Search Results (42)

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that impairs motor function, often causing tremors and difficulty with movement control. A promising diagnostic method involves analyzing hand-drawn patterns, such as spirals and waves, which show characteristic distortions in individuals with PD. This study compares three computational approaches for classifying individuals as Parkinsonian or healthy based on drawing-derived features: (1) Large Language Models (LLMs), (2) traditional machine learning (ML) algorithms, and (3) a fuzzy ontology-based method using fuzzy sets and Fuzzy-OWL2. Each method offers unique strengths: LLMs leverage pre-trained knowledge for subtle pattern detection, ML algorithms excel in feature extraction and predictive accuracy, and fuzzy ontologies provide interpretable, logic-based reasoning under uncertainty. Using three structured handwriting datasets of varying complexity, we assessed performance in terms of accuracy, interpretability, and generalization. Among the approaches, the fuzzy ontology-based method showed the strongest performance on complex tasks, achieving a high F1-score, while ML models demonstrated strong generalization and LLMs offered a reliable, interpretable baseline. These findings suggest that combining symbolic and statistical AI may improve drawing-based PD diagnosis. Full article

Due to climate change, the electric vehicle (EV) industry is rapidly growing and drawing researchers interest. Driving conditions like mountainous roads, slick surfaces, and rough terrains illuminate the vehicles inherent nonlinearities. Under such scenarios, the behavior of power sources (fuel cell, battery, and super-capacitor), power processing units (converters), and power consuming units (traction motors) deviates from nominal operation. The increasing demand for FCHEVs necessitates control systems capable of handling nonlinear dynamics, while ensuring robust, precise energy distribution among fuel cells, batteries, and super-capacitors. This paper presents a DSMC strategy enhanced with Robust Uniform Exact Differentiators for FCHEV energy management. To optimally tune DSMC parameters, reduce chattering, and address the limitations of conventional methods, a hybrid metaheuristic framework is proposed. This framework integrates moth flame optimization (MFO) with the gravitational search algorithm (GSA) and Fractal Heritage Evolution, implemented through three spiral-based variants: MFOGSAPSO-A (Archimedean), MFOGSAPSO-H (Hyperbolic), and MFOGSAPSO-L (Logarithmic). Control laws are optimized using the Integral of Time-weighted Absolute Error (ITAE) criterion. Among the variants, MFOGSAPSO-L shows the best overall performance with the lowest ITAE for the fuel cell (56.38), battery (57.48), super-capacitor (62.83), and DC bus voltage (4741.60). MFOGSAPSO-A offers the most accurate transient response with minimum RMSE and MAE FC (0.005712, 0.000602), battery (0.004879, 0.000488), SC (0.002145, 0.000623), DC voltage (0.232815, 0.058991), and speed (0.030990, 0.010998)—outperforming MFOGSAPSO, GSA, and PSO. MFOGSAPSO-L further reduces the ITAE for fuel cell tracking by up to 29% over GSA and improves control smoothness. PSO performs moderately but lags under transient conditions. Simulation results conducted under EUDC validate the effectiveness of the MFOGSAPSO-based DSMC framework, confirming its superior tracking, faster convergence, and stable voltage control under transients making it a robust and high-performance solution for FCHEV. Full article

Whip restraints based on thin-walled structures are widely used for protection against high-energy pipe breaks in nuclear power plants due to their excellent impact resistance. Recently, biomimetic and hierarchical structures have emerged as focal points in thin-walled structure research, aimed at enhancing energy absorption capacities. Drawing inspiration from the nautilus shell and Fibonacci spiral, based on the nautilus bionic hierarchical multi-cell (NBHMC) structure, this study introduces a novel Nautilus Bionic Double Hierarchical Multi-Cell (NBDHMC) structure. Finite element analysis was employed to evaluate the energy absorption performance of the structure under axial and oblique loads using four crashworthiness parameters. Crashworthiness studies showed that the NBDHMC exhibits superior crashworthiness compared to the NBHMC and hollow circular tube configurations. Finally, the study investigated the influence of combination modes, hierarchical levels, cross-sectional characteristics, and other parameters on the parameterization of the NBDHMC. The results offer innovative insights for the design of highly efficient energy absorbers. Full article

