Search Results (1,955)

Urban wetlands are increasingly recognized for their ecological and cultural benefits, yet remain underutilized due to limited public awareness and environmental literacy. This study investigates how visitors’ perceptions of wetland ecosystem services influence their motivations to engage with a constructed wetland in Putrajaya, Malaysia. By integrating the ecosystem services framework with push-pull motivation theory, the research aims to bridge knowledge gaps and inform sustainable wetland tourism planning. A structured questionnaire was administered to 420 visitors, with 385 valid responses (response rate: 91.7%). Data were analyzed using non-parametric tests (Kruskal–Wallis, Spearman correlation) and multiple regression analysis. Results show that cultural and regulating services are perceived most positively, while emotional restoration and aesthetic appreciation emerged as key motivational drivers. Regression findings reveal that push factors are stronger predictors of ecosystem service engagement than pull factors. These insights highlight the importance of emotional and psychological connections to nature, offering practical implications for urban wetland management, visitor education and environmental communication strategies. Full article

Addressing the growing demand for affordable housing requires innovative solutions that strike a balance between cost efficiency and user-specific needs. Mass customization (MC) presents a promising approach that enables the creation of tailored housing solutions on a scale. In this context, this study introduces a model for mass customization of affordable single-family housing units in the city of Teresina, PI, Brazil. Our approach integrates algorithmic–parametric modeling and BIM technologies, facilitating the flow of information and enabling informed decision-making throughout the design process. Since the early design stages, the work has assumed that these integrated technologies provide real-time control over design variables and associated construction data. To develop the model, the method proceeded through the following phases: (1) analysis of the context and definition of the design language; (2) definition of the design process; (3) definition of the cost calculation method and estimation of construction time; (4) definition of the computing model based on the specified technologies; and (5) quantitative and qualitative evaluation of the computational model. As a result, this research aims to contribute to the state-of-the-art by formalizing the knowledge generated through the systematic description of the processes involved in this workflow, with a special focus on the Brazilian context, where the issue of social housing is a critical challenge. Full article

To analyze the hydrodynamic performance of the Voith–Schneider Propeller (VSP) under high eccentricity (e = 0.9), open-water performance numerical calculations were conducted for the VSP at different eccentricities. The results were compared with experimental data, revealing significant discrepancies at high eccentricity. Analysis identified that during the experiment, the VSP blades did not strictly move according to the prescribed “normal intersection principle” when passing near the eccentric point, which was the primary cause of the errors between the calculation and experiment. Further research demonstrated that when the blades pass near the eccentric point, both the individual blade and the overall propeller exhibit strong unsteady pulsation phenomena. The characteristics of these unsteady forces become more pronounced with increasing eccentricity. For the VSP under high eccentricity (e = 0.9), different Blade Steering Curves near the eccentric point were designed using a parametric method. The hydrodynamic performance of the VSP under these different curves was compared. The study demonstrates that rationally optimizing the motion of blades is a key approach to improving their hydrodynamic performance. At J = 2.4, the adoption of Opt-5 enables a 4.67% increase in thrust, a 25.19% reduction in thrust pulsation, a 12.74% reduction in torque, an 81.94% reduction in torque pulsation, and a 19.95% improvement in efficiency for the VSP. Full article

This research introduces an innovative passive vibration control methodology employing acoustic black hole (ABH) structures to mitigate vibration transmission in electric automotive steering machines—a prevalent issue adversely affecting driving comfort and vehicle safety. Leveraging the inherent bending wave manipulation properties of ABH configurations, we conceive an integrated vibration suppression framework synergizing advanced computational modeling with intelligent optimization algorithms. A high-fidelity finite element (FEM) model integrating ABH-attached steering machine system was developed and subjected to experimental validation via rigorous modal testing. To address computational challenges in design optimization, a hybrid modeling strategy integrating parametric design (using Latin Hypercube Sampling, LHS) with Kriging surrogate modeling is proposed. Systematic parameterization of ABH geometry and damping layer dimensions generated 40 training datasets and 12 validation datasets. Surrogate model verification confirms the model’s precise mapping of vibration characteristics across the design space. Subsequent multi-objective genetic algorithm optimization targeting RMS velocity suppression achieved substantial vibration attenuation (29.2%) compared to baseline parameters. The developed methodology provides automotive researchers and engineers with an efficient suitable design tool for vibration-sensitive automotive component design. Full article

