Pattern Characteristics Research Articles

REGENERATION OF THE DESIGN OF WEAVING AND EMBROIDERY PATTERNS BASED ON ARTIFICIAL INTELLIGENCE TECHNOLOGIES

Purpose of this work is to determine the feasibility of using AIGC (Artificial Intelligence Generated Content) technology to restore the design of traditional Yao patterns in order to protect and preserve the culture of the Yao ethnic group, and at the same time contribute to the modernization of the application of traditional arts. Methodology: In this study, based on the classification and combing through Yao weaving and embroidery patterns through literature research, the two selected design patterns are coded and extracted, and AIGC (Artificial Intelligence Generated Automatic Generation Contents) technology is cited to carry out regeneration design of the patterns. Results: By studying the process of creation and evolution of Yao patterns, it was found that its unique cultural and aesthetic value warrants study and preservation. It was found that patterns created using AIGC technology not only retain the basic elements of traditional patterns, but also introduce various design innovations. These newly created templates have a high visual aesthetic and can be applied to the design of Yao derivative cultures. Scientific novelty of the study consists in the identification of the main characteristics of the traditional patterns of the Yao people, and the proposed approach to their preservation and integration into modern design. By overcoming the limitations of traditional design, this study combines AIGC technology with national traditional culture. Practical significance: This study is of great scientific significance because it not only provides a new method for digital preservation and innovative design of Yao patterns, but also contributes to the study of the application of artificial intelligence in pattern innovation. Research materials can be used both in educational courses and in project design practices.

Research on image design of Fujian paper-cut pattern based on Kansei engineering and WOA-BP neural network

Abstract In order to design Fujian paper-cut patterns that meet the perceptual needs of consumers and better inherit and develop them in modern society, a Fujian paper-cut pattern image design method based on perceptual engineering and the Whale Optimization Algorithm optimized BP neural network (WOA-BP) neural network is proposed. First, based on the theory of Kansei engineering, six representative paper-cut pattern samples and their main modeling features were determined through questionnaire survey, multi-dimensional scaling analysis, cluster analysis, and analytic hierarchy process. Second, the semantic difference method is used to obtain the perceptual image evaluation value of the representative paper-cut pattern, and combined with the principal component analysis method, the representative image vocabulary is extracted. Finally, the WOA-BP neural network is used to construct the mapping relationship between consumers' perceptual images and paper-cut pattern modeling features, and calculate the paper-cut pattern modeling code combinations corresponding to consumers' image needs. At the same time, the paper-cut pattern design using the image vocabulary ‘modern-traditional’ as an example verifies the feasibility of the method in this article. Compared with the existing method of designing paper-cut patterns based solely on the subjective experience of the designer, the method of this article can correlate the perceptual needs of consumers with the corresponding modeling characteristics of paper-cut patterns, making the design of paper-cut patterns targeted, precise and intelligent.

Advancements in Antiviral Therapy: Favipiravir Sodium in Nasal Formulation.

Favipiravir (FPV) is an Active Pharmaceutical Ingredient (API) known to have lower solubility in aqueous solvents. In the current study, efforts were made to generate a crystalline Favipiravir Sodium Salt (NaFPV) for enhanced solubility in aqueous media. The in-house generated NaFPV was characterized by NMR studies and its sodium content was determined by Flame Emission Spectroscopy (FES) as a confirmation of salt formation. Its solubility was determined where-in the solubility of NaFPV in water was about 100 times greater than FVP. FPV and NaFPV nasal spray formulations were prepared and its activity was determined against human coronavirus (hCoV) 229E strain. In the anti-hCoV assay as compared to FPV, NaFPV showed almost threefold higher anti-viral activity than its unmodified counterpart. Accelerated stability and spray pattern characteristics of both the formulations were studied. Interestingly, NaFPV showed higher physical stability during storage at conditions 40 ± 2°C/ 75% ± 5% RH. The nasal spray formulations of both FPV and NaFPV showed ideal plume geometry and spray pattern of acceptablespecifications. Due to its improvement in terms of solubility, NaFPV will have higher rate and extent of absorption, and faster onset of the therapeutic effect and may appear to be a feasible alternative to regular favipiravir for use in solid dosage forms.

