Although air pollution is one of the most significant environmental factors posing a threat to human health worldwide, air quality data are scarce or not easily accessible in most European countries. The current work aims to develop a centralized air quality data hub that enables citizens to contribute to air quality monitoring. In this work, data from official air quality monitoring stations are combined with air pollution estimates from sky-depicting photos and from low-cost sensing devices that citizens build on their own so that citizens receive improved information about the quality of the air they breathe. Additionally, a data fusion algorithm merges air quality information from various sources to provide information in areas where no air quality measurements exist.

We report the experimental determination of the ordinary and extraordinary refractive index of 8 mol% Mg-doped congruent lithium tantalate (MCLT). Refractive index measurements cover a spectral range from 450nm to 1550nm and temperatures varying from 22°C to 200°C. Experimental data are fitted to a temperature-dependent dispersion relation that has not been previously used with this material family. Based on this relation, various optical properties of MCLT are calculated, including thermo-optic coefficient, group velocity dispersion, phase matching curve and temporal walk-off. In an additional quasi-phase-matching second-harmonic-generation experiment it is shown that the proposed dispersion relation may be used to predict grating period with remarkable nanometer-scale accuracy.

Acoustic emission tests were conducted on prismatic Dionysos’ marble specimens subjected to compressive mechanical loading cycles. Two groups of acoustic emission time series were analyzed: one group comprised the number of counts per acoustic emission hit and a second group comprised the RA quantity (RA=rise time/amplitude). Both time series were studied during the load increase period. The investigation of prefailure indicators was carried out using an interdisciplinary approach: using two solid methods from different scientific areas (i.e. cardiology and seismology) we were able to detect failure indicators along before the final collapse. More specific, by means of multiresolution wavelet analysis for the study of temporal variation of the wavelet-coefficients’ standard deviation and natural time analysis using the variance ( κ₁) of natural-time transformed time-series, a common pattern for both domains was discovered, clearly showing that it is feasible to estimate if the applied mechanical load has led the specimen in the nonlinear region (σ^* > 70% σ_f) regarding the stress–strain behavior.

On 27 September 2021, a shallow earthquake with focal depth of 10 km and moment magnitude M_w6.0 occurred onshore in central Crete (Greece). The evolution of possible preseismic patterns in the area of central Crete before the M_w6.0 event was investigated by applying the method of multiresolution wavelet analysis (MRWA), along with that of natural time (NT). The monitoring of preseismic patterns by critical parameters defined by NT analysis, integrated with the results of MRWA as the initiation point for the NT analysis, forms a promising framework that may lead to new universal principles that describe the evolution patterns before strong earthquakes. Initially, we apply MRWA to the interevent time series of the successive regional earthquakes in order to investigate the approach of the regional seismicity towards critical stages and to define the starting point of the natural time domain. Then, using the results of MRWA, we apply the NT analysis, showing that the regional seismicity approached criticality for a prolonged period of ~40 days before the occurrence of the M_w6.0 earthquake, when the κ₁ natural time parameter reached the critical value of κ₁ = 0.070, as suggested by the NT method.

It is common sense that immediate response and action are among the most important terms when it comes to public safety, and emergency response systems (ERS) are technology components strictly tied to this purpose. While the use of ERSs is increasingly adopted across many aspects of everyday life, the combination of them with real-time biometric and location data appears to provide a different perspective. Panic is one of the most important emergency indicators. Until now, panic events of any cause tend to be treated in a local manner. Various attempts to detect such events have been proposed based on traditional methods such as visual surveillance technologies and community engagement systems. The aim of this paper is twofold. First, it presents an innovative multimodal dataset containing biometric and spatiotemporal data associated with the detection of panic state in subjects that perform various activities during a certain period. For this purpose, time-enabled location data are combined with biometrics coming from wearables and smartphones that are analyzed in real-time and produce data indicating possible panic events that are geospatially described. Second, the proposed dataset is used to train various machine learning models, and their applicability to correctly distinguish panic states from normal behavior is thoroughly examined. As a result, the Gaussian SVM classifier ranked first among seven classifiers, achieving an accuracy score of 94.5%. The dataset was also tested in a deep learning framework, achieving an accuracy level of 93.4%. A long short-term memory approach was also used, which reached a top accuracy of 94%. Moreover, the contribution of the various biometric and geospatial features is analyzed in-depth to determine their partial importance in the overall panic detection process. This is moving towards the creation of a smart geo-referenced ERS that could be used to inform the authorities regarding a potentially unpleasant event by detecting possible crowd panic patterns and helping to act accordingly, getting the information right from the source of the event, the human body. The proposed dataset is freely distributed to the scientific community under the third version of GNU General Public License (GPL v3) through the GitHub platform.

The main goal of an earthquake early warning system (EEWS) is to estimate the magnitude of an underway rupture from the first few seconds in order to allow hazard assessment and mitigation before destructive events occur. This letter investigates the application of a wavelet-based algorithm for local magnitude estimation in the South Aegean Sea (focusing on Crete Island) which is covered by a sparse seismological network. A relation between the first few seconds of the first-arriving energy at the surface, the P wave, and the local magnitude of the earthquake has been developed for the area of interest. Results show that the errors produced by the proposed method present less scattering than relevant magnitude rapid estimation methods. It is the first time that such a method is applied in a sparse seismological network since all the previous studies took place in high-density networks. This fact expands the applicability of EEWS and also provides an alternative magnitude estimator for the currently developed EEWS.