Search Results (9,007)

The oxygen evolution reaction (OER) plays a central role as an anode in electrocatalytic processes such as energy conversion and storage and the generation of molecular oxygen from the electrolysis of water. Currently, precious metal oxides such as IrO₂ and RuO₂ are recognized as reference OER electrocatalysts with reasonably high activity; however, their widespread use in practical devices has been severely hindered by their high cost and scarcity. It is essential to design alternative OER electrocatalysts made of low-cost and abundant earth elements with significant activity and robustness. We report four new nanocellulose-derived Fe–zeolite nanocomposites, namely Fe/Zeolite@CCNC (1), Fe/Zeolite@CCNF (2), Fe/Zeolite/CNT@CCNC (3), and Fe/Zeolite/CNT@CCNF (4). Two different types of nanocellulose were investigated: nanocellulose nanofibrils and nanocellulose nanocrystals. Characterization with TEM, SEM-EDS, PXRD, and XPS is reported. The nanocomposites exhibited electrocatalytic activity for OER that varies based on the origin of biocarbon and the composition content. The effect of adding carbon nanotubes to the nanocomposites was studied, and an improvement in OER catalysis was observed. The electrochemical double-layer capacitance and electrochemical impedance spectroscopy of the nanocomposites are reported. The nanocomposite 3 exhibited the highest performance, with an onset potential value of 1.654 V and an overpotential of 551 mV, which exceeds the activity of RuO₂ for OER catalysis at 10 mA/cm² in the glassy carbon electrode. A 24 h chronoamperometry study revealed that the catalyst is active for ~2 h under continuous operating conditions. BET surface analysis showed that the crystalline nanocellulose-derived composite exhibited 301.47 m²/g, and the fibril nanocellulose-derived composite exhibited 120.39 m²/g, indicating that the increased nanoporosity of the former contributes to the increase in OER catalysis. Full article

Computational Fluid Dynamics (CFD) is regarded as an important tool for analyzing the flow field, thermal environment, and air quality around the built environment. However, for built environment applications, the high computational cost of CFD hinders large-scale scenario simulation and efficient design optimization. In the field of built environment research, surrogate modeling has become a key technology to connect the needs of high-fidelity CFD simulation and rapid prediction, whereas the low-dimensional nature of traditional surrogate models is unable to match the physical complexity and prediction needs of built flow fields. Therefore, combining machine learning (ML) with CFD to predict flow fields in built environments offers a promising way to increase simulation speed while maintaining reasonable accuracy. This review briefly reviews traditional surrogate models and focuses on ML-based surrogate models, especially the specific application of neural network architectures in rapidly predicting flow fields in the built environment. The review indicates that ML accelerates the three core aspects of CFD, namely mesh preprocessing, numerical solving, and post-processing visualization, in order to achieve efficient coupled CFD simulation. Although ML surrogate models still face challenges such as data availability, multi-physics field coupling, and generalization capability, the emergence of physical information-driven data enhancement techniques effectively alleviates the above problems. Meanwhile, the integration of traditional methods with ML can further enhance the comprehensive performance of surrogate models. Notably, the online ministry of trained ML models using transfer learning strategies deserves further research. These advances will provide an important basis for advancing efficient and accurate operational solutions in sustainable building design and operation. Full article

The Pennes bioheat equation is the most widely used model for describing heat transfer in living tissue during thermal exposure. It is derived from the classical Fourier law of heat conduction and assumes energy exchange between blood vessels and surrounding tissues. The literature presents various numerical methods for solving the bioheat equation, with exact solutions developed for different boundary conditions and geometries. However, analytical models based on this framework are rarely reported. This study aims to develop an analytical three-dimensional model using MATHEMATICA software, with subsequent mathematical validation performed through COMSOL simulations, to characterize heat transfer in biological tissues induced by laser irradiation under various therapeutic conditions. The objective is to refine the conventional bioheat equation by introducing three key improvements: (a) incorporating a non-Fourier framework for the Pennes equation, thereby accounting for the relaxation time in thermal response; (b) integrating Dirac functions and the telegraph equation into the bioheat model to simulate localized point heating of diseased tissue; and (c) deriving a closed-form analytical solution for the Pennes equation in both its classical (Fourier-based) and improved (non-Fourier-based) formulations. This paper investigates the nuanced relationship between the relaxation time parameter in the telegraph equation and the thermal relaxation time employed in the bioheat transfer equation. Considering all these aspects, the optimal thermal relaxation time determined for these simulations was 1.16 s, while the investigated thermal exposure time ranged from 0.01 s to 120 s. This study introduces a generalized version of the model, providing a more realistic representation of heat exchange between biological tissue and blood flow by accounting for non-uniform temperature distribution. It is important to note that a reasonable agreement was observed between the two modeling approaches: analytical (MATHEMATICA) and numerical (COMSOL) simulations. As a result, this research paves the way for advancements in laser-based medical treatments and thermal therapies, ultimately contributing to more optimized therapeutic outcomes. Full article

