Search Results (52,199)

To address the issues of tedious optimization processes, insufficient fitting accuracy of surrogate models, and low optimization efficiency in drone propeller airfoil design, this paper proposes an aerodynamic optimization method for propeller airfoils based on DBO-BP (Dum Beetle Optimizer-Back-Propagation) and NSWOA (Non-Dominated Sorting Whale Optimization Algorithm). The NACA4412 airfoil is selected as the research subject, optimizing the original airfoil at three angles of attack (2°, 5° and 10°). The CST (Class Function/Shape Function Transformation) airfoil parametrization method is used to parameterize the original airfoil, and Latin hypercube sampling is employed to perturb the original airfoil within a certain range to generate a sample space. CFD (Computational Fluid Dynamics) software (2024.1) is used to perform aerodynamic analysis on the airfoil shapes within the sample space to construct a sample dataset. Subsequently, the DBO algorithm optimizes the initial weights and thresholds of the BP neural network surrogate model to establish the DBO-BP neural network surrogate model. Finally, the NSWOA algorithm is utilized for multi-objective optimization, and CFD software verifies and analyzes the optimization results. The results show that at the angles of attack of 2°, 5° and 10°, the test accuracy of the lift coefficient is increased by 45.35%, 13.4% and 49.3%, and the test accuracy of the drag coefficient is increased by 12.5%, 39.1% and 13.7%. This significantly enhances the prediction accuracy of the BP neural network surrogate model for aerodynamic analysis results, making the optimization outcomes more reliable. The lift coefficient of the airfoil is increased by 0.04342, 0.01156 and 0.03603, the drag coefficient is reduced by 0.00018, 0.00038 and 0.00027, respectively, and the lift-to-drag ratio is improved by 2.95892, 2.96548 and 2.55199, enhancing the convenience of airfoil aerodynamic optimization and improving the aerodynamic performance of the original airfoil. Full article

To address the inefficiencies and time-consuming nature of traditional hot-air anti-icing system designs, reduced-order models (ROMs) and machine learning techniques are introduced to predict anti-icing surface temperature distributions. Two models, AlexNet combined with Proper Orthogonal Decomposition (POD-AlexNet) and multi-CNNs with GRU (MCG), are proposed by comparing several classic neural networks. Design variables of the hot-air anti-icing cavity are used as inputs of the two models, and the corresponding surface temperature distribution data serve as outputs, and then the performance of these models is evaluated on the test set. The POD-AlexNet model achieves a mean prediction accuracy of over 95%, while the MCG model reaches 96.97%. Furthermore, the proposed model demonstrates a prediction time of no more than 5.5 ms for individual temperature samples. The proposed models not only provide faster predictions of anti-icing surface temperature distributions than traditional numerical simulation methods but also ensure acceptable accuracy, which supports the design of aircraft hot-air anti-icing systems based on optimization methods such as genetic algorithms. Full article

Background: Cashmere, known as “soft gold”, is a highly prized fiber from Cashmere goats, produced by secondary hair follicles. Dermal papilla cells, located at the base of these follicles, regulate the proliferation and differentiation of hair matrix cells, which are essential for hair growth and cashmere formation. Recent studies emphasize the role of microRNAs (miRNAs) in controlling gene expression within these processes. Methods: This study centered on exploring the targeted regulatory interaction between miR-144 and the Lhx2 gene. Utilizing methodologies like miRNA target prediction, luciferase reporter assays, and quantitative PCR, they assessed the interplay between miR-144 and Lhx2. Dermal papilla cells derived from Cashmere goats were cultured and transfected with either miR-144 mimics or inhibitors to observe the subsequent effects on Lhx2 expression. Results: The results demonstrated that miR-144 directly targets the Lhx2 gene by binding to its mRNA, leading to a decrease in Lhx2 expression. This modulation of Lhx2 levels influenced the behavior of dermal papilla cells, affecting their ability to regulate hair matrix cell proliferation and differentiation. Consequently, the manipulation of miR-144 levels had a significant impact on the growth cycle of cashmere wool. Conclusions: The findings suggest miR-144 regulates hair follicle dynamics by targeting Lhx2, offering insights into hair growth mechanisms. This could lead to innovations in enhancing cashmere production, fleece quality, and addressing hair growth disorders. Future research may focus on adjusting miR-144 levels to optimize Lhx2 expression and promote hair follicle activity. Full article

