Sea ice concentration decline in an important Adélie penguin molt area.

Unlike in many polar regions, the spatial extent and duration of the sea ice season have increased in the Ross Sea sector of the Southern Ocean during the satellite era. Simultaneously, populations of Adélie penguins, a sea ice obligate, have been stable or increasing in the region. Relationships between Adélie penguin population growth and sea ice concentration (SIC) are complex, with sea ice driving different, sometimes contrasting, demographic patterns. Adélie penguins undergo a complete molt annually, replacing all their feathers while fasting shortly after the breeding season. Unlike most penguin species, a majority of Adélies are thought to molt on sea ice, away from the breeding colonies, which makes this period particularly difficult to study. Here, we evaluate the hypothesis that persistent areas of high SIC provide an important molting habitat for Adélie penguins. We analyzed data from geolocating dive recorders deployed year-round on 195 adult penguins at two colonies in the Ross Sea from 2017 to 2019. We identified molt by detecting extended gaps in postbreeding diving activity and used associated locations to define two key molting areas. Remotely sensed data indicated that SIC during molt was anomalously low during the study and has declined in the primary molt area since 1980. Further, annual return rates of penguins to breeding colonies were positively correlated with SIC in the molt areas over 20 y. Together these results suggest that sea ice conditions during Adélie penguin molt may represent a previously underappreciated annual bottleneck for adult survival.

SkyPole-A method for locating the north celestial pole from skylight polarization patterns.

True north can be determined on Earth by three means: magnetic compasses, stars, and via the global navigation satellite systems (GNSS), each of which has its own drawbacks. GNSS are sensitive to jamming and spoofing, magnetic compasses are vulnerable to magnetic interferences, and the stars can be used only at night with a clear sky. As an alternative to these methods, nature-inspired navigational cues are of particular interest. Celestial polarization, which is used by insects such as Cataglyphis ants, can provide useful directional cues. Migrating birds calibrate their magnetic compasses by observing the celestial rotation at night. By combining these cues, we have developed a bioinspired optical method for finding the celestial pole during the daytime. This method, which we have named SkyPole, is based on the rotation of the skylight polarization pattern. A polarimetric camera was used to measure the degree of skylight polarization rotating with the Sun. Image difference processes were then applied to the time-varying measurements in order to determine the north celestial pole's position and thus the observer's latitude and bearing with respect to the true north.

Explore variation of salivary bacteria across time and geolocations.

Saliva is an informative body fluid that can be found at various crime scenes, and the salivary bacterial community has been revealed it is a potential auxiliary target for forensic identification. However, the variation of salivary bacterial community composition across time and geolocation needs to be explored. The study was designed to be carried out during the winter vacation that was across about 50 days and eight geographic locations. The high throughput sequencing was performed with the V3-V4 region of the16S rRNA gene to explore salivary bacterial community composition. An overall slight fluctuation of the salivary bacteria was observed, which primarily occurred in the relative abundance of the salivary bacterial taxa. The results of principal coordinate analysis and hierarchical clustering showed samples were clustered by the individuals. All individuals could be correctly identified with the random forest model. In summation, although the relative abundance of salivary bacteria varied across the changes of time and geolocation, the individualized characteristic of salivary bacteria remained steady, which is beneficial for the salivary bacterial application in personal identification.

Use of strontium isotope ratios in potential geolocation of Ajnala skeletal remains: a forensic archeological study.

