Search Results (2,333)

Benefiting from their outstanding porosity, considerable specific surface area, and natural flexibility, cellulose nanofibers (CNFs)/MOF materials have emerged as competitive candidates for advanced flexible energy storage devices. However, conventional CNFs/MOFs aerogels or films often suffer from poor recoverability under compression, bending, and folding, accompanied by severe plastic deformation that compromises the cycling and structural stability of devices. To address this issue, we report a rationally designed flexible PU/CNFs/ZIF-8/PANI composite foam with an interconnected micro-mesoporous structure. Using polyurethane foam as a soft substrate and CNFs/ZIF-8 as building blocks, the composite was fabricated through a combined strategy of impregnation, in situ ZIF-8 growth, hot-pressing, and in situ aniline polymerization with simultaneous etching of the ZIF-8. The incorporation of carboxylated CNFs enhances the hydrophilicity of the PU skeleton. This, in combination with the hot-pressed framework, establishes an interconnected 3D network, thereby effectively preventing the agglomeration of active materials. Meanwhile, the hierarchical pores derived from the sacrificial ZIF-8 template provide abundant electroactive sites, accelerate ion transport, and facilitate high PANI loading. By virtue of this synergistic architectural effect, the resultant electrode achieves a high specific capacitance of 449 F/g at 0.2 A/g, with 97% capacitance retention after 2000 cycles at 5 A/g. Furthermore, the composite foam demonstrates excellent mechanical flexibility, with a tensile strength of 0.87 MPa and an elongation at break of 230%. This work offers a feasible approach for developing high-performance flexible supercapacitors and provides novel perspectives for the rational design of portable energy storage devices. Full article

This paper introduces a hybrid metaheuristic approach for the reconfiguration of electric distribution networks, integrating Simulated Annealing (SA) and Tabu Search (TS) to accelerate convergence and enhance exploration of the solution space. The method employs a selective mesh-based neighbor generation strategy, which substantially reduces the search space while maintaining operational feasibility (radial topology, voltage, and current limits). The approach was implemented in Python and integrated with DIgSILENT PowerFactory, enabling the direct evaluation of losses, voltages, and currents for reproducible and scalable analysis. Validation on 5-, 16- and 33-bus benchmark systems consistently reached the global optimum across 100 simulation runs, demonstrating robustness and computational efficiency. A real-world application was performed on the 10 kV primary distribution network of Huancayo, Peru, where the proposed method achieved a 10.4% reduction in active losses, improved the minimum voltage from 0.931 to 0.949 p.u., and partially relieved feeder overloads. These results confirm the method’s suitability for both academic benchmarking and practical deployment in Latin American distribution systems. Full article

Dynamic covalent bonds are commonly used to maintain the self-healing properties of polyurethanes and facilitate resource recycling. However, relying on a single type of dynamic covalent bond often makes it difficult to effectively regulate both mechanical and self-healing properties across a wide temperature range. In this study, a self-synthesized chain extender containing disulfide bonds was introduced into a polyurethane system, leading to the development of a novel dual-dynamic covalent bond self-healing polyurethane (SSDA-PU). Innovatively, this SSDA-PU demonstrates self-healing properties across a wide temperature range. The successful synthesis of the chain extender and the incorporation of both disulfide bonds and Diels–Alder (DA) bonds were confirmed using FTIR and Raman spectroscopy. The physical characteristics and self-healing performance were comprehensively evaluated through multi-scale testing and characterization, including thermogravimetric analysis (TGA), dynamic mechanical analysis (DMA), hardness testing, mechanical tensile tests, and self-healing experiments. The underlying synergistic self-healing mechanism was subsequently elucidated. Findings showed that a higher R-value (isocyanate index) in SSDA-PU leads to over-crosslinking, while an R-value of 1.7 achieves the best overall mechanical performance, with tensile strength and elongation at break reaching 21.1 MPa and 755.17%, respectively. Additionally, SSDA-PU demonstrated the capacity for multiple healing cycles, with an initial self-healing efficiency of 90.38%, which remained notably high at 59.21% even after three damage-healing cycles. Importantly, SSDA-PU exhibited healing capabilities even at relatively low temperatures. Cracks in SSDA-PU can be effectively repaired through the synergistic action of disulfide bond exchange, hydrogen bond dissociation, and thermally reversible DA reactions. SSDA-PU also shows excellent recyclability, offering valuable insights for the practical engineering application of functional polyurethanes. Full article

