Autocorrelation Characteristics Research Articles

Vibration-Based Anomaly Detection for Induction Motors Using Machine Learning

Predictive maintenance of induction motors continues to be a significant challenge in ensuring industrial reliability and minimizing downtime. In this study, machine learning techniques are utilized to enhance fault diagnosis through the use of the Machinery Fault Database (MAFAULDA). A detailed extraction of statistical features was performed on multivariate time-series data to capture essential patterns that could indicate potential faults. Three machine learning algorithms—deep neural networks (DNNs), support vector machines (SVMs), and K-nearest neighbors (KNNs)—were applied to the dataset. Optimization strategies were carefully implemented along with oversampling techniques to improve model performance and handle imbalanced data. The results achieved through these models are highly promising. The SVM model demonstrated an accuracy of 95.4%, while KNN achieved an accuracy of 92.8%. Notably, the combination of deep neural networks with fast Fourier transform (FFT)-based autocorrelation features produced the highest performance, reaching an impressive accuracy of 99.7%. These results provide a novel approach to machine learning techniques in enhancing operational health and predictive maintenance of induction motor systems.

On the use of autocorrelation properties for diffuse reflection by coding metasurfaces

Abstract One-bit coding metasurfaces combine two basic unit cells with out-of-phase responses. Their potential in achieving diffuse scattering has already been demonstrated. These metasurfaces can subsequently be applied to radar-signature control. This paper presents a theoretical analysis linking the scattered field to the autocorrelation of the code that encodes the metasurface. This analysis leads to a focus on Minimum Peak Sidelobes codes with autocorrelation characteristics similar to the unit impulse. Advances in other research areas have greatly enhanced the search for these kind of codes, making them directly usable for coding diffuse scattering metasurfaces. This approach is compared with existing codes, specifically examining how it performs against the optimal code found through exhaustive search in small-scale scenarios. Then, it is shown that this coding strategy facilitates the design of metasurfaces with any and large electrical sizes, achieving results comparable to those obtained through optimization-based approaches, at a significantly reduced computational workload.

Design of Anti-Interference Ultra-Wideband Fuze Waveform Based on Chaotic Pulse Position Modulation

Abstract In modern warfare, the increasingly complex electromagnetic environment and the enhanced capabilities of intelligent jamming techniques pose a threat to the survival rate of radio fuzes on the battlefield. To improve the anti-jamming performance and thereby enhance the battlefield survivability of radio fuzes, this paper proposes a chaotic pulse position modulation (PPM) method for ultra-wideband (UWB) radio fuzes based on nonlinear mapping. Simulations validate that the proposed method exhibits better autocorrelation characteristics and superior anti-jamming performance compared to unmodulated UWB radio fuzes.

A Generalized Autoregressive Conditional Heteroscedasticity GARCH for Forecasting and Modeling Crude Oil Price Volatility

This current study explores the application of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models to forecast and model crude oil price volatility. Crude oil is a vital commodity whose price fluctuations significantly impact global economies, energy markets, and strategic decisions of both National Oil Companies (NOCs) and International Oil Corporations (IOCs). Using the GARCH(1,1) and GARCH(1,2) models, this study evaluates the effectiveness of these models in capturing the dynamic nature of oil price volatility. The findings indicate that while both models fit the data well, the GARCH(1,1) model is preferred due to its parsimonious nature and comparable forecast accuracy. Despite including an additional lag in the GARCH(1,2) model, it did not significantly outperform the GARCH(1,1) model in predictive performance. The study further analyzes the residuals and autocorrelation characteristics, highlighting the potential for model refinement. The study underscores the importance of selecting an appropriate model complexity, incorporating external factors, and exploring advanced methodologies to enhance forecast accuracy. These insights are critical for developing effective risk management strategies and informing policy decisions in volatile crude oil markets.

