Search Results (819)

Salicornia europaea L. is a spontaneous halophytic plant, widespread in coastal environments, recognized for its high polyphenol content and bioactivities. In this study, a sustainable extraction strategy was developed by coupling natural deep eutectic solvents (NADESs) with ultrasound-assisted extraction (UAE) to recover bioactive compounds from autochthonous S. europaea collected in the Apulia region of southern Italy. Sixty-one NADES combinations were screened using COSMOtherm software, based on the predicted solubility of isorhamnetin, the major flavonol in Salicornia spp, to identify optimal hydrogen-bond donor (HBD) and acceptor (HBA) pairs. Six selected and prepared NADESs (B:CA, B:Suc, ChCl:U, ChCl:Xil, CA:Glc and Pro:MA) were used to extract S. europaea, and the resulting extracts were evaluated for total phenolic content (TPC), antioxidant capacity (DPPH, ABTS, FRAP) and antibacterial activity against four ATCC bacterial strains (Enterococcus faecalis, Escherichia coli, Klebsiella pneumoniae and Staphylococcus aureus). Among the tested extracts, Pro:MA exhibited the highest TPC (6.79 mg GAE/g) and interesting antioxidant activity (DPPH IC₅₀ = 0.09 mg GAE/g; ABTS = 8.12 mg TE/g; FRAP = 2.41 mg TE/g). In the antibacterial assays, the Pro:MA extract demonstrated the highest activity, with minimum inhibitory concentrations (MICs) ranging from 0.1% to 0.4% v/v and minimum bactericidal concentrations (MBCs) from 0.2% to 0.8% v/v. In addition, the Pro:MA extract maintained TPC stability over a 90-day storage period. These findings support the NADES-UAE system as a green and efficient approach for the recovery of bioactive compounds and for the valorization of halophyte plants, such as S. europaea, with promising ready-to-use applications in the food, pharmaceutical and cosmeceutical sectors. Full article

In this paper, the multi-objective optimization of the 12-pole radial active magnetic bearing (RAMB) is investigated. In the optimization of the RAMB, the decision-maker is more interested in the Pareto-optimal solutions in a certain region. This paper proposes a decomposition-based and preference-based multi-objective evolutionary algorithm (MOEA/D-Pref). The proposed MOEA/D-Pref not only allows the number of Pareto-optimal solutions to be more concentrated in the region of interest but also preserves solutions in other regions. These preserved solutions enable decision-makers to observe a more complete Pareto front, thus gaining more comprehensive insights. In this paper, a mathematical model of the 12-pole RAMB is established, and, with the help of this model and the proposed algorithm, the optimal design of the 12-pole RAMB is completed. The difference between the current stiffness coefficients of the optimized RAMB, calculated by the proposed algorithm and by the finite element method, is 2.3%. The difference between the displacement stiffness coefficient of the optimized RAMB as calculated by the proposed algorithm and by the finite element method is 3.9%. These differences, being less than 4%, are relatively low and verify the reliability of the mathematical model established. Full article

Effective water quality monitoring is very important for detecting pollution and protecting public health. However, traditional methods are slow, relying on costly equipment, central laboratories, and expert staffing, which delays real-time measurements. At the same time, significant advancements have been made in the field of plasmonic sensing technologies, making them ideal for environmental monitoring. However, their reliance on large, expensive spectrometers limits accessibility. This work aims to bridge the gap between advanced plasmonic sensing and practical water monitoring needs, by integrating plasmonic sensors with mobile technology. We present BioColor, a mobile platform that consists of a plasmonic sensor setup, mobile application, and cloud services. The platform processes captured colorimetric sensor images in real-time using optimized image processing algorithms, including region-of-interest segmentation, color extraction (mean and dominant), and comparison via the CIEDE2000 metric. The results are visualized within the mobile app, providing instant and automated access to the sensing outcome. In our validation experiments, the system consistently measured color differences in various sensor images captured under media with different refractive indices. A user experience test with 12 participants demonstrated excellent usability, resulting in a System Usability Scale (SUS) score of 93. The BioColor platform brings advanced sensing capabilities from hardware into software, making environmental monitoring more accessible, efficient, and continuous. Full article

