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Purpose: This study was conducted to forecast the monthly number of tuna landings between 2023 and 2030 and determine whether the estimated number meets the government's target. Design/methodology/approach: The ARIMA and seasonal ARIMA (SARIMA) models were employed for time series forecasting of tuna landings from the Malaysian Department of Fisheries. The best ARIMA (p, d, q) and SARIMA(p, d, q) (P, D, Q)12 model for forecasting were determined based on model identification, estimation and diagnostics. Findings: SARIMA(1, 0, 1) (1, 1, 0)12 was found to be the best model for forecasting tuna landings in Malaysia. The result showed that the fluctuation of monthly tuna landings between 2023 and 2030, however, did not achieve the target. Research limitations/implications: This study provides preliminary ideas and insight into whether the government's target for fish landing stocks can be met. Impactful results may guide the government in the future as it plans to improve the insufficient supply of tuna. Practical implications: The outcome of this study could raise awareness among the government and industry about how to improve efficient strategies. It is to ensure the future tuna landing meets the targets, including increasing private investment, improving human capital in catch and processing, and strengthening the system and technology development in the tuna industry. Originality/value: This paper is important to predict the trend of monthly tuna landing stock in the next eight years, from 2023 to 2030, and whether it can achieve the government's target of 150,000 metric tonnes. [ABSTRACT FROM AUTHOR]

Based on the panel data of atmospheric environmental pollution in Hunan Province from 2016 to 2023, the autoregressive integrated moving average model (ARIMA) is introduced to evaluate and predict the current status of atmospheric environmental quality in Hunan Province of China, and the constructed ARIMA model has an excellent prediction effect on the atmospheric environmental quality in Hunan Province. The following conclusions are obtained through the prediction and analysis based on the ARIMA model: (1) the atmospheric environmental quality in Hunan Province shows a year-on-year improvement trend; (2) the ARIMA model prediction method is reliable and effective and can accurately analyze and predict the concentrations of air pollutants (PM2.5, PM10, SO2, and CO) and atmospheric environmental quality, and the prediction results show that the outdoor air quality of Hunan Province will improve gradually each year from 2024 to 2028; (3) this study contributes a better understanding of the ambient air quality in Hunan Province during 2016–2023 and provides good forecasting results for air pollutants during the period of 2024–2028. [ABSTRACT FROM AUTHOR]

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.)

Background: Respiratory infectious diseases have the highest incidence among infectious diseases worldwide. Currently, global monitoring of respiratory pathogens primarily focuses on influenza and coronaviruses. This study included influenza and other common respiratory pathogens to establish a local respiratory pathogen spectrum. We investigated and analyzed the co-infection patterns of these pathogens and explored the impact of lifting non-pharmaceutical interventions (NPIs) on the transmission of influenza and other respiratory pathogens. Additionally, we used a predictive model for infectious diseases, utilizing the commonly used An autoregressive comprehensive moving average model (ARIMA), which can effectively forecast disease incidence. Methods: From June 2023 to February 2024, we collected influenza-like illness (ILI) cases weekly from the community in Xuanwu District, Nanjing, and obtained 2046 samples. We established a spectrum of respiratory pathogens in Nanjing and analysed the age distribution and clinical symptom distribution of various pathogens. We compared age, gender, symptom counts, and viral loads between individuals with co-infections and those with single infections. An autoregressive comprehensive moving average model (ARIMA) was constructed to predict the incidence of respiratory infectious diseases. Results: Among 2046 samples, the total detection rate of respiratory pathogen nucleic acids was 53.37% (1092/2046), with influenza A virus 479 cases (23.41%), influenza B virus 224 cases (10.95%), and HCoV 95 cases (4.64%) being predominant. Some pathogens were statistically significant in age and number of symptoms. The positive rate of mixed infections was 6.11% (125/2046). There was no significant difference in age or number of symptoms between co-infection and simple infection. After multiple iterative analyses, an ARIMA model (0,1,4), (0,0,0) was established as the optimal model, with an R2 value of 0.930, indicating good predictive performance. Conclusions: The spectrum of respiratory pathogens in Nanjing, Jiangsu Province, was complex in the past. The primary age groups of different viruses were different, causing various symptoms, and the co-infection of viruses did not correlate with the age and gender of patients. The ARIMA model estimated future incidence, which plateaued in subsequent months. [ABSTRACT FROM AUTHOR]