Thermal barrier materials play a crucial role in reducing heat transfer, suppressing thermal runaway (TR) propagation, and mitigating the risk of fire and explosion. Among the various types of thermal barrier materials, polymer-based thermal barrier materials, including polyimide (PI), aramid, epoxy resin (ER), polyurethane (PU), phenolic resin (PR), and silicone, have been widely applied in lithium-ion battery (LIB) safety protection owing to their excellent thermal stability, structural tunability, and favorable processability. This review provides a systematic and comprehensive overview of polymer-based thermal barrier materials for mitigating thermal runaway propagation in LIBs. The propagation pathways of TR in battery systems are first outlined to clarify the functional requirements of thermal barrier materials. Subsequently, representative classes of polymer materials are reviewed with emphasis on their structural characteristics and advantages. Strategies for enhancing thermal insulation, flame retardancy, heat absorption capacity, and mechanical robustness are then summarized in the context of thermal safety protection. Finally, key challenges associated with polymer-based thermal barrier materials are discussed, and future development directions are proposed. Full article

The study develops and implements a methodology for prioritizing power units (PUs) of captive power plants (CPPs) to support the development of maintenance and repair (M&R) programs considering their actual technical condition (TC) and reliability indicators. The proposed approach is based on the joint assessment of the technical condition index (TCI), the consumed technical resource (CTR), and the risk level (RL) of the PUs. To describe the statistical patterns of failures, a two-parameter Weibull distribution is applied, while the temporal change in the TCI is approximated by a linear relationship that accounts for differences between actual and nominal operating conditions. The CTR is defined as an integral characteristic reflecting the deviation between the actual and nominal TCI degradation functions. The RL is evaluated as a function of the probability of failure and the consequences of PU failure. Based on these individual indicators, an integrated priority index is formed to provide an unambiguous ranking of PUs. The methodology was implemented using actual operational data from a fleet of PUs of an energy company. The results demonstrate that using the TCI alone does not fully reflect the actual TC of the PUs, whereas the combined consideration of TC, CTR, and RL enables a more justified formation of M&R programs. The practical significance of the study lies in the possibility of applying the developed methodology for reliability management of PUs at CPPs under resource constraints. Full article

This study examines the impact of ChatGPT-assisted learning on the academic achievement of hospitality and tourism students in Egyptian public universities, with particular emphasis on the mediating roles of perceived usefulness and self-regulated learning. Drawing conceptually on the Technology Acceptance Model (TAM), the study adopts a contextualized framework that emphasizes perceived usefulness while incorporating ChatGPT-assisted learning effectiveness as a learning-oriented driver within generative AI-supported educational environments. A quantitative research design was employed using an online survey administered to students who actively used ChatGPT for academic purposes. A total of 689 valid responses were collected from nine public universities and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the proposed hypotheses. The findings indicate that ChatGPT-Assisted Learning Effectiveness (CALE) has a statistically significant and positive direct effect on academic achievement (AA; β = 0.386, T = 3.946, p < 0.001, 95% CI = 0.192–0.561) and strongly predicts perceived usefulness (β = 0.673, T = 9.274, p < 0.001, 95% CI = 0.581–0.742) and self-regulated learning (β = 0.707, T = 10.734, p < 0.001, 95% CI = 0.621–0.779). In turn, PU (β = 0.281, T = 3.854, p < 0.001, 95% CI = 0.142–0.417) and SRL (β = 0.220, T = 2.418, p = 0.016, 95% CI = 0.041–0.356) significantly enhance academic achievement. Mediation analyses further confirm that PU (β = 0.189, T = 2.366, p = 0.018, 95% CI = 0.031–0.284) and SRL (β = 0.156, T = 3.699, p < 0.001, 95% CI = 0.102–0.301) partially mediate the relationship between CALE and academic achievement. These findings offer important theoretical insights by contextualizing TAM’s performance-related logic within generative AI-driven learning environments and refining its application to academic outcome settings, while highlighting self-regulated learning as a critical explanatory mechanism. From a practical perspective, the study provides valuable implications for educators and policymakers by emphasizing the need to promote students’ perceived usefulness of ChatGPT and foster learner autonomy, positioning generative AI as a powerful pedagogical support tool for enhancing academic success in hospitality and tourism education. Full article

