Search Results (95)

This study suggests a hybrid approach that combines improved feature selection and intelligent diagnosis to increase the operational safety and intelligent diagnosis capabilities of hydropower units. In order to handle the vibration data, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used initially. A novel comprehensive index is constructed by combining the Pearson correlation coefficient, mutual information (MI), and Kullback–Leibler divergence (KLD) to select intrinsic mode functions (IMFs). Next, feature extraction is performed on the selected IMFs using Refined Composite Multiscale Fluctuation Dispersion Entropy (RCMFDE). Then, time and frequency domain features are screened by calculating dispersion and combined with IMF features to build a hybrid feature vector. The vector is then fed into a CNN-GRU-Attention model for intelligent diagnosis. The improved slime mold algorithm (ISMA) is employed for the first time to optimize the hyperparameters of the CNN-GRU-Attention model. The experimental results show that the classification accuracy reaches 96.79% for raw signals and 93.33% for noisy signals, significantly outperforming traditional methods. This study incorporates entropy-based feature extraction, combines hyperparameter optimization with the classification model, and addresses the limitations of single feature selection methods for non-stationary and nonlinear signals. The proposed approach provides an excellent solution for intelligent optimized diagnosis of hydropower units. Full article

This study investigates the complex dynamics of new-type urbanization in resource-based county-level cities, using Wuan City in Hebei Province, China, as a representative case. As China pursues a high-quality development agenda, cities historically dependent on resource extraction face profound challenges in achieving sustainable and inclusive urban growth. This research employs a multi-method approach—including Theil index analysis, industrial shift-share analysis, a Cobb–Douglas production function model, and a composite urbanization index—to quantitatively diagnose the constraints on Wuan’s development and assess its transformation efforts. Our empirical results reveal a multifaceted situation: while the urban–rural income gap has narrowed, rural income streams remain fragile. The shift-share analysis indicates that although Wuan’s traditional industries have regained competitiveness, the city’s economic structure is still burdened by a persistent negative structural component, hindering diversification. Furthermore, the economy exhibits characteristics of a labor-intensive growth model with inefficient capital deployment. These underlying issues are reflected in a comprehensive urbanization index that, after a period of rapid growth, has recently stagnated, signaling the exhaustion of the city’s traditional development mode. In response, Wuan attempts an “industrial transformation-driven new-type urbanization” path. This study details the three core strategies being implemented: (1) incremental population urbanization through development at the urban fringe and in industrial zones; (2) in situ urbanization of the existing rural population; and (3) the cultivation of specialized “characteristic small towns” to create new, diversified economic nodes. The findings from Wuan offer critical, actionable lessons for other resource-dependent regions. The case demonstrates that successful urban transformation requires not only industrial upgrading but also integrated, spatially aware planning and robust institutional support. We conclude that while Wuan’s model provides a valuable reference, its strategies must be adapted to local contexts, emphasizing the universal importance of institutional innovation, human capital investment, and a people-centered approach to achieving resilient and high-quality urbanization. Full article

Accurate short-term photovoltaic (PV) power forecasting is crucial for ensuring the stability and efficiency of modern power systems, particularly given the intermittent and nonlinear characteristics of solar energy. This study proposes a novel hybrid forecasting model that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the jellyfish search (JS) optimization algorithm, and a bidirectional long short-term memory (BiLSTM) neural network. First, the original PV power signal was decomposed into intrinsic mode functions using a modified CEEMDAN method to better capture the complex nonlinear features. Subsequently, the fast Fourier transform and improved Pearson correlation coefficient (IPCC) were applied to identify and merge similar-frequency intrinsic mode functions, forming new composite components. Each reconstructed component was then forecasted individually using a BiLSTM model, whose parameters were optimized by the JS algorithm. Finally, the predicted components were aggregated to generate the final forecast output. Experimental results on real-world PV datasets demonstrate that the proposed CEEMDAN-JS-BiLSTM model achieves an $R^2$ of 0.9785, a MAPE of 8.1231%, and an RMSE of 37.2833, outperforming several commonly used forecasting models by a substantial margin in prediction accuracy. This highlights its effectiveness as a promising solution for intelligent PV power management. Full article

