According to the water resource protection plans published by the European Union (EU), groundwater needs to be protected and sanitized. Groundwater may contain heavy metals in terms of its geological and physicochemical content. These heavy metals can accumulate in the body in the long term and cause significant health problems. One of these heavy metals is arsenic, which is a toxic element that is harmful to human health. Low-cost, porous structure, larger surface area, functional groups, and carbon negative texture have made agro-industrial biochar an effective adsorbent for removing arsenic (As) from groundwater. In order to determine the effect of irrigation on arsenic concentration, samples were taken from the Yaygılı groundwater source in Harran Plain, where arsenic concentration was previously determined, in the pre-irrigation (March) and post-irrigation (October) periods. This study demonstrated that use of malt dust-derived biochar has dual benefits as an arsenic adsorbent and also waste minimization technique in terms of a circular economy approach. On average, an efficiency rate of 98.39% for arsenic removal from potable water has been reported using malt dust-derived biochar in the post-irrigation period. A potable water quality index (PWQI) was developed based on treatment performance and water quality parameters obtained via Monte Carlo simulation. The potable water quality index was in the range of 97.3-99.1% in terms of water remediation by agro-industrial biochar adsorption for arsenic-polluted groundwater. Also, biochar is recyclable and regenerative material. The waste biochar could be regenerated after arsenic adsorption in terms of zero-waste management and circular economy policies.

Photodynamic inactivation (PDI) has emerged as an effective and selective strategy for microbial inactivation, where close contact between microorganisms and the photosensitizer (PS) is essential to ensure its efficacy. Achieving proper dispersion of the PS in aqueous media is crucial for its performance. In this work, magnetic nanoparticles (MNPs) were designed as platforms to transport PSs in aqueous environments.

Iron oxide (Fe₃Oâ‚„) MNPs were synthesized using the conventional co-precipitation method. FeClâ‚‚·4Hâ‚‚O and FeCl₃·6Hâ‚‚O were dissolved in water and reacted under mild conditions. The resulting MNPs were then coated with metasilicate, incorporating Naâ‚‚SiO₃·5Hâ‚‚O, to provide colloidal stability. The obtained MNPs-SiOâ‚‚ was washed by magnetic decantation and functionalized with aminopropyltriethoxysilane (APTES) in a toluene/THF (1:1) mixture. This spacer introduced aliphatic amine groups capable of reacting through nucleophilic aromatic substitution (S_NAr) with the PSs. Finally, the MNPs-SiOâ‚‚-NHâ‚‚ was suspended in DMF to react with 5,10,15,20-tetrakis(pentafluorophenyl)chlorin (TPCF₂₀) and its Zn(II) complex (ZnTPCF₂₀). This covalent coupling allowed for the preparation of MNPs-TPCF₂₀ and MNPs-ZnTPCF₂₀, which remained stable in aqueous solutions. Moreover, the magnetic nature of the MNPs-PS facilitated their removal from the medium using external magnetic fields. Spectroscopic characterizations confirmed the retention of the photophysical properties of the attached chlorins. The materials showed the ability to generate reactive oxygen species (ROS) and photodynamic efficacy against both Gram-positive and Gram-negative microorganisms. These results highlight the potential of chlorin-functionalized magnetic nanoparticles as effective antimicrobial agents for applications in aqueous environments.

Artificial intelligence (AI) has generated a new technological revolution in education. AI can be an excellent tool for solving a wide range of problems. For example, the appropriate use of AI can have a beneficial impact on public education in Mexico and can help thousands of students avoid dropping out of school. Even the most acute socio-educational problem is school dropout, and in rural regions of Mexico, this problem is even more serious. Because once students leave school, they are exposed to violence and crime. Therefore, the aim of this work is to use statistical regression analysis through Generative AI, specifically using DeepSeek, to identify those students with a higher probability of school dropout in rural regions of Mexico. This work can be a first pilot test and subsequently be implemented in all rural regions, helping hundreds of students. Fundamentally, a survey was implemented to identify the socioeconomic and academic conditions of the students. The data obtained was analyzed using DeepSeek, and the students most likely to drop out of school were identified. Aditionally, DeepSeek is an attractive option for teachers and the general public without a math degree, as developing a sophisticated mathematical model to predict the probability of dropping out of school is not an easy task. Among the key results of this work is a significant reduction in the dropout rate. DeepSeek's simple features allow for the rapid monitoring of the socioeconomic and academic status of a large number of students.

