The backbone of the economy, security and sustainability of a state is inseparably linked to the security of its critical infrastructure. Critical infrastructures define goods, systems or subsystems ...that are essential to maintain the vital functions of society, health, physical protection, security plus economic and social well-being of citizens. The digital security of critical infrastructures is a very important priority for the well-being of every country, especially nowadays, because of the direct threats dictated by the current international conjuncture and due to the emerging interactions or interconnections developed between the National Critical Infrastructures, internationally. The aim of this research is the development and testing of an
Anomaly Detection intelligent
algorithm that has the advantage to run very fast with a small portion of the available data and to perform equally well with the existing approaches. Such a system must be characterized by high efficiency and very fast execution. Thus, we present the Gryphon advanced intelligence system. Gryphon is a Semi-
Supervised Unary Anomaly Detection System
for big industrial data which is employing an
evolving Spiking Neural Network
(eSNN)
One
-
Class Classifier
(eSNN-OCC). This machine learning algorithm corresponds to a model capable of detecting very fast and efficiently, divergent behaviors and abnormalities associated with cyberattacks, which are known as
Advanced Persistent Threat
(APT). The training process is performed on data related to the normal function of a critical infrastructure.
The analysis of air quality and the continuous monitoring of air pollution levels are important subjects of the environmental science and research. This problem actually has real impact in the human ...health and quality of life. The determination of the conditions which favor high concentration of pollutants and most of all the timely forecast of such cases is really crucial, as it facilitates the imposition of specific protection and prevention actions by civil protection. This research paper discusses an innovative threefold intelligent hybrid system of combined machine learning algorithms HISYCOL (henceforth). First, it deals with the correlation of the conditions under which high pollutants concentrations emerge. On the other hand, it proposes and presents an ensemble system using combination of machine learning algorithms capable of forecasting the values of air pollutants. What is really important and gives this modeling effort a hybrid nature is the fact that it uses clustered datasets. Moreover, this approach improves the accuracy of existing forecasting models by using unsupervised machine learning to cluster the data vectors and trace hidden knowledge. Finally, it employs a Mamdani fuzzy inference system for each air pollutant in order to forecast even more effectively its concentrations.
An intriguing natural phenomenon occurs every polar spring, namely the bromine explosion, in which plumes of tropospheric bromine monoxide (BrO) are formed. These plumes are observed in the BrO ...vertical column densities (VCDs), retrieved from satellite sensors. Tropospheric BrO depletes tropospheric ozone and facilitates the deposition of mercury. Bromine molecules are mainly released from young sea ice, and meteorological parameters determine the formation and evolution of enhanced BrO VCD plumes. Due to the complexity of the physicochemical processes involved in the bromine explosion, the modeling of tropospheric BrO VCDs in chemical transport models is challenging and not yet adequate. The first of its type, this study demonstrates the potential of using an artificial neural network (ANN), which uses meteorological parameters and sea ice age as inputs to simulate and predict tropospheric BrO VCDs in the Arctic. The ANN is trained and validated using a 22-year satellite remote sensing dataset of Arctic tropospheric BrO VCDs. A generally satisfactory spatial agreement between observed and simulated tropospheric BrO VCDs is observed. However, the magnitude of the observed BrO VCD plumes is underestimated. Air temperature and mean sea level pressure are the most important parameters influencing the magnitude of tropospheric BrO VCD simulations. Although the changing spatial distribution of tropospheric BrO VCDs over time is well captured, the trend reported in the observations of tropospheric BrO VCDs is not reproduced by the ANN, suggesting that additional parameters not included in the ANN also influence the formation of tropospheric BrO VCD plumes.
•The prediction of BrO plumes is required to assess the loss of tropospheric ozone•We present an artificial neural network, which simulates Arctic tropospheric BrO•The neural network reproduces spatial patterns of many tropospheric BrO plumes•The trend reported in the observations is not evident in the simulations
Every polar spring, phenomena called bromine explosions
occur over sea ice. These bromine explosions comprise photochemical
heterogeneous chain reactions that release bromine molecules, Br2, to
the ...troposphere and lead to tropospheric plumes of bromine monoxide, BrO.
This autocatalytic mechanism depletes ozone, O3, in the boundary layer
and troposphere and thereby changes the oxidizing capacity of the
atmosphere. The phenomenon also leads to accelerated deposition of metals
(e.g., Hg). In this study, we present a 22-year (1996 to 2017) consolidated
and consistent tropospheric BrO dataset north of 70∘ N, derived from
four different ultraviolet–visible (UV–VIS) satellite instruments (GOME, SCIAMACHY, GOME-2A and
GOME-2B). The retrieval data products from the different sensors are
compared during periods of overlap and show good agreement (correlations of
0.82–0.98 between the sensors). From our merged time series of
tropospheric BrO vertical column densities (VCDs), we infer changes in the
bromine explosions and thus an increase in the extent and magnitude of
tropospheric BrO plumes during the period of Arctic warming. We determined
an increasing trend of about 1.5 % of the tropospheric BrO VCDs per year
during polar springs, while the size of the areas where enhanced
tropospheric BrO VCDs can be found has increased about 896 km2 yr−1. We infer from comparisons and correlations with sea ice age data that
the reported changes in the extent and magnitude of tropospheric BrO VCDs
are moderately related to the increase in first-year ice extent in the
Arctic north of 70∘ N, both temporally and spatially, with a
correlation coefficient of 0.32. However, the BrO plumes and thus bromine
explosions show significant variability, which also depends, apart from sea
ice, on meteorological conditions.
