Growth Trends Research Articles

Dynamics and Seasonal Fluctuations of Soybean Prices in Domestic and International Markets

Background: Soybean, being one of the most widely cultivated crops globally, plays a significant role in global agriculture and food production. However, soybean prices have experienced significant fluctuations over the past few years, causing imbalances in resource allocation and generating demand and supply gaps. Thus, analyzing past price trends is crucial to understand the current scenario and formulate appropriate strategies to improve the marketing system. Methods: This study examines the trends and seasonal variations in soybean prices in local, national and international markets using compound growth rate, trend and seasonality analysis. Monthly and yearly time series data of soybean prices in selected markets were collected from July 2006 to June 2021. The finding revealed a continuous rise in soybean prices in India over the 15 years. Result: The results revealed that the rise in soybean price during this period was due to inflation and farmers were not benefited. Furthermore, the study identified seasonal variation in soybean prices, with peak prices observed during may in most markets, when the crop comes to harvest. The study findings can be useful for policymakers and farmers to understand the trends and seasonal patterns in soybean prices.

Evolution of artificial intelligence in healthcare: a 30-year bibliometric study

IntroductionIn recent years, the development of artificial intelligence (AI) technologies, including machine learning, deep learning, and large language models, has significantly supported clinical work. Concurrently, the integration of artificial intelligence with the medical field has garnered increasing attention from medical experts. This study undertakes a dynamic and longitudinal bibliometric analysis of AI publications within the healthcare sector over the past three decades to investigate the current status and trends of the fusion between medicine and artificial intelligence.MethodsFollowing a search on the Web of Science, researchers retrieved all reviews and original articles concerning artificial intelligence in healthcare published between January 1993 and December 2023. The analysis employed Bibliometrix, Biblioshiny, and Microsoft Excel, incorporating the bibliometrix R package for data mining and analysis, and visualized the observed trends in bibliometrics.ResultsA total of 22,950 documents were collected in this study. From 1993 to 2023, there was a discernible upward trajectory in scientific output within bibliometrics. The United States and China emerged as primary contributors to medical artificial intelligence research, with Harvard University leading in publication volume among institutions. Notably, the rapid expansion of emerging topics such as COVID-19 and new drug discovery in recent years is noteworthy. Furthermore, the top five most cited papers in 2023 were all pertinent to the theme of ChatGPT.ConclusionThis study reveals a sustained explosive growth trend in AI technologies within the healthcare sector in recent years, with increasingly profound applications in medicine. Additionally, medical artificial intelligence research is dynamically evolving with the advent of new technologies. Moving forward, concerted efforts to bolster international collaboration and enhance comprehension and utilization of AI technologies are imperative for fostering novel innovations in healthcare.

Global trends and hotspots of type 2 diabetes in children and adolescents: A bibliometric study and visualization analysis.

Epidemiological surveys indicate an increasing incidence of type 2 diabetes mellitus (T2DM) among children and adolescents worldwide. Due to rapid disease progression, severe long-term cardiorenal complications, a lack of effective treatment strategies, and substantial socioeconomic burdens, it has become an urgent public health issue that requires management and resolution. Adolescent T2DM differs from adult T2DM. Despite a significant increase in our understanding of youth-onset T2DM over the past two decades, the related review and evidence-based content remain limited. To visualize the hotspots and trends in pediatric and adolescent T2DM research and to forecast their future research themes. This study utilized the terms "children", "adolescents", and "type 2 diabetes", retrieving relevant articles published between 1983 and 2023 from three citation databases within the Web of Science Core Collection (SCI, SSCI, ESCI). Utilizing CiteSpace and VoSviewer software, we analyze and visually represent the annual output of literature, countries involved, and participating institutions. This allows us to predict trends in this research field. Our analysis encompasses co-cited authors, journal overlays, citation overlays, time-zone views, keyword analysis, and reference analysis, etc. A total of 9210 articles were included, and the annual publication volume in this field showed a steady growth trend. The United States had the highest number of publications and the highest H-index. The United States also had the most research institutions and the strongest research capacity. The global hot journals were primarily diabetes professional journals but also included journals related to nutrition, endocrinology, and metabolism. Keyword analysis showed that research related to endothelial dysfunction, exposure risk, cardiac metabolic risk, changes in gut microbiota, the impact on comorbidities and outcomes, etc., were emerging keywords. They have maintained their popularity in this field, suggesting that these areas have garnered significant research interest in recent years. Pediatric and adolescent T2DM is increasingly drawing global attention, with genes, behaviors, environmental factors, and multisystemic interventions potentially emerging as future research hot spots.

