Search Results (167)

Luminescent cellulose-based fibers are promising materials for anti-counterfeiting applications because they can provide covert and spectrally distinguishable optical signatures compatible with paper- and textile-based authentication systems. In this study, Lyocell fibers modified with selected inorganic and organic luminescent compounds were subjected to accelerated xenon-lamp aging in order to evaluate their functional durability under simulated environmental exposure. The effects of aging on the mechanical properties and luminescent behavior of the fibers were investigated. The results showed that accelerated aging led to a reduction in tensile strength and elongation at break for all fiber variants, although the extent of these changes depended on the type of modifier. Spectroscopic analysis indicated that, despite changes in emission intensity, the characteristic luminescent responses of the modified fibers remained detectable after aging. These findings suggest that luminescent Lyocell fibers can retain their practical identification potential under the applied test conditions and may be considered promising candidates for use as covert security elements. The observed stability is attributed to the immobilization of luminophores within the cellulose matrix and the intrinsic photostability of the applied luminescent systems. At the same time, the study highlights the need for further investigations into the structural and photophysical stability of such systems under long-term environmental exposure. Full article

Smart contracts raise persistent challenges regarding compliance with traditional contract formalities, including writing, signature, notarization, and in certain transactions, registration. These issues are particularly significant in high-value and public-facing transactions such as real estate, where formalities determine legal validity, evidentiary sufficiency and publicity effects. While existing scholarly work has examined these challenges from either doctrinal or technological perspectives, limited attention has been given to how the functional roles of formalities interact with blockchain architecture, practitioner perceptions and institutional legal frameworks. This study addresses this gap through a mixed-methods approach combining doctrinal legal analysis with qualitative socio-legal research based on 27 semi-structured interviews with legal professionals including attorneys, judges, and academic scholars. The analysis is grounded in a civil law framework, with particular reference to the Jordanian legal system, while references to the European Union’s eIDAS Regulation are used illustratively to demonstrate regulatory approaches to digital authentication. The findings demonstrate that blockchain-based systems can effectively support the evidentiary and attribution functions of contractual formalities through cryptographic verification, consensus mechanisms, and automated execution. However, they do not independently satisfy formalities that perform cautionary, constitutive, protective or public order function, namely notarization and registration, which remain dependent on institutional validation and legal recognition. The analysis further shows that practitioner concerns reflect not only doctrinal constraints but also institutional roles and varying levels of technical familiarity. To address these limitations, the study proposes a function-based analytical framework for evaluating smart contract formalities and identifies two complementary pathways for legal adaptation: (i) institutional integration, including registry-linkage systems and hybrid contracts; and (ii) technological adaptation, including digital authentication frameworks and legal oracles that connect on-chain execution to off-chain legal conditions. The study concludes that smart contract formalities’ challenges arise not solely from technological limitations, but from the interaction between legal doctrine, institutional structures, and system design. It advances a functional framework for aligning automation with the evidentiary, protective, and publicity functions of contractual formalities. Full article

Despite the undisputable ecosystem importance of honeybees, human activities have a substantial impact on their health. Since foraging is directly linked to a wide range of crops and bee-attracting flowers, plant protection products are at the forefront of chemical scrutiny, along with contamination of pollen, nectar, beehive components and water by other xenobiotics. In this study, a non-targeted Liquid Chromatography-High-Resolution Mass Spectrometry (LC-HRMS) screening was applied to 25 honeybee samples collected after reported death incidents in Greece. This approach led to the tentative annotation of over 50 compounds across various chemical classes, including pesticides, PFAS candidates not included in the EFSA “PFAS-4”, pharmaceuticals, antibiotics, industrial chemicals, and natural product constituents. In parallel, targeted pesticide residue analysis using liquid and gas chromatography coupled to tandem mass spectrometry (LC-MS/MS and GC-MS/MS) was performed, covering more than 250 active substances and providing direct quantitative results, revealing 11 active substances in concentrations ranging from <limit of quantification (LOQ) to 0.95 mg/kg, overlapping substantially with the HRMS detection. Overall, this study does not allow concrete causal attribution of mortality to specific chemicals; however, it documents complex co-occurrence patterns (pesticides together with other xenobiotics and plant bioactives), not excluding sublethal and mixture-toxicity effects. Quantified pesticide concentrations were below acute LD50-based thresholds, yet selected samples combined neonicotinoid/pyrethroid/fungicide signatures and other contaminants, supporting the need for mixture-toxicity frameworks and effect-based follow-ups. Full article

