The spread of Xylella fastidiosa, a xylem-limited bacterial pathogen, has caused widespread mortality among olive trees in Apulian region, Italy in more than a decade, and represents a significant threat to Mediterranean agroecosystems. To encourage evidence-based containment strategies, we developed a stochastic, spatiotemporal simulation model that represents pathogen transmission at the individual-tree level. This work integrates high-resolution georeferenced olive-tree data and implicitly incorporates vector population dynamics through a tree-specific vulnerability index, which considers local host density and landscape connectivity. Vector dispersal is approximated using a radial transmission kernel, which preserves host-vector spatial interactions while avoiding the explicit modeling of insect trajectories. The system's spatial structure is additionally formulated as a proximity graph, facilitating network-based analysis of spread pathways. A series of Monte Carlo simulation experiments is employed for calibration against the observed epidemic footprint, while validation utilizes independent infection records and global sensitivity analysis of key parameters. The findings indicate that the model effectively replicates realistic propagation patterns, and its calibrated parameters are consistent with out-of-sample data. This makes it an appropriate exploratory tool for scenario testing, assessing the potential impact of intervention strategies, and offering risk-based decision support for handling Xylella fastidiosa outbreaks. Subsequently, graph centrality metrics are used to identify epidemiologically critical trees that function as transmission bridges, thus representing priority targets for surveillance or removal efforts. Thus, multiple tests have been conducted using betweenness and closeness centrality, while comparing both methods leads to effective node-tree removal decisions.

Unstable rock blocks and cliffs pose a widespread geohazard, and their mechanical state can be tracked through their ambient-vibration resonance frequencies, whose decrease anticipates progressive failure. Such monitoring is usually performed with expensive broadband instrumentation, limiting spatial and temporal coverage. Here we assess whether a low-cost, IoT-enabled node—built around a Raspberry Pi single-board computer, a 24-bit sigma-delta digitiser and a force- balance accelerometer (Geobit FBA-200)—can identify the resonance of an unstable coastal rock block at the celebrated “moving rock” of Kounopetra (Paliki peninsula, Kefalonia, Greece), a site historically renowned for visually perceptible rocking boulders. We stress that the low-amplitude 7.7 Hz structural eigenvibration characterised here is a distinct phenomenon from the historically documented ∼0.3 Hz macroscopic, quasi-rigid rocking of the boulder: the former is the ambient–vibration resonance of the fractured rock mass, the latter a large-amplitude rigid-body oscillation. Two identical nodes recorded ground acceleration simultaneously for nine hours: one on the fractured Kounopetra rock mass and one on stable ground 25 m away, used as a reference. The rock station exhibits a clear, temporally stable fundamental resonance at f0 = 7.7 Hz (Q ≈ 50, damping ζ ≈ 1%), amplified by up to an order of magnitude relative to the reference and entirely absent from it, whereas a narrow 20.5 Hz line present on both nodes is identified as instrument-related and discarded. A simultaneous two-station analysis further shows that the ambient sources are extremely local (only 0.4% of transient activity is common to the two nodes 25 m apart), quantifying a design constraint for differential schemes. The results demonstrate that a low-cost force-balance node is sufficient to establish a resonance baseline for an unstable rock block, opening the way to dense, affordable early-warning networks; the main limitations are the single vertical component and the short record, which preclude polarisation analysis and long-term tracking of f0.

This study theoretically investigates the role of plant moisture content and its spatial heterogeneity in wildfire dynamics using Cellular Automata models. The model incorporates varying moisture levels and ignition probabilities across different grid configurations, including homogeneous moisture grids and heterogeneous setups with elliptical and segmented high-moisture zones. The relationship between moisture content and ignition probability is modeled using a nonlinear formulation, reflecting threshold-like combustion dynamics observed in real ecosystems. Simulation results show that introducing high-moisture zones significantly reduces the rate of fire spread, with segmented configurations providing the most effective firebreaks. In this context, the ‘suppression effect’ denotes the reductions in forward spread and total burned area attributable to high-moisture regions acting as low-ignitability barriers. The effect is more pronounced when ignition probability depends nonlinearly on moisture, since the nonlinear mapping produces a steeper decline in ignitability above a critical moisture range, which reduces successful transmission across the barrier and increases the likelihood of fire isolation. In particular, the results highlight how modeling can be used as a decision-support tool for the strategic placement of firebreaks. By evaluating alternative spatial configurations of moisture, the approach helps identify barrier designs that maximize containment effectiveness while minimizing ecological and economic costs. This positions the methodology not only as a theoretical contribution but also as a practical framework for guiding firebreak planning and wildfire prevention policies. While the model successfully captures critical fire dynamics, its assumptions of static moisture content and simplified environmental conditions warrant further investigation. Future work will focus on integrating real-time moisture data and refining parameters with observational wildfire data to enhance the model’s predictive capabilities. This study provides valuable insights into the interplay between moisture content and wildfire spread, contributing to the development of decision-support tools for effective wildfire management.

