Search Results (2,260)

Achieving sustainable agricultural intensification in inland valleys while limiting the adverse environmental impacts and uncertainties related to water availability requires an analysis of the long-term hydrological behavior of the catchment. Such a task is particularly challenging in West Africa and Benin due to the limited availability of climate and hydrological data. This study evaluates the applicability of the lumped GR4J model for simulating streamflow in three inland valleys of the Sudanian zone of Benin (Lower-Sowé, Bahounkpo and Nalohou). Additionally, we test the reliability of satellite-based rainfall data (GPM-IMERG, CHIRPS or GSMAP) in modeling hydrological dynamics in these small catchments. The results demonstrate that the GR4J model is effective in simulating daily discharge in the three inland valleys (KGE > 0.5 during both calibration and validation periods), with particularly interesting performance in mean-flow conditions. The modeling using GPM-IMERG and GSMAP rainfall data shows mitigated results with acceptable performance at Nalohou and less accurate results at Bahounkpo and Lower-Sowé. CHIRPS emerged as the most consistent among the evaluated products, providing a sound basis for reconstructing general trends and seasonal variations in historical streamflow time series. The approach of combining historical CHIRPS data and the GR4J model provides insights and can support decision-making related to water resource management in terms of resource capacity and volume in the study area. Except for Nalohou (KGE = 0.19 with GPM-IMERG data), we observe limitations in predicting high flows with satellite-based climatic data at Bahounkpo (KGE = 0.02 with GPM-IR) and Lower-Sowé (KGE = −0.01 with CHIRPS), where the near-zero KGE scores indicate marginal improvement over a mean-flow benchmark. Future work should explore how hybrid or flexible modeling approaches can improve the accuracy of runoff simulations in inland valleys, particularly for extreme (low- and high-) flow conditions. Additionally, the analysis of the trends of indicators of hydrological alteration (IHA) must be deepened in these important ecosystems, especially under climate and land-use change scenarios. Full article

Vertical shafts are key channels for underground energy storage, mineral exploitation, and related engineering fields. Yet in deeply buried complex strata and high ground stress environments, traditional passive supports are prone to lining failure, while linear yield criteria cannot accurately characterize rock masses’ nonlinear mechanical behavior, limiting their use in shaft analysis. The core mechanical process of shaft construction aligns with the cavity contraction–expansion mechanism: excavation induces cavity unloading and contraction, causing shaft deformation and plastic zone expansion in surrounding rock; support enables cavity reverse expansion via preset shaft wall counter loads to actively control surrounding rock deformation. Based on this, this study integrates the Hoek–Brown nonlinear yield criterion, large-strain theory, and non-associated flow rules; couples cavity contraction–expansion semi-analytical solutions with the composite shaft wall mechanical model; and establishes a composite shaft wall–surrounding rock interaction analysis method. This research clarifies excavation-induced surrounding rock mechanical responses, reveals shaft wall counter loads’ regulatory effect on surrounding rock, and develops a systematic excavation support calculation workflow. Parameter analysis shows that increasing lining thickness is the most direct way to reduce inner wall tensile stress and improve safety; composite linings optimize stress distribution and enhance structural collaborative performance; and safety assessment confirms the lining inner wall as a structural weak zone. The proposed method and findings fill the gap in applying cavity contraction–expansion theory to shaft construction, providing reliable theoretical and practical guidance for deep shaft design, construction, and safety evaluation. Full article

Vibration-assisted technology has been employed to satisfy various requirements for different polymeric products due to its excellent performance, but because of the large inertia of the vibration excitation system, these attempts are strictly limited to several fixed vibration amplitudes and frequencies in small extruders or injectors. The purpose of this study is to carry out a numerical investigation via smoothed particle hydrodynamics (SPH) and to perform a comparative analysis of physical parameters among different cases from various perspectives on the fluid channel in twin-screw extruders (TSEs). The results demonstrate that certain combinations of larger vibration amplitudes and frequencies can significantly enhance the velocity, pressure, and particle distribution characteristics within the flow channel. However, no monotonic (i.e., strictly increasing or decreasing) trends are observed with respect to either amplitude or frequency alone. These findings are in excellent agreement with previously reported experimental studies and confirm that the meshless smoothed particle hydrodynamics (SPH) method is a robust and effective computational tool for investigating how various vibrational parameters influence flow behavior in twin-screw extruders (TSEs). Moreover, the results underscore that optimal amplitude and frequency selections must be tailored to the specific rheological and thermal properties of the polymer being processed. This work establishes a solid theoretical and numerical foundation for integrating superimposed vibration-assisted technology into the design optimization of TSE systems. Full article