Augmented pre-training-based carbon emission accounting method using electricity data under small-sample condition

IntroductionAccurate and rapid carbon accounting method for the power industry is crucial to support China’s low-carbon transformation. Currently, carbon emission accounting methods are based on slowly updated fuel statistics or expensive monitoring equipment, resulting in high costs and delays in carbon emission estimation. Power data offers high real-time availability, accuracy, and resolution, and exhibits a strong correlation with carbon emissions. These characteristics provide a pathway for achieving rapid and precise annual carbon emission accountings. However, carbon emission data inherently exhibits small sample characteristics, making these methods less effective in small sample conditions and leading to lower accounting accuracy.MethodsTherefore, this paper proposes an augmented pre-training-based “electricity-to-carbon” method under small sample conditions.ResultsThis approach utilizes the correlation between electricity and carbon data as well as the autocorrelation characteristics of carbon emission data to construct a machine learning-based electricity-carbon fitting model for rapid and accurate carbon emission estimation. To address the challenges of small sample learning, this paper introduces an interpolation pre-training method to optimize the model’s hyperparameters and conserve samples for model training, thereby improving the model’s generalization and robustness.DiscussionCase studies on a real dataset verifies the effectiveness of the proposed method. The findings of this study can promote the development of carbon measurement technology and facilitate the low-carbon transition of developing countries.

Does county urban shrinkage in China affect the concentration of lung-accessible particulate matter? An analysis from spatial effects and EKC perspectives.

The environmental effects of urban shrinkage have gradually attracted academic attention. Based on spatial effects and EKC perspectives, this study constructs an environmental impact assessment framework and examines the impact of urban shrinkage on the concentration of lung-accessible particulate matter at the county level in China from 2013 to 2020. The results show that, from a two-dimensional perspective in space and time, lung-accessible particulate matter concentrations show a decreasing trend in the time series and an obvious autocorrelation feature in space. Urban shrinkage can affect lung-accessible particulate matter concentrations through spatial effects. Through direct effects, urban shrinkage promotes the increase of lung-accessible particulate matter concentrations. Through spillover effects, urban shrinkage suppresses the growth of lung-accessible particulate matter concentrations. The effect of urban shrinkage on lung-accessible particulate matter concentration is nonlinear (EKC). This may be the result of the "siphon effect." Furthermore, in the extended STIRPAT model, it was found that population and economic and technological factors differentially affect lung-accessible particulate matter concentrations in shrinking cities. This study gives a new idea and insight into the changes in environmental pollution in shrinking cities.

A new deep learning-based model for reconstructing high-quality NDVI time-series data in heavily cloudy areas: fusion of Sentinel 1 and 2 data

ABSTRACT Reconstructing high-quality Normalized Difference Vegetation Index time series data is essential for ecological and agricultural applications but remains challenging in heavily cloudy areas. Fusing Sentinel SAR and optical data with deep learning could be helpful but is also challenging for stable models due to unstable SAR-NDVI relationships caused by imaging mechanism differences and environmental complexities. In this study, we developed a new Bidirectional Recurrent Imputation for Optical-SAR fusion (BRIOS) model to reconstruct high-quality Sentinel-2 NDVI time series data. BRIOS designs a two-layer recurrent architecture that integrates the autocorrelation of discrete, cloud-free NDVI observations into the model for a more stable SAR-NDVI relationship. Evaluating BRIOS against three baseline methods (GF-SG spatiotemporal fusion, Harmonic regression interpolation, and MCNN-Seq deep learning) across three full Sentinel-2 tiles in reconstructing 8-day NDVI time series, BRIOS consistently outperformed in scenarios of either random or continuously missing data, as evidenced by lower RMSE values (e.g. 0.075 for BRIOS vs. 0.108 for GF-SG vs. 0.143 for Harmonic regression vs. 0.303 for MCNN-Seq), better Edge index, and high linear correlation coefficients (R values up to 0.97). Further ablation experiments revealed that deep integration of NDVI autocorrelation features and SAR temporal change patterns has improved the stability and generalization of BRIOS. Discussions on the model's scalability across various cloud sizes and training dataset sizes affirm its practicality for broad-scale application in vegetation monitoring under challenging cloudy conditions.

