In recent decades, governments and NGOs--in an effort to promote democracy, freedom, fairness, and stability throughout the world--have organized teams of observers to monitor elections in a variety ...of countries. But when more organizations join the practice without uniform standards, are assessments reliable? When politicians nonetheless cheat and monitors must return to countries even after two decades of engagement, what is accomplished? Monitoring Democracy argues that the practice of international election monitoring is broken, but still worth fixing. By analyzing the evolving interaction between domestic and international politics, Judith Kelley refutes prevailing arguments that international efforts cannot curb government behavior and that democratization is entirely a domestic process. Yet, she also shows that democracy promotion efforts are deficient and that outside actors often have no power and sometimes even do harm.
Analyzing original data on over 600 monitoring missions and 1,300 elections, Kelley grounds her investigation in solid historical context as well as studies of long-term developments over several elections in fifteen countries. She pinpoints the weaknesses of international election monitoring and looks at how practitioners and policymakers might help to improve them.
Observation of surface water is a functional requirement for studying ecological and hydrological processes. Recent advances in satellite‐based optical remote sensors have promoted the field of ...sensing surface water to a new era. This paper reviews the current status of detecting, extracting, and monitoring surface water using optical remote sensing, especially progress in the last decade. It also discusses the current status and challenges in this field, including spatio‐temporal scale issues, integration with in situ hydrological data and elevation data, obscuration caused by clouds and vegetation, and the growing need to map surface water at a global scale. Historically, sensors have exhibited a contradiction in resolutions. Techniques including pixel unmixing and reconstruction, and spatio‐temporal fusion have been developed to alleviate this contradiction. Spatio‐temporal dynamics of surface water have been modeled by combining remote sensing data with in situ river flow. Recent studies have also demonstrated that the river discharge can be estimated using only optical remote sensing imagery, providing valuable information for hydrological studies in ungauged areas. Another historical issue for optical sensors has been obscuration by clouds and vegetation. An effective approach of reducing this limitation is to combine with synthetic aperture radar data. Digital elevation model data have also been employed to eliminate cloud/terrain shadows. The development of big data and cloud computation techniques makes the increasing demand of monitoring global water dynamics at high resolutions easier to achieve. An integrated use of multisource data is the future direction for improved global and regional water monitoring.
Plain Language Summary
Observing surface water is essential for ecological and hydrological studies. This paper reviews the current status of detecting, extracting, and monitoring surface water using optical remote sensing, especially progress in the last decade. It also discusses the current status and challenges in this field. For example, it was found that pixel unmixing and reconstruction, and spatio‐temporal fusion are two common and low‐cost approaches to enhance surface water monitoring. Remote sensing data have been integrated with in situ river flow to model spatio‐temporal dynamics of surface water. Recent studies have also proved that the river discharge can be estimated using only optical remote sensing imagery. This will be a breakthrough for hydrological studies in ungauged areas. Optical sensors are also easily obscured by clouds and vegetation. This limitation can be reduced by integrating optical data with synthetic aperture radar data and digital elevation model data. There is increasing demand of monitoring global water dynamics at high resolutions. It is now easy to achieve with the development of big data and cloud computation techniques. Enhanced global or regional water monitoring in the future requires integrated use of multiple sources of remote sensing data.
Key Points
Satellite‐based optical sensors are an efficient means for observing surface water regionally and globally
Pixel unmixing and reconstruction, and spatio‐temporal fusion are two common and low‐cost approaches to enhance surface water monitoring
The potential to estimate flow using only optical remote sensing has greatly enriched the data source of hydrological studies
Intraoperative neural monitoring (IONM) during thyroid and parathyroid surgery has gained widespread acceptance as an adjunct to the gold standard of visual nerve identification. Despite the ...increasing use of IONM, review of the literature and clinical experience confirms there is little uniformity in application of and results from nerve monitoring across different centers. We provide a review of the literature and cumulative experience of the multidisciplinary International Neural Monitoring Study Group with IONM spanning nearly 15 years. The study group focused its initial work on formulation of standards in IONM as it relates to important areas: 1) standards of equipment setup/endotracheal tube placement and 2) standards of loss of signal evaluation/intraoperative problem‐solving algorithm. The use of standardized methods and reporting will provide greater uniformity in application of IONM. In addition, this report clarifies the limitations of IONM and helps identify areas where additional research is necessary. This guideline is, at its forefront, quality driven; it is intended to improve the quality of neural monitoring, to translate the best available evidence into clinical practice to promote best practices. We hope this work will minimize inappropriate variations in monitoring rather than to dictate practice options. Laryngoscope, 121:S1–S16, 2011
Automated methods of monitoring ecosystems provide a cost‐effective way to track changes in natural system's dynamics across temporal and spatial scales. However, methods of recording and storing ...data captured from the field still require significant manual effort.
