Search Results (581)

The rapid growth of civilian unmanned aircraft systems (UAS) for various applications, such as logistics, inspection and surveillance has enabled increasingly complex UAS operations in shared airspace and in close proximity to third parties. European regulations for civilian UAS provide a comprehensive framework for operational approval, based on operational rules, risk-based approval processes, and airspace management concepts. While regulatory frameworks and current international standards provide detailed guidance for operational authorization for UAS, they do not prescribe how UAS should be developed and verified at a system and software level to support safety assurance in a structured and traceable manner. This paper addresses this gap by proposing a method for extracting system-level and software-level safety requirements from regulatory artifacts. The method interprets regulatory safety intent–expressed through operational constraints, mitigation measures, and robustness expectations–and translates it into development-relevant safety requirements under explicit operational assumptions. Building on these requirements, the paper introduces a software-centered system lifecycle for UAS development. The proposed lifecycle integrates regulatory safety intent, risk-proportionate assurance, and staged verification. Finally, through a cross-domain analysis, the paper positions the proposed approach relative to established practices from the automotive and the avionics domains, aiming to identify transferable and necessary adaptations for the development of unmanned aircraft systems. Full article

Manufacturing enterprises face increasing complexity in managing the complete life cycle of production systems, requiring integration of information from diverse sources to support timely maintenance, diagnostics, and operational decisions. This paper presents a comprehensive service-oriented architecture (SOA) for decision support in industrial life-cycle management, integrating real-time monitoring, predictive maintenance, and collaborative problem-solving across extended manufacturing enterprises. The architecture implements a three-layer service model comprising eight core collaborative services, three application services, and six life-cycle management services, orchestrated through a risk assessment module that monitors life-cycle parameters and triggers appropriate maintenance, diagnostics, or hazard prevention actions. The system was developed in the context of a European research project and validated in two industrial settings: automotive assembly lines at a German SME and air conditioning manufacturing at a Portuguese company. Results demonstrated substantial operational improvements, including reduced problem resolution time, lower diagnostic travel requirements, reduced spare-parts consumption, and increased structured problem registration. The original SOAP-based web-services implementation is further contextualized within the contemporary Industry 4.0 landscape through comparison with microservices architectures and discussion of integration paths involving OPC UA, Asset Administration Shells, and digital twins. The paper contributes a validated reference architecture for service-based industrial life-cycle management and clarifies its relevance as an early precursor of contemporary smart manufacturing approaches. Full article

Humulus lupulus L. (hops), belonging to the Cannabaceae family, is grown mainly for brewing, with 98% of global production directed to this sector. Moreover, large volumes of female cone residues are generated as by-products, representing a valuable source of bioactive compounds that can be valorized under green chemistry principles. This study aimed to extract bioactive compounds from hop cone residues sourced from craft breweries using ultrasound-assisted (EH-UA) and microwave-assisted (EH-MA) extraction methods. Hydroalcoholic extracts (70%) were analyzed for chemical composition, antioxidant, antimicrobial, antiproliferative, nitric oxide (NO)-production inhibition, and photoprotective activities. GC-MS identified 32 compounds in EH-MA and 30 in EH-UA, including terpenes, sesquiterpenes, oxygenated sesquiterpenes, and fatty acids. Both extracts demonstrated strong antioxidant activity in cell-based (TBARS, OxHLIA) and chemical (DPPH, ABTS, FRAP) assays, particularly EH-MA. Significant antibacterial activity was observed, especially against Enterobacter cloacae, Pseudomonas aeruginosa, and Staphylococcus aureus (MIC 1–10 mg/mL), as well as antifungal activity against Aspergillus brasiliensis (MIC 2–2.5 mg/mL). Selective antiproliferative activity was observed against tumor cell lines Caco-2 and MCF-7 (GI₅₀ 25 μg/mL), without cytotoxicity toward nontumor cell lines Vero and PLP2 (GI₅₀ > 400 μg/mL). All extracts inhibited the production of the inflammation mediator NO, with EH-MA showing the most potent effect (IC₅₀ of 35 μg/mL), followed by EH-UA (IC₅₀ of 55 μg/mL). Photoprotective potential was also demonstrated, with SPF values of 19 (EH-MA) and 18 (EH-UA). In conclusion, hop cone residues can yield multifunctional extracts with antioxidant, antimicrobial, antiproliferative, anti-inflammatory, and photoprotective activities, which support their sustainable upcycling for pharmacological, nutraceutical, and cosmetic applications. Full article

