Arrhythmia constitutes a problem with the rate or rhythm of the heartbeat, and an early diagnosis is essential for the timely inception of successful treatment. We have jointly optimized the entire multi-stage arrhythmia classification scheme based on 12-lead surface ECGs that attains the accuracy performance level of professional cardiologists. The new approach is comprised of a three-step noise reduction stage, a novel feature extraction method and an optimal classification model with finely tuned hyperparameters. We carried out an exhaustive study comparing thousands of competing classification algorithms that were trained on our proprietary, large and expertly labeled dataset consisting of 12-lead ECGs from 40,258 patients with four arrhythmia classes: atrial fibrillation, general supraventricular tachycardia, sinus bradycardia and sinus rhythm including sinus irregularity rhythm. Our results show that the optimal approach consisted of Low Band Pass filter, Robust LOESS, Non Local Means smoothing, a proprietary feature extraction method based on percentiles of the empirical distribution of ratios of interval lengths and magnitudes of peaks and valleys, and Extreme Gradient Boosting Tree classifier, achieved an F₁-Score of 0.988 on patients without additional cardiac conditions. The same noise reduction and feature extraction methods combined with Gradient Boosting Tree classifier achieved an F₁-Score of 0.97 on patients with additional cardiac conditions. Our method achieved the highest classification accuracy (average 10-fold cross-validation F₁-Score of 0.992) using an external validation data, MIT-BIH arrhythmia database. The proposed optimal multi-stage arrhythmia classification approach can dramatically benefit automatic ECG data analysis by providing cardiologist level accuracy and robust compatibility with various ECG data sources.

Hyperspectral systems have improved significantly through recent advancements in sensor technology, which have made possible to acquire data with several hundred channels. These advances provide the possible benefit of not only collecting more detailed information than previously possible, but also of producing more accurate data. Some of the major challenges in handling such large data sets are removing redundant information and assuring the continued relevance of vital information to the application at hand. For example, conventional methods for land use and land cover classifications may not be applicable, due to the large data volumes used to characterize hyperspectral cubes. Therefore, these conventional methods may require a preprocessing step, namely dimension reduction. Dimension reduction can be seen as a transformation from a high order dimension to a low order dimension in order to conquer the so- called "curse of the dimensionality," which eliminates data redundancy. Principal Component Analysis (PCA) is one such data reduction technique, which is often used when analyzing remotely sensed data. In computing the principal components, the eigenvalues of the covariance matrix of the 3-D image must be computed. Since this is a global operation it requires high computational resources and requires whole image to be stored which increases memory requirements. This paper reports an hierarchical algorithm, which can effectively reduce the hyperspectral data to intrinsic dimensionality. In the hierarchical PCA, we break the image into various parts and then perform PCA on each part separately and then combine the results. The classification results over the original and the resulting reduced data have been compared. The results show that reduced data obtained by hierarchical PCA can compare favorably to the results obtained from original data.

Global warming or the increase of the surface and atmospheric temperatures of the Earth, is increasingly discernible in the polar, sub-polar and major land glacial areas. The Himalayan and Tibetan Plateau Glaciers, which are the largest glaciers outside of the Polar Regions, are showing a large-scale decrease of snow cover and an extensive glacial retreat. These glaciers such as Siachen and Gangotri are a major water resource for Asia as they feed major rivers such as the Indus, Ganga and Brahmaputra. Due to scarcity of ground measuring stations, the long-term observations of atmospheric temperatures acquired from the Microwave Sounding Unit (MSU) since 1979–2008 is highly useful. The lower and middle tropospheric temperature trend based on 30 years of MSU data shows warming of the Northern Hemisphere's mid-latitude regions. The mean month-to-month warming (up to 0.048±0.026°K/year or 1.44°K over 30 years) of the mid troposphere (near surface over the high altitude Himalayas and Tibetan Plateau) is prominent and statistically significant at a 95% confidence interval. Though the mean annual warming trend over the Himalayas (0.016±0.005°K/year), and Tibetan Plateau (0.008±0.006°K/year) is positive, the month to month warming trend is higher (by 2–3 times, positive and significant) only over a period of six months (December to May). The factors responsible for the reversal of this trend from June to November are discussed here. The inequality in the magnitude of the warming trends of the troposphere between the western and eastern Himalayas and the IG (Indo-Gangetic) plains is attributed to the differences in increased aerosol loading (due to dust storms) over these regions. The monthly mean lower-tropospheric MSU-derived temperature trend over the IG plains (dust sink region; up to 0.032±0.027°K/year) and dust source regions (Sahara desert, Middle East, Arabian region, Afghanistan-Iran-Pakistan and Thar Desert regions; up to 0.068±0.033°K/year) also shows a similar pattern of month-to-month oscillation and six months of enhanced and a statistically significant warming trend. The enhanced warming trend during the winter and pre-monsoon months (December–May) may accelerate glacial melt. The unequal distribution of the warming trend over the year is discussed in this study and is partially attributed to a number of controlling factors such as sunlight duration, CO₂ trends over the region (2003–2008), water vapor and aerosol distribution.