Stable isotope methods for provenance of unidentified human remains are relatively a newer field of enquiry in forensic archeology. It is of great interest for forensic experts these days. The application of strontium isotope analyses for estimating geolocation of archeological remains is of great interest in bioarcheology and modern forensics. The strontium (Sr) isotope composition of human bones and teeth has been widely used to reconstruct an individual's geo-affiliation, residential mobility, and migration history. Thousands of unknown human remains, reportedly belonging to 282 Indian soldiers of 26th Native Bengal regiment and killed in 1857, were exhumed non-scientifically from an abandoned well situated underneath a religious structure at Ajnala (Amritsar, India). Whether these remains belonged to the individuals, local or non-local to the site, was the important forensic archeological question to be answered by doing their thorough forensic anthropological examinations. In the present study, 27 mandibular teeth (18 s molars, 6 first molars, and 3 premolars) collected from the Ajnala skeletal assemblage were processed for strontium isotope analysis, and the measured ratios were compared with published isotope baseline data to estimate the locality status of these remains. The Sr isotopic values were concentrated in the range of 0.7175 to 0.7270. The comparative analysis of isotopic ratios revealed that most individuals buried in the Ajnala well have 87Sr/86Sr values close to the river as well as groundwater of the Gangetic plain (less radiogenic 87Sr/86Sr ~ 0.716); most likely originated near Varanasi (Uttar Pradesh, India) region, whereas the individuals with higher 87Sr/86Sr ratios (~ 0.7200) probably resided in the West Bengal and Bihar areas where the river as well as groundwater of the Gangetic plain is relatively more radiogenic. Thus, the strontium isotope results reveal that the Ajnala individuals did not grow up or live in the Amritsar region during their childhood, and this observation complemented the previous forensic anthropological and molecular findings. There is very little Indian data on the bioavailable strontium, so the inferences from the present study estimating Sr isotope abundances are expected to provide baseline data for future forensic provenance studies that will contribute to the global efforts of mapping Sr isotope variations by the isotope community.

Exposure density and neighborhood disparities in COVID-19 infection risk.

Although there is increasing awareness of disparities in COVID-19 infection risk among vulnerable communities, the effect of behavioral interventions at the scale of individual neighborhoods has not been fully studied. We develop a method to quantify neighborhood activity behaviors at high spatial and temporal resolutions and test whether, and to what extent, behavioral responses to social-distancing policies vary with socioeconomic and demographic characteristics. We define exposure density ([Formula: see text]) as a measure of both the localized volume of activity in a defined area and the proportion of activity occurring in distinct land-use types. Using detailed neighborhood data for New York City, we quantify neighborhood exposure density using anonymized smartphone geolocation data over a 3-mo period covering more than 12 million unique devices and rasterize granular land-use information to contextualize observed activity. Next, we analyze disparities in community social distancing by estimating variations in neighborhood activity by land-use type before and after a mandated stay-at-home order. Finally, we evaluate the effects of localized demographic, socioeconomic, and built-environment density characteristics on infection rates and deaths in order to identify disparities in health outcomes related to exposure risk. Our findings demonstrate distinct behavioral patterns across neighborhoods after the stay-at-home order and that these variations in exposure density had a direct and measurable impact on the risk of infection. Notably, we find that an additional 10% reduction in exposure density city-wide could have saved between 1,849 and 4,068 lives during the study period, predominantly in lower-income and minority communities.

Bridging and Bonding Social Capital by Analyzing the Demographics, User Activities, and Social Network Dynamics of Sexual Assault Centers on Twitter: Mixed Methods Study.

BACKGROUND: Social media platforms have gained popularity as communication tools for organizations to engage with clients and the public, disseminate information, and raise awareness about social issues. From a social capital perspective, relationship building is seen as an investment, involving a complex interplay of tangible and intangible resources. Social media-based social capital signifies the diverse social networks that organizations can foster through their engagement on social media platforms. Literature underscores the great significance of further investigation into the scope and nature of social media use, particularly within sectors dedicated to service delivery, such as sexual assault organizations. OBJECTIVE: This study aims to fill a research gap by investigating the use of Twitter by sexual assault support agencies in Canada. It seeks to understand the demographics, user activities, and social network structure within these organizations on Twitter, focusing on building social capital. The research questions explore the demographic profile, geographic distribution, and Twitter activity of these organizations as well as the social network dynamics of bridging and bonding social capital. METHODS: This study used purposive sampling to investigate sexual assault centers in Canada with active Twitter accounts, resulting in the identification of 124 centers. The Twitter handles were collected, yielding 113 unique handles, and their corresponding Twitter IDs were obtained and validated. A total of 294,350 tweets were collected from these centers, covering >93.54% of their Twitter activity. Preprocessing was conducted to prepare the data, and descriptive analysis was used to determine the center demographics and age. Furthermore, geolocation mapping was performed to visualize the center locations. Social network analysis was used to explore the intricate relationships within the network of sexual assault center Twitter accounts, using various metrics to assess the network structure and connectivity dynamics. RESULTS: The results highlight the substantial presence of sexual assault organizations on Twitter, particularly in provinces such as Ontario, British Columbia, and Quebec, underscoring the importance of tailored engagement strategies considering regional disparities. The analysis of Twitter account creation years shows a peak in 2012, followed by a decline in new account creations in subsequent years. The monthly tweet activity shows November as the most active month, whereas July had the lowest activity. The study also reveals variations in Twitter activity, account creation patterns, and social network dynamics, identifying influential social queens and marginalized entities within the network. CONCLUSIONS: This study presents a comprehensive landscape of the demographics and activities of sexual assault centers in Canada on Twitter. This study suggests that future research should explore the long-term consequences of social media use and examine stakeholder perceptions, providing valuable insights to improve communication practices within the nonprofit human services sector and further the missions of these organizations.