Coffee, as a perennial commodity crop, plays a crucial role in global agricultural markets, regional livelihoods, and poverty alleviation. Yunnan Province of China (21°8′–29°15′N) represents the northernmost coffee-growing region worldwide, and its production has gained increasing attention in international markets. However, the absence of a spatially explicit and high-resolution coffee distribution dataset has constrained environmental assessment, land-use analysis, and policy-making in this subtropical and marginal growing region. In this study, we developed the first 10 m resolution Arabica coffee distribution dataset for Yunnan Province for the year 2023 using Sentinel-2 optical imagery and Shuttle Radar Topographic Mission (SRTM) terrain data within the Google Earth Engine (GEE) platform. An object-based workflow was implemented to generate spatially coherent mapping units, followed by supervised classification to identify coffee plantations. The resulting map achieved an overall accuracy (OA) of 0.87, with user accuracy (UA), producer accuracy (PA), and F1 score of 0.90, 0.96, and 0.93 for the coffee class, demonstrating its reliability for regional-scale applications. Feature contribution analysis indicates that shortwave infrared (SWIR) and red-edge information, particularly during the dry season, plays an important role in coffee discrimination. These results enhance confidence in the ecological relevance and stability of the mapping framework. The proposed workflow provides a practical and transferable approach for perennial crop mapping in complex mountainous environments. More importantly, the generated high-resolution coffee distribution dataset establishes a spatial baseline for monitoring land-use dynamics, assessing ecological impacts, and supporting sustainable coffee development in southwestern China. Full article

This study investigated the effects of different application rates of spent mushroom substrate (SMS) from Morchella sextelata on soil properties and microbial communities in a moso bamboo (Phyllostachys edulis) plantation. Three SMS rates (2.4, 4.7, and 9.4 kg·m⁻²) were applied, and soil samples were collected at 6 and 12 months from two depths (0–20 cm and 20–40 cm). One year after application, topsoil total phosphorus (TP) increased 12–20 fold, while available phosphorus (AP) and potassium (AK) were significantly elevated. Soil pH initially decreased but partially recovered, whereas electrical conductivity (EC) continued to rise, indicating salt accumulation. Urease (UA) and sucrase (SA) activities increased 10–17 fold and 3–5 fold, respectively, while catalase (CAT) and acid phosphatase (ACP) were temporarily suppressed. SMS application significantly altered microbial community composition, with Acidobacteriota and Basidiomycota becoming more abundant. Correlation analysis identified pH, organic matter, AP, and UA as key factors linked to microbial changes. The medium application rate (4.7 kg·m⁻²) provided the best balance between soil improvement and environmental risk. These findings demonstrate that M. sextelata SMS can effectively enhance soil fertility while modulating microbial communities, but salt accumulation and short-term acidification warrant attention. Full article

Drought-induced tree mortality is a growing threat to Mediterranean ecosystems, which host high biodiversity but face increasing water stress under climate change. Detecting mortality over large areas with satellite data remains challenging due to open canopies and mixed pixels that obscure vegetation signals. This study evaluates the performance of two widely used vegetation indices—the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)—alongside a novel application of Spectral Unmixing derived vegetation cover Spectral Unmixing (SU) within the LandTrendr algorithm to track tree mortality in southwest Crete, Greece. High-resolution Unmanned Aerial System (UAS) imagery was used to validate satellite observations, demonstrating strong agreement with field data ($R^2$ = 0.95) and confirming its suitability as reference data. LandTrendr applied to NDVI, NDWI, and SU detected major mortality events between 1995 and 2008, with SU identifying the largest affected area. While NDVI and NDWI achieved higher accuracy in distinguishing unaffected plots, SU performed best at detecting mortality. Regression analysis revealed a limited ability of all approaches to quantify mortality magnitude, though SU improved when high-mortality plots were excluded. Overall, NDVI effectively tracked canopy changes, NDWI provided early warnings of drought stress, and SU reduced soil interference to better capture mortality patterns. By integrating satellite time series with UAS validation, this study demonstrates a scalable approach for detecting forest decline and offers actionable insights to guide Mediterranean forest management under increasing drought pressure. Full article