Background/Objectives: Virtual non-contrast (VNC) images derived from dual-energy CTA (DE-CTA) could potentially replace true non-contrast (TNC) scans while reducing radiation exposure. This study evaluated the image quality of VNC compared to TNC for assessing native arteries and bypass grafts in patients with peripheral arterial disease (PAD). Methods: We retrospectively analyzed 175 patients (111 men, 64 women, mean age: 69.3 ± 9.5 years) with PAD who underwent lower extremity DE-CTA. Mean attenuation and image noise values of TNC and VNC images were measured in native arteries and bypass grafts at six arterial levels, from the aorta to the popliteal arteries, using circular regions of interest (ROI). Signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) were calculated. Three independent radiologists evaluated the subjective image quality of VNC images compared to baseline TNC scans for overall quality (4-point Likert scale), and for residual contrast medium (CM), calcium subtractions, and bypass graft visualization (3-point Likert scales). Radiation dose parameters (DLP, CTDIvol) were recorded to estimate effective dose values (ED) and the potential radiation dose reduction. Differences between TNC and VNC measurements and radiation dose parameters were compared using a paired t-test. Interobserver agreement was assessed with Gwet’s AC2. Results: VNC attenuation and noise values were significantly lower across all native arterial levels (p < 0.05, mean difference: 4.7 HU–10.8 HU) and generally lower at all bypass regions (mean difference: 2.2 HU–13.8 HU). Mean image quality scores were 3.03 (overall quality), 2.99 (residual contrast), 2.04 (subtracted calcifications), and 3.0 (graft visualization). Inter-reader agreement was excellent for each assessment (AC2 ≥ 0.81). The estimated radiation dose reduction was 36.8% (p < 0.0001). Conclusions: VNC reconstructions demonstrated comparable image quality to TNC in a PAD assessment and offer substantial radiation dose reduction, supporting their potential as a promising alternative in clinical practice. Further prospective studies and optimization of reconstruction algorithms remain essential to confirm diagnostic accuracy and address remaining technical limitations. Full article

Network connectivity and area coverage are the most important aspects in the applications of wireless sensor networks (WSNs). The resource and energy constraints of sensor nodes, operational conditions, and network size pose challenges to the optimal coverage of targets in the region of interest (ROI). The main idea is to achieve maximum area coverage and connectivity with strategic deployment and the minimal number of sensor nodes. This work addresses the problem of network area coverage in randomly distributed WSNs and provides an efficient deployment strategy using an enhanced version of cuckoo search optimization (ECSO). The “sequential update evaluation” mechanism is used to mitigate the dependency among dimensions and provide highly accurate solutions, particularly during the local search phase. During the preference random walk phase of conventional CSO, particle swarm optimization (PSO) with adaptive inertia weights is defined to accelerate the local search capabilities. The “opposition-based learning (OBL)” strategy is applied to ensure high-quality initial solutions that help to enhance the balance between exploration and exploitation. By considering the opposite of current solutions to expand the search space, we achieve higher convergence speed and population diversity. The performance of ECSO-OBL is evaluated using eight benchmark functions, and the results of three cases are compared with the existing methods. The proposed method enhances network coverage with a non-uniform distribution of sensor nodes and attempts to cover the whole ROI with a minimal number of sensor nodes. In a WSN with a 100 m² area, we achieved a maximum coverage rate of 98.45% and algorithm convergence in 143 iterations, and the execution time was limited to 2.85 s. The simulation results of various cases prove the higher efficiency of the ECSO-OBL method in terms of network coverage and connectivity in WSNs compared with existing state-of-the-art works. Full article

An optical power splitter (OPS) with arbitrary splitting ratios has attracted significant research interest for its broad applications in photonic integrated circuits. A series of OPSs with arbitrary splitting ratios based on silicon nitride (Si₃N₄) platforms are presented. The devices are designed with ultra-compact dimensions using three-dimensional finite-difference time-domain (3D FDTD) analysis and an inverse design algorithm. Within a 50 nm bandwidth (1525 nm to 1575 nm), we demonstrated a 1 × 2 OPS with splitting ratios of 1:1, 1:1.5, and 1:2; a 1 × 3 OPS with ratios of 1:2:1 and 2:1:2; and a 1 × 4 OPS with ratios of 1:1:1:1 and 2:1:2:1. The target splitting ratios are achieved by optimizing pixel distributions in the coupling region. The dimensions of the designed devices are 1.96 × 1.96 µm², 2.8 × 2.8 µm², and 2.8 × 4.2 µm², respectively. The designed devices achieve transmission efficiencies exceeding 90% and exhibit excellent power splitting ratios (PSRs). Full article

