Search Results (7,916)

Since Reynolds’ work, turbulence has been one of the most important subjects in fluid dynamics. Although its complete understanding seems still out of reach, there is at least one established physical basis that turbulence is a phenomenon of a random but non-trivially correlated assembly of vortices. The knowledge of vortices has thus become a prerequisite for promoting our understanding of the nature of turbulence. In this article, we first review the simple, compact vortex solutions to the Navier–Stokes equations for incompressible viscous fluids and a unified view of a certain type of vortices including Burgers, Sullivan and Bellamy-Knights solutions. The non-equivalence of the inviscid limit of the Navier–Stokes equations and the Euler equations is emphasized. Introducing the notion of observational non-uniqueness, which differs from the non-uniqueness in a certain class of differential equations, of solutions to the Navier–Stokes equations, the observation problem associated with the dense distribution of non-equivalent solutions is argued. The origin of the extreme sensitivity of the solutions to the boundary conditions is clarified. A few examples of vortex phenomena in the real world are also surveyed. We next review the works of constructing turbulence as a random assembly of simple, compact vortices. An attempt to combine the vortex model of turbulence with the Kármán–Howarth equation for the velocity correlation functions of anisotropic turbulence is presented. It is pointed out that the studies in this direction suggested that Kolmogorov’s 2/3 scaling law was generally compatible with anisotropy. A few quantities are proposed as candidates to measure anisotropy in turbulence experiments. Full article

Precise identification of weeds at the maize seedling stage is pivotal for implementing Site-Specific Weed Management and minimizing herbicide environmental pollution. However, the performance of existing lightweight detectors is severely bottlenecked by unstructured field environments, characterized by the “green-on-green” spectral similarity between crops and weeds, diminutive seedling targets, and complex mutual occlusion of leaves. To address these challenges, this study proposes SLD-YOLO11, a topology-reconstructed lightweight detection model tailored for complex field environments. First, to mitigate the feature loss of tiny targets, a Lossless Downsampling Topology based on Space-to-Depth Convolution (SPD-Conv) is constructed, transforming spatial information into depth channels to preserve fine-grained features. Second, a Decomposed Large Kernel Attention (D-LKA) mechanism is designed to mimic the wide receptive field of human vision. By modeling long-range spatial dependencies with decomposed large-kernel attention, it enhances discrimination under severe occlusion by leveraging global structural context. Third, the DySample operator is introduced to replace static interpolation, enabling content-aware feature flow reconstruction. Experimental results demonstrate that SLD-YOLO11 achieves an mAP@0.5 of 97.4% on a self-collected maize field dataset, significantly outperforming YOLOv8n, YOLOv10n, YOLOv11n, and mainstream lightweight variants. Notably, the model achieves Zero Inter-class Misclassification between maize and weeds, establishing high safety standards for weeding operations. To further bridge the gap between visual perception and precision operations, a Visual Weed-Crop Competition Index (VWCI) is innovatively proposed. By integrating detection bounding boxes with species-specific morphological correction coefficients, the VWCI quantifies field weed pressure with low cost and high throughput. Regression analysis reveals a high consistency (R² = 0.70) between the automated VWCI and manual ground-truth coverage. This study not only provides a robust detector but also offers a reliable decision-making basis for real-time variable-rate spraying by intelligent weeding robots. Full article

Non-invasive electrocardiogram (ECG) and respiratory signals are easy to acquire via low-cost sensors, making them promising alternatives for sleep staging. However, existing methods using these signals often yield insufficient accuracy. To address this challenge, we incrementally optimized the sleep staging model by designing a structured experimental workflow: we first preprocessed respiratory and ECG signals, then extracted fused features using an enhanced feature selection technique, which not only reduces redundant features, but also significantly improves the class discriminability of features. The resulting fused features serve as a reliable feature subset for the classifier. In the meantime, we proposed a hybrid optimization algorithm that integrates the snake optimization algorithm (SO) and ant colony optimization algorithm (ACO) for automated hyperparameter optimization of support vector machines (SVMs). Experiments were conducted using two PSG-derived public datasets, the Sleep Heart Health Study (SHHS) and MIT-BIH Polysomnography Database (MIT-BPD), to evaluate the classification performance of multimodal features compared with single-modal features. Results demonstrate that the bimodal staging using SHHS multimodal signals significantly outperformed single-modal ECG-based methods, and the overall accuracy of the SHHS dataset was improved by 12%. The SVM model optimized using the hybrid Snake–ACO algorithm achieved an average accuracy of 89.6% for wake versus sleep classification on the SHHS dataset, representing a 5.1% improvement over traditional grid search methods. Under the subject-independent partitioning experiment, the wake versus sleep classification task maintained good stability with only a 1.8% reduction in accuracy. This study provides novel insights for non-invasive sleep monitoring and clinical decision support. Full article

