Variation Coefficient Method Research Articles

Exploring Sustainable Innovation Level, Spatial Inequities, and Convergence Trends in China’s Wood Industry

The importance of sustainable innovation in the wood industry is growing, but there is a lack of comprehensive analysis of its evolution, regional differences, and patterns of convergence in China. Based on the panel data of 31 provinces in China from 2011 to 2021, the sustainable innovation index of the wood industry is measured by the projection pursuit method. On this basis, the kernel density estimation method and Dagum Gini coefficient are used to study the dynamic evolution trend, regional differences, and sources of the index, and the convergence characteristics are examined using the coefficient of variation method. The study shows that (1) China’s overall wood industry sustainable innovation index shows a decreasing trend from 2011 to 2021. (2) The differences in the four regions mainly come from inter-regional differences. (3) The index shows significant nonequilibrium characteristics and progressive evolution patterns, and the spatial agglomeration is significant. The magnitude of the index deviation from the average did not decrease over time for the northern and southwestern forest regions. (4) The wood industry sustainable innovation index of the four major forest regions has obviously converged to the same level; under the condition of considering multifactors differentiation, the growth rate of the index of the lower regions is significantly higher than that of the higher regions. The study concludes that current regional imbalances in sustainable innovation in China impede progress and equitable distribution of benefits in the wood industry, and that the impact of regional differences on the β-convergence of sustainable innovation varies according to specific regional characteristics and conditions. These findings provide important theoretical contributions and practical guidance for the development of targeted innovation strategies for the sustainable development of the wood industry, as well as for the promotion of balanced regional development.

A Groundwater Quality Assessment Model for Water Quality Index: Combining Principal Component Analysis, Entropy Weight Method, and Coefficient of Variation Method for Dimensionality Reduction and Weight Optimization, and Its Application.

Groundwater underpins water supply for most of the world's regions, yet its sustainable utilization has been markedly compromised by inappropriate exploitation and a multitude of pollution sources. Water quality evaluation has emerged as an essential strategy to guarantee the optimized utilization and vigilant conservation of water resources. In this study, principal component analysis (PCA), entropy weight method (EWM), coefficient of variation method (CVM), and Water Quality Index (WQI) were used to construct an integrated WQI groundwater quality assessment model that integrates PCA-CVM-EWM for dimensionality reduction and weight optimization. Taking a village in Shandong Province, China, as an example, PCA identified seven evaluation indicators. The CVM-EWM were coupled to calculate comprehensive weights through the principle of minimum information entropy, followed by a comprehensive assessment of groundwater quality based on WQI values. The results indicated that Class III groundwater predominated in the study area, accounting for 74%, with localized pollution present. The hydrochemical type of the groundwater was primarily SO4·HCO3-Ca, significantly influenced by human activities. The coefficients of variation for Fe, Mn, and NH4-N all exceeded 1. Compared to other methods, the optimized WQI model demonstrated superior performance in the selection of evaluative indicators, weight distribution, and comprehensive water quality assessment, showing a distinct advantage for water quality data with numerous hydrochemical indicators and substantial coefficients of variation. The findings provided a scientific reference for diagnosing groundwater quality issues and formulating preventive and control measures. PRACTITIONER POINTS: A comprehensive water quality index evaluation model was constructed. Optimized steps for selecting indicators and assigning weights for the water quality index model. Selection of evaluation indicators based on indicator correlation analysis. The variability of hydrochemical data is considered.