The objective of this research was to conduct a comprehensive review of rainbow trout (Oncorhynchus mykiss) culture in recirculating aquaculture systems (RAS), identify knowledge gaps, and propose strategies oriented towards intelligent and sustainable aquaculture. A systematic review and bibliometric analysis of 387 articles published between 1941 and 2025 in the Scopus database was carried out. Since 2011, there has been a sustained growth in scientific production, with the United States, Denmark, Finland, and Germany standing out as the main contributors. The journals with the highest number of publications were Aquacultural Engineering, Aquaculture, and Aquaculture Research. The conceptual analysis revealed the following three thematic clusters: experimental studies on physiology and metabolism; research focused on nutrition, growth, and yield; and technological developments for water treatment in RAS. This evolution reflects a transition from basic approaches to applied technologies oriented towards sustainability. There was also evidence of a thematic transition toward molecular tools such as proteomics, transcriptomics, and real-time PCR. However, there is still limited integration of smart technologies such as the IoT. It is recommended to incorporate self-calibrating multi-parametric sensors, machine learning models, and autonomous systems for environmental regulation in real time. Full article

Formative research is an essential component of higher education, aimed at developing research competencies in students, with an emphasis on critical thinking, academic autonomy, and analytical capacity. Its purpose is not the production of original knowledge but the systematic preparation for research activity. Within this framework, the objective of this study is to analyze how conceptual appropriation, which encompasses theoretical, methodological, procedural, and normative knowledge, is related to students’ perceived research skills. This study is grounded in the imperative of fostering higher education that cultivates critical, autonomous, and ethically responsible researchers. For this purpose, a quantitative methodology was used, with a non-experimental and correlational design, applying a census sampling to 10,536 students from a higher education institution. Data were collected through a structured survey on conceptual appropriation and perceived research skills. After the removal of inconsistent records, the data were processed statistically using non-parametric tests, particularly Spearman’s correlation, due to the non-normal distribution of the variables. The results reveal strong and significant correlations between conceptual appropriation and key research skills such as hypothesis formulation, critical thinking, and motivation for research, demonstrating that greater conceptual mastery promotes a more solid and engaged research training. These findings reinforce the need to systematically and progressively integrate research content into the university curriculum, fostering an authentic, reflective, and contextualized education. Full article

This research aims to analyze the evolution of productivity and efficiency in tractors featuring varying propulsion system designs through the development of a parametric modeling approach. Recognizing that large row-crop tractors represent a significant capital investment—ranging from USD 0.4 to over 0.8 million for current-generation models—and that machinery costs constitute a substantial share of farm production expenses, this study addresses the urgent need for data-driven decision-making in agricultural enterprises. Utilizing consolidated OECD Code 2 tractor test data for all large row-crop John Deere tractors from the MFWD era to the latest generation, the study evaluates tractor performance across multiple productivity and efficiency indicators. The analysis culminates in the creation of a robust, user-friendly parametric model (R² = 0.9337, RMSE = 1.0265), designed to assist stakeholders in making informed decisions regarding tractor replacement or upgrading. By enabling the optimization of productivity and efficiency while accounting for agronomic and timeliness constraints, this model supports sustainable and profitable management practices in modern agriculture. Full article

