Search Results (81)

This study presents a numerical and experimental evaluation of axial load transfer mechanisms in deep foundations constructed in stratified cohesive soils in İzmir, Türkiye. A full-scale bi-directional static load test equipped with strain gauges was conducted on a barrette pile to investigate depth-dependent mobilization of shaft resistance. A finite element model was developed and calibrated using field-observed load–settlement and strain data to replicate the pile–soil interaction and deformation behavior. The analysis revealed a shaft-dominated load transfer behavior, with progressive mobilization concentrated in intermediate-depth cohesive layers. Sensitivity analysis identified the undrained stiffness (E_u) as the most influential parameter governing pile settlement. A strong polynomial correlation was established between calibrated E_u values and SPT N₆₀, offering a practical tool for preliminary design. Additionally, strain energy distribution was evaluated as a supplementary metric, enhancing the interpretation of mobilization zones beyond conventional stress-based methods. The integrated approach provides valuable insights for performance-based foundation design in layered cohesive ground, supporting the development of site-calibrated numerical models informed by full-scale testing data. Full article

The Sustainable Development Goals (SDGs), launched by the United Nations in 2015, outline 17 interconnected objectives designed to promote human well-being and sustainable development worldwide. Education is recognized by the United Nations as a key factor in promoting sustainable development. To cultivate students with both global perspectives and local engagement, it is essential to integrate sustainability education into elementary curricula. Accordingly, this study aimed to enhance elementary school students’ understanding of the SDGs by designing a structured instructional activity and developing a hybrid digital-physical board game. The game was implemented as a supplementary review tool to traditional classroom teaching, leveraging the motivational and knowledge-retention benefits of physical board games while incorporating digital features to support learning process monitoring. To address the limitations of conventional review approaches—such as reduced student engagement and increased cognitive load—the instructional model incorporated the board game during review sessions following formal instruction. This was intended to maintain student attention and reduce unnecessary cognitive effort, thereby supporting learning in sustainability-related content. A quasi-experimental design was employed to evaluate the effectiveness of the instructional intervention and the board game system, focusing on three outcome variables: learning motivation, cognitive load, and learning achievement. The results indicated that students in the game-based Sustainable Development Goals group achieved significantly higher delayed posttest scores (M = 72.91, SD = 15.17) than the traditional review group (M = 61.30, SD = 22.82; p < 0.05). In addition, they reported significantly higher learning motivation (M = 4.40, SD = 0.64) compared to the traditional group (M = 3.99, SD = 0.69; p < 0.05) and lower cognitive load (M = 1.84, SD = 1.39) compared to the traditional group (M = 2.66, SD = 1.30; p < 0.05), suggesting that the proposed approach effectively supported student learning in sustainability education at the elementary level. Full article

This study examines the mechanical and physical performance of lightweight concretes incorporating acidic pumice aggregate, with a particular focus on their behavior under thermal exposure. Pumice sourced from the Bitlis-Tatvan region was used as a partial replacement for limestone aggregate at volumetric substitution levels of 50%, 60%, and 70% (designated LC50, LC60, and LC70, respectively), alongside a conventional control mix (NC). Experimental investigations included flexural and compressive strength tests, capillary water absorption measurements, and mass loss assessments at elevated temperatures (100 °C, 200 °C, and 300 °C). The results indicate that increasing pumice content leads to a significant reduction in mechanical strength, as evidenced by a strong negative correlation (e.g., −0.994 for compressive strength), and results in increased water absorption due to the higher porosity of pumice. Thermal exposure caused more pronounced weight loss in pumice-rich mixtures, primarily attributable to moisture evaporation and the formation of surface voids, particularly in LC60 and LC70 specimens. Although the incorporation of pumice effectively reduces the unit weight of concrete, it compromises both strength and durability, highlighting a critical trade-off between weight reduction and structural performance. Future studies are recommended to quantitatively assess the relationship between compressive and flexural strengths to address current limitations. Additionally, advanced microstructural analyses (e.g., SEM, XRD), fire resistance evaluations at higher temperatures, and the development of hybrid mixes incorporating supplementary cementitious materials (SCMs) should be further explored. Full article

