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.

Dynamic Scheduling for Veterans Health Administration Patients using Geospatial Dynamic Overbooking.

The Veterans Health Administration (VHA) is plagued by abnormally high no-show and cancellation rates that reduce the productivity and efficiency of its medical outpatient clinics. We address this issue by developing a dynamic scheduling system that utilizes mobile computing via geo-location data to estimate the likelihood of a patient arriving on time for a scheduled appointment. These likelihoods are used to update the clinic's schedule in real time. When a patient's arrival probability falls below a given threshold, the patient's appointment is canceled. This appointment is immediately reassigned to another patient drawn from a pool of patients who are actively seeking an appointment. The replacement patients are prioritized using their arrival probability. Real-world data were not available for this study, so synthetic patient data were generated to test the feasibility of the design. The method for predicting the arrival probability was verified on a real set of taxicab data. This study demonstrates that dynamic scheduling using geo-location data can reduce the number of unused appointments with minimal risk of double booking resulting from incorrect predictions. We acknowledge that there could be privacy concerns with regards to government possession of one's location and offer strategies for alleviating these concerns in our conclusion.

Utilization of the social media platform Snapchat in the care of an acutely suicidal individual.

Background/Case Presentation: An 18-year-old female college student shared an electronic message with her friends in which she discussed plans to harm herself through the ingestion of a highly toxic substance. Concerned friends activated emergency healthcare resources, who were dispatched to the individual's residence. Based on the known toxicity of the substance, locating the individual expeditiously to ensure her safety and well-being remained paramount. Upon arrival of collegiate-based health services, emergency medical services, and law enforcement, however, the individual was unable to be located. University healthcare and prehospital providers quickly recognized that the individual's recent location was visible to her friends through the social media platform Snapchat. Based on information gathered from her shared friends on the social media platform, a geo-location was identified. Law enforcement and EMS personnel canvased the identified area, successfully locating the individual. Conclusion: This case highlights the innovative use of smartphone technology to locate a person experiencing an acute medical emergency who hadn't summoned the services on her own.

Snakes and Ladders: Unpacking the Personalisation-Privacy Paradox in the Context of AI-Enabled Personalisation in the Physical Retail Environment.

Artificial intelligence (AI) is expected to bring to the physical retail environment the kind of mass personalisation that is already common in online commerce, delivering offers that are targeted to each customer, and that adapt to changes in the customer's context. However, factors related to the in-store environment, the small screen where the offer is delivered, and privacy concerns, create uncertainty regarding how customers might react to highly personalised offers that are delivered to their smartphones while they are in a store. To investigate how customers exposed to this type of AI-enabled, personalised offer, perceive it and respond to it, we use the personalisation-privacy paradox lens. Case study data focused on UK based, female, fashion retail shoppers exposed to such offers reveal that they seek discounts on desired items and improvement of the in-store experience; they resent interruptions and generic offers; express a strong desire for autonomy; and attempt to control access to private information and to improve the recommendations that they receive. Our analysis also exposes contradictions in customers' expectations of personalisation that requires location tracking. We conclude by drawing an analogy to the popular Snakes and Ladders game, to illustrate the delicate balance between drivers and barriers to acceptance of AI-enabled, highly personalised offers delivered to customers' smartphones while they are in-store.

Smartphone-based mobility metrics capture daily social motivation and behavior in schizophrenia.

Impaired social functioning contributes to reduced quality of life and is associated with poor physical and psychological well-being in schizophrenia, and thus is a key psychosocial treatment target. Low social motivation contributes to impaired social functioning, but is typically examined using self-report or clinical ratings, which are prone to recall biases and do not adequately capture the dynamic nature of social motivation in daily life. In the current study, we examined the utility of global positioning system (GPS)-based mobility data for capturing social motivation and behavior in people with schizophrenia. Thirty-one participants with schizophrenia engaged in a 60-day mobile intervention designed to increase social motivation and functioning. We examined associations between twice daily self-reports of social motivation and behavior (e.g., number of social interactions) collected via Ecological Momentary Assessment (EMA) and passively collected daily GPS mobility metrics (e.g., number of hours spent at home) in 26 of these participants. Findings suggested that greater mobility on a given day was associated with more EMA-reported social interactions on that day for four out of five examined mobility metrics: number of hours spent at home, number of locations visited, probability of being stationary, and likelihood of following one's typical routine. In addition, greater baseline social functioning was associated with less daily time spent at home and lower probability of following a daily routine during the intervention. GPS-based mobility thus corresponds with social behavior in daily life, suggesting that more social interactions may occur at times of greater mobility in people with schizophrenia, while subjective reports of social interest and motivation are less associated with mobility for this population.

COVID-19 Vaccine Hesitancy: Analysing Twitter to Identify Barriers to Vaccination in a Low Uptake Region of the UK.

To facilitate effective targeted COVID-19 vaccination strategies, it is important to understand reasons for vaccine hesitancy where uptake is low. Artificial intelligence (AI) techniques offer an opportunity for real-time analysis of public attitudes, sentiments, and key discussion topics from sources of soft-intelligence, including social media data. In this work, we explore the value of soft-intelligence, leveraged using AI, as an evidence source to support public health research. As a case study, we deployed a natural language processing (NLP) platform to rapidly identify and analyse key barriers to vaccine uptake from a collection of geo-located tweets from London, UK. We developed a search strategy to capture COVID-19 vaccine related tweets, identifying 91,473 tweets between 30 November 2020 and 15 August 2021. The platform's algorithm clustered tweets according to their topic and sentiment, from which we extracted 913 tweets from the top 12 negative sentiment topic clusters. These tweets were extracted for further qualitative analysis. We identified safety concerns; mistrust of government and pharmaceutical companies; and accessibility issues as key barriers limiting vaccine uptake. Our analysis also revealed widespread sharing of vaccine misinformation amongst Twitter users. This study further demonstrates that there is promising utility for using off-the-shelf NLP tools to leverage insights from social media data to support public health research. Future work to examine where this type of work might be integrated as part of a mixed-methods research approach to support local and national decision making is suggested.