Elemental migration and enrichment are important processes influencing tea plant growth and the assembly of rhizosphere bacterial communities within the rock–soil–plant continuum. This study explores how soil parent materials (granite, quartz schist, and sericite schist) are potentially associated with these processes and their observed associations with the elemental composition of tea leaves. Exploratory statistical analyses revealed distinct, lithology-specific biogeochemical patterns that serve as a foundation for hypothesis generation. In granite soils, chlorite correlated with the mobility of Cr, Pb, Cu, Ni, Mg, and Na, coinciding with shifts in the relative abundances of Verrucomicrobia, Armatimonadetes, and Chloroflexi. In quartz schist, kaolinite exhibited notable correlations with the dynamics of Pb, Cr, Ni, Zn, and As, which were statistically linked to Planctomycetes, Proteobacteria, and Acidobacteria. Complex mineral–microbe interactions were observed in sericite schist soils, where clay minerals (e.g., chlorite, illite) were closely associated with the migration of multiple elements (Pb, K, Ca, Cd, As, Al, Fe, Zn), paralleling structural variations in communities of Actinobacteria, Planctomycetes, Chloroflexi, and Proteobacteria. Potassium (K), calcium (Ca), and manganese (Mn) showed bioaccumulation tendencies in tea leaves across all lithologies, with an enrichment capacity order of Ca > K > Mn > Mg > Na > Al. Exploratory Classification and Regression Tree (CART) analysis suggested that the migration of K, Ca, Cu, Zn, and Hg corresponded most closely with their soil concentrations. Manganese (Mn) exhibited a mineral-associated trend, with kaolinite content as a potential correlate, while cadmium (Cd) migration was statistically linked to the relative abundance of Armatimonadetes. These findings highlight potential candidate relationships between mineralogy, microbes, and elemental mobility rather than confirming causal mechanisms, emphasizing the need for further validation in larger or experimental datasets. Full article

Background: Diabetic foot ulcers (DFUs) are considered a prevalent complication of diabetes mellitus, frequently accompanied with compromised peripheral circulation, slower healing, as well as high risk of infection in addition to risk of amputation. Additional treatments that enhance microvascular perfusion and lessen plantar pressure may accelerate the healing process. This study was carried out to examine the impact of pulsed electromagnetic field (EMF) therapy as well as customized silicone gel insoles in terms of peripheral circulation in addition to vascular indices in patients with DFUs. Methods: A randomized, controlled clinical trial, including sixty-six adults diagnosed with type II diabetes as well as plantar DFUs (Wagner grade I–II) were divided into three groups (n = 22 each): Group A was given low-frequency electromagnetic field therapy (15–50 Hz, 2–5 mT, 30 min, three times per week for 8 weeks), Group B was given a customized silicone gel insoles produced for ulcer offloading, and Group C (control) was given conventional physiotherapy along with wound care. Peripheral microcirculation as well as tissue perfusion were the primary outcomes, and they were measured using Laser Doppler Flowmetry (LDF), Photoplethysmography (PPG), in addition to the Toe–Brachial Index (TBI). The secondary outcome included the Ankle–Brachial Pressure Index (ABPI). A blinded assessor measured the outcomes at the beginning of the study, after the intervention (week 8), and again after the follow-up (week 16). Results: EMF therapy significantly improved LDF (baseline: 45.2 ± 6.5 PU; week 8: 62.5 ± 7.2 PU), PPG (0.42 ± 0.08 mV to 0.68 ± 0.10 mV), TBI (0.64 ± 0.07 to 0.82 ± 0.08), and ABPI (0.88 ± 0.06 to 0.97 ± 0.05) compared with insoles and controls (p < 0.001, partial η² 0.25–0.37). The insole group exhibited moderate enhancements, whereas the control group demonstrated minor changes. Between-group analyses showed substantial differences in favor of EMF therapy across all measured variables (F = 13.5–19.9, p < 0.001). Improvements continued at the 8-week follow-up. Conclusions: Patients with DFUs who receive EMF therapy experience a significant improvement in their peripheral microcirculation, tissue perfusion, as well as vascular indices. This is more effective than just mechanical offloading, and custom insoles offer extra benefits by redistributing pressure. Combining EMF therapy with regular DFU care may speed up healing and lower the risk of problems. Additional research should investigate the efficacy of combined EMF as well as off-loading interventions and their long-term outcomes. Full article