Evaluation Method of Water Injection Development Effects in Fractured Vuggy Carbonate Reservoirs: Case Study of FI7 Strike-Slip Fault Zone

As the main method of secondary development, water injection has been widely used in fractured vuggy carbonate reservoirs. By using an evaluation index system combined with an integrated approach to objectively evaluate and support targeted and operational adjustments of water injection development effects in fractured vuggy carbonate reservoirs, a comprehensive evaluation method is established in this study. The CRITIC method is used as the main approach, and the analytic hierarchy process, the entropy weight method, and the coefficient of variation method are used as sub-methods. Additionally, the clustering centres are divided using the clustering method to reduce the error caused by the irrational distribution of the relevant evaluation index data. The method is used to evaluate and analyse the effects of water injection development, using the FI7 fault zone of the Halahatang oilfield as an example. The application results show that the evaluation method is feasible and effective for cases with small data volume, fewer computational resources, and less time. This study has a certain reference significance for the evaluation of the effect of water injection development in similar fractured vuggy reservoirs.

Digitally Driven Urban Governance: Framework and Evaluation in China

With the rapid development of digital technology, the role of digitalisation in urban governance continues to emerge. Building a theoretical analysis framework and evaluation system of digitally driven urban governance has important theoretical and practical significance for stimulating the efficiency of digital technology tools and improving the energy level of urban digital governance. This paper aims to explore the mechanism of urban governance enabled by digital technology, innovatively change the previous thinking mode that only attaches importance to facility construction and e-government platforms, adopt ecological thinking, and comprehensively consider the role of “soft elements” such as strategic support, industrial support, the security environment, talent support, and the market environment. Then, the extreme value variance method and the coefficient of variation method are used to calculate the overall capacity and secondary index scores of each city, and the standard deviation of secondary index scores is used to represent the sub-environmental balance of the cross-sectional data of China’s provinces. In order to further explore which indicators restrict the improvement of China’s urban digital governance capacity, this study also constructs an obstacle degree model. The results show the following: (1) The overall capability of China’s digitally driven urban governance is low, with a total score of 27.25, indicating that China’s digitally driven urban governance is in its infancy. (2) There is a significant development imbalance among Chinese provinces, with Beijing ranking first with a score of 81.16, and Tibet, Qinghai, Xinjiang, Heilongjiang, and Ningxia scoring less than 13.30 points, ranking as the bottom 5 among the 31 provinces. (3) The shortcomings of talent support, industrial support, and the security environment restrict the improvement of the entire digital ecological governance ability.

Evaluation of water richness in coal seam roof aquifer based on factor optimization and random forest method.

Water richness evaluation of coal seam roofs is a crucial prerequisite for preventing and controlling water hazards in coal seam roofs. For this purpose, Spearman correlation and GeoDetector were employed for factor optimization to investigate the significance of lithological and structural factors and the impact of interactions among different factors on the water richness of coal seam roofs. Water richness evaluation models of coal seam roofs were separately established via the entropy weight method (EWM), coefficient of variation method (CVM), and random forest method (RFM), and the predictive accuracies of these models were compared. Eleven lithological and structural factors were collected. Through Spearman correlation analysis, 6 factors were identified to have significant influences on water richness. By utilizing the interaction detection of GeoDetector, the effects of interfactor interactions on water richness were explored, and 3 combination factors were selected. AICc was used to determine the model's superiority. The evaluation results of the study area based on factor optimization and three methods were further compared and validated via pumping tests, workface water inflow tests, and three-dimensional high-density electrical method. The results indicated that the RFM exhibited higher prediction accuracy than did the entropy weight and coefficient of variation methods. Additionally, within each method, factor optimization led to improved model accuracy.

Characterizing Ecological Sensitivity of Yangtze River Delta Urban Agglomeration in China

