Search Results (1,517)

The avian respiratory system, characterized by its caudocranial unidirectional pulmonary airflow, has long fascinated scientists. To date, the mechanisms underlying the unidirectional airflow and the formation of aerodynamic valving, especially the expiratory valve, remain poorly understood. To provide new insights into pulmonary airflow characteristics and to deepen our understanding of the role of aerodynamic valving in generating unidirectional pulmonary airflow, the respiratory tract of a domestic chicken was reconstructed based on CT-scanned data. CFD simulations were performed under various conditions after validation of the numerical model. Flow parameters were analyzed qualitatively and quantitatively during inspiration and expiration phases, respectively. The results showed that the coupled interaction of the distinctive bronchial topology and air-sac pressure conditions preferentially routed inspired air toward the caudal-sac pathways during inspiration. During expiration, aerodynamic valving was primarily associated with a momentum barrier generated by the cranial-sac airstream, which suppressed retrograde caudal-sac airflow through the primary bronchus and redirected it toward the dorsobronchial outlets leading to the physiological paleopulmonic pathway. Moreover, the ‘segmentum accelerans’ structure facilitated the formation of aerodynamic valving, whereas its removal did not abolish major-bronchial flow rectification, although higher air-sac boundary pressures were required during expiration. Finally, fluid density substantially affected inspiratory aerodynamic valving, whereas its effect on the critical pressures associated with expiratory valving remained below 5%. Full article

Reliable object detection is essential for agricultural perception under illumination changes, occlusion, and complex backgrounds. This study investigates three complementary object-detection paradigms for multiclass pepper harvesting using 1004 RGB images annotated for human, pepper, pepper group, box, and pushcart. First, Faster R-CNN, SSD, and YOLOv7 were evaluated with alternative CNN backbones to examine the effect of feature extraction and training configuration. Second, the contemporary YOLO12n, YOLO26n, and RT-DETR-L detectors were evaluated under domain-specific closed-set training. Finally, YOLOE-26n was assessed under a zero-shot open-vocabulary setting without fine-tuning on the pepper-harvesting dataset. Among the backbone-adapted models, YOLOv7–GoogLeNet achieved 99.6% test mAP@0.5. Across five independent training seeds, YOLO12n achieved the highest mean performance among the contemporary detectors with $86.01\pm 0.53$% mAP@0.5 and $71.00\pm 0.62$% mAP@0.5:0.95, while YOLO26n provided the most favorable efficiency profile at 47.06 FPS with 2.38 million parameters. Despite receiving no pepper-domain training, YOLOE-26n achieved 41.89% mAP@0.5 and reached 92.43% AP@0.5 for human, demonstrating that pretrained visual–semantic representations can transfer effectively to selected categories in field-acquired agricultural scenes, although performance remained strongly class-dependent. Quantitative and qualitative analyses further highlighted differences in object recovery, localization, computational efficiency, and failure modes among the evaluated paradigms. Overall, the study provides a common experimental reference for understanding the trade-offs among backbone adaptation, contemporary closed-set detection, and zero-shot semantic recognition in pepper-harvesting perception. Because the three paradigms followed different training and benchmarking protocols, their results are interpreted within their respective experimental settings rather than as a strict cross-paradigm ranking. Full article

Conventional solid-weighted drilling fluids suffer from particle settling under high-temperature and high-pressure (HTHP) conditions and easily induce irreversible reservoir pore damage, limiting safe drilling in deep HTHP reservoirs. To address these drawbacks, two solids-free water-in-oil emulsion drill-in fluid systems based on GTL synthetic oil and saturated potassium formate brine were developed using composite ET/MT emulsifiers. The thermal stability, HTHP filtration, droplet size, interfacial rheology, clay inhibition, and permeability recovery performances were systematically evaluated. The 45:55 oil/water formulation exhibited excellent long-term thermal homogeneity, whereas the 20:80 system achieved higher density. The optimized emulsions presented low HTHP fluid loss, stable interfacial films, prominent clay inhibition, and over 90% core permeability recovery, demonstrating favorable reservoir protection capacity. Molecular dynamics simulations qualitatively clarified the interfacial adsorption behavior of composite emulsifiers and the formation of compact interfacial films. Notably, most tests were single determinations without replicates, blank core controls were absent, and bulk rheological data were unavailable, leading to qualitative mechanistic analysis. This work verifies the feasibility of the proposed solids-free fluid system, while further replicated tests and complete parameter validation are essential for its quantitative performance interpretation and field application. Full article

