Search Results (11,353)

This study examines the hydrodynamic and acoustic performance of plain NACA0018 hydrofoil and modified NACA0018 hydrofoils (foil with a cavity on suction surface) at a Reynolds number ($Re$) of $40,000$, which is indicative of small-scale turbines and marine applications. A cavity was created on suction side surface at 40–50% of the chord length, which is chosen for its efficacy in cavitation control. The present analysis examines the impact of the cavity on lift-to-drag-ratio ($L/D$) and cavity length at three cavitation numbers ($1.7$, $1.2$, and $0.93$) for plain and modified hydrofoils. Simulations demonstrate a significant enhancement of 7% in the lift-to-drag ratio relative to traditional designed foils. Contrary to earlier observations, the cavity length increases instead of decreasing for the modified hydrofoil. Both periodic steady and turbulent inflow conditions are captured that simulate the complex cavity dynamics and flow–acoustic interactions. It is found that a reduction in RMS velocity with modified blade suggests flow stabilization. Spectral analysis using Mel-frequency techniques confirms the cavity’s potential to reduce low-frequency flow-induced noise. These findings offer new insights for designing quieter and more efficient hydrofoils and turbine blades. Full article

Cone Penetration Tests with pore pressure measurements (CPTu) are widely used in geotechnical site investigations due to their high-resolution profiling capabilities. However, traditional interpretation methods—such as the Soil Behavior Type Index ($I_c$)—often fail to capture the internal heterogeneity typical of mining tailings deposits. This study presents a machine learning-based approach to enhance stratigraphic interpretation from CPTu data. Four unsupervised clustering algorithms—k-means, DBSCAN, MeanShift, and Affinity Propagation—were evaluated using a dataset of 12 CPTu soundings collected over a 19-year period from an iron tailings dam in Brazil. Clustering performance was assessed through visual inspection, stratigraphic consistency, and comparison with $I_c$-based profiles. k-means and MeanShift produced the most consistent stratigraphic segmentation, clearly delineating depositional layers, consolidated zones, and transitions linked to dam raising. In contrast, DBSCAN and Affinity Propagation either over-fragmented or failed to identify meaningful structures. The results demonstrate that clustering methods can reveal behavioral trends not detected by $I_c$ alone, offering a complementary perspective for understanding depositional and mechanical evolution in tailings. Integrating clustering outputs with conventional geotechnical indices improves the interpretability of CPTu profiles, supporting more informed geomechanical modeling, dam monitoring, and design. The approach provides a replicable methodology for data-rich environments with high spatial and temporal variability. Full article

The COVID-19 pandemic was a profoundly influential global occurrence in recent history, impacting daily life, economics, and healthcare systems for an extended period. The abundance of data has been essential in creating models to simulate and forecast the dissemination of infectious illnesses, aiding governments and health organizations in making educated decisions. This research primarily focuses on designing a control technique that incorporates the five most important inputs that impact the spread of COVID-19 on the Romanian territory. Quantitative analysis and data filtering are two crucial aspects to consider when developing a mathematical model. In this study the transfer function principle was used as the most accurate method for modeling the system, based on its superior fit demonstrated in a previous study. For the control strategy, a PI (Proportional-Integral) controller was designed to meet the requirements of the intended behavior. Finally, it is showed that for such complex models, the chosen control strategy, combined with fine tuning, led to very accurate results. Full article

This paper presents a novel four-dimensional (4D) chaotic system exhibiting parametric symmetry breaking and multistability. Through equilibrium stability analysis, attractor reconstruction, Lyapunov Exponent spectra (LEs), and bifurcation diagrams, we reveal a continuous transition from symmetric period attractors to asymmetric chaotic states and rich dynamical behaviors. Additionally, considering the potential of this system in practical applications, a feedback control simulation circuit is designed and implemented to ensure its stability and effectiveness under real-world conditions. Finally, among various control strategies, this paper proposes an innovative Fixed-Time Sliding Mode Synchronization (FTSMS) strategy, determines its synchronization convergence time, and provides an important theoretical foundation for the practical application of the system. Full article