Ecological sensitivity, as one of the most important indicators to evaluate regional environmental issues, holds significant implications for ecological governance and management in the related area. This study utilized remote sensing imagery of Landsat Thematic Mapper (TM) from the Yangtze River Delta (YRD) in 2014 and 2018, combined with field surveys and socio-economic data. Considering the local ecological and environmental conditions in the region, nine factors related to seven aspects, soil erosion, topography, humidity, habitat, water environment, human interference, and climate, were selected to create an ecological sensitivity evaluation framework for the YRD urban agglomeration. The coefficient of variation method was applied to determine factor weights, while the zonal statistics and spatial overlay methods were used for a comprehensive analysis of ecological sensitivity in a geographic information system (GIS). The YRD urban agglomeration was categorized into five ecological sensitivity levels: extremely sensitive, highly sensitive, moderately sensitive, slightly sensitive, and insensitive. The analysis results revealed spatial variations in the distribution of ecological sensitivity across the YRD urban agglomeration, with the overall ecological sensitivity level being slightly sensitive. The proportions of the total area occupied by extremely sensitive, highly sensitive, moderately sensitive, slightly sensitive, and insensitive zones were 14.30%, 12.02%, 25.29%, 30.34%, and 18.05% in 2014, and 14.30%, 24.01%, 16.33%, 27.32%, and 18.05%, respectively, in 2018. Based on these results, relevant ecological vulnerabilities for the YRD urban agglomeration were discussed.

The influence of erosion on the dynamic process of landslide in Xinmo Village, Maoxian

A large-scale, high-speed, long-runout landslide occurred in Xinmo Village, Maoxian, Sichuan Province, China, on June 24, 2017. It was characterized by fast sliding speed, rapid volume growth, and large impact area. The dynamic process of such landslides and the influence of erosion on the dynamic process are studied by field investigation, numerical inversion and simulation. The results showed that entrainment erosion was a major factor of landslide volumetric change and a salient feature of the landslide process. An exponential equation relating the erosion rate and the deposition volume was established. Moreover, the study found that a slight change of the erosion rate (1e-4) also had a significant impact on the lateral spreading, longitudinal runout, and vertical erosion. As the erosion rate increased, the lateral spreading, longitudinal runout, and vertical erosion of this type of landslide became more obvious. By using the coefficient of variation method, it was obtained that the erosion rate had a greater influence on the vertical erosion than on the lateral spreading and longitudinal runout. In the study of the velocity of the rock avalanche under different erosion conditions, it was found that the erosion amount and the landslide velocity were not strictly linearly related. This study has important significance for understanding the dynamic process and erosion effect of rock avalanche, and provides useful references and insights for future research in related fields.

Population Distribution in Guizhou’s Mountainous Cities: Evolution of Spatial Pattern and Driving Factors

Guizhou is a typical mountainous province and is also one of the lowland regions in China that has attracted a population influx. Here, using population density data from 2000 to 2020 as the basic dataset and the coefficient of variation method and standard deviation ellipse analysis, we investigated the spatial characteristics across different years. The results show: Firstly, Guizhou’s population has a distinct spatial distribution, characterized by a lower population density in the southeast and a higher density in the northwest as well as an increasing polarization of population concentration toward the centers of prefecture-level cities and provincial capitals. Fluctuations in population density resemble a central siphon effect, which is particularly pronounced in the provincial capital and show a significant gravitational pull. Secondly, the coefficient of variation in population density across Guizhou’s counties is spatially divided by Guiyang, showing higher values in the east and lower values in the west. Furthermore, the ellipse of the standard deviation of population density is gradually shrinking, indicating an increasingly concentrated population distribution. Thirdly, the explanatory power of the population and socio-economic systems on the population distribution in Guizhou is significantly greater than that of the natural systems. Population distribution and migration patterns have shifted from purely “economic driven” to coexisting with “economic and comfort-oriented” trends, and there is an urgent need to improve the comfort level of public services as a typical supply, in order to boost Guizhou’s population attraction.