Is High-Productivity Growth Returning?

Productivity growth has shown a notable pickup since the fourth quarter of 2019, and some commentators cite artificial intelligence and other factors as reasons why technological progress can sustain this faster pace. Motivated by this consideration, we use a model designed to detect trend shifts to examine the behavior of productivity growth in the postwar period. The model allows for shifts between high- and low-growth productivity regimes and estimates the probability of being in one regime or the other. Currently, the model shows that the recent data provide some support, albeit limited and preliminary, for the view that productivity growth may have shifted to a higher trend growth rate.

PENINGKATAN DAYA SAING MELALUI PLATFORM SHOPEE DENGAN ANALISIS SWOT (STUDI KASUS : MTRSTORE.ID)

MTRSTORE.ID is a thrift shop business operating in the fashion industry, offering imported thrift products such as jeans, chinos, and cargo pants. This business was chosen due to the rapid trend growth in the sector, particularly among teenagers. This study aims to analyze the competitiveness of MTRSTORE.ID, a thrift shop operating on the Shopee e-commerce platform, using the SWOT analysis approach. In the context of globalization and advancements in information technology, businesses like MTRSTORE.ID face challenges to remain relevant and competitive in an increasingly tight market. The analysis results show that MTRSTORE.ID has strengths, including high sales potential through Shopee's large user base and attractive promotions. However, the business also faces weaknesses, such as intense competition and high administrative costs. Opportunities to boost sales include participating in discount programs and utilizing Shopee's advertising features, while threats such as price competition and challenges in maintaining product quality need to be addressed. This research employs a qualitative methodology with a case study approach. It recommends that MTRSTORE.ID implement these strategies to enhance its competitiveness on Shopee, utilizing the SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis framework.

Modelling future urban growth utilizing multilayer perceptron and Markov chain models in West Himalayan urban centre: Srinagar Municipal Corporation

The urban spatial planning challenges encountered in areas situated in hilly regions, such as the Srinagar in Jammu and Kashmir, are distinguished by the swift growth of population, disorganized expansion of built-up areas, and insufficient data on alterations in urban expansion and land utilization. The study was conducted to examine the trends in urban growth in order to implement sustainable urban management within the Srinagar Municipal Corporation. The complexities arising from the rapid aggregation of population and unregulated urbanization necessitate a proactive approach to urban spatial planning. The aim of this investigation is to contribute to the development of effective strategies by analysing the current patterns of urban growth and anticipating future changes. The findings of this investigation display a notable trend in which the predicted LULC shows the rapid expansion of built-up areas from 134.72 km2 to 170.89 km2 and corresponds with a continuous decrease in agricultural and Fallow lands to 16.64 km2 and 6.62 km2 respectively. This emphasizes the transformative impact on the land-use, highlighting the requirement for comprehensive surveillance and predictive tools to direct the dynamic interplay between human activities and the environment. Also, the study could improve the city's planning, resource allocation and decision-making processes by providing probabilistic insights into various urban issues especially land-use planning in hilly regions.