During polar spring, ozone depletion events (ODEs) are often observed in combination with bromine explosion events (BEEs) in Ny-Ålesund. In this study, two long-term ozone data sets (2010–2021) from ...ozonesonde launches and in situ ozone measurements have been evaluated between March and May of each year to study ODEs in Ny-Ålesund. Ozone concentrations below 15 ppb were marked as ODEs. We applied a composite analysis to evaluate tropospheric BrO retrieved from satellite data and the prevailing meteorological conditions during these events. During ODEs, both data sets show a blocking situation with a low-pressure anomaly over the Barents Sea and anomalously high pressure in the Icelandic Low area, leading to transport of cold polar air from the north to Ny-Ålesund with negative temperature and positive BrO anomalies found around Svalbard. In addition, a higher wind speed and a higher, less stable boundary layer are noticed, supporting the assumption that ODEs often occur in combination with polar cyclones. Applying a 20 ppb ozone threshold value to tag ODEs resulted in only a slight attenuation of the BrO and meteorological anomalies compared to the 15 ppb threshold. Monthly analysis showed that BrO and meteorological anomalies are weakening from March to May. Therefore, ODEs associated with low-pressure systems, high wind speeds, and blowing snow more likely occur in early spring, while ODEs associated with low wind speed and stable boundary layer meteorological conditions seem to occur more often in late spring. Annual evaluations showed similar weather patterns for several years, matching the overall result of the composite analysis. However, some years show different meteorological patterns deviating from the results of the mean analysis. Finally, an ODE case study from the beginning of April 2020 in Ny-Ålesund is presented, where ozone was depleted for 2 consecutive days in combination with increased BrO values. The meteorological conditions are representative of the results of the composite analysis. A low-pressure system arrived from the northeast to Svalbard, resulting in high wind speeds with blowing snow and transport of cold polar air from the north.
Satellite observations have shown large areas of elevated bromine monoxide (BrO) covering several thousand square kilometres over the Arctic and Antarctic sea ice regions in polar spring. These ...enhancements of total BrO columns result from increases in stratospheric or tropospheric bromine amounts or both, and their occurrence may be related to local meteorological conditions. In this study, the spatial distribution of the occurrence of total BrO column enhancements and the associated changes in meteorological parameters are investigated in both the Arctic and Antarctic regions using 10 years of Global Ozone Monitoring Experiment-2 (GOME-2) measurements and meteorological model data. Statistical analysis of the data presents clear differences in the meteorological conditions between the 10-year mean and episodes of enhanced total BrO columns in both polar sea ice regions. These differences show pronounced spatial patterns. In general, atmospheric low pressure, cold surface air temperature, high surface-level wind speed, and low tropopause heights were found during periods of enhanced total BrO columns. In addition, spatial patterns of prevailing wind directions related to the BrO enhancements are identified in both the Arctic and Antarctic sea ice regions. The relevance of the different meteorological parameters on the total BrO column is evaluated based on a Spearman rank correlation analysis, finding that tropopause height and surface air temperature have the largest correlations with the total BrO vertical column density. Our results demonstrate that specific meteorological parameters can have a major impact on the BrO enhancement in some areas, but in general, multiple meteorological parameters interact with each other in their influence on BrO columns.
For more than 2 decades, satellite observations from instruments such as GOME, SCIAMACHY, GOME-2, and OMI have been used for the monitoring of bromine monoxide (BrO) distributions on global and ...regional scales. In October 2017, the TROPOspheric Monitoring Instrument (TROPOMI) was launched on board the Copernicus Sentinel-5 Precursor platform with the goal of continuous daily global trace gas observations with unprecedented spatial resolution. In this study, sensitivity tests were performed to find an optimal wavelength range for TROPOMI BrO retrievals under various measurement conditions. From these sensitivity tests, a wavelength range for TROPOMI BrO retrievals was determined and global data for April 2018 as well as for several case studies were retrieved. Comparison with GOME-2 and OMI BrO retrievals shows good consistency and low scatter of the columns. The examples of individual TROPOMI overpasses show that due to the better signal-to-noise ratio and finer spatial resolution of 3.5×7 km2, TROPOMI BrO retrievals provide good data quality with low fitting errors and unique information on small-scale variabilities in various BrO source regions such as Arctic sea ice, salt marshes, and volcanoes.
Mining hidden knowledge from available datasets is an extremely time-consuming and demanding process, especially in our era with the vast volume of high-complexity data. Additionally, validation of ...results requires the adoption of appropriate multifactor criteria, exhaustive testing and advanced error measurement techniques. This paper proposes a novel Hybrid Fuzzy Semi-Supervised Forecasting Framework. It combines fuzzy logic, semi-supervised clustering and semi-supervised classification in order to model Big Data sets in a faster, simpler and more essential manner. Its advantages are clearly shown and discussed in the paper. It uses as few pre-classified data as possible while providing a simple method of safe process validation. This innovative approach is applied herein to effectively model the air quality of Athens city. More specifically, it manages to forecast extreme air pollutants’ values and to explore the parameters that affect their concentration. Also it builds a correlation between pollution and general climatic conditions. Overall, it correlates the built model with the malfunctions caused to the city life by this serious environmental problem.