Spatiotemporal Correlation Analysis of Landscape Pattern and Habitat Quality in and Around China’s Tropical Rainforest National Park

With the establishment of the national park system in China, balancing ecological conservation within national parks and development in surrounding areas has become a critical issue. Despite its importance, studies on the scale effects of landscape fragmentation and its relationship with habitat quality (HQ) in these regions remain scarce. Therefore, this study focuses on the Hainan Tropical Rainforest National Park (HTRNP) in China and the surrounding nine cities and counties. The optimal scale for analyzing the spatiotemporal characteristics of landscape patterns, we assessed the best grain size and extent of landscape pattern indices using the regional information loss assessment model, area loss index, and semivariance function analysis. Based on this, the Fragstats and InVEST models analyzed the spatiotemporal evolution of landscape patterns and HQ from 1980 to 2020. Additionally, bivariate spatial autocorrelation theory was applied to examine the spatial correlation between the two. The results show that (1) the landscape indices in the study area vary with grain size and extent. The optimal grain size is 300 m, and the optimal extent is 2 km × 2 km. (2) Over the past 40 years, the landscape pattern of HTRNP has remained stable. In contrast, the surrounding areas exhibit notable spatial differentiation, with landscapes in coastal town centers becoming increasingly fragmented. (3) Over the past 40 years, the overall HQ in the study area has been relatively high, showing a spatial distribution pattern that decreases gradually from the center of HTRNP outward to rural areas, towns, and coastal regions. HTRNP has maintained a high and stable HQ, while the surrounding areas exhibited a decreasing trend in HQ, although the decline was relatively modest. (4) The spatial distribution of HQ aligns closely with landscape patterns, indicating a significant spatial correlation. The landscape connectivity of HTRNP remains high, with consistently superior HQ. In contrast, coastal town centers are characterized by highly fragmented landscapes and poor HQ. This study reveals the spatiotemporal dynamics of landscape patterns and HQ, along with their spatial associations. These findings provide a scientific basis for ecological conservation in national parks and the planning optimization of surrounding areas.

Research on the Development and Management of Ecological Agriculture Characteristic Tourism Based on Artificial Intelligence Technology for Water Resources

In order to improve the effectiveness of ecological agriculture tourism development and management of water resources, this article combines artificial intelligence technology to study the development and management of ecological agriculture tourism. This article conducts a hierarchical analysis based on the characteristics of tourism symbol aggregation and spatial distribution patterns within a certain period of time, determines the threshold for extracting the optimal tourism behavior boundary, and strives to solve the problems caused by the complexity and dynamism of tourism behavior. In addition, this article also developed an intelligent water resources agricultural characteristic tourism management system to improve the development effect of agricultural characteristic tourism and promote precise control of water conservancy irrigation systems in ecological agricultural tourism areas. Finally, this article combines the proposed algorithm model to construct an ecological agriculture characteristic tourism development and management system. Simulation evaluation shows that the ecological agriculture characteristic tourism development and management model based on artificial intelligence technology proposed in this paper has good effects.

Changing characteristics of land cover, landscape pattern and ecosystem services in the Bohai Rim region of China

Since the Anthropocene, ecosystems have been continuously deteriorating due to global climate change and human intervention. Exploring the changing characteristics of land use/land cover (LULC), landscape pattern and ecosystem service (ES) and their drivers is crucial for regional ecosystem management and sustainable development. Taking the Bohai Rim region of China as an example, we used the land use transfer matrix, landscape pattern index and InVEST model to analyze the changing characteristics of LULC, landscape pattern and six key ESs [crop production (CP), water yield (WY), carbon storage (CS), soil conservation (SC), habitat quality (HQ), landscape aesthetics (LA)] during 2000–2020. Detailed analysis of the natural and anthropogenic factors affecting the landscape pattern and ES changes has been considered in this study. The results show that the areas of forest, water and impervious land increased, while those of cropland, shrubs, grassland and barren land decreased during 2000–2020. The landscape was fragmented, complex and decentralized during 2000–2015, while the three trends eased during 2015–2020. From 2000 to 2020, CP, WY, and SC capacity show an increasing trend, while CS, HQ, and LA capacity tend to a decline. Natural factors (e.g., precipitation, temperature, altitude) and human factors (e.g., technological progress, policy changes, and LULC forms) are the main factors affecting landscape pattern and ESs. The present study can provide theoretical basis for ecological restoration, ecological product value realization, and land planning in the typical developed urban area.

