A low-altitude public air route network for UAV management constructed by global subdivision grids

With an increasing number of unmanned aerial vehicles (UAVs), the difficulty of UAV management becomes more challenging, especially for low-altitude airspace due to complicated issues of security, privacy and flexibility. Existing management approaches to UAV flights include implementing registration of flight activity for supervision purposes, limiting the maximum flight height, setting different zones for different flight activities and prohibiting flights. In this research, we proposed a new air traffic management method for UAVs based on global subdivision theory. We designed four types of low-altitude air routes from grids, which correspond to grid sizes of 1.85 km, 128 m, 64 m and 32 m. Utilization of the subdivision grids transforms the complex spatial computation problem into a query process in the spatial database, which provides a new approach to UAV management in the fifth-generation (5G) era. We compared the number and data size of stored track records using longitude and latitude and different grid levels, computed time consumption for air route trafficability and simulated UAV flight to verify the feasibility of constructing this type of air traffic highway system. The amount of data storage and time consumption for air route trafficability can be substantially reduced by subdivision. For example, the data size using traditional expressions of latitude and longitude is approximately 1.5 times that of using a 21-level grid, and the time consumption by coordinates is approximately 1.5 times that of subdivision grids at level 21. The results of the simulated experiments indicate that in the 5G environment, gridded airspace can effectively improve the efficiency of UAV trajectory planning and reduce the size of information storage in the airspace environment. Therefore, given the increasing number of UAVs in the future, gridded highways have the potential to provide a foundation for various UAV applications.

Drone for medical products transportation in maternal healthcare: A systematic review and framework for future research

INTRODUCTION

Medical products transportation has become an important research topic requiring multidisciplinary collaboration among experts in medicine, engineering, and health economics. Current modes of transportation are unable to overcome the limited settings in maternal healthcare, particularly during the event of obstetric emergencies. The drone is a promising medical product aerial transportation (MedART) that holds an enormous potential for delivery of medical supplies in the healthcare system. We conducted a systematic review to examine scientific evidence of positive impact of drone transportation on maternal health.

METHODS

The following electronic databases were searched from inception to July 2019: ScienceDirect, PubMed, and EMBASE. The report was made in accordance with the principles of PRISMA guidelines. The search terms used were related to drones including unmanned aerial vehicle (UAV) and unmanned aerial system (UAS), and related to obstetric/maternal including obstetric emergencies and postpartum hemorrhage. Studies were selected if the intervention used were drones, and if any direct or indirect maternal health indicators were reported. Meta-analysis was not done throughout the study in view of the anticipated heterogeneity of each study.

RESULTS

Our initial search yielded a total of 244 relevant publications, from which 236 were carried forward for a title and abstract screening. After careful examination, only two were included for systematic synthesis. Among the reasons for exclusion were irrelevance to maternal health purpose, and irrelevance to drone applications in healthcare. An updated search yielded one additional study that was also included. Overall, two studies assessed drones for blood products delivery, and one study used drones to transport blood samples.

CONCLUSION

A significant deficiency was found in the number of reported studies analyzing mode of medical products transportation and adaptation of drones in maternal healthcare. Future drone research framework should focus on maternal healthcare-specific drone applications in order to reap benefits in this area.

Passive Monitoring of Mental Health Status in the Criminal Forensic Population

Current approaches to monitoring patients' mental status rely heavily on self-reported symptomatology, clinician observation, and self-rated symptom scales. The limitations inherent in these methodologies have implications for the accuracy of diagnosis, treatment planning, and prognosis. Certain populations are particularly affected by these limitations because of their unique situations, including criminal forensic patients, who have a history of both criminal behavior and mental disorder, and experience increased stigma and restrictions in their access to mental health care. This population may benefit particularly from recent developments in technology and the growing use of mobile devices and sensors to collect behavioral information via passive monitoring. These technologies offer objective parameters that correlate with mental health status and create an opportunity to use Big Data and machine learning to refine diagnosis and predict behavior in a way that represents a marked shift from current practices. This article reviews the approaches to and limitations of psychiatric assessment and contrasts this with the promise of these new technologies. It then discusses the ethics concerns associated with these technologies and explores their potential relevance to criminal forensic psychiatry and the broader implications they carry for health and criminal justice policy.