The formation of thermal stable salts (TSS) in amine-based natural gas sweetening processes is a major operational challenge that compromises the efficiency and longevity of solvent systems. This study explores the degradation mechanisms of methyldiethanolamine (MDEA) and diethanolamine (DEA) solvents used in the gas treatment units at the Shurtan Oil and Gas Production Department. It was identified that the presence of impurities such as COS and chloride ions—introduced from upstream zeolite purification units—significantly accelerates solvent degradation, leading to the accumulation of TSS, surfactants, and organometallic compounds. These degradation products not only reduce absorption efficiency but also promote corrosion, foaming, and viscosity increases, resulting in higher energy consumption and more frequent solvent replacement. Elemental analysis of degraded solvent samples revealed high concentrations of sulfur, calcium, potassium, and corrosion-related metals such as iron, chromium, and manganese. To address these issues, a two-stage vacuum distillation system was designed and evaluated. The first stage involves atmospheric distillation to concentrate the solution, followed by vacuum steam stripping in the second stage to remove degradation products and recover purified amine. Experimental results demonstrate a solvent recovery rate exceeding 90%, with significant reductions in TSS and degradation byproduct concentrations. This method offers a practical and efficient approach to solvent reclamation, extending the service life of amine solutions and improving the stability and sustainability of gas sweetening operations. The proposed technology aligns with international practices and holds potential for broader adoption in natural gas processing facilities where solvent degradation is a persistent issue.

The need for gentle, efficient, and sustainable harvesting solutions in modern agriculture is becoming increasingly important as global demand for fresh produce continues to rise. Conventional rigid robotic grippers often cause mechanical damage to delicate fruits and vegetables, leading to significant post-harvest losses and food waste. To address these challenges, soft robotic technologies are emerging as effective alternatives due to their compliance, adaptability, and ability to interact safely with biological materials. This project presents a low-cost pneumatic soft robotic system designed for fruit harvesting, integrating pressure and color sensing to detect ripeness and prevent bruising. The gripper is fabricated from silicone Ecoflex using a custom 3D-printed mold and actuated through an air pump–valve assembly. A pressure sensor detects contact pressure to ensure safe gripping, while a TCS34725 color sensor evaluates ripeness based on fruit surface color. The control is implemented with an Arduino Uno, which coordinates actuation by integrating sensor data for decision-making in real time. Experimental trials are carried out on different fruits with varying hardness and ripeness levels. The results demonstrate reliable ripeness detection with over 80% accuracy and successful picking with minimal visible surface damage, confirming the effectiveness of integrating pneumatic actuation with low-cost sensing. Overall, the developed system provides a practical, sustainable, and accessible approach to precision agriculture, contributing to reduced food waste, improved crop handling, and advancement toward intelligent, automated harvesting technologies.

Vision Foundation Models (VFMs) offer transformative potential for geospatial AI, but their application in data-constrained regions like Algeria is hindered by massive data requirements and high computational costs. This work introduces a novel framework that integrates VFMs with Few-Shot Learning (FSL) and an advanced ensemble technique, delivering a high-performance, data-efficient solution for mapping cereal crops, which are vital for Algerian food security.

Our objective was to develop a semantic segmentation pipeline for cereal mapping using a very limited, custom-collected dataset. We fine-tuned two architecturally distinct VFMs: the ViT-based Prithvi and the Swin Transformer-based Satlas. We then developed a Mixture of Experts (MoE) system, which combines these two fine-tuned "expert" models. A lightweight, trainable "gating network" learns to dynamically weigh the output of each expert on a per-image basis, synergistically leveraging their unique strengths.

The results highlight the exceptional performance of VFMs in a low-data regime. The fine-tuned Satlas model achieved a remarkable Overall Accuracy of 96.93% and a Cereal Class Intersection over Union (IoU) of 94.12%. The MoE system advanced this performance further, setting a new benchmark with an Overall Accuracy of 97.82% and a Cereal Class IoU of 95.58%. The MoE model demonstrated rapid convergence, showcasing its efficiency.

This study validates a highly effective framework for precision agriculture, proving that VFM ensembles can overcome data scarcity and deliver state-of-the-art performance, providing a tangible pathway for nations like Algeria to leverage cutting-edge AI for food security and sustainable resource management.

This study examines the precipitation and temperature evolution over Greece covering the historical period and 21^st century (the years from 1850 to 2100). Timeseries from multi-model mean precipitation and temperature, averaged over continental Greece, are calculated from twelve (12) CMIP6 (6th Phase of Coupled Model Intercomparison Project) model simulations to study future changes under different Shared Socioeconomic Pathways (namely: SSP1-2.6, SSP3-7.0, SSP2-4.5 and SSP5-8.5, respectively). The analysis focuses on the investigation of projected precipitation changes both for annual and seasonal temporal scales. Results indicate a reduction in annual precipitation over continental Greece. The maximum changes are shown under SSP3-7.0 and SSP5-8.5 scenarios. Annual precipitation is projected to decrease about 20% by the end of 21^st century (relative to the historical period that covers the years from 1980 to 2005; basis period). Spring and autumn shows a reduction of precipitation that rangers between 15 and 30% for SSP3-7.0 and SSP5-8.5. Additionally, for JJA - the dryer season in Greece – a decrease of about 0.1 to 0.2 mm/day (about 30 to 40% relative to the basis period) is projected under SSP3-7.0 and SSP5-8.5 scenarios too. Regarding temperature, the CMIP6 multi-model mean indicates a substantial warming mainly during the last period of 21^st century. In particular, temperature over continental Greece is projected to increase about 4.0 and 6.0 °C by the end of 21^st century under SSP3-7.0 and SSP5-8.5 scenarios, respectively

