Search Results (100,002)

A very important method of reducing carbon emissions is to make sure industrial plants are operated at optimal energy efficiency. The oil and petrochemical industries spend large amounts of energy in the transportation of petroleum and its various products that have high viscosities. A critical component in these plants is abrupt pipe contraction. Large amounts of energy are lost in pipe contractions. In this paper we investigate the energy losses in pipe contraction using the local entropy generation method after solving the detailed flow field around an abrupt pipe contraction. We have applied the method at various Reynolds numbers covering laminar and turbulent flow regimes. Furthermore, we have used an integral entropy analysis and found excellent agreement between the differential and integral entropy methods when the computational grid is well refined. The differential analysis was able to predict the local entropy generation and find where the large losses are located and therefore be able to minimize these losses effectively. Based on the detailed entropy generation field, it is recommended to use rounded contraction in order to reduce the losses. By introducing rounded contractions in laminar flow, the losses have been reduced by 22%. In the case of the turbulent flow regime, the losses were reduced by 96% by introducing a rounding radius to diameter ratio r/D₂ of 10%. The turbulent flow results for the case of pipe entrance, which is a special case of abrupt contraction (D₂/D₁ goes to zero) agree very well with the present results. This work addresses a large range of D₂/D₁ for laminar and turbulent flows. It is recommended that companies involved in designing petrochemical plants and installations take these findings into consideration to reduce carbon emissions. These recommendations also extend to the design of equipment and piping systems for the food industry and micro-device flows. Full article

Amphibian populations have experienced a severe decline over the past 40 years, driven primarily by environmental pollution, habitat destruction, climate change, and disease. This work reports, for the first time, saprolegniosis in Pelophylax perezi egg masses and saprolegniosis in amphibians in Portugal. After isolation and phylogenetic analysis, the pathogen was identified as Saprolegnia australis. Following this, the present work intended to screen a collection of P. perezi skin bacteria for the existence of bacterial strains with inhibitory action against the newly identified S. australis SC1 and two other species, Saprolegnia diclina SAP 1010 UE and Saprolegnia australis SAP 1581 UE. The results showed that various bacterial species could inhibit the growth of these three species of oomycetes. Bacteria with the most significant antagonistic action against Saprolegnia spp. predominantly belonged to the genus Bacillus, followed by Serratia, Pseudomonas, and Aeromonas. Despite variations in bacterial diversity among frog populations, the present study also demonstrated the presence of bacteria on frogs’ skin that were capable of inhibiting Saprolegnia spp., as evidenced by in vitro challenge assays. These findings highlight the protective function of bacteria present in amphibian skin. The observed bacterial diversity may contribute to the metabolic redundancy of the frog skin microbiome, helping to maintain its functional capacity despite shifts in the community composition. Additionally, the study found that, when providing a more advantageous environment for pathogen growth—in this case a peptone–glucose (PG) medium instead of R2A—the percentage of bacteria with moderate-to-strong antagonistic activity dropped by 13% to 4%. In conclusion, the presence of bacteria capable of inhibiting Saprolegnia spp. in adult individuals and across different environmental conditions may contribute to lowering the susceptibility of frog adults towards Saprolegnia spp., compared with that in the early stages of development, like the tadpole or egg stages. Full article