Monitoring the height of secondary forest regrowth is essential for assessing ecosystem recovery, but current methods rely on field surveys, airborne or UAV LiDAR, and 3D reconstruction from high-resolution UAV imagery, which are often costly or limited by logistical constraints. Here, we address the challenge of scaling up canopy height monitoring by evaluating a recent deep learning model, trained on data from the Amazon and Atlantic Forests, developed to extract canopy height from RGB-NIR Planet NICFI imagery. The research questions are as follows: (i) How are canopy height estimates from the model affected by slope and orientation in natural forests, based on a large and well-balanced experimental design? (ii) How effectively does the model capture the growth trajectories of Pinus and Eucalyptus plantations over an eight-year period following planting? We find that the model closely tracks Pinus growth at the parcel scale, with predictions generally within one standard deviation of UAV-derived heights. For Eucalyptus, while growth is detected, the model consistently underestimates height, by more than 10 m in some cases, until late in the cycle when the canopy becomes less dense. In stable natural forests, the model reveals seasonal artifacts driven by topographic variables (slope × aspect × day of year), for which we propose strategies to reduce their influence. These results highlight the model’s potential as a cost-effective and scalable alternative to field-based and LiDAR methods, enabling broad-scale monitoring of forest regrowth and contributing to innovation in remote sensing for forest dynamics assessment. Full article

Portland cement production is a major source of global CO₂ emissions due to its high energy consumption and calcination processes. This study proposes a sustainable alternative through the partial replacement of cement with alkali-activated pumice, a naturally occurring aluminosilicate material with high regional availability. Mixes with 0%, 10%, 20%, and 30% cement replacement were designed for pedestrian slabs exposed to humid tropical conditions. Compressive strength was evaluated using non-destructive testing over a period of 364 days, and carbonation was analyzed at different ages. The results show that mixes with up to 30% pumice maintain adequate strength levels for light-duty applications, although with a more gradual strength development. A significant reduction in carbonation depth was also observed, especially in the mix with the highest replacement level, suggesting greater durability in aggressive environments. These findings support the use of pumice as a viable and sustainable supplementary cementitious material in tropical regions, promoting low-impact construction practices. Full article

Building performance simulation is crucial for the design and optimization of sustainable buildings. However, the increasing complexity of building systems necessitates advanced modeling techniques capable of handling multi-domain interactions. This paper presents a novel application of the bond graph (BG) methodology to simulate and analyze the thermal behavior of an integrated trigeneration system within an experimental test cell. Unlike conventional simulation approaches, the BG framework enables unified modeling of thermal and hydraulic subsystems, offering a physically consistent and energy-based representation of system dynamics. The study investigates the system’s performance under both dynamic and steady-state conditions across two distinct climatic periods. Validation against experimental data reveals strong agreement between measured and simulated temperatures in heating and cooling scenarios, with minimal deviations. This confirms the method’s reliability and its capacity to capture transient thermal behaviors. The results also demonstrate the BG model’s effectiveness in supporting predictive control strategies, optimizing energy efficiency, and maintaining thermal comfort. By integrating hydraulic circuits and thermal exchange processes within a single modeling framework, this work highlights the potential of bond graphs as a robust and scalable tool for advanced building performance simulation. Full article

Motivation remains a central concern in second language (L2) and English as a foreign language (EFL) education, yet its underlying mechanisms are insufficiently understood. This study employs the theory of Directed Motivational Currents (DMCs) to explore periods of intense, sustained L2 motivation among Chinese adolescent EFL learners across secondary and tertiary levels. Through in-depth interviews with ten participants, this research identified the conditions (e.g., collaborative peer dynamics, vivid goal visualization) that triggered their DMC experiences. The data also highlighted how facilitative elements—such as clear starting points, personalized goal alignment, behavioral routines, and timely feedback—played a crucial role in initiating and sustaining these motivational currents. These findings contribute to DMC theory by revealing how intrinsic and extrinsic factors jointly foster and maintain high levels of motivation over time, offering valuable insights for designing targeted interventions to enhance EFL motivation and learning among Chinese adolescents. Full article