Monitoring forearm muscle contraction force in home-based rehabilitation remains challenging. Electromyography (EMG), as a standard technique, is considered impractical and complex for independent use by patients at home, which poses a risk of device misattachment and inaccurate recorded data. Considering the muscle-related modality, several studies have demonstrated an excellent correlation between stretch sensors and EMG, which provides significant potential for addressing the monitoring issue at home. Additionally, due to its flexible nature, it can be attached to the finger, which facilitates the logging of the kinematic mechanisms of a finger. This study proposes a method for estimating forearm muscle contraction in a cylinder grasping environment during home-based rehabilitation using a stretch-sensor glove. This study employed support vector machine (SVM), multi-layer perceptron (MLP), and random forest (RF) to construct the estimation model. The root mean square (RMS) of the EMG signal, representing the muscle contraction force, was collected from 10 participants as the target learning for the stretch-sensor glove. This study constructed an experimental design based on a home-based therapy protocol known as the graded repetitive arm supplementary program (GRASP). Six cylinders with varying diameters and weights were employed as the grasping object. The results demonstrated that the RF model achieved the lowest root mean square error (RMSE) score, which differed significantly from the SVM and MLP models. The time series waveform comparison revealed that the RF model yields a similar estimation output to the ground truth, which incorporates the contraction–relaxation phases and the muscle’s contraction force. Additionally, despite the subjectivity of the participants’ grasping power, the RF model could produce similar trends in the muscle contraction forces of several participants. Utilizing a stretch-sensor glove, the proposed method demonstrated great potential as an alternative modality for monitoring forearm muscle contraction force, thereby improving the practicality for patients to self-implement home-based rehabilitation. Full article

Predicting the flexural behavior of fiber-reinforced ultra-high-performance concrete (UHPC) remains a significant challenge due to the complex interactions among numerous mix design parameters. This study presents a machine learning-based framework aimed at accurately estimating the modulus of rupture (MOR) of UHPC. A comprehensive dataset comprising 566 distinct mixtures, characterized by 41 compositional and fiber-related variables, was compiled. Seven regression models were trained and evaluated, with Random Forest, Extremely Randomized Trees, and XGBoost yielding coefficients of determination (R²) exceeding 0.84 on the test set. Feature importance was quantified using Shapley values, while partial dependence plots (PDPs) were employed to visualize both individual parameter effects and key interactions, notably between fiber factor, water-to-binder ratio, maximum aggregate size, and matrix compressive strength. To validate the predictive performance of the machine learning models, an independent experimental campaign was carried out comprising 26 UHPC mixtures designed with varying binder compositions—including supplementary cementitious materials such as fly ash, ground recycled glass, and calcium carbonate—and reinforced with mono-fiber (straight steel, hooked steel, and PVA) and hybrid-fiber systems. The best-performing models were integrated into a hybrid neural network, which achieved a validation accuracy of R² = 0.951 against this diverse experimental dataset, demonstrating robust generalizability across both material and reinforcement variations. The proposed framework offers a robust predictive tool to support the design of more sustainable UHPC formulations incorporating supplementary cementitious materials without compromising flexural performance. Full article

Managers often need to stay motivated and effectively motivate others. Therefore, they should rely on evidence-based interventions to effectively motivate and self-motivate. This research investigated how self-determination theory-based interventions affect employees’ motivation dynamics and motivational consequences within short time frames (i.e., within an hour, within a few weeks or months) in two empirical studies. Study one focused on assessing the effectiveness of a one-day training workshop in helping to improve managers’ work motivation, basic psychological needs satisfaction/frustration, subordinates’ motivation, and perceptions of managers’ needs-supportive/thwarting behaviors within a few weeks. Results support the effectiveness of the training, as managers were rated by their direct subordinates as having fewer needs-thwarting behaviors and reported self-improvement in needs satisfaction and frustration six weeks after completing the training program. Study two used the mean and covariance structure analysis and tested the impact of three types of basic psychological needs-supportive/thwarting and control conditions (3 × 2 × 1 factorial design) on participants’ situational motivation, vitality, and general self-efficacy for playing online word games within 30 min. Multi-group confirmatory factor analysis (CFA) confirmed the scalar measurement invariance, then latent group mean comparison results show consistently lower controlled motivation across the experimental conditions. During a quick online working scenario, the theory-based momentary intervention effectively changed situational extrinsic self-regulation in participants. Supplementary structural equation modeling (SEM; cross-sectional) analyses using experience samples supported the indirect dual-path model from basic needs satisfaction to vitality and general efficacy via situational motivation. We discussed the theoretical implications of the temporal properties of work motivation, the practical implications for employee training, and the limitations. Full article

