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At present, the method of using coupled models to model different frequency subseries of precipitation series separately for prediction is still lacking in the research of precipitation prediction, thus in this paper, a coupled model based on Ensemble Empirical Mode Decomposition (EEMD), Long Short-Term Memory neural network (LSTM) and Autoregressive Integrated Moving Average (ARIMA) is proposed for month-by-month precipitation prediction. The monthly historical precipitation data of Luoyang City from 1973 to 2021 were used to build the model, and the modal components of different frequencies obtained by EEMD decomposition were divided into high-frequency series part and low-frequency series part using the Permutation Entropy (PE) algorithm, the LSTM model is used to predict the high-frequency sequence part, while the ARIMA model is used to predict the low-frequency sequence part. Monthly precipitation forecasts are obtained by superimposing the results of the two models. Finally, the predictive performance is evaluated using several assessment metrics. The indicators show that the model predictive performance outperforms the EMD-LSTM (Empirical Mode Decomposition), EEMD-LSTM, EEMD-ARIMA combined models and the single models, and the model has high confidence in the prediction results of future precipitation.

Wu Zetian’s 武則天 Bright Hall 明堂 was an unprecedented structure, serving as both a political hub and a ceremonial center of the state, symbolizing the image of Wu Zetian’s regime. While it inherited some traditional design concepts, the core structure—such as the central pillar—differed significantly from earlier Bright Halls, aligning more closely with the Sudharmā Hall 善法堂 of the deity Indra in Buddhism. Furthermore, both the Bright Hall and the Sudharmā Hall were used for court gatherings and decision making, bearing the nature of the palace of heavenly gods. The high degree of similarity suggests that the design of Wu Zetian’s Bright Hall was likely modeled after the Sudharmā Hall. This design highlighted Wu Zetian’s identity as both the Pure Light Heavenly Maiden 淨光天女 and the Cakravartin 轉輪聖王, thereby reinforcing the political legitimacy and sanctity of her rule. The Bright Hall, together with the Celestial Pillar 天樞 which represented Mount Meru 須彌山 and the Heavenly Hall 天堂 symbolizing Tuṣita Heaven 兜率天, form a representation of Mount Meru’s cosmic landscape.

The late Palaeozoic Yong’an–Meizhou depression belt is an important iron (Fe) and polymetallic metallogenic belt in southern China. It has undergone a transformation from Tethys to the circum-Pacific tectonic domain. The Luoyang deposit is one of the typical Fe skarn deposits in the Yong’an–Meizhou depression belt of eastern China. Garnet is a characteristic mineral in the deposit. Two generations of garnets are detected in the deposit based on their textural characteristics and trace-element contents, and are represented by Fe-enriched andradite. The first generation of garnets (Grt1) have two types of garnets (Grt1-A and Grt1-B). Type A garnets of the first generation (Grt1-A) (Adr80-88) replaced by massive diopside-magnetite assemblage exhibit distinct oscillatory zonings and display patterns of enriched light rare earth elements (LREE) to weak heavy rare earth elements (HREE), with weak negative to positive Eu anomalies, and highest U, ΣREE and Sn contents. Type B garnets of the first generation (Grt1-B) are irregular zones (Adr94-96) coexisting with magnetite, in which Grt1-A is generally dissolved, and have obviously LREE-enriched and HREE-depleted patterns, with weak negative to positive Eu anomalies, and moderate U, ΣREE and Zn contents. Garnets of the second generation (Grt2) (Adr96-99) that replaced massive magnetite together with sphalerite show unzoned patterns, with a flat REE pattern and pronounced negative Eu anomalies as well as contents of lowest U and ΣREE, and highest W. The substitution of REEs in garnets occurs as [X2+]VIII –1[REE3+]VIII +1[Si4+]IV –1[Z3+]IV +1in an Al-enriched environment. Luoyang hydrothermal fluids shifted from reducing conditions with relatively high-U and -ΣREE characteristics to oxidizing conditions with relatively low-U and -ΣREE characteristics. The reduced siderophile elements and increased fO2 in fluid during Grt1-B formation caused magnetite mineralization and reduced Zn contents during Grt2 formation, causing the deposition of sphalerite. All garnets formed from magmatic fluid and were controlled by infiltrative metasomatism in an opened system.

