Search Results (4,340)

An eco-friendly composite coating was developed for blueberry preservation through the incorporation of thymol-loaded β-cyclodextrin microcapsules (THY@β-CD) into a potato starch (PO) matrix. Microencapsulation at an optimal wall-to-core ratio of 13:1 achieved a THY encapsulation efficiency of 73.24%. Structural analyses confirmed the successful formation of an inclusion complex, which enhanced thermal stability and provided a controlled release profile governed by Fickian diffusion mechanisms. When applied to blueberries, the coating significantly reduced weight loss by 22%, delayed softening, and more effectively preserved anthocyanin content compared to uncoated fruit during 10-day storage. Furthermore, it well-maintained the sensory quality and visual appeal of the fruit. These results demonstrate that the THY@β-CD/PO coating synergistically integrates sustained antimicrobial delivery with matrix compatibility, offering a promising natural alternative to synthetic preservatives for extending the shelf life of blueberries. Full article

Background: Peptide-loaded antigen-presenting cell (APC)-based vaccines have been under investigation as a therapeutic approach for treating cancer. However, in general they have demonstrated limited efficacy in clinical trials. Dendritic cells (DCs) have been the primary choice for APC-based vaccines given their ability to cross-present antigens. B cells have been less studied as APCs for vaccines. Here we compare the phenotype and anti-tumor activity of activated T cells that result from peptide-specific priming using either B cells or DCs. Methods: B cells and DCs were isolated from C57Bl/6 mice, and either treated or not treated with lipopolysaccharide (LPS) for maturation, and then either loaded or not loaded with SIINFEKL peptide to prime CD8+ T cells from OT-1 mice. Activated T cells were then analyzed for their phenotype and anti-tumor efficacy. Results: We report that both immature B cells and immature DCs were similarly capable of activating antigen-specific CD8+ T cells. However, LPS-matured DCs generated a stronger CD8+ T cell activation profile in vitro compared to LPS-matured B cells. Immature B cells, mature DCs and immature DCs all generated a similar anti-tumor response upon adoptive transfer of primed CD8+ T cells to tumor-bearing mice. Conclusion: Collectively, our data suggests that B cells and DCs are each capable of priming CD8+ T cells and generating anti-tumor responses. Given that B cells are relatively easier to culture and expand compared to DCs, our study suggests that, following further validation, B cells could be further investigated as APCs for peptide-based human cancer vaccines. Full article

Background/Objectives: Hepatitis B virus (HBV) infection poses a major global health challenge, with current therapies like nucleos(t)ide analogs and pegylated interferon alpha offering limited functional cure rates due to persistent HBsAg-driven immune tolerance. This study aimed to develop a targeted immunoadsorption system using a high-affinity humanized anti-HBsAg monoclonal antibody for efficient HBsAg and viral particle clearance, providing a novel approach to overcome therapeutic bottlenecks in chronic hepatitis B (CHB). Methods: A murine anti-HBsAg monoclonal antibody was humanized via complementarity-determining region grafting, resulting in HmAb-12 (equilibrium dissociation constant, K_D = 0.36 nM). A stable Chinese Hamster Ovary K1 (CHO-K1) cell line was established for high-yield expression (fed-batch yield: 8.31 g/L). The antibody was covalently coupled to agarose microspheres (coupling efficiency > 95%) to prepare the immunoadsorbent. Efficacy was evaluated through in vitro dynamic circulation assays with artificial sera and preclinical trials using an integrated blood purification system in two CHB participants. Clearance rates for HBsAg and HBV DNA were quantified, with safety assessed via blood component monitoring. Results: In vitro, a single treatment cycle achieved HBsAg clearance rates of 70.14% (high antigen load, >10⁵ IU/mL) and 92.10% (low antigen load, ~3000 IU/mL). Preclinically, one treatment session resulted in acute HBsAg reductions of 78.30% and 74.31% in participants with high and moderate antigen loads, respectively, alongside HBV DNA decreases of 65.66% and 73.55%. Minimal fluctuations in total protein and albumin levels (<15%) confirmed favorable safety profiles, with no serious adverse events observed. Conclusions: Preliminary findings from this study indicate that the HBsAg-specific immunoadsorption system can achieve efficient HBV antigen clearance with an initial favorable safety profile in a small cohort. These results support its further investigation as a potential therapeutic strategy for functional cure in CHB. Future work will focus on validating these findings in larger studies and exploring the system’s combinatory potential with existing blood purification platforms. Full article

