Search Results (218)

: The objective of the current research is to investigate the role of Neusilin US2 as a porous carrier for improving the drug loading and stability of Ezetimibe (EZB) by hot melt extrusion (HME). The amorphous solid dispersions (ASDs) were developed from 10–40% of drug loading using Kollidon VA 64 (Copovidone) as a polymer matrix and Neusilin US2 as a porous carrier. The solid-state characterization of EZB was studied using differential scanning calorimetry (DSC), powder x-ray diffraction (PXRD), and Fourier transform infrared spectroscopy (FTIR). The formulation blends were characterized for flow properties, and CTC (compressibility, tabletability, compactibility) profile. The in-vitro drug release profiles were studied in 0.1N HCl (pH 1.2). The incorporation of Neusilin US2 has facilitated the development of ASDs up to 40% of drug loading. The CTC profile has demonstrated excellent tabletability for the ternary (EZB, copovidone and Neusilin) dispersions over binary dispersion (EZB and copovidone) formulations. The tablet formulations with binary (20%) and ternary (30% and 40%) dispersions have demonstrated complete dissolution of the drug in 30 min in 0.1N HCl (pH 1.2). The incorporation of copovidone has prevented the recrystallization of the drug in the solution state. Upon storage of formulations at accelerated conditions, the stability of ternary dispersion tablets was preserved attributing to the entrapment of the drug within Neusilin pores thereby inhibiting molecular mobility. Based on the observations, the current research concludes that it is feasible to incorporate Neusilin US2 to improve the drug loading and stability of ASD systems. Full article

The development of sustainable cementitious materials is crucial to reduce the environmental footprint of the construction industry. Alkali-activated materials (AAMs) have emerged as promising environmentally friendly alternatives; however, their compatibility with natural stone in heritage structures remains poorly understood, especially regarding salt migration and related damage to stones. This study presents a novel methodology for assessing salt movement in solid materials between two types of stones—Boñar and Silos—and two types of binders: blended Portland cement (BPC) and an AAM. The samples underwent capillarity and immersion tests to evaluate water absorption, salt transport, and efflorescence behavior. The capillarity of the Silos stone was 0.148 kg·m⁻²·t^−0.5, whereas this was 0.0166 kg·m⁻²·t^−0.5 for the Boñar stone, a ninefold difference. Conductivity mapping and XRD analysis revealed that AAM-based mortars exhibit a significantly higher release of salts, primarily sodium sulfate, which may pose a risk to adjacent porous stones. In contrast, BPC showed lower salt mobility and different salt compositions. These findings highlight the importance of evaluating the compatibility between alternative binders and heritage stones. The use of AAMs may pose significant risks due to their tendency to release soluble salts. Although, in the current experiments, no pore damage or mechanical degradation was observed, additional studies are required to confirm this. A thorough understanding of salt transport mechanisms is therefore essential to ensure that sustainable restoration materials do not inadvertently accelerate the deterioration of structures, a process more problematic when the deterioration affects heritage monuments. Full article

Ubiquitous blended learning, leveraging mobile devices, has democratized education by enabling autonomous and readily accessible knowledge acquisition. However, its reliance on traditional interfaces often limits learner immersion and meaningful interaction. The emergence of the wearable metaverse offers a compelling solution, promising enhanced multisensory experiences and adaptable learning environments that transcend the constraints of conventional ubiquitous learning. This research proposes a novel framework for ubiquitous blended learning in the wearable metaverse, aiming to address critical challenges, such as multi-source data fusion, effective human–computer collaboration, and efficient rendering on resource-constrained wearable devices, through the integration of embodied interaction and multi-agent collaboration. This framework leverages a real-time multi-modal data analysis architecture, powered by the MobileNetV4 and xLSTM neural networks, to facilitate the dynamic understanding of the learner’s context and environment. Furthermore, we introduced a multi-agent interaction model, utilizing CrewAI and spatio-temporal graph neural networks, to orchestrate collaborative learning experiences and provide personalized guidance. Finally, we incorporated lightweight SLAM algorithms, augmented using visual perception techniques, to enable accurate spatial awareness and seamless navigation within the metaverse environment. This innovative framework aims to create immersive, scalable, and cost-effective learning spaces within the wearable metaverse. Full article