This paper examines the thermal properties of a Bardeen-type black hole (BH) embedded in a cloud-of-strings and a quintessence background within the Tsallis non-extensive entropy framework. Within the extended thermodynamic phase space, generalized expressions are derived for the Tsallis thermodynamic temperature, heat capacity, equation of state, Gibbs free energy, and the associated critical parameters. Particular attention is paid to how the cloud-of-strings parameter, the magnetic charge and the quintessence field influence the resulting thermodynamic behaviour. A divergence in the heat capacity marking the boundary of local thermodynamic stability is identified from the heat-capacity analysis. The equation of state is further examined using the standard inflection-point conditions. For the representative parameter set considered in this work, the resulting mathematical critical solution has negative critical temperature and pressure and therefore lies outside the physical thermodynamic domain. Hence, no physically admissible Van der Waals-type critical point is obtained for the chosen parameters. Furthermore, the thermodynamic geometry is investigated using the HPEM metric, whose Ricci scalar provides an independent geometric characterization of the phase-transition structure. The results demonstrate that the cloud-of-strings parameter, magnetic charge, and quintessence field significantly influence the thermodynamic stability and phase structure of the BH, whereas the Tsallis entropy modifies the quantitative thermodynamic behaviour while preserving the overall qualitative phase characteristics. Full article

Bird activity levels present an increasing trend in China. Bird strike risks for the aviation industry are positively correlated with bird activity. Migratory birds pose greater threats to airplanes due to their broader distribution and larger mass. A reliable broad-area surveillance solution for migratory birds is critical for bird strike warning and prevention. This paper introduces a broad-area migratory bird identification model based on weather radar systems. A customized feature space and a sample-separation optimization strategy are proposed for more accurate identification of bird targets in weather radar resolution volumes. A new bird density calculation model is proposed to accommodate nonuniform bird distributions and provide a more reasonable bird quantity interpretation. The relevance between bird density and weather parameters is modeled by specific data sampling and a numerical regression model. Weather radar data files from two stations in China are used to verify the effectiveness of the proposed method. Results indicate that the bird identification model could provide reasonable bird surveillance information, and the density prediction models present good consistency with existing datasets. Differences among radar stations and seasons prove the necessity of building individual bird density prediction models. Identification and prediction models are expected to be further optimized with more data support and cross-validation information. Quantitative and qualitative prediction products provide valuable references for bird strike prediction and prevention. Full article

Nonsteroidal anti-inflammatory drugs (NSAIDs), such as indomethacin (ID), may produce gastrointestinal adverse effects associated with cyclooxygenase-1 (COX-1) inhibition, motivating the investigation of microencapsulation strategies for modulating drug release. However, protein-based encapsulating materials remain comparatively underexplored using molecular simulation relative to synthetic polymers. In this study, the interaction tendencies of ID with cottonseed flour protein (CFP) and maltodextrin (MD) as potential encapsulating matrices were explored in silico using molecular dynamics simulations under NPT and NVT ensembles, cohesive energy density and Hildebrand solubility parameter (δ) calculations, the Flory–Huggins interaction parameter (χ), and mixing energy (E_mix). The protein cohesive properties exhibited substantial force-field dependence, while the absolute χ values obtained for the binary systems were considerably higher than those reported for comparable drug–excipient systems, limiting their quantitative interpretation as predictors of miscibility. Nevertheless, within the same computational framework, CFP–ID exhibited lower χ and E_mix values than MD–ID, indicating comparatively less unfavorable calculated interactions. As a complementary analysis independent of the formulation assessment, molecular docking of free ID against COX-1 and COX-2 showed a relative computational trend qualitatively consistent with published enzyme-inhibition data. Overall, the results support the use of the present workflow as an exploratory computational pre-screening approach and identify CFP as the candidate warranting further investigation among the two encapsulating materials evaluated, subject to experimental validation. Full article