Background: This study assessed the global trends and burden of severe chest injury, including rib fractures, lung contusions, and heart injuries from 1990 to 2019. Herein, we predicted the burden patterns and temporal trends of severe chest injuries to provide epidemiological evidence globally and in China. Methods: In our analysis, the age-standardized incidence rate (ASIR), prevalence rate (ASPR), and years lived with disability rate (ASYR) of severe chest injury were analyzed by gender, age, sociodemographic index, and geographical region between 1990 and 2019 using data from the Global Burden of Disease study 2019. Trends were depicted by calculating the estimated annual percentage changes (EAPCs). The impact of age, period, and cohort factors was assessed using an Age-Period-Cohort model. Autoregressive integrated moving average (ARIMA) model was employed to predict severe chest injury trends from 2020 to 2050. Results: In 2019, the global number of severe chest injury cases reached 7.95 million, with the highest incidence rate observed in Central Europe (209.61). Afghanistan had the highest ASIRs at 277.52, while North Korea had the lowest ASIRs at 41.02. From 1990 to 2019, the Syrian Arab Republic saw significant increases in ASIR, ASPR, and ASYR, with EAPCs of 10.4%, 9.31%, and 10.3%, respectively. Burundi experienced a decrease in ASIR with an EAPC of − 6.85% (95% confidence interval [CI] − 11.11, − 2.37), while Liberia's ASPR and ASYR declined with EAPCs of − 3.22% (95% CI − 4.73, − 1.69) and − 5.67% (95% CI − 8.00, − 3.28), respectively. Falls and road injuries remained the most common causes. The relative risk of severe chest injury by age, period, and cohort demonstrated a complex effect globally and in China. The ARIMA model forecasted a steady increase in global numbers from 2020 to 2050, while in China, it forecasted an increase in incidence, a decrease in ASIR and ASYR, and an increase in ASPR. Conclusions: This study provides a groundbreaking analysis of global severe chest injury, shedding light on its measures and impact. These findings highlight the need for timely, specialized care and addressing regional disparities to mitigate the severe chest injury burden. [ABSTRACT FROM AUTHOR]

Background: The aim of this study is to analyze the trend of acute onset of chronic cor pulmonale at Chenggong Hospital of Kunming Yan'an Hospital between January 2018 and December 2022.Additionally, the study will compare the application of the ARIMA model and Holt-Winters model in predicting the number of chronic cor pulmonale cases. Methods: The data on chronic cor pulmonale cases from 2018 to 2022 were collected from the electronic medical records system of Chenggong Hospital of Kunming Yan'an Hospital. The ARIMA and Holt-Winters models were constructed using monthly case numbers from January 2018 to December 2022 as training data. The performance of the model was tested using the monthly number of cases from January 2023 to December 2023 as the test set. Results: The number of acute onset of chronic cor pulmonale in Chenggong Hospital of Kunming Yan'an Hospital exhibited a downward trend overall from 2018 to 2022. There were more cases in winter and spring, with peaks observed in November to December and January of the following year. The optimal ARIMA model was determined to be ARIMA (0,1,1) (0,1,1)12, while for the Holt-Winters model, the optimal choice was the Holt-Winters multiplicative model. It was found that the Holt-Winters multiplicative model yielded the lowest error. Conclusion: The Holt-Winters multiplicative model predicts better accuracy. The diagnosis of acute onset of chronic cor pulmonale is related to many risk factors, therefore, when using temporal models to fit and predict the data, we must consider such factors' influence and try to incorporate them into the models. [ABSTRACT FROM AUTHOR]

Copyright of China Fiber Inspection / Zhongguo Xian-Jian is the property of China Fiber Inspection 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.)

Copyright of Journal of Agriculture & Nature / Kahramanmaraş Sütçü İmam Üniversitesi Tarım & Doğa Dergisi is the property of Kahramanmaras Sutcu Imam Universitesi 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.)

