The integration of LiDAR and Artificial Intelligence (AI) has revolutionized feature detection in urban environments. LiDAR systems, which utilize pulsed laser emissions and reflection measurements, produce detailed 3D maps of urban landscapes. When combined with AI, this data enables accurate identification of urban features such as buildings, green spaces, and infrastructure. This synergy is crucial for enhancing urban development, environmental monitoring, and advancing smart city governance. LiDAR, known for its high-resolution 3D data capture capabilities, paired with AI, particularly deep learning algorithms, facilitates advanced analysis and interpretation of urban areas. This combination supports precise mapping, real-time monitoring, and predictive modeling of urban growth and infrastructure. For instance, AI can process LiDAR data to identify patterns and anomalies, aiding in traffic management, environmental oversight, and infrastructure maintenance. These advancements not only improve urban living conditions but also contribute to sustainable development by optimizing resource use and reducing environmental impacts. Furthermore, AI-enhanced LiDAR is pivotal in advancing autonomous navigation and sophisticated spatial analysis, marking a significant step forward in urban management and evaluation. The reviewed paper highlights the geometric properties of LiDAR data, derived from spatial point positioning, and underscores the effectiveness of machine learning algorithms in object extraction from point clouds. The study also covers concepts related to LiDAR imaging, feature selection methods, and the identification of outliers in LiDAR point clouds. Findings demonstrate that AI algorithms, especially deep learning models, excel in analyzing high-resolution 3D LiDAR data for accurate urban feature identification and classification. These models leverage extensive datasets to detect patterns and anomalies, improving the detection of buildings, roads, vegetation, and other elements. Automating feature extraction with AI minimizes the need for manual analysis, thereby enhancing urban planning and management efficiency. Additionally, AI methods continually improve with more data, leading to increasingly precise feature detection. The results indicate that the pulse emitted by continuous wave LiDAR sensors changes when encountering obstacles, causing discrepancies in measured physical parameters.

Opuntia ficus-indica (L.) Mill., commonly known as prickly pear, is a versatile plant widely used for food, medicinal, and cosmetic purposes in various regions of the Mediterranean and North Africa. This study provides a comparative ethnobotanical review of prickly pear uses in Algeria, Morocco, Tunisia, and Italy. In total, 74 articles were selected from ethnobotanical uses in Italy, Algeria, Morocco and Tunisia. In the Maghreb, the plant is used for fruit production and processed products, such as jams, oils, and flours, as well as for traditional medicinal purposes. In Algeria and Morocco, the fruits and cladodes are used to treat digestive disorders, diabetes, and skin diseases. In Tunisia, products derived from O. ficus-indica are also applied in the cosmetic industry and for erosion control. In Italy, particularly in Sicily and Calabria, the prickly pear is a vital resource, used for both food consumption and for medicinal purposes. The cladodes, rich in mucilage, are applied as topical remedies for skin problems, while the fruits are a key ingredient in the preparation of traditional desserts. Furthermore, O. ficus-indica has historically been used as forage and to produce natural dyes. Results indicate that the versatility of this species, combined with its ability to adapt to extreme climates, makes it a valuable resource for the development of new nutraceutical and cosmetic products. However, further scientific research is necessary to explore the bio-functional potential of this plant and to promote its broader and more sustainable use, especially in arid and semi-arid regions.

Silver nanoparticles (Ag⁺NPs) contamination in the environment is a serious concern. This study investigated selected heavy metal (Ag⁺, Cd^2+, Cr²⁺ and Pb²⁺) concentrations at different sampling points to assess the risk to human health (infants, children, and adults). To do this, an enclosed area (laboratory) of 12.6 m X 8.5 m (107.1 m²) was clearly marked at different coded distances of S1, S2, S3, and S4 representing 2, 4, 6, and 8 m, while unpolluted atmosphere at 50 m away without Ag⁺NPs served as the control (S5). The silver fireworks were allowed to burn for an approximate 00h03m30s at each sampling points using a high-volume air sampler mounted at the Environmental Engineering Departmental Laboratory, Rivers State University, with windows and doors closed to simulate indoor conditions. Samples were digested using a mixture of analytical-grade nitric acid, analytical-grade hydrochloric acid and analyzed to evaluate the levels of heavy metals by atomic absorption spectrophotometry. The Ag⁺ result at S1 shows 30,000 µg/cm³, S2 was 29,000 µg/cm³, while S3 was 28000 µg/cm³ and then S4 was 13,000 µg/cm³. These results exceeded the permissible values of the United States National Ambient Air Concentration for rural, urban and industrial areas (0.0005, 0.004 and 0.6 µg/cm³, respectively). The result for the control (S5) (0.037 µg/cm³) was within the maximum allowable value. Results from other heavy metals such as Cd were 1000, 743, 401, 153, 0.001 µg/cm³, Cr was 5000, 4000, 3729, 2960, 0.002 µg/cm³, Pb was 0.048, 0.041, 0.035, 0.034 and 0.01, µg/cm³, respectively. However, higher values of Ag⁺, Cd, and Cr indicated a higher propensity for the metals to be toxic (bioavailable). In addition, the assessment of the potential health risk posed by these metals proved contaminated and harmful. Visitors recorded high values in exposure concentration (EC) and low values in average daily dose (ADD).

Recent field investigations, performed across some of the less floristically known sectors of Calabria, have led to the discovery of Salsola tragus and Thymelaea gussonei, two new species for the regional vascular flora. By providing crucial biogeographical and taxonomic remarks, this work aims to contribute to the improvement of the knowledge of the rich, diversified and still poorly known native flora of Calabria. The biogeographical value of these new findings underlines the decisive role of field survey in the enhancement of the information on regional biodiversity, the essential basis for every further ecological study and conservation effort.