Mean corpuscular volume ($MCV$) is a routinely measured hematological parameter that influences blood viscosity by altering red blood cell volume and packing density. Although $MCV$ is physiologically linked to hemorheological behavior, to the authors’ knowledge, its direct role in modulating large-artery hemodynamics has not been systematically quantified. This study introduces an $MCV$-driven effective Newtonian viscosity mode to evaluate the first-order impact of $MCV$ variation on carotid bifurcation flow. Rather than employing shear-dependent constitutive laws, blood viscosity was scaled through an $MCV$-based formulation, yielding three Newtonian fluids corresponding to clinically relevant $MCV$ levels of 70, 90, and 110 fL. Pulsatile CFD simulations were performed in four idealized carotid bifurcation geometries (40°, 50°, 65°, and 100°) to assess the combined influence of vascular geometry and $MCV$-dependent viscosity variation. Hemodynamic indices including time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), and relative residence time (RRT) were quantified, and a two-way analysis of variance (ANOVA) was employed to distinguish the relative contributions of geometric configuration and $MCV$. Across the investigated $MCV$ range, increasing $MCV$ produced a geometry-dependent modulation of shear-based indices, with TAWSS increasing by up to approximately 11%, while OSI and RRT decreased by about 20–25% and 10%, respectively, particularly in geometries exhibiting pronounced flow separation. Although vascular geometry remained the dominant determinant of overall hemodynamic patterns, $MCV$-induced viscosity scaling significantly modulated low-shear and recirculation regions. These findings suggest that $MCV$-dependent viscosity scaling can complement patient-specific hemodynamic assessments and provide a rational baseline for future shear-dependent and personalized rheological modeling frameworks. Full article

Background: Accurate staging of bladder cancer is critical for guiding clinical management, particularly the distinction between non–muscle-invasive (T1) and muscle-invasive (T2–T4) disease. Although MRI offers superior soft-tissue contrast, image interpretation remains opera-tor-dependent and subject to inter-observer variability. This study proposes an automated deep learning framework for MRI-based bladder cancer staging to support standardized radio-logical interpretation. Methods: A sequential AI-based pipeline was developed, integrating hybrid tumor segmentation using YOLOv11 for lesion detection and DeepLabV3 for boundary refinement, followed by three deep learning classifiers (VGG19, ResNet50, and Vision Transformer) for MRI-based stage prediction. A total of 416 T2-weighted MRI images with radiology-derived stage labels (T1–T4) were included, with data augmentation applied during training. Model performance was evaluated using accuracy, precision, recall, F1-score, and multi-class AUC. Performance un-certainty was characterized using patient-level bootstrap confidence intervals under a fixed training and evaluation pipeline. Results: All evaluated models demonstrated high and broadly comparable discriminative performance for MRI-based bladder cancer staging within the present dataset, with high point estimates of accuracy and AUC, particularly for differentiating non–muscle-invasive from muscle-invasive disease. Calibration analysis characterized the probabilistic behavior of predicted stage probabilities under the current experimental setting. Conclusions: The proposed framework demonstrates the feasibility of automated MRI-based bladder cancer staging derived from radiological reference labels and supports the potential of deep learning for stand-ardizing and reproducing MRI-based staging procedures. Rather than serving as an independent clinical decision-support system, the framework is intended as a methodological and work-flow-oriented tool for automated staging consistency. Further validation using multi-center datasets, patient-level data splitting prior to augmentation, pathology-confirmed reference stand-ards, and explainable AI techniques is required to establish generalizability and clinical relevance. Full article