Anti-analysis techniques, also known as evasive techniques, enable Windows malware to detect and evade dynamic inspection environments, undermining the effectiveness of virtual-machine and sandbox-based inspection. Despite extensive prior research, no unified classification has been paired with a large-scale empirical evaluation of static detection capabilities for these behaviors. This paper addresses this gap by presenting a comprehensive classification and detection framework. We consolidate 94 anti-analysis techniques from academic, community, and threat-intelligence sources into nine mechanistic categories and derive corresponding YARA rules for static identification. In total, 82 YARA signatures were authored or refined and evaluated on 459,508 malware and 92,508 goodware samples. After iterative refinement using precision thresholds, 42 rules achieved high accuracy (≥75%), 16 showed moderate precision (50–75%), and 24 were discarded due to unreliability. The results indicate strong static detectability for firmware- and BIOS-based checks, but limited precision for timing-based evasions, which frequently overlap with benign behavior. Although YARA provides broad coverage of observable artifacts, its static nature limits detection under obfuscation or runtime mutation; our measurements therefore represent conservative estimates of technique prevalence. All validated rules are released in an open-source repository to support reproducibility, improve incident-response workflows, and strengthen prevention and mitigation against real-world threats. Future work will explore hybrid validation, container-evasion extensions, and forensic attribution based on signature co-occurrence patterns. Full article

Remote sensing advancements have enhanced defoliation monitoring in forests, but distinguishing insect-specific damage from general canopy stress remains challenging due to overlapping spectral signatures. This study addresses this gap by analyzing multispectral reflectance changes in Eucalyptus dunnii caused by Gonipterus sp. n. 2 larval feeding and artificial defoliation (AD). A randomized complete block design with five replicates tested four treatments: No Damage, Medium (100 larvae/tree) and High (200 larvae/tree) larval inoculation, and AD (80% leaf removal). Twenty potted E. dunnii trees were monitored over 16 days using UAV-based multispectral 10-band imagery. Five multispectral flights were conducted during the experiment. The reduction in visible and near-infrared (NIR) reflectance likely reflects structural changes in canopy composition, namely an increased proportion of mature foliage. Both larval feeding and AD treatments decreased reflectance in these spectral regions, probably due to the removal of young leaves and exposure of older, darker leaves. This explanation is inferred from morphological observations; further biochemical measurements would be required to confirm the underlying mechanisms. Larval feeding and AD reduced chlorophyll-related vegetation indices (CVI, NDRE), decreased anthocyanin-related vegetation indices (mARI, ARI), and also caused a drop in relative carotene content (MTVI, CTRI/RE). The effects were strongest in the AD and peaked soon after the treatment, indicating that these pigment effects can mostly and also be attributed to the older leaves becoming more exposed. Statistically significant interactions between date and treatment were found for the pigment-sensitive indices, the Anthocyanin Reflectance Index (ARI) and the Chlorophyll Vegetation Index (CVI). They displayed opposite reflectance trends—CVI increased while ARI decreased—but followed a consistent pattern aligned with insect feeding. EVI values also exhibited a distinguishable pattern that matched this trend. Due to the inherent difficulty of studying insect feeding in natural settings, AD trials may serve as a practical proxy for assessing the impact of pest-induced damage on vegetation reflectance and physiological indices. Full article