Multi-Year Mapping of Major Crop Yields in an Irrigation District from High Spatial and Temporal Resolution Vegetation Index

Crop yield estimation is important for formulating informed regional and national food trade policies. The introduction of remote sensing in agricultural monitoring makes accurate estimation of regional crop yields possible. However, remote sensing images and crop distribution maps with coarse spatial resolution usually cause inaccuracy in yield estimation due to the existence of mixed pixels. This study aimed to estimate the annual yields of maize and sunflower in Hetao Irrigation District in North China using 30 m spatial resolution HJ-1A/1B CCD images and high accuracy multi-year crop distribution maps. The Normalized Difference Vegetation Index (NDVI) time series obtained from HJ-1A/1B CCD images was fitted with an asymmetric logistic curve to calculate daily NDVI and phenological characteristics. Eight random forest (RF) models using different predictors were developed for maize and sunflower yield estimation, respectively, where predictors of each model were a combination of NDVI series and/or phenological characteristics. We calibrated all RF models with measured crop yields at sampling points in two years (2014 and 2015), and validated the RF models with statistical yields of four counties in six years. Results showed that the optimal model for maize yield estimation was the model using NDVI series from the 120th to the 210th day in a year with 10 days' interval as predictors, while that for sunflower was the model using the combination of three NDVI characteristics, three phenological characteristics, and two curve parameters as predictors. The selected RF models could estimate multi-year regional crop yields accurately, with the average values of root-mean-square error and the relative error of 0.75 t/ha and 6.1% for maize, and 0.40 t/ha and 10.1% for sunflower, respectively. Moreover, the yields of maize and sunflower can be estimated fairly well with NDVI series 50 days before crop harvest, which implicated the possibility of crop yield forecast before harvest.

The Ethics of Smart Pills and Self-Acting Devices: Autonomy, Truth-Telling, and Trust at the Dawn of Digital Medicine

Digital medicine is a medical treatment that combines technology with drug delivery. The promises of this combination are continuous and remote monitoring, better disease management, self-tracking, self-management of diseases, and improved treatment adherence. These devices pose ethical challenges for patients, providers, and the social practice of medicine. For patients, having both informed consent and a user agreement raises questions of understanding for autonomy and informed consent, therapeutic misconception, external influences on decision making, confidentiality and privacy, and device dependability. For providers, digital medicine changes the relationship where trust can be verified, clinicians can be monitored, expectations must be managed, and new liability risks may be assumed. Other ethical questions include direct third-party monitoring of health treatment, affordability, and planning for adverse events in the case of device malfunction. This article seeks to lay out the ethical landscape for the implementation of such devices in patient care.

Remote sensing of plant-water relations: An overview and future perspectives

Vegetation is a highly dynamic component of the Earth surface and substantially alters the water cycle. Particularly the process of oxygenic plant photosynthesis determines vegetation connecting the water and carbon cycle and causing various interactions and feedbacks across Earth spheres. While vegetation impacts the water cycle, it reacts to changing water availability via functional, biochemical and structural responses. Unravelling the resulting complex feedbacks and interactions between the plant-water system and environmental change is essential for any modelling approaches and predictions, but still insufficiently understood due to currently missing observations. We hypothesize that an appropriate cross-scale monitoring of plant-water relations can be achieved by combined observational and modelling approaches. This paper reviews suitable remote sensing approaches to assess plant-water relations ranging from pure observational to combined observational-modelling approaches. We use a combined energy balance and radiative transfer model to assess the explanatory power of pure observational approaches focussing on plant parameters to estimate plant-water relations, followed by an outline for a more effective use of remote sensing by their integration into soil-plant-atmosphere continuum (SPAC) models. We apply a mechanistic model simulating water movement in the SPAC to reveal insight into the complexity of relations between soil, plant and atmospheric parameters, and thus plant-water relations. We conclude that future research should focus on strategies combining observations and mechanistic modelling to advance our knowledge on the interplay between the plant-water system and environmental change, e.g. through plant transpiration.