A comprehensive numerical analysis has been conducted to investigate unsteady natural convection (UNC), bifurcation behavior, and heat transfer (HT) in a rectangular enclosure containing thermally stratified air. The enclosure comprises a uniformly heated bottom wall, thermally stratified vertical sidewalls, and a cooled top wall. To assess thermal performance, square and rectangular cavities with identical boundary conditions and working fluid are considered. The finite volume method (FVM) is used to solve the governing equations over a wide range of Rayleigh numbers (Ra = 10¹ to 10⁹) for air with a Prandtl number (Pr) of 0.71. Flow dynamics and thermal performance are analyzed using temperature time series (TTS), limit point–limit cycle behavior, average Nusselt number (Nu_avg), average entropy generation (S_avg), average Bejan number (Be_avg), and the ecological coefficient of performance (ECOP). In the rectangular cavity, the transition from steady to chaotic flow exhibits three bifurcations: a pitchfork bifurcation at Ra = 3 × 10⁴–4 × 10⁴, a Hopf bifurcation at Ra = 3 × 10⁶–4 × 10⁶, and the onset of chaotic flow at Ra = 9 × 10⁷–2 × 10⁸. The comparative analysis indicates that Nu_avg remains nearly identical for both cavities within Ra = 10⁵ to 10⁷. However, at Ra = 10⁸, the HT rate in the rectangular cavity is 29.84% higher than that of the square cavity, while S_avg and Be_avg differ by 39.32% and 37.50%, respectively. Despite higher HT and S_avg in the rectangular enclosure, the square cavity demonstrates superior overall thermal performance by 13.52% at Ra = 10⁸. These results offer significant insights for optimizing cavity geometries in thermal system design based on energy efficiency and entropy considerations. Full article

This investigation evaluates the viscosity and flow performance of cationic polyacrylamide (CPAA) solutions by assessing the effect of CPAA concentrations, shear rate, temperature, and electrolyte salt types. The study aims to characterize the flow behavior of CPAA solutions for different industrial utilizations under some challenging conditions of high salinity of two different electrolytes and high-temperature environments. In addition, the study addresses the critical shear rate thresholds at which the transition from shear-thinning to shear-thickening occurs. An Anton Paar rotational rheometer was employed to evaluate the flow behavior of cationic polyacrylamide solutions over the range of 20–80 °C at 20 °C intervals. Polymer samples were prepared from CPAA powder in a concentration range of 500–5000 ppm. To determine the electrolyte effects, NaCl and CaCl₂ were incorporated into the polymer solutions with a concentration range of 0–10 Wt.%. This study revealed that shear stress is vastly sensitive to CPAA concentration at shear rates less than 200 s⁻¹, whereas this sensitivity reduces at higher shear rates where the resulting profiles converge. Moreover, a considerable decrease in shear stress was reported with temperature as a result of the thermal influence on the molecular interaction forces. Rheological analysis of the CPAA solutions shows they exhibit strong non-Newtonian shear-thinning behaviors with viscosity decreasing significantly as the shear rate approaches 200 s⁻¹. On the contrary, a transition to a shear-thickening profile is observed at a shear rate above this limit of 200 s⁻¹. The results show that the dynamic viscosity of the CPAA solutions rises significantly as the concentration increases from 500 to 5000 ppm. At a shear rate of 10 s⁻¹, the dynamic viscosity increased from 2.4 to 33.8 mPa·s as the CPAA concentration increased from 500 to 5000 ppm (exactly 2.4, 11.8, 16.6, and 33.8 mPa.s for 500, 1500, 2500, and 5000 ppm, respectively). Additionally, increasing the temperature from 20 to 80 °C exerts a strong negative influence on dynamic viscosity. Specifically, for the 5000 ppm concentration at a shear rate of 10 s⁻¹, the dynamic viscosity decreased from 33.8 to 18.3 mPa.s as the temperatures rose from 20 to 80 °C (recorded as 33.8, 27.9, and 18.3 mPa.s at 20, 40, and 80 °C, respectively). Furthermore, the introduction of different electrolytes, such as NaCl and CaCl₂, significantly reduces the viscosity flow profiles. Full article

