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This article examines the influence of specific time series attributes on the efficacy of fuel demand forecasting. By utilising autoregressive models and Markov chains, the research aims to determine the impact of these attributes on the effectiveness of specific models. The study also proposes modifications to these models to enhance their performance in the context of the fuel industry's unique fuel distribution. The research involves a comprehensive analysis, including identifying the impact of volatility, seasonality, trends, and sudden shocks within time series data on the suitability and accuracy of forecasting methods. The paper utilises ARIMA, SARIMA, and Markov chain models to assess their ability to integrate diverse time series features, improve forecast precision, and facilitate strategic logistical planning. The findings suggest that recognising and leveraging these time series characteristics can significantly enhance the management of fuel supplies, leading to reduced operational costs and environmental impacts. [ABSTRACT FROM AUTHOR]

Transit crashes are a serious social problem for any country, with a significant loss of human lives and economic consequences that are difficult to quantify. This article proposes a characterization of the transit crash rate for urban road safety using time series. A quantitative descriptive study was conducted, characterizing the variables of each crash extracted from the National Traffic Agency of Ecuador (NTA); the data were processed at a descriptive and predictive level for the city of Guayaquil. The first step was an exploration of the scientific interest of the topic with the processing of bibliographic data taken from Scopus and Web of Science articles. Among the results obtained, there is a growing trend of research related to the evaluation of traffic crash through applied statistics. Every day, approximately 155 people die as a result of a traffic crash. In addition, traffic crashes are analyzed based on three indicators: number of crashes, injuries and onsite fatalities. Finally, an adequate performance is found, with very few differences in the forecast of incidents using three times series models, autoregressive integrated moving average (ARIMA). It is expected that this study will be valuable for data analysts and decision makers at the security level to reduce human losses related to these events in urban cities with similar characteristics to the analyzed cases. [ABSTRACT FROM AUTHOR]

From an ethical standpoint, combating corruption is crucial for promoting justice and equality. The rule of law and ethical governance involves clear standards of behavior for public servants and mechanisms for ensuring these standards are upheld. Corruption undermines public trust in government and democratic institutions and exacerbates social inequality and injustice (disproportionately affects the poor and marginalized groups, denying them access to essential services and opportunities). The fight against corruption during a full-scale war has ethical and purely pragmatic implications for Ukraine, the data of which formed the basis of this study. The lack of tangible progress in the fight against corruption in general, the lack of transparency of many institutional mechanisms in public administration, the revealed corruption schemes in the distribution of international military and humanitarian aid, as well as in the field of public defence procurement, the lack of punishment for corrupt officials in the highest echelons of power threaten the loyalty of international donors and allies, reduce Ukraine’s authority in the international arena, slows down Ukraine’s movement towards the EU, and significantly affects the decisions of foreign partners. The article demonstrates the results of forecasting the future level of corruption in Ukraine (for 2024‒2027) based on the retrospective dynamics of the Corruption Perceptions Index by Transparency International for 1998‒2023. Two economic and mathematical models are used for forecasting: Autoregressive Integrated Moving Average (ARIMA), which better reflects long-term historical trends and fluctuations, and the exponential smoothing method, which is more sensitive to the latest values of the time series. The statistical analysis package STATISTICA was used for the calculations. The forecasting results are disappointing since both methods showed an expected decrease in the level of corruption in 2024-2027, but in critically low volumes: by 1 point according to the ARIMA model and by 3-4 points according to the exponential smoothing method. The results of this study can serve as a basis for public advocacy campaigns as an argument for the need to radically revise the existing format of anti-corruption policy in Ukraine given its European future.

Copyright of Panorama Económico is the property of Universidad de Cartagena and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Being the largest consumer of sugar, India had relied on sugar imports during the first decade of twenty-first century. However, advent of wonder varieties developed by Sugarcane Breeding Institute of the Indian Council of Agricultural Research (ICAR-SBI) has revolutionized sugar production since 2010, and the country has turned into a sugar exporter since 2016. The objective of the study is to analyse the impact of such wonder varieties on sugar production, consumption and prices. Here, sugar prices were compared with the WPI of primary articles and analysed for a possible difference in the prices with and without adjusting for inflation. It was found that, sugar prices were lesser than the inflation adjusted sugar price during the study period (2010–2022), depicting the impact of production surplus in meeting the rise in consumption demand. Although domestic sugar prices were over and above the global sugar prices, the prices remained stable with no short-term spike in the sugar prices. It is therefore evident that the wonder varieties have not only created production surplus but also stabilized sugar prices during the study period. Using the historic time series data, ARIMA results revealed that the demand for sugar would be around 341.64 lakh tonnes by 2032–33, which is expected to grow at an annual average rate of 1.55 per cent per annum. The study suggests a sustained sugarcane and sugar production in the country to protect the domestic market from global price shocks taking into account the fuel ethanol production for EBP (ethanol blended petroleum) programme. [ABSTRACT FROM AUTHOR]