An accurate identification and spatial characterization method for the development degree of preferential flow paths in water-flooded reservoir

Water flooding is one of the most important methods for oil field development, It accounts for more then 70% of China's crude oil production. However, in the progress of water flooding, preferential flow paths often formed between oil and water wells, which seriously restricts production rate. How to effectively identify the preferential flow paths has become the key to improving the effect of water flooding. In this paper, to solve the problem of difficult identification of preferential flow paths in reservoirs, through the change of core seepage law with high pore volume water flooding experiment, parameters such as multiple of water flux, change value of permeability, and water saturation were selected for analysis. The weight coefficient for each parameter was determined by the variation coefficient method of objective weight. Subsequently, a comprehensive reservoir identification index was obtained by weighting each parameter, which was used to describe the development degree of the preferential flow paths. Finally, quantitative criteria of preferential flow paths were given. The spatial characterization of preferential flow paths was realized by post processing of the Eclipse software. The new method for identifying preferential flow paths fully considers the changes in physical properties and fluid mobility of water-flooded reservoirs. The results of the new applied to a typical water-flooded reservoir in the Bohai Bay Basin show that the preferential flow paths calculated by the new method were highly consistent with the judgment results of tracers. It can accurately and quickly identify the preferential flow paths. This study provides a scientific basis for adjusting measures of water-flooded reservoirs to further enhance oil recovery. Moreover, the new method holds broader prospects for application in the field of porous media transport.

Regional Disaster Risk Assessment Based on ARIMA Prediction and Comprehensive Evaluation Model

The occurrence of extreme weather events affects the natural economic and social security, and has a serious impact on some industries, so the prediction and evaluation of disaster risk level is particularly important. This paper collected data on nine indicators from 25 regions and divided them into four areas: extreme weather, natural, social, and economic. the ARIMA prediction model is used to predict the probability and intensity of extreme weather in the future. The combined weights of each index were calculated by Composite Weighting Method, including principal component analysis, entropy weight method and coefficient of variation method, and the Risk Assessment Index (RAI) formula was constructed, and then the WNSE prediction and evaluation model was constructed. It is divided into four risk levels by fuzzy cluster analysis. We use WNSE prediction and evaluation model to calculate New York City and Switzerland’s RAI and coverage levels. Using this prediction, we can assess the severity of the consequences of natural disasters and prevent or remedy them. The assessment of regional disaster risk levels can give relevant reference to the government, and the government can make reasonable resource allocation.

A fire risk assessment method for offshore platforms: applications of 24Model with BN

ABSTRACT This paper integrates the 24Model with a BN and proposes a fire risk assessment method for offshore platforms. Triangular fuzzy membership function fuzzy set theory, coefficient of variation method and the combination of node state distance function and fuzzy inference are used to address the subjectivity and uncertainty due to insufficient objective data and conflicting opinions of multiple experts. Based on the proposed method, the human, organisational and technical factors of an offshore platform are comprehensively analysed from the perspective of the system. Based on diagnostic inference and sensitivity analysis, the main affecting factors and causal path of the risk scenario are determined. The results show that the proposed method could help in identifying the root causes of the failures and provide new insights into the probabilities and relationships among different factors.

New Electric Power System Stability Evaluation Based on Game Theory Combination Weighting and Improved Cloud Model

The development of new electric power system is a requirement for China to realize its “carbon peaking and carbon neutrality goals”, so it is of great importance that we assess the stability of a new electric power system under certain conditions. Firstly, according to the factors affecting the stability of the new power system, the characteristics of the new electric power system are analyzed in depth, so as to establish a stability evaluation index system including four first-level indices of safety, adequacy, flexibility, and adaptability. Secondly, a stability evaluation model is proposed. Based on the game theory, the entropy weight method, criteria importance though intercrieria correlation (CRITIC) method, and coefficient of variation method are combined and assigned, and the cloud model is improved through the combination of weights, which is used for evaluating the stability of the new electric power system. Finally, the applicability of the proposed evaluation model is verified by an arithmetic example analysis, which can identify the vulnerability of the new electric power system and provide suggestions for improving its stability. The model can provide a theoretical basis for promoting energy transition and sustainable development.

Greater baseline intra-individual variation in telephone-based cognitive screening predicts cognitive and diagnostic outcomes at 2-year follow-up.