Flood forecasting in Mediterranean rivers remains challenging due to the highly intermittent nature of flow and the limited availability of real-time hydrological observations. This study presents a low-cost system for water level monitoring and short-term flood prediction using high-frequency sensor measurements and meteorological data collected in Kefalonia, Greece. A multi-stage preprocessing pipeline was applied to improve data quality, including session-based filtering, outlier removal, and Savitzky–Golay smoothing. An XGBoost regression model was then trained to predict the maximum water level within a 24-hour horizon using hydrological lag features and lagged meteorological variables. The proposed model achieved strong predictive performance (R² = 0.924, RMSE = 0.293 cm), demonstrating its ability to capture the temporal dynamics of flood events. The results also confirm a strong relationship between precipitation accumulation and river response.

Xylella fastidiosa is a devastating pathogen that has significantly impacted olive cultivation, particularly in Southern Italy, since 2013. Monitoring and early identification are crucial for managing the spread of this disease and minimising associated economic losses. This study presents a low-cost system employing electrical conductivity (EC) probes to monitor the water status of olive tree stems, enabling the early detection of X. fastidiosa. The system provides real-time data by detecting changes in EC, which are possibly correlated with the presence of the pathogen. In contrast to traditional detection methods like PCR, which are expensive and require laboratory facilities, this system offers a practical, field-deployable, and sustainable solution. In a testbed, EC probes were installed on an olive tree in Southern Italy, with data transmitted via a GSM network to a central server for analysis. Powered by solar energy, the system successfully detected changes in the stem’s water content, which were subsequently confirmed to be associated with pathogen presence. This low-cost EC probe system initially demonstrates promising potential for the early detection of X. fastidiosa, enabling proactive management strategies. Future research will focus on improving the system’s sensitivity and conducting extensive field trials across diverse environments and applications.

Low-Code and No-Code (LCNC) platforms are gaining popularity due to their affordability and accessibility, enabling users without extensive technical expertise to create software solutions. Artificial Intelligence (AI) is increasingly integrated to enhance these platforms, enriching their capabilities in software and product development. However, this convergence raises ethical concerns regarding biases embedded in the underlying algorithms and trained datasets of these AI models. This paper investigates the intersection of AI and LCNC platforms, focusing on gender bias. Specifically, it explores whether gender stereotypes are perpetuated within AI-Powered LCNC solutions, contributing to unfair decision-making and reinforcing societal inequalities. To examine this, we developed an AI-Powered Low-Code solution incorporating several well-established AI tools, including those from OpenAI, and designed two experiments. The first experiment tested gender-role biases in professional associations, while the second explored gender biases in sentence completion tasks. Our findings indicate that AI-Powered LCNC solutions can inadvertently perpetuate gender biases, reinforcing stereotypes related to professions, education, and traditional gender roles. This paper also discusses the implications of these findings, offering insights to support ongoing efforts to promote fairness and reduce bias in AI-Powered LCNC solutions.

This paper presents a simulation study investigating the possible dispersal of the red palm weevil, a highly destructive pest affecting various palm species, across the island of Corfu, Greece. The simulation incorporates ecological modeling and geographical data to analyze the dynamics and the spread of red palm weevil populations over time and space. Key findings indicate that factors such as tree density and spatial distribution significantly influence infestation rates, with densely populated areas being more susceptible to rapid spreading. The study underscores the importance of early detection and targeted interventions to control red palm weevil populations and to mitigate their impact on affected regions. This research contributes to the development of effective pest management strategies that could potentially be adapted to address similar invasive species challenges in other agricultural contexts.