Comprehensive analysis of feature‐algorithm interactions for fall detection across age groups via machine learning

AbstractFall detection in daily activities hinges on both feature selection and algorithm choice. This study delves into their intricate interplay using the Sisfall dataset, testing 10 machine learning algorithms on 26 features encompassing diverse falls and age groups. Individual feature analysis yields key insights. RFC with the autocorrelation feature outperformed the other classifiers, achieving 97.94% accuracy and 97.51% sensitivity (surpassing F3‐SVM at 96.18% and F17‐LightGBM at 95.79%). The F3‐SVM exhibited exceptional specificity (98.72%) for distinguishing daily activities. Time‐series features employed by SVM achieved a peak accuracy of 98.60% on unseen data, exceeding motion, basic statistical, and frequency domain features. Feature combinations further excel: the Quintuple approach, fusing top‐performing features, reaches 98.69% accuracy, 98.28% sensitivity, and 99.08% specificity with the ETC, demonstrating notable sensitivity owing to its adaptability. This study underscores the crucial interplay of features and algorithms, with the Quintuple‐ETC approach emerging as the most effective. Rigorous hyperparameter tuning strengthens its performance in real‐world fall‐detection applications. Furthermore, the study investigates algorithm transferability, training models on young participants' data and applying them to the elderly—a significant challenge in machine learning. This highlights the importance of understanding the data transfer between age groups in healthcare, aging management, and medical diagnostics.

Efficiency Evaluation and Spatio-Temporal Differentiation Analysis of tourism Industry in Cities Along the Beijing Hangzhou Grand Canal Based on Three-Stage DEA

The Beijing Hangzhou Grand Canal has become an important component of China’s national cultural park construction. In order to improve the tourism efficiency of cities along the Beijing Hangzhou Grand Canal, this paper constructs an evaluation index system for tourism efficiency of cities along the Beijing Hangzhou Grand Canal based on spatial production theory. The three-stage DEA model are used to evaluate the tourism efficiency of 22 cities along the Beijing Hangzhou Grand Canal in 2015, 2017, and 2019, and analyze the spatiotemporal differences in tourism efficiency. Research has found that firstly, the development of tourism in Beijing, Tianjin, Tai’an, Zhenjiang, Huzhou, and Hangzhou is efficient. The comprehensive tourism efficiency of Xingtai, Zaozhuang, Suqian, and Huai’an is greatly affected by scale efficiency, while the comprehensive tourism efficiency of Xuzhou and Changzhou is more affected by pure technical efficiency. The tourism efficiency of other cities is influenced by the alternating effects of two decomposition efficiencies. Secondly, “tourism dependence” and “actual utilization of foreign investment” have a positive effect on promoting the overall efficiency and scale efficiency of tourism, but are not conducive to the improvement of pure technological efficiency. Thirdly, the global spatial autocorrelation characteristics of tourism efficiency in cities along the Beijing Hangzhou Grand Canal are not significant. Locally developed tourism areas exhibit “H-H” characteristics, while underdeveloped tourism areas exhibit “H-L” and “L-L” distribution characteristics. The findings of this study can provide reference for the development of tourism resources in cities along the Beijing Hangzhou Grand Cana.

Automobile-Demand Forecasting Based on Trend Extrapolation and Causality Analysis

Accurate automobile-demand forecasting can provide effective guidance for automobile-manufacturing enterprises in terms of production planning and supply planning. However, automobile sales volume is affected by historical sales volume and other external factors, and it shows strong non-stationarity, nonlinearity, autocorrelation and other complex characteristics. It is difficult to accurately forecast sales volume using traditional models. To solve this problem, a forecasting model combining trend extrapolation and causality analysis is proposed and derived from the historical predictors of sales volume and the influence of external factors. In the trend-extrapolation model, the historical predictors of sales series was captured based on the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Polynomial Regression (PR); then, Empirical Mode Decomposition (EMD), a stationarity-test algorithm, and an autocorrelation-test algorithm were introduced to reconstruct the sales sequence into stationary components with strong seasonality and trend components, which reduced the influences of non-stationarity and nonlinearity on the modeling. In the causality-analysis submodel, 31-dimensional feature data were extracted from influencing factors, such as date, macroeconomy, and promotion activities, and a Gradient-Boosting Decision Tree (GBDT) was used to establish the mapping between influencing factors and future sales because of its excellent ability to fit nonlinear relationships. Finally, the forecasting performance of three combination strategies, namely the boosting series, stacking parallel and weighted-average parallel strategies, were tested. Comparative experiments on three groups of sales data showed that the weighted-average parallel combination strategy had the best performance, with loss reductions of 16.81% and 4.68% for data from the number-one brand, 25.60% and 2.79% for data from the number-two brand, and 46.26% and 14.37% for data from the number-three brand compared with the other combination strategies. Other ablation studies and comparative experiments with six basic models proved the effectiveness and superiority of the proposed model.