Accurate estimation of petrophysical properties from well log data is paramount for reliable reservoir characterization and informed decision-making in hydrocarbon exploration and production. Conventional methods often struggle with the inherent complexities and non-linear relationships within geological datasets, leading to suboptimal prediction accuracy. To overcome these limitations, we propose a novel hybrid machine learning method that integrates multiple predictive models with a unique residual adjustment strategy. A novel aspect of the methodology involves fitting a Weibull distribution to the residuals of machine learning models, such as random forest, support vector regression, and artificial neural networks as foundational learners. A distinctive aspect of this approach is the application of Weibull distribution analysis to model and subsequently adjust the residuals generated by these individual base models, thereby enhancing individual model predictive accuracy. These adjusted base models were then combined into a stacked ensemble, utilizing a ridge regressor as the final meta-learner to further consolidate their predictive strengths. Performance evaluation, conducted using metrics such as R-squared (R²) and root mean squared error (RMSE), demonstrated that the proposed stacked ensemble model significantly outperformed individual models, achieving a superior predictive capability for the photoelectric factor. The integration of residual analysis through Weibull distribution further contributed to the overall predictive robustness. This research demonstrates the efficacy of advanced ensemble machine learning techniques, particularly when combined with detailed residual distribution analysis, in accurately characterizing complex subsurface properties. The developed method offers a powerful and reliable tool for enhancing reservoir modeling and supporting more effective decision-making in geological applications.

“NHSOS” is a multidisciplinary sustainability project that has been implemented in the islandic area of the Southeastern Aegean basin. Chalki (Halki) is a small island of about 500 citizens located in the climate-sensitive area of the Eastern Mediterranean. Focusing on Chalki Island, the project aims to reduce environmental degradation throughout three key pillars of “GReco” Island and Smart Specialization Strategy (S3) initiatives, namely waste management optimization, air quality monitoring, and carbon footprint mitigation. The project engages with authorities, the community, and tourism stakeholders in order to promote the circular economy and low emissions strategies. This study evaluates current and projected waste generation trends and outlines sustainable waste management strategies tailored to the island’s unique geographic and socioeconomic context. Air quality and meteorological recordings from sensors are used in order to investigate the concentration of air pollutants as well as the impact of meteorology and traffic activity on the variability of pollutant concentrations. All carbon emission sources are qualitatively characterized following Life Cycle Assessment (LCA) standards. Subsequently, sustainable interventions and energy autonomy solutions are outlined to reduce the island’s total carbon footprint. The ”NHSOS” project provides solutions for islandic sustainability in Mediterranean regional-based ecosystems, addressing the challenges of climate change, air pollution, and unsustainable resource use.

Transformer-based models struggle with long-document summarization due to fixed input length constraints. To mitigate this issue, hybrid approaches typically perform an extractive preprocessing step, selecting salient sentences as input to an abstractive summarization model. However, most unsupervised extractive methods, such as TextRank and LexRank, rely on shallow heuristics and fail to preserve semantic coherence or minimize redundancy. We propose GravSpike, a neuro-inspired preprocessing framework for extractive–abstractive summarization. GravSpike integrates SBERT-based sentence embeddings with a gravitational ranking model that scores sentences based on lexical salience, positional weight, and semantic proximity, modeled using the gravitational force equation. To further enhance content diversity and reduce redundancy, we introduce a spiking neuron-inspired filtering mechanism that iteratively activates informative sentences based on adaptive firing thresholds. A multi-objective Ant Colony Optimization (ACO) algorithm then selects an optimal subset, balancing ROUGE-based relevance and SBERT-based semantic cohesion. We evaluate GravSpike on three long-document datasets, BillSum, PubMed, and arXiv, by comparing abstractive summaries generated by BART and T5 with and without GravSpike preprocessing. Experimental results show that GravSpike-enhanced inputs consistently yield higher ROUGE-1, ROUGE-2, and ROUGE-L scores than the same models applied directly to truncated or full-length documents. On the BillSum dataset, GravSpike achieves ROUGE-1, ROUGE-2, and ROUGE-L scores of 58.83, 37.63, and 44.47, respectively (p < 0.01). These findings demonstrate GravSpike’s effectiveness as a modular, unsupervised filtering pipeline that significantly improves the performance of large language models on long-form summarization tasks.