Search Results (5,044)

Maritime unmanned aerial vehicles (UAVs) are increasingly transforming ocean, coastal, port, and offshore operations through rapid sensing, intelligent communication, autonomous perception, and cooperative decision support. This trend-oriented review analyses 471 peer-reviewed journal articles published between 2020 and March 2026, supported by selected foundational pre-2020 studies, to examine the evolution, technological structure, methodological maturity, and deployment readiness of maritime UAV systems. The bibliometric analysis shows rapid publication growth, from 22 papers in 2020 to 160 in 2025, and identifies five interconnected research clusters centred on maritime communications and networking; artificial intelligence (AI)-enabled perception, surveillance, and search and rescue; navigation and cooperative operations; distributed and federated intelligence; and multi-agent autonomous systems. The literature further shows a transition from task-specific aerial monitoring toward integrated maritime cyber–physical systems combining UAVs with vessels, unmanned surface vehicles (USVs), unmanned underwater vehicles (UUVs), edge computing, next-generation communication networks, digital twins, and AI-enabled decision support. However, evidence maturity remains uneven: many AI, communication, optimisation, and cooperative-autonomy methods are still evaluated predominantly using datasets or simulation, while fewer studies provide integrated system-level evidence under representative maritime conditions. The review therefore advocates a layered validation strategy combining high-fidelity scenario-based simulation, hardware-in-the-loop testing, controlled experiments, and representative maritime trials, with traceability between simulated and observed system behaviour. Deployment readiness additionally depends on environmental robustness, interoperability, cybersecurity, energy feasibility, human oversight, maintainability, and regulatory assurance. The review therefore proposes an evidence-gated roadmap to progress maritime UAV technologies from promising algorithms and prototypes to reliable, operationally deployable smart-ocean systems. Full article

Groundwater salinization near salt domes is a major threat to water resources in arid and semi-arid plains, causing a persistent decline in water quality worldwide. Machine learning offers an effective approach for evaluating this risk. This study tested three algorithms—Artificial Neural Network (ANN), Support Vector Machine (SVM), and Random Forest (RF)—to estimate groundwater salinity, focusing on the combined roles of land use and climate. The Darab Plain (Iran), where eight salt domes influence groundwater quality, was selected as the study area, and the spatial distribution of the sodium adsorption ratio (SAR) and electrical conductivity (EC) in its groundwater was estimated for 2003, 2013, and 2022, together with their controlling factors. The results showed that the Random Forest model (with an R² value in 2003 of 0.76 for EC and 0.62 for SAR) achieved the highest accuracy and was selected as the best-performing model. Salinity zoning revealed that the eight salt domes exert a strong direct effect on the salinity of adjacent aquifers and an indirect effect on more distant aquifers along groundwater flow paths. Among the 29 variables examined, dependence-plot analysis of the trained models showed that two collinear terrain-elevation proxies, land surface temperature and channel network base level, most strongly explained both EC and SAR in nearly every modeled year, with temperature and precipitation also consistently influential where climatic data were available. Given the projected warming trend in the region, local authorities and stakeholders should prioritize improved irrigation practices before considering water-management strategies that anticipate reduced agricultural productivity and associated population migration. Full article

Insects are sensitive indicators of environmental change, but their responses to large hydropower landscapes remain insufficiently understood because dam areas combine hydrological regulation, shoreline modification, artificial surfaces, vegetation management, and local microclimatic change. This study investigated insect diversity (species richness, inverse Simpson index and exponential Shannon index) across seven survey sites in the Three Gorges Dam area during May, August, and October in 2022 and 2023. Field surveys were combined with environmental, landscape, and vegetation variables to examine associations between compound habitat conditions and insect assemblages. The results showed that insect diversity displayed remarkable seasonal variation, with the highest values (species richness: 16.14, inverse Simpson index: 11.69, exponential Shannon index: 13.57) in August and the lowest values in May (species richness: 5.43, inv Simpson index: 2.35, exponential Shannon index: 3.07), consistent with the plant growing season. Artificial building area proportion was negatively associated with observed species richness across the seven survey sites, whereas no clear association was detected for inverse Simpson or exponential Shannon diversity. Vegetation mixed-effects models indicated positive associations between NDVI and insect diversity after accounting for month, year, and repeated observations within sites. Other site-level environmental correlations were treated as exploratory because of the limited number of independent sites. These findings suggest that insect diversity in dam landscapes is associated with built-surface cover, seasonal vegetation dynamics, and local habitat conditions. Integrating insect surveys with remote-sensing indicators may support biodiversity monitoring and ecological management in large hydropower regions. Full article