Negative self-perceptions of aging have been linked to poorer health and quality of life and predict significantly depressive symptomatology. The support provided by the partner may have an impact on the effects of self-perceptions of aging on depressive symptoms; a close relationship can go along with additional stress or resources and benefits. The present study analyzes the relationship between negative self-stereotypes and depressive symptomatology, considering positive and negative dyadic coping (DC) as moderator variables in this association. Method: Participants were 365 individuals (convenience sample) 40 years or older (M = 60.86) involved in a partner relationship. Participants completed a questionnaire that included the following variables: negative self-perceptions of aging, positive DC (e.g., “My partner shows empathy and understanding to me”), negative DC (e.g., “When I am stressed, my partner tends to withdraw”), and depressive symptomatology. Two moderation models were tested by linear regression. Results: The effect of negative self-perceptions of aging on depressive symptoms was moderated by positive and negative DC only in women. The effect of negative self-perceptions of aging appears to be smaller among those women with higher levels of positive DC and lower levels of negative DC. Conclusions: Positive DC might buffer the association between negative self-perceptions of aging and depressive symptoms. Negative DC might amplify this association, as it is associated with lower well-being among women who express negative self-perceptions of aging. Implications: Training couples in strategies for providing supportive dyadic coping may be a resource to buffer the negative effect of negative self-perceptions of aging on well-being. Full article

Investigation into the post-processing machinability of Ti6Al4V alloy is increasingly crucial in the manufacturing industry, particularly in the machining of additively manufactured (AM) Ti6Al4V alloy to ensure effective machining parameters. This review article summarizes various AM techniques and machining processes for Ti6Al4V alloy. It focuses on powder-based fusion AM techniques such as electron beam melting (EBM), selected laser melting (SLM), and direct metal deposition (DMD). The review addresses key aspects of machining Ti6Al4V alloy, including machining parameters, residual stress effects, hardness, microstructural changes, and surface defects introduced during the additive manufacturing (AM) process. Additionally, it covers the qualification process for machined components and the optimization of cutting parameters. It also examines the application of finite element analysis (FEA) in post-processing methods for Ti6Al4V alloy. The review reveals a scarcity of articles addressing the significance of post-processing methods and the qualification process for machined parts of Ti6Al4V alloy fabricated using such AM techniques. Consequently, this article focuses on the AM-based techniques for Ti6Al4V alloy parts to evaluate and understand the performance of the Johnson–Cook (J–C) model in predicting flow stress and cutting forces during machining of the alloy. Full article

Most rice growth stage predictions are currently based on a few rice varieties for prediction method studies, primarily using linear regression, machine learning, and other methods to build growth stage prediction models that tend to have poor generalization ability, low accuracy, and face various challenges. In this study, multispectral images of rice at various growth stages were captured using an unmanned aerial vehicle, and single-plant rice silhouettes were identified for 327 rice varieties by establishing a deep-learning algorithm. A growth stage prediction method was established for the 327 rice varieties based on the normalized vegetation index combined with cubic polynomial regression equations to simulate their growth changes, and it was first proposed that the growth stages of different rice varieties were inferred by analyzing the normalized difference vegetation index growth rate. Overall, the single-plant rice contour recognition model showed good contour recognition ability for different rice varieties, with most of the prediction accuracies in the range of 0.75–0.93. The accuracy of the rice growth stage prediction model in recognizing different rice varieties also showed some variation, with the root mean square error between 0.506 and 3.373 days, the relative root mean square error between 2.555% and 14.660%, the Bias between1.126 and 2.358 days, and the relative Bias between 0.787% and 9.397%; therefore, the growth stage prediction model of rice varieties can be used to effectively improve the prediction accuracy of the growth stage periods of rice. Full article