Optical profilometry based on heterodyne interferometry was used to characterize the lateral surface topography of reflective surfaces with microstructures. A 633 nm laser beam is split into an unshifted object beam and a reference beam that is frequency-shifted by 80 MHz using a traveling-wave acousto-optic modulator. The object beam is focused onto the sample through a 100× microscope objective, recombined with the reference beam, and detected using a photodetector with a bandwidth of 150 MHz. At each scan position, a Universal Software Radio Peripheral (USRP) acquires the beat signal, and Fourier analysis isolates the heterodyne component. Electrical tests verified the recovery of a prescribed low-amplitude modulation, whereas translation of a plane mirror through focus identified a monotonic region for displacement-sensitive operation. Reflective silicon microstructures and a nominal 300 lines mm${}^{-1}$ grating were examined. The reconstructed maps reproduced the principal lateral features observed by scanning electron microscopy (SEM). For the grating-period comparison, the SEM-derived mean was 3.545 $\mathsf{\mu }$m, whereas the heterodyne reconstruction yielded a representative period of approximately 3.50 $\mathsf{\mu }$m, corresponding to an absolute difference of approximately 0.045 $\mathsf{\mu }$m (1.3%). The present experiments establish Fourier-domain signal recovery and lateral morphological characterization. Because a traceable axial calibration and an uncertainty budget were not available, the reconstructed vertical coordinate remains qualitative, and the results do not yet establish quantitative surface-roughness parameters. Full article

University libraries are critical campus venues characterized by high-density occupancy, making comprehensive fire evacuation safety assessment essential for protecting lives and cultural assets. Conventional evaluation methods often suffer from either subjective bias or a lack of engineering realism, while standalone cloud models lack robust weight allocation mechanisms. To address these deficiencies, this study proposes a fire evacuation safety evaluation method integrating combined weighting and the cloud model. An evaluation framework consisting of 21 indicators is established across four dimensions: human factors, evacuation facilities, emergency management, and venue environment. For weight determination, the Analytic Hierarchy Process (AHP) and the entropy method are synthesized to balance expert experience with data-driven objectivity. The cloud model is then employed to handle the inherent fuzziness and randomness in safety assessment, utilizing the forward and backward cloud generators to map qualitative linguistic descriptions to quantitative values based on three digital characteristics: expectation (Ex), entropy (En), and hyper-entropy (He). A case study of a university library in Wuhan is conducted. The results demonstrate that the library’s overall fire evacuation safety level is "Excellent," with a comprehensive cloud parameter set of (83.577, 0.620, 0.116). The assessment reveals that while evacuation facilities perform optimally, the complex architectural layout represents a structural vulnerability, and personnel safety literacy requires further stabilization. This integrated framework effectively overcomes the limitations of single-weighting strategies and provides a reliable, fine-grained quantitative tool for fire safety management and targeted risk mitigation in high-density public buildings. Full article

High-temperature alkali metal heat pipes are key passive heat-transfer components of heat pipe cooled reactors, space nuclear power sources, and advanced non-light-water reactors; their long-term reliability sets the safety envelope and service life of the system. This review is organized around a four-level hierarchy—stressors, degradation mechanisms, component failure modes, and system-level consequences—and surveys failure modes, the reliability databases, reliability analysis methods, and verification techniques. The material–working-fluid interface is shown to be a leading constraint on early degradation, with dissolution corrosion driven by oxygen impurities, carbon migration, and non-condensable-gas generation as the main pathways. Independently inventorying the available data reveals gaps in long-term irradiation aging data, in key-parameter measurement, and in the link between manufacturing processes and performance degradation. A two-axis classification of reliability analysis methods is then established, separating the analysis paradigm from whether a quantitative failure probability is produced and covering deterministic flow and thermal-resistance-network simulation, statistical assessment by FMECA/FTA/Weibull/Bayesian updating, and probabilistic fracture mechanics, with the irradiation-failure subfield identified as the weakest. Finally, four research priorities are ranked by the authors’ qualitative judgment—experimental data, modeling–operation matching, measurement and detection, and engineering application. Full article

Background/Objectives: Cholera remains a major public health problem, particularly in regions with limited access to safe drinking water and sanitation. Traditional compartmental models may underrepresent adaptive human behavioral responses during outbreaks. This study aims to investigate the epidemiological effects of adaptive risk perception within a deterministic SIQR-B framework for cholera transmission. Methods: We developed a deterministic SIQR-B model incorporating adaptive behavior through a nonlinear risk-perception function that modulates environmental transmission. The model includes susceptible, infected, quarantined, and recovered individuals together with the environmental concentration of Vibrio cholerae. Positivity, boundedness, equilibria, the basic reproduction number, and stability were analyzed, complemented by numerical simulations and robustness analysis across alternative transmission regimes. Results: Under the baseline parameter setting, adaptive behavior reduced the infected peak by $19.03\%$ and delayed its occurrence by approximately $1.13$ days. In contrast, cumulative incidence over the 200-day simulation horizon decreased by only approximately $0.033\%$. Additional simulations across alternative transmission regimes preserved the qualitative effect of adaptive behavior on peak mitigation and epidemic timing, although the magnitude of these effects varied with the transmission configuration and behavioral intensity. Conclusions: Within the proposed theoretical framework, adaptive behavioral feedback primarily mitigates short-term peak burden and alters epidemic timing rather than substantially reducing cumulative transmission. These findings highlight the importance of incorporating behavioral responses into mathematical models of cholera dynamics while recognizing that the quantitative effects depend on the assumed parameter regime. Full article

