once, version 10) model to deliver very high accuracy and speed in identifying and localising hotspots under defined regions of interest (ROIs). The proposed method begins with taking thermal images from multiple solar PV installations while capturing a range of operational conditions and fault types. These images are annotated with hotspot regions to create a robust training dataset. Given YOLO's capability for real-time object detection with high precision and mean average precision (mAP), the YOLOv10 model is then implemented to train on this dataset. Multiple changes were made to the YOLOv10 hyperparameters to optimise them for detecting hotspots in thermal images. The experimental scenario of this paper demonstrates that this approach indeed performs significantly better than standard image processing methods as well as prior deep learning models for detecting both hotspot accuracy as well as speed of processing the images. The YOLOv10 method demonstrated the highest available classification performance with a mAP at intersection over union (IoU) of 0.5 accuracy of 0.91 with inference time suitable for realtime applications. The sample results demonstrated the model's continuous ability to detect hotspots and provide location data under various conditions, such as crossing through different times of day and weather. The results of this study demonstrate that YOLOv10 can be used for improved detection of hotspots in the explicably thermal imagery of solar PV arrays. [ABSTRACT FROM AUTHOR]