Search Results (20,481)

Wine processing industries require a substantial amount of water and generate considerable volumes of wastewater. Winery wastewater (WWW) is notable for its high concentrations of biodegradable organic matter, while containing relatively low levels of nutrients. Due to seasonal variability in wastewater generation, treatment processes must be both efficient and adaptable. A range of wastewater treatment technologies are currently implemented at pilot and full scales, and ongoing research continues to yield innovative solutions in laboratory settings. This paper provides a comprehensive review of advancements in WWW treatment technologies, pinpoints gaps, and highlights future research directions. The treatment methods discussed include aerobic reactors, anaerobic systems, constructed wetlands (CWs) and biosand filters (BSFs), as well as advanced oxidation processes (AOPs). The advantages and limitations of these techniques, along with key factors affecting their performance, are examined. CWs are regarded as cost-effective and efficient solutions for small to medium wineries, whereas activated sludge and anaerobic digestion processes, which require a smaller footprint, are suitable for managing higher loads at large wineries. While anaerobic processes offer reduced operating costs, they often produce effluents of lower quality compared to aerobic processes, necessitating subsequent polishing prior to discharge. Advances in AOPs present promising alternatives for pre/post-treatment, facilitating the breakdown of persistent organics and achieving acceptable chemical oxygen demand (COD) levels. Nevertheless, further research is required to address operational optimization and reduce associated costs. Full article

Sweeping jet actuators (SJAs) are promising for active flow control in aerospace systems, but integrating actuator-resolved unsteady CFD into full-configuration simulations is often impractical due to small geometric scales and $\mathcal{O}\left({10}^2\right)$ Hz oscillations that demand fine grids and small time steps. This work develops a reduced-order modeling (ROM) framework to generate time-resolved boundary conditions at the actuator exit from SJA flow data. Dynamic mode decomposition (DMD) is particularly attractive for this purpose because it provides a linear, data-driven input–output representation of the actuator effect, even though it does not explicitly model the underlying nonlinear switching mechanism. We introduce an eigenvalue-sorted dynamic mode decomposition (ES-DMD) method that performs stability-aware mode ranking based on the discrete-time DMD eigenvalues, prioritizing modes with $\Re \left(\lambda \right)$ closest to unity to retain near-neutrally stable oscillatory dynamics, improving robustness relative to conventional amplitude-based selections for high-frequency oscillatory flows. The method is evaluated across multiple operating conditions, with detailed analysis performed for the highest mass-flow case ($\dot{m}=0.01$ lb/s), representing the most dynamically demanding condition considered. Across multiple operating conditions, ES-DMD yields consistent reconstructions of the dominant switching dynamics. For one-dimensional exit-plane profiles, combining ES-DMD with time-delay embedding enables accurate reconstruction and multi-period prediction using only 20 modes (7.6% of the full system rank). The proposed approach provides a practical pathway to incorporate unsteady SJA effects into large-scale aerospace CFD through compact, predictive boundary-condition models. Full article

Assessing genetic diversity in various native poultry breeds, including bantam/dwarf ones, is instrumental for their conservation as genetic resources, identifying their specific genetic features, and exploring the history of their genetic divergence. Rare chicken breeds are usually carriers of peculiar phenotypic traits, including adaptations to local conditions, disease resistance, and unique performance features. Here, we report for the first time SNP-based genetic characterization of the Russian Korolyok, translated as “kinglet,” relative to five other dwarf/small breeds: Cochin Bantam, Hamburg Bantam Silver Spangled, Polish White-crested Black, Red White-tailed Dwarf and Silkie White. We estimated phenotypes, heterozygosity, inbreeding, effective population size, and runs of homozygosity (ROHs). Some breeds had higher genetic diversity and others showed elevated inbreeding rates in their genomes. With lower effective population sizes (both presently and in the past), rare breeds came from a limited number of ancestors or were under strong selection pressure over many generations. Within 22 ROHs, we identified 26 prioritized candidate genes (GRB10, RPRD1A, APOOL, EAF2, SEMA5, HACD2, GALANT1, DACH2, CHM, POF1B, HDX, SLC15A2, PDIA5, SEC22, NR2F2, ARRDC4, IGF1R, SYNM, TMEM263, etc.). Our data offer whole-genome insights into genetic variability, history, phylogeny, selective sweeps, and candidate genes of a distinct indigenous Russian chicken breed and other bantam/dwarf breeds. Full article