In this study, an analytical and dynamical study of the classical Kolmogorov–Petrovsky– Piskunov (KPP) equation is carried out by means of the improved generalized Riccati equation mapping (IGREM) method. An appropriate traveling-wave transformation is used to reduce the governing equation and several families of exact analytical solutions are systematically built, including hyperbolic, trigonometric, rational, and exponential solutions. The solutions are different nonlinear wave structures that are shown to be useful for the IGREM framework of nonlinear evolution equations. In addition to the exact solution construction, qualitative dynamics of the reduced system are explored using phase portraits, bifurcation analysis, periodic perturbations, Lyapunov exponents, chaotic dynamics, and multistability. The Lyapunov analysis gives a quantitative description of sensitivity to initial conditions, and the presence of two different attractors means that the system is multi-stable. The results show that the parameters of the system and the external periodic perturbations can lead to transitions between regular and complex dynamical regimes. The present study is essentially mathematical in nature, but the wave structures and the nonlinear dynamical features that have been obtained could be of theoretical interest in various contexts of nonlinear wave phenomena in the context of microwave propagation under appropriate modeling assumptions. The proposed framework therefore provides a unified and systematic approach for investigating both exact traveling-wave solutions and complex dynamical behaviors in nonlinear reaction–diffusion systems. Full article

Zinc (Zn) is an essential micronutrient for optimum plant growth; however, Zn deficiency is widespread in agricultural soils due to the low availability of plant-accessible or soluble forms of Zn. Therefore, harnessing microorganisms’ ability to solubilize Zn can be a significant, environmentally beneficial contribution to sustainable agriculture. This study isolated Zn-solubilizing bacteria (ZnSB) from rhizosphere soil and assessed their capacity to solubilize Zn minerals (ZnO, ZnCO₃). Based on qualitative and quantitative evaluation, the highest solubilization efficiency (345%) and 954 ± 46 mg Zn L⁻¹, respectively, were observed in the AEYTU11 strain and were associated with a strong pH decrease in the culture medium. Based on phenotypic characterization and 16S rRNA sequence analysis, strain AEYTU11 was identified as Serratia marcescens. The in vitro solubilization potential of microbes is significantly influenced by process parameters, emphasizing the importance of optimizing the culture conditions to maximize microbial solubilization efficiency. A Box–Behnken design (BBD) experiment with 17 combinations of varying levels of insoluble Zn compound (0.5–1.5 g/L), pH (5–9), and incubation time (3–5 days) was used to model their effects on Serratia marcescens AEYTU11 strain-mediated Zn release from ZnCO₃. Following process optimization, the solubilization efficiency of the AEYTU11 strain increased, achieving a maximum Zn solubilization capacity of 1608 ± 51 mg/L. The resulting model had high predictive ability (R² = 0.9099) and showed that ZnCO₃ concentrations were the most significant factor for the amounts of Zn released. In addition to Zn solubilization, AEYTU11 possess potassium (K) solubilization and extracellular enzyme production for protease and cellulose activity. The optimization findings highlight the value of statistical optimization tools for maximizing microbial Zn-solubilizing activity and improving biofertilizer development. Full article