Passive Polarized Vision for Autonomous Vehicles: A Review.

This review article aims to address common research questions in passive polarized vision for robotics. What kind of polarization sensing can we embed into robots? Can we find our geolocation and true north heading by detecting light scattering from the sky as animals do? How should polarization images be related to the physical properties of reflecting surfaces in the context of scene understanding? This review article is divided into three main sections to address these questions, as well as to assist roboticists in identifying future directions in passive polarized vision for robotics. After an introduction, three key interconnected areas will be covered in the following sections: embedded polarization imaging; polarized vision for robotics navigation; and polarized vision for scene understanding. We will then discuss how polarized vision, a type of vision commonly used in the animal kingdom, should be implemented in robotics; this type of vision has not yet been exploited in robotics service. Passive polarized vision could be a supplemental perceptive modality of localization techniques to complement and reinforce more conventional ones.

Combining acoustic telemetry with archival tagging to investigate the spatial dynamic of the understudied pollack, Pollachius pollachius.

Combining fish tracking methods is a promising way of leveraging the strengths of each approach while mitigating their individual weaknesses. Acoustic telemetry provides presence information as the fish move within receiver range, eliminating the need for tag recovery. Archival tags, on the other hand, record environmental variables on tag retrieval, enabling continuous path reconstruction of a fish beyond coastal regions. This study capitalizes on the combination of both methods for geolocating pollack, Pollachius pollachius, an understudied species of the northeast Atlantic, where declining stocks are raising concern. Essential knowledge of population structure and connectivity between essential habitats is critically lacking and could help inform stock assessment and management. The aims of the study were (1) to evaluate the feasibility of double-tagging pollack, known for being challenging to tag, and (2) to track seasonal movements across the Channel to gain first insights into pollack spatial ecology. In 2022, an extensive network of acoustic receivers was been deployed in the Channel along the French, English, and Belgian coasts as part of the Fish Intel project. We tagged 83 pollack with acoustic transmitters, among which 48 were double-tagged with data storage tags. Post-tagging survival assessment, conducted on a subset of 35 individuals, revealed a successful procedure with a 97% short-term survival rate. By October 2023, the acoustic telemetry network detected 30 out of 83 pollack at least once, with no large-scale movements observed across the Channel. Presence in the network fluctuates seasonally, peaking in summer, particularly among immature fish. Integrating acoustic detections with temperature and depth time series in a geolocation model enabled trajectory reconstruction of 10 recaptured pollack, seven of which were detected by the network. This combined tracking approach revealed coastal movements along the coast of Brittany in France, highlighting the ecological significance of the Iroise Sea for pollack throughout the year, particularly in summer. The geolocation model also suggested movements towards the entrance of the western Channel. This study highlights the complementarity of acoustic telemetry and archival tagging in reconstructing fish movements in their natural environment. As data accumulate, these innovative tracking methods promise to continually unveil new insights into the spatial ecology of the understudied pollack, which is essential for the species' management.