Spatiotemporal Evolution of Urban Land Green Utilization Efficiency and Driving Factors: An Empirical Study Based on Spatial Econometrics

Green development is an inevitable choice for sustainable development under the constraints of environmental resources. This paper attempts to explore the connotation of urban land green utilization efficiency (LGUE) and reveal its spatial differentiation characteristics. This study adopts the super-SBM model to measure LGUE from 2009 to 2022 and analyzes the spatiotemporal variation rules. Then, the study reveals the spatial influencing factors of LGUE, drawing the following conclusions: (1) the average efficiency value of LGUE at the national level is still at a low level, but it is on an upward trend. There are significant differences in LGUE among the eastern, central, and western regions, with the highest LGUE in the eastern region and the lowest in the western region. (2) The spatial distribution of LGUE in various cities across the country is not entirely random but shows significant spatial autocorrelation characteristics. The improvement in LGUE in a region can improve the surrounding region's LGUE. (3) Economic development level promotes the improvement of local city LGUE, but its impact on LGUE of surrounding neighboring cities is not significant; local city industrial structure upgrading can improve LGUE in both local and neighboring cities; foreign investment in local cities can promote LGUE in both local and neighboring cities; the increase in population density will hinder LGUE in local cities but improve surrounding cities LGUE. The intervention degree of local city government will suppress the improvement of LGUE in both local and neighboring cities.

Carbon emission intensity measurement and spatial effect of high energy consuming industries: evidence from China

<p style="text-indent:20.0pt"><span style="font-size:10.5pt"><span style="font-family:"Times New Roman",serif"><span style="color:black"><span class="fontstyle01" style="font-family:仿宋"><span style="color:black"><span lang="EN-US" style="font-size:10.0pt"> High energy consumption industry is an important source of carbon dioxide emissions, and reducing pollution and carbon is an important measure for China to achieve the goal of "2030 carbon peak 2060 carbon neutral". Based on the improvement of the traditional calculation method of IPCC carbon emission intensity, this paper measures the carbon emission intensity, selects the data of high energy consuming industries in 30 provinces in China from 1997 to 2022 as samples, uses the stipat model and Moran index to analyze the correlation of the influencing factors of carbon emission, and uses the spatial measurement model to study the spatial effect of carbon emission intensity. The results show that: first, the overall carbon emission intensity of high energy consuming industries shows a downward trend, with typical spatial heterogeneity. During the sample period, the carbon emission intensity of high energy consuming industries in 30 provinces in China was calculated based on the IPPC method, with the overall decline. Second, the carbon emission intensity of high energy consuming industries has significant spatial autocorrelation characteristics. According to the global Moran index, the center of gravity moves from east to West as a whole. Third, the carbon emission intensity of high energy consuming industries is affected by multiple environmental factors. Industrial structure (INS), regional gross domestic product (GDP) and regional economic development (ECO) have a significant impact. Fourth, the carbon emission intensity of high energy consuming industries has a significant spatial spillover effect. According to the regression results of spatial Dobbin model with double fixed effects, the direct and indirect effects of carbon emission intensity of high energy consuming industries are significant.</span></span></span></span></span></span></p>

Explicit Incorporation of Spatial Autocorrelation in 3D Deep Learning for Geospatial Object Detection