Yuxiao Yang,1,2 Xingyuan Gao,3 Hongmei Liang,4 Qiuying Yang1,2 1School of Biomedical Engineering, Capital Medical University, Beijing, People’s Republic of China; 2Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, People’s Republic of China; 3Design Department, Beijing HANHAIZHONGJIA Hydraulic Machinery Co., Ltd, Beijing, People’s Republic of China; 4Nursing Department, China Railway 17th Bureau Group Central Hospital, Taiyuan, People’s Republic of ChinaCorrespondence: Qiuying Yang, School of Biomedical Engineering, Capital Medical University, Beijing, People’s Republic of China, Tel +86 13691283439, Email yangqiuying@ccmu.edu.cnPurpose: AIDS presents serious harms to public health worldwide. In this paper, we used five single models: ARIMA, SARIMA, Prophet, BP neural network, and LSTM method to model and predict the number of monthly AIDS incidence cases and mortality cases in China. We have also proposed the LSTM-SARIMA combination model to enhance the accuracy of the prediction. This study provides strong data support for the prevention and treatment of AIDS.Methods: We collected data on monthly AIDS incidence cases and mortality cases in China from January 2010 to February 2024. Among them, for modeling, we used data from January 2010 to February 2021 and the rest for validation. Treatments were applied to the dataset based on its characteristics during modeling. All models in our study were performed using Python 3.11.6. Meanwhile, we used the constructed model to predict monthly incidence and mortality cases from March 2024 to July 2024. We then evaluated our prediction results using RMSE, MAE, MAPE, and SMAPE.Results: The deep learning methods of LSTM and BPNN outperform ARIMA, SARIMA, and Prophet in predicting the number of mortality cases. When predicting the number of AIDS incidence cases, there is little difference between the two types of methods, and the LSTM method performs slightly better than the rest of the methods. Meanwhile, the average error in predicting AIDS mortality cases is significantly lower than in predicting AIDS incidence cases. The LSTM-SARIMA method outperforms other methods in predicting AIDS incidence and mortality.Conclusion: Due to the different characteristics of the AIDS incidence and mortality cases series, the performance of distinct methods is slightly different. The AIDS mortality series is smoother than the incidence series. The combined LSTM-SARIMA model outperforms the traditional method in prediction and the LSTM method alone, which is of practical significance for optimizing the prediction results of AIDS.Keywords: AIDS, ARIMA model, Prophet model, deep learning model, LSTM-SARIMA combination model

This research paper addresses the forecasting of Ukraine’s macroeconomic dynamics amidst a full-scale war, which has profoundly impacted its economy, causing disruptions in key sectors like agriculture and energy. The significance of this research lies in its focus on an economy facing severe wartime disruptions and providing crucial forecasts for recovery and policy planning. The study uses the ARIMA (Autoregressive Integrated Moving Average) model to analyse various economic indicators, including GDP, inflation, unemployment, public debt, foreign direct investment, and currency devaluation. ARIMA models are chosen for their effectiveness in handling time series data that exhibit autocorrelation, making them suitable for analysing macroeconomic trends in volatile environments. Data was collected from a wide range of national and international sources, and the ARIMA model was applied to identify correlations, trends, and potential scenarios for Ukraine’s economy. The research finds that Ukraine’s economy has suffered significantly due to the war, with indicators like GDP and the unemployment rate experiencing extreme fluctuations. The destruction of infrastructure, displacement of millions, and blockades of key sectors have led to a sharp contraction in GDP. Furthermore, inflation and currency devaluation have persisted due to supply chain disruptions and energy shortages. The analysis reveals strong positive autocorrelations in economically active population figures and the unemployment rate, indicating consistent trends over short lags. In contrast, weak but statistically significant autocorrelations are found in foreign exchange reserves and public debt. The study also observes that foreign direct investment in Ukraine demonstrates cyclical behaviour, with downturns during crises like the war and the global financial crisis. The monetary policy responses by the National Bank of Ukraine, particularly interest rate hikes, have played a key role in stabilizing inflation, but inflationary pressures remain high. The war's impact on critical sectors such as agriculture, energy, and industrial production suggests that reconstruction and recovery will be contingent on external financial support and strategic economic policies. The paper discusses the challenges and complexities of forecasting economic dynamics in conflict zones, where traditional economic models are insufficient to account for the uncertainties and shocks caused by conflict. The use of ARIMA models has proven effective for short-term forecasting, but the paper emphasizes the need for dynamic models that incorporate war-related variables like military expenditures, sanctions, and international aid inflows. The research underscores the crucial role of international institutions, such as the IMF and the World Bank, in aiding Ukraine's recovery through accurate macroeconomic forecasts. These forecasts guide the disbursement of international aid and shape policies for the country's reconstruction. Moreover, the paper notes the potential for Ukraine’s economy to undergo structural transformations toward energy independence, export diversification, and industrial reconstruction. This research is highly relevant for policymakers and international stakeholders involved in Ukraine's post-war economic planning, offering insights into the country's macroeconomic dynamics and potential paths for stabilization and recovery. Accurate forecasts are pivotal for guiding resource allocation, managing inflation, and ensuring long-term economic stability.