Old-growth forests provide a key biodiversity reservoir due to their high amount of deadwood and abundance of tree-related microhabitats (TreMs). This research investigates the abundance and diversity of deadwood and TreMs in old-growth Caledonian pine forests located in the Cairngorms National Park, Scotland. The study area is a Scots pine (Pinus sylvestris L.)-dominated forest. A field survey campaign was conducted in 15 sample plots to collect data on stand and deadwood characteristics, and TreMs by category. Within circular plots of 531 m², the diameter at breast height, height, and insertion height of the canopy of all the living trees were measured, and the three deadwood components (snags, fallen deadwood, and stumps) and TreMs were recorded. The results showed a total deadwood volume of 37.53 ± 32.39 m³ ha⁻¹, mostly in the form of snags (68.9% of total volume) and in the lowest degree of decay (first decay class equals 36.8%). The average number of deadwood elements is 217 ha⁻¹, distributed to 127 snags ha⁻¹, 64 fallen deadwood ha⁻¹, and 26 stumps ha⁻¹. The results showed an average of 89.1 TreMs ha⁻¹ on snags and 26.4 ha⁻¹ on living trees. The abundance and diversity of TreMs are significantly related to the volume of snags (R² = 0.712), the deadwood diversity (R² = 0.664), and the degree of decomposition (R² = 0.416). Full article

We study a hybrid stochastic SIS co-infection model for two primary strains and a co-infected class with Crowley–Martin incidence, Markovian regime switching, and Lévy jumps. The model is a four-dimensional regime-switching Lévy-driven SDE system with state-dependent diffusion and jump coefficients. Under natural integrability conditions on the jumps and a mild structural assumption on removal rates, we prove uniform high-order moment bounds for the total population, establish pathwise sublinear growth, and derive strong laws of large numbers for all Brownian and Lévy martingales, reducing the long-time analysis to deterministic time averages. Using logarithmic Lyapunov functionals for the infective classes, we introduce four noise-corrected effective growth parameters ${\lambda }_1,\dots ,{\lambda }_4$ and two interaction matrices $A,B$ that encode the combined impact of Crowley–Martin saturation, regime switching, and jump noise. In terms of explicit inequalities involving ${\lambda }_k$ and the entries of $A,B$, we obtain sharp almost-sure criteria for extinction of all infectives, persistence with competitive exclusion, and coexistence in mean of both primary strains, together with the induced long-term behaviour of the co-infected class. Numerical simulations with regime switching and compensated Poisson jumps illustrate and support these thresholds. This provides, to our knowledge, the first rigorous extinction-exclusion-coexistence theory for a multi-strain SIS co-infection model under the joint influence of Crowley–Martin incidence, Markov switching, and Lévy perturbations. Full article

Emergency and Disaster Assembly Areas (EDAA) are designated safe zones where basic needs can be met until temporary shelters are established following natural or man-made disasters like floods, fires, earthquakes, explosions, or chemical incidents. Promptly relocating disaster victims to these areas is crucial for minimizing loss of life and facilitating effective search and rescue operations by maintaining an uninterrupted flow of information. To prepare for disasters like earthquakes, which cause significant material and emotional damage to large populations, sustainable disaster management must be ensured to evaluate site suitability, correct deficiencies, and avoid inappropriate locations. This study will examine the evaluation criteria for EDAAs established by the Tunceli Provincial Disaster and Emergency Management Authority (AFAD) in terms of area, structure, security, and accessibility, taking into account the region’s specific characteristics. Based on a literature review, eleven criteria have been proposed and ranked using the Besson mean ranking method. Areas have been classified into four categories (e.g., adequate, not suitable) using the optimistic, pessimistic, and comprise approaches of the Assignment Rule Driven by Attitudes (ARDA) and the ORESTE-Sort method. The examination of 19 EDAA provides two perspectives: an optimistic view that recommends classifying eleven areas as first class and using all areas as they are, and a pessimistic view that calls for urgent improvements in three areas and states that one area (EDAA 1) is deemed unsuitable due to its assignment to class ${\mathrm{K}}_4$. It is also advised that the second area should not be used, despite being rated as class ${\mathrm{K}}_3$, due to its proximity to the river and its slope characteristics. The study also performs a sensitivity analysis of the method and provides recommendations for future research. Full article