Background: Composite materials are not easy to model using traditional experiments and physics-based approaches due to their diverse properties and multiscale nature. This review summarizes how artificial intelligence (AI) methodologies, including machine learning, deep learning, and generative AI, have been increasingly transforming and how they have been increasingly applied to composite materials research. It combines their possible use across the prediction of their properties, damage detection, structural health monitoring, and materials design. Methods: Searches were performed in Scopus and Google Scholar for articles published using combined keywords such as “(artificial intelligence OR machine learning OR deep learning OR Generative AI) AND (composite OR fiber reinforced) AND (prediction OR optimization OR characterization)”. The search strategy was designed to capture both fundamental developments in AI methodologies relevant to materials research and their specific applications to composite materials. The identified publications underwent a two-step selection process: (i) an initial review of titles and abstracts, and (ii) a detailed full-text evaluation. Studies were included if they provided explicit descriptions of AI models, clearly specified datasets, and applied quantitative performance assessments to composite materials-related applications such as material property prediction, damage detection, characterization, or manufacturing process optimization. For each selected article, relevant information was extracted in a structured and consistent manner, including the type of composite material, AI methodology, input parameters, dataset characteristics and size, validation methods, and application domain. These data were used to support both quantitative trend analysis and the qualitative identification of research gaps and future research opportunities in composite materials research. Results: Supervised learning methods, in particular artificial neural networks, support vector machines, random forests, and decision trees, were commonly used and exhibited durable potential accuracy in predicting properties of composite materials. Unsupervised methods such as principal component analysis (PCA) and clustering were comparatively underused. Generative AI, including generative adversarial networks (GANs) and variational autoencoders (VAEs), and physics-informed models emerged as growing approaches for synthetic data generation and improved interpretability, respectively. Limitations: The reviewed studies were frequently limited by small or heterogeneous datasets, weak generalizability testing beyond training conditions, high computational requirements and limited model interpretability (“black box” behavior); a risk-of-bias assessment across included studies was not formally conducted. Conclusions: AI is shifting composite materials research from trial-and-error toward data-driven, predictive design. Realizing its full potential will require open, well-annotated datasets, physics-aware and explainable models, and closed-loop, active-learning workflows linking prediction to experimental validation. Full article

Background/Objectives: Vestibular dysfunction is an important feature of cerebellar ataxia, neuropathy, and vestibular areflexia syndrome (CANVAS) associated with biallelic RFC1 expansions. This study aimed to characterize both quantitative and qualitative vHIT findings in genetically confirmed CANVAS patients. Methods: Fifteen patients with genetically confirmed CANVAS underwent a clinical neuro-otological assessment including horizontal video Head Impulse Test (vHIT). Quantitative analysis included vestibulo-ocular reflex (VOR) gain measurements, while qualitative analysis focused on corrective saccade characteristics, including number, peak velocity, latency interval, temporal organization, and predominant compensatory pattern. Correlations between vestibular parameters, disease duration, and clinical severity assessed using the Scale for the Assessment and Rating of Ataxia (SARA) were explored. The study specifically focused on the quantitative and qualitative characteristics. Results: All patients exhibited bilateral vestibular dysfunction, with markedly reduced VOR gains and generally low interaural asymmetry, confirming severe and largely symmetric vestibular hypofunction. Disease duration negatively correlated with VOR gain and positively correlated with the number of corrective saccades. Lower VOR gains were significantly associated with larger corrective saccades and wider latency intervals, while saccadic amplitude and latency were positively correlated. Qualitative analysis revealed a consistent pattern of predominantly overt, temporally dispersed (anisochronous) corrective saccades, with no evidence of predominant covert responses or isochronous clustering. The relationship between vestibular impairment and clinical severity was variable, suggesting partially independent progression of vestibular and cerebellar dysfunction. Conclusions: In this cohort of genetically confirmed CANVAS patients, severe, bilateral, and largely symmetric vestibular hypofunction was observed, together with a predominantly overt and temporally dispersed corrective saccade pattern. Qualitative analysis of horizontal vHIT responses may provide complementary information to conventional VOR gain measurements and may contribute to a more detailed characterization of vestibular and oculomotor findings in RFC1-related disease. Full article

Material Extrusion of metals (MEX/M) followed by debinding and sintering is a cost-effective multi-step route for producing complex metallic components. However, the inherent staircase effect and sintering-induced distortions often necessitate aggressive post-treatment processes to meet industrial requirements. The aim of this study is to investigate the effect of multi-stage surface mechanical treatment on a comprehensive benchmark sample containing diverse geometric primitives such as overhang prismatic features, cylinders, holes, pyramids, etc. A 3D structured light scanner was adopted to evaluate the evolution of dimensional accuracy and geometrical characteristics. The results indicate that sequential treatment effectively mitigates the layer-by-layer deposition. However, this effect carried an improvement in circular features such as cylinders or hollow rings, while for prismatic features such as prisms or pyramids it caused a measurable edge roll-off effect, highlighting a critical trade-off between smoothness and sharp-feature retention. Dimensional analysis reveals strong orientation-dependent shrinkage behavior during sintering, which is further modified by the material removal rates of the surface mechanical treatment stages. This work provides a qualitative and quantitative analysis of the final geometrical limits of the MEX/M technology cycle and offers insights into optimizing surface post-treatment parameters for high-performance applications. Full article