This study evaluated the effects of the dietary administration of a glycosylated form of active vitamin D (calcitriol, 1,25(OH)₂D₃) combined with ursolic acid (UA) and oleanolic acid (OA) triterpenes on sow health and productivity. Twenty-four third-parity Landrace × Large White sows were allocated at day 108 of gestation into three groups: a control group receiving 1800 IU/kg of vitamin D₃, and two treatment groups receiving the control diet supplemented with either 0.64 µg/kg (ACTD1) or 0.96 µg/kg (ACTD2) of glycosylated 1,25(OH)₂D₃ plus 140 or 210 µg/kg of UA + OA (4:1 ratio), respectively. Diets were administered from late gestation through the end of lactation. Farrowing duration, sow body weight, backfat thickness, and litter growth were recorded. Blood samples collected at key physiological stages were analyzed for pro-inflammatory cytokines, mineral homeostasis, endocrine markers, and serum proteome. Farrowing time was reduced in both treatment groups compared with the control (p < 0.05). Treated sows exhibited lower backfat thickness at the end of lactation and improved litter weights at farrowing, after cross-fostering, and at weaning (p < 0.05). Plasma pro-inflammatory cytokines (TNF-α, IL-1α, and IL-1β) were reduced at the end of lactation in ACTD1 and ACTD2 sows, with TNF-α and IL-1β already decreased after farrowing (p < 0.05). Treated sows also displayed decreased plasma parathormone concentrations at the end of lactation, along with increased circulating 1,25(OH)₂D₃ and calcium concentrations after farrowing and at lactation end (p < 0.05), while plasma phosphate levels remained unchanged. Proteomic analysis supported the systemic availability of the supplemented compounds and their involvement in metabolic and inflammatory pathways rather than calcium transport or vitamin D binding mechanisms. Overall, this nutritional strategy influenced the immune modulation while maintaining mineral homeostasis via modest endocrine adaptations. Larger-scale trials are warranted to confirm these results and to evaluate their practical applicability under commercial production conditions. Full article

Monitoring uric acid (UA) concentration is crucial for human health, enabling early detection and prevention of metabolic disorders as well as assessing renal function and overall metabolic balance. Herein, we developed a field-effect transistor (FET)-based UA biosensor using hydrothermally synthesized vertical zinc oxide (ZnO) nanorods (NRs) and uricase. The fabricated FET biosensor was tested in phosphate-buffered saline (PBS) at increasing UA concentrations to evaluate its biosensing performance. The FET biosensor yields a sensitivity of 12.45 μA·mM⁻¹·cm⁻², covering a dynamic range of 0.05–2.75 mM. The calculated detection limit was ~0.0043 mM. The improved sensing performance results in a substantial enhancement of both detection sensitivity and limit of detection compared to the traditional lateral electrode setup. Additionally, selectivity, storage stability, fabrication reproducibility, and applicability for serum UA detection were evaluated. Overall, the vertical electrode configuration of the UA biosensor has the potential to be further extended for the sensitive detection of additional biomarkers. Full article

Hyperuricemia (HUA) is a metabolic syndrome caused by elevated levels of uric acid (UA) serum, posing a significant threat to human health. Lactic acid bacteria degrade or adsorb UA precursors such as purine nucleosides and metabolites. By inhibiting intestinal nucleoside absorption, UA synthesis is reduced and HUA alleviated. A total of 60 fermented food samples and 20 soil samples were collected for screening. Strains were selected based on their inosine and guanosine degradation efficiency, and all degradation assays were performed in triplicate (n = 3). We isolated a strain that efficiently degrades inosine and guanosine at rates of 93.99% and 98.88%, respectively. This strain was identified as Pediococcus pentosaceus (P. pentosaceus) via 16S rDNA sequencing and named WQ-30. Whole-genome assembly yielded one chromosome and one plasmid, with 1705 coding sequences. The key gene rihC, encoding a nucleoside hydrolase, was identified through gene functional annotation. Heterologous expression and purification confirmed that RihC was approximately 36 kDa. Recombinant RihC exhibited optimal nucleoside hydrolase activity at pH 7 and 37 °C. This study provides a promising strain for functional food development and a mechanistic basis for the application of P. pentosaceus with purine nucleoside degradation and UA-lowering activities. Full article

