Monitoring Network Research Articles

Identification of anthropogenic activities interference in the seismic catalog for Banat and Danubian region, Romania

Abstract Banat and Danubian regions are among the most active zones in terms of crustal seismicity in Romania. Even though active seismic monitoring started in the early 20th century, the data sets got more performant with the development and expansion of seismic network monitoring in the late 1970’s. One particularity regarding seismic data acquisition is that in many cases anthropogenic activity, such as mining and quarry exploitation, interferes with natural seismicity. We aim to bring forward a series of data extracted from the ROMPLUS catalog to identify anthropogenic interference. To this aim, we propose a procedure based on multiple discrimination criteria, such as local time of event occurrence, magnitude, location concerning the nearest exploitation site, depth, and P wave input analysis (polarity and waveform). Identifying and filtering the anthropogenic events from the catalog will result in better imaging and characterizing of the natural seismic phenomenon in Banat and Danubian zones, improving the regional seismic hazard assessment this way.

Spatio-Temporal Correlation Between Radon Emissions and Seismic Activity: An Example Based on the Vrancea Region (Romania)

Radon gas anomalies have been investigated as potential earthquake precursors for many years. In this work, we have studied the possible correlations between radon emissions and the seismic activity rate for a given region to test if the existing correlation may be later used to forecast the occurrence of earthquakes larger than a given magnitude. The Vrancea region (Romania) was chosen as a study area since it is being surveilled by a multidisciplinary real-time monitoring network, and at least seven earthquakes with magnitudes greater than 4.5 Mw have occurred in this area in the period from 2016 to 2020. Our research followed several steps: First, the recorded radon signals were preprocessed (detrended, deseasoned and smoothed). Then, the station’s signals were correlated in order to check which stations are recording radon anomalies due to the same regional tectonic process. On the other hand, the seismic activity rate was computed using the earthquakes in the main catalogue of the region that are able to generate radon emissions and can be registered at several stations. The obtained results indicate a significant correlation between the seismic activity rate and the temporal series of radon anomalies. A temporal lag between the seismic activity rate and the radon anomalies was found, which can be related to the proximity to the epicentre of the main earthquake in each of the studied subperiods. Changes in the regional tectonic stress field could explain why the seismic activity rate and radon anomalies are correlated over time. Further research could focus on obtaining a function to forecast the seismic activity rate using the following as dependent variables: the radon anomalies recorded at several stations, the distance from the stations, and tectonic factors such as the fault system, azimuth, type of soil, etc.

A Multivariate Approach for Modeling Spatio‐Temporal Agrometeorological Variables

ABSTRACTOne of the main issues facing agrometeorological studies involves measuring and modeling the evolution of different environmental variables over time; this often requires a dense monitoring network. Spatio‐temporal geostatistics has the potential to provide techniques and tools to estimate the spatio‐temporal multiple covariance function and define an appropriate multivariate correlation function capable of reliable predictions. This paper presents a spatio‐temporal multivariate geostatistical modeling approach based on the joint diagonalization of the empirical covariance matrix evaluated at different spatio‐temporal lags. The possibility to consider a reduced number of uncorrelated variables (lower than the number of observed variables) and separately model the spatio‐temporal evolution of these uncorrelated components represents a substantial simplification for multivariate modeling. A space–time linear coregionalization model (ST‐LCM) with appropriate parametric models for the latent components was fitted to the matrix‐valued covariance function estimated for five relevant agrometeorological variables, including evapotranspiration, minimum and maximum humidity, maximum temperature, and precipitation. The analyses highlight how to identify space–time components and choose the corresponding model by evaluating some characteristics of these components, such as symmetry, separability, and type of non‐separability. The predictive results of this multivariate study will be of interest for agriculture, in particular for addressing drought emergencies.

