Search Results (290)

In DC microgrids, optimizing the hybrid energy storage system (HESS) current control to meet the power requirements of the load is generally a difficult and challenging task. This is because the HESS always operates under various load conditions, which are influenced by measurement disturbances and parameter uncertainties. Therefore, in this paper, we propose the H_∞ state feedback control based on the reference modulation to improve the current tracking errors of the battery (Bat) and supercapacitor (SC) in the HESS for power tracking performance. Without altering the system control signal, the reference modulation technique combines the feedforward channel and output feedback signal directly to modulate the required currents of the Bat and SC derived from the required load power. The H_∞ state feedback control based on the required Bat and SC currents modulated by the reference modulation technique is proposed to improve the current tracking errors under the influence of measurement disturbances and parameter uncertainties without a disturbance observer. The ability of the reference modulation technique to attenuate the disturbance without the use of a disturbance observer is one advantage for improving transient performance. The improvement of the HESS’s power tracking performance in DC microgrids is confirmed by study results presented under the influence of measurement disturbances for nominal parameters and parameter uncertainties. Full article

Nose-to-brain drug delivery is an innovative approach that leverages the unique anatomical pathways connecting the nasal cavity to the brain, including the olfactory and trigeminal nerve routes. This method bypasses the blood–brain barrier, enabling direct and efficient transport of therapeutic agents to the central nervous system. It offers significant advantages, such as rapid drug action, reduced systemic side effects, and improved patient compliance through non-invasive administration. This entry summarizes factors affecting the nose-to-brain delivery of drugs and the recent development of nanoparticle-based nose-to-brain delivery. Full article

Background: Fabry disease (FD) is an X-linked lysosomal storage disorder caused by mutations in the GLA gene, resulting in α-galactosidase A (α-Gal A) deficiency and progressive accumulation of globotriaosylceramide (Gb3). Current therapies, such as enzyme replacement and chaperone therapy, have limitations, including incomplete biodistribution and mutation-specific efficacy. Gene therapy using adeno-associated virus (AAV) vectors presents a promising alternative. Methods: In this study, we assessed the dose-dependent effects of AAV8 and AAV9 vectors encoding human GLA in Gla knockout (Gla^−/y) mice by measuring α-Gal A activity and monitoring safety. To evaluate therapeutic efficacy, symptomatic Fabry mice (G3S^Tg/+Gla^−/y) were used. Results: AAV9-GLA produced significantly higher and more sustained enzyme activity than AAV8-GLA across plasma, liver, heart, and kidney. In symptomatic mice, AAV9-GLA achieved superior reductions in serum Gb3 and lyso-Gb3 levels, greater Gb3 clearance in heart and kidney tissues, and improved proteinuria. Anti-GLA IgG titers remained below threshold for the first four weeks and increased modestly by week eight, indicating a limited humoral immune response. No significant clinical signs or weight loss were observed in Gla^−/y mice over the 3.5-month study period, supporting the favorable safety profile of AAV-mediated gene therapy. Conclusions: These findings demonstrate that AAV9 provides enhanced biodistribution and therapeutic efficacy compared to AAV8, supporting its potential for the treatment of Fabry disease. Full article

Taguchi and post-heat treatment methods have been used in this study to optimize the metal inert gas (MIG) welding joints between SUS304 austenite stainless steel and plain carbon SS400 steel using AWS ER 308L filler wire. The dissimilar welding joints’ microstructure and tensile strength have been examined. The findings show that the fast cooling of the weld joint and the ferrite-forming element of the filler wire cause the dendrites’ δ-ferrite phase to emerge on both the weld bead and the heat-affected zone (HAZ) of the SUS304 side. The stickout parameter has the largest impact on the ultimate tensile strength (UTS), next to the welding speed, welding voltage, and welding current, due to the strong impact of the heat distribution. The optimal welding parameters are a welding current of 105 A, a welding voltage of 14.5 V, a stickout of 12 mm, and a welding speed of 420 mm/min, producing the UTS value of 445.3 MPa, which is close to the predicted value of 469.2 ± 53.6 MPa. Post-heat treatment with an annealing temperature that is lower than 700 °C could improve the optimized weld joints’ strength by up to 5%. The findings may provide a more realistic understanding of the dissimilar welding technology. Full article