The Middle East frontal sand and dust storms (SDS) occur in non-summer seasons, and represent an important phenomenon of this region's climate. Among the mentioned type, spring SDS are the most common. Trend analysis was used in the current study to investigate the spatial-temporal variability of springtime dust events in the Middle East using synoptic station observation from 2011 to 2022. The plausible changes in some controlling factors of dust activity at selected important dust sources in the Middle East were also studied during this time period. Our results showed a statistically significant spike in springtime dust events across the Middle East, particularly in May 2022. To evaluate the relative importance of controlling factors, the applied feature of importance analysis using random forest (RF) showed the higher relative importance of topsoil layer wetness, surface soil temperature, and surface wind speed in dust activity over the Middle East between 2011 and 2022. Long-term trend analysis of topsoil moisture and temperature, using the Mann-Kendall trend test, showed a decrease in soil moisture and an increase in soil temperature in some selected important dust sources in the Middle East. Moreover, our predictions using ARIMA models showed a high tendency to dust activities in selected major dust origins (domain 2 and domain 5) with a statistically significant increase (p-value < 0.05) between 2023 and 2029. Observed spatial and temporal changes within SDS hotspots can act as the first step to build up for the first time an SDS precise intensity scale, as well as establishing an SDS early warning system in future. [ABSTRACT FROM AUTHOR]

Predicting soil moisture accurately is the precondition of realizing accurate irrigation and improving the utilization rate of water resource and the necessary step of developing water‐saving agriculture, which can alleviate the water shortage in our agricultural effectively. In order to further improve the accuracy of soil water content prediction, a combined soil water content prediction model based on Autoregressive moving average model (ARIMA model) and back propagation neural network (BP neural network) neural network is proposed. The model considers the linear and nonlinear characteristics of soil water content data, combines them according to the characteristics of the model itself, gives full play to the advantages of ARIMA model and BP neural network. At the same time, two data smoothing methods were used to establish the ARIMA model, and the adaptive moment estimation algorithm (Adam algorithm) and mind evolutionary algorithm (MEA) optimization BP neural network model were used to propose an improved combined prediction model to predict soil water content data. The experimental results show that the average relative error of the improved combinatorial prediction model is 1.51%, which is 4.18%, 0.95% and 3.1% lower than the combinatorial prediction model, BP neural network model and ARIMA model, respectively, and the overall prediction effect is better, which can be used to save agricultural water and provide a strong basis for the development of water‐saving agriculture in China. At the same time, it can also ensure that crop production is increased and the purpose of national food security is guaranteed. [ABSTRACT FROM AUTHOR]

The networks of wireless sensors provide the ground for a range of applications, including environmental monitoring and industrial operations. Ensuring the networks can overcome obstacles like power and communication reliability and sensor coverage is the crux of network optimization. Network infrastructure planning should be focused on increasing performance, and it should be affected by the detailed data about node distribution. This work recommends the creation of each sensor's specs and radius of influence based on a particular geographical location, which will contribute to better network planning and design. By using the ARIMA model for time series forecasting and the Al-Biruni Earth Radius algorithm for optimization, our approach bridges the gap between successive terrains while seeking the equilibrium between exploration and exploitation. Through implementing adaptive protocols according to varying environments and sensor constraints, our study aspires to improve overall network operation. We compare the Al-Biruni Earth Radius algorithm along with Gray Wolf Optimization, Particle Swarm Optimization, Genetic Algorithms, and Whale Optimization about performance on real-world problems. Being the most efficient in the optimization process, Biruni displays the lowest error rate at 0.00032. The two other statistical techniques, like ANOVA, are also useful in discovering the factors influencing the nature of sensor data and network-specific problems. Due to the multi-faceted support the comprehensive approach promotes, there is a chance to understand the dynamics that affect the optimization outcomes better so decisions about network design can be made. Through delivering better performance and reliability for various in-situ applications, this research leads to a fusion of time series forecasters and a customized optimizer algorithm. [ABSTRACT FROM AUTHOR]

This research enhances air quality predictions in Abu Dhabi by employing Autoregressive Integrated Moving Average (ARIMA) models on comprehensive air quality data collected from 2015 to 2023. We collected hourly data on nitrogen dioxide (NO2), particulate matter (PM10), and fine particulate matter (PM2.5) from 19 well-placed ground monitoring stations. Our approach utilized ARIMA models to forecast future pollutant levels, with extensive data preparation and exploratory analysis conducted in R. Our results found a significant drop in NO2 levels after 2020 and the highest levels of particulate matter observed in 2022. The findings of our research confirm the effectiveness of the models, indicated by Mean Absolute Percentage Error (MAPE) values ranging from 7.71 to 8.59. Additionally, our study provides valuable spatiotemporal insights into air pollution historical evolution, identifying key times and areas of heightened pollution, which can help in devising focused air quality management strategies. This research demonstrates the potential of ARIMA models in precise air quality forecasting, aiding in proactive public health initiatives and environmental policy development, consistent with Abu Dhabi's Vision 2030. [ABSTRACT FROM AUTHOR]

