Search Results (413)

In this work, biochars were produced by pyrolysis of exhausted olive pomace and evaluated as low-cost adsorbents for the removal of methylene blue (MB) from aqueous solutions. The biochar obtained at 400 °C for 1 h, which exhibited the best adsorption performance, was characterized by FTIR, N₂ adsorption–desorption isotherms, SEM-EDX, and proximate analysis, revealing a mesoporous structure with a relatively low specific surface area but enriched in surface functional groups, likely due to the partial degradation of lignocellulosic components. Adsorption experiments were conducted to optimize operational parameters such as solid particle size (2–3 mm), agitation speed (75 rpm), and bioadsorbent dosage (1 g per 0.05 L of MB solution), which allowed for dye removal efficiencies close to 100%. Kinetic studies showed that MB adsorption followed a pseudo-second-order model, while equilibrium data at 30 °C were best described by the Langmuir isotherm (R² = 0.999; SE = 4.25%), suggesting monolayer coverage and strong adsorbate–adsorbent affinity. Desorption trials using water, ethanol, and their mixtures resulted in low MB recovery, whereas the addition of 10% acetic acid significantly improved desorption performance. Under optimal conditions, up to 52% of the retained dye was recovered. Full article

Although the benefits of rational grazing by polygastric animals are well known, little is understood about how chicken grazing affects soil biological health and its capacity to store organic matter. This study aimed to assess the impact of long-term free-range chicken grazing in an olive grove on the soil chemical and biochemical properties, including the total organic carbon (TOC), total nitrogen (TN), microbial biomass (Cmic), basal respiration, and microbial community structure, as well as the soil’s capability to stock organic carbon and total nitrogen. A field experiment was conducted in an olive grove grazed by chickens for over 20 years, with the animal load decreasing with distance from the poultry houses. At 20 m, where the chicken density was highest, the soils showed reduced OC and TN contents and a decline in fungal biomass. This was mainly due to the loss of both aboveground vegetation and root biomass from intensive grazing. At 50 m, where grazing pressure was lower, the soil OC, TN, and microbial community size and activity were similar to those in a control, ungrazed area. These findings suggest that high chicken density can negatively affect soil health, while moderate grazing allows for the recovery of vegetation and soil organic matter. Rational management of free-range chicken grazing, particularly through the control of chicken density or managing grazing time and frequency, is therefore recommended to preserve soil functions and fertility. Full article

Federated Learning (FL) enables decentralized Machine Learning (ML), focusing on preserving data privacy, but faces a unique set of optimization challenges, such as dealing with non-IID data, communication overhead, and client drift. Adaptive optimizers like AdaGrad, Adam, and Adam variations have been applied in FL, showing good results in convergence speed and accuracy. However, it can be quite challenging to combine good convergence, model generalization, and stability in an FL setup. Data-bound adaptive methods like AdaDB have demonstrated promising results in centralized settings by incorporating dynamic, data-dependent bounds on Learning Rates (LRs). In this paper, FedAdaDB is introduced, which is an FL version of AdaDB aiming to address the aforementioned challenges. FedAdaDB uses the AdaDB optimizer at the server-side to dynamically adjust LR bounds based on the aggregated client updates. Extensive experiments have been conducted comparing FedAdaDB with FedAvg and FedAdam on three different datasets (EMNIST, CIFAR100, and Shakespeare). The results show that FedAdaDB consistently offers better and more robust outcomes, in terms of the measured final validation accuracy across all datasets, for a trade-off of a small delay in the convergence speed at an early stage. Full article

Automated handwritten signature verification continues to pose significant challenges. A common approach for developing writer-independent signature verifiers involves the use of a dichotomizer, a function that generates a dissimilarity vector with the differences between similar and dissimilar pairs of signature descriptors as components. The Dichotomy Transform was applied within a Euclidean or vector space context, where vectored representations of handwritten signatures were embedded in and conformed to Euclidean geometry. Recent advances in computer vision indicate that image representations to the Riemannian Symmetric Positive Definite (SPD) manifolds outperform vector space representations. In offline signature verification, both writer-dependent and writer-independent systems have recently begun leveraging Riemannian frameworks in the space of SPD matrices, demonstrating notable success. This work introduces, for the first time in the signature verification literature, a Riemannian dichotomizer employing Riemannian dissimilarity vectors (RDVs). The proposed framework explores a number of local and global (or common pole) topologies, as well as simple serial and parallel fusion strategies for RDVs for constructing robust models. Experiments were conducted on five popular signature datasets of Western and Asian origin, using blind intra- and cross-lingual experimental protocols. The results indicate the discriminative capabilities of the proposed Riemannian dichotomizer framework, which can be compared to other state-of-the-art and computationally demanding architectures. Full article