The construction industry is one of the main areas of energy consumption and carbon emissions, and strengthening research on the thermal performance of building facades can effectively promote energy conservation and emission reduction. Compared with traditional static enclosure structures, dynamic skin can adapt its functions, characteristics, and methods based on constantly changing environmental conditions and performance requirements. It has great potential in adapting to the environment, reducing energy consumption, adjusting shading and natural ventilation, and improving human thermal and visual comfort. To comprehensively understand the key technologies of dynamic skin energy-saving design, previous research results were comprehensively compiled from relevant databases. The research results indicate that various types of dynamic skins, intelligent materials, multi-layer facades, dynamic shading, and biomimetic facades are commonly used core technologies for dynamic facades. Parametric modeling, computer simulation, and multi-objective algorithms are commonly used to optimize the performance of dynamic skin. In addition, integrated technology design, interaction design, and lifecycle design should be effective methods for improving dynamic skin energy efficiency, resident satisfaction, and economic benefits. Despite current challenges, dynamic skin energy-saving technology remains one of the most effective solutions for future sustainable building design. Full article

This paper develops a novel design formula to estimate the residual strength of corroded stiffened cylindrical structures. It extends a previously established ultimate strength formulation for intact cylinders by introducing a corrosion-induced strength reduction factor. The foundational formula considers failure mode interactions like yielding, local buckling, overall buckling, and stiffener tripping. This research utilizes recent experimental and numerical investigations on corroded ring-stiffened cylinder models. Experimental results validate the numerical analysis method, showing good agreement in collapse pressures (2–4% difference) and shapes. The validated numerical method is then subject to an extensive parametric study, systematically varying corrosion characteristics. Results indicate a clear relationship between corrosion volume and strength reduction, with overall buckling being more sensitive. Based on these comprehensive results, a new empirical strength reduction factor (${\rho }_c$) is derived as a function of the corrosion volume ratio ($V_{non}$). This factor is integrated into the existing ultimate strength formula, allowing direct residual strength estimation for corroded structures. The proposed formula is rigorously verified against experimental and numerical data, showing excellent agreement (mean 1.00, COV 5.86%). This research provides a practical, accurate design tool for assessing the integrity and service life of corroded stiffened cylindrical structures. Full article

Background and Objectives: Caring for an individual with dementia encompasses many challenges. This can lead to increased burden, anxiety, and mental health issues among those taking care of them. Limited research exists investigating the care of people with dementia in Kuwait, particularly regarding the mental health of caregivers. There is a need to understand the impact of caregiver burden in this population. This study aimed to assess the level of burden of care, depression, anxiety, and well-being among caregivers of the elderly with dementia in Kuwait. Methods: This study used a descriptive and cross-sectional design. To measure the burden of care, depression, anxiety, and well-being of the caregivers, we utilized the Zarit Burden Interview, the Hospital Anxiety and Depression Scale, and the World Health Organization-Five Well-Being Index. A sensitivity analysis was conducted to compare the results of the parametric and non-parametric methods. Results: This study included 180 (65%) caregivers for the elderly with dementia and 98 (35%) without dementia. The descriptive statistics showed that caregivers for the elderly with dementia and caregivers for the elderly without dementia experienced moderate burden (17.21 ± 9.09 and 14.51 ± 8.08, respectively), borderline abnormal anxiety (9.92 ± 5.15 and 8.61 ± 4.79, respectively), borderline abnormal depression (8.69 ± 4.35 and 8.06 ± 4.24, respectively), and low mental health well-being (54.40 ± 25.10 and 58.90 ± 23.42, respectively). The t-test of independent samples and Mann–Whitney U test results showed that the burden and anxiety in the caregivers for the elderly with dementia group were statistically significantly higher than those in the caregivers for the elderly without dementia group (p = 0.015 and p = 0.039; p = 0.026 and p = 0.027, respectively). The ANOVA test and Kruskal–Wallis test revealed that the caregivers for the elderly with dementia group had statistically significant differences in burden (p < 0.001; p < 0.001), anxiety (p = 0.048; p = 0.043), depression (p = 0.017; p = 0.009), and mental health well-being (p = 0.001; p = 0.002) scores across various durations of care. The multiple linear regression showed that caregiving was a significant predictor of burden of care and anxiety, indicating that caregivers of the elderly with dementia experienced a higher burden of care than those caring for the elderly without dementia. In addition, confounders with significant influence were duration of care (p < 0.001), education level (p = 0.002), employment status (p = 0.008), and gender (p = 0.02). Conclusions: Family caregivers experienced significant levels of burden of care and anxiety when caring for the elderly with dementia. A multidimensional holistic approach is needed to provide family caregivers of the elderly with dementia with valuable interventions. Full article