Background: Osteoradionecrosis (ORN) following head and neck radiotherapy has been demonstrated to induce structural and functional alterations of the upper airway, with the potential to complicate the process of tracheal intubation. Despite its clinical relevance, there is a paucity of systematic evidence on airway characteristics in ORN and reliable predictors of difficult tracheal intubation. This study compares preoperative airway parameters and tracheal intubation outcomes in irradiated patients with and without ORN and introduces a novel preoperative ORN-Difficult-Airway Score for risk stratification. Methods: In this retrospective cohort study, airway assessments, tracheal intubation methods, and perioperative visualization parameters were evaluated in 105 patients following head and neck radiotherapy. Group differences between non-ORN and ORN were analyzed using chi-square tests. A preoperative ORN-Difficult-Airway Score was constructed using exclusively bedside parameters, based on statistically and clinically relevant predictors. Results: Patients with ORN showed significantly restricted mouth opening (p < 0.001), higher Mallampati classes, particularly Mallampati IV, and a greater need for fiberoptic tracheal intubation (p < 0.01). Direct laryngoscopy (DL) was significantly less feasible in ORN, while hyperangulated videolaryngoscopy (VL) yielded consistently positive visualization (first-pass success (FPS) 100% in both groups). Under DL, FPS was lower in ORN (54.2% vs. 79.5%), resulting in an odds ratio of 0.305. Based on observed predictors, ORN status, mouth opening <3 cm, Mallampati class, restricted neck reclination, and history of difficult intubation, a preoperative ORN-Difficult-Airway Score was developed. Conclusions: ORN has been associated with distinct alterations in airway anatomy and visualization, resulting in increased tracheal intubation complexity after head and neck radiotherapy. The proposed ORN-Difficult-Airway Score presents a clinically practical, bedside-applicable approach to stratifying the risk of tracheal intubation in this population. Prior to clinical implementation, prospective validation in larger cohorts is warranted. Full article

Constrained by observation conditions and high inter-class similarity, effective feature extraction and classification of synthetic aperture radar (SAR) targets in few-shot scenarios remains a persistent challenge. To address this issue, this article proposes a few-shot fine-grained SAR target classification method based on multi-scale embedding network and meta-contrastive learning (MSMC). Specifically, the MSMC integrates two complementary training pipelines; the first employs metric-based meta-learning to facilitate few-shot classification, while the second adopts an auxiliary training strategy to enhance feature diversity through contrastive learning. Furthermore, a shared multi-scale embedding network (MSEN) is designed to extract discriminative multi-scale features via adaptive candidate region generation and joint multi-scale embedding. The experimental results on the MSTAR dataset demonstrate that the proposed method achieves superior few-shot fine-grained classification performance compared to existing methods. Full article

Diamond has been extensively examined as an appealing material for use in quantum optics and quantum information processing owing to the existence of various classes of optically active defects, referred to as “color centers,” which can be engineered into its crystal structure. Among these defects, the negatively charged nitrogen-vacancy center (NV⁻) stands out as the most prominent type. Despite the progress made, the number of emitters characterized by reproducible fabrication processes within the desired spectral range at room temperature, with limited or no damage to the parent diamond lattice, remains restricted. Herein, we are proposing for the first time the creation of the FV⁻ center in diamond via low-energy implantation, which is particularly interesting as it possesses characteristic light absorption and magnetic properties similar to NV⁻ centers. The low-energy ion-implanted FV centers in diamond show more desirable optical emission properties at room temperature (RT). Additionally, as per DFT calculations, the flat bands near the Fermi energy indicate dominant electron–electron interactions, an important prerequisite for observing emergent behavior as seen in systems such as twisted bi-layer graphene. Consequently, as-developed new luminescent defects such as Fluorine Vacancy Centers (FV) with desirable spectral and quantum emission properties would be a significant breakthrough in diamond-based quantum materials. Full article