The "allergic nose as a pollen detector" concept: e-Diaries to predict pollen trends.

Hirst pollen traps and operator pollen recognition are worldwide used by aerobiologists, providing essential services for the diagnosis and monitoring of allergic patients. More recently, semiautomated or fully automated detector systems have been developed, which facilitate prediction of pollen exposure and risk for the individual patient. In parallel, smartphone apps consisting of short questionnaires filled in daily by the patient/user provide daily scores, time trajectories, and descriptive reports of the severity of respiratory allergies in patients with pollen allergy. The usual scientific and clinical approach to this matter is to monitor the environment (pollen concentration) in order to predict the risk of symptoms (allergic rhinitis) in a population. We discuss here the opposite, contraintuitive possibility, that is, the use of e-diaries to collect daily information of mono-sensitized pollen-allergic patients in order to predict the clinically efficient airborne exposure to a given pollen, area, and time period. In line with the "Patient as Sensor" concept, proposed in 2013 by Bernd Resch, the "allergic nose" may be used as a pollen detector in addition to existing calibrated hardware sensors, namely the pollen stations, thus contributing with individual measurements, sensations, and symptoms' perception. The target of this review is to present a novel concept of pollen monitoring based on "pollen-detector" patients to inspire future cooperative studies aimed at investigating and hopefully validating our hypothesis.

Capturing emergency dispatch address points as geocoding candidates to quantify delimited confidence in residential geolocation.

BACKGROUND: In response to citizens' concerns about elevated cancer incidence in their locales, US CDC proposed publishing cancer incidence at sub-county scales. At these scales, confidence in patients' residential geolocation becomes a key constraint of geospatial analysis. To support monitoring cancer incidence in sub-county areas, we presented summary metrics to numerically delimit confidence in residential geolocation. RESULTS: We defined a concept of Residential Address Discriminant Power (RADP) as theoretically perfect within all residential addresses and its practical application, i.e., using Emergency Dispatch (ED) Address Point Candidates of Equivalent Likelihood (CEL) to quantify Residential Geolocation Discriminant Power (RGDP) to approximate RADP. Leveraging different productivity of probabilistic, deterministic, and interactive geocoding record linkage, we simultaneously detected CEL for 5,807 cancer cases reported to North Carolina Central Cancer Registry (NC CCR)- in January 2022. Batch-match probabilistic and deterministic algorithms matched 86.0% cases to their unique ED address point candidates or a CEL, 4.4% to parcel site address, and 1.4% to street centerline. Interactively geocoded cases were 8.2%. To demonstrate differences in residential geolocation confidence between enumeration areas, we calculated sRGDP for cancer cases by county and assessed the existing uncertainty within the ED data, i.e., identified duplicate addresses (as CEL) for each ED address point in the 2014 version of the NC ED data and calculated ED_sRGDP by county. Both summary RGDP (sRGDP) (0.62-1.00) and ED_sRGDP (0.36-1.00) varied across counties and were lower in rural counties (p < 0.05); sRGDP correlated with ED_sRGDP (r = 0.42, p < 0.001). The discussion covered multiple conceptual and economic issues attendant to quantifying confidence in residential geolocation and presented a set of organizing principles for future work. CONCLUSIONS: Our methodology produces simple metrics - sRGDP - to capture confidence in residential geolocation via leveraging ED address points as CEL. Two facts demonstrate the usefulness of sRGDP as area-based summary metrics: sRGDP variability between counties and the overall lower quality of residential geolocation in rural vs. urban counties. Low sRGDP for the cancer cases within the area of interest helps manage expectations for the uncertainty in cancer incidence data. By supplementing cancer incidence data with sRGDP and ED_sRGDP, CCRs can demonstrate transparency in geocoding success, which may help win citizen trust.

Geolocation prediction from STR genotyping: a pilot study in five geographically distinct global populations.