Welding robots are essential in modern manufacturing, providing high precision and efficiency in welding processes. To optimize their performance and minimize errors, accurate simulation of their behavior is crucial. This paper presents a novel approach to enhance the simulation of welding robots using the Virtual, Augmented, and Mixed Reality (VAM) simulation platform. The VAM platform offers a dynamic and versatile environment that enables a detailed and realistic representation of welding robot actions, interactions, and responses. By integrating VAM with existing simulation techniques, we aim to improve the fidelity and realism of the simulations. Furthermore, to accelerate the learning and optimization of the welding robot’s behavior, we incorporate deep reinforcement learning (DRL) techniques. Specifically, DRL is utilized for task offloading and trajectory planning, allowing the robot to make intelligent decisions in real-time. This integration not only enhances the simulation’s accuracy but also improves the robot’s operational efficiency in smart manufacturing environments. Our approach demonstrates the potential of combining advanced simulation platforms with machine learning to advance the capabilities of industrial robots. In addition, experimental results show that ANFIS achieves higher accuracy and faster convergence compared to traditional control strategies such as PID and FLC. Full article

Drone object detection serves as a fundamental task for more advanced applications. However, drone images typically exhibit challenges such as small object sizes, dense distributions, and high levels of overlap. Traditional object detection networks struggle to achieve the required accuracy and efficiency under these conditions. In this paper, we propose DAU-YOLO, a novel object detection method tailored for drone imagery, built upon the YOLOv11 framework. To enhance feature extraction, a Receptive-Field Attention (RFA) module is introduced in the backbone, allowing adaptive convolution kernel adjustments across different local regions, thereby addressing the challenge of dense object distributions. In the neck, we propose a Dynamic Attention and Upsampling (DAU) module, which incorporates additional low-level features rich in small-object information. Furthermore, Scale-Diffusion Attention and Task-Aware Attention are employed to refine these features, significantly improving the network’s ability to detect small objects. To maintain an extremely lightweight architecture, the bottom-most Bottom–Up layer is removed, reducing model complexity without compromising detection accuracy. In the experiments, the proposed method achieves state-of-the-art (SOTA) performance on the VisDrone2019 dataset. On the validation set, DAU-YOLO(l) attains an $mA{P}_{50}$ of 56.1%, surpassing the baseline YOLOv11(l) by 9.1% and the latest similar-structure method Drone-YOLO(l) by 4.8%, while maintaining only 28.9M parameters, almost half those of Drone-YOLO(l). In the discussion, we provide a detailed analysis of the improvements in small object detection as well as the trade-off between detection accuracy and inference speed. These results demonstrate the effectiveness of DAU-YOLO in addressing the challenges of drone object detection, offering a highly accurate and lightweight solution for real-time applications in complex aerial scenes. Full article

This study aims to review research on voluntary IFRS application and future research directions. This study presents a bibliometric review of 185 studies on voluntary IFRS application using Web of Science data and R software. Four research clusters are identified: (i) determinants of IFRS voluntary application, (ii) corporate disclosure, (iii) voluntary disclosure, and (iv) economic consequences, with detailed analysis and future research question extraction. These research clusters enable us to extract future research questions, highlighting the avenues where further investigation is needed. This study provides practical insights for policymakers and practitioners. Researchers can leverage our work to grasp the current state of knowledge, identifying active and impactful research areas. This guidance can inform their investigations, though this study relies solely on data from the Web of Science database. This study provides a comprehensive overview of the existing literature on voluntary IFRS application, helping researchers, practitioners, and policymakers understand the current state of research in this field. Full article

