Search Results (46,124)

Climate change threatens Tetraclinis articulata, a Mediterranean plant endangered by habitat loss, logging, and aridification. This study used the MaxEnt model to analyze factors affecting its distribution under current and future climate scenarios (SSP1-2.6 to SSP5-8.5) for 2040–2100, highlighting its vulnerability to drought and urgent conservation needs. Results showed that: (a) the model demonstrated excellent predictive power with an AUC of 0.92; (b) the highly suitable habitat for T. articulata is projected to expand by 6.5%–6.7% (5.24–5.38 million km²) by 2100 under SSPs 2-4.5, 3-7.0, and 5-8.5, compared to current conditions (6.1%, 4.92 million km²); (c) the centroid of suitable habitats shifts from northwest Algeria (1.394° N, 33.538° E) to various locations under future climate scenarios: west Morocco (SSP1-2.6, −3.429° S, 33.588° E), east Tunisia (SSP2-4.5, 11.091° N, 32.501° E), northwest Morocco (SSP3-7.0, −1.947° S, 34.098° E), and southwest Morocco (SSP5-8.5, −2.985° S, 34.707° E); (d) key environmental variables influencing T. articulata distribution include annual precipitation (bio12, 41.7%), mean annual temperature (bio1, 27.9%), and precipitation during the driest month (bio14, 16.1%). This study concluded that climate change significantly influenced the distribution of T. articulata in the Mediterranean, highlighting the urgent need for conservation strategies to mitigate the risk of local extinction driven by both anthropogenic activities and climate impacts. Full article

Early intervention is pivotal for optimizing neurodevelopmental outcomes in children with language delay, where increased language stimulation can optimize therapeutic outcomes. Extending speech–language therapy from clinical settings to the home is a promising strategy; however, practical barriers and a lack of scalable, customizable home-based models limit the implementation of this approach. The integration of AI-powered digital interactive tools could bridge this gap. This pilot feasibility study adopted a single-arm pre–post (before–after) design within a two-phase, mixed-methods framework to evaluate a generative AI-powered interactive platform supporting home-based language therapy in children with either idiopathic language delay or autism spectrum disorder (ASD)-related language impairment: two conditions known to involve heterogeneous developmental profiles. The participants received clinical language assessments and engaged in home-based training using AI-enhanced tablet software, and 2000 audio recordings were collected and analyzed to assess pre- and postintervention language abilities. A total of 22 children aged 2–12 years were recruited, with 19 completing both phases. Based on 6-week cumulative usage, participants were stratified with respect to hours of AI usage into Groups A (≤5 h, n = 5), B (5 < h ≤ 10, n = 5), C (10 < h ≤ 15, n = 4), and D (>15 h, n = 5). A threshold effect was observed: only Group D showed significant gains between baseline and postintervention, with total words (58→110, p = 0.043), characters (98→192, p = 0.043), type–token ratio (0.59→0.78, p = 0.043), nouns (34→56, p = 0.043), verbs (12→34, p = 0.043), and mean length of utterance (1.83→3.24, p = 0.043) all improving. No significant changes were found in Groups A to C. These findings indicate the positive impact of extended use on the development of language. Generative AI-powered digital interactive tools, when they are integrated into home-based language therapy programs, can significantly improve language outcomes in children who have language delay and ASD. This approach offers a scalable, cost-effective extension of clinical care to the home, demonstrating the potential to enhance therapy accessibility and long-term outcomes. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly integral to construction site management, supporting monitoring, inspection, and data collection tasks. Effective UAV path planning is essential for maximizing operational efficiency, particularly in complex and dynamic construction environments. While previous BIM-based approaches have explored representation models such as space graphs, grid patterns, and voxel models, each has limitations. Space graphs, though common, rely on predefined spatial spaces, making them less suitable for projects still under construction. Voxel-based methods, considered well-suited for 3D indoor navigation, suffer from three key challenges: (1) a disconnect between the BIM and voxel models, limiting data integration; (2) the computational cost and time required for voxelization, hindering real-time application; and (3) inadequate support for 4D BIM integration during active construction phases. This research introduces a novel framework that bridges the BIM–voxel gap via an enriched voxel map, eliminates the need for repeated voxelization, and incorporates 4D BIM and additional model data such as defined workspaces and safety buffers around fragile components. The framework’s effectiveness is demonstrated through path planning simulations on BIM models from two real-world construction projects under varying scenarios. Results indicate that the enriched voxel map successfully creates a connection between BIM model and voxel model, while covering every timestamp of the project and element attributes during path planning without requiring additional voxel map creation. Full article