The construction industry’s escalating environmental footprint, coupled with the underutilization of construction waste streams, necessitates innovative approaches to sustainable material design. This study investigates the dual functionality of recycled clay brick powder (RCBP) as both a supplementary cementitious material (SCM) and an alkali–silica reaction (ASR) inhibitor in hybrid mortar systems incorporating recycled glass (RG) and recycled clay brick (RCB) aggregates. Leveraging the pozzolanic activity of RCBP’s residual aluminosilicate phases, the research quantifies its influence on mortar durability and mechanical performance under varying substitution scenarios. Experimental findings reveal a nonlinear relationship between RCBP dosage and mortar properties. A 30% cement replacement with RCBP yields a 28-day activity index of 96.95%, confirming significant pozzolanic contributions. Critically, RCBP substitution ≥20% effectively mitigates ASRs induced by RG aggregates, with optimal suppression observed at 25% replacement. This threshold aligns with microstructural analyses showing RCBP’s Al³⁺ ions preferentially reacting with alkali hydroxides to form non-expansive gels, reducing pore solution pH and silica dissolution rates. Mechanical characterization reveals trade-offs between workability and strength development. Increasing RCBP substitution decreases mortar consistency and fluidity, which is more pronounced in RG-RCBS blends due to glass aggregates’ smooth texture. Compressively, both SS-RCBS and RG-RCBS mortars exhibit strength reduction with higher RCBP content, yet all specimens show accelerated compressive strength gain relative to flexural strength over curing time. Notably, 28-day water absorption increases with RCBP substitution, correlating with microstructural porosity modifications. These findings position recycled construction wastes and glass as valuable resources in circular economy frameworks, offering municipalities a pathway to meet recycled content mandates without sacrificing structural integrity. The study underscores the importance of waste synergy in advancing sustainable mortar technology, with implications for net-zero building practices and industrial waste valorization. Full article

This study evaluates the effects of steel slag powder (SSP), fly ash (FA), and steel slag sand (SSS) on mortar compressive strength. A response surface methodology (RSM) based on central composite design (CCD) was employed to model 7-day, 28-day, and 91-day strength development, considering three quantitative variables: SSP, FA, and SSS. Statistical results confirmed the reduced cubic models were significant and predictive (R² > 0.97), with non-significant lack of fit and adequate precision. Experimental results revealed that SSP and FA negatively affected early-age strength due to dilution effects and low initial reactivity, whereas SSS slightly improved it by enhancing particle packing. At later ages, SSP exhibited nonlinear effects, where moderate dosages enhanced strength, while excessive replacement led to strength reduction. SSS showed a continuously positive contribution across all ages, particularly at 91 days. Perturbation plots, contour maps, and gradient analyses indicated that SSS played a dominant role at later stages and that maintaining a proper balance among supplementary cementitious materials (SCMs) and aggregate replacements is crucial. The developed models and response surfaces provide practical guidance for designing slag-based mortars with improved mechanical properties and enhanced sustainability. Full article