Here, we introduce an open source, inexpensive, fully autonomous ecosystem monitoring unit for capturing and remotely transmitting continuous data streams from field sites over long time‐periods. We provide a modular software framework for deploying various sensors, together with implementations to demonstrate proof of concept for continuous audio monitoring and time‐lapse photography.
We show how our system can outperform comparable technologies for fractions of the cost, provided a local mobile network link is available. The system is robust to unreliable network signals and has been shown to function in extreme environmental conditions, such as in the tropical rainforests of Sabah, Borneo.
We provide full details on how to assemble the hardware, and the open‐source software. Paired with appropriate automated analysis techniques, this system could provide spatially dense, near real‐time, continuous insights into ecosystem and biodiversity dynamics at a low cost.
While A1C is well established as an important risk marker for diabetes complications, with the increasing use of continuous glucose monitoring (CGM) to help facilitate safe and effective diabetes ...management, it is important to understand how CGM metrics, such as mean glucose, and A1C correlate. Estimated A1C (eA1C) is a measure converting the mean glucose from CGM or self-monitored blood glucose readings, using a formula derived from glucose readings from a population of individuals, into an estimate of a simultaneously measured laboratory A1C. Many patients and clinicians find the eA1C to be a helpful educational tool, but others are often confused or even frustrated if the eA1C and laboratory-measured A1C do not agree. In the U.S., the Food and Drug Administration determined that the nomenclature of eA1C needed to change. This led the authors to work toward a multipart solution to facilitate the retention of such a metric, which includes renaming the eA1C the glucose management indicator (GMI) and generating a new formula for converting CGM-derived mean glucose to GMI based on recent clinical trials using the most accurate CGM systems available. The final aspect of ensuring a smooth transition from the old eA1C to the new GMI is providing new CGM analyses and explanations to further understand how to interpret GMI and use it most effectively in clinical practice. This Perspective will address why a new name for eA1C was needed, why GMI was selected as the new name, how GMI is calculated, and how to understand and explain GMI if one chooses to use GMI as a tool in diabetes education or management.
Pakistan ranks third in the world in terms of mortality attributable to air pollution, with aerosol mass concentrations (PM2.5) consistently well above WHO (World Health Organization) air quality ...guidelines (AQG). However, regulation is dependent on a sparse network of air quality monitoring stations and insufficient ground data. This study utilizes long-term observations of aerosols and trace gases to characterize and rank the air pollution scenarios and pollution characteristics of 80 selected cities in Pakistan. Datasets used include (1) the Aqua and Terra (AquaTerra) MODIS (Moderate Resolution Imaging Spectroradiometer) Level 2 Collection 6.1 merged Dark Target and Deep Blue (DTB) aerosol optical depth (AOD) retrieval products; (2) the CAMS (Copernicus Atmosphere Monitoring Service) reanalysis PM1, PM2.5, and PM10 data; (3) the MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) reanalysis PM2.5 data, (4) the OMI (Ozone Monitoring Instrument) tropospheric vertical column density (TVCD) of nitrogen dioxide (NO2), and VCD of sulfur dioxide (SO2) in the Planetary Boundary Layer (PBL), (5) the VIIRS (Visible Infrared Imaging Radiometer Suite) Nighttime Lights data, (6) MODIS Collection 6 Version 2 global monthly fire location data (MCD14ML), (7) population density, (8) MODIS Level 3 Collection 6 land cover types, (9) AERONET (AErosol RObotic NETwork) Version 3 Level 2.0 data, and (10) ground-based PM2.5 concentrations from air quality monitoring stations. Potential Source Contribution Function (PSCF) analyses were performed by integrating with ground-based PM2.5 concentrations and the NOAA (National Oceanic and Atmospheric Administration) HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) air parcel back trajectories to identify potential pollution source areas which are responsible for extreme air pollution in Pakistan. Results show that the ranking of the top polluted cities depends on the type of pollutant considered and the metric used. For example, Jhang, Multan, and Vehari were characterized as the top three