Research on the Optimization and Development of Low Carbon Energy Economy from the Perspective of Water Conservancy Engineering

In order to explore the optimal development path of low-carbon energy economy in universities, this paper proposes a new method based on complex network theory to describe the global evolution and transformation characteristics of multivariate time series correlation patterns. In order to more intuitively see the impact of product extraction inputs as endogenous factors on economic growth, this article explores the relationship between economic growth and product extraction inputs by constructing a Hamiltonian function and using optimization methods. When considering whether the extraction input share of products with different extraction ratios is different, this article found the relationship between the total extraction input of products with different extraction ratios and the input share of green and low-carbon products, and analyzed consumers' preferences; Extractive investment of products under budget constraints and policy constraints. In addition, this article uses the dynamic equation of household utility function and environmental quality to prove that the extractive input of products not only promotes carbon reduction, but also has a positive effect on promoting economic growth. The simulation results show that the low-carbon economic development model based on energy efficiency proposed in this paper can effectively improve the development path of low-carbon energy economy.

A spatiotemporal analysis framework of the ecological security pattern based on the ecological “source–surface–corridor” and its implications

A reasonable ecological security pattern can improve landscape connectivity, promote biological circulation, maintain ecological processes, and improve regional ecosystem service functions and human well-being. Accordingly, systematically and scientifically analyzing the spatiotemporal evolution characteristics of the regional ecological security pattern has essential theoretical and practical significance. This research proposed that the analysis framework of the ecological security pattern should include a complete system composed of ecological sources, resistance surfaces, and corridors. Thus, this research uses the ecological network method to systematically analyze the spatiotemporal evolution characteristics of the ecological security pattern in the Qinba–Dabie intersection zone in China, a complete natural ecological unit across administrative regions, from 2000 to 2020. Results revealed the following: (1) The distribution change law of the ecological sources in the study area was consistent with the distribution change law of their centrality. (2) In the northeast of the study area, the disappearance of the small ecological sources, which play a stepping stone role, led to a longer length and a weaker centrality of ecological corridors. (3) The resistance surface in the study area showed an overall growth trend, while the ecological corridor network showed an overall deterioration trend. Based on the results, this research suggests that the systematic analysis and targeted protection of ecological sources are the basis for maintaining the ecological security pattern, and targeted improvement of ecological resistance surface is the key to improving the ecological security pattern. Therefore, measures such as the protection of core ecological sources, the rational layout of ecological sources as stepping stones, and the improvement of resistance values at ecological key points should be paid attention to in the future.

Spatio-temporal evolution and driving factors of landscape pattern in minority villages: a case study of Zahan Village in Hainan Province

Ethnic minority settlements, as an important medium for the transmission of ethnic cultures, are also a key resource for accelerating the development of ethnic minorities and the regions where they are located. Currently, research on landscape patterns focuses on traditional villages and ancient villages, whereas there is a relative lack of discussion on ethnic minority settlements. This study focuses on the multi-ethnic Zahan Village in Hainan Province, adopting the analysis methods of landscape pattern index and land-use transfer matrix, based on the theoretical framework of landscape ecology, to systematically analyze the spatial and temporal characteristics of the landscape pattern of the village and its patterns between 2007 and 2022, and to qualitatively analyze the influencing factors of its landscape changes from two dimensions, namely, humanities and nature. Research findings: (1) As the dominant landscape type, the proportion of woodland (although decreasing year by year) still exceeds 80%, whereas other land types, such as watersheds and grasslands, are gradually transformed into construction land and arable land, whose increment is significant. (2) During the study period, Throughout the study period, the landscape homogeneity of Zahan Village became better and better, the landscape types tended to be richer, and the spatial heterogeneity of the landscape also increased. (3) The area of woodland landscapes shows a decreasing trend from year to year, whereas construction land and arable land show an overall increasing trend, and the area of watersheds and meadows also decreases slightly. (4) The village landscape is mainly spatially “clustered,” concentrated in the center and southern part of the village, with a few “dots” distributed in the east and northwest, and the overall trend is spreading from the center to the periphery. (5) The evolution of village landscapes is influenced by a combination of human factors, including demographic, economic, and policy factors, as well as natural geographic factors, such as topography, climate change, and precipitation. The study provides theoretical support and practical guidance for the sustainable development of Zahan Village, as well as valuable experience and inspiration for the optimal development of other minority villages.