Three-dimensional (3D) geospatial object detection has become essential for 3D geospatial studies driven by explosive growth in 3D data. It is extremely labor- and cost-intensive, though, as it often requires manual detection. Deep learning has been recently used to automate object detection within 3D context. Yet, addressing spatial dependency in 3D data and how it might inform deep learning for 3D geospatial object detection remains a significant challenge. Traditional models focus on the use of spatial properties, often overlooking color and contextual information. Exploiting these nonspatial attributes for 3D geospatial object detection thus becomes crucial. Our study pioneers explicit incorporation of spatial autocorrelation of color information into 3D deep learning for object detection. We introduce an innovative framework to estimate spatial autocorrelation, addressing challenges in unstructured 3D data sets. Our experiments suggest the effectiveness of incorporating spatial autocorrelation features in enhancing the accuracy of 3D deep learning models for geospatial object detection. We further investigate the uncertainty of such contextual information brought by diverse configurations, exemplified by the number of nearest neighbors. This study advances 3D geospatial object detection via using spatial autocorrelation to inform deep learning algorithms, strengthening the connection between GIScience and artificial intelligence and, thus, holding implications for diverse GeoAI applications.

Influence of the atmospheric environment on spatial variation of lung cancer incidence in China.

Conducting this research contributes to a deeper understanding of the correlation between atmospheric environmental quality and lung cancer incidence, and provides the scientific basis for formulating effective environmental protection and lung cancer prevention and control strategies. Lung cancer incidence in China has strong spatial variation. However, few studies have systematically revealed the characteristics of the spatial variation in lung cancer incidence, and have explained the causes of this spatial variation in lung cancer incidence from the perspectives of multiple components of the atmospheric environment to explain this spatial variation in lung cancer incidence. To address research limitations, we first analyze the spatial variation and spatial correlation characteristics of lung cancer incidence in China. Then, we build a spatial regression model using GeoDa software with lung cancer incidence as the dependent variable, five atmospheric environment factors-particulate matter 2.5 (PM2.5) concentration, temperature, atmospheric pressure, and elevation as explanatory variables, and four socio-economic characteristics as control variables to systematically analyze the influence and intensity of these factors on lung cancer incidence. The results show that lung cancer incidence in China has apparent changes in geographical and spatial unevenness, and spatial autocorrelation characteristics. In China, the lung cancer incidence is relatively high in Northeast China, while some areas of high lung cancer incidence still exist in Central China, Southwest China and South China, although the overall lung cancer incidence is relatively low. The atmospheric environment significantly affects lung cancer incidence. Different elements of the atmospheric environment vary in the direction and extent of their influence on the development of lung cancer. A 1% increase in PM2.5 concentration is associated with a level of 0.002975 increase in lung cancer incidence. Atmospheric pressure positively affects lung cancer incidence, and an increase in atmospheric pressure by 1% increases lung cancer incidence by a level of 0.026061. Conversely, a 1% increase in temperature is linked to a level of 0.006443 decreases in lung cancer incidence, and a negative correlation exists between elevation and lung cancer incidence, where an increase in elevation by 1% correlates with a decrease in lung cancer incidence by a level of 0.000934. The core influencing factors of lung cancer incidence in the seven geographical divisions of China exhibit variations. This study facilitates our understanding of the spatial variation characteristics of lung cancer incidence in China on a finer scale, while also offering a more diverse perspective on the impact of the atmospheric environment on lung cancer incidence.

Research on multiscale OpenStreetMap in China: data quality assessment with EWM-TOPSIS and GDP modeling