Buildings are a significant component of urban space and are essential to smart cities, catastrophe monitoring, and land use planning. However, precisely extracting building polygons from remote sensing images remains difficult because of the variety of building designs and intricate backgrounds. This paper proposes an end-to-end polygon dynamic adjustment algorithm (PDAA) to improve the accuracy and geometric consistency of building contour extraction by dynamically generating and optimizing polygon vertices. The method first locates building instances through the region of interest (RoI) to generate initial polygons, and then uses four core modules for collaborative optimization: (1) the feature enhancement module captures local detail features to improve the robustness of vertex positioning; (2) the contour vertex tuning module fine-tunes vertex coordinates through displacement prediction to enhance geometric accuracy; (3) the learnable redundant vertex removal module screens key vertices based on a classification mechanism to eliminate redundancy; and (4) the missing vertex completion module iteratively restores missed vertices to ensure the integrity of complex contours. PDAA dynamically adjusts the number of vertices to adapt to the geometric characteristics of different buildings, while simplifying the prediction process and reducing computational complexity. Experiments on public datasets such as WHU, Vaihingen, and Inria show that PDAA significantly outperforms existing methods in terms of average precision (AP) and polygon similarity (PolySim). It is at least 2% higher than existing methods in terms of average precision (AP), and the generated polygonal contours are closer to the real building geometry. Values of 75.4% AP and 84.9% PolySim were achieved on the WHU dataset, effectively solving the problems of redundant vertices and contour smoothing, and providing high-precision building vector data support for scenarios such as smart cities and emergency response. Full article

Magnetic tiles are an important component of permanent magnet motors, and the quality of magnetic tiles directly affects the performance and service life of a motor. It is necessary to perform defect detection on magnetic tiles in industrial production and remove those with defects. The YOLOv8-AHF algorithm is proposed to improve the ability of network feature information extraction and solve the problem of missed detection or poor detection results in surface defect detection due to the small volume of permanent magnet motor tiles, which reduces the deviation between the predicted box and the true box simultaneously. Firstly, a hybrid module of a combination of atrous convolution and depthwise separable convolution (ADConv) is introduced in the backbone of the model to capture global and local features in magnet tile detection images. In the neck section, a hybrid attention module (HAM) is introduced to focus on the regions of interest in the magnetic tile surface defect images, which improves the ability of information transmission and fusion. The Focal-Enhanced Intersection over Union loss function (Focal-EIoU) is optimized to effectively achieve localization. We conducted comparative experiments, ablation experiments, and corresponding generalization experiments on the magnetic tile surface defect dataset. The experimental results show that the evaluation metrics of YOLOv8-AHF surpass mainstream single-stage object detection algorithms. Compared to the You Only Look Once version 8 (YOLOv8) algorithm, the performance of the YOLOv8-AHF algorithm was improved by 5.9%, 4.1%, 5%, 5%, and 5.8% in terms of mAP@0.5, mAP@0.5:0.95, F1-Score, precision, and recall, respectively. This algorithm achieved significant performance improvement in the task of detecting surface defects on magnetic tiles. Full article

Spatiotemporal correlations between meteo-inputs and wind–solar outputs in an optimal regional scale are crucial for developing robust models, reliable in mid-term prediction time horizons. Modelling border conditions is vital for early recognition of progress in chaotic atmospheric processes at the destination of interest. This approach is used in differential and deep learning; artificial intelligence (AI) techniques allow for reliable pattern representation in long-term uncertainty and regional irregularities. The proposed day-by-day estimation of the RE production potential is based on first data processing in detecting modelling initialisation times from historical databases, considering correlation distance. Optimal data sampling is crucial for AI training in statistically based predictive modelling. Differential learning (DfL) is a recently developed and biologically inspired strategy that combines numerical derivative solutions with neurocomputing. This hybrid approach is based on the optimal determination of partial differential equations (PDEs) composed at the nodes of gradually expanded binomial trees. It allows for modelling of highly uncertain weather-related physical systems using unstable RE. The main objective is to improve its self-evolution and the resulting computation in prediction time. Representing relevant patterns by their similarity factors in input–output resampling reduces ambiguity in RE forecasting. Node-by-node feature selection and dynamical PDE representation of DfL are evaluated along with long-short-term memory (LSTM) recurrent processing of deep learning (DL), capturing complex spatio-temporal patterns. Parametric C++ executable software with one-month spatial metadata records is available to compare additional modelling strategies. Full article

