Analysis of the Current and Future Prediction of Land Use/Land Cover Change Using Remote Sensing and the CA-Markov Model in Majang Forest Biosphere Reserves of Gambella, Southwestern Ethiopia.

This study aimed to evaluate land use/land cover changes (1987-2017), prediction (2032-2047), and identify the drivers of Majang Forest Biosphere Reserves. Landsat image (TM, ETM+, and OLI-TIRS) and socioeconomy data were used for the LU/LC analysis and its drivers of change. The supervised classification was also employed to classify LU/LC. The CA-Markov model was used to predict future LU/LC change using IDRISI software. Data were collected from 240 households from eight kebeles in two districts to identify LU/LC change drivers. Five LU/LC classes were identified: forestland, farmland, grassland, settlement, and waterbody. Farmland and settlement increased by 17.4% and 3.4%, respectively; while, forestland and grassland were reduced by 77.8% and 1.4%, respectively, from 1987 to 2017. The predicted results indicated that farmland and settlement increased by 26.3% and 6.4%, respectively, while forestland and grassland decreased by 66.5% and 0.8%, respectively, from 2032 to 2047. Eventually, agricultural expansion, population growth, shifting cultivation, fuel wood extraction, and fire risk were identified as the main drivers of LU/LC change. Generally, substantial LU/LC changes were observed and will continue in the future. Hence, land use plan should be proposed to sustain resource of Majang Forest Biosphere Reserves, and local communities' livelihood improvement strategies are required to halt land conversion.

Utilization of New Technologies in the Production of Pharmaceutical Olive Oil.

Olive oil is a key ingredient in the Mediterranean diet and offers many health benefits. However, many factors affect the quality and quantity of olive oil such as olive tree diseases and olive-related pests. Unfortunately, the procedure of identifying pests or the outbreak of a disease is time-consuming, and it depends heavily on the size of the olive grove. Through the use of ICT, remote monitoring of the olive grove can be achieved, by collecting environment-related data and having an overview of the olive grove's overall health. In this paper we propose a low-cost dense network of sensors that collects daily data regarding the olive grove, thus, providing the possibility to prevent infestation of olive fruit fly and/or the outbreak of olive tree-related disease.

The Ethics of Emergent Health Technologies: Implications of the 21st Century Cures Act for Nursing.

The 21st Century Cures Act, passed in December 2016 by the United States Congress, is a public law aimed at accelerating the time it takes to get pharmaceutical drugs and medical devices into the market, in addition to shifting connected review processes from randomized controlled trials to real-world efficacy tests. As of December 2019, efforts are underway to introduce a "Cures Act 2.0" bill, with particular attention to the implementation of digital health within health systems. Research on the development of emergent health technologies is nascent; research examining health technology implications of 21st Century Cures Act for the health care workforce is nonexistent. This article fills a crucial gap in public awareness, discussing ethical implications of the 21st Century Cures Act and centering nursing. Nursing is a profession frequently acknowledged as practicing on "the front lines of care" and frequently responsible for the trialing of products in clinical settings. The article summarizes and evaluates key components of the 21st Century Cures Act related to health technology development. Discrete health technologies addressed are (a) breakthrough devices, (b) digital health software, and (c) combination products. It then connects these provisions to ethical considerations for nursing practice, research, and policy. The article concludes by discussing the relevance of emerging digital health technologies to the crafting of a "Cures 2.0" bill, with particular attention to this moment in light of digital care precedents set during the COVID-19 pandemic.

Cardioprotective effect of remote ischemic preconditioning with postconditioning on donor hearts in patients undergoing heart transplantation: a single-center, double-blind, randomized controlled trial.

BACKGROUND: The cardioprotective effect of remote ischemic preconditioning (RIPC) in cardiovascular surgery is controversial. This study investigated whether RIPC combined with remote ischemic postconditioning (RIPostC) reduces myocardial injury to donor hearts in patients undergoing heart transplantation. METHODS: One hundred and twenty patients scheduled for orthotopic heart transplantation were enrolled and randomly assigned to an RIPC+RIPostC group (n = 60) or a control (n = 60) group. In the RIPC+RIPostC group, after anesthesia induction, four cycles of 5-min of ischemia and 5-min of reperfusion were applied to the right upper limb by a cuff inflated to 200 mmHg (RIPC) and 20 min after aortic declamping (RIPostC). Serum cardiac troponin I (cTnI) levels were determined preoperatively and at 3, 6, 12, and 24 h after aortic declamping. Postoperative clinical outcomes were recorded. The primary endpoint was a comparison of serum cTnI levels at 6 h after aortic declamping. RESULTS: Compared with the preoperative baseline, in both groups, serum cTnI levels peaked at 6 h after aortic declamping. Compared with the control group, RIPC+RIPostC significantly reduced serum cTnI levels at 6 h after aortic declamping (38.87 ± 31.81 vs 69.30 ± 34.13 ng/ml, P = 0.02). There were no significant differences in in-hospital morbidity and mortality between the two groups. CONCLUSION: In patients undergoing orthotopic heart transplantation, RIPC combined with RIPostC reduced myocardial injury at 6 h after aortic declamping, while we found no evidence of this function provided by RIPC+RIPostC could improve clinical outcomes. TRIAL REGISTRATION: Trial Registration Number: chictr.org.cn . no. ChiCTR-INR-16010234 (prospectively registered). The initial registration date was 9/1/2017.