Density functional theory (DFT) calculations of metallic surfaces routinely employ periodic slab models in which spurious coupling between top and bottom surfaces artificially splits surface states, yet the thickness required to eliminate this artifact remains poorly characterized. Here, we systematically investigate convergence of surface states at the $\overline{\mathsf{\Gamma }}$ point of Al(001) slabs as a function of slab thickness (11–81 atomic layers) and relaxation depth (3–11 layers). The splitting and energy position of the surface states are largely insensitive to the relaxation depth, but depend strongly on the slab thickness. At $\overline{\mathsf{\Gamma }}$, for symmetric relaxation, the splitting decays exponentially with slab thickness, requiring at least 55 layers to fall below 10 meV; for asymmetric relaxation, a persistent splitting of ∼35 meV does not vanish even in the thick-slab limit. We interpret this behavior using a two-state coupling model. For practical comparison with experimental binding energies, the midpoint energy E_mid of a symmetric 31-layer slab offers a computationally efficient alternative to full decoupling for the $\overline{\mathsf{\Gamma }}$ surface states. For asymmetric slabs, however, the lower-energy branch E_low, corresponding to the relaxed surface, should be used instead of midpoint averaging. These results establish validated convergence criteria for Al(001) and provide a methodological framework transferable to other metallic optoelectronic materials. Full article

Intelligent transportation systems (ITSs), Vehicle-to-Everything (V2X) communication and autonomous driving technologies have brought about significant changes in the modern vehicular network. At the same time, the cyber-attack surface has grown, leading to new and existing advanced security threats for Vehicular Ad hoc Networks (VANETs) and the Internet of Vehicles (IoV). Traditional IDSs that employ static rule-based techniques are often insufficient in the context of the vehicular environment, characterized by dynamic, highly mobile systems with stringent low-latency requirements. Next generation IDS development has shifted to artificial intelligence (AI), which offers adaptive, scalable and data-driven detection capabilities. In this survey, the authors present a comprehensive overview of the state-of-the-art of AI-driven vehicle IDS in terms of machine learning (ML), deep learning (DL), federated learning (FL), reinforcement learning (RL), transformer-based techniques, and graph neural network (GNN) designs. We present a multidimensional taxonomy of vehicular attacks, an analysis of existing datasets and their weaknesses, an evaluation of the performance of AI techniques and a study of potential deployment architectures such as edge, fog and cloud. Furthermore, some of the most important open problems are identified, including data scarcity, adversarial robustness, real-time constraints, data utility-privacy conflicts, and standardization gaps. A research roadmap is provided, including 6G connectivity, blockchain-based IDS, explainable AI (XAI), Digital Twin technologies and post-quantum cryptography. This survey provides a common reference for the designers and developers of secure, efficient and interoperable VIDSs. Full article

Objective: The aim of this study was to comparatively evaluate the effects of fluoride- and casein phosphopeptide–amorphous calcium phosphate (CPP-ACP)-containing products and commercial pediatric dentifrices containing herbal ingredients (propolis, ginger, aloe vera, and rosemary) on artificially induced initial caries lesions in primary tooth enamel using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX). Materials and Methods: Eighty caries-free primary molars were randomly allocated into eight groups (n = 10): two control groups (sound and demineralized enamel), a fluoride group, a CPP-ACP group, and four commercial herbal dentifrice groups containing propolis, ginger, aloe vera, or rosemary. Artificial initial enamel lesions were created in all specimens except those in the positive control group. During a 7-day pH-cycling protocol, the treatment groups received standardized dentifrice slurry applications twice daily for 4 min. At the end of the experimental period, enamel surfaces were evaluated using SEM-EDX. Statistical analyses were performed using one-way analysis of variance (ANOVA) and the Kruskal–Wallis test, as appropriate (p < 0.05). Results: SEM analysis revealed a comparatively compact and homogeneous surface appearance in the fluoride group, while the ginger group showed a relatively homogeneous surface morphology among the commercial herbal dentifrice groups. EDX analysis showed no statistically significant differences among the groups in Ca wt% (p = 0.085), Ca at% (p = 0.077), atomic Ca/P ratio (p = 0.138), or weight Ca/P ratio (p = 0.126). Significant between-group differences were observed for both P wt% (p = 0.008) and P at% (p = 0.005), as well as O wt% (p = 0.022) and O at% (p = 0.020). Conclusions: The tested products produced qualitative differences in enamel surface micromorphology and significant differences in some EDX-derived relative elemental percentages; however, Ca percentages and Ca/P ratios did not differ significantly among the groups. Therefore, the observed numerical and morphological variations should not be interpreted as evidence of superior mineral gain or remineralization efficacy. Further in situ and in vivo studies are required to determine the clinical relevance of these findings. Full article