Analysis of the evolution in the number of manufacturing companies in Portugal between 2009 and 2021 to understand growth and decline trends

This project analyzes the evolution of the manufacturing sector in Portugal from 2009 to 2021, focusing on the variations in the number of active companies across various subcategories, such as food, textiles, and metal product industries. The goal of this analysis is to understand the dynamics of growth and contraction within each sector, providing insights for companies to adjust their market and operational strategies. Key objectives include analyzing the overall evolution in the number of companies, identifying subcategories with notable changes, and providing a comprehensive analysis of observed trends and patterns. The study is based on data from PORDATA 2024, and the research employs temporal trend analysis, linear and quadratic regression, and the Pareto representation to identify patterns of growth and decline. By comparing annual data, the project uncovers periods of growth and decline, allowing for a deeper understanding of the sector’s dynamics. The findings also highlight variations in periods of economic crises and during the Covid-19 pandemic, and recommendations for action are presented to support businesses resilience and continuity. These results are valuable for companies within the manufacturing sectors analyzed and policy makers, guiding strategic decisions to navigate the complexities of the market dynamics and to ensuring long-term organizational sustainable success.

Hot Spots and Frontiers of Livestreaming Communication Research in China in the Past 10 Years---- Bibliometric Analysis and Knowledge Graph Visualization Using Cite Space

Chinese research on livestreaming communication lacks systematic theoretical analysis and comprehensive understanding; hotspots and frontiers are still unclear. Based on the relevant literature in the past ten years (2013-2023) on CNKI, this paper explores the development trends, research status, and keyword clustering in this field. It was found that academic articles on livestreaming communication in China show a significant growth trend. The past decade's research can be divided into three stages of development, and the establishment of strong collaboration among prolific authors, represented by Yu Guoming, Liu Jia, Hua Jie, and Yan Sanjiu, remains elusive. Eight hotspots emerged in live streaming communication research during the four evolutionary phases: webcasting, livestreaming reports, mainstream media, e-commerce live streaming, sports events, media convergence, livestreaming bandwagons, and live streaming platforms. Moreover, five major research frontiers were identified: mobile live streaming, live streaming with goods, slow live streaming, live streaming with goods, and e-commerce live streaming. Regarding knowledge structure, in the cluster analysis, webcasting, e-commerce live streaming, and livestreaming coverage scale are more significant and are the current areas of concern for scholars. This study helps scholars in livestreaming communication to discover new frontiers and distinguish key knowledge subfields.

Navigating the Challenges of Sustainability in the Food Processing Chain: Insights into Energy Interventions to Reduce Footprint

This review paper examines the critical intersection of energy consumption and environmental impacts within the global food system, emphasizing the substantial footprint (including land usage, costs, food loss and waste, and carbon and water footprints) associated with current practices. The study delineates the high energy demands and ecological burdens of food production, trade, and consumption through a comprehensive bibliographic analysis of high-impact research papers, authoritative reports, and databases. The paper systematically analyzes and synthesizes data to characterize the food industry’s current energy use patterns and environmental impacts. The results underscore a pressing need for strategic interventions to enhance food system efficiency and reduce the footprint. In light of the projected population growth and increasing food demand, the study advocates for a paradigm shift towards more sustainable and resilient food production practices, adopting energy-efficient technologies, promoting sustainable dietary habits, and strengthening global cooperation among stakeholders to achieve the Sustainable Development Goals. Investigations have revealed that the food system is highly energy-intensive, accounting for approximately 30% of total energy consumption (200 EJ per year). The sector remains heavily reliant on fossil fuels. Associated greenhouse gas (GHG) emissions, which constitute 26% of all anthropogenic emissions, have shown a linear growth trend, reaching 16.6 GtCO2eq in 2015 and projected to approach 18.6 GtCO2eq in the coming years. Notably, 6% of these emissions result from food never consumed. While the water footprint has slightly decreased recently, its demand is expected to increase by 20% to 30%, potentially reaching between 5500 and 6000 km3 annually by 2050. Energy efficiency interventions are estimated to save up to 20%, with a favorable payback period, as evidenced by several practical implementations.