Internet of Things in Marine Environment Monitoring: A Review.

Marine environment monitoring has attracted more and more attention due to the growing concern about climate change. During the past couple of decades, advanced information and communication technologies have been applied to the development of various marine environment monitoring systems. Among others, the Internet of Things (IoT) has been playing an important role in this area. This paper presents a review of the application of the Internet of Things in the field of marine environment monitoring. New technologies including advanced Big Data analytics and their applications in this area are briefly reviewed. It also discusses key research challenges and opportunities in this area, including the potential application of IoT and Big Data in marine environment protection.

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.

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.

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.

Battery drain in daily practice and medium-term projections on longevity of cardioverter-defibrillators: an analysis from a remote monitoring database.

AIMS: The longevity of generators is a crucial determinant of the cost-effectiveness of therapy with implantable cardioverter-defibrillators (ICDs) and cardiac resynchronization therapy defibrillators (CRT-D). We evaluated the trend of device-measured residual battery capacity and longevity projections over 5-year follow-up. We also investigated possible factors associated with battery drain. METHODS AND RESULTS: Data from 4851 patients in the European LATITUDE(®) database who were followed up for a minimum of 3 years were analysed. The factors associated with battery drain (i.e. year-to-year decrease in residual battery capacity), and thus potentially impacting on device longevity, were mainly the pacing parameters in CRT-D devices and the number of shocks delivered and diverted in both ICD and CRT-D (all P < 0.01 on linear regression analysis). Over the first 5 years, the longevity estimates provided by devices showed low intra-patient variability and increased with time. The estimates exceeded 10 years for CRT-D and 13 and 12 years for single- and dual-chamber ICDs, respectively. In CRT-D patients, the expected patient age on replacement was 80 ± 12 years, and the expected probability of undergoing device replacement was 63 ± 13% for New York Heart Association (NYHA) II patients and 37 ± 16% for NYHA III patients. For comparison, the probabilities of replacing a CRT-D lasting 5 years were 78 ± 8 and 59 ± 13%, respectively (both P < 0.001). CONCLUSION: Battery drain was mainly associated with pacing output in CRT-D devices and with the number of capacitor charges in both ICD and CRT-D devices. The longevity estimates provided by the devices were consistent and conservative. According to these estimates, among CRT-D recipients a low proportion of patients should require device replacement.

Airborne Optical and Thermal Remote Sensing for Wildfire Detection and Monitoring.

For decades detection and monitoring of forest and other wildland fires has relied heavily on aircraft (and satellites). Technical advances and improved affordability of both sensors and sensor platforms promise to revolutionize the way aircraft detect, monitor and help suppress wildfires. Sensor systems like hyperspectral cameras, image intensifiers and thermal cameras that have previously been limited in use due to cost or technology considerations are now becoming widely available and affordable. Similarly, new airborne sensor platforms, particularly small, unmanned aircraft or drones, are enabling new applications for airborne fire sensing. In this review we outline the state of the art in direct, semi-automated and automated fire detection from both manned and unmanned aerial platforms. We discuss the operational constraints and opportunities provided by these sensor systems including a discussion of the objective evaluation of these systems in a realistic context.

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.

Manpower and outpatient clinic workload for remote monitoring of patients with cardiac implantable electronic devices: data from the HomeGuide Registry.

BACKGROUND: This study aimed to assess manpower and resource consumption of the HomeGuide workflow model for remote monitoring (Biotronik Home Monitoring [HM], Biotronik SE & Co. KG, Berlin, Germany) of cardiac implantable electronic devices in daily clinical practice. METHODS: The model established a cooperative interaction between a reference nurse (RN) for ordinary management, and a responsible physician (RP) for medical decisions in each outpatient clinic. RN reviewed remote transmissions and alerts, addressing critical cases to the RP. RESULTS: A total of 1,650 patients were enrolled in 75 sites: 25% pacemakers (PM), 22% dual-, 27% single-chamber implantable defibrillators (ICD), 2% PM with cardiac resynchronization therapy (CRT), and 24% ICD-CRT. During a median follow-up of 18 (10-31) months, 3,364 HM sessions were performed (74% by the RN, 26% by the RP) to complete 18,478 remote follow-ups. Median duration of remote follow-ups was 1.2 (0.6-2.0) minutes, corresponding to a manpower of 43.3 (4.2-94.8) minutes/month every 100 patients for nurses and 10.2 (0.1-31.1) for physicians (P < 0.0001). RN submitted 15% of remote transmissions to RP, who decided unscheduled follow-ups in 12% of the cases. The median manpower for phone calls was 1.9 (0.8-16.5) minutes/month every 100 contacted patients. There were 2.84 in-hospital visits/patient, 0.46 of which triggered by HM findings. A cumulative per-patient HM follow-up time of 15.4 minutes (20% of total follow-up time) allowed remote detection of 73% of actionable events. CONCLUSIONS: HM implemented in the HomeGuide workflow model required <1 hour/month every 100 patients to detect the majority of actionable events with limited administrative workload.

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.