Crop aeroponic cultivation still faces issues such as insufficient precision in water supply control and scientifically-based irrigation scheduling. To address this challenge, the present study aims to establish a precision irrigation protocol adapted to the characteristics of crop aeroponic cultivation. Using coriander (Coriandrum sativum L.) as the experimental subject, crop water requirements were estimated utilizing both the FAO56 P-M equation and its revised form. The RMSE between the water requirement measured values and the calculated values using the P-M formula is 2.12 mm, the MAE is 2.0 mm, and the MAPE is 14.29%. The RMSE between the water requirement measured values and the calculated values using the revised P-M formula is 0.88 mm, the MAE is 0.82 mm, and the MAPE is 5.78%. The results indicate that the water requirement values calculated using the revised P-M formula are closer to the measured values. For model development, this study used coriander evapotranspiration as a basis. Major environmental variables influencing water requirement were selected as input features, and the daily reference water requirement served as the output. Three modeling approaches were implemented: Random Forest (RF), Bagging, and M5P Model Tree algorithms. The results indicate that, in comparing various input combinations (C1: air temperature, relative humidity, atmospheric pressure, wind speed, radiation, photoperiod; C2: air temperature, relative humidity, wind speed, radiation; C3: air temperature, relative humidity, radiation), the RF model based on C1 input demonstrated superior performance with RMSE = 0.121 mm/d, MAE = 0.134 mm/d, MAPE = 2.123%, and R² = 0.971. It significantly outperforms the RF models with other input combinations, as well as the Bagging and M5P models across all input scenarios, in terms of convergence rate, determination coefficient, and comprehensive performance. Its predictions aligned more closely with observed data, showing enhanced accuracy and adaptability. This optimized prediction model demonstrates particular suitability for forecasting water requirements in aeroponic coriander production and provides theoretical support for efficient, intelligent water-saving management in crop aeroponic cultivation. Full article

Background: Timely and accurate identification of wound infections is essential for effective management, yet remains clinically challenging. This study evaluated the utility of a near-infrared autofluorescence imaging system (Fluobeam^®, Fluoptics, Grenoble, France) and a thermal imaging system (FLIR^®, Teledyne LLC, Thousand Oaks, CA, USA) for detecting bacterial and fungal infections in chronic wounds. Fluobeam^® enables real-time visualization of microbial autofluorescence without exogenous contrast agents, whereas FLIR^® detects localized thermal changes associated with infection-related inflammation. Methods: This retrospective clinical study included 33 patients with suspected wound infections. All patients underwent autofluorescence imaging using Fluobeam^® and concurrent thermal imaging with FLIR^®. Imaging findings were compared with microbiological culture results, clinical signs of infection, and semi-quantitative microbial burdens. Results: Fluobeam^® achieved a sensitivity of 78.3% and specificity of 80.0% in detecting culture-positive infections. Fluorescence signal intensity correlated strongly with microbial burden (r = 0.76, p < 0.01) and clinical indicators, such as exudate, swelling, and malodor. Pathogens with high metabolic fluorescence, including Pseudomonas aeruginosa and Candida spp., were consistently identified. Representative cases demonstrate the utility of fluorescence imaging in guiding targeted debridement and enhancing intraoperative decision-making. Conclusions: Near-infrared autofluorescence imaging with Fluobeam^® and thermal imaging with FLIR^® offer complementary, noninvasive diagnostic insights into microbial burden and host inflammatory response. The combined use of these modalities may improve infection detection, support clinical decision-making, and enhance wound care outcomes. Full article

Ultra-high-performance concrete (UHPC) is increasingly employed in long-span and heavily loaded structural applications; however, the accurate prediction of its flexural capacity remains a significant challenge because of the complex interactions among geometric parameters, reinforcement details, and advanced material properties. Existing design codes and single-architecture machine learning models often struggle to capture these nonlinear relationships, particularly when experimental datasets are limited in size and diversity. This study proposes a compact hybrid CNN–Transformer model that combines convolutional layers for local feature extraction with self-attention mechanisms for modeling long-range dependencies, enabling robust learning from a database of 120 UHPC beam tests drawn from 13 laboratories worldwide. The model’s predictive performance is benchmarked against conventional design codes, analytical and semi-empirical formulations, and alternative machine learning approaches including Convolutional Neural Networks (CNN), eXtreme Gradient Boosting (XGBoost), and K-Nearest Neighbors (KNN). Results show that the proposed architecture achieves the highest accuracy with an R² of 0.943, an RMSE of 41.310, and a 25% reduction in RMSE compared with the best-performing baseline, while maintaining strong generalization across varying fiber dosages, reinforcement ratios, and shear-span ratios. Model interpretation via SHapley Additive exPlanations (SHAP) analysis identifies key parameters influencing capacity, providing actionable design insights. The findings demonstrate the potential of hybrid deep-learning frameworks to improve structural performance prediction for UHPC beams and lay the groundwork for future integration into reliability-based design codes. Full article