Solid propellant grain, as a typical polymer, are the thrust generation devices and core load-bearing components of solid rocket motor (SRM) and are also known as SRM grain. They are constantly exposed to extreme service environments such as high temperatures, high pressures, and dynamic shocks, and have a relatively high failure rate in the field use of SRM. Its life and reliability are the shortcomings that restrict the improvement of weapons and equipment capability in China at present. This paper summarizes the typical fault types of SRM grain at present, and compares and analyzes the research progress of reliability design and analysis technology, reliability optimization technology, life test technology and reliability evaluation technology of SRM grain at home and abroad; This paper analyzes the deficiencies and reasons in the research and application of SRM grain reliability technology in China, and points out the technical difficulties and challenges faced by the integrated design of performance and reliability of SRM independent innovation design according to the needs of the forward research and development system of SRM. Based on the existing design level and industrial foundation in China, the basic research suggestions that should be carried out to consolidate the design ability of SRM grain in China are given. Full article

Weeksella virosa (W. virosa) is a rare, non-saccharolytic Gram-negative bacterium initially described in the 1970s, later proposed as a distinct genus in 1986. The genus Weeksella currently contains two species, namely W. virosa and W. massiliensis. Although primarily considered non-pathogenic, recent evidence has linked W. virosa to a limited number of clinical infections, mostly in immunocompromised patients. This review aims to consolidate the current body of knowledge on W. virosa, encompassing its microbiological and biochemical characteristics, involvement in human and animal infections, antimicrobial susceptibility profiles, and a critical evaluation of existing diagnostic methodologies. This review includes 13 case reports detailing 16 human cases retrieved from multiple databases, highlighting diagnostic inconsistencies and a lack of standardized antimicrobial susceptibility testing. Although W. virosa is generally susceptible to most antibiotics with the exception of aminoglycosides, recent reports seem to suggest a possible emerging resistance trend. The presence of this organism in hospital environments raises concerns about its potential transmission within healthcare settings. While biochemical testing appears to offer reasonably accurate identification of W. virosa, molecular confirmation may be warranted in some cases mainly due to the organism’s rarity. The reliability of MALDI-TOF MS for the identification of W. virosa remains currently uncertain. Further studies, including electron microscopy and genome-wide analysis, are urgently needed to clarify the pathogenic potential of this bacterium and guide clinical management. This review underscores the necessity for awareness among clinicians and microbiologists regarding this underrecognized pathogen. Full article

Aiming at the goal of preparing high-quality coatings, this paper reviews the progress on circular arc source structure and magnetic field arc controlling technology in arc ion plating (AIP), with a focus on design characteristics of the different structures and configuration optimization of the corresponding magnetic fields. The circular arc source, due to its simple structure, convenient installation, flexible target combination, high cooling efficiency, and high ionization rate and deposition rate, has shown significant application potential in AIP technology. In terms of magnetic field arc controlling technology, this paper delves into the design progress of various magnetic field configurations, including fixed magnetic fields generated by permanent magnets, dynamic rotating magnetic fields, axially symmetric magnetic fields, rotating transverse magnetic fields, and multi-mode alternating electromagnetic coupling fields. By designing the magnetic field distribution reasonably, the trajectory and velocity of the arc spot can be controlled precisely, thus reducing the generation of macroparticles, improving target utilization, and enhancing coating uniformity. In particular, the introduction of multi-mode magnetic field coupling technology has broken through the limitations of traditional single magnetic field structures, achieving comprehensive optimization of arc spot motion and plasma transport. Hopefully, these research advances provide an important theoretical basis and technical support for the application of AIP technology in the preparation for high-quality decorative and functional coatings. Full article