This study investigates the effects of transcutaneous electroacupuncture stimulation (TEAS) on eyeblink rate, EEG, and heart rate variability (HRV), emphasising whether eyeblink data—often dismissed as artefacts—can serve as useful physiological markers. Sixty-six participants underwent four TEAS sessions with different stimulation frequencies (2.5, 10, 80, and 160 pps, with 160 pps as a low-amplitude sham). EEG, ECG, PPG, and respiration data were recorded before, during, and after stimulation. Using non-parametric statistical analyses, including Friedman’s test, Wilcoxon, Conover–Iman, and bootstrapping, the study found significant changes across eyeblink, EEG, and HRV measures. Eyeblink laterality, particularly at 2.5 and 10 pps, showed strong frequency-specific effects. EEG power asymmetry and spectral centroids were associated with HRV indices, and 2.5 pps stimulation produced the strongest parasympathetic HRV response. Blink rate correlated with increased sympathetic and decreased parasympathetic activity. Baseline HRV measures, such as lower heart rate, predicted participant dropout. Eyeblinks were analysed using BLINKER software (v. 1.1.0), and additional complexity and entropy (‘CEPS-BLINKER’) metrics were derived. These measures were more predictive of adverse reactions than EEG-derived indices. Overall, TEAS modulates multiple physiological markers in a frequency-specific manner. Eyeblink characteristics, especially laterality, may offer valuable insights into autonomic function and TEAS efficacy in neuromodulation research. Full article

Renewable energy stands as a pivotal solution to environmental concerns, prompting substantial research and development endeavors to promote its adoption and enhance energy efficiency. Despite the recognized environmental superiority of renewable energy systems, there is a lack of globally standardized indicators specifically focused on renewable energy efficiency. This study aims to develop and apply a non-parametric data envelopment analysis (DEA) indicator, termed the Renewable Energy Indicator (REI), to measure environmental performance at the national level and to identify differences in renewable energy efficiency across countries grouped by development status and income level. The REI incorporates new factors such as agricultural methane emissions (thousand metric tons of ${\mathrm{CO}}_2$ equivalent), PM2.5 air pollution exposure (µg/m³), and aspects related to electricity, including consumption (as % of total final energy consumption), production from renewable sources, excluding hydroelectric (kWh), and accessibility in rural and urban areas (% of population with access), aligning with the emerging paradigm outlined by the United Nations. By segmenting the REI into global, developmental, and income group classifications, this study conducts the Mann–Whitney U test and the Kruskal–Wallis H tests to identify variations in renewable energy efficiency among different country groups. Our findings reveal top-performing countries globally, highlighting both developed (e.g., Sweden) and developing nations (e.g., Costa Rica, Sri Lanka). Central and North European countries demonstrate high efficiency, while those facing political and economic instability perform poorly. Agricultural-dependent nations like Australia and Argentina exhibit lower REI due to significant methane emissions. Disparities between developed and developing markets underscore the importance of understanding distinct socio-economic dynamics for effective policy formulation. Comparative analysis across income groups informs specific strategies tailored to each category. Full article