Three years (2006–2008) of ground-based observations of the Aerosol Optical Depth (AOD) in the urban environment of Athens, in the Eastern Mediterranean, are analysed in this work. Measurements were acquired with a Multi-Filter Rotating Shadowband Radiometer at five wavelengths. The daily average AOD at 500 nm is 0.23, and the mean Ångström coefficient calculated between 415 and 867 nm is 1.41. The annual variability of AOD has a spring maximum dominated by coarse dust particles from the Sahara (AOD 0.34–0.42), while the diurnal pattern is typical for urban sites, with AOD steadily increasing throughout the day. The greatest contribution to the annually averaged AOD, accounting for almost 40%, comes from regional and local sources (namely the Istanbul metropolitan area, the extended areas of biomass burning around the north coast of the Black Sea, power plants spread throughout the Balkans and the industrial area in the Po valley, with average daily AOD in the range of 0.25–0.35). An additional important contribution (23%) is dust from Africa, whereas the rest of Europe contributes another 22%. The geographical distribution of the above sources in conjunction with the prevailing synoptic situation and contribution of local sources, lead to mixed types of aerosols over Athens, with highly variable contribution of fine and coarse particles to AOD in the range 10%–90%. This is the first long-term, ground based data set available for Athens, and it has also been used for the validation of satellite derived AOD by MODIS, showing good agreement on an annual basis, but with an overestimation of satellite AODs in the warm period.

Owing to continuing touristic developments in Hurghada, Egypt, several coral reef habitats have suffered major deterioration between 1987 and 2013, either by being bleached or totally lost. Such alterations in coral reef habitats have been well observed in their varying distributions using change detection analysis applied to a Landsat 5 image representing 1987, a Landsat 7 image representing 2000, and a Landsat 8 image representing 2013. Different processing techniques were carried out over the three images, including but not limited to rectification, masking, water column correction, classification, and change detection statistics. The supervised classifications performed over the three scenes show five significant marine-related classes, namely coral, sand subtidal, sand intertidal, macro-algae, and seagrass, in different degrees of abundance. The change detection statistics obtained from the classified scenes of 1987 and 2000 reveal a significant increase in the macro-algae and seagrass classes (93 and 47%, respectively). However, major decreases of 41, 40, and 37% are observed in the sand intertidal, coral, and sand subtidal classes, respectively. On the other hand, the change detection statistics obtained from the classified scenes of 2000 and 2013 revealed increases in sand subtidal and macro-algae classes by 14 and 19%, respectively, while major decreases of 49%, 46% and 74% are observed in the sand intertidal, coral, and seagrass classes, respectively.

On 5 July 2011, a massive dust storm struck Phoenix, Arizona (USA), raising concerns for increased cases of valley fever (coccidioidomycosis, or, cocci). A quasi-operational experimental airborne dust forecast system predicted the event and provides model output for continuing analysis in collaboration with public health and air quality communities. An objective of this collaboration was to see if a signal in cases of valley fever in the region could be detected and traced to the storm – an American haboob. To better understand the atmospheric life cycle of cocci spores, the DREAM dust model (also herein, NMME-DREAM) was modified to simulate spore emission, transport and deposition. Inexact knowledge of where cocci-causing fungus grows, the low resolution of cocci surveillance and an overall active period for significant dust events complicate analysis of the effect of the 5 July 2011 storm. In the larger context of monthly to annual disease surveillance, valley fever statistics, when compared against PM10 observation networks and modeled airborne dust concentrations, may reveal a likely cause and effect. Details provided by models and satellites fill time and space voids in conventional approaches to air quality and disease surveillance, leading to land–atmosphere modeling and remote sensing that clearly mark a path to advance valley fever epidemiology, surveillance and risk avoidance