Autonomous ground vehicles (AGVs) are of major importance in exploration missions since they perform difficult tasks in changing or harmful environments. Mapping and exploration is crucial in hazardous areas, or areas inaccessible to humans, demanding autonomous navigation. This paper proposes a lightweight, low-cost AGV platform, which will be used in resource-constrained situations and aimed at scenarios like exploration missions (e.g., cave interiors, biohazard environments, or fire-stricken buildings) where there are serious security threats to humans. The proposed system relies on simple ultrasonic sensors when navigating and applied traversal algorithms (e.g., BFS, DFS, or A*) during path planning. Since on-board microcomputers have limited memory, the traversal data and direction decisions are stored in a file located on an SD card, which supports long-term, energy-saving navigation and risk-free backtracking. A fish-eye camera set on a servo motor captures three photos ordered from left to right and stores them on the SD card for further off-line processing, integrating each frame into a low-frame-rate video. Moreover, when the battery level falls below 50%, the exploration path does not extend further and the AGV returns to the base station, thus combining a secure backtracking procedure with energy-efficient decisions. The resultant platform is low-cost, modular, and efficient at augmenting; thus it is suitable for exploring missions with applications in search and rescue, educational robotics, and real-time applications in low-infrastructure environments.

Coastal lagoons are marine environments with high economic importance due to their great role in seafood production. However, only a small fraction of coastal lagoons has been investigated for microplastic pollution, and considering hypersaline lagoons, the percentage drops even further. This study investigated microplastics (MPs) pollution for the first time in a Mediterranean hypersaline coastal lagoon. Our work reports an average abundance of 60 MPs/L with a statistically significant positive correlation to salinity. The microplastic particles identified in this study were primarily fragments of semi-synthetic fibers of cellulose acetate. A small amount was determined as polyester polyethylene terephthalate (PET), originating from fishing and touristic activities that occur in the area and potentially transported by the surface circulation of the NE Ionian Sea. Finally, this study shows that the mechanisms controlling the hypersaline conditions in the lagoon (atmospheric forcing and seawater intrusions) are also responsible for the higher concentration of MPs compared to other lagoons.

The use of a dense network of commercial high-cost seismographs for earthquake monitoring is often financially unfeasible. A viable alternative to address this limitation is the development of a network of low-cost seismographs capable of monitoring local seismic events with a precision comparable to that of high-cost instruments within a specified distance from the epicenter. The primary aim of this study is to compare the performance of an advanced, contemporary low-cost seismograph with that of a commercial, high-cost seismograph. The proposed system is enhanced through the integration of a 24-bit analog-to-digital converter board and an optimized architecture for a low-noise signal amplifier employing active components for seismic signal detection. To calibrate and assess the performance of the low-cost seismograph, an installation was deployed in a region of high seismic activity in Evgiros, Lefkada Island, Greece. The low-cost system was co-located with a high-resolution 24-bit commercial digitizer, equipped with a broadband (30 s—50 Hz) seismometer. An uninterrupted dataset was collected from the low-cost system over a period of more than two years, encompassing 60 local events with magnitudes ranging from 0.9 to 3.2, epicentral distances from 5.71 km to 23.45 km, and focal depths from 1.83 km to 19.69 km. Preliminary findings demonstrate a significant improvement in the accuracy of earthquake magnitude estimation compared to the initial configuration of the low-cost seismograph. Specifically, the proposed system achieved a mean error of ±0.087 when benchmarked against the data collected by the high-cost commercial seismograph. These results underscore the potential of low-cost seismographs to serve as an effective and financially accessible solution for local seismic monitoring.

The early detection of smoke signals due to wildfires is vital in containing the extent of loss and reducing response time, particularly in inaccessible or forested areas. For lightweight object detection, this study contrasts the YOLOv9-tiny, YOLOv10-nano, YOLOv11-nano, YOLOv12-nano, and YOLOv13-nano algorithms in determining wildfire smoke at extended ranges. We present a robustness- and generalization-checking five-fold cross-validation. This study is also the first of its kind to train and publicly benchmark YOLOv10-nano up to YOLOv13-nano on the given dataset. We investigate and compare the detection performance against the standard performance metrics of precision, recall, F1-score, and mAP50, as well as the performance metrics regarding computational efficiency, including the training and testing time. Our results offer practical implications regarding the trade-off between pre-processing methods and model architectures for smoke detection when applied in real time on ground-based cameras installed on mountains and other high-risk fire locations. The investigation presented in this work provides a model in which implementations of lightweight deep learning models for wildfire early-warning systems can be achieved.