Biotite in mica-bearing phosphate ores is prone to sliming and forms fine intergrowths with apatite, impairing flotation selectivity. Taurine, sodium lignosulfonate, chitosan, hydroxypropyl distarch phosphate, and carboxymethyl chitosan (CMCS) were screened by single-mineral and artificial-mixture flotation tests. Contact angle, adsorption, zeta potential, FTIR, and XPS analyses focused on the −0.023 mm fraction. At pH 9 and a collector dosage of 268 mg/L, CMCS retained selectivity in favor of apatite. In single-mineral tests, CMCS dosages of 30, 60, and 150 mg/L produced apatite–biotite recovery differences of 47.57, 40.51, and 20.59 percentage points for the +0.074, −0.074 + 0.023, and −0.023 mm fractions, respectively. In artificial-mixture tests, selected dosages of 20, 120, and 180 mg/L yielded P₂O₅ recoveries of 95.50%, 95.58%, and 74.37% and grades of 28.02%, 26.47%, and 21.10%, respectively. As particle size decreased, the required CMCS dosage increased and separation deteriorated. Contact-angle measurements showed that CMCS pretreatment preserved the strong collector-induced hydrophobicity of apatite while slightly weakening that of biotite, thereby increasing the wettability contrast between the two minerals. Biotite showed a larger negative zeta-potential shift and greater CMCS adsorption than apatite; at 180 mg/L, the adsorption amounts were 1.17 and 0.76 mg/g, respectively. Together with the adsorption results, FTIR and XPS suggested stronger CMCS–biotite interfacial interactions involving carboxyl and hydroxyl-containing groups, with possible participation of Mg-related surface sites. These findings demonstrate the particle-size dependence of CMCS-assisted apatite-biotite flotation and reveal preferential CMCS interaction with biotite under fine-particle conditions. Full article

Anthracnose, caused by several Colletotrichum species, is the most destructive disease of olive fruit. Successful infection depends on the perception of host surface signals that regulate conidial germination and appressorium formation. Although olive fruit epicuticular waxes form a hydrophobic barrier, they can also serve as chemical cues regulating fungal spore differentiation. However, their role in olive susceptibility to Colletotrichum spp. remains poorly understood. In this study, we evaluated the effects of artificial and natural olive epicuticular waxes on conidial germination and appressorium formation in C. godetiae and C. nymphaeae, the two dominant species in Mediterranean olive-growing regions. Surface hydrophobicity strongly favoured conidial differentiation. The addition of olive epicuticular waxes restored conidial germination and appressorium formation when added to hydrophilic surfaces. Increasing wax concentration progressively enhanced both processes. Waxes extracted from olive fruits and leaves stimulated both developmental processes in both species, although waxes from ripe fruits induced a stronger response than those from unripe fruits. In contrast, waxes from the non-host avocado completely inhibited conidial germination. Although olive epicuticular waxes are generally regarded as physical barriers protecting plants against abiotic and biotic stresses, our results suggest that, in the olive-Colletotrichum pathosystem, they may also provide host-derived chemical cues that stimulate the early stages of infection. Full article

Artificial intelligence (AI) is increasingly used in biosensing, yet its role is often limited to post-processing or high-accuracy regression on simulation-generated datasets. This critical narrative review examines physics-informed and explainable AI for nanomaterial-based biosensors, emphasizing graphene and other two-dimensional materials, surface plasmon resonance (SPR), terahertz (THz) metasurfaces, electrochemical platforms, field-effect transistors, surface-enhanced Raman spectroscopy, and wearable systems. Recent peer-reviewed studies and relevant technical guidance were critically compared with respect to sensing physics, data provenance, AI task, interpretability, validation strategy, and evidence level. Across these modalities, AI supports forward surrogate modeling, inverse design, spectral interpretation, classification, calibration, and uncertainty-aware decision support, but predictive accuracy alone does not establish translational maturity. Particular attention is given to explainable AI, the distinction between physics-guided and genuinely physics-informed learning, small-data validation, fabrication tolerance, drift, and the simulation-to-experiment gap. Quantitative comparisons place reported SPR and THz sensitivities in context, while independent experimental studies provide benchmarks for real-device and biomedical validation. Clinically credible intelligent biosensors should combine mechanistic constraints, uncertainty quantification, device- and batch-aware validation, interpretable features, realistic biological matrices, and prospective evaluation. Full article