polluted cities in Pakistan when considering AquaTerra DTB AOD products; for PM1, PM2.5, and PM10 Lahore, Gujranwala, and Okara were the top three; for tropospheric NO2 VCD Lahore, Rawalpindi, and Islamabad and for PBL SO2 VCD Lahore, Mirpur, and Gujranwala. The results demonstrate that Pakistan's entire population has been exposed to high PM2.5 concentrations for many years, with a mean annual value of 54.7 μg/m3, over all Pakistan from 2003 to 2020. This value exceeds Pakistan's National Environmental Quality Standards (Pak-NEQS, i.e., <15 μg/m3 annual mean) for ambient air defined by the Pakistan Environmental Protection Agency (Pak-EPA) as well as the WHO Interim Target-1 (i.e., mean annual PM2.5 < 35 μg/m3). The spatial analyses of the concentrations of aerosols and trace gases in terms of population density, nighttime lights, land cover types, and fire location data, and the PSCF analysis indicate that Pakistan's air quality is strongly affected by anthropogenic sources inside of Pakistan, with contributions from surrounding countries. Statistically significant positive (increasing) trends in PM1, PM2.5, PM10, tropospheric NO2 VCD, and SO2 VCD were observed in ~89%, ~67%, ~48%, 91%, and ~ 88% of the Pakistani cities (80 cities), respectively. This comprehensive analysis of aerosol and trace gas levels, their characteristics in spatio-temporal domains, and their trends over Pakistan, is the first of its kind. Results will be helpful to the Ministry of Climate Change (Government of Pakistan), Pak-EPA, SUPARCO (Pakistan Space and Upper Atmosphere Research Commission), policymakers, and the local research community to mitigate air pollution and its effects on human health.
•Lahore, Gujranwala, and Okara are the most polluted city based on PM2.5.•Jhang, Multan, and Vehari are the most polluted cities based on AOD.•Aerosols, nighttime lights, population, cropland, and fire show same spatial patterns.•Pakistan's entire population is exposed to long-term PMx (x = 1, 2.5, & 10).•Pakistan's air quality is mainly affected by local anthropogenic sources.
ABSTRACT
Ecosystem monitoring is fundamental to our understanding of how ecosystem change is impacting our natural resources and is vital for developing evidence‐based policy and management. However, ...the different types of ecosystem monitoring, along with their recommended applications, are often poorly understood and contentious. Varying definitions and strict adherence to a specific monitoring type can inhibit effective ecosystem monitoring, leading to poor program development, implementation and outcomes. In an effort to develop a more consistent and clear understanding of ecosystem monitoring programs, we here review the main types of monitoring and recommend the widespread adoption of three classifications of monitoring, namely, targeted, surveillance and landscape monitoring. Landscape monitoring is conducted over large areas, provides spatial data, and enables questions relating to where and when ecosystem change is occurring to be addressed. Surveillance monitoring uses standardised field methods to inform on what is changing in our environments and the direction and magnitude of that change, whilst targeted monitoring is designed around testable hypotheses over defined areas and is the best approach for determining the causes of ecosystem change. The classification system is flexible and can incorporate different interests, objectives, targets and characteristics as well as different spatial scales and temporal frequencies, while also providing valuable structure and consistency across distinct ecosystem monitoring programs. To support our argument, we examine the ability of each monitoring type to inform on six key types of questions that are routinely posed for ecosystem monitoring programs, such as where and when change is occurring, what is the magnitude of change, and how can the change be managed? As we demonstrate, each type of ecosystem monitoring has its own strengths and weaknesses, which should be carefully considered relative to the desired results. Using this scheme, scientists and land managers can design programs best suited to their needs. Finally, we assert that for our most serious environmental challenges, it is essential that we include information from each of these monitoring scales to inform on all facets of ecosystem change, and this is best achieved through close collaboration between the scales. With a renewed understanding of the importance of each monitoring type, along with greater commitment to monitor cooperatively, we will be well placed to address some of our greatest environmental challenges.