Usnic acid (UA) is one of the most extensively studied specialized metabolites of lichens, attracting considerable interest due to its antimicrobial, anti-inflammatory, and cytotoxic properties. The efficiency of UA extraction from lichens depends on multiple interrelated biological and technological factors. This systematic review aims to synthesize and critically evaluate reported strategies for UA extraction from wild-grown lichen biomass, with particular emphasis on extraction efficiency, practicality, and application potential. This systematic literature review, based on the Scopus database was conducted by including original research articles reporting UA extraction from wild-growing lichens. The analysis covered species selection, sample pre-treatment, solvent type, and extraction methodology. A total of 117 studies were included. Due to the predominantly non-polar nature of UA, higher extraction efficiencies were generally achieved using solvents, including acetone, supercritical CO₂, vegetable oils, and lipophilic green solvent systems. Pre-treatment strategies such as grinding or flaking significantly enhanced extraction performance by improving mass transfer. Alongside conventional methods (maceration, reflux, Soxhlet), non-conventional techniques such as Supercritical Fluid Extraction (SFE), Ultrasound- (UAE), and Microwave-Assisted Extraction (MAE) enabled faster and more selective UA extraction with reduced solvent use. Notably, SFE have been reported as particularly promising in terms of selectivity, process control, and potential suitability for scale-up, with commercially available supercritical CO₂ extracts of Usnea species supporting the feasibility of this approach. This review provides a consolidated and application-oriented overview of UA extraction, highlighting strategies that balance efficiency, selectivity, sustainability, and practical implementation. Full article

The growing number of older bridges has resulted in an increase in structural flaws, demanding frequent inspections and maintenance. Structural degradation accelerates post-damage recovery, emphasizing the necessity of preventive interventions. The use of Uncrewed Aerial Vehicle Systems (UASs) for bridge inspections represents a significant development in structural health monitoring (SHM). Traditional inspection methods are labor-intensive, time-consuming, expensive, and require access to high or difficult-to-reach areas, posing safety risks to inspectors. This study focuses on identifying drones that can efficiently support bridge inspection activities. Key factors influencing UAS selection include flight performance, flying modes, cost, sensor capabilities, payload capacity, and controller communication. The primary objective of this paper is to provide guidance to inspectors and transportation agencies regarding the capabilities and limitations of commercially available drones. It also outlines potential cost considerations associated with drone selection, including pilot skill level, platform cost, and sensor integration. These factors may vary depending on the type and complexity of the bridge being inspected. By addressing these aspects, this paper aims to assist decision-makers in making informed choices regarding the use of UASs for bridge inspection applications. Full article

We report a highly sensitive and interference-free electrochemical sensor for dopamine (DA) detection in the presence of uric acid (UA) and ascorbic acid (AA), based on an in situ deposited graphitic carbon nitride (g-C₃N₄) and polymethyl thymol blue (PMTB) nanohybrid modified screen-printed carbon electrode (SPCE). The as-fabricated g-C₃N₄/PMTB/SPCE was thoroughly characterized using various physicochemical techniques. The electrochemical behavior of the modified electrode was systematically investigated by cyclic voltammetry (CV) and differential pulse voltammetry (DPV). The g-C₃N₄/PMTB/SPCE exhibited excellent electrocatalytic activity toward the selective oxidation of DA under optimized experimental conditions, including pH and scan rate. Interference-free detection of DA in the presence of AA and UA was achieved using DPV and chronoamperometric methods, revealing a wide linear concentration range, an ultralow limit of detection, and high sensitivity. Furthermore, the practical applicability of the proposed sensor was validated by determining DA in artificial biofluid samples, including blood serum, and urine. The recovery results obtained good agreement with those obtained using high-performance liquid chromatography (HPLC), confirming the reliability and accuracy of the developed sensing platform. Full article