Background: Successful human resources selection is considered the main step for every organization. Previous research has identified many challenges and innovations concerning the application of Artificial Intelligence/Machine Learning in Human Resources Management. Purpose: The purpose of this study was to apply machine learning algorithms in order to match human qualifications to position’s standards and finally to establish a rapid and more reliable procedure either for initial selection or for authority positions and additionally, to optimize the selection coefficients of properly chosen variables that describe qualifications and, in parallel, to optimize the best fit algorithms’ parameters in order to achieve the greatest accuracy. Finally, this procedure may support automatic mobility. Approach: This study was based on civil section data in order to match human qualifications to position’s standards using machine learning algorithms and finally to establish a rapid and more reliable procedure mainly for initial selection but also for authority positions. Supervised machine learning algorithms were applied. Optimization of selection coefficients of properly chosen variables was performed, followed by algorithms’ parameters optimization in order to achieve the greatest accuracy. Findings: Metrics of algorithms were improved at about 3% for accuracy and F-Measure, especially for J48, which found to be the best algorithm for matching with accuracy close to 97% and pruning simplified the final tree and thus visual classification. This procedure may also be useful in order to support a system of automatic mobility (internal and external) of highly qualified executives.

Microplastic pollution has affected every region of the marine environment and every level of the food chain. The potential health risks associated with the consumption of seafood products affect the worldwide seafood industry. Crustaceans are the fastest-growing fisheries industry. However, due to their predatory feeding behavior and benthic habitats, crabs are at higher risk of consuming MPs than other marine organisms. This is especially true for blue crabs (Callinectes sapidus) who spent most of their lives in coastal environments (such as lagoons and estuaries), which are among the most burdened environments by microplastic pollution. This study evaluated the occurrence of MPs within juvenile blue crabs from Antinioti lagoon, Greece. The findings showed an average abundance of 0.28 MPs/Ind with MPs being identified as nylon and polyethylene (PE), by Raman microscopy. Hence, blue crabs are affected by microplastic pollution from the early stages of their lives.

Olive tree production has been of paramount importance to nutrition and culture since the early fifth millennium B.C. The most serious pest of olive groves is the olive fruit fly, known as Bactrocera Oleae or Dacus Oleae, which can lead to loss of production up to 80–90

This paper provides a concise and comprehensive analysis of forest fire spread simulation. The Alexandridis model was implemented and enhanced utilizing stochastic methods in a Python environment. The impact of various factors influencing fire spread was individually studied. Results were empirically analyzed based on model-generated images, addressing the lack of real fire data. Multiple scenarios and parameter values were explored, highlighting the effectiveness of the developed method. In summary, this paper provides a concise and comprehensive analysis of forest fire spread simulation, demonstrating the efficacy of the Alexandridis model. Findings suggest the potential for developing a more effective model to combat forest fires and improve prediction and prevention methods.

Microplastics (MPs) are plastic debris that have been accumulating in the marine environment since the 1950s, becoming a hazardous pollutant, especially of the marine coastal ecosystems. MPs have been reported in almost every marine environment around the world. The Mediterranean Sea is considered to be one of the most affected areas of our planet. This chapter presents the current situation of microplastic pollution in coastal seas around the world, and most importantly, in the Mediterranean Sea. More specifically, the sources, threats, strategies, and research on microplastic pollution are being demonstrated, followed by a discussion on current issues for the research on microplastic pollution and the future challenges.

Coastal lagoons are important ecosystems that contribute to the production of seafood worldwide. The impact of climate change on these vulnerable ecosystems can be evaluated by studying the zooplankton community due to its sensitivity to changes in environmental conditions. In this study, the authors investigated the effect of future climate conditions on the zooplankton community of a Mediterranean coastal lagoon (Antinioti lagoon, Greece), by incorporating field measurements and projected future temperatures in a multivariate autoregressive (MAR) stochastic model. The findings indicated a strong relationship between future water temperatures in Antinioti lagoon and the abundance of specific zooplankton groups. Furthermore, the productivity of Antinioti lagoon will be positively influenced by global warming, and the future zooplankton community's structure will be subjected to competitive interactions among Copepods, Others, and Mysidacea.

The importance of monitoring earthquakes for disaster management, public safety, and scientific research can hardly be overstated. The emergence of low-cost seismic sensors offers potential for widespread deployment due to their affordability. Nevertheless, vehicular noise in low-cost seismic sensors presents as a significant challenge in urban environments where such sensors are often deployed. In order to address these challenges, this work proposes the use of an amalgamated deep neural network constituent of a DNN trained on earthquake signals from professional sensory equipment as well as a DNN trained on vehicular signals from low-cost sensors for the purpose of earthquake identification in signals from low-cost sensors contaminated with vehicular noise. To this end, we present low-cost seismic sensory equipment and three discrete datasets that-when the proposed methodology is applied-are shown to significantly outperform a generic stochastic differential model in terms of effectiveness and efficiency.