Ultra-high molecular weight polyethylene (UHMWPE) fiber-reinforced concrete (UHMWPE-FRC) is a hard–soft multiphase hybrid composite with exceptional toughness and impact resistance compared to conventional concrete. However, its cutting characteristics and processing performance have not been sufficiently investigated, potentially causing accelerated saw blade wear, higher energy consumption, and poor cutting quality, thus increasing project costs and duration. In order to intelligently evaluate the performance of diamond saw blades when cutting UHMWPE-FRC, a feature extraction method, based on feature classification and an improved Hilbert–Huang transform (HHT), is proposed, which consider Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and wavelet threshold de-noising. By conducting the cutting experiments, the cutting force was analyzed by the improved HHT, in terms of noise reduction and time-frequency. Five types of characteristics were preliminarily screened, including depth of cut (a_p), cutting speed (V_c), feed rate (V_f), concrete strength, and the type of concrete. A feature correlation analysis method for UHMWPE-FRC cutting, based on feature classification, is proposed. The five features were classified into continuous variable features and unordered categorical variable features; correlation analyses were carried out by Spearman correlation coefficient testing and Kruskal–Wallis and Dunn’s testing, respectively. It was found that the a_p and concrete strength exhibited a strong positive correlation with cutting force, making them the primary influencing factors. Meanwhile, the influence of aggregates on cutting force can be identified in the low-frequency range, while the influence of fibers can be identified in the high-frequency range. The feature classification-based correlation analysis effectively distinguishes the influence of V_c on cutting force. Full article

Precision rolling bearings serve as critical components in a range of diverse industrial applications, where their continuous health monitoring is essential for preventing costly downtime and catastrophic failures. Early-stage bearing defects present significant diagnostic challenges, as they manifest as weak, nonlinear, and non-stationary transient features embedded within high-amplitude random noise. While entropy-based methods have evolved substantially since Shannon’s pioneering work—from approximate entropy to multiscale variants—existing approaches continue to face limitations in their computational efficiency and information preservation. This paper introduces the Adaptive Composite Multiscale Slope Entropy (ACMSlE) framework, which overcomes these constraints through two innovative mechanisms: a time-window shifting strategy, generating overlapping coarse-grained sequences that preserve critical signal information traditionally lost in non-overlapping segmentation, and an adaptive scale optimization algorithm that dynamically selects discriminative scales through entropy variation coefficients. In a comparative analysis against recent innovations, our integrated fault diagnosis framework—combining Fast Ensemble Empirical Mode Decomposition (FEEMD) preprocessing with Particle Swarm Optimization-Extreme Learning Machine (PSO-ELM) classification—achieves 98.7% diagnostic accuracy across multiple bearing defect types and operating conditions. Comprehensive validation through a multidimensional stability analysis, complexity discrimination testing, and data sensitivity analysis confirms this framework’s robust fault separation capability. Full article

The ongoing debate over whether polycentric urban structures reduce commuting times has yielded conflicting conclusions, highlighting the need for empirical findings in diverse urban contexts and analyses that consider a range of influencing factors. This study analyzed the effects of employment center characteristics on commuting times, using the Seoul Metropolitan Area (SMA) as a case study. A cutoff method identified employment centers within the SMA. Differences in commuting behavior, including average commuting time and mode share, were observed among workers at different employment centers. A multilevel regression model estimated the effect of employment center characteristics, such as industry composition and nearby housing prices, on workers’ commuting time. Key findings include a positive relationship between public transportation (PT) density and commuting time, suggesting that well-designed PT systems may encourage longer commutes. Manufacturing and finance, insurance, and real estate (FIRE) industries negatively impacted commuting times, with manufacturing being associated with the geographic location of centers and FIRE industries being associated with high-income workers, which likely contributed to shorter commutes. On the other hand, the positive relationship between housing prices and commuting times highlights the need for affordable housing near employment centers to reduce commuting times. These findings underscore the complex interactions between each employment center’s characteristics and workers’ socioeconomic factors in shaping commuting behavior. Full article