This study investigates the application of deep learning (DL) techniques for predicting temperature fields in the SM1 swirl-stabilized turbulent non-premixed flame. Two distinct DL approaches were developed using a comprehensive CFD database generated via the steady laminar flamelet model coupled with the SST k-ω turbulence model. The first approach employs a fully connected dense neural network to directly map scalar input parameters—fuel velocity, swirl ratio, and equivalence ratio—to high-resolution temperature contour images. In addition, a comparison was made with different deep learning networks, namely Res-Net, EfficientNetB0, and Inception Net V3, to better understand the performance of the model. In the first approach, the results of the Inception V3 model and the developed Dense Model were found to be better than Res-Net and Efficient Net. At the same time, file sizes and usability were examined. The second framework employs a U-Net-based convolutional neural network enhanced by an RGB Fusion preprocessing technique, which integrates multiple scalar fields from non-reacting (cold flow) conditions into composite images, significantly improving spatial feature extraction. The training and validation processes for both models were conducted using 80% of the CFD data for training and 20% for testing, which helped assess their ability to generalize new input conditions. In the secondary approach, similar to the first approach, studies were conducted with different deep learning models, namely Res-Net, Efficient Net, and Inception Net, to evaluate model performance. The U-Net model, which is well developed, stands out with its low error and small file size. The dense network is appropriate for direct parametric analyses, while the image-based U-Net model provides a rapid and scalable option to utilize the cold flow CFD images. This framework can be further refined in future research to estimate more flow factors and tested against experimental measurements for enhanced applicability. Full article

This paper presents an integrated computational framework for predicting temperature fields in glulam beam–column connections under fire conditions, combining finite element modeling, automated parametric analysis, and deep learning techniques. A high-fidelity heat transfer finite element model was developed, incorporating the anisotropic thermal properties of wood and temperature-dependent material behavior, validated against experimental data with strong agreement. To enable large-scale parametric studies, an automated Abaqus model modification and data processing system was implemented, improving computational efficiency through the batch processing of geometric and material parameters. The extracted temperature field data was used to train a DeepONet neural network, which achieved accurate temperature predictions (with a L2 relative error of 1.5689% and an R² score of 0.9991) while operating faster than conventional finite element analysis. This research establishes a complete workflow from fundamental heat transfer analysis to efficient data generation and machine learning prediction, providing structural engineers with practical tools for the performance-based fire safety design of timber connections. The framework’s computational efficiency enables comprehensive parametric studies and design optimizations that were previously impractical, offering significant advancements for structural fire engineering applications. Full article

Background. Major Depressive Disorder (MDD) is a highly prevalent and disabling condition frequently accompanied by cognitive deficits, impaired psychosocial functioning, and biological rhythm disturbances. Despite extensive literature on individual associations between depression and circadian disruptions, the mediating role of biological rhythms in the functional outcomes of MDD remains underexplored. Objectives. This study aimed to explore the associations between depression severity, biological rhythms, sleep quality, and psychosocial functioning, and to assess whether biological rhythm disturbances mediate the impact of depression on functioning. Methods. Sixty-one inpatients diagnosed with moderate-to-severe MDD were assessed using standardized instruments: BDI-II for depressive symptoms, BRIAN for biological rhythms, PSQI for sleep quality, and FAST for global functioning. Group comparisons, non-parametric correlations, and a mediation analysis were conducted to test direct and indirect effects. Results. Participants showed severe depressive symptoms, impaired functioning, disrupted biological rhythms, and poor sleep. Women reported more depressive episodes, reduced autonomy, and worse sleep than men. Depression severity was associated with circadian and sleep disturbances, which in turn related to functional impairment. Mediation analysis suggested that biological rhythms partially mediate the impact of depression on functioning. Conclusions. Findings from this preliminary analysis suggest that biological rhythm disturbances may play a mediating role in the relationship between depressive symptoms and daily psychosocial functioning. While not conclusive, these results highlight the potential relevance of chronobiological factors in understanding functional outcomes in MDD. Further research using longitudinal and controlled designs is needed to clarify these associations and their clinical implications. Full article