Prays oleae Bernard, 1788, or the olive moth, is a significant pest in Croatian olive groves. This study aims to develop a functional model based on an artificial neural network to detect olive moths in real time. This study was conducted in two different orchards in Zadar County, Croatia, in the periods from April to September 2022 and from May to July 2023. Moth samples were collected by placing traps with adhesive pads in these orchards. Photos of the pads were taken every week and were later annotated and used to develop the dataset for the artificial neural network. This study primarily focused on the average precision parameter to evaluate the model’s detection capabilities. The average AP value for all classes was 0.48, while the average AP value for the Olive_trap_moth class, which detected adult P. oleae, was 0.59. The model showed the best results at an IoU threshold of 50%, achieving an AP50 value of 0.75. The AP75 value was 0.56 at an IoU = 75%. The mean average precision (mAP) was 0.48. This model is a promising tool for P. oleae detection; however, further research is advised. Full article

Agricultural practices significantly influence agroecosystem biodiversity, driving a growing focus on the development of environmentally sustainable management strategies. Olive (Olea europaea L.) is one of the most widely cultivated tree crops in the Mediterranean basin and other regions with a Mediterranean climate. In this study, we employed a split-plot design with whole plots arranged as a randomized complete block design (RCBD) to evaluate the effects of minimum tillage and the application of wood distillate to olive canopies on wild vascular plant and soil-dwelling springtail communities in a conventionally managed olive grove in central Italy. Biotic communities were sampled twice, in November and April. Tillage caused a marginally significant decrease in springtail species richness in April and significantly influenced the composition of both plant and springtail communities in April. All the plant species showed a decrease in abundance under tillage, whereas the abundance of springtail species responded to tillage in a species-specific way. Wood distillate had no effect on any community attribute in either season. Springtail total abundance was not affected by any treatment in either season. Our findings confirm that tillage practices affect the diversity of plant and springtail communities. Moreover, we had evidence that spring tillage may have more negative impacts on the studied communities with respect to autumn tillage. Moreover, we suggest that the application of low-concentration wood distillate to olive canopies can be considered, in the short-term, a sustainable agricultural practice that does not negatively affect agroecosystem biodiversity. Full article

Bactrocera oleae (Rossi) (Diptera: Tephritidae) is a key pest of olive groves. Adult monitoring is carried out by means of attractant traps of different shapes, which give relevant information for pest control such as the presence of adult flies in the field and their trend, female maturity and sex ratio. However, it is still not entirely clear whether a given density is sufficient for providing a reliable representation of flies in an olive grove. To investigate this question, an experiment was planned, consisting of arranging a high-density network of unbaited sticky panels (UTs) between panels baited with ammonium carbonate (BTs) deployed at a density of 2 traps/ha. The experiment was carried out in Greece, Italy, Lebanon, Spain and Tunisia. The percentage of BT over UT catches varied significantly among the different countries, with BTs ranging from 82% of catches in Italy to 27% in Greece. The Pearson correlation between BTs and UTs was significant under high captures but not significant at low densities. The index of aggregation showed an inverse relationship with baited catches. The distributions of males and females were nearly always positively spatially associated. According to the field data, BTs at the density of 2/ha provide a realistic estimate of the population in the field in the cases of established populations. However, in the periods without population establishment, a denser monitoring trap network is likely required to obtain a reliable estimation of the field population. Full article

Climate crisis in the Mediterranean region has severely affected olive tree cultivation, especially due to the long, dry summers, when temperature often rises above 40 °C. In order to overcome such climate challenges in the olive sector, the particle film technology (PFT) was used, as an environmentally friendly alleviation technique, due mainly to the reflecting properties of clay materials. Three clay materials—attapulgite, talc, and kaolin—were applied foliarly to olive trees (both rainfed and irrigated) in July and August. At harvest, yield and oil production per tree were assessed, alongside olive oil quality and functional properties. Under irrigated conditions, trees treated with kaolin or talc in July exhibited the highest yields, whereas under rainfed conditions, trees treated with attapulgite in August, followed by those treated with talc in August, showed the greatest yields. Oil production exceeded that of controls in rainfed trees across nearly all clay treatments. Oils from irrigated trees treated with talc in August and rainfed trees treated with talc in July exhibited high phenolic content, though antioxidant capacity peaked in oils from trees treated with talc in August. These oils, along with those from trees treated with attapulgite in August, contained the highest concentrations of hydroxytyrosol and oleacein. In rainfed trees, most clay treatments resulted in oils with elevated oleic acid (C18:1) and reduced linoleic acid levels, yielding a high monounsaturated-to-polyunsaturated fatty acid ratio. In irrigated groves, August applications produced oils with distinct differences from controls, whereas in rainfed conditions, these differences were evident regardless of application timing. Clay materials offer a promising approach for mitigating abiotic stress under Mediterranean summer conditions; however, further research is needed to elucidate their mechanisms of action. This study represents the first report of foliar attapulgite application in plants and talc application in olive trees. Full article