This study explores the application of Eastern philosophy in craft innovation education, identifying opportunities for interdisciplinary learning. Drawing on the I Ching and Laozi’s thought, it examines human needs in craft across three dimensions: Qi-form (material), Xin-form (psychological), and Dao-form (philosophical). Taiji theory’s Yin–Yang balance highlights the importance of interdisciplinary thinking in craft innovation. This study introduces the “Spiral Innovation Theory” as a framework for craft education, implemented in the 2024 Taiwan Craft Academy Summer Program with 43 participants. The curriculum covered lacquer, wood, metal, and ceramics, employing a multi-mentor system. Using the Learning Motivation Strategies Scale, Imaginative Thinking Scale, and interviews, the findings reveal that different crafts foster distinct creative abilities. The ANOVA results show woodworking enhances ideation, metalwork and ceramics improve fluency, ceramics and woodworking strengthen flexibility, while woodworking and lacquer work boost creativity. A significant correlation between learning motivation and imagination was found. These findings offer insights into future craft education, advocating the dual mentorship model as a strategy for interdisciplinary innovation. Full article

Background: Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder worldwide. People suffering from PD exhibit motor symptoms that affect the control of upper and lower limb movement. Among daily activities that depend on proper upper limb control is the handwriting process, which has been studied in state-of-the-art research, mainly considering non-semantic drawings like spirals, geometric figures, cursive lines, and others. Objectives: This paper analyzes the suitability of modeling the handwriting process of digits from 0 to 9 to automatically discriminate between PD patients and healthy control subjects. The main hypothesis is that modeling these numbers allows a more natural evaluation of upper limb control. Methods: Two approaches are considered: modeling of the images resulting from the strokes collected by the digital tablet and modeling of the time series yielded by the digital tablet while performing the strokes, i.e., time-dependent signals. The first approach is implemented by fine-tuning a CNN-based architecture, while the second approach is based on hand-crafted features measured upon the time series, namely pressure and kinematic measurements. Features extracted from time-dependent signals are represented following two strategies, one based on statistical functionals and the other one based on creating Gaussian Mixture Models (GMMs). Results: The experiments indicate that pressure-based features modeled with functionals are the ones that yield the highest accuracy, indicating that PD-related symptoms are better modeled with dynamic approaches than those based on images. Conclusions: The dynamic approach outperformed the image-based model, indicating that the writing process, modeled with signals collected over time, reveals motor symptoms more clearly than images resulting from handwriting. This finding is in line with previous results in the state-of-the-art research and constitutes a step forward to create more accurate and informative methods to detect and monitor PD symptoms. Full article

In this work, reverse osmosis and forward osmosis membranes were prepared using base cellulosic polymers with ZrO₂. The prepared membranes were rolled on the spiral-wound configuration module. The modules were tested on a pilot unit to investigate the efficiency of the RO membrane and the hydraulic pressure effect on both sides of the FO membranes. The RO membrane provided a rejection of 99% for the seawater desalination, and the brine was used as a draw solution for the FO system. First, seawater was used as a draw solution to indicate the best hydraulic pressure, where the best one was 3 bar for the draw solution side, and 2 bar for the feed side, where the water flux reached 48.89 L/m²·h (LMH) with a dilution percentage of 80% and a low salt reverse flux of 0.128 g/m²·h (gMH) after 5 h of operation time. The integrated system of RO and forward-assisted osmosis (PAO) was investigated using river water as a feed and RO brine as a draw solute, where the results of PAO indicate a high-water flux of 68.6 LMH with a dilution of 93.2% and a salt reverse flux of 0.18 gMH. Therefore, using PAO improves the performance of the system. Full article