Forecasting and modelling of price dynamics of financial instruments and their volatility is an essential element of technical analysis of financial markets. The real sector is also interested in changes in volatility as it seeks to maintain stability in financial and commodity markets. The article aims to develop methodological approaches to modelling the dynamics and volatility of the Ukrainian corporate credit market using asymmetric GARCH approaches. It has been established that the risky nature of financial markets is a prerequisite for analyzing and modeling the volatility of their dynamics in order to correctly respond to possible spikes in volatility, as well as to predict their duration. The analysis was based on daily data on interest rates on the corporate credit market. A graph of the initial time series, autocorrelation functions was plotted, the series was checked for stationarity by the Dickey–Fuller test, which led to its differentiation and subsequent formation of the optimal ARIMA specification. When checking the residuals for autocorrelation and the ARCH effect, positive results were obtained, which led to the use of the GARCH model. Going through various GARCH specifications made it possible to choose GJR-GARCH for modeling, which takes into account the asymmetry of the impact of information shocks on the profitability management of active bank operations. The resulting model was tested by the Leung–Box test, the ARCH LM test, and the Pearson test for the optimality of the specification. The model was compared with actual time series data. All the results confirmed the correctness of the built models, which allows them to be used for analysis and forecasting for further periods.

An accelerating global shift towards sustainable development has made the diffusion of green technologies a critical area of focus, particularly within OECD economies. This study aims to use a machine-learning approach to explore the future diffusion of green technology across OECD countries. It provides detailed forecasts from 2023 to 2037, highlighting the varying rates of green technology diffusion (GTD) among different nations. To achieve this, the Autoregressive Integrated Moving Average (ARIMA) model is employed to offer new evidence on how the progress of green technology can be predicted. Based on empirical data, the study categorizes countries into high, moderate, and low GTD growth. The findings suggest that Japan, Germany, and the USA will experience significant growth in GTD, while countries like Australia, Canada, and Mexico will see moderate increases. Conversely, some nations, including Ireland and Iceland, face challenges with low or negative GTD values. The study concludes that applying this machine-learning model provides valuable insights and future predictions for policymakers aiming to enhance green technology adoption in their respective countries.

Abstract Background Respiratory infectious diseases have the highest incidence among infectious diseases worldwide. Currently, global monitoring of respiratory pathogens primarily focuses on influenza and coronaviruses. This study included influenza and other common respiratory pathogens to establish a local respiratory pathogen spectrum. We investigated and analyzed the co-infection patterns of these pathogens and explored the impact of lifting non-pharmaceutical interventions (NPIs) on the transmission of influenza and other respiratory pathogens. Additionally, we used a predictive model for infectious diseases, utilizing the commonly used An autoregressive comprehensive moving average model (ARIMA), which can effectively forecast disease incidence. Methods From June 2023 to February 2024, we collected influenza-like illness (ILI) cases weekly from the community in Xuanwu District, Nanjing, and obtained 2046 samples. We established a spectrum of respiratory pathogens in Nanjing and analysed the age distribution and clinical symptom distribution of various pathogens. We compared age, gender, symptom counts, and viral loads between individuals with co-infections and those with single infections. An autoregressive comprehensive moving average model (ARIMA) was constructed to predict the incidence of respiratory infectious diseases. Results Among 2046 samples, the total detection rate of respiratory pathogen nucleic acids was 53.37% (1092/2046), with influenza A virus 479 cases (23.41%), influenza B virus 224 cases (10.95%), and HCoV 95 cases (4.64%) being predominant. Some pathogens were statistically significant in age and number of symptoms. The positive rate of mixed infections was 6.11% (125/2046). There was no significant difference in age or number of symptoms between co-infection and simple infection. After multiple iterative analyses, an ARIMA model (0,1,4), (0,0,0) was established as the optimal model, with an R2 value of 0.930, indicating good predictive performance. Conclusions The spectrum of respiratory pathogens in Nanjing, Jiangsu Province, was complex in the past. The primary age groups of different viruses were different, causing various symptoms, and the co-infection of viruses did not correlate with the age and gender of patients. The ARIMA model estimated future incidence, which plateaued in subsequent months.