The segmentation and classification of color are crucial stages in image processing, computer vision, and pattern recognition, as they significantly impact the results. The diverse, hand-labeled datasets in the literature are applied for monochromatic or color segmentation in specific domains. On the other hand, synthetic datasets are generated using statistics, artificial intelligence algorithms, or generative artificial intelligence (AI). This last one includes Large Language Models (LLMs), Generative Adversarial Neural Networks (GANs), and Variational Autoencoders (VAEs), among others. In this work, we propose VitralColor-12, a synthetic dataset for color classification and segmentation, comprising twelve colors: black, blue, brown, cyan, gray, green, orange, pink, purple, red, white, and yellow. VitralColor-12 addresses the limitations of color segmentation and classification datasets by leveraging the capabilities of LLMs, including adaptability, variability, copyright-free content, and lower-cost data—properties that are desirable in image datasets. VitralColor-12 includes pixel-level classification and segmentation maps. This makes the dataset broadly applicable and highly variable for a range of computer vision applications. VitralColor-12 utilizes GPT-5 and DALL·E 3 for generating stained-glass images. These images simplify the annotation process, since stained-glass images have isolated colors with distinct boundaries within the steel structure, which provide easy regions to label with a single color per region. Once we obtain the images, we use at least one hand-labeled centroid per color to automatically cluster all pixels based on Euclidean distance and morphological operations, including erosion and dilation. This process enables us to automatically label a classification dataset and generate segmentation maps. Our dataset comprises 910 images, organized into 70 generated images and 12 pixel segmentation maps—one for each color—which include 9,509,524 labeled pixels, 1,794,758 of which are unique. These annotated pixels are represented by RGB, HSL, CIELAB, and YCbCr values, enabling a detailed color analysis. Moreover, VitralColor-12 offers features that address gaps in public resources such as violin diagrams with the frequency of colors across images, histograms of channels per color, 3D color maps, descriptive statistics, and standardized metrics, such as ΔE76, ΔE94, and CIELAB Chromacity, which prove the distribution, applicability, and realistic perceptual structures, including warm, neutral, and cold colors, as well as the high contrast between black and white colors, offering meaningful perceptual clusters, reinforcing its utility for color segmentation and classification. Full article

As a novel approach, polyfloral honey originating from the three regions of the Caribbean Island of the Dominican Republic (D.R.) was analyzed. Using the HPLC-DAD technique, 10 specific flavonoids (FLV) together with 9 phenolic acids (PHA) were identified and compared with Spanish polyflorals (commercial brands, artisanal beekeepers, and experimental apiaries). On average, the total content of FLV and PHA was much higher in Spanish (14.2 and 20.1 mg/kg) than in D.R. (10.8 and 4.5 mg/kg) honeys. Unlike in Dominican honeys, chrysin (in FLV) and vanillic acid (in PHA) had the greatest impact on Spanish honey, with the latter alone accounting for more than 50% of the quantified PHAs. Unsupervised Principal Component Analysis (PCA) showed that the information provided by both FLV and PHA allowed us to differentiate honeys according to their geographical origin, particularly at the country level. Furthermore, a stepwise discriminant-analysis identified the PHA ferulic acid followed by the FLVs apigenin-7-glucoside, chrysin, and naringenin as the most influential compounds for distinguishing among groups of honeys. The resulting model correctly classified 80.3% of the original and 71.2% of the cross-validated cases, indicating acceptable efficiency and robustness. These findings highlight the potential of the analyzed compounds for the geographical authentication of honey, providing the beekeeping sector with valuable tools for ensuring honey provenance. Full article

Background/Objectives: The relationship between multiple sclerosis (MS) disability and co-occurring non-motor symptomatology is not well understood. This study examined the association between disability status and non-motor symptoms—sleep quality, depression, anxiety, and fatigue—in people with multiple sclerosis (MS), as well as the contribution of sleep quality to the prediction of fatigue, depression, and anxiety in MS. Methods: A cross-sectional study included 469 MS and 369 control subjects. Disability status of MS subjects was assessed with the Expanded Disability Status Scale (EDSS), while fatigue, depression, anxiety, and sleep quality were evaluated with the Fatigue Severity Scale (FSS), the Hospital Anxiety and Depression Scale (HADS), and the Pittsburgh Sleep Quality Index (PSQI), respectively. Statistical analyses encompassed group comparisons, Pearson correlations, and hierarchical regression models adjusted for age, sex, and EDSS. Results: The results show that MS subjects exhibited higher FSS, HADS-D, and PSQI scores than controls, with intercorrelations and only weak associations with EDSS severity (r = 0.15–0.29). Moreover, PSQI global and HADS-D scores increased with higher EDSS severity, while FSS scores peaked in the moderate EDSS range (4.5–6.5). Global PSQI score independently predicted FSS, HADS-D, and HADS-A. Daytime dysfunction, sleep disturbances, and sleep medication use significantly predicted FSS, HADS-D, and HADS-A scores. Conclusions: Study findings highlight the role of sleep quality in exacerbating depression, anxiety, and fatigue in MS. Full article