Noise contamination is a common challenge in the analysis of time series data, where stochastic perturbations can obscure deterministic dynamics and complicate the interpretation of signals from chaotic and physiological systems. Reliable identification of noise regimes and their intensity is therefore essential for robust analysis of dynamical and biomedical signals, where incorrect attribution of stochastic perturbations can lead to misleading interpretations of system behavior. For this reason, the present study examines the role of complexity-based descriptors for identifying stochastic perturbations in time series and analyzes how these metrics respond to different noise regimes across heterogeneous dynamical systems. A supervised learning approach based on complexity descriptors was developed to analyze controlled perturbations in multiple signal types. Gaussian, pink, and low-frequency noise disturbances were injected at predefined intensity levels into the Rössler and Lorenz chaotic systems, the Hénon map, and synthetic electrocardiogram signals, while AR(1) processes were used for validation on inherently stochastic signals. From these systems, eighteen entropy-based, fractal, statistical, and singular value decomposition-based complexity metrics were extracted from either raw signals or reconstructed phase spaces. These features were used to perform three classification tasks that capture different aspects of noise characterization, including detecting the presence of noise, identifying the perturbation type, and discriminating between different noise intensities. In addition to predictive modeling, the study evaluates the complexity profiles and feature relevance of the metrics under varying perturbation regimes. The results show that no single complexity metric consistently discriminates noise regimes across all systems. Instead, system-specific relevance patterns emerge. Under given experimental constraints (data partitioning, machine learning algorithm, etc.), Approximate Entropy provides the strongest discrimination for the Lorenz system and the Hénon map, the Coefficient of Variation, Sample and Permutation Entropy dominate classification for ECG signals, and the Condition Number and Variance of first derivative together with Fisher Information are most informative for the Rössler system. Across all datasets, the proposed framework achieves an average accuracy of 99% for noise presence detection, 98.4% for noise type classification, and 98.5% for noise intensity classification. These findings demonstrate that complexity metrics capture structural and statistical signatures of stochastic perturbations across a diverse set of dynamic systems. Full article

A substantial proportion of wildfires in Mediterranean regions continue to be recorded without information about the cause or source of ignition, limiting our ability to understand ignition drivers and design effective prevention strategies. In this study, we develop a spatially harmonised wildfire database for mainland Spain by integrating ignition records from the Spanish General Fire Statistics (EGIF) with fire perimeters generated from satellite images. We then apply a Random Forest classifier to infer ignition causes for events lacking cause attribution. To interpret model behaviour, we use Shapley Additive Explanation (SHAP) values at both global and local scales. Results indicate that human-caused ignitions are dominant, with intentional and negligence-related fires accounting for 52.13% of all known events, although they are associated with contrasting climatic and land-use settings. Negligence-related fires tend to occur under hot, dry and windy conditions, often in agricultural interfaces, whereas intentional fires are more frequent under cooler and wetter conditions and in areas with higher population density and land-use change. Lightning-caused fires represent a small fraction of total ignitions (3%) but exhibit a distinct climatic signature, occurring primarily in sparsely populated areas, under intermediate moisture conditions, and often leading to larger burned areas. Despite strong overall model performance (F1-score = 0.82), minority classes (e.g., lightning and fire rekindling, 0.17%) remain challenging to classify, reflecting both data imbalance and uncertainty in causal attribution. Overall, the combined use of machine learning and explainable AI provides a coherent spatial characterisation of wildfire ignition drivers across mainland Spain, highlights systematic differences among ignition causes, and identifies key limitations in existing fire cause records. This framework represents a practical step towards improving fire cause information by integrating remote sensing products with field-based fire reports, thereby supporting more targeted and evidence-based fire risk management. Full article

Indonesia is among the most landslide-prone countries in the world, with thousands of fatalities and widespread infrastructure damage recorded over recent decades. Despite this high hazard level, regional-scale landslide monitoring remains constrained by the limitations of conventional bitemporal satellite imagery, which is susceptible to cloud contamination, dependent on precise acquisition timing, and unable to capture the full temporal dynamics of landslide occurrence and recovery. While the LandTrendr (Landsat-based Detection of Trends in Disturbance and Recovery) algorithm has been widely applied for detecting vegetation disturbances such as forest loss and land-use change, its potential for landslide detection in tropical environments has not been sufficiently explored. This study aims to evaluate the applicability of LandTrendr applied to long-term Landsat time series imagery for automated regional-scale landslide detection and mapping in Indonesia. The method integrates temporal segmentation of the Normalized Difference Vegetation Index (NDVI) derived from Landsat imagery spanning 2000–2022 with slope information from the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) to identify the characteristic drop-recovery spectral signature associated with landslide events. The algorithm was applied and evaluated in two geologically distinct study areas: Lombok, West Nusa Tenggara, and Pasaman, West Sumatra. Detection accuracies of 25.9% by location and 20.3% by area were achieved in Lombok and 76.3% by location and 85.3% by area in Pasaman. The lower accuracy in Lombok is primarily attributed to the predominance of small landslides below the sensor’s spatial resolution and rapid vegetation recovery. The proposed approach demonstrates the unique capability of LandTrendr to model the entire life cycle of a mass movement event, from pre-event stability through abrupt disturbance to ecological recovery within a single unified framework, providing a scalable and cost-effective tool for long-term landslide monitoring applicable to other tropical, landslide-prone regions. Full article