BACKGROUND: Traditional CE-based STR profiles are highly useful for the purpose of individualisation. However, they do not give any additional information without the presence of the reference sample for comparison. AIM: To assess the usability of STR-based genotypes for the prediction of an individual's geolocation. SUBJECTS AND METHODS: Genotype data from five geographically distinct populations, i.e. Caucasian, Hispanic, Asian, Estonian, and Bahrainian, were collected from the published literature. RESULTS: A significant difference (p < 0.05) in the observed genotypes was found between these populations. D1S1656 and SE33 showed substantial differences in their genotype frequencies across the tested populations. SE33, D12S391, D21S11, D19S433, D18S51, and D1S1656 were found to have the highest occurrence of "unique genotype's" in different populations. In addition, D12S391 and D13S317 exhibited distinct population-specific "most frequent genotypes." CONCLUSIONS: Three different prediction models have been proposed for genotype to geolocation prediction, i.e. (i) use of unique genotypes of a population, (ii) use of the most frequent genotype, and (iii) a combinatorial approach of unique and most frequent genotypes. These models could aid the investigating agencies in cases where no reference sample is available for comparison of the profile.

Application of Gaussian Mixtures in a Multimodal Kalman Filter to Estimate the State of a Nonlinearly Moving System Using Sparse Inaccurate Measurements in a Cellular Radio Network.

Kalman filter is a well-established accuracy correction method in control, guidance, and navigation. With the popularity of mobile communication and ICT, Kalman Filter has been used in many new applications related to positioning based on spatiotemporal data from the cellular network. Despite the low accuracy compared to Global Positioning System, the method is an excellent supplement to other positioning technologies. It is often used in sensor fusion setups as a complementary source. One of the reasons for the Kalman Filter's inaccuracy lies in naive radio coverage approximation techniques based on multivariate normal distributions assumed by previous studies. Therefore, in this paper, we evaluated those disadvantages and proposed a Gaussian mixtures model to address the non-arbitrary shape of the radio cells' coverage area. Having incorporated the Gaussian mixtures model into Switching Kalman Filter, we achieved better accuracy in positioning within the cellular network.

An Improved TDOA Method for Land-Based Long-Range HF Skywave Source Geolocation and Experimental Results.

The real-time information of the unknown ionospheric environments is difficult to obtain, plaguing the timely and accurate geolocation of high frequency (HF) sources. In this paper, we propose an improved HF skywave source geolocation method based on the time-difference-of-arrival (TDOA) with the semidefinite programming (SDP), and model HF signal propagation paths as paths with significant non-line-of-sight (NLOS) biases. With this method, no priori information about the ionosphere, especially the priori ionospheric virtual heights of reflection, is necessary while timely and accurately geolocating the HF sources. Furthermore, we use the ray tracing technique and build a 3D ionospheric electron density gridded matrix model to simulate realistic HF signal propagation paths. In the simulations, the proposed method is compared with existing methods, and detailed geolocation error distribution maps are given. In the experiments, HF I/Q data captured from different types of HF transmitters are located by six receivers with time synchronization. Simulated and experimental results show that the proposed method improves the positioning accuracy by about 50% compared with existing methods under the same conditions, and the average relative positioning error is less than 2.7%.

Genomics Reveals the Origins of Historical Specimens.

Centuries of zoological studies have amassed billions of specimens in collections worldwide. Genomics of these specimens promises to reinvigorate biodiversity research. However, because DNA degrades with age in historical specimens, it is a challenge to obtain genomic data for them and analyze degraded genomes. We developed experimental and computational protocols to overcome these challenges and applied our methods to resolve a series of long-standing controversies involving a group of butterflies. We deduced the geographical origins of several historical specimens of uncertain provenance that are at the heart of these debates. Here, genomics tackles one of the greatest problems in zoology: countless old specimens that serve as irreplaceable embodiments of species concepts cannot be confidently assigned to extant species or population due to the lack of diagnostic morphological features and clear documentation of the collection locality. The ability to determine where they were collected will resolve many on-going disputes. More broadly, we show the utility of applying genomics to historical museum specimens to delineate the boundaries of species and populations, and to hypothesize about genotypic determinants of phenotypic traits.

Emotions and Topics Expressed on Twitter During the COVID-19 Pandemic in the United Kingdom: Comparative Geolocation and Text Mining Analysis.