Precipitation prediction is one of the important issues in meteorology and hydrology, and it is of great significance for water resources management, flood control, and disaster reduction. In this paper, a precipitation prediction model based on the empirical mode decomposition–variational mode decomposition–long short-term memory (EMD–VMD–LSTM) is proposed. This model is coupled with EMD, VMD, and LSTM to improve the accuracy and reliability of precipitation prediction by using the characteristics of EMD for noise removal, VMD for trend extraction, and LSTM for long-term memory. The monthly precipitation data from 2000 to 2019 in Luoyang City, Henan Province, China, are selected as the research object. This model is compared with the standalone LSTM model, EMD–LSTM coupled model, and VMD–LSTM coupled model. The research results show that the maximum relative error and minimum relative error of the precipitation prediction using the EMD–VMD–LSTM neural network coupled model are 9.64 and −7.52%, respectively, with a 100% prediction accuracy. This coupled model has better accuracy than the other three models in predicting precipitation in Luoyang City. In summary, the proposed EMD–VMD–LSTM precipitation prediction model combines the advantages of multiple methods and provides an effective way to predict precipitation.

Urban parks can mitigate the urban heat island (UHI) and effectively improve the urban microclimate. In addition, quantifying the park land surface temperature (LST) and its relationship with park characteristics is crucial for guiding park design in practical urban planning. The study’s primary purpose is to investigate the relationship between LST and landscape features in different park categories based on high-resolution data. In this study, we identified the land cover types of 123 parks in Luoyang using WorldView-2 data and selected 26 landscape pattern indicators to quantify the park landscape characteristics. The result shows that the parks can alleviate UHI in most seasons, but some can increase it in winter. While the percentage of bare land, PD, and PAFRAC have a positive impact on LST, AREA_MN has a significant negative impact. However, to deal with the current urban warming trend, a compact, clustered landscape configuration is required. This study provides an understanding of the major factors affecting the mitigation of thermal effects in urban parks (UP) and establishes a practical and feasible urban park renewal method under the idea of climate adaptive design, which provides valuable inspiration for urban park planning and design.

Metasurfaces and metagratings offer new platforms for electromagnetic wave control with significant responses. However, metasurfaces based on abrupt phase change and resonant structures suffer from the drawback of high loss and face challenges when applied in water waves. Therefore, the application of metasurfaces in water wave control is not ideal due to the limitations associated with high loss and other challenges. We have discovered that non-resonant metagratings exhibit promising effects in water wave control. Leveraging the similarity between bridges and metagratings, we have successfully developed a water wave metagrating model inspired by the ancient Luoyang Bridge in China. We conduct theoretical calculations and simulations on the metagrating and derive the equivalent anisotropic model of the metagrating. This model provides evidence that the metagrating has the capability to control water waves and achieve unidirectional surface water wave. The accuracy of our theory is strongly supported by the clear observation of the unidirectional propagation phenomenon during simulation and experiments conducted using a reduced version of the metagrating. It is the first time that the unidirectional propagation of water waves has been seen in water wave metagrating experiment. Above all, we realize the water wave metagrating experiment for the first time. By combining complex gratings with real bridges, we explore the physics embedded in the ancient building - Luoyang Bridge, which are of great significance for the water wave metagrating design and provide a new method for analyzing the effects of water waves on bridges. At the same time, this discovery also provides a new idea for ocean cargo transportation, ocean garbage cleaning, and the development and protection of ancient bridges.