Evaluating user experience (UX) is essential for optimizing educational chatbots to enhance learning outcomes and student productivity. This study introduces a novel weighted composite metric integrating interface usability assessment (via the Chatbot Usability Questionnaire, CUQ), engagement measurements (via the User Engagement Scale—Short Form, UES-SF), and objective performance indicators (through error rates and response times), addressing gaps in existing evaluation methods across interaction modes (text-based, menu-based, and hybrid) and question complexities. A 3 × 3 within-subject experimental design (n = 30) was conducted, measuring these distinct UX dimensions through standardized instruments and performance metrics, supplemented by qualitative feedback. Principal Component Analysis (PCA) was used to derive weights for the composite UX metric based on empirical patterns in user interactions. Repeated-measures ANOVA revealed that the hybrid interaction mode outperformed the others, achieving significantly higher usability (F(2,58) = 89.32, p < 0.001) and engagement (F(2,58) = 8.67, p < 0.001), with fewer errors and faster response times under complex query conditions. These findings demonstrate the hybrid mode’s adaptability across question complexities. The proposed framework establishes a standardized method for evaluating educational chatbots, providing actionable insights for interface optimization and sustainable learning tools. Full article

The issue of power quality disturbances in modern power systems has become increasingly complex and severe, with multiple disturbances occurring simultaneously, leading to a decrease in the recognition accuracy of traditional algorithms. This paper proposes a composite power quality disturbance identification method based on the integration of improved Complementary Ensemble Empirical Mode Decomposition (CEEMDAN), Hilbert Transform (HT), and Extreme Learning Machine (ELM). Addressing the limitations of traditional signal processing techniques in handling nonlinear and non-stationary signals, this study first preprocesses the collected initial power quality signals using the improved CEEMDAN method to reduce modal aliasing and spurious components, thereby enabling a more precise decomposition of noisy signals into multiple Intrinsic Mode Functions (IMFs). Subsequently, the HT is utilized to conduct a thorough analysis of the reconstructed signals, extracting their time-amplitude information and instantaneous frequency characteristics. This feature information provides a rich data foundation for subsequent classification and identification. On this basis, an improved ELM is introduced as the classifier, leveraging its powerful nonlinear mapping capabilities and fast learning speed to perform pattern recognition on the extracted features, achieving accurate identification of composite power quality disturbances. To validate the effectiveness and practicality of the proposed method, a simulation experiment is designed. Upon examination, the approach introduced in this study retains a fault diagnosis accuracy exceeding 95%, even amidst significant noise disturbances. In contrast to conventional techniques, such as Convolutional Neural Network (CNN) and Support Vector Machine (SVM), this method achieves an accuracy enhancement of up to 5%. Following optimization via the Particle Swarm Optimization (PSO) algorithm, the model’s accuracy is boosted by 3.6%, showcasing its favorable adaptability. Full article

A genetic linkage between U–Cu–Mo mineralization with feldspar and chlorite minerals and the discrimination of different mineralization events in the hydrothermal and metasomatic system in the Rohil polymetallic uranium deposit in India is presented on the basis of textural relationships and mineral chemistry. Field and EPMA studies reveal that the chlorite formed in two possible modes, viz. (a) replacement of ferromagnesian minerals of the host rock and (b) precipitated directly from hydrothermal solutions. Chlorites follow a distinctive composition from Al-saturated to Al-undersaturated and, in most cases, from Mg- to Fe-rich species as alteration progressed. The chlorites show a wide range of Fe content (1.86–5.06 apfu), high Mg content (3.96–6.28 apfu), and Si contents (5.99–6.90 apfu) with an Fe/(Fe + Mg) ratio (0.23–0.56), leading to their classification as Diabantite/Pycnochlorite. Empirical and thermodynamic geothermometers have been used to determine the temperature of chlorite formation based on chemical composition, which revealed a large variation in temperatures from 130 °C to 260 °C. The feldspar geothermometry reveals a temperature range of 158 to 236 °C, which is in congruence with that of chlorites. Geothermometry by two different methods provides the range of temperature that prevailed in the study area during and succeeding the crystallization of uraninite and associated ore minerals. Mineral chemistry vis-à-vis geothermometry of feldspars and chlorite can provide impetus to geochemical evolution in the North Delhi Fold Belt (NDFB) and similar geological setups in metasomatite-type uranium deposits. Full article