Low-latency and robust beamforming are vital for sustaining signal quality and spectral efficiency in emerging high-mobility 5G and future 6G wireless networks. Conventional beam management approaches, which rely on periodic Channel State Information feedback and static codebooks, as outlined in 3GPP standards, are insufficient in rapidly varying propagation environments. In this work, we propose a Dominance-Enforced Adaptive Clustered Sliding Window Regression (DE-ACSW-R) framework for predictive beamforming in O-RAN Split 7-2x architectures. DE-ACSW-R leverages a sliding window of recent angle of arrival (AoA) estimates, applying in-window change-point detection to segment user trajectories and performing both Linear Regression (LR) and curvature-adaptive Support Vector Regression (SVR) for short-term and non-linear prediction. A confidence-weighted fusion mechanism adaptively blends LR and SVR outputs, incorporating robust outlier detection and a dominance-enforced selection regime to address strong disagreements. The Open Radio Unit (O-RU) autonomously triggers localised MUSIC scans when prediction confidence degrades, minimising unnecessary full-spectrum searches and saving delay. Simulation results demonstrate that the proposed DE-ACSW-R approach significantly enhances AoA tracking accuracy, beamforming gain, and adaptability under realistic high-mobility conditions, surpassing conventional LR/SVR baselines. This AI-native modular pipeline aligns with O-RAN architectural principles, enabling scalable and real-time beam management for next-generation wireless deployments. Full article

Low achievement in mathematics across educational levels has long been a concern for researchers. Recent evidence points to equipping teachers with skills and competencies that align with the demands of the modern, technology-rich world. This systematic review explored how technology-facilitated heutagogical practices contribute to the professional development of preservice and in-service mathematics teachers. Drawing on 21 empirical studies published between 2017 and 2024, this review identified three major findings. First, technology-enhanced heutagogical practices promote teaching skills by fostering learner autonomy, self-reflection, and professional identity development. Second, tools such as mobile apps, Massive Open Online Courses (MOOCs), adaptive learning platforms, and collaborative digital environments support the integration of heutagogical principles. Third, implementation is challenged by limited digital access, institutional constraints, and the need for gradual adaptation to self-determined learning models. These findings prove the need for policy and institutional investment in digital infrastructure, blended learning models, and teacher support. Theoretically, this study affirms heutagogy as a relevant pedagogical approach for preparing mathematics teachers in dynamic learning contexts. There is also a need for more empirical studies to investigate scalable models for technology-driven heutagogy, especially in resource-constrained settings. Full article

This study explores the potential of biopolymers as sustainable alternatives to synthetic polymers in enhanced oil recovery (EOR), aiming to reduce reliance on partially hydrolyzed polyacrylamides (HPAM). Mucilage extracted from Opuntia ficus-indica cladodes was investigated individually and in combination with HPAM in an 80/20 blend. The objective was to evaluate the physicochemical and rheological properties of these formulations, and their efficiency in improving oil recovery under realistic reservoir conditions. The materials were characterized using thermogravimetric analysis (TGA), X-ray diffraction (XRD), scanning electron microscopy (SEM), and Fourier-transform infrared spectroscopy (FTIR). Rheological tests showed that both Opuntia mucilage and the HPAM–mucilage blend displayed favorable viscoelastic behavior in saline environments (2% NaCl) at high concentrations (10,000 ppm). The mucilage also exhibited thermal stability above 200 °C, making it suitable for harsh reservoir conditions. Core flooding experiments conducted at 120 °C using core plugs from Algerian reservoirs revealed enhanced oil recovery performance. The recovery factors were 63.3% for HPAM, 84.35% for Opuntia mucilage, and 94.28% for the HPAM–mucilage blend. These results highlight not only the synergistic effect of the blend but also the standalone efficiency of the natural biopolymer in improving oil mobility and pore permeability. This study confirms the viability of using locally sourced biopolymers in EOR strategies. Opuntia ficus-indica mucilage offers a cost-effective, eco-friendly, and thermally stable alternative to conventional polymers for enhanced oil recovery, particularly in saline and high-temperature reservoirs such as Hassi Messaoud in Algeria. Full article