Evaluation of an aquatic liverwort and terrestrial moss as biomonitors of heavy metals associated with particulate matter

In this study, the capacity of the aquatic liverwort Ricciocarpus natans L. and the terrestrial moss Entodon serrulatus Mitt. as biomonitors of heavy metals associated with particulate matter from a highly polluted urban area was evaluated, and concentrations in moss tissues were correlated with concentrations of PM10 and PM2.5 present in the atmosphere. The two species were exposed by the moss bag technique to the pollution of the Toluca Valley Metropolitan Area (TVMA) for two periods of 6 months, using the sites of the Automatic Atmospheric Monitoring Network of the Government of the State of Mexico, and were subsequently analyzed using elemental and structural characterization techniques. The results show that mainly the functional groups -OH and -NH, N-H and C-O on the surface of the liverwort and moss participate in the adsorption of heavy elements. The average enrichment factors of Cd and Pb show to be highly enriched (> 10) in the study area while chromium is not enriched (< 2). The statistical results indicate a temporary variation in the concentration of metals and particles in the atmosphere, where there is a lower concentration of these pollutants in the rainy and dry-cold season and a higher concentration in the dry-hot season and a possible association of Cr and Cd with PM10 and PM2.5. In addition, except for Cr, both species accumulate the metals associated with airborne particulate matter at equivalent levels. There is strong association between PM2.5 and PM10 particles and between the metals Cr-Pb-Fe in R. natans and between PM2.5-PM10 and Fe and between Cd-Cr-Pb in E. serrulatus and these pollutants are mainly associated with sampling sites with the highest concentrations of metals in the TVMA. Although terrestrial moss showed slightly better characteristics than aquatic liverwort as a biomonitor of heavy metals associated with atmospheric particles, these differences were not statistically significant for all metals, so both species could be useful for heavy metal biomonitoring in highly polluted urban areas.

Identification of particulate matter (PM10 and PM2.5) sources using bivariate polar plots and k-means clustering in a South American megacity: Metropolitan Area of Lima-Callao, Peru.

The identification of different air pollution sources is essential to effectively control atmospheric pollution, particularly in megacities of emerging countries with rapid economic development, such as the Metropolitan Area of Lima-Callao (MALC). The objective of this research was to identify the main sources of particulate matter pollution by applying bivariate polar plots and the k-means clustering algorithm. These statistical techniques were applied to hourly in situ data of four variables collected over a 5-year period (2015-2019) by the Automatic Air Quality Monitoring Network of the MALC: wind direction, wind speed, PM10, and PM2.5 concentrations. Average PM10 concentrations ranged from 34μgm-3 (CDM station) to 126.7μgm-3 (VMT station), while average PM2.5 concentrations ranged from 16.8μgm-3 (CDM station) to 41.2μgm-3 (ATE station). The diurnal variation of PM presented two peaks, one in the morning (from 0800 to 1000h) and the other at night (from 1900 to 2300h), with the highest concentrations of PM10 recorded at the ATE (0800h: 155.8μgm-3) and VMT (2100h: 154.6μgm-3) stations, and PM2.5 at the ATE station (0800h: 60.3μgm-3 and 2300h: 37.5μgm-3). The results showed that the contributions of PM10 are directly related to emissions from industrial activities, automotive fleet, construction, demolition, wind erosion, and the suspension and resuspension of particulates from unpaved roads. Meanwhile, high concentrations of PM2.5 are mainly attributed to vehicle exhaust emissions, industrial emissions, secondary particulate formation, and drag by the action of the winds. The major source of particulate matter contamination is the vehicle fleet, and within this, automobiles, station wagons, combi vans, and 2 and 3-wheel motorcycles are those that have the greatest contribution. These results were supported by non-parametric statistical tests such as Kruskal-Wallis and Mann-Whitney U and validated by the conditional bivariate probability function. The findings of this work may help to implement pollution prevention and control strategies in the future within this South American megacity.