Parkinson’s Disease (PD) is a progressive neurodegenerative disorder affecting millions worldwide. Early detection is crucial for improving patient outcomes. Spiral drawing analysis has emerged as a non-invasive tool to detect early motor impairments associated with PD. This study examines the performance of hybrid deep learning and machine learning models in detecting PD using spiral drawings, with a focus on the impact of data augmentation techniques. We compare the accuracy of Vision Transformer (ViT) with K-Nearest Neighbors (KNN), Convolutional Neural Networks (CNN) with Support Vector Machines (SVM), and Residual Neural Networks (ResNet-50) with Logistic Regression, evaluating their performance on both augmented and non-augmented data. Our findings reveal that ViT with KNN, initially achieving 96.77% accuracy on unaugmented data, experienced a notable decline across all augmentation techniques, suggesting it relies heavily on global patterns in spiral drawings. In contrast, ResNet-50 with Logistic Regression showed consistent improvement with data augmentation, reaching 93.55% accuracy when rotation and flipping techniques were applied. These results highlight that hybrid models respond differently to augmentation, and careful selection of augmentation strategies is necessary for optimizing model performance. Our study provides important insights into the development of reliable diagnostic tools for early PD detection, emphasizing the need for appropriate augmentation techniques in medical image analysis. Full article

Background: Parkinson’s disease (PD) diagnosis benefits significantly from advancements in artificial intelligence (AI) and image processing techniques. This paper explores various approaches for processing hand-drawn Archimedean spirals in order to detect signs of PD. Methods: The best approach is selected to be integrated in a neurodegenerative disease management platform called NeuroPredict. The most innovative aspects of the presented approaches are related to the employed feature extraction techniques that convert hand-drawn spirals into a frequency spectra, so that frequency features may be extracted and utilized as inputs for various classification algorithms. A second category of extracted features contains information related to the thickness and pressure of drawings. Results: The selected approach achieves an overall accuracy of 95.24% and allows acquiring new test data using only a pencil and paper, without requiring a specialized device like a graphic tablet or a digital pen. Conclusions: This study underscores the clinical relevance of AI in enhancing diagnostic precision for neurodegenerative diseases. Full article

Drawing on data from 133 Chinese New Energy Vehicle (NEV) policy documents from 2007 to 2023, this study utilizes Dynamic Topic Modelling (DTM), social network analysis and a quantitative model to investigate the evolutionary path of policy themes and the coordination effects. The following results were obtained. (1) A thematic cross-sectional analysis identified six core policy themes, namely, coordinated promotion of technology and finance, industry development and safety standardisation, market service and technical support systems, promotion strategy and urban cluster development, industrial capital and safety supervision mechanisms, and policy support and market expansion. The analysis also mapped the distribution of hot spots within these themes. (2) The keyword co-occurrence network of the NEV policy indicated that the network structure evolved from an initial ‘overall dispersion–theme concentration’, comprising 16 policy themes, to an ‘overall stability–theme coordination’, consisting of 14 policy themes. (3) The coordination degrees across the three types of policies exhibited a consistent upward spiral, with the comprehensive coordination index surging from 30 in 2007 to 951 in 2023, underscoring the complementary effects among policy instruments. These conclusions offer valuable insights for government departments to understand NEV development trends and dynamically adjust policy themes accordingly. Full article

Aiming at the problem that the Osprey Optimization Algorithm (OOA) does not have high optimization accuracy and is prone to falling into local optimum, an Improved Osprey Optimization Algorithm Based on a Two-Color Complementary Mechanism for Global Optimization (IOOA) is proposed. The core of the IOOA algorithm lies in its unique two-color complementary mechanism, which significantly improves the algorithm’s global search capability and optimization performance. Firstly, in the initialization stage, the population is created by combining logistic chaos mapping and the good point set method, and the population is divided into four different color groups by drawing on the four-color theory to enhance the population diversity. Secondly, a two-color complementary mechanism is introduced, where the blue population maintains the OOA core exploration strategy to ensure the stability and efficiency of the algorithm; the red population incorporates the Harris Hawk heuristic strategy in the development phase to strengthen the ability of local minima avoidance; the green group adopts the strolling and wandering strategy in the searching phase to add stochasticity and maintain the diversity; and the orange population implements the optimized spiral search and firefly perturbation strategies to deepen the exploration and effectively perturb the local optimums, respectively, to improve the overall population diversity, effectively perturbing the local optimum to improve the performance of the algorithm and the exploration ability of the solution space as a whole. Finally, to validate the performance of IOOA, classical benchmark functions and CEC2020 and CEC2022 test sets are selected for simulation, and ANOVA is used, as well as Wilcoxon and Friedman tests. The results show that IOOA significantly improves convergence accuracy and speed and demonstrates high practical value and advantages in engineering optimization applications. Full article