This work presents a method to encapsulate plant extracts within a binary polysaccharide carrier and to characterize the physicochemical and rheological performance of the resulting biocomposites in the context of food use. Using a starch/psyllium matrix, extracts from Sambucus nigra (SN), Aronia melanocarpa (AM), and Echinacea purpurea (EP) were effectively protected and incorporated through a stepwise workflow encompassing matrix preparation, encapsulation, structural verification, and functional assessment. SEM revealed a porous network containing uniformly distributed, extract-loaded spherical structures (~800–1500 nm), while FTIR supported the presence of hydrogen bonding and hydrophobic interactions that contributed to system stability. The prepared nanoemulsions showed shear-thinning (pseudoplastic) behavior, indicating favorable processing characteristics, whereas most physicochemical and bioactivity measurements were performed on lyophilized composites. The dried materials preserved extract-specific color signatures (ΔE > 5) and exhibited distinct thermal responses: AM produced a pronounced plasticizing effect (Tg reduced by >20 °C), while the incorporation of extracts generally delayed thermal degradation, consistent with polyphenol–starch interactions. Phase-transition behavior was also altered, with melting peaks suppressed for SN and AM and melting temperatures lowered for EP. Surface analysis indicated increased hydrophobicity and a reduced polar component of surface free energy, suggesting improved moisture barrier potential. Antioxidant capacity closely tracked total phenolic content (r > 0.94), with caffeic acid contributing strongly, particularly in EP-based systems. Antimicrobial activity depended on extract type (broad-spectrum for EP, selective for SN, minimal for AM), and the comparatively higher sensitivity of Gram-negative bacteria points to improved phenolic availability and membrane interactions upon encapsulation. Collectively, these results highlight the starch/psyllium matrix as a flexible platform for stabilizing plant extracts while enabling tunable functional attributes for functional food applications. Full article

Sand production in an offshore hydrocarbon wells poses significant operational and integrity challenges, particularly in deviated wells, where complex flow geometries intensify particle transport and erosion risks. The traditional sand-monitoring method utilizes stationary acoustic sensors attached to the production flowline at the surface. However, these sensors provide limited spatial coverage and intermittent measurements, restricting their ability to detect early sanding onset or precisely localize sanding intervals. By combining with vertical seismic profiling (VSP), Distributed Acoustic Sensing (DAS) delivers continuous, high-density data along the entire length of the wellbore and is increasingly recognized as a powerful diagnostic tool for real-time downhole monitoring. This study presents a field application of DAS-VSP for detecting and characterizing sand transport in a deviated offshore production well equipped with 350 distributed fiber-optic channels spanning 0–1983 m true vertical depth (TVD) at 8 m spacing. A multistage workflow was developed, including SEGY ingestion and shot merging, channel and time window selection, trace normalization, and low-pass filtering below 20 Hz. Multi-domain signal analysis, such as RMS energy, spike-based time-domain attributes, FFT, PSD spectral characterization, and time–frequency decomposition, were used to isolate the characteristic im-pulsive low-frequency (<20 Hz) signatures associated with sand impact. An adaptive thresholding and event-clustering scheme was then applied to discriminate sanding bursts from background noise and integrate their acoustic energy over depth. The processed DAS section revealed distinct, depth-localized sand ingress zones within the production interval (1136–1909 m TVD). The derived sand log provided a quantitative measure of sand intensity variations along the deviated wellbore, with normalized RMS amplitudes ranging from 0.039 to 1.000 a.u., a mean value of 0.235 a.u., and 137 analyzed channels within the production interval. These results indicate that sand production is highly clustered within discrete depth intervals, offering new insights into sand–fluid interactions during steady-state flow. Overall, the findings confirm that DAS-VSP enables continuous real-time monitoring of the sanding behavior with a far greater depth resolution than conventional tools. This approach supports proactive sand management strategies, enhances well-integrity decision-making, and underscores the potential of DAS to evolve into a standard surveillance technology for hydrocarbon production wells. Full article