Wearable electronics is a rapidly growing field that recently started to introduce successful commercial products into the consumer electronics market. Employment of biopotential signals in wearable ...systems as either biofeedbacks or control commands are expected to revolutionize many technologies including point of care health monitoring systems, rehabilitation devices, human–computer/machine interfaces (HCI/HMIs), and brain–computer interfaces (BCIs). Since electrodes are regarded as a decisive part of such products, they have been studied for almost a decade now, resulting in the emergence of textile electrodes. This study presents a systematic review of wearable textile electrodes in physiological signal monitoring, with discussions on the manufacturing of conductive textiles, metrics to assess their performance as electrodes, and an investigation of their application in the acquisition of critical biopotential signals for routine monitoring, assessment, and exploitation of cardiac (electrocardiography, ECG), neural (electroencephalography, EEG), muscular (electromyography, EMG), and ocular (electrooculography, EOG) functions.
Continuous glucose monitoring (CGM) provides real-time assessment of glucose levels and may be beneficial in reducing hypoglycemia in older adults with type 1 diabetes.
To determine whether CGM is ...effective in reducing hypoglycemia compared with standard blood glucose monitoring (BGM) in older adults with type 1 diabetes.
Randomized clinical trial conducted at 22 endocrinology practices in the United States among 203 adults at least 60 years of age with type 1 diabetes.
Participants were randomly assigned in a 1:1 ratio to use CGM (n = 103) or standard BGM (n = 100).
The primary outcome was CGM-measured percentage of time that sensor glucose values were less than 70 mg/dL during 6 months of follow-up. There were 31 prespecified secondary outcomes, including additional CGM metrics for hypoglycemia, hyperglycemia, and glucose control; hemoglobin A1c (HbA1c); and cognition and patient-reported outcomes, with adjustment for multiple comparisons to control for false-discovery rate.
Of the 203 participants (median age, 68 interquartile range {IQR}, 65-71 years; median type 1 diabetes duration, 36 IQR, 25-48 years; 52% female; 53% insulin pump use; mean HbA1c, 7.5% SD, 0.9%), 83% used CGM at least 6 days per week during month 6. Median time with glucose levels less than 70 mg/dL was 5.1% (73 minutes per day) at baseline and 2.7% (39 minutes per day) during follow-up in the CGM group vs 4.7% (68 minutes per day) and 4.9% (70 minutes per day), respectively, in the standard BGM group (adjusted treatment difference, -1.9% (-27 minutes per day); 95% CI, -2.8% to -1.1% -40 to -16 minutes per day; P <.001). Of the 31 prespecified secondary end points, there were statistically significant differences for all 9 CGM metrics, 6 of 7 HbA1c outcomes, and none of the 15 cognitive and patient-reported outcomes. Mean HbA1c decreased in the CGM group compared with the standard BGM group (adjusted group difference, -0.3%; 95% CI, -0.4% to -0.1%; P <.001). The most commonly reported adverse events using CGM and standard BGM, respectively, were severe hypoglycemia (1 and 10), fractures (5 and 1), falls (4 and 3), and emergency department visits (6 and 8).
Among adults aged 60 years or older with type 1 diabetes, continuous glucose monitoring compared with standard blood glucose monitoring resulted in a small but statistically significant improvement in hypoglycemia over 6 months. Further research is needed to understand the long-term clinical benefit.
ClinicalTrials.gov Identifier: NCT03240432.
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