Landscape pattern evolution and ecological security assessment based on land use changes in mining cities: a case study of Heihe city

The development of mining cities has led to changes in land use and the evolution of landscape patterns. Constructing an ecological security evaluation system can reflect the ecological security status of mining city areas and provide planning references for these cities. This study, based on Heihe City’s land use data from 1980 to 2020, systematically analyzes the spatial and temporal evolution characteristics of land use, landscape patterns, and landscape ecological security levels by constructing a land use transfer matrix and calculating landscape pattern indices and landscape ecological security indices. The results show that: 1) Forest land is the main type of land use in Heihe City, accounting for over 50% of the total area. Land use changes primarily occurred between 2000–2010 and 2010–2020, with the spatial pattern characterized by overall stability and localized dramatic shifts, mainly involving the conversion of forest land to farmland and unused land. 2) From 1980 to 2020, the landscape ecological security pattern in Heihe City improved. Landscape diversity and landscape contagion increased, while landscape fragmentation, the largest patch area, and the average patch area decreased. Land use was optimized overall, but the trend of forest fragmentation became noticeable. 3) Between 1980 and 2020, the landscape ecological security level in Heihe City improved significantly. Driven by ecological restoration policies, the ecological security level in the southern region greatly improved, while the spatial pattern continued to show a trend of lower security in the south and higher security in the north. Specifically, the area proportions of low-security and relatively low-security areas increased by 6.23% and 9.55%, respectively. The spatial clustering of landscape ecological security levels is evident, with high-high value clustering mainly in the north and low-low value clustering mainly in the south. It is necessary to further promote ecological protection in the north to ensure the ecological barrier function, while strengthening ecological restoration in the south to improve ecological security levels. Additionally, continuous macro-policy regulation is needed to maintain long-term ecological security in Heihe City. The ecological security level of mining city landscapes is influenced by economic dependence, policy constraints, and environmental issues. This study can provide guidance for planning improvements in the study area and other mining cities.

Identification of Urban Renewal Potential Areas and Analysis of Influential Factors from the Perspective of Vitality Enhancement: A Case Study of Harbin City’s Core Area

In the context of people-centered and sustainable urban policies, identifying renewal potential based on vitality enhancement is crucial for urban regeneration efforts. This article collected population density data, house price data, and built environment data to examine the spatial pattern characteristics of Harbin’s core area using spatial autocorrelation analysis. Building on these findings, a geographically weighted regression (GWR) model was constructed to further analyze the influencing mechanisms of the relevant factors. The analysis revealed significant spatial development imbalances within Harbin’s core area, characterized by differentiated and uneven development of social and economic vitality between the old city and newly constructed areas. Notably, in certain regions, the construction intensity does not align with the levels of social and economic vitality, indicating potential opportunities for urban renewal. Furthermore, the examination of key influencing factors highlighted that the accessibility of commercial facilities and development intensity had the most substantial positive impact on social vitality. In contrast, the age of construction and the distribution of educational facilities demonstrated a strong positive correlation with economic vitality. By clearly delineating specific areas with urban renewal potential, this study provided a detailed characterization of the urban development pattern in Harbin. Additionally, by depicting the local variations in influencing factors, it established analytical foundations and objective references for urban planning in targeted locations. Ultimately, this research contributes new insights and frameworks for urban renewal analyses applicable to other regions.

Revolutionizing Facial Recognition: A Dolphin Glowworm Hybrid Approach for Masked and Unmasked Scenarios

Machine learning has several essential applications, including classification and recognition. Both people and objects may be identified using the Machine learning technique. It is particularly important in the verification process since it recognizes the characteristics of human eyes, fingerprints, and facial patterns. With the advanced technology developments, nowadays, Facial recognition is used as one of the authentication processes by utilizing machine learning and deep learning algorithms and it has been the subject of several academic studies. These algorithms performed well on faces without masks, but not well on faces with masks. since the masks obscured the preponderance of the facial features. As a result, an improved algorithm for facial identification with and without masks is required. After the Covid-19 breakout, deep learning algorithms were utilized in research to recognize faces wearing masks. Those algorithms, however, were trained on both mask- and mask-free faces. Hence, in this, the cropped region for the faces is only used for facial recognition. Here, the features were extracted using the texture features, and the best-optimized features from the glow worm optimization algorithm are used in this paper. With these features set, the hybrid Dolphin glow worm optimization is used for finding the optimal features and spread function value for the neural network. The regression neural network is trained with the optimized feature set and spread function for the face recognition task. The performance of the suggested method will be compared to that of known approaches such as CNN-GSO and CNN for face recognition with and without masks using accuracy, sensitivity, and specificity will next be examined.