To solve the problems of low efficiency, poor uniformity and high labor intensity in the manual distribution of rehydrated pellet feed for young silkworms, a distributing machine for silkworm rehydrated pellet feed was developed. According to the feeding requirements of 1st- and 2nd-instar silkworms, the required feed mass per rearing tray was determined to be 1350 g and 3640 g, respectively, with distribution widths of 375 mm and 750 mm, and a continuous distributing device composed of a rehydrated pellet feed conveyor, a vibrating feeder and a silkworm tray conveyor was designed. A discrete element model of the rehydrated pellet feed was established using the Hertz–Mindlin with JKR contact model, and the particle surface energy was calibrated using the angle of repose as the target; the resulting surface energies of the 1st- and 2nd-instar silkworm feeds were 0.095 J/m² and 0.05 J/m², respectively. The effect of the vibration direction angle on the particle conveying characteristics was analyzed, and among the tested angles of 30°, 35° and 40°, the average tangential velocity of the particles was the highest at 30°, indicating better conveying performance. The model was validated by high-speed photography for both feeds: the measured distance between the particle and the trough surface was 19.27 ± 5.98 mm and 18.97 ± 4.69 mm for the 1st- and 2nd-instar feeds, compared with 20.23 ± 2.60 mm and 20.01 ± 2.65 mm in the simulation, giving relative differences of 4.75% and 5.20%. Prototype tests showed that the measured feed mass per tray was 1320.91 g for the 1st-instar feed and 3659.44 g for the 2nd-instar feed, deviating by −2.15% and +0.53% from the required values, that the distribution width was 387 mm and 750 mm, and that the distributing time per tray was 15 s and 20 s, respectively. The machine met the feeding requirements of young silkworms reared on pellet artificial feed. Full article

High-density polyethylene (HDPE) has attracted attention as an alternative material for marine structures because of its chemical resistance, processability, and recyclability. However, internal pores formed during extrusion welding can reduce weld quality and mechanical performance. This study investigated pore formation mechanisms in HDPE extrusion welds by considering material throughput, filler wire storage condition, and artificial defect volume fraction. Porosity increased from 0.06% to 0.37% as material throughput increased from 0.96 to 2.00 kg/h and slightly decreased to 0.33% at 2.60 kg/h. Under these process conditions, tensile strength changed within approximately 1.49%, whereas elongation decreased more clearly with increasing porosity. In contrast, residual surface moisture on the filler wire caused severe pore formation and reduced tensile strength by approximately 55.53%. Artificial defect tests also confirmed that increasing internal defect volume directly reduced tensile strength. Overall, controlling material throughput and removing surface moisture are essential for improving HDPE extrusion weld quality. Full article

Fixed orthodontic appliances create retention sites for bacterial biofilm, increasing the risk of white spot lesions and periodontal complications. Silver ion (Ag) implantation offers a potential solution by conferring antimicrobial properties while preserving mechanical integrity. This study evaluated the physicochemical properties, Ag release, biocompatibility, and antibacterial efficacy of AISI 301 stainless-steel orthodontic brackets (Ormco Bios) modified by silver ion implantation using a Pulsed Filtered Cathodic Arc Vacuum System. Physical and micro-structural surface characterizations—including SEM/EDS, interferometric roughness analysis, contact angle measurements, nanoindentation hardness testing, and friction coefficient determination (under dry and saliva-lubricated conditions)—were conducted directly on the modified brackets. In compliance with international testing standards requiring defined geometric areas, AISI 301 flat samples of same chemical composition and microstructure were used to quantify Ag release in artificial saliva (cumulative silver ion release in artificial saliva was quantified via ICP-MS over time, with measured concentrations corrected for the volume extracted at each sampling interval), as well as to perform in vitro biological (human fibroblast cytocompatibility) and microbiological assays against seven bacterial strains (Streptococcus mutans, Streptococcus gordonii, Lactobacillus salivarius, Lactobacillus acidophilus, Porphyromonas gingivalis, Enterococcus faecalis, and Staphylococcus aureus). SEM/EDS confirmed successful Ag incorporation (0.93 wt.%) on the brackets. Surface roughness remained unchanged (0.16–0.17 mm), whereas hydrophilicity, surface energy, and nano-hardness increased significantly. Friction coefficients decreased under both dry and saliva-lubricated conditions. ICP-MS analysis revealed Ag release stabilizing at 119 ± 5.0 ppb after one week. Biocompatibility testing demonstrated no cytotoxic effects on fibroblasts. Furthermore, modified surfaces exhibited marked antibacterial activity against Lactobacillus spp., P. gingivalis, E. faecalis, and S. aureus, though no significant inhibition was observed against S. mutans or S. gordonii. Ag ion implantation directly improves the mechanical and tribological properties of stainless-steel orthodontic brackets. The use of standardized specimens confirmed steady Ag release, cytocompatibility, and selective antibacterial efficacy, supporting Ag implantation as a viable surface modification strategy for clinical orthodontic applications. Full article