Intra-Individual Cognitive Variability (IICV) is an emerging clinical tool that has shown promise in predicting cognitive decline and dementia incidence. The present study aims to assess the predictive validity of IICV in remote cognitive screening tests, using nationally representative data. Two waves of cognitive and diagnostic data from the Health and Retirement Study (collected in 2010 and 2012) were utilized to investigate whether baseline IICV can predict cognitive decline and dementia pathology. Middle-aged and older adults who were cognitively intact and completed all cognitive tests at both baseline and follow-up were recruited in the study, resulting in a sample of 6,050 participants. With the coefficient of variation method, the IICV-dispersion was calculated based on cognitive screeners to predict follow-up mean cognitive performance, global cognition, suspected cognitive impairment, and self-reported dementia diagnosis. After accounting for demographics, depressive symptoms, and baseline cognitive performance, the results provide support for the predictive validity of IICV. Specifically, the study demonstrated that IICV-dispersion significantly predicted cognitive and diagnostic outcomes in a concave pattern where the prediction was more sensitive toward the higher end of IICV. IICV explained about 0.2%-2.3% of the variance of outcomes variables. IICV retrieved from cognitive screening tests in telemedicine settings offers insight into future cognitive functioning and neurocognitive diagnostic status, which can be cost-effective and reduce the burden on both patients and health care providers, especially benefitting individuals with low socioeconomic status and rural residents. Potential avenues for future research were also discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Evolution and Spatiotemporal Response of Ecological Environment Quality to Human Activities and Climate: Case Study of Hunan Province, China

Human beings are facing increasingly serious threats to the ecological environment with industrial development and urban expansion. The changes in ecological environmental quality (EEQ) and their driving factors are attracting increased attention. As such, simple and effective ecological environmental quality monitoring processes must be developed to help protect the ecological environment. Based on the RSEI, we improved the data dimensionality reduction method using the coefficient of variation method, constructing RSEI-v using Landsat and MODIS data. Based on RSEI-v, we quantitatively monitored the characteristics of the changes in EEQ in Hunan Province, China, and the characteristics of its spatiotemporal response to changes in human activities and climate factors. The results show the following: (1) RSEI-v and RSEI perform similarly in characterizing ecological environment quality. The calculated RSEI-v is a positive indicator of EEQ, but RSEI is not. (2) The high EEQ values in Hunan are concentrated in the eastern and western mountainous areas, whereas low values are concentrated in the central plains. (3) A total of 49.40% of the area was experiencing substantial changes in EEQ, and the areas with significant decreases (accounting for 2.42% of the total area) were concentrated in the vicinity of various cities, especially the Changsha–Zhuzhou–Xiangtan urban agglomeration. The areas experiencing substantial EEQ increases (accounting for 16.97% of the total area) were concentrated in the eastern and western forests. (4) The areas experiencing substantial EEQ decreases, accounting for more than 60% of the area, were mainly affected by human activities. The areas surrounding Changsha and Hengyang experienced noteworthy decreases in EEQ. The areas where the EEQ was affected by precipitation and temperature were mainly concentrated in the eastern and western mountainous areas. This study provides a valuable reference for ecological environment quality monitoring and environmental protection.

Research on extreme weather insurance and real estate decision model based on AHP-EWM-CVM and K-Means clustering technology

Aiming at the challenge of extreme weather to insurance industries and real estate industries, this paper constructs an insurance model and a real estate decision model. The insurance model uses analytic hierarchy Process (AHP), entropy weight method (EWM), coefficient of variation method (CVM) and K-Means clustering algorithm to evaluate the risk level in different regions and predict the values of risk indicators up to the year 2035, and then calculate future insurance premiums. At the same time, multiple constraints are used to determine the future risk safety interval, which provides the basis for insurance companies to make decisions. In the case application, this paper finds that China is currently medium risk region and will be a medium risk region in the future, and obtains the time point when insurance companies can underwrite policies in this region. In addition, this paper also constructs a real estate decision model, which comprehensively considers three factors: risk, development potential and risk cost, and provides decision making basis for real estate developers through GE matrix. In the case application, this paper predicts that in the next nearly ten years, China is a medium willingness and medium risk area, suggesting that real estate developers should be cautious in deciding whether to develop. This study provides decision support for insurance industries and real estate industries to deal with extreme weather, which is of theoretical and practical significance.