BACKGROUND: In recent years, the COVID-19 pandemic has brought great changes to public health, society, and the economy. Social media provide a platform for people to discuss health concerns, living conditions, and policies during the epidemic, allowing policymakers to use this content to analyze the public emotions and attitudes for decision-making. OBJECTIVE: The aim of this study was to use deep learning-based methods to understand public emotions on topics related to the COVID-19 pandemic in the United Kingdom through a comparative geolocation and text mining analysis on Twitter. METHODS: Over 500,000 tweets related to COVID-19 from 48 different cities in the United Kingdom were extracted, with the data covering the period of the last 2 years (from February 2020 to November 2021). We leveraged three advanced deep learning-based models for topic modeling to geospatially analyze the sentiment, emotion, and topics of tweets in the United Kingdom: SenticNet 6 for sentiment analysis, SpanEmo for emotion recognition, and combined topic modeling (CTM). RESULTS: We observed a significant change in the number of tweets as the epidemiological situation and vaccination situation shifted over the 2 years. There was a sharp increase in the number of tweets from January 2020 to February 2020 due to the outbreak of COVID-19 in the United Kingdom. Then, the number of tweets gradually declined as of February 2020. Moreover, with identification of the COVID-19 Omicron variant in the United Kingdom in November 2021, the number of tweets grew again. Our findings reveal people's attitudes and emotions toward topics related to COVID-19. For sentiment, approximately 60% of tweets were positive, 20% were neutral, and 20% were negative. For emotion, people tended to express highly positive emotions in the beginning of 2020, while expressing highly negative emotions over time toward the end of 2021. The topics also changed during the pandemic. CONCLUSIONS: Through large-scale text mining of Twitter, our study found meaningful differences in public emotions and topics regarding the COVID-19 pandemic among different UK cities. Furthermore, efficient location-based and time-based comparative analysis can be used to track people's thoughts and feelings, and to understand their behaviors. Based on our analysis, positive attitudes were common during the pandemic; optimism and anticipation were the dominant emotions. With the outbreak and epidemiological change, the government developed control measures and vaccination policies, and the topics also shifted over time. Overall, the proportion and expressions of emojis, sentiments, emotions, and topics varied geographically and temporally. Therefore, our approach of exploring public emotions and topics on the pandemic from Twitter can potentially lead to informing how public policies are received in a particular geographical area.

Precise Target Geo-Location of Long-Range Oblique Reconnaissance System for UAVs.

High-precision, real-time, and long-range target geo-location is crucial to UAV reconnaissance and target strikes. Traditional geo-location methods are highly dependent on the accuracies of GPS/INS and the target elevation, which restricts the target geo-location accuracy for LRORS. Moreover, due to the limitations of laser range and the common, real time methods of improving the accuracy, such as laser range finders, DEM and geographic reference data are inappropriate for long-range UAVs. To address the above problems, a set of work patterns and a novel geo-location method are proposed in this paper. The proposed method is not restricted by conditions such as the accuracy of GPS/INS, target elevation, and range finding instrumentation. Specifically, three steps are given, to perform as follows: First, calculate the rough geo-location of the target using the traditional method. Then, according to the rough geo-location, reimage the target. Due to errors in GPS/INS and target elevation, there will be a re-projection error between the actual points of the target and the calculated projection ones. Third, a weighted filtering algorithm is proposed to obtain the optimized target geo-location by processing the reprojection error. Repeat the above process until the target geo-location estimation converges on the true value. The geo-location accuracy is improved by the work pattern and the optimization algorithm. The proposed method was verified by simulation and a flight experiment. The results showed that the proposed method can improve the geo-location accuracy by 38.8 times and 22.5 times compared with traditional methods and DEM methods, respectively. The results indicate that our method is efficient and robust, and can achieve high-precision target geo-location, with an easy implementation.

An Edge Computing and Ambient Data Capture System for Clinical and Home Environments.