This study investigates the sustainable reuse of industrial enamel frit production waste generated during enamel application processes and evaluates its potential from a process-oriented glass-forming and -shaping perspective. Enamel frit waste collected from an industrial production line in Türkiye was subjected to comprehensive characterization, including XRD, XRF, TG/DTA, dilatometry, and CIE Lab* color analysis, with the primary aim of assessing forming compatibility rather than final product performance. Following calcination and controlled fritting, the waste material was processed through mold-based glass-forming experiments using firing schedules derived from thermal analysis. The results reveal pronounced chemical and thermal heterogeneity among enamel frit production wastes, leading to variable melting behavior across samples. Nevertheless, selected waste compositions exhibited sufficient viscous flow for shaping at reduced firing temperatures of approximately 850 °C. This study demonstrates that selected enamel frit production wastes—obtained from industrial enameling processes in slurry, powder, or granular form—can be reshaped into glass forms under controlled low-temperature conditions. The novelty of this study lies in investigating industrial enamel production frit waste as a reusable material within a circular economy framework, specifically focusing on its application in mold-based glass forming for artistic and educational contexts, thereby fostering collaboration between industrial waste management and glass art practice. Full article

Reducing fuel costs, maximizing waste utilization, and improving energy efficiency are critical challenges in agricultural thermal processes. This study addresses these issues by developing and evaluating a mixed-fuel burner and furnace system for steaming mushroom substrate cubes using crude glycerol and recycled vegetable oil as low-cost alternative energy sources. The experimental investigation assessed boiler thermal efficiency, combustion efficiency, exhaust-gas composition, temperature distribution, steam generation, and combustion-gas dispersion within the furnace. In parallel, analytical modeling of pressure, temperature, and gas-flow behavior was performed to validate the experimental observations. Five fuel compositions were examined, including 100% used vegetable oil, 100% crude glycerol, and blended ratios of 50/50, 25/75, and 10/90 (glycerol/vegetable oil), with all tests conducted in accordance with DIN EN 203-1 standards. The results demonstrate that blending used vegetable oil with glycerol significantly improves flame stability, increases peak combustion temperatures, and suppresses incomplete-combustion byproducts compared with pure glycerol operation. Combustion efficiencies of 90–99% and boiler thermal efficiencies of 72–73% were achieved. Among the tested fuels, the optimal balance between combustion stability, efficiency, and cost was achieved with a 25% glycerol and 75% used vegetable oil mixture. Economic analysis revealed that the proposed mixed-fuel system offers superior viability compared with LPG, reducing annual fuel costs by approximately 50%, shortening steaming time by 2 h per batch, and achieving a payback period of only 3.26 months. These findings confirm the feasibility of the proposed waste-to-energy system for small- and medium-scale agricultural applications. To further enhance sustainability and renewable fuel utilization, future work should focus on improving air–fuel mixing for higher glycerol fractions, scaling the system for larger farms, and extending its application to other agricultural thermal processes. Full article

This study presents a multi-index performance system that is systematically used to assess the binder synergy and fly ash reactivity of eco-sustainable cementitious composite (ESCC) using the Strength Activity Index (SAI) as a reference in line with ASTM C618. The partial replacements of fly ash with high and low calcium fly ash (HCFA and LCFA) were added to the fly-ash-to-sand (FA/S) ratios of 0, 10, 20, and 30% with a constant mix parameter, such as a 50% ratio of water to slag and a 20% ratio of activator to slag. The Initial Flow Index (IFI) and Flow Retention Index (FRI) were used to measure fresh-state performance, and compressive-, tensile-, and flexural-based indices, i.e., the SAI, Tensile Strength Index (TSI), and Flexural Strength Index (FSI), were used to measure mechanical performance. The results indicate that flowability and workability retention decrease with an increase in the FA/S ratio, with LCFA-based mixtures having better flow retention than HCFA systems. The optimum mechanical performance at a replacement level of 20% FA/S produced the maximum SAI values of about 112% HCFA and 110% LCFA with a consistent increase in TSI and FSI values at 28 days. When the replacement levels were increased (30% FA/S), all strength indices decreased with the effect of dilution and decreased the packing efficiency of the binder. Comparisons of the SAI with the respective TSI and FSI values through correlation analysis showed that the quantitative relationship between compressive, tensile, and flexural behavior was definite and showed that compressive strength alone is not enough to extrapolate mechanical performance. Collectively, the proposed framework provides a reasonable performance-based basis for the manner in which fly ash could be utilized in the most effective way in eco-sustainable cementitious compositions. Full article