To address the dynamic characteristics of vehicle semi-active suspension systems under special operating conditions and multi-source excitations, this paper proposes a fuzzy adaptive proportional–integral–derivative model predictive control (PID-MPC) strategy aimed at enhancing ride comfort during vehicle operation. The proposed approach employs MPC as the primary controller to optimize suspension performance, incorporating a fuzzy adaptive PID compensation mechanism for real-time adjustment of PID parameters, thereby improving control efficacy. A half-car semi-active suspension model was established on the MATLAB/Simulink (2020b) platform, with simulation validation conducted across diverse road profiles, including speed bump road surface, Class B road surface, and Class C road surface. Simulation results demonstrate that the proposed strategy achieves a significant reduction in both vehicle vertical acceleration and vehicle pitch angle acceleration while maintaining appropriate suspension deflection and tire dynamic loads, effectively elevating occupant ride comfort. Research demonstrates that the fuzzy adaptive PID-MPC control strategy exhibits commendable performance under typical road operating conditions, possessing notable potential for practical engineering implementation. Full article

Candida albicans infections are associated with high morbidity and mortality worldwide. Current antifungal therapies are limited by adverse effects and the emergence of resistant strains, which compromise long-term efficacy. Previous studies have shown that Origanum vulgare L. essential oil (OvEO) possesses strong antifungal activity; however, its volatility and physicochemical instability hinder clinical application. The aim of this study was to encapsulate OvEO in a hydrogel and evaluate its release kinetics, chemical composition, structural properties, and antifungal activity. We assessed its release kinetics, chemical composition, structural characteristics (FTIR; SEM), and antifungal activity against C. albicans. OvEO was successfully encapsulated into hydrogel beads, enabling a gradual release profile, with in vitro release of phenolic compounds reaching 100% at 48 min. SEM revealed an irregular surface with small pores and crystalline aggregates distributed across the bead surface. OvEO-loaded hydrogel beads inhibited C. albicans growth with an IC₅₀ of 0.15 ± 0.05 mg/L for strain 90029 and 0.2 ± 0.06 mg/L for strain 10231. At these concentrations, adhesion to abiotic surfaces was reduced by 60–80%. These findings support the potential of OvEO-loaded hydrogel beads as an alternative approach for the treatment of fungal infections, offering a complementary strategy to current antifungal agents. Full article

High penetration of distributed photovoltaics (DPV) in distribution networks can lead to voltage violations, increased network losses, and renewable energy curtailment, posing significant challenges to both economic efficiency and operational stability. To address these issues, this study develops a coordinated planning framework for DPV and energy-storage systems (ESS) that simultaneously achieves cost minimization and operational reliability. The proposed method employs a cluster partitioning strategy that integrates electrical modularity, active and reactive power balance, and node affiliation metrics, enhanced by a net-power-constrained Fast-Newman Algorithm to ensure strong intra-cluster coupling and rational scale distribution. On this basis, a dual layer optimization model is developed, where the upper layer minimizes annualized costs through optimal siting and sizing of DPV and ESS, and the lower layer simultaneously suppresses voltage deviations, reduces network losses, and maximizes PV utilization by employing an adaptive-grid multi-objective particle-swarm optimization approach. The framework is validated on the IEEE 33-node test system using typical PV generation and load profiles. The simulation results indicate that, compared with a hybrid second-order cone programming method, the proposed approach reduces annual costs by 6.6%, decreases peak–valley load difference by 22.6%, and improves PV utilization by 28.9%, while maintaining voltage deviations below 6.3%. These findings demonstrate that the proposed framework offers an efficient and scalable solution for enhancing renewable hosting capacity, and provides both theoretical foundations and practical guidance for the coordinated integration of DPV and ESS in active distribution networks. Full article