It aims to improve the application efficiency of modern science and technology in farm operation, enhance the prediction effect of machine learning and blockchain technology on farm supply and demand issues, and help enterprise managers to better manage and operate enterprise projects. First, the agricultural super business model under the blockchain + Internet of Things (IoT) is analysed, and the farm management system of the IoT + blockchain is constructed. Then, the demand forecasting model of agricultural products is established. Autoregressive Integrated Moving Average model (ARIMA) and Support Vector Machine (SVM) models are used to predict demand. Finally, the characteristics of the two models in prediction are discussed. The results show that the ARIMA model can reach 99.2% in the application efficiency evaluation of farm operation. Moreover, in the case of proper selection of model indicators, the prediction result is close to the actual value, the prediction error fluctuation is small, and the maximum error value is 0.4318. The local error of SVM model in predicting the demand of farm supply chain is slightly larger, and the maximum error value is 8.5430. By comparing the error results of the two models, the prediction accuracy of ARIMA model is higher than that of SVM model. The ARIMA model designed in this work can provide effective product demand prediction for farm operation and provide a strong technical reference for farm operation. The research not only provides technical support for machine learning and blockchain technology to predict farm supply and demand, but also contributes to the integrated management and sustainable development of enterprises. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Dalian University of Technology / Dalian Ligong Daxue Xuebao is the property of Journal of Dalian University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The COVID-19 pandemic has far-reaching impacts on the global economy and public health. To prevent the recurrence of pandemic outbreaks, the development of short-term prediction models is of paramount importance. We propose an ARIMA-LSTM (autoregressive integrated moving average and long short-term memory) model for predicting future cases and utilize multi-source data to enhance prediction performance. Firstly, we employ the ARIMA-LSTM model to forecast the developmental trends of multi-source data separately. Subsequently, we introduce a Bayes-Attention mechanism to integrate the prediction outcomes from auxiliary data sources into the case data. Finally, experiments are conducted based on real datasets. The results demonstrate a close correlation between predicted and actual case numbers, with superior prediction performance of this model compared to baseline and other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Time series analysis (TSA) is a statistical approach rooted in data mining that measures outcomes that consistently fluctuate over time. It is uniquely positioned as a methodology for preventive intervention research due to its capacity to measure long‐term impact in natural settings. With the ability to detect temporal patterns in a series of chronological data points, it allows researchers to detect naturalistic patterns, forecast future observations, and explain a change in data after an intervention is put in place. However, its application is historically sparse across behavioral research. This article provides counseling researchers with an overview of TSA and presents a step‐by‐step guide for conducting TSA analyses in counseling research. The author discusses the key concepts, steps for implementation, and the strengths and challenges of conducting TSA. [ABSTRACT FROM AUTHOR]

In the dynamic environment of fresh food supermarkets, managing the short shelf life and varying quality of vegetable products presents significant challenges. This study focuses on optimizing restocking and pricing strategies to maximize profits while accommodating the diverse and time-sensitive nature of vegetable sales. We analyze historical sales, pricing data, and loss rates of six vegetable categories in Supermarket A from 1 July 2020 to 30 June 2023. Using advanced data analysis techniques like K-means++ clustering, non-normal distribution assessments, Spearman correlation coefficients, and heat maps, we uncover significant correlations between vegetable categories and their sales patterns. The research further explores the implications of cost-plus pricing, revealing a notable relationship between pricing strategies and sales volumes. By employing Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models, we forecast sales and determine optimal restocking volumes. Additionally, we use price elasticity theories and a comprehensive model to predict net profit changes, aiming to enhance profit margins by 47%. The study also addresses space constraints in supermarkets by proposing an effective assortment of salable items and individual product restocking plans, based on FP-Growth algorithm analysis and market demand. Our findings offer insightful strategies for sustainable and economic growth in the supermarket industry, demonstrating the impact of data-driven decision-making on operational efficiency and profitability. [ABSTRACT FROM AUTHOR]

This study reveals Spatio-temporal prognostication of macro and micronutrients present in entisols, inceptisols and alfisols soil groups at Anaimalai block of Coimbatore district, India through empirical analysis. The soil primary macronutrients and micronutrients for the period 2021–2032 were procured from Autoregressive Integrated Moving Average (ARIMA), Linear Regression (LR) and Logistic Regression (Log R) models by setting out 2005–2020 soil nutrient datasets as input. 1627 soil samples were taken out through composite sampling method in the month of February 2021. The laboratory soil analysis was carried out for macro and micronutrients computation. Computed values were validated with the 2021 soil nutrient empirical models. R2 values 0.88, 0.83, 0.85, 0.89, 0.88, 0.92, 0.90, 0.92 and 0.85 for soil pH, EC, Available nitrogen, Available phosphorus, Available potassium, Iron, Manganese, Copper and Zinc respectively divulges the reliability of ARIMA model in forecasting the soil nutrients and are spatially exposed. Spatio-temporal visualization of soil nutrients for a decade with large nutrient database as input along with field validation brings out promising forecasting model that reverberates on futuristic policy making for attaining sustainable agro-productivity. [ABSTRACT FROM AUTHOR]