Carbon Peak and Carbon Neutrality Scenarios for Key Industries in Jiangsu Province Based on LEAP Model

In this study, strategies for meeting the carbon peak and carbon neutral targets are developed through an in-depth analysis of future energy demand and carbon emission scenarios in Jiangsu Province. The LEAP model was used to forecast energy demand and carbon emissions under different scenarios from 2021 to 2060. Under the baseline scenario, the energy demand of key industries in Jiangsu Province showed a high rate of growth, with energy consumption increasing by 2.35 times from 2021 to 2060, with an average annual growth rate of 3.46%. In contrast, under the Policy Implementation, Sustainable Development, and Innovation Promotion scenarios, total energy consumption decreased significantly by 45.98%, 58.96%, and 86.13%, respectively, demonstrating varying degrees of energy-saving potential. Projections of carbon emissions showed that the total carbon emissions under the baseline scenario showed a continuous growth trend, reaching 599 million t in 2060, with an average annual growth rate of 4.47%. In contrast, under the Policy Implementation, Sustainable Development, and Innovation Advancement Scenario, carbon emissions were significantly lower, as evidenced by a peak reaching 2025 or 2030 and a gradual decline. The industrial sector decreased year by year, the transportation sector showed an increasing trend, and the construction sector was relatively stable. Overall, with industrial restructuring and technological upgrading, Jiangsu Province is expected to achieve a gradual reduction in carbon emissions, but the increasing trend of carbon emissions from the transportation sector requires further monitoring.

Intelligent optoelectrowetting digital microfluidic system for real-time selective parallel manipulation of biological droplet arrays.

Optoelectrowetting technology generates virtual electrodes to manipulate droplets by projecting optical patterns onto the photoconductive layer. This method avoids the complex design of the physical circuitry of dielectricwetting chips, compensating for the inability to reconstruct the electrode. However, the current technology relies on operators to manually position the droplets, draw optical patterns, and preset the droplet movement paths. It lacks real-time feedback on droplet information and the ability for independent droplet control, which can lead to droplet miscontrol and contamination. This paper presents a combination of optoelectrowetting with deep learning algorithms, integrating software and a photoelectric detection platform, and develops an optoelectrowetting intelligent control system. First, a target detection algorithm identifies droplet characteristics in real-time and automatically generate virtual electrodes to control movement. Simultaneously, a tracking algorithm outputs trajectories and ID information for efficient droplet arrays tracking. The results show that the system can automatically control the movement and fusion of multiple droplets in parallel and realize the automatic arrangement and storage of disordered droplet arrays without any additional electrodes and sensing devices. Additionally, through the automated control of the system, the cell suspension can be precisely cultured in the specified medium according to experimental requirements, and the growth trend is consistent with that observed in the well plate, significantly enhancing the experiment's flexibility and accuracy. In this paper, we propose an intelligent method applicable to the automated manipulation of discrete droplets. This method would play a crucial role in advancing the applications of digital microfluidic technology in biomedicine and other fields.

Normalized Hurricane Damage in the United States: 1900–2022

Abstract Since 1900, landfalling hurricanes have been the costliest of all weather-related disasters to afflict the contiguous United States. To provide a present-day (2022) reevaluation of this risk, this study employs an improved normalization approach to better understand potential economic event losses in the context of contemporary societal conditions. The updated methodology identifies impacted coastal counties using the newly available radius of maximum winds at landfall. Hurricane Katrina is the most expensive hurricane since 1900, with a likely 2022 normalized cost of $234 billion. Combined losses from the 50 most expensive hurricane events are ∼ $2.9 trillion in normalized economic losses. The study also explores some “analog storms” where comparisons can be made between two historic storms with similar landfall locations. For example, category 5 Andrew (1992) has lower 2022 normalized losses than category 4 Great Miami (1926), at $125 billion versus $178 billion, most likely due to the significantly different radius of maximum wind size (10 vs 20 n mi; 1 n mi = 1.852 km). As with previous studies, we conclude that increases in inflation, coastal population, regional wealth, and higher replacement costs remain the primary drivers of observed increases in hurricane-related damage. These upsurges are especially impactful for some coastal regions along the U.S. Gulf and Southeast Coasts that have seen exceptionally high rates of population/housing growth in comparison to countrywide growth. Exposure growth trends are likely to continue in the future and, independent of any influence of climate change on tropical cyclone behavior, are expected to result in greater hurricane-related damage costs than have been previously observed.