Ground-source heat pump (GSHP) systems are widely regarded as an energy-efficient solution for building heating and cooling. However, their actual performance in large commercial buildings is often limited by rigid control strategies, insufficient equipment coordination, and suboptimal load matching. In the Liuzhou Fengqing Port commercial complex, the seasonal coefficient of performance (SCOP) of the GSHP system remains at a relatively low level of 3.0–3.5 under conventional operation. To address these challenges, this study proposes a gray-box-model-based cooperative optimization and group control strategy for GSHP systems. A hybrid gray-box modeling approach (YFU model), integrating physical-mechanism modeling with data-driven parameter identification, is developed to characterize the energy consumption behavior of GSHP units and variable-frequency pumps. On this basis, a multi-equipment cooperative optimization framework is established to coordinate GSHP unit on/off scheduling, load allocation, and pump staging. In addition, continuous operational variables (e.g., chilled-water supply temperature and circulation flow rate) are globally optimized within a hierarchical control structure. The proposed strategy is validated through both simulation analysis and on-site field implementation, demonstrating significant improvements in system energy efficiency, with annual electricity savings of no less than 3.6 × 10⁵ kWh and an increase in SCOP from approximately 3.2 to above 4.0. The results indicate that the proposed framework offers strong interpretability, robustness, and engineering applicability. It also provides a reusable technical paradigm for intelligent energy-saving retrofits of GSHP systems in large commercial buildings. Full article

With the increasing competition in elite sports, pre-competition anxiety has become increasingly prevalent among high-level competitive athletes, and high levels of such anxiety may impair sports performance and threaten athletes’ psychological health. Traditional psychological interventions (e.g., cognitive-behavioral therapy) are often poorly accepted and costly; however, pre-competition anxiety in these athletes may be alleviated through multiple pathways of traditional mind–body exercises like Tai Chi. Yet, the psychological mechanism by which mind–body exercises such as Tai Chi training influence pre-competition anxiety remains insufficiently explored, particularly the chain-mediating effect of the “flow experience → mental toughness” pathway. This study thus aimed to investigate the impact of Tai Chi training on pre-competition anxiety in high-level competitive athletes and verify the chain-mediating role of the “flow experience → mental toughness” pathway, thereby providing a theoretical basis and practical reference for sports psychology interventions. Using a randomized controlled experimental design, 86 high-level competitive athletes were randomly divided into an experimental group (n = 43) and a control group (n = 43). The experimental group received standardized Tai Chi training for 8 weeks, while the control group maintained their regular training regimen. Data were collected at baseline, week 4, and week 8 of the intervention using the Competition State Anxiety Inventory-2 (CSAI-2), Flow State Scale-2 (FSS-2), and Sport Mental Toughness Questionnaire (SMTQ), and chain-mediating effects were tested via hierarchical regression analysis and the bootstrap method with 5000 resamples. The results indicated that Tai Chi training could reduce pre-competition anxiety levels (β = −0.30, p < 0.5), and both flow experience (β = 0.38, p < 0.5) and mental toughness (β = 0.21, p < 0.5) exerted significant mediating effects. The chain mediation model further revealed that Tai Chi training alleviated pre-competition anxiety by enhancing flow experience and improving mental toughness sequentially (β = 0.01, 95% CI [0.00, 0.03]), accounting for 78.9% of the total mediated effect. In conclusion, Tai Chi training is associated with reduced pre-competition anxiety in high-level competitive athletes, and this relationship is statistically mediated by the sequential pathway of flow experience and mental toughness. These findings offer a new theoretical basis and practical direction for mind–body interventions in sports psychology. It should be noted that future research could further optimize and refine the intervention protocol, and explore the underlying mechanism of mind–body interventions at the neurobiological level. Full article