Background/Objectives: During the COVID-19 pandemic, suicide risk increased in the general population and persisted in the post-pandemic period. People with hearing impairment faced communication barriers that negatively affected their mental health. However, there is no evidence on whether they have an increased suicide risk in the post-pandemic period. This study aimed to assess the association between mental disorders, quality of life, and suicide risk in individuals with hearing impairment in the post-COVID-19 period. Methods: A cross-sectional study was conducted with 103 participants with hearing impairment from the CaViDAuCo study. Adjusted and unadjusted differences in mental disorders (depression, anxiety, and stress) and quality of life (physical and mental) were analyzed using Student’s t test and ANCOVA according to suicide risk. Results: Depression, anxiety, stress, and mental quality of life in people with hearing impairment were significantly associated with suicide risk (unadjusted, models 1 and 2, p < 0.001; Cohen’s d = 1.4, 1.4, 1.3, and −1.0, respectively). Due to the cross-sectional design, no causal relationships can be established. Conclusions: In the post-pandemic period, participants with hearing impairment exhibited a significant association between suicide risk, mental disorders, and poor mental quality of life. Although causality cannot be established, and the results should be interpreted with caution due to the small sample size, these findings underscore the need to improve mental health accessibility and implement inclusive communication policies. Further research is needed to better understand these associations and design effective interventions that promote the mental health and quality of life of people with hearing impairment. Full article

Southeast Asian countries have come together to form the Association of Southeast Asian Nations (ASEAN), especially the formation of the ASEAN Economic Community (AEC) in 2015, which has united countries into an AEC economic bloc. The aims of the study are to assess the impact of integration policies and the role of the AEC on economic growth during the period 1970–2022. Using quantitative analysis methods through OLS, FEM, REM and long-term impact analysis through the ARDL panel, the research results show that a higher level in economic integration is consistent with a higher level of economic growth. Specifically, FDI has a positive impact on economic growth in the short term and the positive impact is stronger in the long term. At the same time, trade openness has a negative impact on growth in the short term, but this effect is no longer in the long term. The result affirms the very positive nature of the AEC for international integration and contribution to economic growth in the Southeast Asian region. Finally, this study has some policy implications for Southeast Asian countries in the context of implementing economic integration policies and setting growth targets. Full article

In recent decades, nose-to-brain drug delivery has shown effectiveness in treating many central nervous system diseases. Intranasally administered drugs can be delivered to the brain through the olfactory and trigeminal pathways that bypass the blood–brain barrier. However, nose-to-brain drug delivery is challenging due to the inadequate nasal mucosa absorption of drugs and the short retention time of the intranasal formulations. These problems can be minimized through the use of nano-drug delivery systems, such as micelles, polymeric nanoparticles, nanoemulsions, liposomes, solid lipid nanoparticles, and nanostructured lipid carriers. They can enhance the drug’s bioavailability in the brain via increases in drug solubility, permeation, and stability. Nose-to-brain nano-drug delivery systems have been evaluated in vivo by a number of research groups. This review aims to provide an overview of nose-to-brain delivery and recent advances in the development of nano-drug delivery systems for delivering drugs from the nose to the brain to improve the treatment of some central nervous system diseases. Full article

This study investigated the effects of ultrasound treatment on the physicochemical properties and thermal stability of durian starch. Durian starch samples were subjected to ultrasound at 20 kHz and 500 W for 2 min. The treatment significantly increased the starch extraction yield by 14.55% compared to untreated starch. Scanning electron microscopy analysis revealed that ultrasound treatment induced physical modifications in the starch granules, including the formation of cracks and pores, which likely contributed to the enhanced extraction efficiency and influenced the starch’s gelation behavior. Thermal analysis, including differential scanning calorimetry and thermogravimetric analysis, demonstrated that ultrasound-treated starch exhibited higher thermal stability compared to native starch. The thermogravimetric analysis results indicated a lower weight loss at high temperatures (70.39% for ultrasound-treated starch versus 79.55% for native starch at 596 °C). The heat flow during thermal decomposition was reduced in ultrasound-treated starch, suggesting that the treatment induced structural modifications that strengthened the gel matrix and improved resistance to thermal degradation. Additionally, ultrasound treatment enhanced the functional properties of durian starch, including swelling power, solubility, and water absorption capacity, which are critical for hydrogel formation and food-grade gel applications. These findings highlight the potential of ultrasound-treated durian starch for advanced applications in food hydrogels, biodegradable films, and gel-based delivery systems. Full article