ABSTRACT OpenStreetMap (OSM) is a voluntary platform designed to provide free and up-to-date geographic data. Since OSM is based on multisource geographic data provided by the public, the quality of the data has become a concern of researchers. In this study, a unified measure for evaluating OSM data quality was constructed, and the entropy weight method (EWM) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) model were used to evaluate the quality of multiscale OSM data in China from 2014 to 2020. In addition to evaluating the data quality, the use of OSM data quality index factors in economic modeling at different spatial scales in China was explored by using a geographic information system (GIS) analysis method and a geographically and temporally weighted regression (GTWR) model. Four machine learning models, SVM, RF, XGBoost and CatBoost, were used to simulate the grid-scale GDP, and the effectiveness of these simulations was discussed. The results showed that (1) the weights of OSM data quality indicator factors vary across different spatial scales. (2) From 2014 to 2020, the quality of national-scale OSM data first increased, then decreased and then gradually stabilized. In addition, the quality of OSM data at the provincial and municipal scales is significantly different, and the distribution is affected by the population and geographical environment. (3) Over time, the spatial clustering characteristics of OSM data quality at different spatial scales in China has continuously strengthened. In addition, the quality of Chinese OSM data displays obvious local spatial autocorrelation characteristics, which are dominated by H-H clustering and L-L clustering. (4) The GTWR model performs well in predicting and revealing the spatiotemporal correlation characteristics between GDP and OSM data quality indicators at the provincial and municipal scales in China. The correlations increase with decreasing spatial scale (provincial to municipal). Moreover, the GDP modeling ability is better in economically underdeveloped Northwest China and economically developed East China. (5) Four machine learning models coupled with road network length completeness, relative linear density, road name attribute completeness, POI name attribute completeness, road network accuracy, road network update frequency, POI update frequency, topological consistency and directional similarity yielded the best grid-scale GDP simulation values. Notably, the CatBoost model provided the best accuracy, and the results further verified that it is feasible to use the proposed OSM data quality index system to predict the regional economic development level in China.

GSEA analysis identifies potential drug targets and their interaction networks in coronary microcirculation disorders

Coronary microcirculation dysfunction (CMD) is one of the main causes of cardiovascular disease. Traditional treatment methods lack specificity, making it difficult to fully consider the differences in patient conditions and achieve effective treatment and intervention. The complexity and diversity of CMD require more standardized diagnosis and treatment plans to clarify the best treatment strategy and long-term outcomes. The existing treatment measures mainly focus on symptom management, including medication treatment, lifestyle intervention, and psychological therapy. However, the efficacy of these methods is not consistent for all patients, and the long-term efficacy is not yet clear. GSEA is a bioinformatics method used to interpret gene expression data, particularly for identifying the enrichment of predefined gene sets in gene expression data. In order to achieve personalized treatment and improve the quality and effectiveness of interventions, this article combined GSEA (Gene Set Enrichment Analysis) technology to conduct in-depth research on potential drug targets and their interaction networks in coronary microcirculation dysfunctions. This article first utilized the Coremine medical database, GeneCards, and DrugBank public databases to collect gene data. Then, filtering methods were used to preprocess the data, and GSEA was used to analyze the preprocessed gene expression data to identify and calculate pathways and enrichment scores related to CMD. Finally, protein sequence features were extracted through the calculation of autocorrelation features. To verify the effectiveness of GSEA, this article conducted experimental analysis from four aspects: precision, receiver operating characteristic (ROC) curve, correlation, and potential drug targets, and compared them with Gene Regulatory Networks (GRN) and Random Forest (RF) methods. The results showed that compared to the GRN and RF methods, the average precision of GSEA improved by 0.11. The conclusion indicated that GSEA helped identify and explore potential drug targets and their interaction networks, providing new ideas for personalized quality of CMD.

The spatial spillover effects of clean energy consumption and production on sustainable economic development in China

The massive consumption of fossil energy has resulted in high CO2 emissions, posing a formidable challenge to global sustainable economic development (SED). As countries endeavor to shift from fossil to clean energy sources to achieve SED, research on the impact of clean energy is scarce, and quantitative analysis is lacking. This study measured China's SED and used a spatial econometric model to examine the impact of clean energy consumption and production on SED across 30 provinces in China from 2008 to 2020. Results show that (1) China's SED exhibits significant positive spatial autocorrelation characteristics, forming a “point-to-area” development pattern. (2) Clean energy consumption, production, and consumption structure all contribute to the promotion of SED in the region and have positive spatial spillover effects. (3) A considerable regional disparity exists in the spatial impact of clean energy on SED. The eastern and central regions have significant positive spatial spillover effects, whereas the western region is opposite. Notably, the estimated coefficient of the spatial Durbin model is relatively small, reflecting China's ongoing transition to clean energy and its limited role in promoting economic sustainability. Joint efforts and differentiated policies are essential to develop clean energy and sustainable economic.