Rail surface defects pose significant risks to the operational efficiency and safety of industrial equipment. Traditional visual defect detection methods typically rely on high-quality RGB images; however, they struggle in low-light conditions due to small, low-contrast defects that blend into complex backgrounds. Therefore, this paper proposes a novel defect segmentation method leveraging a dual-stream feature fusion network that combines polarization images with DeepLabV3+. The approach utilizes the pruned MobileNetV3 as the backbone network, incorporating a coordinate attention mechanism for feature extraction. This reduces the number of model parameters and enhances computational efficiency. The dual-stream module implements cascade and addition strategies to effectively merge shallow and deep features from both the original and polarization images. This enhances the detection of low-contrast defects in complex backgrounds. Furthermore, the CBAM is integrated into the decoding area to refine feature fusion and mitigate the issue of missing small-target defects. Experimental results demonstrate that the enhanced DeepLabV3+ model outperforms existing models such as U-Net, PSPNet, and the original DeepLabV3+ in terms of MIoU and MPA metrics, achieving 73.00% and 80.59%, respectively. The comprehensive detection accuracy reaches 97.82%, meeting the demanding requirements for effective rail surface defect detection. Full article

The rapid advancement of sustainable materials has driven the need for high-performance polymer nanocomposites with superior mechanical, thermal, and structural properties. In this study, a novel RPET/PA-11/Joncryl^® nanocomposite reinforced with halloysite nanotubes (HNTs) is developed for the first time, marking a significant breakthrough in polymer engineering. Six different proportions of HNT (0, 1, 2, 3, 4, and 5 phr) are introduced to the blend of rPET/PA-11/Joncryl^® through a twin-screw extruder and injection moulding machine. The incorporation of HNTs into the RPET/PA-11 matrix, coupled with Joncryl^® as a compatibilizer, results in a synergistic enhancement of material properties through improved interfacial adhesion, load transfer efficiency, and nanoscale reinforcement. Comprehensive characterization reveals that the optimal formulation with 2 phr HNT (NCS-H2) achieves remarkable improvements in tensile strength (56.14 MPa), flexural strength (68.34 MPa), and Young’s modulus (895 MPa), far exceeding conventional polymer blends. Impact resistance reaches 243.46 J/m, demonstrating exceptional energy absorption and fracture toughness. Thermal analysis confirms enhanced stability, with an onset degradation temperature of 370 °C, attributing the improvement to effective matrix–filler interactions and restricted chain mobility. Morphological analysis through FESEM validates uniform HNT dispersion at optimal loading, eliminating agglomeration-induced stress concentrators and reinforcing the polymer network. The pioneering integration of HNT into RPET/PA-11/Joncryl^® nanocomposites not only bridges a critical gap in sustainable polymers but also establishes a new benchmark for polymer nanocomposites. This work presents an eco-friendly solution for engineering applications, offering mechanical robustness, thermal stability, and recyclability. The results form the basis for next-generation high-performance materials for industrial use in automotive, aerospace, and high-strength structural applications. Full article

The immiscibility of thermoplastic starch (TPS) and polybutylene succinate (PBS) complicates the thermal processing of these materials. This study provides the first comparative assessment of two compatibilizers with differing reaction mechanisms, Joncryl^® ADR 4468 and epoxidized linseed oil (ELO), for the optimization of biobased TPS/PBS blends. A total of 13 batches, varying in compatibilizer and blend composition, were processed via hot melt extrusion and injection molding to produce pellets. Blends were analyzed using tensile and impact testing, differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR), rheology, and scanning electron microscopy (SEM). The findings suggest that both compatibilizers can improve the compatibility of these blends, as evidenced by higher glass transition temperatures (T_g) compared to the reference batch (100-0-N/A). Joncryl^® ADR 4468 batches exhibit superior tensile strength and Young’s moduli, while ELO batches demonstrate greater elongation at break. The enhanced processability observed in Joncryl^® ADR 4468 is attributed to the increased polymer chain entanglement and molecular weight, whereas ELO facilitates greater chain mobility due to its plasticizing effect. These differences arise from the distinct mechanisms of action: Joncryl^® ADR 4468 promotes chain extension and crosslinking, whereas ELO mainly enhances flexibility through plasticization. Overall, this study provides a comparative assessment of these compatibilizers in TPS/PBS blends, laying the groundwork for future investigations into optimizing compatibilizer concentration and blend composition. Full article