Anaerobic digestion of manures, crop residues, food waste, and sludge frequently yields biogas with elevated hydrogen sulfide concentrations, which accelerate corrosion and reduce biogas quality. Microaeration, defined as the controlled addition of oxygen at 1 to 5% of the biogas production rate, has been investigated as a low-cost desulfurization strategy. This review synthesizes studies from 2015 to 2025 spanning laboratory, pilot, and full-scale anaerobic digester systems. Continuous sludge digesters supplied with ambient air at 0.28–14 m³ h⁻¹ routinely achieved 90 to 99% H₂S removal, while a full-scale dairy manure system reported a 68% reduction at 20 m³ air d⁻¹. Pure oxygen dosing at 0.2–0.25 m³ O₂ (standard conditions) per m³ reactor volume resulted in greater than 99% removal. Reported methane yield improvements ranged from 5 to 20%, depending on substrate characteristics, operating temperature, and aeration control. Excessive oxygen, however, reduced methane yields in some cases by inhibiting methanogens or diverting carbon to CO₂. Documented benefits of microaeration include accelerated hydrolysis of lignocellulosic substrates, mitigation of sulfide inhibition, and stimulation of sulfur-oxidizing bacteria that convert sulfide to elemental sulfur or sulfate. Optimal redox conditions were generally maintained between −300 and −150 mV, though monitoring was limited by low-resolution oxygen sensors. Recent extensions of the Anaerobic Digestion Model No. 1 (ADM1), a mathematical framework developed by the International Water Association, incorporate oxygen transfer and sulfur pathways, enhancing its ability to predict gas quality and process stability under microaeration. Economic analyses estimate microaeration costs at 0.0015–0.0045 USD m⁻³ biogas, substantially lower than chemical scrubbing. Future research should focus on refining oxygen transfer models, quantifying microbial shifts under long-term operation, assessing effects on digestate quality and nitrogen emissions, and developing adaptive control strategies that enable reliable application across diverse substrates and reactor configurations. Full article

In this paper, the rail mean vertical alignment is estimated through double integration of wheel–rail contact forces measured using dynamometric wheelsets on a dedicated track recording vehicle (TRV). A simplified three degrees of freedom (DOF) linear model of half a train coach has been developed for this purpose. The model’s ability to simulate the average left and right longitudinal level has been tested using vertical contact force recordings from a constant speed track section, as measured by the TRV. The results are compared with available track geometry (TG) data, recorded by the optical system of the same vehicle, used for condition monitoring of the Italian railway infrastructure. Model parameters, such as masses, stiffness, and damping of the suspensive system have been optimized. An error analysis has been conducted on results. A good agreement is found between simulated and recorded vertical alignment at the D1 level, suggesting the feasibility of using contact forces measured with instrumented wheelsets for railway TG condition monitoring. This computationally efficient approach highlights the potential of strain gauges and instrumented wheelsets as alternative or complementary technologies to the widely adopted accelerometers, rate gyros, and optical devices for railway condition monitoring. Given its low computational cost, embedded and real-time TG estimation could be further investigated. Full article

Background/Objectives: Parents achieving recommended eicosapentaenoic (EPA) and docosahexaenoic (DHA) acid intake can improve the health of parents and their children. Evidence links higher DHA intake to lower preterm birth (PTB) risk. With parental intake poorly defined, the objective is to characterize EPA and DHA intake by parents with children in households in a diverse, urban city. Methods: Parents with ≥1 child in the household completed a validated seven-question food frequency questionnaire to assess consumption of foods contributing most to EPA and DHA intake in American diets during the cross-sectional Voices of Child Health in Chicago Panel Survey (May–July 2022). Female respondents reported prior PTB. Home/residence information was linked to the Childhood Opportunity Index (COI). Multivariable linear regression and survey-weighted models evaluated parental characteristics associated with EPA+DHA intake. Pairwise comparisons estimated intake differences (mean (SE)) among groups. Results: Chicago parents (n = 1057) reported lower-than-recommended EPA+DHA intake and mothers consumed less compared to fathers (difference: 27.1 (11.4) mg/d; p = 0.02). Prior PTB was associated with lower EPA+DHA intake, yet DHA-containing supplement use, which occurred in ~25% of parents, was associated with higher intake (p < 0.05). Lower household income and a lower COI were associated with lower intake while parental race and ethnicity categories were also associated with intake (all p < 0.05); intake differed for mothers and fathers based on Black race and Hispanic ethnicity categories. Conclusions: The findings suggest that efforts aimed at improving parental EPA+DHA intake to improve the health of families should account for multidimensional influences on household food choices. Full article