Imidazole is, from a structural point of view, a heterocycle consisting of three C atoms and two N atoms, belonging to the class of diazoles, having two N atoms at the first and third positions in the aromatic ring. Being a polar and ionizable aromatic compound, it has the role of improving the pharmacological properties of lead molecules, thus being used to optimize their solubility and bioavailability. Imidazole is a constituent of many important biological compounds, like histidine, histamine, and purine compounds, the most widespread heterocyclic compound in nature. In current practice, substituted imidazole derivatives play a major role in antifungal, antibacterial, anti-inflammatory, CNS active compounds, antiprotozoal, as well as anticancer therapy. Thus, imidazole derivatives have demonstrated significant anticancer activities by inhibiting the key metabolic pathways essential for tumor cell growth and survival. Nitroimidazoles, for instance, have been employed as hypoxia-directed therapeutic agents, targeting oxygen-deprived tumor tissues, while mercaptopurine derivatives are well-established in oncological treatments. Structural modifications of the imidazole nucleus have led to the novel compounds exhibiting increased selective cytotoxicity against cancer cells, while sparing normal healthy cells. In accordance with what has been stated, this review highlights recent research on the medicinal and pharmaceutical interest of novel imidazole derivatives, emphasizing their potential in the development of new drugs. Full article

A complex of Fusarium species frequently infects maize, causing root, ear, and stem rot, yield losses, reduced seed quality, and mycotoxin accumulation. To quantify Fusarium species composition and mycotoxin contamination, we conducted a first nationwide monitoring in Swiss commercial grain maize over three years (2008–2010), followed by grain maize hybrid experiments across five sites (2011–2013). Samples were analysed for species incidence, fungal DNA, and the mycotoxins deoxynivalenol, zearalenone, and fumonisins. For each field, crop management data were collected. Fusarium graminearum, F. verticillioides, F. subglutinans, and F. proliferatum were predominant, and deoxynivalenol was the most frequent toxin, with 55% of the samples exceeding the European pig feed guidance value (0.9 mg kg⁻¹). Overall, fumonisin contamination was low: only 11% of samples were above the limit of detection. The year, the length of the growing period, and the timing of the harvest were the principal determinants of F. graminearum infection and deoxynivalenol/zearalenone accumulation, whereas other agronomic factors, including crop rotation, soil management, and maturity class, showed only limited or inconsistent effects. Results from this study provide evidence that farmers should avoid long growing periods and late harvests to reduce the risk of high deoxynivalenol/zearalenone content. The maize hybrid experiments confirmed the overriding influence of weather conditions on Fusarium species incidence and mycotoxin content, leading to high inter-annual variability. These results highlight the need for standardised, long-term field experiments to disentangle agronomic effects and environmental drivers. Full article

Antimicrobial resistance (AMR) in hospital wastewater is a recognized public health concern, yet the dynamics of its emergence remain poorly understood. This study aimed to characterize the quantitative and qualitative changes in the microbial community of a newly built internal medicine intensive care hospital wing following the start of patient treatment. Wastewater samples were collected regularly from eight relevant sites, including seven patient-associated locations within the intensive care ward and the central sanitary sewer where all effluent converged. Culture-based analyses targeted the “ESCAPE-SO” bacterial and fungal groups (“Enterococci”, “Staphylococci”, “Candida”, “Acinetobacter”, “Pseudomonas”, “Enterobacteriaceae”, “Stenotrophomonas”, “Others”). Comparisons were made between a 12-month pre-operation period (only flushing every 72 h to prevent contamination of the drinking water system) and the first 12 months of patient treatment. The results showed a significant increase in mean bacterial concentrations from 53 [0–349] CFU/mL before patient treatment to 8423 [3054–79,490] CFU/mL during patient treatment (p = 0.0224) with a particular focus on Pseudomonas spp. as the dominant genus. Resistance against all four main antibiotic classes of the WHO AWaRe essential “watch” list (carbapenems, third-generation cephalosporins, broad-spectrum penicillin and ciprofloxacin) emerged within the first twelve months and depended on the amount of prescribed antibiotics and the number of patients treated. These findings indicate that hospital activity drives rapid development of antimicrobial resistance in wastewater microbial communities, highlighting the critical role of clinical antibiotic use in shaping environmental resistomes. This study provides quantitative evidence that resistance can emerge within months of hospital operation, emphasizing the need for early monitoring and targeted interventions to mitigate the spread of AMR from hospital effluents into broader environmental systems. Full article