Future runoff trends in the mang river basin of China: Implications of carbon emission paths.

In recent years, the rapid development of the global economy has led to an increasing impact of the ongoing climate warming phenomenon on the hydrological cycle. In this context, the runoff changes affected by human activities are more severe. This study classifies climate scenarios based on carbon emission levels into "low-carbon" (SSP1-2.6, SSP2-4.5) and "high-carbon" (SSP3-7.0, SSP5-8.5) dual carbon development paths, and analyzes the evolution characteristics of runoff in the Mang River Basin in the near future (2021-2060) and far future (2061-2100) through driving the SWAT model. The main conclusions are as follows: (1) The suitability of the SWAT model in the Mang River Basin was confirmed with a high accuracy (R2>0.65, NSE > 0.8), and the parameters ESCO and SOL_AWC were found to have a high sensitivity to runoff. (2) Increased precipitation fluctuation and continuous temperature rise will be the climate change trend in the basin under the dual carbon pathway. It is estimated that by the end of the 21st century, the overall highest temperature will increase by 1.37-5.02°C, with temperature increases of 0.53-0.63°C/10a and 0.17-0.38°C/10a under the "high carbon" and "low carbon" pathways, respectively. Furthermore, the far future precipitation levels are expected to be higher than near future levels across various climate scenarios, with this trend being especially significant under the "low-carbon" pathway. (3) With the influence of climate change, there is a larger increase in runoff volume under the "high-carbon" pathway, with the growth rate of SSP5-8.5 being the fastest at 0.099m3/s·a, resulting in an overall runoff change of 30.65%. On the other hand, the runoff volume under the "low-carbon" pathway shows a slow growth trend, with an increasing rate that accelerates after the mid-21st century. The runoff change rates range from 0.046 to 0.079m3/s·a. (4) Climate change will significantly alter the overall runoff conditions of the basin. With the passage of time and the increase of carbon concentration emission, the impact of temperature on basin runoff will become increasingly stronger. However, precipitation was still the dominant factor leading to changes in runoff. The overall climate environment within the basin will shift towards a warmer and wetter direction. This research will help in the future to adopt appropriate measures for soil and water conservation and ecological protection strategies in ecologically vulnerable areas affected by human activities and located in different geographical basins under the background of climate change.

Trends in growth and nutritional status of Korean toddlers and preschoolers: a cross-sectional study using 2010–2021 Korea National Health and Nutrition Examination Survey data

Objectives: We aimed to analyze trends in growth and nutrient intake patterns in Korean toddlers and preschoolers and generate data for international comparisons of early childhood growth status.Methods: Overall, 3,661 children aged 1–4 years were included. This study used the data from the 5th–8th Korea National Health and Nutrition Examination Survey. The weight-for-height, height-for-age, weight-for-age, and body mass index-for-age z-scores of the participants were calculated using the World Health Organization 2006 child growth z-score reference cutoff points. The 24-hour recall method was used to determine dietary intake, which was assessed according to the Dietary Reference Intake for Koreans. All statistical analyses were conducted and weighted according to a complex sample design.Results: Most Korean toddlers and preschoolers demonstrated normal growth, with an increasing trend noted over the survey period. Concurrently, the prevalence of overweight decreased. The mean intake of energy from fat exhibited an upward trend, paralleling that of protein and fat. Conversely, the intake of several essential micronutrients declined. The prevalence of nutritional inadequacy showed significant variation throughout the survey period; the proportion of inadequacy in carbohydrate intake decreased, whereas those of iron, vitamin A, thiamin, niacin, and vitamin C increased.Conclusion: South Korea is making significant progress in supporting toddlers and preschoolers, as evidenced by consistent increases in the proportion of children with normal growth and decreases in the prevalence of malnutrition. Future research should focus on exploring dietary patterns and analyzing specific food groups that are essential for promoting optimal growth and nutritional status in children.