Objective: Since specific bioelectrical reference values for Italian adults are lacking, this study aims to define specific values and test their suitability in pathological cases and athletes. Methods: A sample of 1049 Italian individuals (441 men, 608 women) aged 30–65 years was considered. Competitive athletes (bodybuilding, streetlifting and tennis) were identified within the general sample, and an independent group of individuals with obesity or anorexia nervosa was analyzed for comparison. Anthropometric (weight, kg; stature, mid-upper arm, waist and calf circumferences; cm) and bioelectrical (resistance and reactance, at 50 kHz) variables were taken. Resistivity, (Rsp, Ωcm), reactivity (Xcsp, Ωcm), impedivity (Zsp, Ωcm) and phase angle (PhA, °) were calculated. Two-way ANOVA and Hotelling’s T² test were applied to assess group differences. These data were then pooled with existing datasets to create a comprehensive reference for individuals aged 18 to 100 years. Results: The specific bioelectrical variables were: Rsp = 352.3 ± 55.5, Xcsp = 41.8 ± 9.1, PhA = 6.8 ± 1.0, r (Rsp, Xcsp) = 0.67 (men); Rsp = 384.9 ± 71.2, Xcsp = 40.7 ± 9.4, PhA = 6.1 ± 1.0, r (Rsp, Xcsp) = 0.72 (women). Men showed higher PhA values (p < 0.001), reflecting higher muscle mass and quality, and shorter vectors (p < 0.001), indicative of lower relative fat mass (FM%), than women. Advancing age was associated with lower PhA and longer vectors (p < 0.001). Bioelectrical vectors of individuals with obesity or anorexia nervosa were outside the 95% variability, indicating abnormal values of FM%, whereas those of athletes fell within the lower left quadrant. Conclusions: The specific tolerance ellipses for the Italian adult population fill a gap in the existing literature, providing essential new tools for evaluating body composition in clinical and sports settings, and for comparative analyses. Full article

We propose a novel Expert-Driven Conditional Auxiliary Classifier Generative Adversarial Network (AC-GAN) framework tailored for heterogeneous multi-modal federated learning at edge AI devices such as the NVIDIA Jetson Orin Nano. Unlike prior works that assume idealized distributions or rely on centralized data, our approach jointly addresses statistical non-IID data, model heterogeneity, privacy protection, and resource constraints through an expert-guided training pipeline and hierarchical model updates. Specifically, we introduce a collaborative synthesis and aggregation mechanism where local experts guide conditional data generation, enabling realistic data augmentation on resource-constrained edge nodes and enhancing global model generalization without sharing raw data. Through hierarchical updates between client and server levels, our method mitigates bias from skewed local distributions and significantly reduces communication overhead compared to classical federated averaging baselines. We demonstrate that while “perfect precision” is theoretically unattainable under non-IID and real-world conditions, our framework achieves substantially improved precision and false positive trade-offs (e.g., precision 0.89) relative to benchmarks, validating robustness in practical multi-modal settings. Extensive experiments across synthetic and real datasets show that the proposed AC-GAN approach consistently outperforms federated baselines in accuracy, convergence stability, and privacy preservation. Our results suggest that expert-guided conditional generative modeling is a promising direction for scalable, privacy-aware edge intelligence. Full article

