The generation of structured light beams with prescribed shapes, such as Bessel or Gaussian beams, is typically achieved through analytical formulations based on known parametric expressions. However, when synthesizing beams with arbitrary, free-form contours, these methods require a laborious process to first analyze and determine the specific parametric equations that compose the desired shape. This traditional, continuous approach is not only computationally intensive but also time consuming, limiting its application in scenarios demanding rapid responses.

In this work, a novel strategy is proposed, departing entirely from this analytical framework by adopting a digital image processing approach. The method works directly from a binary mask of the target shape, where the algorithm detects the contour edges and performs all subsequent operations in the discrete domain. By replacing symbolic parameterization and continuous integration with discrete differentiation and cumulative integration, it can achieve a substantial reduction in computational complexity. Numerical simulations demonstrate that this new method achieves high fidelity synthesis for a wide range of arbitrary contours, with the resulting light beams accurately tracing the input shapes. This research provides a new pathway for synthesizing complex light beams, transitioning from a time intensive analytical formulation to a streamlined image based process that enhances both computational efficiency and flexibility.

National satellite programs offer strategic autonomy but are often constrained by sensor hardware limitations. Algeria's ALSAT-2B satellite lacks the critical Short-Wave Infrared (SWIR) and Red-Edge spectral bands essential for quantitative forest monitoring, limiting the utility of this sovereign data asset. This research introduces a novel AI-driven framework to digitally enhance ALSAT-2B imagery, unlocking its full potential for national forest management (SDG 13 & 15).

Our methodology employs a synergistic, two-stage process. Stage 1 (Spectral Enrichment): A Generative Adversarial Network (GAN) with a SwinUNet architecture was trained to synthetically reconstruct the five missing spectral bands by learning the relationship between ALSAT-2B and Sentinel-2 data. Stage 2 (Spatial Enhancement): The now nine-band data was fused with ALSAT-2B’s 2.5m panchromatic band using the Gram–Schmidt pansharpening algorithm, enhancing its spatial resolution.

The framework's efficacy was validated through a forest cover classification task. Results showed a dramatic improvement at each stage. The classification F1-Score on the baseline four-band, 10m ALSAT-2B data was 60.28%. After spectral enrichment, the score increased to 70.08%. The final, fully enhanced nine-band, 2.5m product achieved an F1-Score of 74.03%—a total improvement of nearly 14 percentage points.

This research presents a powerful framework for valorizing sovereign satellite data through AI. By successfully generating critical spectral bands and enhancing spatial detail, we can transform existing imagery into a high-value, analysis-ready product, providing Algerian institutions with a tangible, deployable methodology to significantly improve national forest monitoring and management.

Coastal geomorphological changes significantly affect the environmental integrity and socio-economic resilience of vulnerable coastal zones. This study investigates the geomorphological evolution of the artificial inlets of Bardawil Lagoon, located along Egypt’s Mediterranean coast, using multi-temporal satellite remote sensing data from Landsat 8/9 (2015–2025) and digital elevation models (DEMs). The accuracy of digital image processing techniques was validated using high-resolution Google Earth imagery. Bardawil Lagoon, a critical ecological and coastal system, has undergone substantial changes driven by both anthropogenic activities and natural processes, including sediment transport, tidal fluctuations, and engineered inlet modifications. Spatiotemporal monitoring using satellite remote sensing has become a vital tool in recent decades for mapping and assessing coastal dynamics. In this study, high-resolution imagery was processed within a GIS-based framework to analyze shoreline change rates, inlet migration, sediment deposition within navigational canals, and the performance of coastal protection structures. The Digital Shoreline Analysis System (DSAS) was used to quantify shoreline retreat and advance. Results reveal significant morphological variability in the artificial inlets, characterized by pronounced eastward migration and seasonal sediment accumulation that influence lagoon connectivity and hydrodynamic behavior. This research underscores the effectiveness of integrating remote sensing and GIS for monitoring coastal inlet dynamics and provides critical insights for sustainable coastal management, infrastructure planning, and ecological conservation in semi-enclosed systems such as Bardawil Lagoon. The approach is adaptable to other coastal environments facing similar natural and anthropogenic pressures.

The "black-box" nature of deep learning models remains a critical barrier to their adoption in high-stakes fields like precision agriculture, where trust and accountability are paramount. This research addresses this challenge by developing and validating two novel Explainable AI (XAI) frameworks designed to make crop segmentation models both transparent and highly accurate.