Accurate prediction of shear strength at the interface between new and old concrete is vital for the structural performance of repaired and composite systems. However, the underlying shear transfer mechanism is highly nonlinear and influenced by multiple interdependent factors, which limit the applicability of conventional empirical models. To address this challenge, an interpretable machine-learning (ML) framework is proposed. The latest database of 247 push-off specimens was compiled from the recent literature, incorporating diverse interface types and design parameters. The hyperparameters of the adopted ML models were optimized via a grid search to ensure the predictive performance on the updated database. Among the evaluated algorithms, eXtreme Gradient Boosting (XGBoost) demonstrated the best predictive performance, with R² = 0.933, RMSE = 0.663, MAE = 0.486, and MAPE = 12.937% on the testing set, outperforming Support Vector Regression (SVR), Random Forest (RF), and adaptive boosting (AdaBoost). Compared with the best empirical model (AASHTO, R² = 0.939), XGBoost achieved significantly lower prediction errors (e.g., RMSE was reduced by 67.8%), enhanced robustness (COV = 0.176 vs. 0.384), and a more balanced mean ratio (1.054 vs. 1.514). The SHapley Additive exPlanations (SHAP) method was employed to interpret the model predictions, identifying the shear reinforcement ratio as the most influential factor, followed by interface type, interface width, and concrete strength. These results confirm the superior accuracy, generalizability, and explainability of XGBoost in modeling the shear behaviors of new–old concrete interfaces. Full article

The formation, development, and spatial distribution of CUHIs are influenced by urban spatial heterogeneity, yet the scale and seasonal dependencies of the effects of urban morphology modulation on CUHIs have not been fully explored, needing further study. Based on multi-source data for the Yangtze-Huaihe River Valley (YHRV), this study employs the XGBoost model to systematically investigate the effects of two-dimensional (2D)/three-dimensional (3D) urban morphological indicators on CUHIs and their inherent scale–seasonal dependencies. Results show significant provincial heterogeneity in YHRV’s CUHIs: Shanghai exhibits the highest CUHI intensity (CUHII) across all seasons, with a peak of 1.55 °C in winter, followed by Zhejiang and Jiangsu. Seasonally, winter CUHII averages 0.6–0.8 °C (the highest), followed by autumn, while spring and summer have lower values. The effect of the modulation of urban morphology on CUHIs exhibits distinct spatiotemporal dependence: in winter and autumn, CUHII is mainly dominated by the percentage of landscape (PLAND) and largest patch index (LPI) at the 4 km buffer scale (correlation coefficients r = 0.475 and 0.406 for winter); in spring and summer, the 2 km buffer scale shows a more balanced regulatory role of multiple urban morphological indicators. Notably, 2D indicators of urban morphology are consistently more influential in regulating CUHIs than 3D indicators. The Hefei station case effectively validates the model’s sensitivity to changes in urban morphology. This study provides a quantitative basis for season–scale collaborative regulation of urban thermal environments in the YHRV. Future research will integrate climatic factors into XGBoost via screening, reconstruction, and interaction quantification to enhance its predictability for transient heat island processes. Full article