International comparison of China's digitalization level and its enlightenment.

The global digital wave has flourished in recent years, and the digital technology revolution has emerged. Digitalization plays an undeniable role in promoting modern economic and social development in multiple aspects such as economy, society, innovation, public services and sustainable development, China's digitalization application is also developing rapidly. In order to better measure the current situation of China's digitalization development level, this paper constructs a comprehensive evaluation index system of digitalization development from four dimensions of talents in the digital field, digital infrastructure construction, digitalization innovation ability and international competitiveness, and tests the index system. The entropy method is used to measure the level of digitalization development between China and the United States, the United Kingdom, France and other major developed countries in the world, and the coefficient of variation method, kernel density estimation and Dagum Gini coefficient method are used to analyze the temporal and spatial characteristics and regional differences of digitalization development level of seven countries. This paper makes a comparative analysis between China and major developed countries from the historical perspective of the evolution of the techno-economic paradigm. With a view to summarizing and exploring from it and drawing on the advanced experiences accumulated by the developed countries over a long period of time, so as to provide China with useful insights and bases for further enhancing its digitalization development level.

Analysis of landscape pattern vulnerability in Dasi river basin at the optimal scale

Since the reform and opening up in 1978, the Dasi River Basin within Jinan’s startup area from replacing old growth drivers with new ones (startup area) has experienced rapid urbanization and industrialization, and the landscape pattern has changed significantly, resulting in a series of eco-environmental problems. In order to more accurately identify the vulnerable areas of landscape pattern, understand their cause mechanism and changing laws, and provide a theoretical basis for the implementation of sustainable landscape pattern planning and management in the region. Four Landsat images of 2002, 2009, 2015 and 2020 were taken as data sources, and the optimal granularity of landscape pattern analysis was determined from the perspective of landscape level and class level by using the coefficient of variation method, granularity effect curve and information loss model, and the optimal amplitude was determined by using the grid method and semi-variance function. Then, the landscape vulnerability assessment model was constructed based on the optimal scale, and its spatiotemporal evolution characteristics and spatial autocorrelation were analyzed. The result showed that: (1) The optimal granularity of landscape pattern analysis in this study area was 80 m, and the optimal amplitude was 350 × 350 m. (2) During 2002–2020, the overall vulnerability of landscape pattern in the southern part of the study area showed an increasing trend, while that in the middle and northern parts showed a decreasing trend. (3) The mean values of the vulnerability index of the overall landscape pattern in 2002, 2009, 2015 and 2020 were 0.1479, 0.1483, 0.1562 and 0.1625, respectively, showing an increasing trend year by year. In terms of land use, during 2002–2020, the average vulnerability indices of forestland and built up land increased by 23.18% and 21.43%, respectively, followed by water body and bare land, increased by 12.18% and 9.52%, respectively, while the changes of cropland and grassland were relatively small, increasing by 5.36% and 5.65%, respectively. (4) During 2002–2020, the landscape pattern vulnerability showed a significant spatial positive correlation in terms of spatial distribution. The Low-Low areas were generally transferred from the southeastern and midwestern to the middle and northern, and the High–High areas were mainly transferred from the middle to the southern. Overall, the degree of the spatial agglomeration of the landscape pattern vulnerability showed an increasing trend.