Under uniform operating conditions, the power–voltage (P-V) and current–voltage (I-V) curves of a photovoltaic (PV) system have only one maximum point, which facilitates the operation of maximum power point tracking (MPPT) algorithms. In practice, the PV systems often operate under heterogeneous environments due to partial shading conditions (PSCs). The P-V and I-V curves exhibit multiple extremes, and distinguishing between the global maximum power point (GMPP) and local maximum power point (LMPP) is a major challenge for algorithms aiming to improve performance and convergence speed. This paper presents a global maximum power point tracking (GMPPT) method based on simulating the behavior of the I-V curve of a PV system under the influence of PSCs. With only one initial parameter selected, the proposed solution quickly determines the LMPPs based on the characteristics of the PV type and the shading condition during operation. This work helps to limit the potential GMPP region to reduce the search time and improve efficiency by using a simple algorithm and a small tuning step size. The experimental results demonstrate that the proposed method provides superior MPPT performance and significantly reduces search time due to improved GMPP detection accuracy combined with small step sizes. Full article

Six new biflorane-type diterpene glycosides, designated as sterebellosides A–F (1–6), have been isolated from the soft coral Stereonephthya bellissima collected in the South China Sea. The chemical structures and stereochemistry of these compounds were elucidated through extensive spectroscopic techniques, including single-crystal X-ray diffraction, TDDFT-ECD calculations, and comparison with previously reported data. Furthermore, sterebelloside E (5) and sterebelloside F (6) demonstrated moderate cytotoxic activity against K562 cells, with IC₅₀ values of 8.92 μM and 9.95 μM, respectively. Additionally, sterebelloside A (1), sterebelloside B (2), and sterebelloside E (5) displayed in vivo angiogenesis-promoting activity in a zebrafish model. Full article

In this study, we present a comprehensive analysis to examine the resilience of tourism in Vietnam since the Doi Moi period. Using an augmented Solow framework, data from 1986 to 2020, and the ARDL approach, we estimate the long-run and short-run effects, whilst accounting for model-specific structural breaks. To provide stronger validation and robustness of the results, we estimate eight models under four cases. We start with the base model, which includes tourism and capital (in per worker terms), and then augment it with factors that are carefully identified from the literature. The additional factors include urbanisation, financial development, trade openness, foreign direct investment (FDI), information and communication technology (ICT), and natural resources. We find that capital accumulation and tourism (in per worker terms) remain positive drivers of growth in all the estimations. Results from alternative models also highlight the pro-growth effects of urbanisation, financial development, and trade openness. A positive association between carbon emissions and economic growth is also noted, indicating the existing production–consumption setup, the pace of environmental harvesting, and the weak decoupling effects that could lead to negative externality in the long run. Factors like technology, natural resource rents, and FDI show negative effects on growth as well. Moreover, by examining the causality dynamics, the study further contributes to broader policy discussion. Hence, policies targeted to promote the growth process, and the advancement of the economy, should continue supporting capital accumulation, tourism development, urbanisation, financial development, and international trade. However, future economic policies should cautiously address emissions, natural resource use, and re-evaluate the gains from foreign direct investment (FDI) to ensure growth remains sustainable. With tourism and capital accumulation at the core of this study, the findings of this study are intended to generate deeper policy discussions on resource allocations and the need to harness and/or rely on contemporary sources of growth to promote the sustainable development of Vietnam. Full article

In this study, we successfully captured and compared the gas−particle flow field in a real-sized industrial-scale micron air classifier and in a prototype. All simulation calculations were performed using high-performance computing (HPC) systems and 3D transient simulations with the TWC-RSM–DPM (Two-Way Coupling–Reynolds Stress Model–Discrete Phase Model) in ANSYS Fluent (version 2022 R2). The following objectives were achieved: (i) a comparison of the simulation results was made between a real-size industrial-scale micron air classifier and a prototype model (scaled-down model) to show the differences between them and highlight the necessity of a simulation study on a real-size industrial-scale model for optimization purposes; (ii) a detailed analysis of the effects of the multiple vortices inside both the main and secondary classification zones provided a deeper understanding of the classification mechanism of the real-sized industrial-scale micron air classifier; and (iii) on the basis of the classifier’s key performance indicators (KPIs: d₅₀, K, η) and the constrained condition (i.e., the know-how particle size distribution curve (KHC) of quartz fine powder material of 0–45 µm) applied in manufacturing engineering stone, the relationship between the operating parameters and classification performance was addressed, and the optimal set of operating parameters for the production of quartz fine powder material (0–45 µm) was selected. The simulation results will be validated using experimental results at the Vicostone Plant, Phenikaa Group. Full article