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.

Change in the use of remote monitoring of cardiac implantable electronic devices in Italian clinical practice over a 5-year period: results of two surveys promoted by the AIAC (Italian Association of Arrhythmology and Cardiac Pacing).

AIMS: The aim of this study was to evaluate the use of remote monitoring in Italian clinical practice and its trend over the last 5 years. METHODS: In 2012 and 2017, two surveys were conducted. Both were open to all Italian implanting centres and consisted of 25 questions on the characteristics of the centre, their actual use of remote monitoring, applied organizational models and administrative and legal aspects. RESULTS: The questionnaires were completed by 132 and 108 centres in 2012 and 2017, respectively (30.6 and 24.7% of all Italian implanting centres). In 2017, significantly fewer centres followed up fewer than 200 patients by remote monitoring than in 2012, while more followed up more than 500 patients (all P < 0.005). In most of the centres (77.6%) that responded to both surveys, the number of patients remotely monitored significantly increased from 2012 to 2017.In both surveys, remote monitoring was usually managed by physicians and nurses. Over the period, primary review of transmissions by physicians declined, while it was increasingly performed by nurses; the involvement of technicians rose, while that of manufacturers' technical personnel decreased. The percentage of centres in which transmissions were submitted to the physician only in critical cases rose (from 28.3 to 64.3%; P < 0.001). In 86.7% of centres, the lack of a reimbursement system was deemed the main barrier to implementing remote monitoring. CONCLUSION: In the last 5 years, the number of patients followed up by remote monitoring has increased markedly. In most Italian centres, remote monitoring has increasingly been managed through a primary nursing model. The lack of a specific reimbursement system is perceived as the main barrier to implementing remote monitoring .

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.

Remote sensing of ecosystem health: opportunities, challenges, and future perspectives.

Maintaining a healthy ecosystem is essential for maximizing sustainable ecological services of the best quality to human beings. Ecological and conservation research has provided a strong scientific background on identifying ecological health indicators and correspondingly making effective conservation plans. At the same time, ecologists have asserted a strong need for spatially explicit and temporally effective ecosystem health assessments based on remote sensing data. Currently, remote sensing of ecosystem health is only based on one ecosystem attribute: vigor, organization, or resilience. However, an effective ecosystem health assessment should be a comprehensive and dynamic measurement of the three attributes. This paper reviews opportunities of remote sensing, including optical, radar, and LiDAR, for directly estimating indicators of the three ecosystem attributes, discusses the main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system, and provides some future perspectives. The main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system are: (1) scale issue; (2) transportability issue; (3) data availability; and (4) uncertainties in health indicators estimated from remote sensing data. However, the Radarsat-2 constellation, upcoming new optical sensors on Worldview-3 and Sentinel-2 satellites, and improved technologies for the acquisition and processing of hyperspectral, multi-angle optical, radar, and LiDAR data and multi-sensoral data fusion may partly address the current challenges.

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.

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.

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.