This study experimentally investigates the wake-induced vibration (WIV) behavior of a bio-inspired harbor seal whisker model subjected to upstream propeller-generated unsteady flows. Vibration amplitudes, frequencies, and wake–whisker interactions were systematically evaluated under various flow conditions. The test matrix included propeller rotational speed N_p = 0~5000 r/min, propeller diameter D_p = 60~100 mm, incoming flow velocity U_∞ = 0~0.2 m/s, and separation distance between the whisker model and the propeller L/D = 10~30 (D = 16 mm, diameter of the whisker model). Results show that inline (IL) and crossflow (CF) vibration amplitudes increase significantly with propeller speed and decrease with increasing separation distance. Under combined inflow and wake excitation, non-monotonic trends emerge. Frequency analysis reveals transitions from periodic to subharmonic and broadband responses, depending on wake structure and coherence. A non-dimensional surface fit using L/D and the advance ratio (J = U_∞/(N_pD_p)) yielded predictive equations for RMS responses with good accuracy. Phase trajectory analysis further distinguishes stable oscillations from chaotic-like dynamics, highlighting changes in system stability. These findings offer new insight into WIV mechanisms and provide a foundation for biomimetic flow sensing and underwater tracking applications. Full article

Recently we studied a collocation–quadrature method in weighted ${\mathbf{L}}^2$ spaces as well as in the space of continuous functions for a Volterra-like integral equation of the form $u\left(x\right)-{\int }_{\alpha x}^{1}h(x-\alpha y)u\left(y\right)dy=f\left(x\right),0<x<1,$ where $h\left(x\right)$ (with a possible singularity at $x=0$) and $f\left(x\right)$ are given (in general complex-valued) functions, and $\alpha \in (0,1)$ is a fixed parameter. Here, we want to investigate the same method for the case when $\alpha =1.$ More precisely, we consider (in general weakly singular) Volterra integral equations of the form $u\left(x\right)-{\int }_0^{x}h(x,y){(x-y)}^{\kappa }u\left(y\right)dy=f\left(x\right),0<x<1,$ where $\kappa >-1$, and $h:D⟶\mathbb{C}$ is a continuous function, $D=\left\{(x,y)\in {\mathbb{R}}^2:0<y<x<1\right\}.$ The passage from $0<\alpha <1$ to $\alpha =1$ and the consideration of more general kernel functions $h(x,y)$ make the studies more involved. Moreover, we enhance the family of interpolation operators defining the approximating operators, and, finally, we ask if, in comparison to collocation–quadrature methods, the application of the Nyström method together with the theory of collectively compact operator sequences is possible. Full article

Lance nematodes, Hoplolaimus spp., feed on the roots of many kinds of plants, including agronomic crops. In this study, morphological and molecular analyses of several Hoplolaimus species and populations are provided. We were able to collect and characterize the topotype materials of H. galeatus from Arlington, Virginia; H. stephanus syn. n. from Nichols, South Carolina; and H. concaudajuvencus from Pensacola, Florida, and several additional populations and species from the United States, Israel, and India. Phylogenetic analyses of several hundred sequences of the D2–D3 expansion regions of 28S rRNA, ITS rRNA, and COI genes of Hoplolaimus species obtained from published and original datasets were given. Fifty-three new D2–D3 of 28S rRNA, 43 new ITS rRNA, and 47 new COI sequences from 23 isolates of Hoplolaimus spp. and one isolate of Peltamigratus christiei were obtained in this study. New molecular identities for H. concaudajuvencus and H. galeatus were proposed. Hoplolaimus stephanus syn. n. was considered a synonym of H. galeatus based on the morphological and molecular similarity of these two species. Analysis of morphology and molecular data did not reveal significant differences among H. columbus syn. n., H. indicus syn. n., and H. seinhorsti, and the first two species were synonymized with H. seinhorsti. A new species, H. floridensis sp. n., was described from many locations in Florida, USA. It was separated from other representatives of the genus Hoplolaimus by its morphological and molecular characteristics. Maps with geographical distribution of several lance nematode species in North America were reconstructed based on published and original molecular identification of samples. Full article