Efficient oil transportation in field-deployed mobile pipelines is critical, but mixed-oil zones at interfaces reduce quality and increase waste, necessitating effective interface detection and cutting. Existing online densitometers, such as vibrating tube or high-accuracy magnetic suspension types, typically require external power, limiting their use in remote or emergency/temporary field operations. A self-powered device is presented that leverages gravitational force variations acting on a float to detect density changes and trigger automatic cutting. Validated with gasoline, diesel, kerosene, and water, it achieves a 10 kg/${\mathrm{m}}^3$ resolution, deemed sufficient for functional batch separation in its target application, with switching times of 61–395 s for density differences (760–835 kg/${\mathrm{m}}^3$). It supports 20–90% blending ratios, with a vent mitigating gas effects. The modular, robust, self-powered design suits emergency operations, offering a practical alternative to powered systems. Future work targets improved resolution and environmental testing. Full article

Polymer blending enhances material properties by combining different polymers, which requires careful consideration of both thermodynamic and technological compatibility. This study investigates the compatibility of polyvinyl chloride (PVC), thermoplastic polyurethane (TPU), and a bio-plasticizer in blends produced via roll milling at various mixing ratios. Compatibility and morphology were analyzed using thermally stimulated discharge (TSD), dynamic mechanical analysis (DMA), and scanning electron microscopy (SEM), while mechanical and thermal properties were assessed by mechanical testing and thermogravimetric analysis (TGA). The PVC/TPU (100/30) blend exhibited superior phase compatibility over PVC/TPU (100/50), as indicated by a single relaxation peak in TSD and DMA, along with a more homogeneous morphology and enhanced tensile properties. The PVC/TPU/bio-plasticizer (100/20/50) blend showed a well-balanced mechanical performance and improved phase homogeneity. The TSD peak maxima trends for the TPU/bio-plasticizer blend highlighted the bio-plasticizer’s dual role in enhancing flexibility at low concentrations while restricting molecular mobility at higher concentrations. TGA revealed TPU’s positive effect on PVC’s degradation profile, while the bio-plasticizer reduced thermal stability. These findings demonstrate that blending PVC, TPU, and bio-plasticizer creates compatible materials with enhanced and diverse properties, making them suitable for industrial applications. Full article

Legged robots, designed for high adaptability, are poised for deployment in hazardous tasks traditionally undertaken by humans, particularly in unstructured terrains where their wheeled counterparts cannot operate. Nevertheless, using them in unstructured settings necessitates advanced control techniques to maneuver without depending entirely on visual signals or pre-programmed instructions. To address these challenges, this research proposes a novel walking algorithm for quadruped robots that blends a stable blind gait without needing any visual cues with deep reinforcement learning to enhance mobility across diverse terrains. The algorithm’s effectiveness was evaluated virtually, emphasizing the ability to regulate the robot’s leg movements and posture when reaching obstacles. Our results demonstrated a success rate of over 90% in the stair-climbing task, suggesting that the algorithm improved the robot’s mobility and stability. Although emphasizing a steady blind gait reduces reliance on visual cues, incorporating the algorithm with further sensory inputs and environmental awareness may improve the robot’s functionality and versatility in practical situations. More dynamic gaits and a wider variety of static and dynamic obstacles will be the focus of future algorithm development. Furthermore, validation in the real world will aid in detecting any shortcomings or potential areas for enhancement in the algorithm, thereby improving its adaptability and resilience in diverse settings and assignments. Full article