Fully Passive Electrochemical Oxygen Sensor Enabled With Organic Electrochemical Transistor

AbstractWireless and battery‐free sensor operation is essential for sustainable sensor networks for environmental and health monitoring. Achieving such a fully passive design in electrochemical sensors is challenging due to the need for amplification and control electronics as realized in potentiostats. To address this issue, organic electrochemical transistors (OECTs) are introduced as amplified sensors and demonstrate an OECT‐based oxygen sensor that exploits the electrochemical reduction of oxygen, akin to the Clark electrode. To build the sensor, the transistor thin film fabrication protocol is integrated with the deposition of a gas permeable silicone membrane and a hydrogel electrolyte. To assess the sensor's power consumption, a model that links power consumption to the OECT geometry and the material properties of the semiconducting channel is established. This model identifies the optimized OECT design that is compatible with commercial near‐field communication (NFC) microcontrollers. In the optimized design, the interrogating NFC radio signal is sufficient to power the microcontroller, achieve sensor readout, and transmit the digitized sensor current, enabling oxygen monitoring in air or water without wired connections or batteries. These findings provide a first example and quantitative argumentation on exploiting OECTs in low‐power electrochemical sensor architectures.

Harnessing the power of multicountry networks for influenza vaccine monitoring.

Harnessing the power of multicountry networks for influenza vaccine monitoring.

A novel physical information neural network for real-time monitoring and sparse reconstruction of thermal environments with turbulent natural convection in nacelles

A novel physical information neural network for real-time monitoring and sparse reconstruction of thermal environments with turbulent natural convection in nacelles

Using the long-term genetic monitoring network ARMS-MBON to detect marine non-indigenous species along the European coasts

The increasing prevalence of non-indigenous species (NIS) in marine ecosystems poses significant challenges for biodiversity conservation and ecosystem management. Advances in molecular techniques enable early detection and long-term monitoring of such taxa, especially when coupled with spatio-temporally wide sampling by networks such as the European ARMS Marine Biodiversity Observation Network (ARMS-MBON). This initiative performs standardised sampling campaigns using autonomous reef monitoring structures (ARMS) along European coasts and adjacent regions, providing open-access DNA metabarcoding data sets. We tested the potential of genetic observatory networks to detect and monitor marine NIS by analysing all publicly available ARMS-MBON cytochrome c oxidase subunit I (COI) and 18S rRNA amplicon sequencing data as of February 2024 using a customised bioinformatic pipeline. Screening against the World Register of Introduced Marine Species (WRiMS) and applying manual curation, we identified 63 marine taxa considered non-indigenous at one or more locations. This included widespread taxa and potential new introductions, such as Eucheilota menoni in the Adriatic Sea. We found no significantly higher number of NIS in samples from locations particularly impacted by maritime traffic compared to other areas. Our results suggest that the genetic observatory network approach is powerful for detecting and monitoring marine NIS, and that manual curation still is an essential step for obtaining reliable results. We recommend key improvements including more spatially intense sampling across diverse environments as well as enhancement of NIS reference checklists and genetic databases to ensure accurate identification of both known and unknown NIS across Europe.

Design of Geodetic Monitoring Networks by Minimal Detectable Displacement Evaluation under Confidence and Sensitivity Analysis

Design of Geodetic Monitoring Networks by Minimal Detectable Displacement Evaluation under Confidence and Sensitivity Analysis

Fortifying industrial cybersecurity: a novel industrial internet of things architecture enhanced by honeypot integration

The industrial internet of things (IIoT) has significantly transformed the industrial sectors by connecting devices, machines, and systems to enhance automation, efficiency, and decision-making. However, the increased interconnectivity also poses significant security challenges because IIoT devices control critical infrastructures and processes. Our work presents an implementation of a robust industrial cybersecurity strategy with a segmented network architecture, collaborative efforts between information technology (IT) and operational technology (OT) teams for enhanced resilience and effectiveness, and vertical honeypots across all Industry 4.0 levels integrated with Wazuh for log transmission and proactive threat response, alongside Snort intrusion detection system (IDS) monitoring network traffic. Additionally, we reinforce our architecture by Wazuh with Elasticsearch and Kibana as a security information and event management solution, facilitating data analysis and compliance enforcement through custom rulesets and cybersecurity threat intelligence (CTI) integration, with automatic updates for continuous adaptation against emerging threats.

Auto-induced uplink 4G and 5G RF-EMF exposure assessment using a network monitoring application in different microenvironments across seven European countries.