The first framework, SpectroXAI-LLaMA, is a post hoc tool that synergizes multiple attribution methods (e.g., SHAP, LIME) and uses a Chain-of-Thought (CoT) reasoning engine to generate logical, human-readable explanations. The second, IMPACTX-GC-RS, is a self-explaining U-Net architecture trained to simultaneously predict segmentation masks and generate its own Grad-CAM explanation heatmap, thereby making interpretability an intrinsic part of the model.

The results were transformative. The SpectroXAI-LLaMA framework successfully produced detailed explanations that were faithful to model behavior and consistent with agronomic principles. Most remarkably, the IMPACTX-GC-RS model, by learning to explain its own reasoning process, became more accurate than its non-explainable baseline. The mean Intersection over Union (IoU) increased from 0.9625 to 0.975, and the model completely eliminated a key misclassification error between cereal and potato classes.

This work makes a significant contribution by demonstrating that, contrary to the assumed trade-off, integrating explainability directly into AI models can enhance their predictive performance. Our frameworks provide a vital pathway to developing accountable, verifiable, and trustworthy AI systems, accelerating their adoption for sustainable agriculture and other critical applications.

Nitric oxide (NO) is a vital signaling molecule involved in the regulation of many physiological and pathological processes, including cancer, bacterial infections and neurodegenerative disease. [1,2] However, its biological effects are strictly dependent on its concentration, potentially shifting from beneficial to harmful. [2] For these reasons, the real-time monitoring of NO is highly desirable, especially due to its high reactivity with various biological targets. This scenario has pushed many efforts towards the development of novel methodologies for NO detection that combine selectivity, sensitivity and fast responsiveness. In this study, we introduce a novel fluorescent probe incorporating a BODIPY fluorophore covalently bound to a trimethoxy aniline derivative, specifically designed for fast and selective NO detection. [3] The probe undergoes nitrosation at its electron-rich amino site via the secondary oxide N₂O₃, leading to enhanced BODIPY fluorescence (Figure 1) and a notable shift in fluorescence lifetime amplitudes. It exhibits an ultra-fast response time (≤ 0.1 s), high sensitivity (LOD = 35 nM), reactivity toward ONOO⁻, independence of the fluorescence response across a broad pH range, remarkable selectivity over numerous analytes, and minimal interference from physiological glutathione levels. Validation in melanoma cell lines highlights its effectiveness for real-time NO detection in complex biological environments, establishing it as a promising tool for biomedical research.

Accurate urban building footprint data are essential for sustainable urban planning (SDG 11). This study introduces a novel framework that overcomes the dual challenges of extracting detailed features from medium-resolution Sentinel-2 imagery and the inherent quality issues of crowdsourced OpenStreetMap (OSM) data. We achieve this by integrating Graph Neural Networks (GNNs) with a rigorous, multi-source data assessment pipeline.

Our objective was to evaluate an UrbanGraphSAGE GNN architecture for segmenting building footprints in Algiers. A foundational component was the creation of a high-confidence ground truth dataset by cross-validating OSM data against Google Open Buildings and Overture Maps, followed by a temporal stability analysis. Our model departs from standard CNNs by first segmenting the imagery into superpixels, which then serve as nodes in a graph. The GNN classifies these nodes by learning from both their spectral features and their spatial context, allowing it to model complex urban morphologies effectively.

The results confirmed the framework's robustness. The final augmented model achieved a strong Test F1-Score of 0.7579 and an excellent recall of 0.9192. This high recall is critical for creating comprehensive urban inventories, as it minimizes the number of missed buildings.

This study validates a powerful framework for leveraging GNNs and rigorously assessed open data for urban monitoring. The methodology offers a scalable and low-cost solution for creating reliable building footprint datasets, providing a valuable tool for planners in rapidly urbanizing cities.

Environmental pollution has been exacerbated by widely used pharmaceuticals, in particular, paracetamol. At the same time, studies on the ecotoxicity of paracetamol in vascular plants are limited. In biotesting of toxicants, Lepidium sativum L. is used as a sensitive test plant. The aim of this study was to study the toxic properties of aqueous solutions of paracetamol in a growth test with L. sativum. The methodology consisted of an experimental study of germination energy, seed germination, and biometric–morphometric indicators of L. sativum seedlings in a growth test lasting 5 days under the influence of aqueous solutions with a paracetamol content of 0.002 % to 0.2 % and the calculation of phytotoxic indices. Statistical data processing was used. It was found that a solution of paracetamol at a concentration of 0.002 % (which is higher than that observed for wastewater—0.7 × 10^-8–0.246 × 10^-4 %) does not significantly change the test indicators of L. sativum. It is shown that the toxic properties of this compound for L. sativum differ from previously studied test plants and confirm the species specificity in sensitivity to the toxic effects of paracetamol. However, a comparison of the results of bioassays from different publications should be treated with caution, and it is important to carry them out in cases where the same time of treatment with the toxicant is used. Further studies should focus on assessing the toxicity of paracetamol solutions with concentrations recorded for wastewater, in particular, using other plant species, e.g., Allium cepa L.