In order to solve the problem of mine water surge prediction, we introduce deep learning theory, combine long short-term memory network (LSTM) and gated circulation unit (GRU), select mine water surge as the research object, and establish a mine water surge prediction model based on LSTM-GRU. Taking the mine water inflow of a mine in Shaanxi Province as sample data, the data set was divided into a training set and a test set using a 7∶3 ratio, and the gradient descent algorithm with good model training effect was selected to determine the network model parameters and regularization parameters. In order to prove the prediction accuracy of the LSTM-GRU model, the prediction results were compared with those obtained by the traditional ARIMA model and the LSTM model to predict mine water gusher, respectively. The results show that: the mean absolute percentage error (RMSE), root mean square error (MAE), mean absolute error (MAPE) and coefficient of determination (R2) of the LSTM-GRU composite model are 70.51, 53.4, 2.80% and 0.86, indicating that the model has high prediction accuracy and reliability. The prediction effect is better than the traditional ARIMA model and LSTM model.

Abstract Background The aim of this study is to analyze the trend of acute onset of chronic cor pulmonale at Chenggong Hospital of Kunming Yan’an Hospital between January 2018 and December 2022.Additionally, the study will compare the application of the ARIMA model and Holt-Winters model in predicting the number of chronic cor pulmonale cases. Methods The data on chronic cor pulmonale cases from 2018 to 2022 were collected from the electronic medical records system of Chenggong Hospital of Kunming Yan’an Hospital. The ARIMA and Holt-Winters models were constructed using monthly case numbers from January 2018 to December 2022 as training data. The performance of the model was tested using the monthly number of cases from January 2023 to December 2023 as the test set. Results The number of acute onset of chronic cor pulmonale in Chenggong Hospital of Kunming Yan’an Hospital exhibited a downward trend overall from 2018 to 2022. There were more cases in winter and spring, with peaks observed in November to December and January of the following year. The optimal ARIMA model was determined to be ARIMA (0,1,1) (0,1,1)12, while for the Holt-Winters model, the optimal choice was the Holt-Winters multiplicative model. It was found that the Holt-Winters multiplicative model yielded the lowest error. Conclusion The Holt-Winters multiplicative model predicts better accuracy. The diagnosis of acute onset of chronic cor pulmonale is related to many risk factors, therefore, when using temporal models to fit and predict the data, we must consider such factors’ influence and try to incorporate them into the models.

Background Intestinal infectious diseases are one of the common infectious diseases. Analysis and prediction of their epidemic status can provide certain reference for the prevention and treatment of intestinal infectious diseases. Objective To understand the incidence and mortality of three enteric infectious diseases, including diarrheal diseases, typhoid fever and paratyphoid fever, and invasive non-typhoidal Salmonella intestinal infections in China from 1990 to 2019, and to predict their morbidity and mortality from 2020 to 2030, so as to provide reference for the prevention and control of intestinal infectious diseases. Methods Based on the 2019 Global Burden of Disease Database (GBD), the incidence and mortality data of three enteric infectious diseases, including diarrheal diseases, typhoid fever and paratyphoid fever, and invasive non-typhoidal Salmonella intestinal infections in China from 1990 to 2019 were collected. The change rate (%) and estimated annual percentage change (EAPC) were used to describe the changing trends of the above three intestinal infectious diseases. The autoregressive integrated moving average model (ARIMA) was used to predict the morbidity and mortality of the above three enteric infectious diseases in China from 2020 to 2030. Results There was no statistically significant change in the incidence of diarrheal diseases from 1990 to 2019 (EAPC=0.09, P>0.05), while the incidence of typhoid fever, paratyphoid fever and invasive non-typhoid salmonella intestinal infections showed a downward trend (EAPC were -4.0% and -0.64% respectively, P

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.

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]

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office 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.)

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]

Copyright of Journal of Research in Economics, Politics & Finance / Ekonomi, Politika & Finans Arastirmalari Dergisi is the property of Journal of Research in Economics, Politics & Finance 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.)