The auto-induced uplink (a-UL) radio-frequency electromagnetic field (RF-EMF), often the dominant part of the total RF-EMF exposure, has not been included in previous microenvironmental studies. As 5G exposure depends more on mobile phone usage, monitoring typical transmit power levels is crucial towards more accurate personal exposure assessment. This study describes spatial differences in average mobile phone transmit power and investigates the influence of uplink duty cycles and frequency band usage. A novel methodology using the network monitoring application QualiPoc in fourth-generation (4G) and non-standalone fifth-generation (5G) networks was presented. For the first time, the assessment of 4G and 5G a-UL RF-EMF exposure was conducted simultaneously in a large-scale microenvironmental study in Europe. Measurements were performed along predefined routes in 282 different microenvironments (e.g., parks, residential areas) across seven European countries, during a maximum uplink usage scenario. The Netherlands had the highest average transmit powers per microenvironment (median 20.6 dBm). Transmit powers in villages were 0.6 to 2.1 dB higher than in big cities. The study suggested that base station density is a key predictor of a-UL exposure. Comparing technologies and frequency bands, average transmit powers for 5G were about 3.3 dB lower than for 4G and lowest for frequency bands with a time division duplexing (TDD) scheme due to the low uplink duty cycle (below 20%). This study provides crucial measurement data for epidemiologists and governments to enhance the understanding of the a-UL component of personal RF-EMF exposure.

Network Submission and Monitoring of Power System Operation Safety Monitoring Data using Safe Power Hybrid Classifier (SPHC)

Network Submission and Monitoring of Power System Operation Safety Monitoring Data using Safe Power Hybrid Classifier (SPHC)

Patterns of microbial contamination on Northumberland Strait shores.

The re-emergence of episodic faecal contamination of Parlee and Murray Corner beaches, on the Northumberland Strait of New Brunswick, Canada, in 2017, raised renewed community concerns on the health, environmental and tourism sustainability of these community resources, and led to creation of an Integrated Watershed Management Plan for the Shediac Bay Watershed (October 2021). In response we have to date compiled, curated and made accessible 205,772 microbial water quality data records spanning over 80 years from Southeastern New Brunswick and the Northumberland Strait. This dataset derives in large part from Shellfish Surveys completed by Environment and Climate Change Canada, along with data generated by multiple government agencies, Non-Governmental Organizations and citizen science sources. Records derived from these multiple sources are now deposited in the Gordon Foundation's DataStream (https://atlanticdatastream.ca), an open access common platform for sharing structured information on fresh and marine water health, delivered on a pan-Canadian scale, in collaboration with regional monitoring networks. We herein outline our data assembly, curation and deposition, along with preliminary analyses of contamination patterns at three representative sites on the Northumberland Strait coast of New Brunswick. Our results suggest that cumulative rainfall over 48 h is useful in predicting contamination risk at the developed Parlee Beach, and thereby demonstrate how open data can be used to inform policy and management decisions.

AI-driven cyber threat detection for global logistics in United States

The integration of AI-driven cybersecurity systems is increasingly essential to the security and efficiency of global logistics operations in the United States. This thematic analysis investigates the major themes, challenges, and opportunities of AI in logistics cybersecurity, focusing on its effectiveness in threat detection and prevention, diverse applications, and implementation challenges. AI has been shown to significantly enhance threat detection, reducing response times by up to 65%, and proactively preventing cyber threats through predictive analytics. Key applications include AI-driven network monitoring, endpoint security, and supply chain risk management, each contributing to the overall resilience of logistics networks. However, several challenges hinder the widespread adoption of AI, including high initial costs, integration issues with legacy systems, and a shortage of skilled professionals. Additionally, regulatory compliance and ethical considerations, such as data privacy and transparency, must be addressed to ensure responsible deployment. Despite these barriers, empirical evidence highlights AI’s potential to revolutionize logistics cybersecurity, offering faster threat detection, reduced vulnerabilities, and more secure operations. Overcoming adoption challenges requires a concerted effort from industry stakeholders, with a focus on workforce development, regulatory adherence, and ethical AI practices. Future research should explore strategies to address these challenges, ensuring the full potential of AI in enhancing the security and efficiency of global logistics operations.