Analyzing how the timing and magnitude of electricity consumption drive variations in household electricity-associated emissions on a high-VRE grid

Abstract Electrifying the residential sector is critical for national climate change adaptation and mitigation strategies, but increases in electricity demand could drive-up emissions from the power sector. However, the emissions associated with electricity consumption can vary depending on the timing of the demand, especially on grids with high penetrations of variable renewable energy. In this study, we analyze smart meter data from 2019 for over 100 000 homes in Southern California and use hourly average emissions factors from the California Independent System Operator, a high-solar grid, to analyze household CO2 emissions across spatial, temporal, and demographic variables. We calculate two metrics, the annual household electricity-associated emissions (annual-HEE), and the household average emissions factor (HAEF). These metrics help to identify appropriate strategies to reduce electricity-associated emissions (i.e. reducing demand vs leveraging demand-side flexibility) which requires consideration of the magnitude and timing of demand. We also isolate the portion of emissions caused by AC, a flexible load, to illustrate how a load with significant variation between customers results in a large range of emissions outcomes. We then evaluate the distribution of annual-HEE and HAEF across households and census tracts and use a multi-variable regression analysis to identify the characteristics of users and patterns of consumption that cause disproportionate annual-HEE. We find that in 2019 the top 20% of households, ranked by annual-HEE, were responsible for more emissions than the bottom 60%. We also find the most emissions-intense households have an HAEF that is 1.7 times higher than the least emissions-intense households, and that this spread increases for the AC load. In this analysis, we focus on Southern California, a demographically and climatically diverse region, but as smart meter records become more accessible, the methods and frameworks can be applied to other regions and grids to better understand the emissions associated with residential electricity consumption.

The Design and Development of Dabujiang Patchwork Embroidery for Modern Bags

Background and Aim: Dabujiang patchwork embroidery, an intangible cultural handicraft with the longest heritage in southern Hunan, has garnered increasing attention in recent years due to its unique characteristics. However, these products are facing a situation of decline because they cannot meet the needs of modern people. The objective of this research article is to design and develop modern bags featuring Dabujiang patchwork embroidery patterns for women in contemporary society. Materials and Methods: This research is a combination of qualitative research and quantitative research. The qualitative research method was used to study the color, pattern, and meaning of characteristics from 54 pieces of 6 theme categories of Dabujiang product pictures. Interview intangible cultural inheritors, designers, bag shop owners, and women consumers to understand modern women's design needs for women's bags. Combining the characteristics of the Dabujiang pattern characteristics with the needs of modern women, the design and development of six new Dabujiang women's bags based on 6 theme categories. Through 424 questionnaires for women to assess the women's favorite new bags and analyze the reasons women like and find out the future trend of women's bag development. Results: The results of the research revealed: 1) The patterns based on the six categories have different symbolic meanings respectively and they are mainly bionic and geometric patterns. The lines are curves, and the layout is generally symmetrical and balanced. 2) There are 11 kinds of common colors and mainly based on the five orthodox colors of Chinese tradition. The main colors are red, blue, yellow, black, and white, representing the five traditional Chinese elements, which extend different meanings. 3) Through interviews find out young women prefer large bags of bag 1 style, the Morandi color scheme, and meanings of happiness, love, career success, luck, wealth, and beauty. Consequently, the researcher designed six bag styles based on bag 1 style and six theme categories to meet the needs of modern women associated with these six meanings. Questionnaire responses from 424 young women showed that 23.58% favored the Phoenix pattern bag style, making it the most preferred among the six new bag designs. This style showcases a phoenix motif in blue, white, and black colors, symbolizing career success. Conclusion: The findings indicate that modern women highly value career success and self-worth, prioritizing independence over traditional roles. This conclusion mainly stems from the transformation of modern women's roles and the awakening of self-awareness. In addition to assuming the role of mother in the family, modern women also play the role of professional women in society, and more and more women regard career success as their pursuit goal. In the context of the changing identity and goals of modern women, the products should be designed that meet the needs of modern women based on their needs, combined with the symbolic meaning of cultural patterns. For future product development, incorporating symbolic patterns that align with the career aspirations of modern young women is recommended.