Dynamic voltage restores (DVRs) are usually used to mitigate the effect of voltage sag and guarantee sufficient power supply for sensitive loads. However, three-phase voltage cannot be compensated to the desired balance voltage under unbalanced three-phase loads by traditional DVRs with a three-phase, three-leg inverter. To address this problem, a three-phase, four-leg inverter-based DVR is first introduced in this paper, and then the state space model in its continuous form and discrete form are established, respectively. A two-step predictive method is proposed for the prediction of the output voltage in each switching state by the established voltage prediction model. Finite-control-set model predictive control (MPC) is developed to be used in the three-phase, four-leg inverter-based DVR. Its dynamic response is effectively improved by the proposed MPC method under various voltage sag conditions. The proposed DVR control strategy is validated via MATLAB/Simulink-R2022b simulations, which demonstrate its effectiveness in voltage compensation under various sag conditions. Full article

Existing rotated object detection methods usually use angular parameters to represent the object orientation. However, due to the symmetry and periodicity of these angular parameters, a well-known boundary discontinuity problem often results. More specifically, when the object orientation angle approaches the periodic boundary, the predicted angle may change rapidly and adversely affect model training. To address this problem, this paper introduces a new method that can effectively solve the boundary discontinuity problem related to angle parameters in rotated object detection. Our approach involves a novel vector-based encoding and decoding technique for angular parameters, and a cosine distance loss function for angular accuracy evaluation. By utilizing the characteristics of unit vectors and cosine similarity functions, our method parameterizes the orientation angle as components of the unit vector during the encoding process and redefines the orientation angle prediction task as a vector prediction problem, effectively avoiding the boundary discontinuity problem. The proposed method achieved a mean average precision (mAP) of 87.48% and an average cosine similarity (CS) of 0.997 on the MVTec test set. It also achieved an mAP score of 90.54% on the HRSC2016 test set, which is better than several existing state-of-the-art methods and proves its accuracy and effectiveness. Full article

Background/Objectives: The number of individuals with lower limb loss (LLL) is rising. Therefore, identifying the walking potential in individuals with LLL and prescribing adequate prosthetic systems are crucial. Various factors can influence participants’ walking ability, to different extents. The aim of the present study was to apply machine learning methods to develop a predictive mode. This model can assist rehabilitation and limb loss care teams in making informed decisions regarding prosthesis prescription and predicting walking ability in individuals with LLL. Methods: The present study was designed as a prospective cross-sectional study encompassing 104 consecutively recruited participants with LLL (average age 62.1 ± 10.9 years, 80 (76.9%) men) at the Medical Rehabilitation Clinic. Demographic, physical, psychological, and social status data of patients were collected at the beginning of the rehabilitation program. At the end of the treatment, K-level estimation of functional ability, a Timed Up and Go Test (TUG), and a Two-Minute Walking Test (TMWT) were performed. Support vector machines (SVM) were used to develop the prediction model. Results: Three decision trees were created, one for each output, as follows: K-level, TUG, and TMWT. For all three outputs, there were eight significant predictors (balance, body mass index, age, Beck depression inventory, amputation level, muscle strength of the residual extremity hip extensors, intact extremity (IE) plantar flexors, and IE hip extensors). For the K-level, the ninth predictor was The Multidimensional Scale of Perceived Social Support (MSPSS). Conclusions: Using the SVM model, we can predict the K-level, TUG, and TMWT with high accuracy. These clinical assessments could be incorporated into routine clinical practice to guide clinicians and inform patients of their potential level of ambulation. Full article

This paper presents a numerical analysis of two hot water storage tank configurations—one equipped with an external heat exchanger (Tank-1) and the other with an internal heat exchanger (Tank-2). The objective is to evaluate and compare their thermal performance during charging and discharging processes. The numerical model is developed by solving a system of ordinary differential equations using the 4th-order Runge–Kutta method, implemented in the Python programming language. The results indicate that Tank-1 demonstrated a higher charging efficiency of 94.6%, achieving full charge in approximately 2 h and 20 min. In comparison, Tank-2 required 3 h and 47 min to reach full charge, with a charging efficiency of 85.9%. During discharge, both configurations exhibited similar behavior, with an efficiency of 13.63% over approximately 33 min. The analysis showed that the external heat exchanger configuration led to more effective thermal stratification, supported by the Richardson number analysis, which indicated a significant effect of buoyancy during charging. This design advantage makes Tank-1 particularly suitable for applications requiring rapid heating and minimal heat loss, such as in cold climates or intermittent demand systems. The numerical model demonstrated reliable predictive accuracy, achieving an RMSE of 6.1% for the charging process and 6.8% for the discharging process, thereby validating the model’s reliability. These findings highlight the superior performance of the external heat exchanger configuration for fast and efficient energy storage, particularly for applications in cold climates. Full article