The aim of the article is to improve the methodology of studying the financial market of Ukraine, analyzing the interaction between the components of the financial market, studying the reaction of the components of the financial market to sudden changes and forecasting the behavior of the structural elements of the financial market. The financial market plays a decisive role in the modern market economy of Ukraine, providing a mechanism for the redistribution of capital between creditors and investors through intermediaries based on the principles of supply and demand. The development of the financial market and the interaction of its components is the subject of research by many scholars, whose works laid the foundation for a modern understanding of how financial markets function and how they affect the economy as a whole. Despite a significant number of studies on the functioning and development of financial market infrastructure, many aspects of the interaction of market entities in Ukraine remain unexplored, especially in changing external and internal conditions. To model the interaction of market participants in the financial market of Ukraine, an algorithm has been developed, on the basis of which the interaction of structural elements of the financial market of Ukraine has been analyzed, the reaction of the components of the financial market to sharp changes has been studied, and forecasts of the behavior of its structural elements have been made. The study uses a combination of fundamental conceptions of the financial market and the methodology of expert evaluation to determine the most significant statistical indicators that characterize its constituent components. The cause-and-effect relationships between these components are analyzed, the nature of their mutual relationship is revealed. ARIMA models were used to forecast key indicators of the financial market. On the basis of vector auto-regressive models, the dynamic adaptation of market components to shocks is analyzed. The study showed that the credit and interbank markets show less susceptibility to external factors and can even serve as impetus for changes in other structural elements of the financial market. The results of the study can be used for: development of a more effective policy of financial market regulation; development of new methods and tools of influence on the functioning of financial market entities. [ABSTRACT FROM AUTHOR]

The water resource carrying capacity, as a pivotal component of the overall regional natural resource carrying capacity, plays a fundamental role in determining the feasibility of achieving harmonious coexistence among population, economy, and environment in regions with limited water resources. It holds paramount significance for regional development.“Based on the theoretical framework of the four subsystems, namely water resources, society, economy, and ecology, this article employs the entropy weight method and CRITIC method to calculate individual weights. The combined weights are then determined using game theory. Subsequently, an improved TOPSIS model based on grey relational analysis is applied to comprehensively evaluate the water resources carrying capacity in Gansu Province from 2009 to 2022. The main obstacles impacting water resources carrying capacity are diagnosed using an obstacle degree model. Finally, a time series model is utilized to predict future trends in water resources carrying capacity from 2023 to 2030. The findings suggest that (1)over the study period, Gansu Province exhibited an upward trend in its overall water resource carrying capacity, with a value of 0.55 (weakly bearable) in 2022, representing a significant 20% increase compared to the baseline year of 2009. (2) In terms of regional changes, before 2015, the water resources carrying capacity of Wuwei, Linxia, Dingxi, and other places were in a weakly bearable carrying range [ 0.5,0.6), while other cities and prefectures were in a critical carrying state [ 0.4,0.5). After 2015, the carrying state of most cities and prefectures has improved, especially in Jiuquan, Jiayuguan, Wuwei, and other places, which may be related to the local government’s efforts to increase water resources management and water-saving measures. (3) The key factors influencing the carrying capacity of water resources in Gansu Province include ecological water consumption, industrial wastewater emissions of chemical oxygen demand (COD), urban sewage treatment capacity, effective irrigation area in agricultural land, overall water consumption rate, and per capita daily water consumption in urban areas. To promote sustainable utilization of water resources, it is crucial to strengthen the management and regulation of these major hindering factors. (4) The future carrying capacity of water resources in various cities and states is expected to witness significant improvement. By 2030, it is anticipated that the carrying capacity of all provinces within Gansu Province will reach a bearable state [0.6,1], thereby progressing towards an overall green and sustainable trajectory.