Real-time SERS analysis of programmed cell death patterns based on black Phosphorus–gold nanoparticles

Programmed cell death (PCD) plays a crucial role in the biological processes of living organisms and occurs in various forms, such as apoptosis, necroptosis and ferroptosis. However, traditional methods for PCD analysis are time-consuming and complex. In this paper, we propose a facile surface-enhanced Raman spectroscopy (SERS)-based strategy for the real-time analysis of three PCD patterns utilizing black phosphorus–gold nanoparticles (BP–Au NPs) as the ultrasensitive unlabeled Raman probe. BP–Au NPs, which possess excellent biocompatibility, are capable of detecting dye molecules at concentrations as low as [Formula: see text][Formula: see text]M and remain stable for at least one week in different physiological environments. In view of this, BP–Au NPs-based SERS technique can distinguish the tiny differences in the molecular fingerprints of cancer cells undergoing three PCD patterns (apoptosis, necroptosis and ferroptosis) triggered by doxorubicin, shikonin and erastin, respectively. We also have real-time monitoring of the intracellular molecular events during PCD, which spy the fluctuations of some typical SERS bands assigned to protein, DNA and lipid, revealing the unique phenotypic characteristics of each PCD pattern. This strategy provides a detailed and comprehensive analysis of the mechanisms of drug-induced PCD at the Raman level.

Importance of habit and environmental characteristics in shaping patterns of richness and range size of ferns and lycophytes in the Atlantic Forest.

Ferns and lycophytes, the two spore-bearing lineages of vascular plants, share a unique life cycle, and because of several morpho-ecophysiological similarities, are usually investigated as a unit, but they may have distinct ecological and environmental responses. Understanding the diversity and distribution patterns of ferns and lycophytes separately is essential for designing effective conservation strategies. We assessed species richness and range sizes using range-diversity analyses and investigated environmental (climatic, edaphic) and ecological (endemism, habit diversity, specialization) predictors of these range and richness metrics using generalized linear models. The central region of the Atlantic Forest primarily contained areas with fern and lycophyte species with high richness and small ranges; the northern and southern extremes of the forest had species with low richness and larger ranges. Fern richness was associated with a combination of environmental and ecological variables; lycophyte richness was associated only with ecological variables. Range sizes were positively related to habit diversity for both ferns and lycophytes, but the effect of environmental variables differed between the two lineages. Critical areas were primarily not within protected areas. Fern range sizes and richness were influenced by a combination of ecological and environmental factors such as temperature, precipitation, and soil variables; only ecological variables influenced lycophyte richness and range sizes. The effects of these factors must be considered separately for the two lineages due to their different responses to stressors. Conservation efforts should increase in the species-rich-low-range areas that are outside protected areas to protect these low-range species.

Correlation analysis of landscape patterns and urban thermal environment in Kunming based on a panel data model

With the development of urbanization, the spatial and temporal distribution characteristics of the urban landscape pattern play a decisive role in the intensity of the urban heat island (UHI) effect. A panel data model was constructed to study the relationship between the UHI effect and landscape pattern in Kunming from 1995 to 2020 at four different scales. The results indicate: (1) The landscape pattern of Kunming changed obviously with time, the artificial patch increased, the natural patch decreased, and the UHI effect became more and more obvious; (2) With an increase in scale, the number of influencing variables continues to grow, and the impact of artificial patches gradually intensifies; (3) The normalized difference water index (NDWI) has the greatest influence on the UHI, and the cooling effect is more obvious with the increase in scale. Unlike previous studies, the spatial configuration of the landscape in Kunming City had a stronger effect on the UHI effect than the landscape grouping and vegetation index; (4) This paper introduces the panel data model into the discussion of the UHI for the first time, providing a new method for better understanding the changing patterns of the urban thermal environment.