Accurate field-scale meteorological information is required for precision agriculture, but operational numerical weather prediction products remain spatially coarse and cannot resolve local microclimate variability. This study proposes a data fusion superresolution workflow that combines global GFS predictors (0.25°), regional station observations from Southern Moravia (Czech Republic), and static physiographic descriptors (elevation and terrain gradients) to predict the 2 m air temperature 24 h ahead and to generate spatially continuous high-resolution temperature fields. Several model families (LightGBM, TabPFN, Transformer, and Bayesian neural fields) are evaluated under spatiotemporal splits designed to test generalization to unseen time periods and unseen stations; spatial mapping is implemented via a KNN interpolation layer in the physiographic feature space. All learned configurations reduce the mean absolute error relative to raw GFS across splits. In the most operationally relevant regime (unseen stations and unseen future period), TabPFN-KNN achieves the lowest MAE (1.26 °C), corresponding to an ≈24% reduction versus GFS (1.66 °C). The results support the feasibility of an operational, sensor-infrastructure-compatible pipeline for high-resolution temperature superresolution in agricultural landscapes. Full article

Magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs) offers the flexibility to position sensors closer to the scalp, which improves the signal-to-noise ratio compared to conventional superconducting quantum interference device (SQUID) systems. However, the spatial resolution of OPM-MEG critically depends on sensor placement, especially when the number of sensors is limited. In this study, we present a methodology for optimizing OPM-MEG sensor layouts using each subject’s anatomical information derived from individual magnetic resonance imaging (MRI). We generated realistic forward models from reconstructed head surfaces and simulated magnetic fields produced by equivalent current dipoles (ECDs). We compared multiple simulation strategies, including ECDs randomly distributed across the cortical surface and ECDs constrained to regions of interest. For each simulated magnetic field map (MFM) database, we applied the sequential selection algorithm (SSA) to identify sensor positions that maximized information capture. Unlike previous approaches relying on large measurement databases, this simulation-driven strategy eliminates the need for extensive pre-existing recordings. We benchmarked the performance of the personalized layouts using OPM-MEG datasets of auditory evoked fields (AEFs) derived from real whole-head SQUID-MEG measurements. Our results show that simulation-based SSA optimization improves the coverage of cortical regions of interest, reduces the number of sensors required for accurate source reconstruction, and yields sensor configurations that perform comparably to layouts optimized using measured data. To evaluate the quality of estimated MFMs, we applied metrics such as the correlation coefficient (CC), root-mean-square error, and relative error. Our results show that the first 15 to 20 optimally selected sensors (CC > 0.95) capture most of the information contained in full-head MFMs. Additionally, we performed source localization for the highest auditory response (M100) by fitting equivalent current dipoles and found that localization errors were < 5 mm. The results further indicate that SSA performance is insensitive to individualized head geometry, supporting the feasibility of using representative anatomical models and highlighting the potential of this approach for clinical OPM-MEG applications. Full article

With the rapid evolution of Digital Twins and Embodied AI, achieving fast, dense, and high-precision 3D perception in unknown environments has become paramount. However, existing Visual SLAM paradigms face a critical dilemma: geometry-based methods often fail in texture-less areas due to feature scarcity, while learning-based approaches frequently suffer from scale drift and unphysical deformations. To bridge this gap, we propose VGGT-Geo, a novel SLAM system that synergizes generative priors from Large Foundation Models with multi-modal geometric optimization. Distinguishing itself from simple cascaded architectures, we construct a Probabilistic Geometric Fusion framework, consisting of (1) Generative Warm-start, leveraging the holistic scene understanding capabilities of the VGGT, (2) Confidence-Aware Optimization to extract dense features via DINOv3 and predict their confidence map, and (3) a Multi-Modal Constraint Closure that fuses point-line features and metric depth priors to constrain rotational Degrees of Freedom in Manhattan Worlds. We conducted systematic evaluations on TUM, Replica, Tanks and Temples, and a challenging self-collected dataset featuring extreme lighting and texture-less walls. Experimental results demonstrate that VGGT-Geo exhibits superior robustness and accuracy in unseen environments. On our most challenging dataset, it achieves an Absolute Trajectory Error of 4-5 cm and a Relative Rotation Error of 0.79°, outperforming current state-of-the-art methods by approximately 50% in trajectory accuracy. This study validates that synergizing the intuition of Large Foundation Models with geometric rigor is a viable path toward next-generation robust SLAM. Full article