The interfacial shear performance between ultra-high-performance concrete (UHPC) and normal concrete (NC) is a critical factor in determining the overall performance of composite structures. This paper systematically reviews the research progress on the interfacial shear performance of UHPC-NC, revealing the core mechanisms of bond strength (dominated by mechanical interlocking with chemical bonding as a supplementary factor). It compares the advantages and disadvantages of single-shear, Z-shaped shear, double-shear, and inclined shear tests, clarifying the influence patterns of key parameters such as interface roughness, matrix wetness, curing conditions, and fiber content. This study found that interface treatment is the most significant factor in improving shear strength. Roughening or grooving treatments can increase the strength by more than 40%~80%, while the combination of rebar planting and grooving can further enhance ductility. The matrix wetness (saturated and moist) and UHPC age (within 7 days) need to be strictly controlled to avoid differences in shrinkage stress. Prediction models based on mechanics, finite element analysis, and experimental data each have their advantages and disadvantages and should be selected based on actual working conditions. To address common issues in practical engineering, such as insufficient interface roughness, shrinkage cracking, and fatigue degradation under cyclic loading, it is recommended to adopt composite interface treatment techniques (such as roughening + rebar planting), prestressing design, and optimized fiber distribution (with a steel fiber content of 1.5%~2.5%). This paper provides the theoretical basis and practical guidance for the design optimization and construction control of UHPC reinforcement projects and composite structures. Full article

Medical education faces the dual challenge of addressing cognitive overload and sustaining student engagement, particularly in complex subjects such as pharmacology. This study introduces Cinematic Clinical Narratives (CCNs) as an innovative approach to teaching antiparasitic pharmacology, combining generative artificial intelligence (genAI), edutainment, and mnemonic-based learning. The intervention involved two short films, Alien: Parasites Within and Wormquest, designed to teach antiparasitic pharmacology to first-year medical students. A control group of students only received traditional text-based clinical cases, while the experimental group engaged with the CCNs in an active learning environment. Students who received the CCN material scored an average of 8% higher on exam questions related to the material covered by the CCN compared to students in the control group. Results also showed that the CCNs improved engagement and interest among students, as evidenced by significantly higher scores on the Situational Interest Survey for Multimedia (SIS-M) compared to traditional methods. Notably, students preferred CCNs for their storytelling, visuals, and interactive elements. This study underscores the potential of CCNs as a supplementary educational tool, and suggests the potential for broader applications across other medical disciplines outside of antiparasitic pharmacology. By leveraging genAI and edutainment, CCNs represent a scalable and innovative approach to enhancing the medical learning experience. Full article

Polygain™ (PG), a polyphenolic extract from sugarcane, has recently been identified as a potential additive to reduce methane (CH₄) emissions in livestock. This experiment examined the effects of PG on the enteric CH₄ emission from Holstein Friesian weaned calves. Calves were allocated to annual pasture grazing and received supplementary pellets (200 g/calf/day; Barastoc calf-rearer cubes—Ridley Corporation). The experimental design followed was a completely randomized design (CRD), comprising 24 female calves (4–5 months old) allocated to two equal groups; control (standard pellets) vs. treatment (pellets formulated by adding PG to control pellets to deliver 10 g PG/calf/day). Experimental diets were fed for three months between August and November 2023, including a two-week adaptation period. Calves were weighed at the start and at the end of the study. A GreenFeed (C-Lock Pvt Ltd.) emission monitoring unit (GEM) was used to measure GHG emissions from the experimental calves in their groups in a 2-day rotational cycle. During a visit to the GEM, the calves were encouraged to enter an enclosed area or individual feeding stall where enteric CH₄, CO₂, O₂, H₂, and H₂S measurements were taken. The results indicated a significant effect of PG supplementation on enteric methane emission in calves, with a lower production of CH₄ in calves supplemented with PG (26.66 ± 2.06 g/day) as compared to the control group (35.28 ± 1.39 g/day, p < 0.001). The CO₂/O₂ ratio in the treatment (235 ± 14) and control groups (183 ± 9.6) differed significantly (p < 0.001). Overall, PG supplementation (10 g/calf/day) reduced their average methane emission per day and did not adversely affect the growth and development of experimental calves, confirming its useful anti-methanogenic potential. Full article