P-1351. Occurrence of b-lactamases among Enterobacterales isolates from United States Hospitals in a 10-year period: Report from the International Network for Optimal Resistance Monitoring (INFORM) Program

Abstract Background β-lactamase-production in Enterobacterales challenge the use of β-lactams, the most broadly used and versatile class of antimicrobial agents. The INFORM program surveys β-lactamases and the activity of ceftazidime-avibactam in US hospitals. We analyzed 10 years of data from that study. Yearly distribution of most common β-lactamases among E. coli (EC) and K. pneumoniae (KPN) isolates collected during 2013-2022 in US hospitals Methods 90,956 Enterobacterales isolates were consecutively collected in 22 US hospitals participating in the INFORM program during 2013 to 2022. Susceptibility testing was performed and interpreted according to CLSI. E. coli (EC) and K. pneumoniae (KPN) displaying MICs ≥ 2 mg/L for two of: ceftriaxone, ceftazidime, cefepime or aztreonam; or carbapenem-resistant Enterobacterales (CRE; resistant to meropenem or imipenem) were evaluated for the presence of β-lactamase genes. Genetic analysis was performed by microarray/PCR (2013-2015) or whole genome sequencing (2016-2022). Yearly distribution of CRE isolates collected during 2013-2022 in US hospitals Results ESBLs were detected among 2,334/2,691 (15.6% of 14,969 overall non-CRE isolates) EC and 964/1,051 (13.8% of 6,988) KPN. A decline in EC displaying the phenotypic criteria and CTX-M-15-like isolates (12.6% in 2021 and 8.3% overall in 2022; Figure 1) was noted in 2022. The most common ESBL in both groups was CTX-M-15-like (10.5/58.3% of the EC overall/characterized and 11.0/73.4% of the KPN). SHV ESBLs had a steady decline among KPN since 2015. CRE isolates declined steadily from 1.6% in 2013 to 0.8% in 2022 (Figure 2). Carbapenemase-positive isolates also declined from 1.3% to 0.6%, with a decline in KPC producers but an increase in isolates producing NDM that were initially detected in 2015 and range from 10-16 isolates in the last 3 study years. Ceftazidime-avibactam and the carbapenems are the most active agents against β-lactamase-producing isolates (Figure 3). Activity of ceftazidime-avibactam and comparators against main β-lactamase groups Conclusion Changes in β-lactamase-producing isolates and its susceptibility profiles should be closely monitored in a local and global level since it impacts patient treatment, but longitudinal data such as this information is scarce. Disclosures Rodrigo E. Mendes, PhD, GSK: Grant/Research Support

Advanced Hybrid Models for Air Pollution Forecasting: Combining SARIMA and BiLSTM Architectures

This study explores a hybrid forecasting framework for air pollutant concentrations (PM10, PM2.5, and NO2) that integrates Seasonal Autoregressive Integrated Moving Average (SARIMA) models with Bidirectional Long Short-Term Memory (BiLSTM) networks. By leveraging SARIMA’s strength in linear and seasonal trend modeling and addressing nonlinear dependencies using BiLSTM, the framework incorporates Box-Cox transformations and Fourier terms to enhance variance stabilization and seasonal representation. Additionally, attention mechanisms are employed to prioritize temporal features, refining forecast accuracy. Using five years of daily pollutant data from Romania’s National Air Quality Monitoring Network, the models were rigorously evaluated across short-term (1-day), medium-term (7-day), and long-term (30-day) horizons. Metrics such as RMSE, MAE, and MAPE revealed the hybrid models’ superior performance in capturing complex pollutant dynamics, particularly for PM2.5 and PM10. The SARIMA combined with BiLSTM, Fourier, and Attention configuration demonstrated consistent improvements in predictive accuracy and interpretability, with attention mechanisms proving effective for extreme values and long-term dependencies. This study highlights the benefits of combining statistical preprocessing with advanced neural architectures, offering a robust and scalable solution for air quality forecasting. The findings provide valuable insights for environmental policymakers and urban planners, emphasizing the potential of hybrid models for improving air quality management and decision-making in dynamic urban environments.