Shear strength, which indicates the interfacial bond performance between new and old concrete, is critical in the field of structural reinforcement and rehabilitation. However, the absence of standardized testing equipment has hindered the accurate quantification of this parameter. To address this gap, a dedicated shear-loading apparatus was designed in this study, and finite element modeling was conducted to simulate the shear performance of concrete with different interface roughness. The results show that failure consistently occurs at the interface and that roughness has significant influence on shear capacity. In order to reveal the relationship between shear strength and surface roughness, shear experiments were conducted on new–old concrete using the device we designed. The surface of old concrete was treated by water-jetting, electric hammering, grooving, or grout seal strip to create different profiles, the roughness was quantified by 3D scanning and Fourier transform analysis, and fresh concrete was then cast atop the processed surfaces to form composite specimens. The results show that the correlation between shear strength (τ) and Fourier transform roughness (FTR) can be described with the equation τ (MPa) = 0.546FTR² + 1.832FTR − 0.447. Full article

The integration of photovoltaic (PV) generation into distribution systems supports decarbonization and cost reduction but introduces challenges for secure and efficient operation due to voltage fluctuations and power flow variability. Traditional centralized optimal power flow (OPF) methods require full system observability and significant computational resources, limiting their real-time applicability in active distribution systems. This paper proposes a deep neural network (DNN)-based OPF control framework designed for active distribution systems with high PV penetration under limited measurement availability. The proposed method leverages offline convex chance-constrained OPF (convex-CCOPF) solutions, generated through iterative simulations across a wide range of PV and load conditions, to train the DNN to approximate optimal control actions, including on-load tap changer (OLTC) positions and inverter reactive power dispatch. To address observability constraints, the DNN is trained using a reduced set of strategically selected measurement points, making it suitable for real-world deployment in distribution systems with sparse sensing infrastructure. The effectiveness of the proposed framework is validated on the IEEE 33-bus test system under varying operating conditions. The simulation results demonstrate that the DNN achieves near-optimal performance with a significantly reduced computation time compared to conventional OPF solvers while maintaining voltage profiles within permissible limits and minimizing power losses. Full article

Complete dentures (CD) are prone to biofilm formation, particularly by Candida species, which may lead to denture stomatitis (DS). As edentulism remains highly prevalent among the global ageing population, denture-related infections represent a significant public health concern. The novelty of this study lies in integrating the clinical severity of DS with the prevalence, microbial load, and antifungal susceptibility profile of Candida spp., providing new insights into the pathogenesis and therapeutic management of this condition. Biofilm from the CD and palate was seeded for prevalence and microbial load. The identification of strains was confirmed molecularly, and susceptibility to micafungin, nystatin, fluconazole, and miconazole was assessed by the broth microdilution method. Prevalence was shown in percentage, microbial load was analyzed using a generalized linear model test, and susceptibility was assessed using Pearson’s Chi-square test (p < 0.05). Candida albicans was the most prevalent regardless of DS. However, a higher microbial load of C. albicans was observed with increased severity of DS (p = 0.038). Except for Candida tropicalis, the microbial load of the CD was higher than that of the palate. C. tropicalis showed resistance to fluconazole with increased severity of DS (p = 0.004). All strains were susceptible to nystatin and miconazole, and three were resistant to micafungin. The findings suggest that the prevalence of Candida spp. is not a determining factor in the variation in DS severity. Nevertheless, patients with severe inflammation harbor an increased load of C. albicans on both sites. Nystatin and miconazole appear to be effective treatments for DS. Full article