Perfect Sequences Based on Golay Codes for Communication Systems

In data transmission systems, ensuring reliable communication while maximizing spectrum efficiency is a challenge. Code Division Multiple Access (CDMA) systems, widely used in wireless networks, depend on spreading codes to manage interference and support users. Achieving a balance between low cross-correlation and optimal auto-correlation properties is complex and involves trade-offs that affect system performance, especially as modern systems demand higher data rates and efficiency. In systems that use spectral spreading, achieving optimal autocorrelation characteristics often compromises cross-correlation characteristics, and vice versa. Codes with low cross-correlation values typically exhibit high out-of-phase autocorrelation values. Therefore, a balance between autocorrelation and cross-correlation properties is necessary for an efficient CDMA communication system. These desirable correlation properties are crucial in both periodic and aperiodic contexts. Recent innovations have led to a patented code generator derived from Golay codes/sequences, which exhibits low periodic cross-correlation values and a periodic autocorrelation function characterized by a prominent correlation peak and null values surrounding it. This development not only enhances signal quality but also mitigates interference in multi-user communication scenarios, making it particularly relevant for modern wireless networks. Furthermore, a novel solution is proposed to minimize the Peak-to-Average Power Ratio (PAPR) and reduce the cost of a new Orthogonal Perfect Discrete Fourier Transform Golay (OPDG) power transmission circuit. This approach leverages advanced signal processing techniques to achieve energy efficiency, addressing a critical challenge in high-performance communication systems. Experimental results demonstrate the practicality of these innovations in real-world implementations, paving the way for future advancements in CDMA technology.

Spatio-temporal evolution and scenario analysis of end-recycling governance in China: based on GM-SVR-BP combined forecasting model

ABSTRACT End-recycling governance is the core link for achieving high-quality development of circular economy. This study uses data of 30 provinces in China from 2011 to 2021 as research samples, constructs a comprehensive evaluation index system, and analyzes the achievements and progress of end-recycling governance in China over the years. Based on scenario analysis, a GM-SVR-BP combined model is further used to explore the development trends of end-recycling governance performance in 30 provinces in China from 2022 to 2030. Research finding: (1) The end-recycling governance performance of Chinese provinces presents a dynamic upward trend, but overall, it is low. (2) The barycenter of end-recycling governance in China migrated to the southeast, and the transfer speed was stable, showing a northeast-southwest pattern. There are pronounced differences among different regions. (3) There are apparent spatial autocorrelation characteristics in China’s end-recycling governance. The overall spatial agglomeration degree showed a weakening trend during the observation period. (4) The development trend of end-recycling governance performance in various regions in China during 2022–2030 follows the differentiation characteristics of the observation period in this study. Among them, technological breakthrough is one of the effective ways to improve the performance of end-recycling governance. The effect of the improvement of emotional level on the performance of end-recycling governance showed apparent regional heterogeneity. This study provides a clear and reliable decision-making basis for promoting the improvement of regional end-recycling governance performance and system construction, and has certain reference value for promoting national and regional resource conservation and environmental protection.

Multidimensional Evaluation of Traditional Villages in Jiangnan Region, China: Spatial Pattern, Accessibility and Driving Factors

Assessment of the spatial distribution and accessibility of traditional villages is closely related to their development. However, the impacts of spatial heterogeneity on the accessibility of traditional villages remain largely unknown. A total of 644 national-level traditional villages in the Jiangnan region were used to explore the spatial heterogeneity impact with a geographically weighted regression (GWR) model. We found: (1) spatially, the villages exhibit a predominant aggregation distribution pattern with significant local spatial disparities (R < 1, p < 0.01), predominantly originating from the Song and Ming dynasties (59.21%). Their clustering shifts from northeast to southwest, with over 70% of the villages located on slopes ranging from 0° to 20° and within 1 km of water. (2) The accessibility of these villages is generally low, with 85.66% being accessible within 200 ~ 300 min; it decreases concentrically outwards from Changzhou and exhibits clustering autocorrelation characteristics (Moran’s I > 0, Z > 2.58). (3) Road network density, elevation, and slope are significantly correlated with accessibility (p < 0.05), demonstrating pronounced spatial heterogeneity in their effects, with these factors collectively explaining approximately 85% of the accessibility levels. These findings provide a basis for comprehensive planning and categorized strategies for traditional villages.