This study aimed to develop, by the solvent casting method, poly(vinyl alcohol) (PVA) and hydroxypropyl methylcellulose (HPMC E4M)-based patches for periorbital hyperpigmentation incorporating açaí oil (Euterpe oleracea), barbatimão glycolic extract (Stryphnodendron spp.), and hyaluronic acid, and to evaluate preliminarily their cytotoxicity and ex vivo skin hydration potential using in vitro and ex vivo screening models. Patches were prepared using two drying methods: oven-drying (40 °C for 20 h) or lyophilization (24 h). Structural stability after hydration was assessed. Cytotoxicity was evaluated using the XTT assay in RPE-1 hTERT TP53KO cells, considered a suitable in vitro model for early-stage safety evaluation. Ex vivo hydration potential was measured on porcine ears using a portable bioelectrical impedance device. Lyophilized patches exhibited uniform shape with balanced softness and flexibility. Base and hyaluronic acid-containing patches were nontoxic, maintaining normal cell morphology and viability, whereas açaí oil or barbatimão extract induced cytotoxic effects. The hyaluronic acid-containing patch increased water content in porcine skin in a time-dependent manner, and maintained higher values of water content from 15 to 120 min. PVA and HPMC E4M-based patches were successfully prepared with 24 h of lyophilization. Hyaluronic acid-containing patch showed in vitro preliminary safety and ex vivo skin hydration promoting potential. Cytotoxicity of açaí oil and barbatimão extract highlights the need for further experiments in models that better recapitulate periocular skin complexity. Additional studies using hydration measurement methods using validated equipment and in vivo human models are required to confirm efficacy and safety for periocular applications.
The rapid advancement of metal additive manufacturing (AM) has driven the need for innovative feedstock materials, with nano- to micro-alloyed composite powders playing a pivotal role in the development of new materials. In aluminium alloys, alloying additives are introduced to eliminate hot cracking. Composite feedstock quality is therefore critical, as it influences the effectiveness of additives on the final part properties. The particle morphology, size distribution, and structural integrity of feedstock powders directly affect the density and mechanical performance of additively manufactured components. This study employs X-ray computed tomography (XCT) and scanning electron microscopy (SEM) for a comprehensive, quantitative evaluation of Al–Ta composite powders and their manufactured counterparts in the Laser Powder Bed Fusion (PBF-LB/M) process. The integration of XCT and SEM enables a detailed characterisation of powder features and their potential impact on microstructural integrity. Our findings demonstrate the capability of XCT as a non-destructive, high-resolution technique that complements SEM for precise volumetric analysis of powder morphology, structural homogeneity, and defect identification.
For mathematics to supportive tool in enhancing the understanding of core engineering subjects (physics, mechanics, electronics, etc.), students must comprehend and utilise freely mathematical tools, select them aptly for specific tasks, and effectively apply relevant tools in the context at hand. This is essential for transferring this knowledge and these skills to the workplace, enabling future engineers to use mathematics effectively and efficiently in solving real-world engineering problems. This paper presents and discusses the results of a study conducted among academic teachers of core engineering subjects regarding their expectations of students’ mathematical knowledge and computational proficiency, as well as the extent of computations performed by students. serve as aIt also addresses the use of information technology, particularly software for performing mathematical computations, in mathematics education and the challenges students face related to their mathematical knowledge and skills. Furthermore, the paper outlines the conclusions drawn from the study and presents recommendations based on the views expressed by the academic teachers of core engineering subjects. The findings suggest that students should (1) be familiar with the basics of advanced mathematics; (2) not necessarily memorise all relevant facts but be able to access knowledge from external sources; (3) apply mathematical competencies in other domains, which points to the need for collaboration between mathematicians and core engineering subject teachers; (4) regard the use of computer programmes as a natural part of their engineering training, while placing less emphasis on high-level computational skills; (5) be able to interpret and evaluate the results obtained; and (6) be aware of the significance of mathematics in their education.
The projection-based wave function in density functional theory (WF-in-DFT) embedding enables an efficient description of both the energetics and properties of large and complex chemical systems, with accuracy exceeding that of pure DFT. Recently, we have proposed using the density matrix renormalization group (DMRG) as the WF method for molecules containing strongly correlated fragments [Beran, P. J. Phys. Chem. Lett. 2023, 14, 716-722]. In this work, we demonstrate that the accuracy of the DMRG-in-DFT approach is primarily limited by the approximate treatment of the coupling between the active component and its environment through nonadditive exchange-correlation functionals. To address this issue, we combine exact exchange to reduce the nonadditive exchange error with a multireference adiabatic connection (AC) scheme to recover nonadditive correlation. The performance of the improved DMRG-in-DFT embedding is illustrated on two prototypical strongly correlated systems: the dissociation of the H20 chain and the cleavage of a triple CN bond in propionitrile.
Wireless sensor networks (WSNs) contend with the critical challenge of balancing energy conservation against data transmission delay, a trade-off that protocols such as PEGASIS—while being strong in energy efficiency—fail to manage optimally due to resulting high latency, unbalanced load distribution, and suboptimal cluster formation. To address these limitations, this paper introduces the Enhanced Multi-Objective PEGASIS (EMO-PEGASIS) protocol, which is designed and implemented using a dual-phase machine learning strategy. This multi-objective approach works in two stages. First, it utilises K-means clustering to achieve robust spatial partitioning of the network. Second, it employs K-Nearest Neighbours (K-NN) classification to enable adaptive and intelligent routing. The simulation was performed using MATLAB R2025a, and the results show that EMO-PEGASIS addresses this multi-objective optimisation problem. The proposed EMO-PEGASIS protocol achieves a 45% reduction in average energy consumption, a 38% decrease in end-to-end delay, and a 67% increase in network lifetime compared to the original PEGASIS protocol. Additionally, EMO-PEGASIS demonstrates enhanced stability and effective load balancing under heterogeneous network configurations, while maintaining an excellent packet delivery ratio of 96.8%. These findings underscore the effectiveness of integrating machine learning techniques, which ultimately yield enhanced performance and enable reliable multi-objective optimisation within energy- and delay-constrained WSN environments.