Freemium, a popular business model in the digital economy, offers a basic product for free while charging for advanced features or value-added services. This pricing strategy enables platforms to attract a broad user base and then monetize through premium offerings. Customer characteristics and service price are important factors affecting customer choice behavior in such a model. Based on consumption stickiness, we consider a monopoly that provides value-added services by incorporating a multinomial logit model into a two-stage dynamic pricing model. First, we analyze the optimal pricing of value-added services under a normal sales scenario. We then consider optimal pricing during the marketing period under two strategies—level improvement for value-added services and quality reduction for a basic product—and analyze the applicability of each. The results show that increasing the value-added service level has a positive effect on the optimal price of value-added services, whereas reducing the basic product quality has no effect on the optimal price. Furthermore, the numerical simulation shows that when the depth of consumer stickiness is low, the optimal marketing strategy reduces the quality of the basic product, the price of value-added services should be higher than that in the normal sales period but lower than the price under the level-improvement strategy for value-added services; otherwise, improving the level of the value-added services becomes the optimal approach. This study provides a theoretical basis and decision support for product quality design and service pricing that applies to freemium platforms. Full article

In the era of Industry 5.0, future wireless networks are increasingly shifting from traditional symmetric architectures toward heterogeneous and asymmetric paradigms, driven by the demand for diversified and dynamic services. This architectural evolution gives rise to complex and asymmetric flows, such as the coexistence of periodic and burst flows with varying latency, jitter, and deadline constraints, posing new challenges for deterministic transmission. Traditional time-sensitive networking (TSN) is well-suited for periodic flows but lacks the flexibility to effectively handle dynamic, asymmetric traffi. To address this limitation, we propose a two-stage asymmetric flow scheduling framework with dynamic deadline control, termed A-TSN. In the first stage, we design a Deep Q-Network-based Dynamic Injection Time Slot algorithm (DQN-DITS) to optimize slot allocation for periodic flows under varying network loads. In the second stage, we introduce the Dynamic Deadline Online (DDO) scheduling algorithm, which enables real-time scheduling for asymmetric flows while satisfying flow deadlines and capacity constraints. Simulation results demonstrate that our approach significantly reduces end-to-end latency, improves scheduling efficiency, and enhances adaptability to high-volume asymmetric traffic, offering a scalable solution for future deterministic wireless networks. Full article

Identifying optimal seed sources is critical for the propagation and restoration of Juglans neotropica Diels in the northern Ecuadorian Andes, where populations are declining due to habitat loss and overexploitation. This study evaluated the seed quality and germination performance of Juglans neotropica from three ecologically distinct provenances: a natural regeneration site (Cuyuja), a pure plantation (Natabuela), and an agroforestry system (Pimampiro). Five phenotypically superior trees were selected from each site, and germination was assessed under controlled nursery conditions over a 150-day period using a completely randomized design. Initial viability tests confirmed the physiological integrity of the seeds across all provenances. Germination onset ranged from day 55 to day 73, with significant differences in germination percentage, speed, and uniformity. The agroforestry provenance showed the highest germination rate (69%) and superior performance in all physiological indices, while natural regeneration had the lowest (15%). Post-trial viability assessments indicated that a substantial proportion of non-germinated seeds from Cuyuja remained dormant or deteriorated. These findings underscore the role of agroforestry systems in enhancing seed physiological quality and support their prioritization for large-scale propagation and ecological restoration initiatives involving Juglans neotropica. Full article

Secondary flow phenomena have a significant influence on the generation of losses and the propagation of coolant on the turbine end walls. The majority of film cooling studies are carried out on linear rather than annular cascades due to the structural simplicity and ease of measurement integration of the former. This approach neglects the effects of the radial pressure gradient that is naturally imposed on the vortex flow in annular cascades. The first part of this paper numerically investigates the effect of the radial pressure gradient on the secondary flow under periodic flow conditions by comparing a linear and an annular case. It is shown that the radial pressure gradient has a significant influence on the propagation of the secondary flow induced vortices in the wake of the nozzle guide vanes (NGV). In the second part of the paper, a novel approach of a five-passage annular sector cascade is presented, which avoids the hub boundary layer separation, as is typical for this type of test rig. To increase the periodicity, a benchmark approach is introduced that includes multiple pointwise and integral flow quantities at different axial positions. Based on the optimized best-case design, general design guidelines are derived that allow a straightforward design process for annular sector cascades. Full article