Spatial and temporal evolution of liver cancer incidence

Liver cancer has become a global public health problem and is the fourth leading cause of death and the sixth most common cancer worldwide. The article comprehensively constructed a system of indicators to measure the level of liver cancer incidence from three aspects: economy, diet, and health indicators, and analyzed the influencing factors of liver cancer incidence in 204 countries and regions in the world and the geographical characteristics of the level by using multivariate statistical analyses such as the coefficient of variation method and geodetector. The study shows that (1) the incidence rate of liver cancer is lower in countries with higher GDP levels and urbanized population rates. (2) The combination of caloric intake and protein intake had the largest interaction effect on liver cancer incidence. (3) Excessive fat and protein intake will increase the burden on the liver and cause an increase in the incidence of liver cancer.

Non‐destructive grading technique for mangoes using a flexible impedance sensing system and YOLOv5s_CBAM

AbstractFlexible sensors for food quality control are experiencing rapid development. The purpose of this study is to address the time‐consuming issues associated with traditional fruit grading methods by utilizing a homemade flexible impedance sensing system (FISS). A customized spiral slide system was innovatively designed in the study to simulate the grading process on a fruit assembly line with multimodal features for low power consumption and non‐destructive evaluation. FISS integrated components such as a camera, spiral slider, 3D printed slider, latex ball, flexible impedance electrodes, and back‐end measurement circuits and successfully achieved accurate food quality assessment by using a visual classification model for primary grading based on YOLOv5s‐CBAM and an impedance feature classification model for secondary grading of mangoes based on the coefficient of variation method and threshold coefficient method. With an impressive assessment accuracy of 97.07% and a classification speed of up to 3 s/piece, the system successfully classified mangoes into seven grades covering overripe, fully ripe, ripe, unevenly ripe, unripe, underripe, and rotten states. By eliminating the reliance on complex instruments and expensive equipment, FISS provides a cost‐effective alternative for food quality control, significantly reducing operational costs.Practical applicationsThe purpose of this study is to address the time‐consuming issues associated with traditional fruit grading methods by utilizing a homemade flexible impedance sensing system (FISS). A customized spiral slide system was innovatively designed in the study to simulate the grading process on a fruit assembly line with multimodal features for low power consumption and non‐destructive evaluation. FISS integrated components such as a camera, spiral slider, 3D printed slider, latex ball, flexible impedance electrodes, and back‐end measurement circuits and successfully achieved accurate food quality assessment by using a visual classification model for primary grading based on YOLOv5s‐CBAM and an impedance feature classification model for secondary grading of mangoes based on the coefficient of variation method and threshold coefficient method. With an impressive assessment accuracy of 97.07% and a classification speed of up to 3 s/piece, the system successfully classified mangoes into seven grades covering overripe, fully ripe, ripe, unevenly ripe, unripe, underripe, and rotten states. By eliminating the reliance on complex instruments and expensive equipment, FISS provides a cost‐effective alternative for food quality control, significantly reducing operational costs.

Analysis of the Impact of the “Insurance & Futures” Model on the Price Fluctuation of China's Pig Market

The market price cyclical fluctuation and oscillation "pig cycle" is a typical economic phenomenon in the pig market. In this paper, we use the coefficient of variation method and ARIMA (Auto-Regressive, Integrated, Moving Average) time-series method to analyse the weekly data of local breed pigs, domestic hybrid pigs, exotic hybrid pigs and hog price indexes from 2019 to 2023, and empirically analyse the impact of the "insurance + futures" model on the price of hogs to empirically analyse the degree of influence of the "insurance+futures" model on the volatility of hog prices. It is found that the price change trends of local breed pigs, domestic hybrid pigs, and exotic hybrid pigs are similar, and the "insurance + futures" model has a significant impact on the price fluctuation of exotic hybrid pigs, a weak impact on the price fluctuation of local breed pigs, and a non-significant impact on the price fluctuation of domestic hybrid pigs. During the pilot practice of the "insurance + futures" model, the price volatility of the three types of pigs increased significantly, and the overall pig price showed a downward trend.