In recent years, numerous studies have highlighted the pivotal importance of miRNAs in personalized healthcare, showcasing broad application prospects. miRNAs hold significant potential in disease diagnosis, prognosis assessment, and therapeutic target discovery, making them an integral part of precision medicine. They are expected to enable precise disease subtyping and risk prediction, thereby advancing the development of precision medicine. GNNs, a class of deep learning architectures tailored for graph data analysis, have greatly facilitated the advancement of miRNA-disease association prediction algorithms. However, current methods often fall short in leveraging network node information, particularly in utilizing global information while neglecting the importance of local information. Effectively harnessing both local and global information remains a pressing challenge. To tackle this challenge, we propose an innovative model named DGNMDA. Initially, we constructed various miRNA and disease similarity networks based on authoritative databases. Subsequently, we creatively design a dual heterogeneous graph neural network encoder capable of efficiently learning feature information between adjacent nodes and similarity information across the entire graph. Additionally, we develop a specialized fine-grained multi-layer feature interaction gating mechanism to integrate outputs from the neural network encoders to identify novel associations connecting miRNAs with diseases. We evaluate our model using 5-fold cross-validation and real-world disease case studies, based on the HMDD V3.2 dataset. Our method demonstrates superior performance compared to existing approaches in various tasks, confirming the effectiveness and potential of DGNMDA as a robust method for predicting miRNA-disease associations. Full article

This review provides a comprehensive analysis of the transformative role of artificial intelligence (AI) in predicting and preventing sports injuries across various disciplines. By exploring the application of machine learning (ML) and deep learning (DL) techniques, such as random forests (RFs), convolutional neural networks (CNNs), and artificial neural networks (ANNs), this review highlights AI’s ability to analyze complex datasets, detect patterns, and generate predictive insights that enhance injury prevention strategies. AI models improve the accuracy and reliability of injury risk assessments by tailoring prevention strategies to individual athlete profiles and processing real-time data. A literature review was conducted through searches in PubMed, Google Scholar, Science Direct, and Web of Science, focusing on studies from 2014 to 2024 and using keywords such as ‘artificial intelligence’, ‘machine learning’, ‘sports injury’, and ‘risk prediction’. While AI’s predictive power supports both team and individual sports, its effectiveness varies based on the unique data requirements and injury risks of each, with team sports presenting additional complexity in data integration and injury tracking across multiple players. This review also addresses critical issues such as data quality, ethical concerns, privacy, and the need for transparency in AI applications. By shifting the focus from reactive to proactive injury management, AI technologies contribute to enhanced athlete safety, optimized performance, and reduced human error in medical decisions. As AI continues to evolve, its potential to revolutionize sports injury prediction and prevention promises further advancements in athlete health and performance while addressing current challenges. Full article