A coupled non-destructive testing (NDT) workflow is proposed that integrates active infrared thermography (IRT) with laser-scanning-based reverse engineering (RE) to increase the reliability of detecting and interpreting damage in composite wind turbine blades across laboratory specimens and real components. IRT provides rapid, image-based qualitative localisation of potential anomalies, while 3D scan analysis supplies quantitative, geometry-aware verification and measurement of defect magnitude, reducing both false positives (design-related thermal signatures) and false negatives (weak thermal contrast). On polystyrene-filled profiles, IRT alone produced thermal anomalies unrelated to delamination; co-registered scan maps identified or ruled out local indentation, correctly attributing heat-flow patterns to internal design rather than damage. Outcome: the fused method disambiguates thermal indications and quantifies defect magnitude. On a vertical-axis wind turbine (VAWT) blade, the integration distinguished genuine geometric change from architectural effects under unknown internal structure and without CAD/reference scans, preventing false calls. For three horizontal-axis wind turbine (HAWT) blades, fleet-level scan comparison detected a significant tip deviation despite no clear local IRT anomalies, demonstrating complementary roles: scan = global quantitative homogeneity; and IRT = local qualitative verification. These findings operationalise thermal–geometric cross-validation and outline a path toward UAV-enabled inspections combining passive IRT and laser scanning for hard-to-access structures under real environmental conditions. Full article

Peridotites exposed in the southern Mariana Trench provide a rare opportunity to investigate mantle processes operating at the interface between arc and back-arc tectonic domains. This study presents petrographic observations and major element mineral chemistry of 41 depleted mantle harzburgites collected from three sites (Sites A–C) in the southern Mariana Trench. Site A is located on the east-facing slope of the West Santa Rosa Bank Fault, whereas Sites B and C are situated on the southern slope of the South Mariana Forearc Ridge along the eastern side of the Challenger Deep. The harzburgites exhibit variable microstructures ranging from coarse-grained (>1 mm) to medium-grained (<1 mm) to small-grained (>0.1 mm) textures, with or without porphyroclasts, and commonly contain amphibole associated with orthopyroxene and spinel. Olivine Mg# (Mg/[Mg + Fe]) (0.902–0.925) and spinel Cr# (Cr/[Cr + Al]) (0.304–0.720) indicate a wide range of mantle depletion across the three sites. Based on the integrated chemical characteristics of olivine, spinel, and amphibole, the harzburgites are classified into three distinct compositional trends (Trends 1–3). Trend 1 is characterized by high olivine Mg# (~0.925), high spinel Cr# (>0.6), low TiO₂ contents (<0.1 wt%), and K₂O-enriched but TiO₂-poor amphibole (TiO₂/K₂O < ~0.5), consistent with strongly depleted forearc mantle modified by slab-derived hydrous melts or fluids. In contrast, Trend 2 is defined by relatively high olivine Mg# (>~0.91), lower spinel Cr# (<0.6), slightly higher TiO₂ contents (up to ~0.2 wt%), and amphibole moderately enriched in both K₂O and TiO₂ (TiO₂/K₂O = 1–4), recording an intermediate geochemical signature that cannot be uniquely attributed to a purely forearc origin. Trend 3 is characterized by lower olivine Mg# (~0.90), lower spinel Cr# (<0.6), distinctly higher TiO₂ contents (up to ~0.8 wt%), and TiO₂-rich but K₂O-poor amphibole (TiO₂/K₂O > 4), indicating a back-arc mantle origin related to decompression melting. Trends 1 and 2 occur in harzburgites from Sites B and C of the South Mariana Forearc Ridge, whereas Trend 3 is exclusively identified in harzburgites from Site A of the West Santa Rosa Bank Fault, highlighting the juxtaposition of forearc-type, transitional, and back-arc-type mantle domains within a single forearc region. Full article