Climate change is one of the most pressing global threats, endangering the sustainability of the planet and quality of life, whilst urban mobility significantly contributes to exacerbating its effects. Recently, policies aimed at mitigating these effects have been implemented, emphasizing the promotion of sustainable travel culture. Prior research has indicated that both environmental awareness and regulatory efforts could encourage the shift towards greener mobility; however, factors that affect young people’s travel behavior remain understudied. This study examined whether and how climate change impacts travel behavior, particularly among Generation Z in Greece. A comprehensive online survey was conducted, from 31 March to 8 April 2024, within a Greek academic community, yielding 904 responses from Generation Z individuals. The design of the survey was informed by an adaptation of Triandis’ Theory of Interpersonal Behavior. The study also incorporated a comparative analysis using data from the UK’s National Travel Attitudes Survey (NTAS), offering insights from a different cultural and socio-economic context. Blending an Exploratory Factor Analysis and latent variable ordered probit and logit models, the key determinants of the willingness to reduce car use and self-reported reduction in car use in response to climate change were identified. The results indicate that emotional factors, social roles, and norms, along with socio-demographic characteristics, current behaviors, and local environmental concerns, significantly influence car-related travel choices among Generation Z. For instance, concerns about local air quality are consistently correlated with a higher likelihood of having already reduced car use due to climate change and a higher willingness to reduce car travel in the future. The NTAS data reveal that flexibility in travel habits and social norms are critical determinants of the willingness to reduce car usage. The findings of the study highlight the key role of policy interventions, such as the implementation of Low-Emission Zones, leveraging social media for environmental campaigns, and enhancing infrastructure for active travel and public transport to foster broader cultural shifts towards sustainable travel behavior among Generation Z. Full article

Copolymers of glycidyl azide polymer (GAP) and poly (caprolactone) (PCL) were obtained by introducing PCL molecular chains at both ends or side groups of GAP molecular chains, respectively. GAP/PCL elastomers were prepared via polyurethane curing reaction and compared with GAP/PCL elastomers prepared by physical blending, in order to clarify the relationship between microstructure and macroscopic properties. The results showed that no GAP and PCL phase separation was observed in the chemically bonded GAP/PCL elastomers. The elongation at break of the thermosetting GAP/PCL block copolymer elastomer increased significantly from 268% to 300% due to the increase in molecular weight between crosslinking points. The GAP/PCL graft copolymer, with its longer PCL segment length and higher segment mobility, formed microcrystalline domains within the elastomer, resulting in a significant improvement in tensile strength from 0.32 MPa to 1.07 MPa. In addition, differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) revealed that the glass transition temperature of the GAP/PCL elastomer was 2.6 °C lower than that of the pure GAP elastomer, and the thermal stability was also enhanced. Full article

Zinc oxide (ZnO) has recently gained considerable attention due to its exceptional properties, including higher electron mobility, good thermal conductivity, high breakdown voltage, and a relatively large exciton-binding energy. These characteristics helped engineers to develop low dimensional heterostructures (LDHs)-based advanced flexible/transparent nanoelectronics, which were then integrated into thermal management systems. Coefficients of thermal expansion $\mathsf{\alpha }\left(\mathrm{T}\right),$ phonon dispersions ${\mathsf{\omega }}_{\mathrm{j}}(\overrightarrow{\mathrm{q}})$, and Grüneisen parameters ${\mathsf{\gamma }}_{\mathrm{j}}\left(\overrightarrow{\mathrm{q}}\right)$ can play important roles in evaluating the suitability of materials in such devices. By adopting a realistic rigid-ion model in the quasi-harmonic approximation, this work aims to report the results of a methodical study to comprehend the structural, lattice dynamical, and thermodynamic behavior of zinc-blende (zb) ZnO. Systematic calculations of ${\mathsf{\omega }}_{\mathrm{j}}(\overrightarrow{\mathrm{q}})$, ${\mathsf{\gamma }}_{\mathrm{j}}\left(\overrightarrow{\mathrm{q}}\right),$ and $\mathsf{\alpha }\left(\mathrm{T}\right)$ have indicated negative thermal expansion (NTE) at low T. Soft transverse acoustic shear mode gammas ${\mathsf{\gamma }}_{\mathrm{T}\mathrm{A}}$ at critical points offered major contributions to NTE. Our results of ${\mathsf{\omega }}_{\mathrm{j}}(\overrightarrow{\mathrm{q}})$ at ambient pressure compare reasonably well with Raman scattering spectroscopy measurements and first-principles calculations. By adjusting the layers of materials with positive and negative thermal expansion, it is possible to create LDHs with near-zero $\mathsf{\alpha }\left(\mathrm{T}\right)$. Such a nanostructure might experience a minimal dimensional change with T fluctuations, making it ideal for devices where precise dimensional stability is crucial. Full article