Crude oil is a raw and natural, but nonrenewable, resource. It is one of the world’s most important commodities, and its price can have ripple effects throughout the broader economy. Accurately predicting crude oil prices is vital for investment decisions but it remains challenging. Due to the deficiencies neglecting residual factors when forecasting using conventional combination models, such as the autoregressive moving average and the long short-term memory for prediction, the variational mode decomposition (VMD)-empirical mode decomposition (EMD)-Transformer model is proposed to predict crude oil prices in this study. This model integrates a second decomposition and Transformer model-based machine learning method. More specifically, we employ the VMD technique to decompose the original sequence into variational mode filtering (VMF) and a residual sequence, followed by using EMD to decompose the residual sequence. Ultimately, we apply the Transformer model to predict the decomposed modal components and superimpose the results to produce the final forecasted prices. Further empirical test results demonstrate that the proposed quadratic decomposition composite model can comprehensively identify the characteristics of WTI and Brent crude oil futures daily price series. The test results illustrate that the proposed VMD–EMD–Transformer model outperforms the other three models—long short-term memory (LSTM), Transformer, and VMD–Transformer in forecasting crude oil prices. Details are presented in the empirical study part. Full article

Introduction: Empirical evidence shows that many family carers, especially immigrants, experience considerable health disadvantages and poorer quality of life. Australia has a rapidly increasing multicultural population, officially referred to as Culturally and Linguistically Diverse (CALD) people. This paper explores similarities and differences in the carer profile and physical and mental health of CALD and non-CALD family carers. Methods: A cross-sectional anonymous survey was conducted of self-reported family carers aged 18 years and older. Identical paper and online survey modes were provided to enable choice. Key variables included demographic and carer profile, diagnosed chronic physical health conditions, and validated scales such as CESD-12 and MOS-SF12, including derivative composite Physical and Mental Component Summary (PCS and MCS, respectively) scores. The sample comprised 649 participants (CALD = 347, non-CALD = 302). The analyses included univariate, bivariate, and multivariable linear regression analyses for three outcome variables: PCS, MCS, and CESD-12. Results: CALD carers were comparatively younger and married, and 54% had university-level education (29% in the gfvnon-CALD group). Women were primary carers in both groups (67.4% versus 72.2%). The weekly care hours were higher for non-CALD carers. Both groups had below population-referenced scores for mean PCS and MCS values. For CESD-12, non-CALD respondents had higher scores (17.5 vs. 11.2, p < 0.022). Regression analyses showed significant differences for demographic, carer, and physical health variables across the three outcome variables. Discussion and Conclusion: Women have a higher domestic workload, which, when combined with high care hours, adversely impacts physical and mental health. The need for improved and culturally aligned care support systems is required. Full article

Drought poses a serious threat to Sudan, causing water shortages, crop failures, hunger, and conflict. The relationships between Indo-Pacific teleconnections and drought events in Sudan are examined based on the Standardized Precipitation Index (SPI), anomalies, Empirical Orthogonal Function (EOF), correlation, composite analysis, sequential Mann–Kendall test, and MK-trend test during the period of 1993–2022. The results of the SPI-1 values indicate that the extreme drought in Sudan in 2004 was an exceptional case that affected the entire region, with an SPI-1 value of −2 indicating extremely dry conditions. In addition, Sudan experienced moderate to severe drought conditions for several years (1993, 2002, 2008, 2009 and 2015). The Empirical Orthogonal Function showed that the first EOF mode (42.2%) was the dominant variability mode and had positive loading over most of the country, indicating consistent rainfall variation in the central, eastern, and western regions. Correlation analysis showed a strong significant relationship between June–September rainfall and Indian Ocean sea surface temperature (SST) (r ≤ 0.5). Furthermore, a weak positive influence of the Indian Ocean Dipole (IOD) on JJAS precipitation was observed (r ≤ 0.14). Various time lags in the range of ±12 months were examined, with the highest correlation (0.6) found at 9 month among the time lags of ±12 months. This study contributes to a better understanding of drought dynamics and provides essential information for effective drought management in Sudan. Further research is needed to explore the specific mechanisms driving these interactions and to develop tailored strategies to mitigate the impacts of drought events in the future. Full article