The increasing severity of water scarcity in southern Europe, caused by climate change, requires advanced and more efficient approaches to agricultural water management. In particular, in this paper, we address this problem for olive groves—a cornerstone of the region’s economy. We propose a novel framework for generating high-resolution maps of irrigated olive groves that integrates remote sensing imagery and machine learning. Our approach leverages multi-temporal Sentinel-2 data, specifically the Normalized Difference Vegetation Index (NDVI), to capture seasonal vegetation dynamics. For classification, we explore two distinct models: (1) A Dynamic Time Warping (DTW)-based approach (with and without the Sakoe–Chiba Band constraints), where DTW aligns temporal NDVI sequences to enable robust comparisons of irrigation regimes, followed by a K-Nearest Neighbor classifier (KNN) that classifies plots as irrigated or rainfed. (2) An eXtreme Gradient Boosting (XGBoost) model that directly uses temporal NDVI profiles. Additionally, we compare the dependence of model performance on the length of the NDVI time series (ranging from one to seven seasons), finding that XGBoost requires a shorter time series to achieve optimal results, while KNN with DTW can benefit from longer historical records. Indeed, XGBoost nearly reaches its maximum accuracy using only data based on three seasons, achieving 0.79 compared to its peak performance of 0.80. Hence, our results indicate that this approach can accurately differentiate between irrigated and rainfed plots, enabling the generation of high-resolution irrigation maps for southern Spain. Finally, we argue that the results of this paper go beyond mere mapping: they lay the foundation for a comprehensive management guide that can optimize water use, with broad implications. Such implications range from empowering precision agriculture to providing a roadmap for land management, ensuring both the sustainability and productivity of olive groves in drought-affected regions. Full article

The G5 geomagnetic storm of May 2024 provided a significant opportunity to investigate global ionospheric disturbances using vertical total electron content (VTEC) data derived from 422 GNSS-IGS stations and GIM. This study presents a comprehensive spatio-temporal analysis of VTEC modulation before, during, and after the storm, focusing on hemispheric asymmetries and longitudinal variations. The primary objective of this study is to analyze the spatial and temporal modulation of VTEC under extreme geomagnetic conditions, assess the hemispheric asymmetry and longitudinal disruptions, and evaluate the influence of geomagnetic indices on storm-time ionospheric variability. The indices examined reveal intense geomagnetic activity, with the dst index plunging to −412 nT, the Kp index reaching 9, and significant fluctuations in the auroral electrojet indices (AE, AL, AU), all indicative of severe space weather conditions. The results highlight storm-induced hemispheric asymmetries, with positive storm effects (VTEC enhancement) in the Northern Hemisphere and negative storm effects (VTEC depletion) in the Southern Hemisphere. These anomalies are primarily attributed to penetration electric fields, neutral wind effects, and composition changes in the ionosphere. The storm’s peak impact on DoY 132 exhibited maximum disturbances at ±90° and ±180° longitudes, emphasizing the role of geomagnetic forces in plasma redistribution. Longitudinal gradients were strongly amplified, disrupting the usual equatorial ionization anomaly structure. Post-storm recovery on DoY 136 demonstrated a gradual return to equilibrium, although lingering effects persisted at mid- and high latitudes. These findings are crucial for understanding space weather-induced ionospheric perturbations, directly impacting GNSS-based navigation, communication systems, and space weather forecasting. Full article

The management of Bactrocera oleae (Rossi, 1790) has relied on chemical insecticides, applied as bait or cover sprays. However, concerns over insecticide resistance and environmental impact have driven the search for more effective and eco-friendly alternatives, such as mass trapping. The aim of the study was to assess a novel food-based attractant, derived from Tenebrio molitor Linnaeus, 1758 excreta “Frass”, for its attractiveness to B. oleae adults compared to widely used commercial food-based attractants. Over a four-year period, five field trials were conducted in two organic olive groves in Crete, Greece, using a randomized complete block design with five or six replicate blocks. Results showed that frass-based attractants captured significantly higher number of B. oleae adults than the other tested attractants. Additionally, trap–attractant combinations were assessed to determine the most efficient mass-trapping system. Frass-based attractant deployed in Anel or container traps demonstrated significantly higher attractiveness than all commercial traps and lures tested. The significant advantages of mealworm frass as an attractant highlight its potential to enhance the monitoring and suppression of B. oleae in olive orchards. Its consistent performance, sustainability, and environmental safety make it a promising tool in integrated pest management strategies. Full article