The extensive accumulation of plastic waste causes serious environmental problems, leading to growing interest in biodegradable alternatives. In this study, the structural, chemical, and crystalline characteristics of a pulp-based material incorporating sugarcane bagasse ash (SCBA) were investigated using Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD), and Fourier Transform Infrared Spectroscopy (FTIR). Mechanical properties of the materials were investigated through compression, tensile, and bending tests in order to assess their strength and flexibility, while biodegradability was evaluated through soil burial tests. The results indicate that SCBA addition enhances compressive strength, with optimal performance obtained at 15% SCBA content, while tensile and bending strengths showed an enhancement at 5% content. FTIR and XRD analyses suggested an increase in amorphous regions and notable microstructural interactions between SCBA particles and cellulose fibers, particularly at a 10% concentration. SEM images further confirmed effective particle dispersion and improved porosity in the composite materials. Furthermore, samples incorporating SCBA exhibited superior biodegradability compared to pure pulp. Overall, these findings highlight that incorporating 10–15% SCBA provides a promising balance between mechanical integrity and environmental sustainability, offering a viable strategy for developing eco-friendly, high-performance packaging materials. Full article

The advance of the soil desertification process and water salinisation hinders reforestation actions in the Brazilian semiarid region due to the negative effects on the initial establishment of seedlings. Knowledge of potential species for overcoming the problems of soil and water salinity is of broad interest. This study evaluated the growth of seedlings of the species Handroanthus impetiginosus and Handroanthus spongiosus subjected to the combined stresses of salinity and water deficit. The species were subjected to three water depths (WDs): WD1—50%, WD2—75% and WD3—100% of reference evapotranspiration, and four salinity levels (SL): SL1—0.27 dS m⁻¹, SL2—2.52 dS m⁻¹, SL3—6.35 dS m⁻¹ and SL4—7.38 dS m⁻¹. Biometric data, including plant height, number of leaves, collar diameter and biomass, was obtained. The results showed that H. impetiginosus was more tolerant of the conditions analysed. The species showed greater sensitivity to salt stress, which reduced growth and dry biomass accumulation by up to 98%. Increased water deficit reduced height, collar diameter, number of leaves, root biomass and total biomass. We propose that the optimal water depth for both species is 100% of the reference evapotranspiration. Full article

In recent times, blades that have the ability to change shape passively or actively have garnered interest due to their ability to optimize blade performance for varying flow conditions. Various versions of morphing exist, from simple chord length changes to full blade morphing with multiple degrees of freedom. These blades can incorporate smart materials or mechanical actuators to modify the blade shape to suit the wind conditions. Morphing blades have shown an ability to improve performance in simulations. These simulations show increased performance in Region 2 (partial load) operating conditions. This study focuses on the effects of the wake for a flexible wind turbine with actively variable twist angle distribution (TAD) to improve the energy production capabilities of morphing structures. These wake effects influence wind farm performance for locally clustered turbines by extracting energy from the free stream. Hence, the development of better wake models is critical for better turbine design and controls. This paper provides an outline of some approaches available for wake modeling. FLORIS (FLow Redirection and Induction Steady-State) is a program used to predict steady-state wake characteristics. Alongside that, the Materials and Methods section shows different modeling environments and their possible integration into FLORIS. The Results and Discussion section analyzes the 20 kW wind turbine with previously acquired data from the National Renewable Energy Laboratory’s (NREL) AeroDyn v13 software. The study employs FLORIS to simulate steady-state non-linear wake interactions for the nine TAD shapes. These TAD shapes are evaluated across Region 2 operating conditions. The previous study used a genetic algorithm to obtain nine TAD shapes that maximized aerodynamic efficiency in Region 2. The Results and Discussion section compares these TAD shapes to the original blade design regarding the wake characteristics. The project aims to enhance the understanding of FLORIS for studying wake characteristics for morphing blades. Full article