Grasping objects with a high degree of anthropomorphism is a critical component in the field of highly anthropomorphic robotic grasping. However, the accuracy of contact maps and the irrationality of the grasping gesture become challenges for grasp generation. In this paper, we propose a reasonably improved generation scheme, called Diffangle-Grasp, consisting of two parts: contact map generation based on a conditional variational autoencoder (CVAE), sharing the potential space with the diffusion model, and optimized grasping generation, conforming to the physical laws and the natural pose. The experimental findings demonstrate that the proposed method effectively reduces the loss in contact map reconstruction by 9.59% in comparison with the base model. Additionally, it enhances the naturalness by 2.15%, elevates the success rate of grasping by 3.27%, reduces the penetration volume by 11.06%, and maintains the grasping simulation displacement. The comprehensive comparison and qualitative analysis with mainstream schemes also corroborate the rationality of the improvement. In this paper, we provide a comprehensive account of our contributions to enhancing the accuracy of contact maps and the naturalness of grasping gestures. We also offer a detailed technical feasibility analysis for robotic human grasping. Full article

Background and Objectives: General-purpose multimodal large language models (LLMs) are increasingly used for medical image interpretation despite lacking clinical validation. This study evaluates the diagnostic reliability of ChatGPT-4o and Claude 2 in photographic assessment of adolescent idiopathic scoliosis (AIS) against radiological standards. This study examines two critical questions: whether families can derive reliable preliminary assessments from LLMs through analysis of clinical photographs and whether LLMs exhibit cognitive fidelity in their visuospatial reasoning capabilities for AIS assessment. Materials and Methods: A prospective diagnostic accuracy study (STARD-compliant) analyzed 97 adolescents (74 with AIS and 23 with postural asymmetry). Standardized clinical photographs (nine views/patient) were assessed by two LLMs and two orthopedic residents against reference radiological measurements. Primary outcomes included diagnostic accuracy (sensitivity/specificity), Cobb angle concordance (Lin’s CCC), inter-rater reliability (Cohen’s κ), and measurement agreement (Bland–Altman LoA). Results: The LLMs exhibited hazardous diagnostic inaccuracy: ChatGPT misclassified all non-AIS cases (specificity 0% [95% CI: 0.0–14.8]), while Claude 2 generated 78.3% false positives. Systematic measurement errors exceeded clinical tolerance: ChatGPT overestimated thoracic curves by +10.74° (LoA: −21.45° to +42.92°), exceeding tolerance by >800%. Both LLMs showed inverse biomechanical concordance in thoracolumbar curves (CCC ≤ −0.106). Inter-rater reliability fell below random chance (ChatGPT κ = −0.039). Universal proportional bias (slopes ≈ −1.0) caused severe curve underestimation (e.g., 10–15° error for 50° deformities). Human evaluators demonstrated superior bias control (0.3–2.8° vs. 2.6–10.7°) but suboptimal specificity (21.7–26.1%) and hazardous lumbar concordance (CCC: −0.123). Conclusions: General-purpose LLMs demonstrate clinically unacceptable inaccuracy in photographic AIS assessment, contraindicating clinical deployment. Catastrophic false positives, systematic measurement errors exceeding tolerance by 480–1074%, and inverse diagnostic concordance necessitate urgent regulatory safeguards under frameworks like the EU AI Act. Neither LLMs nor photographic human assessment achieve reliability thresholds for standalone screening, mandating domain-specific algorithm development and integration of 3D modalities. Full article

(1) Background: Human Papillomavirus (HPV)-associated oropharyngeal cancer is the fastest-growing head and neck malignancy, yet vaccination coverage remains suboptimal. (2) Methods: In this cross-sectional survey conducted from April 2022 to April 2023, 400 parents of patients aged 8–18 years (mean ± SD = 12.8 ± 2.6; 59.3% female) reported their child’s HPV vaccination status and willingness to initiate or complete the vaccine series at a dental clinic. For those who were not fully vaccinated, reasons for refusal were documented. (3) Results: Over half (54.5%, n = 218) of the children were not fully vaccinated. Notably, 21% (46/218) of parents indicated an immediate willingness to vaccinate their child if the dentist offered it—a significant potential for improvement compared to general healthcare settings. Reported barriers included preference for a physician’s office (43.6%), indecision (20.3%), unspecified concerns (14.5%), safety worries (8.1%), and religious objections (5.2%). Male and younger patients (9–11 years) showed significantly lower vaccination coverage (p < 0.05). (4) Conclusions: Dentists can substantially impact public health by integrating immunization counseling, interprofessional collaboration, and vaccine administration, thereby addressing critical gaps in HPV-related cancer prevention. These findings highlight the opportunity for dental offices to enhance vaccination rates and prompt further research, education, and policy initiatives to advance the oral and general health of our patients. Full article