Background: Navigational bronchoscopy (NB) enables precise sampling of peripheral and central pulmonary nodules using shape-sensing or electromagnetic guidance. A major challenge is anesthesia-induced atelectasis, which alters lung anatomy, reduces registration accuracy, and is known to lower diagnostic accuracy. To counteract this, ventilatory protocols such as the Ventilatory Strategy to Prevent Atelectasis (VESPA) and the Lung Navigation Ventilation Protocol (LNVP) have been recommended. Their adoption and clinical impact, however, remain uncertain. Methods: We conducted a retrospective review of 224 consecutive NB procedures performed under general anesthesia at a single academic medical center (January 2020–August 2024). Demographic, anesthetic, and ventilatory data were extracted from electronic records. Outcomes included navigational success (ability to reach the lesion) and diagnostic accuracy (concordance between bronchoscopic diagnosis and final clinical diagnosis after follow-up). Ventilatory practices were compared with published VESPA and LNVP recommendations. Results: Navigational success, defined as successful advancement of the bronchoscope to the target lesion with tissue acquisition, was achieved in 89.2% of cases. Overall diagnostic accuracy, defined as concordance between bronchoscopic diagnosis and final clinical diagnosis after follow-up, was 81.7%. Ventilatory management consistently diverged from recommended protocols. Most patients were ventilated with FiO₂ > 0.6, PEEP in the range of 7–10 cm H₂O, and tidal volumes of 300–500 mL. The only recommended maneuver systematically applied was recruitment immediately after intubation. Despite widespread deviation from both VESPA and LNVP, diagnostic performance remained favorable relative to published benchmarks. No major anesthesia-related complications occurred. Conclusions: In this retrospective series, navigational success comparable to published studies that adapted strict ventilation protocols was achieved with also comparable diagnostic accuracy without strict adherence to predefined ventilatory strategies. Recruitment maneuvers may represent the most influential component of current protocols, but institutional factors such as procedural expertise and case volume likely contributed to outcomes. Prospective studies are warranted to determine whether standardized ventilatory protocols are necessary for optimizing NB performance. Full article

Saline–alkali soils significantly hinder agricultural productivity in China’s coastal areas. Although both plant growth-promoting rhizobacteria (PGPR) and biochar have individually demonstrated the capacity to boost crop yield and soil fertility, their synergistic effects on seawater rice and soil ecosystems remain uncertain. In this study, we examined the individual and interactive influences of lychee biochar (2.5% and 5% w/w) and PGPR inoculation on soil physicochemical properties and bacterial community assembly along a soil–root continuum, encompassing bulk soil, rhizosphere soil, rhizoplane, and root endosphere, in a controlled pot experiment with seawater rice. The application of biochar significantly altered soil pH, electrical conductivity, and nutrient availability in both bulk and rhizosphere soils, resulting in pronounced changes in bacterial community composition. The effects generated by biochar were partially mitigated when PGPR was co-applied. The relative abundances of Bacillota and Bacteroidota grew progressively from bulk soil to the root endosphere across all treatments, indicating a significant compartment-dependent selection. Co-occurrence network analysis and FAPROTAX-based functional predictions revealed several taxa and functions that were progressively enriched toward the root, including the halotolerant genera Exiguobacterium and Chryseobacterium, highlighting a significant host-mediated filtration process that functioned independently of the inoculated strains. Multivariate analyses further demonstrated that soil pH was the primary driver of bacterial community structure in bulk and rhizosphere soils, whereas plant-root selection dominated in the rhizoplane and endosphere. Overall, our results demonstrate that, within a seawater-rice and soil ecosystem, the selective influence of the host plant on root-associated microbiomes exceeds that of either biochar amendment or PGPR inoculation. This work improves our understanding of biochar–PGPR–plant interactions in saline–alkali soils and provides insight into sustainable strategies for enhancing rice production under salinity stress. Full article