Background China ranks first in the world in terms of the number of diabetes patients. In recent years, the prevalence and mortality of diabetes have been rising, threatening people's health and placing a heavy burden on the people of China. As the prevalence of obesity continues to rise, the burden of diabetes is expected to continue to rise, and diabetes has become a public health problem that cannot be ignored in China. Objective To describe and analyze the disease burden of type 2 diabetes attributable to high BMI and its trend in China from 1990 to 2019, and predict the disease burden of type 2 diabetes attributable to high BMI in China from 2020 to 2024, so as to provide a basis for the scientific prevention and control of type 2 diabetes in China. Methods In May 2023, data on the burden of disease indicators of type 2 diabetes such as disability-adjusted life years (DALYs) , DALYs rate, standardized DALYs rate, death toll, mortality rate and standardized mortality rate of type 2 diabetes in China from 1990 to 2019 were extracted from the Global Burden of Disease 2019 (GBD 2019) , and the trend was analyzed by annual percentage change (APC) and average annual percentage change (AAPC) using the Joinpoint Regression Model. An autoregressive moving average (ARIMA) model of DALYs rate and mortality rate of type 2 diabetes attributable to high BMI was constructed based on the data from 1990 to 2016 (training set) , and evaluated using the data from 2017 to 2019 (test set) . The relative error between the predicted value and the actual value, the mean absolute error (MAE) , mean absolute percentage error (MAPE) , mean square error (MSE) and root mean square error (RMSE) of the model were used to determine the model prediction effect, and the optimal model was selected to predict the burden of type 2 diabetes attributable to high BMI in China from 2020 to 2024. Results From 1990 to 2019, the burden of disease showed an overall upward trend (AAPC of standardized DALYs rate=2.85%, AAPC of standardized mortality=2.32%, both P

Abstract The Russia–Ukraine conflict has caused a global food security crisis, impacting sustainable development goals. Predicting the crisis’s impact on food security is crucial for global stability by 2030. From a macro-perspective, this paper constructs a food security evaluation indicator system and a food security composite index (FSCI), and using the autoregressive integrated moving average model to predict the variations in the FSCI for different regions of the world from 2023 to 2030 under scenarios with or without the “Russia–Ukraine conflict.” By quantitatively analyzing the differences in these variations, the potential impact of the conflict on regional food security is assessed. The results conclude that the global food security level progressively improved over the past 20 years. The FSCI in Europe, Latin America and Caribbean increased at a faster pace than the global average, with growth rates of 0.035/(10 years) and 0.034/(10 years), respectively. However, the FSCI in the Sub-Saharan Africa showed a declining trend. By 2030, it is expected that the Russia–Ukraine conflict will have a significant impact on the food security of Europe and Sub-Saharan Africa, with a contribution of 1.49% and 0.29%, respectively. However, the impact of the conflict on food security levels in Asia and Latin America and Caribbean is relatively small. This study introduces a new quantitative method to assess and project the overall influence of the Russia–Ukraine conflict on food security. The findings contribute crucial scientific support for effectively evaluating and monitoring the sustainable development objectives related to global food security.

Abstract Background Application of accumulated experience and management measures in the prevention and control of coronavirus disease 2019 (COVID-19) has generally depended on the subjective judgment of epidemic intensity, with the quality of prevention and control management being uneven. The present study was designed to develop a novel risk management system for COVID-19 infection in outpatients, with the ability to provide accurate and hierarchical control based on estimated risk of infection. Methods Infection risk was estimated using an auto regressive integrated moving average model (ARIMA). Weekly surveillance data on influenza-like-illness (ILI) among outpatients at Xuanwu Hospital Capital Medical University and Baidu search data downloaded from the Baidu Index in 2021 and 22 were used to fit the ARIMA model. The ability of this model to estimate infection risk was evaluated by determining the mean absolute percentage error (MAPE), with a Delphi process used to build consensus on hierarchical infection control measures. COVID-19 control measures were selected by reviewing published regulations, papers and guidelines. Recommendations for surface sterilization and personal protection were determined for low and high risk periods, with these recommendations implemented based on predicted results. Results The ARIMA model produced exact estimates for both the ILI and search engine data. The MAPEs of 20-week rolling forecasts for these datasets were 13.65% and 8.04%, respectively. Based on these two risk levels, the hierarchical infection prevention methods provided guidelines for personal protection and disinfection. Criteria were also established for upgrading or downgrading infection prevention strategies based on ARIMA results. Conclusion These innovative methods, along with the ARIMA model, showed efficient infection protection for healthcare workers in close contact with COVID-19 infected patients, saving nearly 41% of the cost of maintaining high-level infection prevention measures and enhancing control of respiratory infections.