This review article presents a detailed investigation into the seismic behavior of structures employing supplementary dampers or additional damping mechanisms over the past decade. The study covers a range of damping systems, including viscous, viscoelastic, and friction dampers, as well as tuned mass dampers and other approaches. A systematic analysis of more than 160 publications in the current literature is undertaken, providing a clear overview of structures equipped with supplementary damping devices and the challenges they face. The theoretical principles that underpin these technologies are examined, along with their practical applications and effectiveness in alleviating seismic effects. Additionally, the article highlights recent developments in the design of damping devices, the challenges related to their implementation, and prospective directions for future research. By synthesizing results from experimental studies, numerical simulations, and real-world applications, this review offers valuable insights for researchers and engineers involved in the design of earthquake-resistant structures. Full article

Six degrees of freedom (6-DoF) object pose estimation is essential for robotic grasping and autonomous driving. While estimating pose from a single RGB image is highly desirable for real-world applications, it presents significant challenges. Many approaches incorporate supplementary information, such as depth data, to derive valuable geometric characteristics. However, the challenge of deep neural networks inadequately extracting features from object regions in RGB images remains. To overcome these limitations, we introduce the Geometry-Focused Attention Network (GFA-Net), a novel framework designed for more comprehensive feature extraction by analyzing critical geometric and textural object characteristics. GFA-Net leverages Point-wise Feature Attention (PFA) to capture subtle pose differences, guiding the network to localize object regions and identify point-wise discrepancies as pose shifts. In addition, a Geometry Feature Aggregation Module (GFAM) integrates multi-scale geometric feature maps to distill crucial geometric features. Then, the resulting dense 2D–3D correspondences are passed to a Perspective-n-Point (PnP) module for 6-DoF pose computation. Experimental results on the LINEMOD and Occlusion LINEMOD datasets indicate that our proposed method is highly competitive with state-of-the-art approaches, achieving 96.54% and 49.35% accuracy, respectively, utilizing the ADD-S metric with a 0.10d threshold. Full article

Recent advancements in intelligent tutoring systems (ITS) driven by artificial intelligence (AI) have attracted substantial research interest, particularly in the domain of computer programming education. Given the diversity in learners’ backgrounds, cognitive abilities, and learning paces, the development of personalized tutoring strategies to support the effective attainment of learning objectives has become a critical challenge. This paper introduces personalized fuzzy logic strategies for intelligent programming tutoring (PerFuSIT), an innovative fuzzy logic-based module designed to select the most appropriate tutoring strategy from five available options, based on individual learner characteristics. The available strategies include revisiting previous content, progressing to the next topic, providing supplementary materials, assigning additional exercises, or advising the learner to take a break. PerFuSIT’s decision-making process incorporates a range of learner-specific parameters, such as performance metrics, error types, indicators of carelessness, frequency of help requests, and the time required to complete tasks. Embedded within the traditional ITS framework, PerFuSIT introduces a sophisticated reasoning mechanism for dynamically determining the optimal instructional approach. Experimental evaluations demonstrate that PerFuSIT significantly enhances learner performance and improves the overall efficacy of interactions with the ITS. The findings highlight the potential of fuzzy logic to optimize adaptive tutoring strategies by customizing instruction to individual learners’ strengths and weaknesses, thereby providing more effective and personalized educational support in programming instruction. Full article

Keeping the epidermis of apple rootstock cuttings moist is important for maintaining physiological activities. It is necessary to monitor the epidermis moisture in real time during the growth process of apple rootstock cuttings. A machine vision-based discrimination model for the moisture degree of cuttings’ epidermis was designed. This model optimizes the structure of the semantic segmentation model U-Net. The model takes the Saturation channel and Value channel information of the cutting images in the HSV color space as the characteristics of the cuttings’ moisture, so that the model has good performance in the blue-purple supplementary light environment. The average accuracy of the improved model is 95.07% for dry and wet cuttings without supplementary light, and 84.83% with supplementary light. The humidification system implanted in the model can control the atomizer to complete the task of moisturizing the cuttings’ epidermis. The average moisture retention rate of the humidification system for cuttings was 92.5%. Compared with the original model, the moisturizing effect of the humidification system increased by 26.87%. The experimental results show that the improved U-Net model has good generalization and high accuracy, which provides a method for the design of an accurate humidification system. Full article