In this study, we outline a modified harmonic balance method for solving non-homogenous integrable dispersionless equations and obtaining the corresponding periodic solutions, a research field which shows limited investigation. This study is the first to solve this nonlinear problem, based on a recently developed harmonic balance method combined with Vieta’s substitution technique. A set of analytical formulas are generated from the modified harmonic balance method and used to compute the approximate periodic solutions of the dispersionless equations. The main advantage of this method is that the computation effort required in the solution procedure can be smaller. The results of the modified harmonic balance method show reasonable agreement with those obtained using the classic harmonic balance method. Our proposed solution method can decouple the nonlinear algebraic equations generated in the harmonic balance process. We also investigated the effects of various parameters on nonlinear periodic responses and harmonic convergence. Full article

Remanufacturing design is a green design model that considers remanufacturability during the design process to improve the reuse of components. However, traditional remanufacturing design scheme decision making focuses on the remanufacturability indicator and does not fully consider the carbon emissions of the remanufacturing process, which will take away the energy-saving and emission reduction benefits of remanufacturing. In addition, remanufacturing design schemes rarely consider the human ergonomics of the product, which leads to uncomfortable handling of the product by the customer. To reduce the remanufacturing carbon emission and improve customer comfort, it is necessary to select a reasonable design scheme to satisfy the carbon emission reduction and ergonomics demand; therefore, this paper proposes an integrated multi-criteria decision-making method for remanufacturing design that considers the carbon emission and human ergonomics. Firstly, an evaluation system of remanufacturing design schemes is constructed to consider the remanufacturability, cost, carbon emission, and human ergonomics of the product, and the evaluation indicators are quantified by the normalization method and the Kansei engineering (KE) method; meanwhile, the hierarchical analysis method (AHP) and entropy weight method (EW) are used for the calculation of the subjective and objective weights. Then, a multi-attribute decision-making method based on the combination of an assignment approximation of ideal solution ranking (TOPSIS) and gray correlation analysis (GRA) is proposed to complete the design scheme selection. Finally, the feasibility of the scheme is verified by taking a household coffee machine as an example. This method has been implemented as an application using Visual Studio 2022 and Microsoft SQL Server 2022. The research results indicate that this decision-making method can quickly and accurately generate reasonable remanufacturing design schemes. Full article

The estimation of runout distances for long return period avalanches is vital in zoning schemes for mountainous countries. There are two broad methods to estimate snow avalanche runout distance. One involves the use of a physical model to calculate speeds along the incline, with runout distance determined when the speed drops to zero. The second method, which is used here, is based on empirical or statistical models from databases of extreme runout for a given mountain range or area. The second method has been used for more than 40 years with diverse datasets collected from North America and Europe. The primary reason for choosing the method used here is that it is independent of physical models such as avalanche dynamics, which allows comparisons between methods. In this paper, data from diverse datasets are analyzed to explain the relation between them to give an overall view of the meaning of the data. Runout is formulated from nine different datasets and 738 values of extreme runout, mostly with average return periods of about 100 years. Each dataset was initially fit to 65 probability density functions (pdf) using five goodness-of-fit tests. Detailed discussion and analysis are presented for a set of extreme value distributions (Gumbel, Frechet, Weibull). Two distributions had exemplary results in terms of goodness of fit: the generalized logistic (GLO) and the generalized extreme value (GEV) distributions. Considerations included both the goodness-of-fit and the heaviness of the tail, of which the latter is important in engineering decisions. The results showed that, generally, the GLO has a heavier tail. Our paper is the first to compare median extreme runout distances, the first to compare exceedance probability of extreme runout, and the first to analyze many probability distributions for a diverse set of datasets rigorously using five goodness-of-fit tests. Previous papers contained analysis mostly for the Gumbel distribution using only one goodness-of-fit test. Given that climate change is in effect, consideration of stationarity of the distributions is considered. Based on studies of climate change and avalanches, thus far, it has been suggested that stationarity should be a reasonable assumption for the extreme avalanche data considered. Full article