[Active cardiac implantable electronic devices: What is possible in ambulatory health care in 2017?]

Telemonitoring (TM) features are implemented in nearly all cardiac implantable electronic devices (CIEDs) that have recently been released to the market. In combination with pacemakers, defibrillators and systems for cardiac resynchronization it is a safe and efficient method for routine technical aftercare of the devices as well as for monitoring heart failure and arrhythmias. Using TM has the potential to optimize patient care with regard to economic, clinical and safety aspects. Despite the good availability of existing data and clear recommendations of the responsible scientific societies, it is often seen as an isolated solution which is not fully integrated into standard care, although it has its own EBM number for implantable cardioverter-defibrillators and cardiac resynchronization therapy systems. The reasons are not only the unsatisfactory reimbursement of costs, different IT structures and the borders between clinics and medical practices, but also acceptance problems of physicians and legal aspects. The compensation of cardiac pacemakers and 'event recorders' is unsolved. TM provides the prospect for optimal and cross-sectoral patient care. Furthermore it has the potential to become the standard method for the care for patients with a CIED.

[Telemetric follow-up of implantable electronic cardiac devices : Optimisation of care in clinical practice]

Remote follow-up of patients with implantable electronic cardiac devices (IECD) is a scientifically well-evaluated and technically mature method. Advantages over conventional follow-up include rapid detection of clinically relevant events (i. e. arrhythmias) and of technical problems. Additionally, telemetric follow-up of IECDs has a high degree of acceptance among both patients as well as health care professionals and carries the potential to reduce health care costs. The implementation of a remote follow-up programme is associated with organisational, infrastructural and legal aspects, which are reviewed.

Living with the animals: animal or robotic companions for the elderly in smart homes?

Although the use of pet robots in senior living facilities and day-care centres, particularly for individuals suffering from dementia, has been intensively researched, the question of introducing pet robots into domestic settings has been relatively neglected. Ambient assisted living (AAL) offers many interface opportunities for integrating motorised companions. There are diverse medical reasons, as well as arguments from animal ethics, that support the use of pet robots in contrast to living with live animals. However, as this paper makes clear, we should not lose sight of the option of living with animals at home for as long as possible and in conformity with the welfare of the animal assisted by AAL technology.

Technology Comes Of Age In The LTC World

The goal of sensor technology is to identify possible future chronic illnesses.

Monitoring trees outside forests: a review

Trees outside forests (TOFs) are an important natural resource that contributes substantially to national biomass and carbon stocks and to the livelihood of people in many regions. Over the last decades, decision makers have become increasingly aware of the importance of TOF, and as a consequence, this tree resource is nowadays often considered in forest monitoring systems. Our review shows that in many cases, TOF are included in national forest inventories, applying traditional methodologies with relatively sparse networks of field sample plots. Only in some countries, such as India, the design of the inventories has considered the special features of how TOFs occur in the landscape. Several research studies utilising remote sensing for monitoring TOF have been conducted lately, but very few studies include comparative studies to optimise sampling strategies for TOF. Our review indicates that methods combining remote sensing and field surveys appear to be very promising, especially when remote sensing techniques that assess both the horizontal and vertical structures of tree resources are applied. For example, two-phase sampling strategies with laser scanning in the first phase and a field survey in the second phase appear to be effective for assessing TOF resources. However, TOFs often exhibit different characteristics than forest trees. Thus, to improve TOF monitoring, there is often a need to develop models, e.g. for biomass assessment, that are specifically adapted to this tree resource. Alternatively, field-based remote sensing methods that provide structural information about individual trees, notably terrestrial laser scanning, could be further developed for TOF monitoring applications. This also would have a potential to reduce the problem of accessing TOF during field surveys, which is a problem, for example, in countries where TOF are present on intensively utilised private grounds like gardens and agricultural fields.