Seed priming studies commonly emphasize growth and physiological responses, yet ionomic regulation and tissue-specific nutrient allocation under salinity stress remain poorly explored, particularly in underutilized crops such as Bambara groundnut (Vigna subterranea L.). This study investigated whether Mg(NO₃)₂ seed priming, previously shown to enhance salt tolerance, is associated with consistent ionomic patterns in contrasting Bambara groundnut genotypes (BGN-14 and BGN-25). Seeds were primed with 0.03% Mg(NO₃)₂ and grown under control or saline conditions (200 mM NaCl) for five weeks. Shoot and root tissues were analyzed for macro- and micronutrient composition using ICP-OES. In BGN-14, salinity caused a marked reduction in shoot fresh weight (−49.5%, p < 0.05), whereas Mg(NO₃)₂ priming largely mitigated this effect under salinity (−0.4%, p > 0.05). Root fresh weight declined numerically under salt stress (−70.1%) and primed + salt conditions (−45.5%), but these changes were not statistically significant. Shoot dry weight increased significantly in primed plants (+83.5%, p < 0.05), while salinity reduced SDW (−58.4%); primed + salt plants maintained SDW near control levels (+2.6%). In BGN-25, root biomass was unaffected by treatments, whereas salinity significantly reduced shoot biomass relative to primed plants, with a consistent trend of primed > control > primed + salt > salt. Salinity increased the Na⁺/K⁺ ratio, particularly in roots. In BGN-14, the root Na⁺/K⁺ ratio increased significantly from 1.07 to 4.49 (p < 0.05), indicating enhanced Na⁺ accumulation, while shoot ratios increased non-significantly. BGN-25 showed a more moderate increase in shoot ratios and a pronounced rise in root ratios. Principal component analysis revealed distinct nutrient clustering, with Na, Fe, and Al loading strongly under salinity, while Ca, K, Mg, and Cu aligned with improved physiological performance. Although differences between salt and primed + salt treatments were often not statistically significant, several ion ratios and nutrient relationships were numerically enhanced under Mg(NO₃)₂ priming. This study builds upon earlier physiological findings (where BGN-14 consistently exhibited a stronger positive response to Mg(NO₃)₂ priming, outperforming BGN-25 under salt stress) and provides exploratory, hypothesis-generating evidence that Mg(NO₃)₂ priming may contribute to salinity tolerance through coordinated ionomic adjustments, including altered Na⁺ allocation and improved nutrient balance, rather than complete Na⁺ exclusion. These findings highlight the relevance of ionomic responses in understanding stress adaptation in underutilized legume crops. Full article

Treacher Collins syndrome (TCS) is a rare disorder within the group of mandibulofacial dysostoses, occurring in 1 in 50,000 live births. It is characterized by anomalies in the maxillary, mandibular, and stapes bones, among others. TCS is caused by pathogenic variants in the TCOF1, POLR1D, POLR1C, and POLR1B genes with autosomal dominant or recessive inheritance patterns. Genetic data from Latin American populations remain scarce. Eleven patients from three different families were recruited. Whole-exome sequencing (WES) was performed on the probands to identify genetic variants, followed by Sanger sequencing for variant validation and familial segregation analysis. Finally, three-dimensional protein structures of wild-type and mutant proteins were predicted. In Family 1, a heterozygous pathogenic splice-site variant in the TCOF1 gene, c.4345 + 1 G > A, was identified and inherited from her mother. In Family 2, a heterozygous pathogenic variant in the TCOF1 gene, c.226_227insC (p.R77fs), was identified and inherited from the paternal lineage. In Family 3, a heterozygous pathogenic POLR1D variant, c.290_291delAG (p.G99fs), was identified among multiple affected relatives; direct parent-of-origin could not be established due to unavailability of one parent, but segregation supports autosomal dominant transmission across three generations. All findings were validated by Sanger sequencing. Our findings highlight the utility of WES for the molecular diagnosis of TCS and underscore the importance of including underrepresented populations in genetic studies to improve diagnosis, genetic counseling, and perinatal planning in at-risk pregnancies. Full article