Organic acids could act as retarders in magnesium oxysulfide (MOS) systems, not only delaying setting and improving fluidity but also enhancing compressive strength and water resistance. These effects are generally attributed to both the presence of H⁺ ions and anion chelation. However, the enhancement efficiency of different organic acids in MOS systems varies significantly due to differences in their molecular structures. To determine the underlying mechanism, this study comparatively investigated the effects of amino trimethylene phosphonic acid (ATMP) and tartaric acid (TA) on the setting time, fluidity, compressive strength, and water resistance of the MOS system, with the two additives incorporated at mole ratios to MgO ranging from 0.002 to 0.006. The mechanism behind it was revealed by discussion on the hydration heat, hydrates, and pH value. Results showed that both ATMP and TA could effectively improve the fluidity, delay the setting process, and enhance the mechanical properties, including strength and water resistance. At a mole ratio of 0.006, the incorporation of ATMP increased the 28 d compressive strength and the softening coefficient by 214.12% and 37.29%, respectively, compared with the blank group. In contrast, under the same dosage, TA led to an increase of 55.13% in the 28 d strength and 22.03% in the softening coefficient. Furthermore, hydration heat, product analysis, and pH measurements indicated that both ATMP and TA inhibited hydration during the initial hours but promoted hydration at later stages. The potential reason could be divided into two aspects: (1) H⁺ ions from ATMP and TA suppressing the formation of Mg(OH)₂; (2) anion chelation with Mg²⁺ in the liquid phase, leading to a supersaturated solution with higher saturation, which further hindered Mg(OH)₂ formation and facilitated the later development of 5Mg(OH)₂·MgSO₄·7H₂O (517 phase). By contrast, under the same mole dosage of H⁺ or anions, the enhancement in compressive strength as well as the water resistance is superior when using ATMP. This was owing to its stronger chelating ability of ATMP, which more effectively inhibited Mg(OH)₂ formation and then promoted the formation of the 517 phase. These findings confirm that the chelating ability of anions exerts an important impact on the retarding effect as well as the enhancement of strength in MOS systems. Full article

This paper aims to implement a fuzzy system for the purpose of forecasting the output of photovoltaic (PV) systems. A bibliometric review was conducted to establish a baseline, involving the exploration of six different configuration of fuzzy systems. These systems were trained and evaluated using a sliding window technique and a validation set. The development of the study utilized data collected from 1 May 2018 to 30 June 2018 at the Universidad Autónoma de Occidente campus. The dataset was analyzed in order to identify any discernible trends, seasonal patterns, and instances of stationarity. A comparison of the six models revealed their ability to predict PV power generation, with the model with 13 lags and five fuzzy sets demonstrating results with a reasonable trade-off between training and test performance. The model achieved an R-squared value of 0.8124 and an RMSE of 29.7025 kWh in the test data, indicating that the predictions were closely aligned with the actual values. However, this suggests that the model may be overly simple or may require additional data to more accurately capture the inherent variability of the data. The paper concludes with a discussion of the model’s limitations and potential avenues for future research. Full article

Accurate fire source location in an aircraft cargo compartment cannot be determined by common design practices. This study proposes an advanced fire location inversion framework based on a Convolutional Long-Short-Term Memory (ConvLSTM) network. A self-designed interpolation preprocessing module is introduced to realize the integration of spatial and temporal sensor data. The model was trained and validated using a comprehensive database generated from large-scale fire dynamics simulations. Hyperparameter optimization, including a learning rate of 0.001 and a 5 × 5 convolution kernel size, can effectively avoid the systematic errors introduced by interpolation preprocessing, further enhancing model robustness. Validation in simplified scenarios demonstrated a mean squared error of 0.0042 m and a mean positional deviation of 0.095 m for the fire source location. Moreover, the present study assessed the model’s timeliness and reliability in full-scale cabin complex scenarios. The model maintained high performance across varying heights within cargo compartments, achieving a correlation coefficient of 0.99 and a mean absolute relative error of 1.9%. Noteworthily, reasonable location accuracy can be achieved with a minimum of three detectors, even in obstructed environments. These findings offer a robust tool for enhancing fire safety systems in aviation and other similar complex scenarios. Full article