Achieving a uniform thickness and defect-free production in the flat die extrusion of polymer sheets and films is a major challenge. Dies are designed for one extrusion scenario, for a polymer grade with specified rheological behavior, and for a given throughput rate. The extrusion of different polymer grades and at different flow rates requires trial-and-error procedures. This study investigated the application of machine learning (ML) to provide guidance for the extrusion of sheets and films with a reduced thickness, non-uniformities, and without defects. A dataset of 200 cases was generated using computer simulation software for flat die extrusion. The dataset encompassed variations in die geometry by varying the gap under a restrictor, polymer rheological and thermophysical properties, and processing conditions, including throughput rate and temperatures. The dataset was used to train and evaluate the following three powerful machine learning (ML) algorithms: Random Forest (RF), XGBoost, and Support Vector Regression (SVR). The ML models were trained to predict thickness variations, pressure drops, and the lowest wall shear rate (targets). Using the SHapley Additive exPlanations (SHAP) analysis provided valuable insights into the influence of input features, highlighting the critical roles of polymer rheology, throughput rate, and the gap beneath the restrictor in determining targets. This ML-based methodology has the potential to reduce or even eliminate the use of trial and error procedures. Full article

The intersection of medical image classification and deep learning has garnered increasing research interest, particularly in the context of breast tumor detection using ultrasound images. Prior studies have predominantly focused on image classification, segmentation, and feature extraction, often assuming that the input images, whether sourced from healthcare professionals or individuals, are valid and relevant for analysis. To address this, we propose an initial binary classification filter to distinguish between relevant and irrelevant images, ensuring only meaningful data proceeds to subsequent analysis. However, the primary focus of this study lies in investigating the performance of a hierarchical two-tier classification architecture compared to a traditional flat three-class classification model, by employing a well-established breast ultrasound images dataset. Specifically, we explore whether sequentially breaking down the problem into binary classifications, first identifying normal versus tumorous tissue and then distinguishing benign from malignant tumors, yields better accuracy and robustness than directly classifying all three categories in a single step. Using a range of evaluation metrics, the hierarchical architecture demonstrates notable advantages in certain critical aspects of model performance. The findings of this study provide valuable guidance for selecting the optimal architecture for the final model, facilitating its seamless integration into a web application for deployment. These insights are further anticipated to advance future algorithm development and broaden the potential of the research applicability across diverse fields. Full article

Land desertification is becoming increasingly significant for the Mediterranean basin, particularly due to the rising pressures on agricultural land. Regarding the olive grove sector, intensive farming methods can have detrimental effects on the provision of various agroecosystem services. Conversely, agroecological approaches, such as reduced tillage/no tillage and the use of cover crops, can help mitigate soil degradation and enhance soil arthropod biodiversity. Herein, an experiment was conducted in a hilly olive grove in southern Peloponnese, a key olive production area in Greece. Different soil treatments were implemented across nine plots (three plots per treatment), including the following: (i) the use of a cover crop mixture (Pisum sativum, Vicia faba, Hordeum vulgare), (ii) herbicide application, and (iii) spontaneous vegetation (control). A comprehensive survey was performed at the plot level for monitoring carbon sequestration and ground-dwelling arthropod diversity. The results indicated that cover crops had a positive impact on soil fertility and structure, leading to an increase in total biomass production per plot, while also contributing to the preservation of key soil arthropod populations when compared to treatments that resulted in bare soil. The findings from this in situ study are meant to be integrated into the frames of a long-term monitoring process in order to be used for climate change mitigation and biodiversity management models, enhancing the resilience and regeneration of degraded land. Full article

In today’s rapidly changing economic landscape, financial stability plays a crucial role in ensuring individual and societal well-being. Millennials encounter unique financial pressures, including shifting labor markets, high housing costs, and economic uncertainty, which may impact their financial stability and broader life choices. This cross-cultural comparative study investigates the interplay between financial stability and environmental sentiment among Greek and Dutch Millennials, exploring how cultural differences influence these dynamics. Utilizing a quantitative research methodology, the study analyzed responses from a convenient sample of 426 participants across Greece and the Netherlands, employing measures such as a multidimensional construct of financial stability and the New Environmental Paradigm (NEP) scale to assess environmental attitudes. The results indicated a significant positive correlation between perceived financial stability and pro-environmental sentiment in both cohorts, suggesting that economic security is a key facilitator of environmental engagement irrespective of cultural context. However, no significant differences were found in environmental sentiment between the two groups, highlighting a possible universal environmental awareness among Millennials transcending economic disparities. These findings suggest that policies aimed at enhancing financial stability may simultaneously foster greater environmental stewardship. The study underscores the importance of integrating economic and environmental policy to promote sustainable development globally among younger populations. Full article