Background Gout is the most common inflammatory arthritis, which causes great harm to people's health, however, there are limited studies on the trend analysis and prediction of gout burden in China. Objective To analyze the changing trend of gout in China from 1990 to 2019, and predict the age-standardized disability-adjusted life years (DALYs) rate, incidence rate and prevalence rate of gout in the next 10 years. Methods The indicators of DALYs, incidence, and prevalence of gout in China were extracted from the Global Burden of Disease Study (GBD 2019), the annual percentage change (APC) and annual average percentage change (AAPC) of the age-standardized DALYs rate, age-standardized incidence rate and age-standardized prevalence rate were calculated using the Joinpoint regression model, autoregressive integrated moving average model (ARIMA model) was used to predict the age-standardized DALYs rate, age-standardized incidence rate and age-standardized prevalence rate in 2020-2029. Results In 1990-2019, the DALYs of gout in China increased from 187 436 to 510 485 person-years, with an increase of 172.35%, the number of cases increased from 1 181 969 to 3 041 329, with an increase of 157.31%, the number of patients increased from 5 864 143 to 16 161 325, with an increase of 175.60% (the corresponding standardized rates increased by 28.45%, 25.92% and 28.63%, respectively). Joinpoint regression results showed an overall increasing trend in the age-standardized DALYs rate, age-standardized incidence rate and age-standardized prevalence rate (AAPC was 0.9%, 0.8%, 0.9%, respectively, P

Biogas is a renewable energy source (RES). The aim of this research was to analyze the perspectives of electricity production from biogas in the European Union. The main source of information was data from Eurostat. We analyzed electricity production from biogas in the European Union (EU). The scope of this research was data from 2012 to 2021. First, we presented biogas production by feedstock type across the world. Then, we presented changes in electricity production from biogas in the EU. We used different methods to evaluate the changes in biogas production. First, we used the ARiMA (Autoregressive Moving Average) model to evaluate the stationarity of the time series. Our electricity production from biogas data proved to be stationary. Second, we elaborated on the prognosis of future changes in electricity production from biogas. The largest producer of biogas is the EU, and it is produced from crops, animal manure, and municipal solid waste. Our research found that the largest production from biogas in 2021 took place in Germany, Italy, and France. These countries have the greatest potential for electricity production from biogas, and they have spent significant funds on facilities and technology. Such countries as Ireland, Greece, Spain, France, Croatia, Poland, Portugal, Romania, Finland, and Sweden increased their electricity production from biogas in 2021 compared to 2020. According to our prognosis, the global production of biogas will increase from 62.300 TWh to 64.000 TWh in 2019–2026 (2.7% increase). In 2022–2026, such countries as Estonia (60.4%), Latvia (29.6%), Croatia (27.6%), Slovenia (10.9%), and Poland (8.2%) will increase their electricity production from biogas the most. In 2022–2026, such countries as Italy (0.68%), Portugal (1.1%), Greece (1.5%), Slovakia (2.3%), and Germany (2.6%) will increase their electricity production from biogas the least. Only Romania (−17.6%), Finland (−11.5%), Lithuania (−9.1%), and Malta (−1.06%) will decrease their production of electricity from biogas in 2022–2026. Such countries as Bulgaria (2344%), Denmark (590.9%), Croatia (449%), and France (183%) increased biogas consumption in 2013–2022. A decrease in the inland consumption of biogas in 2013–2022 was observed in Spain, Cyprus, Latvia, Luxembourg, Austria, and Slovenia. [ABSTRACT FROM AUTHOR]

Oil crops are considered important crops because the demand for them is derived from the demand of production and consumption of vegetable oils. This research aimed to identify the current and future status of vegetable oil consumption in Egypt by estimating the size of the vegetable oil gap and its future expectations to raise the efficiency of the economic and technical performance of vegetable oils in Egypt. This study relied on both descriptive and quantitative methods represented by averages, percentages, annual growth rate, and time trend equations for determining data and presenting its results. In addition, this research relied on estimating the ARIMA model to forecast the future of vegetable oil gap by relying on secondary data published by the Ministry of Agriculture and Land Reclamation, Economic Affairs Sector, and various issues of food balance bulletins during the period from (2007-2021). The most important results were reached that maize crop ranked the first place in both of production, followed by olives which ranked the second place, then the production of cotton seeds, peanut seeds and sesame seeds, while the lowest production of soybeans and sunflower seeds. While olive crop ranked first in the productivity, followed by maize in the second rank, then cotton seeds and sesame seeds were the least in productivity during the period (2007-2021). The quantity of soybean oil production ranked first and contributed the largest share in producing vegetable oils during the study period (2007-2021), followed by production quantity of cotton seed oil, then production quantity of sunflower oil and olive oil, while the production quantity of maize oil came in the last rank. The consumption quantity of palm oil ranked first that contributed with the largest share in consuming of vegetable oils during the study period (2007-2021), followed by the consumption quantity of soybean oil, then sunflower oil and cotton seed oil, while olive oil came in the last rank. By reviewing the results of forecasting of the food gap of vegetable oils, it is cleared that the increasing of gap between local production and national consumption is evident, which indicates the need for the state to make efforts to developed plans and programs to increase production from its various sources. Not only rationalizing consumption but also reducing the increase of vegetable oils gap in Egypt during the study period. [ABSTRACT FROM AUTHOR]