Background/Objectives: Diabetic neuropathy (DN) is one of the most common and disabling complications of diabetes mellitus (DM), affecting motor, sensory, and autonomic nerves. Genetic factors, particularly polymorphisms in the Angiotensin-converting enzyme (ACE) gene, have been proposed as contributors to DN susceptibility. This study aimed to synthesize the scientific evidence on ACE gene polymorphisms and their association with DN through a scoping review combined with scientometric analysis. Methods: A comprehensive search of PubMed, Scopus, and Web of Science was performed in February 2025, following JBI and PRISMA-ScR guidelines. Observational studies involving individuals with DN and the genotyping of ACE polymorphisms were included. Scientometric mapping was conducted using the Bibliometrix package in RStudio to identify publication trends and key thematic terms. Results: From 100 screened articles, 11 met the inclusion criteria. Most studies (72.7%) addressed diabetic peripheral neuropathy, while 27.3% investigated cardiac autonomic neuropathy. All studies analyzed the I/D polymorphism in intron 16 of the ACE gene. The D allele and DD genotype were associated with increased susceptibility to DN in over half of the studies (6/11), while the II genotype was reported as protective in 3/11. Findings varied by ethnicity and study design. The scientometric analysis identified ‘peripheral diabetic neuropathy’, type 2 diabetes’, and ‘ACE gene polymorphism’ as the most frequently co-occurring terms, indicating that research on this topic has been concentrated around these themes, while showing limited diversity in geographic origin and scope. Conclusions: ACE I/D polymorphism appears to modulate susceptibility to DN, though interethnic variability and methodological heterogeneity challenge definitive conclusions. Broader, standardized studies are needed to validate its utility as a predictive biomarker. Full article

Remote sensing objects often exhibit significant scale variations, high aspect ratios, and diverse orientations. The anisotropic spatial distribution of such objects’ features leads to the conflict between feature representation and boundary regression caused by the coupling of different attribute parameters: previous detection methods based on square-kernel convolution lack the overall perception of large-scale or slender objects due to the limited receptive field; if the receptive field is simply expanded, although more context information can be captured to help object perception, a large amount of background noise will be introduced, resulting in inaccurate feature extraction of remote sensing objects. Additionally, the extracted features face issues of feature conflict and discontinuous loss during parameter regression. Existing methods often neglect the holistic optimization of these aspects. To address these challenges, this paper proposes SODE-Net as a systematic solution. Specifically, we first design a multi-scale fusion and spatially orthogonal convolution (MSSO) module in the backbone network. Its multiple shapes of receptive fields can naturally capture the long-range dependence of the object without introducing too much background noise, thereby extracting more accurate target features. Secondly, we design a multi-level decoupled detection head, which decouples target classification, bounding-box position regression and bounding-box angle regression into three subtasks, effectively avoiding the coupling problem in parameter regression. At the same time, the phase-continuous encoding module is used in the angle regression branch, which converts the periodic angle value into a continuous cosine value, thus ensuring the stability of the loss value. Extensive experiments demonstrate that, compared to existing detection networks, our method achieves superior performance on four widely used remote sensing object datasets: DOTAv1.0, HRSC2016, UCAS-AOD, and DIOR-R. Full article

In the three-roll planetary rolling process, excessively high surface temperature of the rolls can easily lead to copper adhesion, deterioration of roll surface quality, shortened rolling lifespan, and severely affect the quality of copper tube products as well as production efficiency. To improve the cooling efficiency of the roll cooling system, this study developed a fluid–solid–heat coupled model and validated it experimentally to investigate the effects of nozzle diameter, spray angle, and axial position of the spray ring on the cooling performance of the roll surface. Given the low computational efficiency of finite element simulations, three machine learning models—Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Support Vector Machine (SVM)—were introduced and evaluated to identify the most suitable predictive model. Subsequently, the Particle Swarm Optimization (PSO) algorithm was employed to optimize the geometric parameters of the spray ring. The results show that the maximum deviation between the coupled model predictions and experimental data was 4.36%, meeting engineering accuracy requirements. Among the three machine learning models, the RF model demonstrated the best performance, achieving RMSE, MAE, and R² values of 1.7336, 1.3203, and 0.9082, respectively, on the test set. The combined RF-PSO optimization approach increased the heat transfer coefficient by 44.72%, providing a robust theoretical foundation for practical process parameter optimization and precision tube manufacturing. Full article