Chronic hepatitis B virus (HBV) infection may influence extrahepatic systems, including endocrine and lipid regulation. In this cross-sectional study, 186 adults were stratified by HBV DNA status and viral load to examine thyroid function, systemic inflammation, and lipid metabolism, with further analyses by age and sex. Thyroid-stimulating hormone (TSH, a pituitary regulator of thyroid function) levels were significantly lower in HBsAg-positive individuals compared with controls; however, this association was attenuated after stratification by viral load, indicating that the relationship is not unequivocally independent of HBV DNA levels, as free thyroxine (FT4, the circulating thyroid hormone reflecting gland activity) levels remained stable. Lipid profiles displayed demographic-specific patterns: males with high viral load exhibited lower HDL cholesterol, whereas younger HBV-positive individuals showed higher LDL cholesterol. CRP levels were unaffected by HBV status or viral load, aligning with the absence of systemic inflammation in early or inactive disease stages. Age was a major determinant across biomarkers, with complex interactions involving sex and viral load. These findings indicate subtle but clinically relevant extrahepatic effects of HBV infection and underscore the need for personalized monitoring and longitudinal studies to clarify metabolic and cardiovascular implications. These subgroup trends should be interpreted with caution given the absence of BMI, liver enzyme, fibrosis, medication, and comorbidity data in this retrospective cohort. Full article

This study develops an optimization-based scheduling framework for coordinating the energy-intensive operations of a steel enterprise with estimated solar power availability. Unlike prior approaches that focus primarily on process efficiency or carbon reduction in isolation, the proposed model integrates demand response with linear programming to improve solar utilization while respecting load priorities. The solar generation profile is derived from typical meteorological year (TMY) irradiance data, adjusted for panel efficiency and system parameters, thereby serving as an estimated input rather than measured data. Simulation results over a 31-day horizon show that coordinated scheduling can reduce grid dependence and increase solar energy utilization by up to 99% under the simulated conditions. While the findings demonstrate the potential of load scheduling for industrial decarbonization, they are based on estimated solar data and a simplified system representation. Future work should incorporate real-world solar measurements and stochastic models to address uncertainty and further validate industrial applicability. Full article

Data-driven forecasting is becoming increasingly central to modern energy management, yet nonspecialists without a background in artificial intelligence (AI) face significant barriers to entry. While Python is the dominant machine learning language, R remains a practical and accessible tool for users with expertise in statistics, engineering, or domain-specific analysis. To inform tool selection, we first provide an evidence-based comparison of R with major alternatives before reviewing 49 peer-reviewed articles published between 2020 and 2025 in Science Citation Index Expanded (SCIE)-level journals that utilized R for energy forecasting tasks, including electricity (regional and site-level), solar, wind, thermal energy, and natural gas. Despite such growth, the field still lacks a systematic, cross-domain synthesis that clarifies which R-based methods prevail, how accessible workflows are implemented, and where methodological gaps remain; this motivated our use of text mining. Text mining techniques were employed to categorize the literature according to forecasting objectives, modeling methods, application domains, and tool usage patterns. The results indicate that tree-based ensemble learning models—e.g., random forests, gradient boosting, and hybrid variants—are employed most frequently, particularly for solar and short-term load forecasting. Notably, few studies incorporated automated model selection or explainable AI; however, there is a growing shift toward interpretable and beginner-friendly workflows. This review offers a practical reference for nonexperts seeking to apply R in energy forecasting contexts, emphasizing accessible modeling strategies and reproducible practices. We also curate example R scripts, workflow templates, and a study-level link catalog to support replication. The findings of this review support the broader democratization of energy analytics by identifying trends and methodologies suitable for users without advanced AI training. Finally, we synthesize domain-specific evidence and outline the text-mining pipeline, present visual keyword profiles and comparative performance tables that surface prevailing strategies and unmet needs, and conclude with practical guidance and targeted directions for future research. Full article