With regard to space telescopes, the processing of large optical mirrors has always been a highlight in the field of optical processing. These mirrors are typically made of hard and brittle materials such as quartz glass, microcrystalline glass, and silicon carbide. These materials have long been considered challenging to work with due to their processing efficiency and propensity for damage. This study proposes a trochoid model considering the actual motion trajectory of the cup-grinding wheel with discrete consolidated abrasive grains. Through the establishment of a process parameter–mathematical model to establish the multi-grain coupled motion trajectory, the uniformity of the trajectory is optimized to increase the material removal rate and reduce the surface damage caused by abrasive interference. The process parameters and model were verified using an optical curve-grinding machine. The results show that the process parameter optimization using this model can effectively reduce the surface roughness of quartz glass grinding. The surface and sub-surface damage caused by grinding stress are significantly reduced, and the edge fracture area of quartz glass is decreased. The large contact area at the end face of the cup-grinding wheel enables a larger grinding depth while ensuring that cracks do not extend to the sub-surface, improving the overall surface integrity of the mirror.

Unauthorized urban development significantly threatens sustainable development objectives (SDG 11), requiring robust and scalable surveillance systems. While Algeria's ALSAT-2 satellite offers consistent territorial coverage, its inherent 10m spatial resolution (2.5m post-pansharpening) proves inadequate for detecting small-scale illegal structures. This study presents a novel dual-stage artificial intelligence pipeline designed to augment existing national satellite data for urban sprawl monitoring. Initially, a Generative Adversarial Network was developed using Sentinel-2 training datasets to execute spectral enhancement, generating higher-resolution imagery with supplementary spectral channels from original ALSAT-2 data. Subsequently, a U-Net architecture processed this refined imagery to automatically identify and delineate unauthorized construction areas. To address local material discrimination challenges, Land Surface Temperature information was incorporated to distinguish concrete surfaces from prevalent alternative building materials in the study region. Performance analysis revealed that spectral enhancement significantly boosted the classification accuracy relative to standard pansharpened baselines, with thermal data integration providing further improvements in the precision and sensitivity of detection. The methodology successfully mapped illegal construction zones, achieving an IoU of 0.7712, Dice coefficient of 0.8350, Precision of 0.8210, Recall of 0.9285, and Overall Accuracy of 0.9516, which was verified through field validation in Arzew, Oran, Algeria, during the years 2022 and 2025. This research demonstrates artificial intelligence's potential to transcend the hardware limitations of satellite sensors, providing an economical and adaptable solution for urban governance, planning compliance, and sustainable territorial management.

Solar Energy is a potential renewable resource; creative methods can increase its efficiency. Using
reflecting silver mirrors to maximize solar energy collection for electricity generation is one such
strategy.
Such reflective mirrors constitute the potential integration areas of CSP systems and PV panels to
enhance the absorption of input light and the conversion of energy. Various CSP technology types
include parabolic troughs, solar towers, and parabolic dishes, which all rely on mirrors to
concentrate the sun's rays on a receiver, producing heat that is used to generate power. Mirrors can
also be placed around PV panels to reflect sunlight and result in more electricity.
This work introduces high-reflectivity silver coatings, appropriate mirror angles, and hybrid CSPPV integration. Experiments have shown that strategically placed mirrors can increase energy
efficiency by 20-30%, depending on the setting. This study found that reflecting surfaces
considerably boost solar radiation absorption.
Future research should concentrate on automatic mirror tracking, affordable materials, and
durability enhancements to achieve the best possible outcomes. This study lays the groundwork
for future advancements in solar energy, opening the door to new high-performing and
environmentally friendly energy options.

The integration of reflective silver mirrors in solar energy systems, particularly in hybrid CSP-PV configurations, presents a promising pathway to significantly enhance solar energy conversion efficiency. By strategically optimizing mirror materials, placement, and tracking, it is possible to unlock new levels of performance in renewable energy generation.