Despite advances in Convolutional Neural Networks (CNNs) for intelligent fault diagnosis in CNC machine tools, bearing fault diagnosis in CNC feed systems remains challenging, particularly in multi-scale feature extraction and generalization across operating conditions. This study introduces an enhanced multi-scale feature network (MSFN) that addresses these limitations through three integrated modules designed to extract critical fault features from vibration signals. First, a Soft-Scale Denoising (S2D) module forms the backbone of the MSFN, capturing multi-scale fault features from input signals. Second, a Multi-Scale Adaptive Feature Enhancement (MS-AFE) module based on long-range weighting mechanisms is developed to enhance the extraction of periodic fault features. Third, a Dynamic Sequence–Channel Attention (DSCA) module is incorporated to improve feature representation across channel and sequence dimensions. Experimental results on two datasets demonstrate that the proposed MSFN achieves high diagnostic accuracy and exhibits robust generalization across diverse operating conditions. Moreover, ablation studies validate the effectiveness and contributions of each module. Full article

The ecological impact of crown-of-thorns starfish (CoTS; Acanthaster spp.) on coral reefs is intrinsically linked to their feeding behaviour. Management thresholds designed to mitigate coral loss driven by elevated densities of crown-of-thorns starfish rely on accurate estimates of individual feeding rates. In this study, structure-from-motion photogrammetry and intensive tracking of adult Pacific CoTS over an extended survey period were used to generate three-dimensional, high-resolution estimates of daily feeding rates. Our findings revealed substantial variation in the areal extent of coral consumed, both across consecutive days and among individuals. Notably, CoTS did not feed consistently; feeding occurred on 65% of observation days, with 2–3 days periods of inactivity common. Despite this variability, mean daily feeding rates aligned with previous studies (1.35 coral colonies d⁻¹; 198.4 cm² day⁻¹ planar area, and 998.83 cm² day⁻¹ three-dimensional surface area). Across all tracked individuals (n = 8), feeding was recorded on 17 coral genera; however, Acropora alone accounted for 51% of colonies consumed and contributed 82% of the total three-dimensional surface area ingested during the survey period. This highlights the disproportionately large feeding yield derived from Acropora-dominated diets and raises important questions about how future declines in Acropora cover may impact CoTS feeding success and energetic intake. Full article

Purpose: This study investigates the test–retest reliability of maximal voluntary contraction (MVC) and integrated object positioning during bimanual box lifting tasks in children with unilateral spastic cerebral palsy (USCP), using the Activities of Daily Living Test and Training Device (ADL-TTD). Materials and Methods: Utilizing an explorative cross-sectional design, the study recruited 47 children with USCP. The ADL-TTD, equipped with an Inertial Measurement Unit (IMU) for precise object positioning, measured MVC, and object position in 3D space in a cross-sectional measurement containing two measurements in a fixed time period. Results: The findings demonstrated good test–retest reliability for MVC, with an ICC_agreement of 0.95 for the mean MVC value. Additionally, good reliability was observed for object positioning in different directions measured with an IMU, with ICC_agreement ranging from 0.82 to 0.86 degrees. Regarding the standard error of measurement (SEM), the SEM_agreement for the mean MVC value was 5.94 kg, while the SEM_agreement for object positioning was 1.48, 5.39, and 3.43 degrees, respectively. Conclusions: These results indicate that the ADL-TTD demonstrates good test–retest reliability for both MVC and object positioning, making it a valuable tool for analyzing this population in cross-sectional research by providing reliable measures of task-oriented strength and object manipulation. However, the relatively high SEM_agreement, particularly in MVC, suggests that caution is needed when using this tool for repeated testing over time. This pioneering approach could significantly contribute to tailored assessment and training for children with USCP, highlighting the importance of integrating task-specific strength and positional accuracy into therapeutic interventions. Full article