Staphylococcus epidermidis is a leading opportunistic pathogen in medical device-associated infections due to its ability to adhere to abiotic materials and develop biofilms that are difficult to eradicate. This study investigated the antibiofilm potential of an ethanolic extract of the red seaweed Gracilaria fisheri and its purified constituent, N-benzylcinnamamide, against S. epidermidis. Antibacterial activity was determined, and antibiofilm effects were assessed using the crystal violet assay and confocal laser scanning microscopy (CLSM). Early bacterial adhesion on glass and polyurethane (PU) surfaces was measured. The effect on catheter-associated biofilms was evaluated by scanning electron microscopy (SEM). Transcripts of biofilm- and quorum-sensing-associated genes (icaA and luxS) were assessed by semi-quantitative RT-PCR. Cytotoxicity was evaluated by MTT assay. At 200 µg/mL, biofilm biomass decreased to 48.21 ± 5.52% with the extract and to 36.65 ± 6.82% with N-benzylcinnamamide. CLSM time-course imaging showed delayed biofilm maturation and less consolidated, discontinuous structures. Surface exposure to the extract markedly reduced early attachment on both materials. On PU catheter segments, SEM demonstrated that N-benzylcinnamamide markedly reduced surface coverage and disrupted three-dimensional biofilm architecture. At the molecular level, transcription of icaA and luxS was reduced. Both the extract and N-benzylcinnamamide showed minimal cytotoxicity in HeLa cells. These findings support further evaluation of these marine-derived agents as candidates for antibiofilm surface treatments to reduce early medical device colonization. Full article

Growing demand for sustainable materials has intensified research into eco-friendly alternatives to conventional and synthetic leathers. Traditional bovine leather and its chromium-tanning process heavily contribute to water pollution, toxic waste generation, and carbon emissions, while synthetic leather derived from Polyvinyl Chloride (PVC) and polyurethane (PU) presents challenges related to fossil fuel dependence and non-biodegradability. This review explores bio-based and sustainable leather substitutes that are made of plants, microbial cellulose, and mycelium fungi. Plant-based leather substitutes such as Vegea^®, Desserto^®, and Piñatex^® use agricultural waste products to create durable, partially biodegradable composites. Microbial cellulose from kombucha fermentation offers material with good physical and aesthetic properties. Mycelium leather, derived from fungal biomass, demonstrates potential for scalable and low-impact production. Comparative analyses of mechanical and physical properties show that mycelium composites are approaching industrial standards, though challenges remain regarding tensile strength, water resistance, and process standardization. Despite current limitations, bio-based leathers, particularly mycelium composites, offer a promising way toward circular material innovation and carbon-neutral manufacturing in fashion, automotive, design and other industries. Full article

In offshore wind farms, modular multilevel converters (MMCs) may operate under a deloading condition to accommodate wind-speed volatility and dispatch constraints. Here, deloading is defined as transmitted power < 0.2 pu (scenario S2, low-power non-reversal). Under this condition, submodule capacitor-voltage fault signatures are weak and exhibit strong operating-point-dependent drift, which degrades conventional threshold-based or offline-trained methods. We propose a lightweight switch-level IGBT open-circuit fault localization framework for deloaded MMCs. Wavelet packet decomposition is used to extract time–frequency energy features, and principal component analysis reduces feature dimensionality for lightweight deployment. An enhanced XGBoost model further integrates severity-index weighting to alleviate class imbalance and incremental learning to adapt to condition drift induced by wind-power fluctuations. MATLAB2024b/Simulink results show 99.6% accuracy in S2 with less than 2 ms inference latency, and robust performance in extended scenarios including partial-power operation and power reversal. Full article

Rigid polyurethane foams are often manufactured in sealed molds, so knowledge of the density distribution in the molded blocks is essential. A study was conducted with the aim to estimate density distribution within a rigid polyurethane foam block (average core density of ≈96 kg/m³) manufactured in a rectangular sealed mold. The density of 150 rectangular samples was determined experimentally. Characteristic locations of the foams’ columns in the block were outlined, having similar foaming conditions. Averaged density in the characteristic columns was calculated for each characteristic location. A mathematical model was developed based on density data of characteristic columns, approximated with second- and third-degree polynomials. Density distribution was calculated, and corresponding color charts with density zones and equidensity lines were constructed for six horizontal and two vertical sections of the block. It was found that the common center of the elliptical equidensity lines is located asymmetrically, ≈17 mm above the geometric center of the untrimmed block. Density gradients were calculated in directions parallel and perpendicular to the foams’ rise direction. The developed mathematical model allowed us to estimate density distribution within the rigid polyurethane foam block manufactured in a rectangular sealed mold. Full article