The utilization of geothermal resources as renewable energy is a subject of interest for the regions that possess these resources. The exploitation of geothermal energy must consider local geological conditions and an integrated approach, which should include practical studies on the chemistry of geothermal waters and their effect on thermal installations. Geothermal waters from Bihor County, Romania, have a variable composition, depending on the crossed geological layers, but also on pressure and temperature. Obviously, water transport and heat transfer are involved in all applications of geothermal waters. This article aims to characterize certain geothermal waters from the point of view of composition and corrosion if used as a thermal agent. Atomic absorption spectroscopy (AAS) and UV–Vis spectroscopy were employed to analyze water specimens. Chemical composition includes calcite (CaCO₃), chalcedony (SiO₂), goethite (FeO(OH)), and magnetite (Fe₃O₄), which confirms the corrosion and scale potential of these waters. Corrosion resistance of mild carbon steel, commonly used as pipe material, was studied by the gravimetric method and through electrochemical methodologies, including chronoamperometry, electrochemical impedance spectroscopy (EIS), potentiodynamic polarization method, and open circuit potential measurement (OCP). Statistical analysis shows that the medium corrosion rate of S235 steel, expressed as penetration rate, is between 0.136 mm/year to 0.615 mm/year. The OCP, EIS, and chronoamperometry experiments explain corrosion resistance through the formation of a passive layer on the surface of the metal. This study proposes an innovative methodology and a systematic algorithm for analyzing chemical processes and corrosion phenomena in geothermal installations, emphasizing the necessity of individualized assessments for each aquifer to optimize operational parameters and ensure sustainable resource utilization. Full article

In response to the growing impact of the climate crisis, many countries are accelerating efforts to develop sustainable and carbon-free energy solutions. This has led to increasing interest in advanced energy storage and conversion technologies, particularly the development of high-temperature molten salt energy systems. Among these, chloride salt-based molten salt systems, which offer excellent thermal properties such as high thermal conductivity, low melting points, and favorable chemical stability, are emerging as strong candidates for thermal energy storage and heat-transfer applications. This study focuses on deriving key thermophysical properties essential for selecting suitable molten salt heat-transfer fluids by examining their eutectic points and thermal stability with respect to various salt compositions. Three chloride mixtures—NaCl-MgCl₂, NaCl-KCl-MgCl₂, and NaCl-KCl-ZnCl₂—were evaluated for potential use in high-temperature molten salt energy systems. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) were employed to measure the melting points and thermal stability of molten salts with various compositions near their eutectic regions. Experimental results were compared with predicted eutectic points to assess the thermal performance of each salt mixture. The findings indicate that the NaCl-KCl-MgCl₂ mixture exhibits the most promising characteristics, including a low melting point below 400 °C and superior thermal stability, making it highly suitable as a heat-transfer fluid in high-temperature molten salt energy systems. In contrast, NaCl-KCl-ZnCl₂ was found unsuitable for such applications due to its high hygroscopicity and poor thermal stability. This study provides essential data for selecting optimal molten salt compositions for the efficient and reliable operation of high-temperature molten salt energy systems. Full article

Background: Radiographic evaluation of bone regeneration following maxillary sinus floor elevation commonly emphasizes volumetric gains. However, the qualitative microarchitecture of the regenerated bone, particularly when assessed via two-dimensional imaging modalities, such as panoramic radiographs, remains insufficiently explored. This study aimed to evaluate early trabecular changes in grafted maxillary sinus regions using fractal dimension, first-order statistics, and gray-level co-occurrence matrix analysis. Methods: This retrospective study included 150 patients who underwent maxillary sinus floor augmentation with bovine-derived xenohybrid grafts. Postoperative panoramic radiographs were analyzed at 6 months to assess early healing. Four standardized regions of interest representing grafted sinus floors and adjacent tuberosity regions were analyzed. Image processing and quantitative analyses were performed to extract fractal dimension (FD), first-order statistics (FOS), and gray-level co-occurrence matrix (GLCM) features (contrast, homogeneity, energy, correlation). Results: A total of 150 grafted sites and 150 control tuberosity sites were analyzed. Fractal dimension (FD) and contrast values were significantly lower in grafted areas than in native tuberosity bone (p < 0.001 for both), suggesting reduced trabecular complexity and less distinct transitions. In contrast, higher homogeneity (p < 0.001) and mean gray-level intensity values (p < 0.001) were observed in the grafted regions, reflecting a more uniform but immature trabecular pattern during the early healing phase. Energy and correlation values also differed significantly between groups (p < 0.001). No postoperative complications were reported, and resorbable collagen membranes appeared to support graft stability. Conclusions: Although the grafted sites demonstrated radiographic volume stability, their trabecular architecture remained immature at 6 months, implying that volumetric measurements alone may be insufficient to assess biological bone maturation. These results support the utility of advanced textural and fractal analysis in routine imaging to optimize clinical decision-making regarding implant placement timing in grafted sinuses. Full article