The cement industry is a major contributor to global CO₂ emissions, creating a need for monitoring techniques that support carbon capture strategies while assessing material performance. This study investigates the accelerated carbonation curing of cement mortar using linear and nonlinear ultrasonic sensing methods, alongside mechanical and gravimetric measurements. Mortar specimens were carbonated for 1–28 days and evaluated using ultrasonic pulse velocity (UPV), the Sideband Peak Count Index (SPC-I) for nonlinear ultrasonic response, compressive strength testing, and mass-based CO₂ uptake analysis. UPV showed sensitivity primarily to bulk material changes, with comparatively less distinction among the observed responses during carbonation curing. In contrast, the SPC-I captured distinct nonlinear responses associated with matrix evolution. Early-age carbonation (<7 days) produced increased nonlinearity, attributed to shrinkage-induced microcracking, whereas extended curing led to reduced SPC-I values, consistent with carbonation curing age. These trends exhibited an inverse correlation with compressive strength, which increased by up to 38.9% on the 28th day compared to the control specimens. Gravimetric analysis confirmed effective CO₂ sequestration, with average specimen mass gains reaching 2.62%. The findings demonstrate that nonlinear ultrasonic sensing provides a sensitive, nondestructive approach for monitoring carbonation curing and linking acoustic signatures to mechanical performance and carbon uptake in cement-based materials. Full article

Texture perception in extruded snacks is commonly evaluated using force-based measurements, although crispness-related oral sensations arise from fracture, sound emission, and lubrication during mastication. This study developed a mechanistically grounded framework for texture characterization of fiber-enriched extruded snacks by integrating instrumental and sensory analyses within an oral-processing context. Extruded snack samples containing soybean residue (okara; 0%, 29%, and 40%) and commercial benchmarks were evaluated using synchronized mechanical–acoustic testing (five-blade Allo-Kramer shear and three-point bending tests), oral tribology, and sensory evaluation combining intensity rating and ranking. Increasing okara content shifted fracture behavior from brittle, sound-emitting failure toward damped, progressive deformation with approximately 3–5-fold lower acoustic envelope amplitudes and smoother force–time profiles. These changes corresponded to lower perceived Crunchiness and Sound Intensity, reflecting diminished crispness-related perception, and higher Hardness and Grittiness/Coarseness attributes (increases of ~25–45%). Oral tribology revealed cohesive, poorly lubricated boli for okara-rich snacks, requiring higher entrainment parameters (Uη₀ ≈ 1.0 × 10⁵–3.5 × 10⁵) to transition from boundary to mixed lubrication compared with commercial benchmarks (Uη₀ ≈ 7.0 × 10⁴–2.0 × 10⁵). Convergent multivariate analyses established instrumentally defined texture phenotypes that translate mechanical–acoustic and tribological signatures into sensory-interpretable texture categories, providing a practical framework for discriminating and optimizing nutritionally enhanced extruded snack products. Full article

Ultrafine particles (UFPs) are particles which can penetrate deeply into the respiratory system due to their small size and can translocate into the bloodstream, where they are linked to oxidative stress, inflammation, and adverse cardiovascular outcomes. Ultrafine particles can originate from direct emissions or processes of new particle formation (NPF) which we investigated in this study. New particle formation is the process by which molecular clusters form and then grow to larger particles and develop to nucleation and Aitken mode particles. This study presents a detailed analysis of ultrafine particle dynamics in the city of Belgrade, Serbia, based on high-resolution particle number size distribution (PNSD) measurements performed at an urban background site in the period from January to March 2020. A total of seven factors were identified using Positive Matrix Factorization (with contributions in brackets): three attributed to traffic, including mixed source (55%), biomass burning (26%), nucleation (11%), and urban diffuse (8%) sources. The results were obtained by measuring size-resolved number concentrations (10–400 nm) and other pollutants (NO, NO₂, NO_x, CO, O₃, PM₁, PM_2.5, PM₁₀, equivalent black carbon, organic carbon). Wind directional analysis revealed clear spatial signatures, with nucleation linked to south-western winds and primary factors associated with major local emission influences. The results provide the first combined characterization of new particle formation processes and source-resolved ultrafine particle contributions in Belgrade, offering new insights into wintertime urban exposure in Southeastern Europe. Full article