An entropy measure-based study on flow pattern of gas–liquid two-phase flow in a U-Tube

U-tubes are widely applied in gas–liquid two-phase transportation in chemical engineering. The diverse flow patterns within these tubes significantly affect the pressure loss, heat transfer efficiency, and even the fluid-induced vibration amplitude of the tubes. This study explores the complex flow pattern features in a U-tube in a vertical plane and focuses on recognizing them. For the acquisition and classification of flow patterns, a Computational Fluid Dynamics (CFD) model for gas–liquid two-phase flow is first established, and its quantitative calculation error is ensured to be less than 5%. Then, the spatiotemporal evolution characteristics of flow patterns is analyzed. The real-time pressure drop response is chosen as the representation signal, and its nonlinear features in the time and frequency domain under different flow patterns are explored. A nonlinear time series is constructed by extracting a segment from the real-time pressure drop data, and six entropy measures are applied to analyze and identify them. Finally, the sensitivity of entropy measures to both the time series lengths and the tested sections are evaluated. Results show that there are six typical flow patterns in a U-tube. According to most entropy measures, the bubble flow has the highest complexity; however, the plug flow presents the lowest complexity. In the U-bend, pressure drop signals for the bubble and annular flows show random fluctuations within a specific range, in contrast to the marked periodicity in plug flow signals, while wavy and slug flows exhibit intermittent peak values. Including the upstream and downstream straight pipes in the analysis, rather than focusing solely on the U-bend, significantly increases the complexity of the stratified, plug, and slug flows. Fuzzy entropy is an effective tool for identifying the six flow patterns, demonstrating good resilience to variations in the length of the data series. This characteristic makes it highly useful for real-time identification of flow patterns in the U-bend sections of non-transparent U-tubes, offering considerable potential in chemical equipment.

Soft sensor model for endpoint carbon content and temperature in BOF steelmaking based on just-in-time learning with multilayer structure preservation and sparse information enhancement

Ensuring precise prediction of the endpoint carbon content and temperature is paramount in controlling the endpoint of the basic oxygen furnace (BOF) steelmaking process. However, due to the frequent fluctuations in real-time blowing conditions, the conventional just-in-time learning soft sensor approach encounters challenges in effectively anticipating the blowing endpoint. To address these aforementioned issues, this research introduces a novel approach known as the pattern-oriented multilayer structure preservation and sparse information enhancement model (POMLSP-SIE). This approach involves dynamically generating a dataset with the same distribution based on the characteristics of the query sample pattern. It is combined with a multi-pathway dimension reduction model to preserve multi-view spatial geometry information while reducing data dimension. The low-dimensional embedding serves as dictionaries containing various hierarchical structural characteristics. Significant information within these dictionaries is sparsely represented to amplify its influence during the regression prediction process while diminishing the impact of less relevant information. This refinement aims to rectify the problem of static regression models being weak to adapt to changing working conditions, ultimately enhancing prediction performance. The effectiveness of the proposed method is substantiated through an experimental study utilising real converter steelmaking process data, thereby confirming its practical applicability.

Spatial Distribution Pattern of Aromia bungii Within China and Its Potential Distribution Under Climate Change and Human Activity.

Aromia bungii is a pest that interferes with the health of forests and hinders the development of the fruit tree industry, and its spread is influenced by changes in abiotic factors and human activities. Therefore, exploring their spatial distribution patterns and potential distribution areas under such conditions is crucial for maintaining forest ecosystem security. This study analyzed the spatial differentiation characteristics of the geographic distribution pattern of A. bungii in China using Moran's I and the Getis-Ord General G index. Hot spot distribution areas were identified using Getis-Ord Gi*. An optimized MaxEnt model was used to predict the potential distribution areas of A. bungii within China under four shared economic pathways by combining multivariate environmental data: (1) prediction of natural environmental variables predicted under current climate models; (2) prediction of natural environmental variables + human activities under current climate models; and (3) prediction of natural environmental variables under the future climate models (2050s and 2070s). Meanwhile, MigClim was used to simulate the unoccupied suitable area in the presence of obstacles under future climate change. The results showed that human activities, minimum temperature of the coldest month, and precipitation of the wettest month had positive effects on the distribution of A. bungii. However, in the current period, human activities drastically reduced the survival area of A. bungii, and its suitable distribution area was mainly concentrated in the eastern and central regions of China. Under the influence of climate change in the future, the habitat of A. bungii will gradually increase. Additionally, the MigClim model indicates that the area unoccupied by A. bungii has been on a continuous increasing trend. This study provides a positive reference for the prevention and control of A. bungii and the maintenance of forest health and ecosystem security, and provides important theoretical guidance for researchers, policymakers, and governments.