The global demand for energy is on the rise, accompanied by increasing requirements for low-carbon environmental protection. In recent years, China’s “double carbon action” initiative has brought about new development opportunities across various sectors. The concept of energy pile foundation aims to harness geothermal energy, aligning well with green, low-carbon, and sustainable development principles, thus offering extensive application prospects in engineering. Drawing from existing research globally, this paper delves into four key aspects impacting the thermodynamic properties of energy piles: the design of buried pipes, pile structure, heat storage materials within the pipe core, and soil treatment around the pile using carbon fiber urease mineralization. Leveraging the innovative mineralization technique known as urease-induced carbonate mineralization precipitation (EICP), this study employs COMSOL Multiphysics simulation software to analyze heat transfer dynamics and establish twelve sets of numerical models for energy piles. The buried pipe design encompasses two types, U-shaped and spiral, while the pile structure includes concrete solid energy piles and tubular energy piles. Soil conditions around the pile are classified into undisturbed sand and carbon fiber-infused EICP mineralized sand. Different inner core heat storage materials such as air, water, unaltered sand, and carbon fiber-based EICP mineralized sand are examined within tubular piles. Key findings indicate that spiral buried pipes outperform U-shaped ones, especially when filled with liquid thermal energy storage (TES) materials, enhancing temperature control of energy piles. The carbon fiber urease mineralization technique significantly improves heat exchange between energy piles and surrounding soil, reducing soil porosity to 4.9%. With a carbon fiber content of 1.2%, the ultimate compressive strength reaches 1419.4 kPa. Tubular energy piles mitigate pile stress during summer temperature fluctuations. Pile stress distribution varies under load and temperature stresses, with downward and upward friction observed at different points along the pile length. Overall, this research underscores the efficacy of energy pile technologies in optimizing energy efficiency while aligning with sustainable development goals. Full article

In the context of Disease X risks, how governments and public health authorities make policy choices in response to potential epidemics has become a topic of increasing concern. The tightness of epidemic prevention policies is related to the effectiveness of the implementation of measures, while the organizational cognition of epidemic risks is related to the rationality of policy choices. During the three years of COVID-19, the Chinese government constantly adjusted the tightness of its prevention policies as awareness of the epidemic risk improved. Therefore, based on the epidemic risk organizational cognition model, the key nodes that affect the tightness of epidemic prevention policies can be explored to find the organizational behavior rules behind the selection of prevention policies. Firstly, through observing the adjustments made to the Chinese government’s prevention strategies during the epidemic, a time-series cross-case comparative analysis reveals how policy tightness shifted from stringent to lenient. This shift coincided with the organizational cognition of epidemic risk evolving from vague to clear. Secondly, by building the “knowledge-cognition” coordinate system to draw the organizational cognition spiral of epidemic risk, it is clear that the changes in the tightness of the prevention policies mainly came from the internalization and externalization of knowledge such as epidemic risk characteristics to promote the level of organizational cognition, which is manifested as expansion and deepening. Thirdly, the node changes in the interaction between organizational cognition development and policy choice proved that different stages of the epidemic had diverse environmental parameters. Moreover, as the epidemic nears its end, the focus of policy tightness is shifting from policy objectives to policy implementation around governance tools. The results indicate that organizational cognition of epidemic risk exhibits significant stages and periodicity. Additionally, epidemic risk characteristics, environmental coupling, and governance tools are crucial factors in determining the tightness of epidemic prevention policies. Full article