Regional analysis of drought and heat impacts on forests: current and future science directions

Accurate assessments of forest response to current and future climate and human actions are needed at regional scales. Predicting future impacts on forests will require improved analysis of species-level adaptation, resilience, and vulnerability to mortality. Land system models can be enhanced by creating trait-based groupings of species that better represent climate sensitivity, such as risk of hydraulic failure from drought. This emphasizes the need for more coordinated in situ and remote sensing observations to track changes in ecosystem function, and to improve model inputs, spatio-temporal diagnosis, and predictions of future conditions, including implications of actions to mitigate climate change.

Assessment of primary and secondary ambient particle trends using satellite aerosol optical depth and ground speciation data in the New England region, United States

The effectiveness of air pollution emission control policies can be evaluated by examining ambient pollutant concentration trends that are observed at a large number of ground monitoring sites over time. In this paper, we used ground monitoring measurements in conjunction with satellite aerosol optical depth (AOD) data to investigate fine particulate matter (PM2.5; particulate matter with aerodynamic diameter ≤ 2.5 µm) trends and their spatial patterns over a large U.S. region, New England, during 2000-2008. We examined the trends in rural and urban areas to get a better insight about the trends of regional and local source emissions. Decreases in PM2.5 concentrations (µg/m(3)) were more pronounced in urban areas than in rural ones. In addition, the highest and lowest PM2.5 decreases (µg/m(3)) were observed for winter and summer, respectively. Together, these findings suggest that primary particle concentrations decreased more relative to secondary ones. This is also supported by the analysis of the speciation data which showed that downward trends of primary pollutants including black carbon were stronger than those of secondary pollutants including sulfate. Furthermore, this study found that ambient primary pollutants decreased at the same rate as their respective source emissions. This was not the case for secondary pollutants which decreased at a slower rate than that of their precursor emissions. This indicates that concentrations of secondary pollutants depend not only on the primary emissions but also on the availability of atmospheric oxidants which might not change during the study period. This novel approach of investigating spatially varying concentration trends, in combination with ground PM2.5 species trends, can be of substantial regulatory importance.

Review of oil spill remote sensing

Remote-sensing for oil spills is reviewed. The use of visible techniques is ubiquitous, however it gives only the same results as visual monitoring. Oil has no particular spectral features that would allow for identification among the many possible background interferences. Cameras are only useful to provide documentation. In daytime oil absorbs light and remits this as thermal energy at temperatures 3-8K above ambient, this is detectable by infrared (IR) cameras. Laser fluorosensors are useful instruments because of their unique capability to identify oil on backgrounds that include water, soil, weeds, ice and snow. They are the only sensor that can positively discriminate oil on most backgrounds. Radar detects oil on water by the fact that oil will dampen water-surface capillary waves under low to moderate wave/wind conditions. Radar offers the only potential for large area searches, day/night and foul weather remote sensing.

Wireless devices incite 'medical spring'

A new breed of medical technology is emerging to make diagnosis and treatment more targeted, effective and efficient. How can hospitals harness all that potential?

Using very high resolution remote sensing for the management of coral reef fisheries: review and perspectives

Coral reef fisheries are critical for food security and as a source of income in developing and developed countries, but they are collapsing in many areas. Following the emergence and routine availability of commercial very high spatial resolution (0.6-10 m) multispectral satellite images, we reviewed the use of these new high-quality remote sensing data and products for coral reef fisheries management. The availability of habitats maps improves management by guiding sampling strategies, mapping resources, involving local communities, identifying conservation areas, and facilitating Ecosystem Based Fishery Management (EBFM) approaches. However, despite their potential, very little use of products designed specifically for fishery management can be reported, likely due to high costs, inherent technology limitations and lack of awareness on the possibilities. Given the theoretical benefits brought by relevant habitat maps in EBFM frameworks, we advocate the use of adequate remote sensing products that integrate fishery technical services demands and local requirements.