As healthcare costs continue to pose significant challenges for governments and policymakers, accurate forecasting of medical inflation has become crucial in the European Union. This study aims to provide insights into the trajectory of medical inflation within the EU using the Autoregressive Integrated Moving Average (ARIMA) model and to check whether this model is an effective tool for predictions of medical inflation. The findings of the study have significant implications across various sectors. With accurate forecasts of medical inflation, policymakers can proactively address challenges, insurers can determine appropriate premiums and develop innovative models, and healthcare entities can allocate resources strategically to ensure financial stability and quality care. [ABSTRACT FROM AUTHOR]

Background: Application of accumulated experience and management measures in the prevention and control of coronavirus disease 2019 (COVID-19) has generally depended on the subjective judgment of epidemic intensity, with the quality of prevention and control management being uneven. The present study was designed to develop a novel risk management system for COVID-19 infection in outpatients, with the ability to provide accurate and hierarchical control based on estimated risk of infection. Methods: Infection risk was estimated using an auto regressive integrated moving average model (ARIMA). Weekly surveillance data on influenza-like-illness (ILI) among outpatients at Xuanwu Hospital Capital Medical University and Baidu search data downloaded from the Baidu Index in 2021 and 22 were used to fit the ARIMA model. The ability of this model to estimate infection risk was evaluated by determining the mean absolute percentage error (MAPE), with a Delphi process used to build consensus on hierarchical infection control measures. COVID-19 control measures were selected by reviewing published regulations, papers and guidelines. Recommendations for surface sterilization and personal protection were determined for low and high risk periods, with these recommendations implemented based on predicted results. Results: The ARIMA model produced exact estimates for both the ILI and search engine data. The MAPEs of 20-week rolling forecasts for these datasets were 13.65% and 8.04%, respectively. Based on these two risk levels, the hierarchical infection prevention methods provided guidelines for personal protection and disinfection. Criteria were also established for upgrading or downgrading infection prevention strategies based on ARIMA results. Conclusion: These innovative methods, along with the ARIMA model, showed efficient infection protection for healthcare workers in close contact with COVID-19 infected patients, saving nearly 41% of the cost of maintaining high-level infection prevention measures and enhancing control of respiratory infections. [ABSTRACT FROM AUTHOR]

The Russia–Ukraine conflict has caused a global food security crisis, impacting sustainable development goals. Predicting the crisis's impact on food security is crucial for global stability by 2030. From a macro-perspective, this paper constructs a food security evaluation indicator system and a food security composite index (FSCI), and using the autoregressive integrated moving average model to predict the variations in the FSCI for different regions of the world from 2023 to 2030 under scenarios with or without the "Russia–Ukraine conflict." By quantitatively analyzing the differences in these variations, the potential impact of the conflict on regional food security is assessed. The results conclude that the global food security level progressively improved over the past 20 years. The FSCI in Europe, Latin America and Caribbean increased at a faster pace than the global average, with growth rates of 0.035/(10 years) and 0.034/(10 years), respectively. However, the FSCI in the Sub-Saharan Africa showed a declining trend. By 2030, it is expected that the Russia–Ukraine conflict will have a significant impact on the food security of Europe and Sub-Saharan Africa, with a contribution of 1.49% and 0.29%, respectively. However, the impact of the conflict on food security levels in Asia and Latin America and Caribbean is relatively small. This study introduces a new quantitative method to assess and project the overall influence of the Russia–Ukraine conflict on food security. The findings contribute crucial scientific support for effectively evaluating and monitoring the sustainable development objectives related to global food security. [ABSTRACT FROM AUTHOR]

In order to improve the high accuracy and reliability of drone pitch angle fault data processing and prediction, and avoid increasing the cost of drone flight testing, long short term memory(LSTM), attention mechanism + LSTM model, and autoregressive integrated moving average model(ARIMA) model were used to predict pitch angle faults data in drone systems. The results show that for ARIMA, mean absolute error(MAE)is 0. 35, root mean square error(RMSE) is 0. 73, mean absolute percentage error (MAPE) is 23. 80% . For LSTM model, MAE = 0. 49, RMSE = 0. 74, MAPE = 45. 20% . For attention mechanism + LSTM model, MAE = 0. 17, RMSE = 0. 53, MAPE = 18. 93% . It can be seen that the attention mechanism + LSTM model is more suitable for predicting the pitch angle of flight tests data than the other two models. The above three methods have practical significance for predicting unmanned aerial vehicle faults. Effective prediction can promote the latest progress in anomaly detection of autonomous aircraft and mobile robots or foreign direct investment research, in order to further improve the safety of automatic and remote flight operations. [ABSTRACT FROM AUTHOR]