The present investigation was designed to elucidate the comparative collective diversity of bacteria in the rumen of buffalo and cattle. For this study, a total of 14,913 16S rRNA gene (rrn) sequences generated through Sanger sequencing of ruminal bacteria deposited in the GenBank database were analyzed, of which 13,432 sequences were from cattle and 1481 sequences were from buffalo. Bacterial sequences of cattle origin represented 18 existing phyla and 165 genera, and those of buffalo origin represented 12 phyla and 67 genera. According to Ribosomal Database Project (RDP) classifier, Firmicutes was the dominant phylum in cattle, representing 47.9% of all sequences. Bacteroidetes was the second most abundant phylum (32.3% of sequences), while Proteobacteria accounted for 8.6% of total sequences. In buffalo, Firmicutes was the dominant phylum with 47.2% of total sequences. Bacteroidetes and Proteobacteria phyla constituted 38.3% and 4.6% of total sequences, respectively. We identified 172 shared non-rare species-level operational taxonomic units (OTUs) between buffalo and cattle, with 17 unique to buffalo belonging to three phyla: Bacteroidetes, Firmicutes, and Fibrobacteres. In cattle, 774 OTUs of unique sequences were assigned to six phyla, namely, Firmicutes (422 OTUs), Bacteroidetes (234 OTUs), Fibrobacteres (99 OTUs), Actinobacteria (7 OTUs), Cyanobacteria (5 OTUs), and SR1 (7 OTUs). This study revealed significant differences in rumen bacterial diversity between buffaloes and cattle, supporting the development of species-specific strategies to enhance fibrous feed utilization. Full article

This study investigated the effects of dietary Rhodotorula mucilaginosa supplementation with different concentrations (0.0 g/kg, 0.1 g/kg, 1.0 g/kg, 10.0 g/kg) on red claw crayfish (Cherax quadricarinatus). Four groups were established: control group (CK, 0.0 g/kg), low-dose group (HL, 0.1 g/kg), medium-dose group (HM, 1.0 g/kg), and high-dose group (HH, 10.0 g/kg). The feeding trial lasted for 56 days. The results showed that, compared with the control group, all supplementation groups exhibited significantly reduced feed conversion ratios (p < 0.05). The HM and HH groups demonstrated significant increases in body length growth rate, specific growth rate, weight gain rate, hepatosomatic index, and survival rate (p < 0.05). All supplemented groups showed significantly enhanced trypsin and lipase activities in intestines and trypsin activity in the hepatopancreas (p < 0.05). The HM and HH groups exhibited elevated α-amylase activity in the hepatopancreas (p < 0.05). Compared with the control group, marine red yeast supplementation reduced colonization of potential pathogens while increasing probiotic abundance, effectively improving intestinal microbiota structure. The HM group significantly improved intestinal villus length, width, and muscular thickness (p < 0.05). All supplemented groups showed considerable upregulation of hepatopancreatic genes related to immunity (heat shock protein 70, down syndrome cell adhesion molecule, crustacean antibacterial peptide, serine proteinase inhibitors, crustacean hyperglycemic hormone, anti-lipopolysaccharide factor, lysozyme, and alkaline phosphatase) and antioxidant defense (superoxide dismutase, glutathione peroxidase, glutathione, and catalase) (p < 0.05). These findings indicate that R. mucilaginosa can significantly enhance digestive enzyme activity, maintain intestinal health, improve antioxidant and immune-related gene expression, and promote growth performance in red claw crayfish, with the HM group (1.0 g/kg R. mucilaginosa) showing optimal promotion effects. Full article