Application of Linear Regression Models in Multidisciplinary Data Analysis: From Economics to Health

This paper introduces the application of regression analysis in multiple fields, with a focus on conducting linear regression analysis on five case studies. First, the study on the Consumer Price Index (CPI) and Retail Price Index (RPI) reveals a positive correlation between the two, indicating that fluctuations in retail prices significantly affect consumer prices. Second, an analysis was conducted on the impact of academic pressure on students’ mental health, finding that academic pressure significantly affects emotional and psychological states. Third, the paper compares the animation markets in Japan and China, showing differences in market development models and growth trends between the two countries. Next, the paper explores the relationship between socioeconomic status and family educational expectations, indicating that family income and parents’ education levels significantly influence educational expectations. Finally, the paper examines the relationship between age and sleep quality and duration, finding that both sleep quality and duration decrease as age increases. This paper uses linear regression models and related statistical methods, providing data support for further research in related fields.

A Survey and Analysis of Youth Unemployment Rate in China

With the continuous restructuring of China’s industrial structure since the reform and opening up, China’s youth unemployment rate has shown a rapid growth trend in recent years, and the Chinese government has ceased to officially publish this data as of June 2023. Through the analysis of the data, it is found that the continuous rapid growth of unemployment rate in China in recent years is caused by a number of factors such as China’s economic transformation, imbalance between supply and demand in the labor market, epidemics, and brain drain. Among these reasons, the difficulty of young people with low educational attainment in finding suitable jobs should be the direct cause of the increase in unemployment, while the imbalance between supply and demand in the labour market should be the root cause. Combined with the analysis of data and special cases, the author predicts that China’s youth unemployment rate will continue to rise in the next five years, and at the same time puts forward a number of feasible solutions to solve the problem from different perspectives.

Reliability evaluation of rolling bearings based on generative adversarial network sample enhancement and maximum entropy method

Aiming at the difficulty of extracting vibration data under actual working conditions of rolling bearings, this paper proposes a bearing reliability evaluation method based on generative adversarial network sample enhancement and maximum entropy method under the condition of few samples. Based on generative adversarial network, data sample enhancement under few samples is carried out, and the reliability analysis model is established by using the maximum entropy principle and Poisson process. The reliability is evaluated according to the reliability variation frequency, variation speed and variation acceleration. The analysis results show that with the gradual increase of running time, the reliability variation frequency shows a nonlinear growth trend, which can be roughly divided into the initial running-in stage, the stable running-in stage and the intense running-in stage. The reliability variation speed is then used to distinguish the specific starting time of the three stages, and finally the preliminary relationship between the reliability variation acceleration and the remaining life is obtained. The experimental results of the XJTU-SY dataset show that compared with the existing reliability evaluation model, the proposed model has the advantages of less samples, no need for preprocessing and higher accuracy. The proposed model has made a beneficial supplement to the existing reliability analysis methods.

Estimation method for karst carbon sinks on the basis of a concentration prediction model.

Karstification can reduce the CO2 concentration in the atmosphere/soil. Accurate estimation of karst carbon sinks is crucial for the study of global climate change. In this study, the Lijiang River Basin was taken as the research area. On the basis of the measured dissolved inorganic carbon (DIC) and dissolved organic carbon (DOC) concentration data from 14 consecutive months, the relationships of DIC and DOC to elevation, slope, aspect, rainfall, and temperature were established. Among six regression algorithms, the random forest (RF), boosted regression tree (BRT) and BP neural network (BP) were selected for stacking integration to construct DIC and DOC concentration prediction models, achieving accuracies of 91% and 83%, respectively. On the basis of these models, the spatial and temporal distributions of DIC and DOC concentrations in the Lijiang River Basin from 2000 to 2022 were predicted. The prediction results reveal that DIC and DOC concentrations have a stable spatial distribution, which is consistent with the lithology distribution in the basin. The solute load method was used to estimate the karst carbon sink in the Lijiang River Basin over 23 years. The carbon sink over 23 years showed an overall growth trend, although with significant fluctuations. On the basis of the estimation results of karst carbon sinks over 23 years, a time series prediction model is used to predict the Lijiang River Basin from 2023 to 2030. The prediction results continue the volatility and trend of the historical data. A comparison of the model verification results with related research findings revealed that the concentration prediction model constructed in this study has high accuracy and good applicability in the estimation of karst carbon sinks at the watershed scale.