The quantitative evaluation of water conservation in the Luoyang area can provide a basis for decision-making on regional water resources development and utilization, ecological environmental protection, and economic development planning. Based on the SWAT model and alternative engineering method, the water conservation and its service value in Luoyang region from 2009 to 2018 were assessed and the reasons for their spatial and temporal changes were analyzed. The results show that during the period of 2009–2018, the total water connotation and its service value reached the highest in 2014, with 16,927,100 m 3 and 103 million yuan, respectively; the total water connotation and its service value reached the lowest in 2011, with 7,073,500 m 3 and 43,224,000 yuan, respectively. Forest ecosystems have a strong water retention and storage capacity, and the highest water conservation and service value. Precipitation is the most important factor influencing water conservation and service value. The value of water-supporting services per unit area of ecosystem in Luoyang area is forest, grassland, arable land, and urban in descending order.

As a key indicator for measuring urban green visibility, the Green View Index (GVI) reflects actual visible greenery from a human perspective, playing a vital role in assessing urban greening levels and optimizing green space layouts. Existing studies predominantly rely on single-source remote sensing image analysis or traditional statistical regression methods such as Ordinary Least Squares and Geographically Weighted Regression. These approaches struggle to capture spatial variations in human-perceived greenery at the street level and fail to identify the non-stationary effects of different drivers within localized areas. This study focuses on the Luolong District in the central urban area of Luoyang City, China. Utilizing Baidu Street View imagery and semantic segmentation technology, an automated GVI extraction model was developed to reveal its spatial differentiation characteristics. Spearman correlation analysis and Multiscale Geographically Weighted Regression were employed to identify the dominant drivers of GVI across four dimensions: landscape pattern, vegetation cover, built environment, and accessibility. Field surveys were conducted to validate the findings. The Multiscale Geographically Weighted Regression method allows different variables to have distinct spatial scales of influence in parameter estimation. This approach overcomes the limitations of traditional models in revealing spatial non-stationarity, thereby more accurately characterizing the spatial response mechanism of the Global Vulnerability Index (GVI). Results indicate the following: (1) The study area’s average GVI is 15.24%, reflecting a low overall level with significant spatial variation, exhibiting a “polar core” distribution pattern. (2) Fractal dimension, normalized vegetation index (NDVI), enclosure index, road density, population density, and green space accessibility positively influence GVI, while connectivity index, Euclidean nearest neighbor distance, building density, residential density, and water body accessibility negatively affect it. Among these, NDVI and enclosure index are the most critical factors. (3) Spatial influence scales vary significantly across factors. Euclidean nearest neighbor distance, building density, population density, green space accessibility, and water body accessibility exert global effects on GVI, while fractal dimension, connectivity index, normalized vegetation index, enclosure index, road density, and residential density demonstrate regional dependence. Field survey results confirm that the analytical conclusions align closely with actual greening conditions and socioeconomic characteristics. This study provides data support and decision-making references for green space planning and human habitat optimization in Luoyang City while also offering methodological insights for evaluating urban street green view index and researching ecological spatial equity.

Shear strength and crack characteristics are crucial factors in determining the engineering properties of expansive soils. In this study, direct shear tests, triaxial shear tests, and crack development tests were implemented to assess the impacts of cement content and dry-wet cycles on the mechanical behavior of expansive soil. The fullsurface fracture indices of the triaxial specimens were extracted, and a comprehensive fracture index was calculated. Subsequently, a Support Vector Machine (SVM) model was established to analyze the development of cohesion. The findings indicated that the shear strength and its parameters decrease as the frequency of dry-wet cycles increases, and they initially rise and then fall with the increase in cement content. The maximum shear strength occurs at a cement content of 6%-8%. The correlation between comprehensive fracture index and strength index is better than that of single fracture index, and the correlation between average crack width is the strongest. The support vector machine model is established by replacing the average width of cracks with dry-wet cycle frequency. The model has a small prediction accuracy error, strong adaptability, and high potential. The findings of this study indicate that crack index analysis can effectively simulate the cohesion of expansive soils, demonstrating significant practical potential for engineering construction in expansive soil regions. This approach provides important theoretical support and technical guidance for construction practices in areas characterized by expansive soils. Keywords: Improved Luoyang expansive soil, Shear strength, Dry-wet cycles, Macro-tomesoscopic correlation analysis, Support vector machine.