The non-contact patient monitoring paradigm moves patient care into their homes and enables long-term patient studies. The challenge, however, is to make the system non-intrusive, privacy-preserving, and low-cost. To this end, we describe an open-source edge computing and ambient data capture system, developed using low-cost and readily available hardware. We describe five applications of our ambient data capture system. Namely: (1) Estimating occupancy and human activity phenotyping; (2) Medical equipment alarm classification; (3) Geolocation of humans in a built environment; (4) Ambient light logging; and (5) Ambient temperature and humidity logging. We obtained an accuracy of 94% for estimating occupancy from video. We stress-tested the alarm note classification in the absence and presence of speech and obtained micro averaged F1 scores of 0.98 and 0.93, respectively. The geolocation tracking provided a room-level accuracy of 98.7%. The root mean square error in the temperature sensor validation task was 0.3°C and for the humidity sensor, it was 1% Relative Humidity. The low-cost edge computing system presented here demonstrated the ability to capture and analyze a wide range of activities in a privacy-preserving manner in clinical and home environments and is able to provide key insights into the healthcare practices and patient behaviors.

Emitter Location Using Frequency Difference of Arrival Measurements Only.

It is desirable to enable emitter location using frequency difference of arrival (FDoA) measurements only, since many signals are characterized by coarse range resolution and fine Doppler resolution. For instance, while using the cross-ambiguity function (CAF) to measure the time difference of arrival (TDoA) and the FDoA of a narrowband signal, it is difficult to obtain accurate TDoA measurements because the Doppler resolution is higher than the range resolution. Grid-based and sample-based algorithms are developed to solve the two-dimensional (2D) emitter location problem, where the solution space is approximated, respectively, by generating deterministic and random emitter location candidates. Simulation results corroborate the viability of both non-iterative algorithms to estimate the emitter location using a single-time snapshot of FDoA measurements only, without any prior location information or any knowledge about the distribution of measurement errors. The achieved accuracies are sufficient for early warning purposes, preparing defenses, and cueing more accurate location sensors by directing additional surveillance resources.

Sexual segregation in juvenile Antarctic fur seals.

Sexual segregation, the differential space, habitat or resource use by males and females, can have profound implications for conservation, as one sex may be more vulnerable to environmental and anthropogenic stressors. The drivers of sexual segregation, such as sex differences in body size, breeding constraints, and social behaviour, have been well studied in adults but are poorly understood in immature animals. To determine whether sexual segregation occurs in juvenile Antarctic fur seals, Arctocephalus gazella, and investigate the underlying drivers, we deployed Global Location Sensors on 26 males and 19 females of 1-3 years of age at Bird Island, South Georgia. Sexual segregation occurred in foraging distribution, primarily in latitude, with females foraging closer to South Georgia and the Polar Front, and males foraging further south near the Antarctic Peninsula. This segregation was particularly evident in Feb-Apr and May-Nov, and males spent more time hauled out than females in May-Nov. Although juveniles have no immediate reproductive commitments, reproductive selection pressures are still likely to operate and drive sex differences in body size, risk-taking, and social roles. These factors, coupled with prey distribution, likely contributed to sexual segregation in juvenile Antarctic fur seals. Consequently, male and female juveniles may compete with different fisheries and respond differently to environmental change, highlighting the importance of considering sex and age groups in species conservation efforts.

Emitter Location with Azimuth and Elevation Measurements Using a Single Aerial Platform for Electronic Support Missions.

Passive ground emitter geolocation techniques are essential to electronic warfare systems, as they provide threat warnings in hostile environments, while ensuring the electronic silence of the mission platform. Geolocation of enemy emitters indicates the position of and type of adversary troops, and allows for the use of guided munition against enemy targets. Three-dimensional geolocation solutions based on least squares and particle filter estimation, using only azimuth and elevation measurements, were considered. Three batch-processing and one instantaneous solution algorithms, i.e., using a single pulse or a single observation point, were developed and investigated. The performance of the proposed solutions was demonstrated by simulations. Results showed that the batch-processing solutions achieved acceptable accuracies with a sufficient number of observation points. The performance degraded with fewer observation points. The instantaneous geolocation solution improved performance with increasing observation points, i.e., working in the sequential mode, and therefore could approach the accuracy of the batch-processing solutions.