Equivalent energy consumption minimization methods of energy management systems have been implemented as a rule-based strategy to enhance electric propulsion system efficiency. This study compares the efficiencies of different systems by applying variable- and constant-speed generators with battery hybrid systems, measuring fuel consumption. In the same scenario, the variable-speed operation showed a notable improvement of 10.36% compared to the conventional system. However, in the verification of hybrid system efficiency, onshore charged energy cannot be considered a reduction in fuel consumption. Instead, when converting onshore energy usage into equivalent fuel consumption for comparative analysis, both hybrid constant- and variable-speed operation modes achieved efficiency enhancements ranging from 5.5% to 9.79% compared to the conventional, nonequivalent constant-speed operation mode. Conversely, the nonequivalent variable-speed operation mode demonstrated an efficiency that was 5.41% higher than that of the hybrid constant-speed operation mode. In contrast, the battery-integrated variable-speed operation mode indicated a system efficiency approximately equal to that of the nonequivalent variable-speed operation mode. For vessels with load profiles characterized by prolonged periods of idling or low-load operations, a battery-integrated hybrid system could be a practical solution. This study demonstrates the necessity of analyzing load profiles, even when aiming for the optimal operational set points of the generator engine. Full article

Unmanned surface vehicles (USVs) have great application prospects in defense, environmental surveillance and offshore energy due to their cost-effectiveness and long-duration mission ability. The structural safety issues induced by the prolonged cyclic wave loading on such small-sized marine structures, such as fatigue failure mechanism, represent an important research topic. In order to characterize the loading process, a piston-type numerical wave flume with wave absorption setting is constructed using the Arbitrary Lagrangian Eulerian (ALE) formulation, and the fluid–structure interaction (FSI) simulations are performed. Simulated wave profiles are measured and compared with corresponding analytical wave solutions to verify the accuracy of target waves. The wave absorption effect is verified by comparing the velocities of water particles in different water regions. Then, different impact scenarios are performed by applying a range of the applicable target waves. Simulated wave forms, impact scenes along with the computed wave load data are presented, and the impact process is analyzed. As a result, the FSI simulations demonstrate cyclic loading characteristics of small-sized floating structures subjected to wave impacts, and the constructed ALE numerical wave flume possesses the extensibility for the simulation of nonlinear water wave impact scenarios. Full article

To enhance the structural performance of concrete-filled steel tube (CFST) columns, various strengthening techniques have been proposed, including the use of internal steel stiffeners, external wrapping with carbon fiber-reinforced polymer (CFRP) sheets, and embedded steel elements. However, the behavior of concrete-filled stainless-steel tube (CFSST) columns remains insufficiently explored. This study numerically investigates the axial performance of square CFSST columns internally strengthened with embedded I-section steel profiles under biaxial eccentric loading. Finite element (FE) simulations were conducted using ABAQUS v. 6.2, and the developed models were validated against experimental results from the literature. A comprehensive parametric study was performed to evaluate the effects of several variables, including concrete compressive strength (f_cu), stainless-steel yield strength (f_y), the depth ratio between the stainless-steel tube and the internal I-section (D_st/D_si), biaxial eccentricities (e_x and e_y), and tube thickness (t). The results demonstrated that the axial performance of CFSST columns was most significantly influenced by increasing the D_st/D_si ratio and load eccentricities. In contrast, increasing the concrete strength and steel yield strength had relatively modest effects. Specifically, the ultimate axial capacity increased by 9.97% when the steel yield strength rose from 550 MPa to 650 MPa and by 33.72% when the tube thickness increased from 3.0 mm to 5.0 mm. A strength gain of only 10.23% was observed when the concrete strength increased from 30 MPa to 60 MPa. Moreover, the energy absorption index of the strengthened columns improved in correlation with the enhanced axial capacities. Full article