Triterpenoids are a diverse class of naturally occurring compounds with a wide range of pharmacological properties, including anticancer, anti-inflammatory, antimicrobial, and antiviral activities. Among them, ursolic acid (UA), oleanolic acid (OA), and betulinic acid (BA) have emerged as key scaffolds due to their broad therapeutic potential and structural versatility. However, the clinical application of these compounds is often limited by issues such as poor solubility, bioavailability, and selectivity. To address these challenges, research conducted between 2015 and 2025 increasingly focused on the development of triterpenoid-based hybrid molecules, in which the triterpenoid scaffolds are chemically linked to other bioactive pharmacophores. This approach aims to enhance therapeutic efficacy through synergistic action, improved pharmacokinetics, and multitarget interactions. This comprehensive review explores recent advancements in the design, synthesis, and evaluation of hybrid derivatives of selected triterpenoids, particularly UA, OA, and BA. Emphasis is placed on the structure–activity relationships (SARs), biological evaluations, and mechanisms of action of these hybrid compounds across various disease models. The review also highlights current challenges, research gaps, and future perspectives in the rational development of triterpenoid-based hybrids as potential leading candidates in drug discovery. Full article

This paper presents test results of the performance comparison of 5G standalone (SA) and non-standalone (NSA) networks in the context of gathering data of remote sensors and machines. The study evaluates key network characteristics such as latency, throughput, jitter and packet loss (for UDP protocol only) using standardized tests to gain insights into the impact of these factors on real-time and data-intensive communication. In addition, a range of communication protocols including OPC UA, Modbus, MQTT, AMQP, CoAP, EtherCAT and gRPC were tested to assess their efficiency, scalability and suitability with different send data sizes. By conducting experiments in a controlled hardware environment, we have analyzed the impact of the 5G architecture on protocol behavior and measured the transmission performance at different data sizes and connection configurations. Particular attention is paid to protocol overhead, data transfer rates and responsiveness, which are crucial for industrial automation and IoT deployments. The results show that SA networks consistently offer lower latency and more stable performance, where robust and low-latency data transfer is essential. In contrast, lightweight IoT protocols such as MQTT and CoAP demonstrate reliable operation in both SA and NSA environments due to their low overhead and adaptability. These insights are equally important for time-critical industrial protocols such as EtherCAT and OPC UA, where stability and responsiveness are crucial for automation and control. The study highlights current limitations of 5G networks in supporting both remote sensing and industrial use cases, while providing guidance for selecting the most suitable communication protocols depending on network infrastructure and application requirements. Moreover, the results indicate directions for configuring and optimizing future 5G networks to better meet the demands of remote sensing systems and Industry 4.0 environments. Full article

Unmanned Aerial Systems (UASs) are playing an increasingly critical role in both civilian and defense applications. However, their heavy reliance on unencrypted Global Navigation Satellite System (GNSS) signals, particularly GPS, makes them highly susceptible to signal spoofing attacks, posing severe operational and safety threats. This paper introduces a comprehensive, AI-driven multi-layer sensor framework that simultaneously enables real-time spoofing detection and secure command-and-control (C2) communication in lightweight UAS platforms. The proposed system enhances telemetry reliability through a refined preprocessing pipeline that includes a novel GPS Drift Index (GDI), robust statistical normalization, cluster-constrained oversampling, Kalman-based noise reduction, and quaternion filtering. These sensing layers improve anomaly separability under adversarial signal manipulation. On this enhanced feature space, a differentiable architecture search (DARTS) approach dynamically generates lightweight neural network architectures optimized for fast, onboard spoofing detection. For secure command and control, the framework integrates a low-latency cryptographic layer utilizing PRESENT-128 encryption and CMAC authentication, achieving confidentiality and integrity with only 1.79 ms latency and a 0.51 mJ energy cost. Extensive experimental evaluations demonstrate the framework’s outstanding detection accuracy (99.99%), near-perfect F1-score (0.999), and AUC (0.9999), validating its suitability for deployment in real-world, resource-constrained UAS environments. This research advances the field of AI-enabled sensor systems by offering a robust, scalable, and secure navigation framework for countering GPS spoofing in autonomous aerial vehicles. Full article