This paper proposes a method to analyze the effect of the rotor’s harmonic winding design and the output of a brushless wound rotor synchronous machine (WRSM) for optimal excitation power transfer. In particular, the machine analyzed by the finite-element method was a 48-slot eight-pole 2D model. The subharmonic magnetomotive force was additionally created in the air gap flux, which induces voltage in the harmonic winding of the rotor. This voltage is rectified and fed to the field winding through a full bridge rectifier. Eventually, a direct current (DC) flows to the field winding, removing the need for external excitation through brushes and sliprings. The effect of the number of harmonic winding turns is analyzed and the field winding turns were varied with respect to the available rotor slot space. Optimization of the harmonic excitation part of the machine will maximize the rotor excitation for regulation purposes and optimize the torque production at the same time. Two-dimensional finite-element analysis has been performed in ANSYS Maxwell 19 to obtain the basic results for the design of the machine. Full article

Background/Objectives: PEGylated zein nanoparticles (PZNs) loaded with losartan potassium (LOS) were developed as a repurposed treatment for vaginal methicillin-resistant Staphylococcus aureus (MRSA) infection. PZNs were prepared using the ethanol injection method with different types and amounts of Brij^® surfactant. Methods: The prepared formulations were optimized using a D-optimal mixture design via Design-Expert^® software version 13. The assessed responses included entrapment efficiency (EE%), particle size (PS), and zeta potential (ZP). Results: The optimized PZNs, composed of 30 mg Brij^® O20 and 10 mg zein, exhibited spherical particles with an EE% of 90.58 ± 1.20%, PS of 200.81 ± 1.39 nm, PDI of 0.395 ± 0.01, and ZP of −36.59 ± 0.05 mV. Confocal laser scanning microscopy confirmed complete deposition of fluorescein-labeled PZNs within vaginal tissues. Ex vivo studies showed that PZNs resulted in prolonged permeation of LOS compared to the LOS solution. In a murine model of MRSA infection, the optimized PZNs demonstrated superior therapeutic efficacy over the LOS solution. Histopathological examinations confirmed the safety of the tested formulations. Conclusions: In conclusion, the optimized PZNs present a promising approach for the treatment of MRSA-related vaginal infections. Full article

Peanut (Arachis hypogaea L.) is an important oil and cash crop, but its growth and productivity are severely constrained by low-temperature stress. Growth-regulating factors (GRFs) are plant-specific transcription factors involved in development and stress responses, yet their roles in peanut remain poorly understood. In this study, we identified AhGRF3b as a direct target of ahy-miR396 using degradome sequencing, which demonstrated precise miRNA-mediated cleavage sites within the AhGRF3b transcript. Expression profiling confirmed that ahy-miR396 suppresses AhGRF3b via post-transcriptional cleavage rather than translational repression. Functional analyses showed that overexpression of AhGRF3b in Arabidopsis thaliana promoted leaf expansion by enhancing cell proliferation. Specifically, leaf length, width, and petiole length increased by 104%, 22%, and 28%, respectively (p < 0.05). Under cold stress (0 °C for 7 days), transgenic lines (OE-2 and OE-6) exhibited significantly better growth than Col-0, with fresh weight increased by 158% and 146%, respectively (p < 0.05). Effect size analysis further confirmed these differences (Cohen’s d = 11.6 for OE-2 vs. Col-0; d = 6.3 for OE-6 vs. Col-0). Protein–protein interaction assays, performed using the yeast two-hybrid (Y2H) system and 3D protein–protein docking models, further supported that AhGRF3b interacts with Catalase 1 (AhCAT1), vacuolar cation/proton exchanger 3 (AhCAX3), probable polyamine oxidase 4 (AhPAO4), and ACT domain-containing protein 11 (AhACR11), which are involved in reactive oxygen species (ROS) scavenging and ion homeostasis. These interactions were associated with enhanced CAT and PAO enzymatic activities, reduced ROS accumulation, and upregulation of stress-related genes under cold stress. These findings suggest that the ahy-miR396/AhGRF3b module plays a potential regulatory role in leaf morphogenesis and cold tolerance, providing valuable genetic resources for breeding cold-tolerant peanut varieties. Full article