To address the limitations of traditional forest above-ground biomass (AGB) retrieval methods—namely, the restricted accuracy of physical models and the limited generalization ability of purely data-driven models—this study proposes a differentiable physical modeling (DPM) approach for forest AGB estimation. The method adopts the water cloud model (WCM) as a physics-based framework, grounded in radiative transfer theory, and integrates C-band synthetic aperture radar (SAR) data with multispectral imagery. Within the PyTorch tensor computation framework, automatic differentiation (AD) is employed to seamlessly couple the WCM with the deep fully connected neural network (DFCNN), enabling a differentiable implementation of the WCM. Using mean squared error (MSE) as the loss function, the neural network parameters are optimized through backpropagation and gradient descent, thereby constructing an end-to-end trainable DPM model that effectively retrieves forest AGB while preserving physical interpretability and generalization capability. To validate the proposed method, two representative test sites were selected: Simao in Pu’er, Yunnan Province, and Genhe in Inner Mongolia. GF-3 PolSAR and RADARSAT-2 data were used to extract backscattering coefficients and compute the radar vegetation index (RVI), while Landsat 8 OLI imagery was employed to calculate the normalized difference vegetation index (NDVI), difference vegetation index (DVI), and soil-adjusted vegetation index (SAVI). These datasets, together with ASTER GDEM, field-measured biomass, and other relevant datasets, were integrated to construct a multisource dataset combining remote sensing and ground observations. The performance of the DPM model was then compared with the traditional WCM and several data-driven models, including the fully connected neural network (FNN), generalized regression neural network (GRNN), RF, and Adaptive Boosting (AdaBoost). The results indicate that the DPM model achieved R² = 0.60, RMSE = 24.23 Mg/ha, Bias = 0.4 Mg/ha, and ubRMSE = 22.43 Mg/ha in Simao, and R² = 0.48, RMSE = 33.29 Mg/ha, Bias = 0.87 Mg/ha, and ubRMSE = 33.28 Mg/ha in Genhe, demonstrating consistently better performance than both the WCM and all tested data-driven models. The DPM model demonstrated consistent performance across ecologically contrasting forest regions. It alleviated the systematic overestimation bias of purely data-driven models and overcame the limitations in predictive accuracy resulting from the simplified structure of the WCM. The differentiability of the WCM enables the loss function errors to be backpropagated through the neural network, thereby allowing the optimization of the physical model parameters. Overall, the DPM framework integrates the advantages of both physical models and data-driven approaches, providing an estimation method with acceptable accuracy for forest AGB retrieval. It also offers theoretical and practical insights for the integration of deep learning and physical knowledge in other research fields. Full article

Permanent magnet synchronous machines (PMSMs) are widely used in high-performance drive systems. However, parasitic torque ripple remains a critical limitation, causing acoustic noise, mechanical vibration, and speed fluctuations. This study presents a compact, model-based torque control strategy for surface-mounted PMSMs (SPMSMs) that suppresses torque ripple by generating a structured current reference. Grounded in the magnetic co-energy principle, the proposed method utilizes a deterministic analytical model to compensate for cogging torque and inductance harmonics, avoiding computationally intensive iterative estimators. A primary contribution involves adapting the harmonic injection profile to varying loads and magnetic saturation levels. Comprehensive finite element analysis (FEA) co-simulations demonstrate that the proposed method reduces torque ripple by approximately 87.5% and speed ripple by over 90% at 1500 RPM compared to conventional maximum torque per ampere (MTPA) strategies. Furthermore, extended dynamic analysis confirms superior robustness during start-up, transients, and low-speed operation (100 RPM), maintaining high control authority even under deep magnetic saturation (2.0 p.u.). Performance evaluations verify that this significant enhancement in torque quality is achieved with a negligible increase in total power losses (~2.1%), presenting a computationally feasible solution for industrial embedded platforms. Full article

The wine aromatic profile is influenced by complex interactions between grapevine genotype and enological practices. Thus, the aim of this study was to investigate the combined effects of grapevine clones and yeast strains on the volatile composition and sensory properties of wines produced from the Croatian indigenous variety Moslavac. Wines from five registered Moslavac clones (PUS-017, PUS-026, PUS-030A, PUS-087, and PUS-111) were produced using two commercially available yeast strains (Lalvin QA23 and Zymaflore Xarom). Significant effects of both clone and yeast strain were observed, particularly for yeast-derived compounds, such as isoamyl alcohol, phenylethyl alcohol, and medium-chain fatty acids. Ester production was generally enhanced by the Xarom yeast strain, although clone differences were also observed. Grape-derived volatile compounds differed significantly among clones, with wines from clones PUS-030A and PUS-087 having higher concentrations of norisoprenoids and terpenes, while PUS-017 wines consistently displayed lower concentrations of volatile compounds. Furthermore, PCA and MLF analyses revealed a clear differentiation between clones, with the yeast strain having a secondary modulatory effect. The sensory results were consistent with chemical data, demonstrating that clonal selection plays a key role in defining aromatic expression and typicity of Moslavac wines. Full article