The diagnosis of faults in wind turbine gearboxes based on signal processing represents a significant area of research within the field of wind power generation. This paper presents an intelligent fault diagnosis method based on ensemble-refined composite multiscale fluctuation-based reverse dispersion entropy (ERCMFRDE) for a wind turbine gearbox vibration signal that is nonstationary and nonlinear and for noise problems. Firstly, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and stationary wavelet transform (SWT) are adopted for signal decomposition, noise reduction, and restructuring of gearbox signals. Secondly, we extend the single coarse-graining processing method of refined composite multiscale fluctuation-based reverse dispersion entropy (RCMFRDE) to the multiorder moment coarse-grained processing method, extracting mixed fault feature sets for denoised signals. Finally, the diagnostic results are obtained based on the least squares support vector machine (LSSVM). The dataset collected during the gearbox fault simulation on the experimental platform is employed as the research object, and the experiments are conducted using the method proposed in this paper. The experimental results demonstrate that the proposed method is an effective and reliable approach for accurately diagnosing gearbox faults, exhibiting high diagnostic accuracy and a robust performance. Full article

This study outlines a method of utilizing the finite element method and a simple mathematical model to predict the behavior of laminated veneer lumber (LVL) beams strengthened with composite sheets. The numerical models were created using the Abaqus 2017 software. The LVL was considered as a linearly elastic or elastic–plastic material, factoring in Hill’s yield criterion. The composites were simulated as linearly elastic–ideally plastic materials. The mathematical models were predicated on the methodology of transformed cross-section. The theoretical and numerical outcomes were juxtaposed with previous empirical investigations. The comparison encompassed load-bearing capacity, stiffness, and deformation under peak force. Furthermore, presentations of normal stress maps in the LVL and composite have been illustrated. The derived maps were juxtaposed with the delineations of failure modes. An adequate correlation was identified between the theoretical, numerical, and empirical values in the case of beams reinforced with aramid, glass, and carbon sheets. The relative deviation varied from several to multiple percentages. This technique is not applicable for evaluating load-bearing capacity and deformation when only dealing with sheets with low elongation of rupture. This is a consequence of their premature failure. The proposed models may be utilized by researchers and engineers in the design of reinforcements for timber structures. Full article

This study reveals the essential load-bearing characteristics of the steel–concrete composite floor system under fire conditions applying the structural stressing state theory. Firstly, the strain data in the entire process of the fire test are modeled as state variables which can present the slab’s stressing state evolution characteristics. Then, the state variables are used to build the stressing state mode and the parameter characterizing the mode. Further, the Mann–Kendall criterion is adopted to detect the leap points in the evolution curves of the characteristic parameters during the entire fire exposure process. Also, the evolution curves of the stressing state modes are investigated to verify the leap profiles around the leap/characteristic points. Finally, the detected leap points are defined as the failure starting points and elastoplastic branching points, which is unseen in past research focusing on the failure endpoint defined at the ultimate load-bearing state of the composite floor system. The failure starting point and the elastoplastic branching point are the embodiment of natural law from quantitative change to quality change in a system rather than an empirical and statistical judgment. Hence, both characteristic points avoidably exist in the strain data of the composite floor system undergoing the fire process, which can be revealed through the proper modeling methods and update the existing theories and methods on structural analysis and design in fire. Full article