Objective To compare the fitting effect of ARIMA model and grey model (GM) (1,1) in the under five mortality rate (U5MR) in Shenzhen, and to predict the U5MR in Shenzhen in the next 3 years, so as to provide scientific basis for the formulation of child health care plan. Methods Taking the U5MR in Shenzhen from 2005 to 2022 as the original data, ARIMA model and GM (1,1) model were constructed to fit the child mortality rate. The mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to compare the fitting effect of the two models. Results From 2005 to 2022, the U5MR in Shenzhen showed a downward trend, lower than the national average. The MAE of the constructed ARIMA model and GM (1,1) model was 030 and 0.24, respectively, and the MAPE was 11.53% and 8.73%, respectively. The fitting effect of the GM (1,1) model was good. The U5MR in Shenzhen from 2023 to 2025 would be 1.27 %-- 1.18 and 1.09 %-- respectively, predicted by GM (1,1) model. Conclusion The fitting effect of GM (1,1) model on U5MR in Shenzhen is better than that of ARIMA model, and it is predicted that the U5MR in Shenzhen will decrease year by year in the next 3 years. [ABSTRACT FROM AUTHOR]

In the present work, the Auto-Regressive Integrated Moving-Average (ARIMA) forecasting method is used to predict the remaining useful life (RUL) of an axial piston pump used in the hydraulic system applied to the sheet metal industry. The leakage volume from the piston pump is considered a significant parameter for the life prediction analysis using the ARIMA model, as the proposed model uses auto-regressive (AR) and moving-average (MA) modelling for forecasting with high prediction accuracy. The life estimation through the ARIMA model is based on the degradation method of remaining useful life prediction. The life prediction results were in an acceptable range with a forecasting root-mean-square error (RMSE) of 0.3. The investigation results concluded that out of the two forecasting models, i.e., ARIMA (1,1,2) and ARIMA (20,1,2), ARIMA (1,1,2) model has the least forecasting error and also shows better adjustment with the historical data. The RUL of the hydraulic pump considered in the present investigation is predicted to be 28 months. Additionally, the model developed can be used to determine the lifetime of the operational pump and plan its maintenance schedule for hydraulic-operated unmanned vehicles used for off-road applications like mining, agriculture, defence, etc. The proposed RUL forecasting technique can help avoid mechanical systems from uncertain breakdowns and will also play a pivotal role in maintenance. [ABSTRACT FROM AUTHOR]

In recent years, under the influence of changes in natural conditions and human social activities, the issue of global warming has become increasingly prominent. So it is crucial to effectively predict the future trend of temperature changes. In this regard, from the perspective of statistical models, this paper studies a new combination model, namely the new GM-ARIMA model, based on linear combination of weight calculation. Furthermore, it also analyzes the prediction effect through comparative experiments and uses multiple performance evaluation indicators, so as to prove the scientificity and effectiveness of the proposed combination model in this paper. Finally, according to the experimental results, it can be clearly found that among the four methods for calculating the weights of linear combination, namely the equal weight method, the variance reciprocal method, the residual reciprocal method and the standard deviation method, the combination model using the standard deviation method for calculation has the highest prediction accuracy, so it is finally decided to use this method to build the combination model (namely S-GM-ARIMA). In addition, the experimental results show that the S-GM-ARIMA model achieves the best prediction results compared to other existing prediction models. Among them, the MAE of S-GM-ARIMA decreases by 10.38% and 16.22% compared to the GM(1,1) model and ARIMA model, respectively. The RMSE of S-GM-ARIMA decreases by 4.52% and 10.03% compared to the GM(1,1) model and ARIMA model, respectively. And the MAPE of S-GM-ARIMA decreases by 10.34% and 16.17% compared to the GM(1,1) model and ARIMA model, respectively. Therefore, the new GM-ARIMA combination model studied in this paper has relatively higher prediction accuracy when making predictions, and can be used to make more effective and accurate predictions of global average temperature. This study can also provide reference for countries in making decisions to address global warming issues. [ABSTRACT FROM AUTHOR]

Objective: To establish a time series model correlating vanilla production with temperature variation to forecast vanilla production in Mexico. Design/methodology/approach: Data on vanilla production in Mexico (Tons) and the annual average temperature were obtained for the period 1985-2020. An ARIMA model was constructed; Granger causality test was conducted to determine the effect of temperature on vanilla production, in addition to evaluating the orthogonal response of the model. A forecast for vanilla production was made for the period 2020-2040. Results: ARIMA (1,1,1) model was found, and the influence of temperature on vanilla production was determined. Both thermal variation and the production of the last three years determine current production. A reduction in the quantity of tons of vanilla produced in the coming years is expected. It is considered that this cultivation is highly sensitive to sudden increases or decreases in temperature. Limitations on study/implications: Vanilla cultivation is sensitive to temperature variation; therefore, in the face of climate change, it is considered necessary to take a series of actions in the present. These actions encompass a genetic perspective, new cultivation methods and locations, as well as technological investment. Findings/conclusions: Vanilla production is influenced by temperature variation and is sensitive to sudden increases or declines. If actions are not taken in the present, a reduction in the national production of vanilla in Mexico is expected due to climate change. [ABSTRACT FROM AUTHOR]