Extracting powerline point clouds from airborne LiDAR data and conducting 3D reconstruction has become a critical technical support for automatic transmission corridor inspection. To enhance data processing efficiency, this paper proposes an automatic method for span segmentation of powerline point clouds that accounts for adjacent powerline interference, aiming to provide “clean” data for the automatic reconstruction of powerline catenary curve models of each span. This method tackles a key challenge in airborne LiDAR data: interference from adjacent or cross-over powerlines when automatically extracting main-line pylon positions and powerline points. Leveraging the spatial relationship between pylons and powerlines in LiDAR point clouds, we developed a fast density clustering algorithm based on a novel point-counting grid (PCGrid), which greatly accelerates DBSCAN clustering while adaptively extracting main-line pylons and powerline point clouds. The method proceeds in three steps: first, using 2D density clustering to extract reliable pylon positions and 3D density clustering to filter out non-main-line point clouds; second, verifying pylon connection combinations via main-line point clouds and identifying the longest line in the connection matrix as the pylons of the main powerline; and third, assigning powerline points to their corresponding spans for segmented reconstruction. Experimental results demonstrate that the proposed PCGrid structure not only significantly improves clustering efficiency, but also enables a fully automated span segmentation process that effectively suppresses adjacent powerline interference, highlighting the novelty of integrating efficient PCGrid-based clustering with spatial-relationship-driven pylon verification into a unified framework for reliable 3D powerline reconstruction. Full article

Residual oil zones (ROZ) arise under the oil–water contact of main pay zones due to diverse geological conditions. Historically, these zones were considered economically unviable for development with conventional recovery methods because of the immobile nature of the oil. However, they represent a substantial subsurface volume with strong potential for CO₂ sequestration and storage. Despite this potential, effective techniques for assessing CO₂-EOR performance coupled with CCUS in ROZs remain limited. To address this gap, this study introduces a machine learning framework that employs artificial neural network (ANN) models trained on data generated from a large number of reservoir simulations (300 cases produced using Latin Hypercube Sampling across nine geological and operational parameters). The dataset was divided into training and testing subsets to ensure generalization, with key input variables including reservoir properties (thickness, permeability, porosity, Sorg, salinity) and operational parameters (producer BHP and CO₂ injection rate). The objective was to forecast CO₂ storage capacity and oil recovery potential, thereby reducing reliance on time-consuming and costly reservoir simulations. The developed ANN models achieved high predictive accuracy, with R² values ranging from 0.90 to 0.98 and mean absolute percentage error (MAPRE) consistently below 10%. Validation against real ROZ field data demonstrated strong agreement, confirming model reliability. Beyond prediction, the workflow also provided insights for reservoir management: optimization results indicated that maintaining a producer BHP of approximately 1250 psi and a CO₂ injection rate of 14–16 MMSCF/D offered the best balance between enhanced oil recovery and stable storage efficiency. In summary, the integrated combination of reservoir simulation and machine learning provides a fast, technically robust, and cost-effective tool for evaluating CO₂-EOR and CCUS performance in ROZs. The demonstrated accuracy, scalability, and optimization capability make the proposed ANN workflow well-suited for both rapid screening and field-scale applications. Full article

Viologens are promising candidates for next-generation electrochromic devices due to their reversible color changes, low operating voltages, and structural tunability. However, their practical performance is often constrained by limited color range, stability issues, and poor charge delocalization. In this study, we present a detailed density functional theory (DFT) and time-dependent DFT (TD-DFT) investigation of asymmetric viologens based on the Benzyl-4,4′-dipyridyl-R (BnV-R) framework. A series of electron-donating and electron-withdrawing substituents (CN, COOH, PO₃H₂, CH₃, OH, NH₂) were introduced via either benzyl or phenyl linkers. Geometry optimizations for neutral, radical cationic, and dicationic states were performed at the CAM-B3LYP/6-31+G(d,p) level with C-PCM solvent modeling. Electronic structure, frontier orbital distributions, and redox potentials were correlated with substituent type and linkage mode. Natural Bond Orbital analysis showed that electron-withdrawing groups stabilize reduced states, while electron-donating groups enhance intramolecular charge transfer and switching kinetics. TD-DFT calculations revealed significant bathochromic and hyperchromic shifts dependent on substitution patterns, with phenyl linkers promoting extended conjugation and benzyl spacers minimizing aggregation. Radical cation stability, quantified via ΔE_red and comproportionation constants, highlighted cyano- and amine-substituted systems as particularly promising. These insights provide predictive design guidelines for tuning optical contrast, coloration efficiency, and electrochemical durability in advanced electrochromic applications. Full article