Tomato leaf diseases cause substantial yield and quality losses worldwide, yet reliable detection in real fields remains challenging. Two practical bottlenecks dominate current research: (i) limited data, including small samples for rare diseases, class imbalance, and noisy field images, and (ii) multimodal heterogeneity, where RGB images, textual symptom descriptions, spectral cues, and optional molecular assays provide complementary but hard-to-align evidence. This review summarizes recent advances in tomato leaf disease detection under these constraints. We first formalize the problem settings of limited and multimodal data and analyze their impacts on model generalization. We then survey representative solutions for limited data (transfer learning, data augmentation, few-/zero-shot learning, self-supervised learning, and knowledge distillation) and multimodal fusion (feature-, decision-, and hybrid-level strategies, with attention-based alignment). Typical model–dataset pairs are compared, with emphasis on cross-domain robustness and deployment cost. Finally, we outline open challenges—including weak generalization in complex field environments, limited interpretability of multimodal models, and the absence of unified multimodal benchmarks—and discuss future opportunities toward lightweight, edge-ready, and scalable multimodal systems for precision agriculture. Full article

The critically endangered Asian giant softshell turtle (Pelochelys cantorii) is a national first-class protected aquatic animal in China, and artificial breeding is vital for its conservation. Given the pivotal role of substrate in captive rearing, this study aimed to investigate the effects of different substrate types on the growth, serum biochemistry, and behavior of juvenile P. cantorii. A total of 45 8-month-old juveniles [(121.11 ± 0.65) g] were randomly allocated to three groups (fine sand [FS], pea gravel [PG], and no substrate [NS]) for an 18-day rearing trial. Results indicated that the FS and PG groups exhibited significantly higher weight gain and specific growth rates than the NS group (p < 0.01). Serum malondialdehyde (MDA) levels were lower in the FS and PG groups than in the NS group (p < 0.05), with no significant difference between FS and PG. Notably, three individuals in the NS group exhibited symptoms of skin ulceration. No significant intergroup differences were observed in glucose (GLU), triglyceride (TG), catalase (CAT), superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), or cortisol (CORT) levels (p > 0.05). Behaviorally, the FS group demonstrated the highest hidden rest frequency and duration (p < 0.01) and significantly lower active avoidance behavior compared to PG and NS (p < 0.01). In conclusion, substrate type significantly influences captive juvenile P. cantorii, with fine sand being optimal as it enhances growth, alleviates oxidative stress, and reduces maladaptive behaviors. Full article

In electroencephalogram (EEG)-based emotion recognition tasks, existing end-to-end approaches predominantly rely on real-valued neural networks, which mainly operate in the time–amplitude domain. However, EEG signals are a type of wave, intrinsically including frequency, phase, and amplitude characteristics. Real-valued architectures may struggle to capture amplitude–phase coupling and spectral structures that are crucial for emotion decoding. To the best of our knowledge, this work is the first to introduce complex-valued neural networks for EEG-based emotion recognition, upon which we design a new end-to-end architecture named Complex-valued EEGNet (CV-EEGNet). Beginning with raw EEG signals, CV-EEGNet transforms them into complex-valued spectra via the Fast Fourier Transform, then sequentially applies complex-valued spectral, spatial, and depthwise-separable convolution modules to extract frequency structures, spatial topologies, and high-level semantic representations while preserving amplitude–phase relationships. Finally, a complex-valued, fully connected classifier generates complex logits, and the final emotion predictions are derived from their magnitudes. Experiments on the SEED (three-class) and SEED-IV (four-class) datasets validate the effectiveness of the proposed method, with t-SNE visualizations further confirming the discriminability of the learned representations. These results show the potential of complex-valued neural networks for raw-signal EEG emotion recognition. Full article