Background: Although computational models are advancing air quality prediction, achieving the desired performance or accuracy of prediction remains a gap, which impacts the implementation of machine learning (ML) air quality prediction models. Several models have been employed and some hybridized to enhance air quality and air quality index predictions. The objective of this paper is to systematically review machine and deep learning techniques for spatiotemporal air prediction challenges. Methods: In this review, a methodological framework based on PRISMA flow was utilized in which the initial search terms were defined to guide the literature search strategy in online data sources (Scopus and Google Scholar). The inclusion criteria are articles published in the English language, document type (articles and conference papers), and source type (journal and conference proceedings). The exclusion criteria are book series and books. The authors’ search strategy was complemented with ChatGPT-generated keywords to reduce the risk of bias. Report synthesis was achieved by keyword grouping using Microsoft Excel, leading to keyword sorting in ascending order for easy identification of similar and dissimilar keywords. Three independent researchers were used in this research to avoid bias in data collection and synthesis. Articles were retrieved on 27 July 2024. Results: Out of 374 articles, 80 were selected as they were in line with the scope of the study. The review identified the combination of a machine learning technique and deep learning techniques for data limitations and processing of the nonlinear characteristics of air pollutants. ML models, such as random forest, and decision tree classifier were among the commonly used models for air quality index and air quality predictions, with promising performance results. Deep learning models are promising due to the hyper-parameter components, which consist of activation functions suitable for nonlinear spatiotemporal data. The emergence of low-cost devices for data limitations is highlighted, in addition to the use of transfer learning and federated learning models. Again, it is highlighted that military activities and fires impact the O₃ concentration, and the best-performing models highlighted in this review could be helpful in developing predictive models for air quality prediction in areas with heavy military activities. Limitation: This review acknowledges methodological challenges in terms of data collection sources, as there are equally relevant materials on other online data sources. Again, the choice and use of keywords for the initial search and the creation of subsequent filter keywords limit the collection of other relevant research articles. Full article

This study evaluated the differences in oral processing and texture perception of breads with varying compositions. The research investigated the dynamic changes in moisture content (MC), reducing sugars (RSs), and chewiness of the bolus formed from white bread (B0) and 50% whole-wheat bread (B50) during oral processing. Hyperspectral imaging (HSI) combined with chemometric methods was used to establish quantitative prediction models for MC, RSs, and chewiness, and to create visual distribution maps of these parameters. The results showed that B0 had a higher moisture content and a faster hydration rate than B50 during the initial stages of oral processing, indicating greater hydrophilicity and ease of saliva wetting. Additionally, the uniformity of moisture distribution in the bolus of B0 was higher than that of B50. B50 exhibited significantly lower RSs content and poorer distribution uniformity compared to B0. The primary differences in chewiness between the two types of bread were observed during the early stages of oral processing, with B50 requiring more chewing effort initially. This study demonstrated that HSI technology can effectively monitor and elucidate the compositional changes in food particles during oral processing, providing new insights into bread texture perception and offering a scientific basis for improving bread processing and texture. Full article

Background: Immunotherapy is the cornerstone of treatment for many cancers. The effectiveness of immunotherapy in hospitalized patients is unknown due to the exclusion of this fragile population from clinical trials. This study evaluates the efficacy of immunotherapy in fragile hospitalized patients. Method: We conducted a single-center retrospective study involving 49 patients who started an immunotherapy (IO) during a hospitalization or within 3 months after a hospitalization at the Centre Hospitalier de l’Université de Sherbrooke (CHUS). Efficacy analysis included objective response rate (ORR), overall survival (OS), and progression-free survival (PFS). Results: Immunotherapy resulted in 30.6% of all grades combined and 18.4% of grade three to four immune-related adverse events (irAE). Efficacy outcomes were inferior in the fragile cohort of patients with ORR of 38.9%, PFS of 2.8 months (95% CI [2.17–3.35]), and OS of 3.2 months (95% CI [1.60–4.84]). Performance status of ECOG three to four compared to ECOG zero predicts poor OS (HR 5.666 [1.207–26.594]; p = 0.028) and PFS (HR 4.136 [0.867–19.733]; p = 0.075). Fitness to receive four to six cycles (HR 0.335 [0.152–0.0.738]; p < 0.007) or more predicts greater OS compared to one to three cycles of immunotherapy. Low levels of serum albumin (HR 0.917 [0.852–0.987]; p = 0.021) and elevated levels of serum LDH (HR 2.224 [1.469–3.367]; p < 0.001) are associated with a reduced OS. Conclusion: The effectiveness of immunotherapy in fragile hospitalized patients is compromised, although they exhibit significant irAE. Excellent performance status, fitness to receive many IO treatments, and normal levels of serum LDH and albumin may be useful in selecting patients who will benefit from immunotherapy. Full article