Understanding the gap in emergency obstetric and neonatal care in Ghana through the PREventing Maternal And Neonatal Deaths (PREMAND) study.

OBJECTIVE: To explore basic and comprehensive emergency obstetric service provision across four districts in rural northern Ghana, and whether women were more likely to deliver at facilities with more skilled care. METHODS: Field workers geo-coded all health facilities in East Mamprusi, Sissala East, Kassena Nankana Municipal, and Kassena Nankana West districts, and administered surveys to assess providers and emergency obstetric care available. Data were also prospectively collected on delivery locations of women and neonates who died, or nearly died (near misses), between September 1, 2015 and April 30, 2017. RESULTS: There were 14 physicians for a population of nearly 360 000 women. Six (6%) facilities could provide basic emergency care, and 3 (3%) could provide comprehensive care. Services were distributed unequally, with 6 (67%) of the emergency facilities located in the least populated district. Among the sample of women and neonates who died or nearly died, 175 (39%) delivered at locations unable to provide basic emergency services. CONCLUSION: Access to emergency obstetric and neonatal care was distributed inequitably across these districts, suggesting the need to revisit geographic placement of facilities relative to population. The study also raised the question of how to ensure facilities are equipped to respond to emergencies.

Geo-location of Oncological Diseases in the Extra-urban Areas of Naples and Creation of Territorial Biobanks: An Important Tool to Study Potential Connections Between Environmental Factors and Cancer.

Many areas of the Campania region of Italy are more frequently at risk of neoplastic diseases due to environmental factors. However, the results of epidemiological studies, although numerous and detailed, do not explain tumor pathogenesis mechanisms in relation to the contribution of exposure to environmental pollutants. The Oncological Biobank of the G. Pascale Foundation (BBI) centralizes the collection and storage of biomaterials, both healthy and pathological human tissues, from urban and extra-urban areas of Naples, associating them with clinicopathological characteristics (type of tumor, histological type, grading, immunohistochemical and molecular profile, etc.). Geo-location of tumor samples is made by an IT platform in which demographic and clinical data are systematically uploaded. For the extra-urban areas of Naples, our experience of tumor sample geolocation highlighted cancer types with high impact of environmental pollutants as being lung, gastric and bladder cancer. In this mini-review, we underline that the possibility of specifically selecting tumor samples in circumscribed territories may allow targeted studies to verify potential connections between environmental factors and cancer. Moreover, the collection of biological fluids (serum, saliva, urine) from healthy individuals from specific areas may be a useful tool for the research of specific genetic polymorphisms linked to individual susceptibility.

Unraveling the Population History of Indian Siddis.

The Siddis are a unique Indian tribe of African, South Asian, and European ancestry. While previous investigations have traced their ancestral origins to the Bantu populations from subSaharan Africa, the geographic localization of their ancestry has remained elusive. Here, we performed biogeographical analysis to delineate the ancestral origin of the Siddis employing an admixture based algorithm, Geographical Population Structure (GPS). We evaluated the Siddi genomes in reference to five African populations from the 1000 Genomes project, two Bantu groups from the Human Genome Diversity Panel (HGDP) and five South Indian populations. The Geographic Population Structure analysis localized the ancestral Siddis to Botsawana and its present-day northeastern border with Zimbabwe, overlapping with one of the principal areas of secondary Bantu settlement in southeast Africa. Our results further indicated that while the Siddi genomes are significantly diverged from that of the Bantus, they manifested the highest genomic proximity to the North-East Bantus and the Luhyas from Kenya. Our findings resonate with evidences supporting secondary Bantu dispersal routes that progressed southward from the east African Bantu center, in the interlacustrine region and likely brought the ancestral Siddis to settlement sites in south and southeastern Africa from where they were disseminated to India, by the Portuguese. We evaluated our results in the light of existing historical, linguistic and genetic evidences, to glean an improved resolution into the reconstruction of the distinctive population history of the Siddis, and advance our knowledge of the demographic factors that likely contributed to the contemporary Siddi genomes.

Engaging research participants to inform the ethical conduct of mobile imaging, pervasive sensing, and location tracking research.

Researchers utilize mobile imaging, pervasive sensing, social media, and location tracking (MISST) technologies to observe and intervene with participants in their natural environment. The use of MISST methods and tools introduces unique ethical issues due to the type and quantity of data, and produces raising new challenges around informed consent, risk assessment, and data management. Since MISST methods are relatively new in behavioral research, there is little documented evidence to guide institutional review board (IRB) risk assessment and inform appropriate risk management strategies. This study was conducted to contribute the participant perspectives when considering ethical and responsible practices. Participants (n = 82) enrolled in an observational study where they wore several MISST devices for 1 week completed an exit survey. Survey items focused on the following: 1-device comfort, 2-informed consent, 3-privacy protections, and 4-bystander engagement. The informed consent process reflected participant actual experience. Device comfort and privacy were raised as concerns to both the participants and bystanders. While the majority of the participants reported a positive experience, it is important to note that the participants were volunteers who were not mandated to wear tracking devices and that persons who are mandated may not have a similar response. Findings support strategies proposed in the Kelly et al. (2013) ethical framework, which emphasizes procedures to improve informed consent, protect privacy, manage data, and respect bystander rights when using a wearable camera.