The application of information systems to the documentation and representation of historical–architectural heritage is currently the focus of research, experimentations, and innovations increasingly geared towards awareness, management, and conservation processes. This involves addressing unresolved challenges arising from the effort of translating the inherent complexity of heritage into knowledge that can be applied to monitoring, conservation, informative, cross-relational, and interdisciplinary actions. Processes involving data classification, segmentation, and semantic association for high-level knowledge clustering, exploiting Artificial Intelligence algorithms, are emerging as a potential—albeit ambivalent—aid in the management of digital information sources. The paper explores some State of the Art procedures in the field and ongoing applied research with a particular focus on the concept of adaptive data management, leveraging parametric modeling and applications within 3D point cloud data for the recognition of surface features and diagnostic purposes. The reconciliation of information between the point cloud segmented through Artificial Intelligence algorithms and the HBIM model starts from heritage building laser scanner and photomodeling datasets, analyzing materials and state of conservation features. The experimentation is focused on a reverse approach—the so-called “HBIM-to-Cloud”—with the aim of generating an enriched information cloud defined by the surfaces of the parametric model. Full article

Machined surfaces can retain features from preceding operations, complicating the identification of their processing history. This study develops a wavelet-based method to identify routes and quantify technological heredity (process-history effects) from surface profiles. Profiles from 40-mm-diameter shafts made of 12Kh18N10T stainless steel (broadly comparable to AISI 321) were analyzed using the continuous wavelet transform with a complex Morlet wavelet. Scalograms were partitioned into nine Taguchi L9 regions and described by five features: peak intensity, relative peak intensity, relative spatial and period coordinates of the peak, and relative high-energy area $S_{75}$. Robustness was evaluated using the Taguchi signal-to-noise metric at artificial noise levels up to 50%. Discrimination was assessed using principal component analysis (PCA), Fisher scores, and nearest-centroid and k-nearest-neighbor classification. Two-way analysis of variance (ANOVA) quantified technological heredity; the unexplained variance ratio $({\mathrm{SS}}_{IJ}+{\mathrm{SS}}_{\mathrm{err}})/{\mathrm{SS}}_{\mathrm{tot}}$ represented the variability not explained by the main effects of period and spatial position. Classification performance depended on the route group: for Group 2, profile-level 1-nearest-neighbor validation gave 7/18 correct classifications from the scalogram descriptors and 18/18 from $R_a$, $R_q$, and $R_z$. Routes involving burnishing exhibited lower unexplained variance and more compact PCA clusters, whereas routes combining rough and finish turning showed stronger heredity signatures and more complex scalogram structures. The framework therefore provides complementary scale-resolved information and a descriptive assessment of process-history-related variability; general route identification requires further validation. The proposed scalogram descriptors are intended to complement, rather than replace, conventional roughness parameters such as $R_a$, $R_z$, and $R_q$, by providing additional information on the scale- and position-resolved structure of the machined surface. Full article

Artificial intelligence (AI) is increasingly applied to image-based and image-formatted geoscientific evidence across mineral exploration, from regional and surface surveys to drilling/core analysis and laboratory characterization. However, the literature remains distributed across different research communities, making it difficult to compare how visual evidence, AI tasks, methodological choices, and geological outputs relate across exploration contexts. This systematic mapping review searched the Web of Science Core Collection, Scopus, and IEEE Xplore for English-language journal articles published between 2016 and 2026. Of 397 identified records, 91 studies were retained after deduplication, screening, and full-text assessment. The studies were mapped across exploration contexts, visual-data domains, AI visual tasks, methodological paradigms, and geological outputs. Regional and surface survey data dominate the evidence base, while drilling/core and laboratory imaging remain less represented. Classification is the most frequent visual task, and CNN-based models remain the dominant methodological paradigm, with hybrid and emerging architectures forming the next major group. The evidence does not indicate a single architecture that is uniformly suitable across mineral-exploration settings; model choice depends on input structure, required geological output, labeled-data availability, and spatial scale. Multisource integration can combine complementary evidence, but differences in spatial support, annotation, sensing conditions, and validation design continue to limit direct comparison and cross-region generalization. Future progress requires more traceable public resources, geographically independent validation, and more systematic integration of complementary geological evidence. Full article