Adapting to change: exploring the distribution dynamics of the alien and potentially invasive aphid species Cinara curvipes (Hemiptera: Aphididae) in the context of global warming

This study investigates the potential ecological niche and distribution of the Nearctic bow-legged fir aphid, Cinara curvipes, an alien and potentially invasive species, across Europe. Utilising climate modelling based on climatic preferences in its native range, we assess the suitability of European climates for facilitating the spread of this aphid. Coupled with the presence of suitable host plants, mostly firs (Abies spp.) or cedars (Cedrus spp.), there is potential for the bow-legged fir aphid to extend its range across most European regions, with projections suggesting potential expansion into Asia Minor. Furthermore, future climate scenarios predict a northward expansion of suitable habitats for this aphid species, confirmed by its detection in Denmark and Norway. Our research reveals that C. curvipes displays notably higher fecundity compared to its Cinara genus counterparts. Specifically, wingless viviparous females of this species achieve peak fecundity at lower temperatures (10–15°C), explaining their spring mass appearances. At 25°C, nymphs fail to moult or reach maturity, leading to summer population declines. Unique survival mechanisms, including the production of numerous winged morphs under overcrowded conditions, enable rapid dispersal facilitated by human-mediated transport, notably through the “Christmas trees” trade. Its high reproductive output allows the species to quickly colonise new habitats and exploit available resources, contributing to its potential invasiveness. We emphasise the critical need for collaborative engagement among researchers, stakeholders, and the public to bolster citizen science initiatives and reinforce environmental monitoring networks, all within the framework of an early detection and rapid response (EDRR) process. This collaborative approach is vital for effective environmental management and mitigating potential invasive threats.

Selection of sampling sites in Germany for the International Moss Survey 2020 using statistics and decision modelling

BackgroundAfter 1990, 1995, 2000, 2005 and 2015, Germany participated in the International Moss Monitoring 2020 (MM2020). The German contribution to MM2020 aimed at pilot studies on the suitability of bioindication with mosses for recording the atmospheric deposition of persistent organic pollutants and microplastics.ResultsThis investigation was based on moss samples collected at 25 sites in Germany: Eight sites at which concentrations of persistent organic pollutants were determined in the Moss Survey 2015 were included. In addition, twelve sites were selected from the pool of the total of 400 moss collection sites in 2015. Further five sites of the German moss monitoring network 2015 were added, at which moss samples were collected in 2020 for developing the sample preparation and for preliminary investigations. The selection of the five test sites was based on the same criteria as for the 20 target sites of the 2020 monitoring to make the analysis data of the test phase usable for later evaluations. To ensure methodological transparency and objectivity, a procedure based on statistical methods and decision modelling was developed for this purpose. The decision algorithm enabled taking into account a large number of technical criteria. Selected features of the three subsamples comprising 8, 20 and 25 sites were compared with those of the full sample (n = 400 sites of Moss Survey 2015) and inferentially tested whether the thinning of the 2015 sampling network (n = 400) to 8, 20 and 25 sites, respectively, leads to significant changes in its information quality or not.ConclusionsMethods of decision modelling and inferential statistics have proven their worth for transparently restructuring the moss monitoring network.

An optimised and secured bio medical signal transmission using CNN

ABSTRACT Real-time transmission of biomedical signals remains challenging, particularly with IoT and cloud-based solutions, due to cybersecurity risks and energy constraints. To ensure secure and power-efficient transmission, we propose a selective transmission-based reconstruction model. Instead of sending the entire signal, only the most relevant features are transmitted to the intended receiver. At the destination, an encrypted version of the signal is reconstructed using a decryption algorithm, preventing data loss. The primary challenge lies in sensor network monitoring, especially regarding energy consumption and data acquisition. To address this, we integrate compressive sensing methods to enhance data reconstruction and prolong network lifetime. This paper introduces the CNN-based Optimized BCAE, an integrated model combining an autoencoder with feature selection, allowing effective extraction of key features for biomedical applications. By distinguishing relevant from irrelevant task units, this approach improves data throughput and optimizes power management. Our model offers a reliable solution for real-time biomedical signal processing, ensuring both efficiency and security.