This research paper addresses the forecasting of Ukraine's macroeconomic dynamics amidst a full-scale war, which has profoundly impacted its economy, causing disruptions in key sectors like agriculture and energy. The significance of this research lies in its focus on an economy facing severe wartime disruptions and providing crucial forecasts for recovery and policy planning. The study uses the ARIMA (Autoregressive Integrated Moving Average) model to analyse various economic indicators, including GDP, inflation, unemployment, public debt, foreign direct investment, and currency devaluation. ARIMA models are chosen for their effectiveness in handling time series data that exhibit autocorrelation, making them suitable for analysing macroeconomic trends in volatile environments. Data was collected from a wide range of national and international sources, and the ARIMA model was applied to identify correlations, trends, and potential scenarios for Ukraine's economy. The research finds that Ukraine's economy has suffered significantly due to the war, with indicators like GDP and the unemployment rate experiencing extreme fluctuations. The destruction of infrastructure, displacement of millions, and blockades of key sectors have led to a sharp contraction in GDP. Furthermore, inflation and currency devaluation have persisted due to supply chain disruptions and energy shortages. The analysis reveals strong positive autocorrelations in economically active population figures and the unemployment rate, indicating consistent trends over short lags. In contrast, weak but statistically significant autocorrelations are found in foreign exchange reserves and public debt. The study also observes that foreign direct investment in Ukraine demonstrates cyclical behaviour, with downturns during crises like the war and the global financial crisis. The monetary policy responses by the National Bank of Ukraine, particularly interest rate hikes, have played a key role in stabilizing inflation, but inflationary pressures remain high. The war's impact on critical sectors such as agriculture, energy, and industrial production suggests that reconstruction and recovery will be contingent on external financial support and strategic economic policies. The paper discusses the challenges and complexities of forecasting economic dynamics in conflict zones, where traditional economic models are insufficient to account for the uncertainties and shocks caused by conflict. The use of ARIMA models has proven effective for short-term forecasting, but the paper emphasizes the need for dynamic models that incorporate war-related variables like military expenditures, sanctions, and international aid inflows. The research underscores the crucial role of international institutions, such as the IMF and the World Bank, in aiding Ukraine's recovery through accurate macroeconomic forecasts. These forecasts guide the disbursement of international aid and shape policies for the country's reconstruction. Moreover, the paper notes the potential for Ukraine's economy to undergo structural transformations toward energy independence, export diversification, and industrial reconstruction. This research is highly relevant for policymakers and international stakeholders involved in Ukraine's post-war economic planning, offering insights into the country's macroeconomic dynamics and potential paths for stabilization and recovery. Accurate forecasts are pivotal for guiding resource allocation, managing inflation, and ensuring long-term economic stability. [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]

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 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]

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]

Research on future land cover changes and carbon emissions is essential for effective land resource management and developing feasible carbon mitigation strategies. This study focused on the Yellow River Basin and employed the Future Land Use Simulation (FLUS) and Autoregressive Integrated Moving Average (ARIMA) models to project future land cover and carbon emissions. Additionally, bivariate spatial autocorrelation was utilized to analyze the relationship between them. Key findings are as follows: 1) Historically, the Yellow River Basin has experienced an expansion in construction land, forests, grasslands, and water, while cropland and unused land have diminished. Notably, construction land displayed the most significant changes, whereas grasslands showed minimal modification. Looking ahead, both the ecological protection and inertial development scenarios exhibit consistent trends with historical patterns across the land type categories. In contrast, the economic priority development scenario forecasts an increase in construction land, cropland, and grasslands, indicating a distinct shift compared to the other scenarios. However, the ecological protection scenario proves to be more sustainable. 2) In the absence of intervention, the simulated carbon emissions from construction land throughout the basin display a linear increase across various scenarios, with provincial-level variations showing an increase from southwest to northeast. However, Henan and Sichuan are expected to experience slower reductions in carbon emissions, compared to other projections. There is a notable positive correlation between carbon emissions and the comprehensive index, indicating that regions with high emissions typically experience substantial land and economic development. 3) Energy consumption projections for 2030 and 2060 indicate that to align with China’s carbon goals, it is essential to reduce energy consumption and adjust the fossil to non-fossil fuel ratio to reduce carbon emissions. Substituting coal with clean energy and enhancing energy efficiency will be more effective for achieving low-carbon emission targets. In summary, this study provides significant guidance for China’s ecological conservation, low-carbon emission strategies, and global carbon emission control efforts.

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.