TRENDS OF FLUCTUATION IN AVERAGE PRICES OF DAIRY PRODUCTS IN UKRAINE IN 2003-2023

Dairy products in Ukraine are in a high demand among the population of all age groups. Milk production is a source of useful nutrients that support vital processes in humans. According to the official statistics, the long-term decrease in milk production in farms is due to the decrease in the number of dairy cattle that is lasting from year to year. At the same time, our country has access to foreign markets and is effectively developing the domestic one. It is worth noting that the internal market has its own peculiarities. It is the main task to understand what the specific features are of this market. Various characteristics can indicate the peculiarities and trends of milk and dairy production. The price value is one of the indicators that can respond to changes in demand, the emergence of competitors, updates in legislation, and other factors. Data from the official website of the State Statistics Service of Ukraine were used to analyze changes in behavior of the dairy industry that may be appeared in the price fluctuations. Investigated data base includes sales prices of milk and dairy products for 254 months for the period of 2003-2023. Fourier transformation was applied in the research. It allowed us to reconstruct general trends and build a forecast for several months ahead. Based on the results of the analysis, formulated conclusions include several points. The first one is about the deterministic trend of price growth with seasonal changes, but almost significant rapid ups and downs (the exception is 2017 year, when there was a rapid increase in product prices). The seasonality is manifested in a double change of direction according to the scheme: half a year of decline and half a year of rise, then the algorithm repeats. As usual, decreases can be observed in spring and summer months. The second point of conclusion is about controlled processes in the price forming in contemporary milk market conditions. Moreover, it would be reasonable to find factors that form such a stable tendency or keep prices on this unchangeable trajectory despite negative impacts, such as inflation, threats of war, population demand, reduction in purchase prices for raw materials, shortage in milk production and others. Data analysis was performed using Python tools.

Dynamic Simulation of the Supply and Demand of Ecosystem Windbreak and Sand Fixation Service in the Wuding River Basin

Wind erosion can cause land degradation and other harmful effects. Examining the ecosystem windbreak and sand fixation service (WSFS) from the perspectives of supply and demand plays a crucial role in the continuous regulation of regional wind erosion. Through the enhancement of the revised wind erosion equation (RWEQ) model, integrated with uncertainty analysis, scenario simulation, and environmental factors calculation, the dynamic simulation of the supply of ecosystem windbreak and sand fixation service (WSFSS) and the demand of ecosystem windbreak and sand fixation service (WSFSD) in the Wuding River Basin in China was achieved, and specifically, a simulation framework for WSFSD and WSFSS was constructed. The results show that: (1) the uncertainty analysis can calculate the upper and lower limits of the range of parameter x (downwind distance) in the RWEQ model, and changes in the parameter x can make the simulation results of WSFSS and WSFSD more reasonable; (2) In the past 20 years, the WSFSS has shown a spatial distribution pattern of high in the northwest and low in the southeast. In terms of time, the annual WSFSS has shown a fluctuating growth trend with a growth rate of 8.06 t/a. The monthly WSFSS has shown a rising-fluctuating-declining trend; (3) The rationality of WSFSD was indirectly verified through the setting of scenario simulation. In terms of time, across the 252 months under study (January 2000–December 2020), 85% of the months witnessed WSFSD within the range of 1.0–1.4 kg/m2 in the Wuding River Basin. At the same time, the WSFSD also presented seasonal variation patterns. The WSFSD was relatively high in spring (March–May) and relatively low in summer (July–September) each year.