Triply periodic minimal surface (TPMS) structures provide high surface area to volume ratios and tunable conduction pathways, but predicting their thermal behavior across different metallic materials remains challenging because multi-material experimentation is costly and full-scale simulations require extremely fine meshes to resolve the complex geometry. This study develops a physics-informed neural network (PINN) that reconstructs steady-state temperature fields in TPMS Gyroid structures using only two experimentally measured materials, Aluminum and Silver, which were tested under identical heat flux and flow conditions. The model incorporates conductivity ratio physics, Fourier-based thermal scaling, and complete spatial temperature profiles directly into the learning process to maintain physical consistency. Validation using the complete Aluminum and Silver datasets confirms excellent agreement for Aluminum and strong accuracy for Silver despite its larger temperature gradients. Once trained, the PINN can generalize the learned behavior to nine additional metals using only their conductivity ratios, without requiring new experiments or numerical simulations. A detailed heat transfer analysis is also performed for Magnesium, a lightweight material that is increasingly considered for thermal management applications. Since no published TPMS measurements for Magnesium currently exist, the predicted Nusselt numbers obtained from the PINN-generated temperature fields represent the first model-based evaluation of its convective performance. The results demonstrate that the proposed PINN provides an efficient, accurate, and scalable surrogate model for predicting thermal behavior across multiple metallic TPMS structures and supports the design and selection of materials for advanced porous heat technologies. Full article

With the increasing number of highly airtight residences, concerns have risen that the negative pressure formed indoors during kitchen hood operation can reduce capture performance and cause unintended infiltration. This study experimentally and numerically (via CFD simulations) examined whether installing an air supply unit on the cooktop beneath a hood can stabilize hood performance and suppress infiltration in small residential spaces. Two cases were established depending on whether air was supplied: Case 1 (hood operation only) and Case 2 (simultaneous operation of the hood and the air supply unit). In the experimental setup, the hood exhaust flow rate, supply airflow rate, sink-drain infiltration rate, and temperature/humidity were measured. The period during which variations in measured values remained within 10% was defined as the steady state. In the CFD analysis, winter conditions were assumed, and the measured values were applied to the wall boundary, after which the temperature and velocity field were analyzed. In Case 2, by supplying 24.11 CMH of air, the hood flow rate remained stable at 75.72 CMH (98.8% of the initial level) throughout the test, and no infiltration was detected. The CFD analysis revealed that the air supply unit generated an “air curtain” effect, enabling rapid capture of hot airflow and reducing the high-temperature region. In conclusion, the interconnected operation of supply and exhaust systems was shown to be effective in enhancing hood exhaust stability, suppressing unintended infiltration, and improving capture reliability in highly airtight small residential buildings. Future studies should include further analyses, such as the effects of actual cooking behaviors and leakage path distributions. Full article

During liquid drainage from intermediate vessels in various industrial processes such as continuous steel casting, aircraft fuel supply, and chemical separation, free-surface vortices commonly occur. The formation and evolution of these vortices not only entrain surface slag and gas, but also lead to deterioration of downstream product quality and abnormal equipment operation. The vortex evolution process exhibits notable three-dimensional unsteadiness, multi-scale turbulence, and dynamic gas–liquid interfacial changes, accompanied by strong coupling effects between temperature gradients and flow field structures. Traditional macroscopic numerical models show clear limitations in accurately capturing these complex physical mechanisms. To address these challenges, this study developed a mesoscopic numerical model for gas-liquid two-phase vortex flow based on the lattice Boltzmann method. The model systematically reveals the dynamic behavior during vortex evolution and the multi-field coupling mechanism with the temperature field while providing an in-depth analysis of how initial perturbation velocity regulates vortex intensity and stability. The results indicate that vortex evolution begins near the bottom drain outlet, with the tangential velocity distribution conforming to the theoretical Rankine vortex model. The vortex core velocity during the critical penetration stage is significantly higher than that during the initial depression stage. An increase in the initial perturbation velocity not only enhances vortex intensity and induces low-frequency oscillations of the vortex core but also markedly promotes the global convective heat transfer process. With regard to the temperature field, an increase in fluid temperature reduces the viscosity coefficient, thereby weakening viscous dissipation effects, which accelerates vortex development and prolongs drainage time. Meanwhile, the vortex structure—through the induction of Taylor vortices and a spiral pumping effect—drives shear mixing and radial thermal diffusion between fluid regions at different temperatures, leading to dynamic reconstruction and homogenization of the temperature field. The outcomes of this study not only provide a solid theoretical foundation for understanding the generation, evolution, and heat transfer mechanisms of vortices under industrial thermal conditions, but also offer clear engineering guidance for practical production-enabling optimized operational parameters to suppress vortices and enhance drainage efficiency. Full article