Stairwells constitute critical escape routes for emergency evacuation during building disasters. The spread of fire smoke and the failure of lighting systems can significantly reduce visibility within stairwells, thereby adversely affecting evacuation speed. This issue is particularly pronounced in super high-rise buildings. In this study, a typical super high-rise building was selected as the experimental site. The variation laws of key parameters such as evacuation time, speed, and heart rate were investigated for groups with different gender proportions in stairwells under different visibility conditions. The experimental results show that: First, collaboration within multi-person groups can effectively mitigate the adverse impact of reduced visibility on evacuation speed. Second, different gender proportions within groups affect evacuation speed, with groups having a higher proportion of males demonstrating relatively faster evacuation speed. Third, under identical visibility conditions, the heart rates of multi-person groups during evacuation are generally lower than those of individual groups; in low-visibility environments, the heart rates of members within the same group are significantly higher than those under normal visibility conditions. Accordingly, this study proposes a mixed-gender group evacuation strategy under low-visibility conditions. The findings provide empirical data support for the formulation of emergency evacuation response strategies in super high-rise buildings. Full article

Coral aggregate concrete (CAC) is a promising sustainable material for construction on remote islands, but it is often limited by relatively low strength and durability. Fiber reinforcement has therefore been introduced as an effective modification strategy. This review focuses on fiber-reinforced coral aggregate concrete (FRCAC), highlighting the roles of different synthetic and natural fibers in improving its performance. Firstly, the characteristics of coral aggregates and the effects of seawater mixing are summarized. Then, the influence of fiber incorporation on the mechanical behavior of CAC under static loading, including compressive, tensile, and flexural responses, is reviewed. In addition, the performance of FRCAC under dynamic and complex loading conditions, such as impact, cyclic, and triaxial loading, is discussed. Overall, fiber reinforcement significantly enhances the tensile strength, ductility, and energy dissipation capacity of CAC, particularly at high strain rates. The maximum reported improvements in splitting tensile strength and flexural strength can reach up to approximately 58% and 68%, respectively, depending on fiber type and dosage. However, the enhancements in compressive strength and elastic modulus are generally limited, with maximum reported increases of about 23% and 7%, respectively. Under multiaxial stress states, fibers mainly contribute to crack control and damage mitigation rather than substantial strength enhancement. Durability and environmental aspects are also addressed. Fiber addition may reduce chloride ingress in CAC, although long-term durability data remain limited. The use of coral aggregate must be balanced with the need to protect coral reefs. Finally, key knowledge gaps and future research directions are identified to support the sustainable application of FRCAC in marine infrastructure. Full article

Old trees function as enduring ecological legacies that preserve historical biodiversity within intensively human-modified landscapes, yet the relative influence of environmental versus anthropogenic drivers on their diversity remains unclear. Here, we aim to disentangle the joint effects of climate, urbanization intensity and cultural preservation on old-tree density and community composition. We analyzed a province-wide census of 21,733 old-tree individuals across 115 counties in Shanxi Province, China, encompassing species origin (native vs. nonnative) and growth form (trees vs. shrubs). Old-tree density was assessed using spatial simultaneous autoregressive error models, while compositional dissimilarity was quantified using generalized dissimilarity modeling. In total, 131 species were recorded, with four dominant species comprising more than 75% of all individuals. Old-tree density increased with mean annual temperature, human population density, and cultural heritage abundance, but declined sharply with cropland coverage. Driver importance varied among groups: native species were primarily governed by climatic conditions, nonnative species by land-use intensity, and tree-form old trees were positively associated with cultural heritage abundance, an effect absent in shrub-form old trees. Compositional dissimilarity was driven mainly by climatic gradients and spatial distance, with additional contributions from human-related variables, particularly for nonnative assemblages. Our findings demonstrate that climate and spatial processes establish the regional framework of old-tree community composition, while cultural and demographic contexts promote local retention of old trees. By explicitly integrating ecological filters with socio-cultural drivers, this study advances old-tree research through a large-scale empirical framework, providing both scientific insight and socially relevant guidance for conservation under land-use intensification and climate warming. Full article