The Hunger Games Search (HGS) is an innovative optimizer that operates without relying on gradients and utilizes a population-based approach. It draws inspiration from the collaborative foraging activities observed in social animals in their natural habitats. However, despite its notable strengths, HGS is subject to limitations, including inadequate diversity, premature convergence, and susceptibility to local optima. To overcome these challenges, this study introduces two adjusted strategies to enhance the original HGS algorithm. The first adaptive strategy combines the Logarithmic Spiral (LS) technique with Opposition-based Learning (OBL), resulting in the LS-OBL approach. This strategy plays a pivotal role in reducing the search space and maintaining population diversity within HGS, effectively augmenting the algorithm’s exploration capabilities. The second adaptive strategy, the dynamic Rosenbrock Method (RM), contributes to HGS by adjusting the search direction and step size. This adjustment enables HGS to escape from suboptimal solutions and enhances its convergence accuracy. Combined, these two strategies form the improved algorithm proposed in this study, referred to as RLHGS. To assess the efficacy of the introduced strategies, specific experiments are designed to evaluate the impact of LS-OBL and RM on enhancing HGS performance. The experimental results unequivocally demonstrate that integrating these two strategies significantly enhances the capabilities of HGS. Furthermore, RLHGS is compared against eight state-of-the-art algorithms using 23 well-established benchmark functions and the CEC2020 test suite. The experimental results consistently indicate that RLHGS outperforms the other algorithms, securing the top rank in both test suites. This compelling evidence substantiates the superior functionality and performance of RLHGS compared to its counterparts. Moreover, RLHGS is applied to address four constrained real-world engineering optimization problems. The final results underscore the effectiveness of RLHGS in tackling such problems, further supporting its value as an efficient optimization method. Full article

The availability of sugar has expanded over the past 50 years, due to improved industrial processes and corn subsidies, particularly in the form of sweetened beverages. This correlates with a surge in the prevalence of cardiometabolic disorders, which has brought this issue back into the spotlight for public health. In this narrative review, we focus on the role of fructose in the genesis of cardiometabolic dyslipidemia (an increase in serum triglyceride-rich lipoproteins (TRL): VLDL, chylomicrons (CM), and their remnants) bringing together the most recent data on humans, which demonstrates the crucial interaction between glucose and fructose, increasing the synthesis while decreasing the catabolism of these particles in a synergistic downward spiral. After reviewing TRL metabolism, we discuss the fundamental principles governing the metabolism of fructose in the intestine and liver and the effects of dysregulated fructolysis, in conjunction with the activation of carbohydrate-responsive element-binding protein (ChREBP) by glucose and the resulting crosstalk. The first byproduct of fructose catabolism, fructose-1-P, is highlighted for its function as a signaling molecule that promotes fat synthesis. We emphasize the role of fructose/glucose interaction in the liver, which enhances de novo lipogenesis, triglyceride (TG) synthesis, and VLDL production. In addition, we draw attention to current research that demonstrates how fructose affects the activity of lipoprotein lipase by increasing the concentration of inhibitors such as apolipoprotein CIII (apoCIII) and angiopoietin-like protein 3 (ANGPTL3), which reduce the catabolism of VLDL and chylomicrons and cause the building up of their atherogenic remnants. The end outcome is a dual, synergistic, and harmful action that encourages atherogenesis. Thus, considering the growing concerns regarding the connection between sugar consumption and cardiometabolic disease, current research strongly supports the actions of public health organizations aimed at reducing sugar intake, including dietary guidance addressing “safe” limits for sugar consumption. Full article

The link between black holes and star formation allows for us to draw a connection between black holes and the places and times when extraterrestrial intelligences (ETIs) had a greater chance of emerging. Within the context of the gap paradigm for black holes, we show that denser cluster environments that led to gas-rich mergers and copious star formation were places less compatible on average with the emergence of ETIs compared to isolated elliptical galaxies by almost two orders of magnitude. The probability for ETIs peaked in these isolated environments around 6 billion years ago and cosmic downsizing shifted the likelihood of ETIs emerging to galaxies with weak black hole feedback, such as in spiral galaxies, at late times. Full article