Mid-term Forecast Research Articles

Predicting the development of optic nerve atrophy in children with cerebral palsy

Introduction. Optic nerve atrophy (ONA) holds a leading position in the nosological structure of ophthalmopathology. In children with cerebral palsy (CP), the presence of concomitant pathology such as ONA significantly worsens their health and quality of life. The combination of ONA and CP, as well as the interrelationship of these diseases, represents a relevant subject for research, particulary in studying the clinical and epidemiological features of ONA in children with CP to find optimal clinical solutions and prognostic approaches. Purpose: to investigate the prevalence of optic nerve atrophy in children with cerebral palsy to determine the prognosis for the development of this pathology in the study group. Materials and methods. A retrospective analysis of medical records of 640 patients diagnosed with cerebral palsy who were hospitalized in the clinic from 2015 to 2020 was conducted. The analysis included data from children with early-onset cerebral palsy (up to 3 years old) who were hospitalized for the first time. MS EXCEL software was used to calculate the main indicators of linear regression. Results. The study revealed that optic nerve atrophy in young children develops more frequently in boys (1.5 times more often than in girls), in rural areas, and in those with spastic cerebral palsy (36.3 %). The second most common form was ataxic cerebral palsy (69 children, 21.6 %), followed by infantile hemiplegia (61 children, 19%). Based on the dynamics of the prevalence of optic nerve atrophy in children with cerebral palsy, a mid-term forecast was made using the main indicators of linear regression. The prevalence of optic nerve atrophy in children with cerebral palsy is increasing, with prognostic estimates indicating further growth (14.9% in 2015, 15.4% in 2020, and 16.2% in 2024). Conclusion. The results of the study showed that cerebral palsy in children is often associated with optic nerve atrophy. ONA in young children is more frequently developed in cases of spastic cerebral palsy. As a result of calculating, an increase in the prevalence of optic nerve atrophy in children with cerebral palsy was observed over time, based on the correlation calculated using Spearman’s non-parametric correlation analysis Prognostic estimates indicate a significant increase in the near future.

A Multivariate Time Series Analysis of Electrical Load Forecasting Based on a Hybrid Feature Selection Approach and Explainable Deep Learning

In the smart grid paradigm, precise electrical load forecasting (ELF) offers significant advantages for enhancing grid reliability and informing energy planning decisions. Specifically, mid-term ELF is a key priority for power system planning and operation. Although statistical methods were primarily used because ELF is a time series problem, deep learning (DL)-based forecasting approaches are more commonly employed and successful in achieving precise predictions. However, these DL-based techniques, known as black box models, lack interpretability. When interpreting the DL model, employing explainable artificial intelligence (XAI) yields significant advantages by extracting meaningful information from the DL model outputs and the causal relationships among various factors. On the contrary, precise load forecasting necessitates employing feature engineering to identify pertinent input features and determine optimal time lags. This research study strives to accomplish a mid-term forecast of ELF study load utilizing aggregated electrical load consumption data, while considering the aforementioned critical aspects. A hybrid framework for feature selection and extraction is proposed for electric load forecasting. Technical term abbreviations are explained upon first use. The feature selection phase employs a combination of filter, Pearson correlation (PC), embedded random forest regressor (RFR) and decision tree regressor (DTR) methods to determine the correlation and significance of each feature. In the feature extraction phase, we utilized a wrapper-based technique called recursive feature elimination cross-validation (RFECV) to eliminate redundant features. Multi-step-ahead time series forecasting is conducted utilizing three distinct long-short term memory (LSTM) models: basic LSTM, bi-directional LSTM (Bi-LSTM) and attention-based LSTM models to accurately predict electrical load consumption thirty days in advance. Through numerous studies, a reduction in forecasting errors of nearly 50% has been attained. Additionally, the local interpretable model-agnostic explanations (LIME) methodology, which is an explainable artificial intelligence (XAI) technique, is utilized for explaining the mid-term ELF model. As far as the authors are aware, XAI has not yet been implemented in mid-term aggregated energy forecasting studies utilizing the ELF method. Quantitative and detailed evaluations have been conducted, with the experimental results indicating that this comprehensive approach is entirely successful in forecasting multivariate mid-term loads.

РИНОК ПРАЦІ ТА РИНОК ОСВІТНІХ ПОСЛУГ В УКРАЇНІ В УМОВАХ ВІЙНИ ТА ПОВОЄННОГО ВІДНОВЛЕННЯ: ПРАВОВЕ ЗАБЕЗПЕЧЕННЯ УСУНЕННЯ ДИСБАЛАНСУ

Several issues caused by disparities between the labor market and the market for educational services in Ukraine have been investigated. Some scientifically substantiated ways to eliminate the professional-qualification imbalance in the labor market have been proposed. Suggestions have been prepared for improving the current legislation in the researched sphere, taking into account the prospects of post-war recovery. In particular, measures have been proposed to prepare a substantively detailed mid-term forecast of the demand for specialists and labor force in the labor market, including information on expected labor market demands for specific professional job titles. Additionally, it has been suggested to ensure annual publication of the mid-term forecast for the demand of specialists and labor force in the labor market. The expediency of amending Article 22, Part 3 of the Law of Ukraine “On the Basic Principles of Youth Policy” has been substantiated, providing that the state guarantee for job placement in the field of expertise for a period of not less than three years applies to young professionals – graduates of vocational (vocational-technical), advanced vocational and higher education institutions, regardless of ownership form, the demand for which was determined by the state and/or regional order. Attention is drawn to the inadmissibility of the state's retreat from guarantees of employment for graduates of vocational (vocational-technical) education institutions, which may create additional demotivating factors and complicate the situation in the labor market, especially during the period of post-war recovery of the Ukrainian economy. The expediency of using direct and indirect means of state regulation separately or simultaneously has been argued, such as reducing the licensed volume of preparation of excess specialists, or paying taxes for excess training of certain specialties by citizens who have chosen them for preparation, which are excessive for the domestic labor market.

ИҚТИСОДИЙ ЎСИШНИНГ АҲОЛИ ТУРМУШ ТАРЗИГА ТАЪСИРИ ВА УНИНГ ПРОГНОЗИ

This article describes the impact of economic growth on the lifestyle of the population. Economic growth and demographic changes in the regions of the Republic of Uzbekistan were studied and statistically analyzed. The state of the republic's regions was evaluated and divided into groups according to the compatibility of economic growth and demographic changes. On the basis of econometric models, mid-term forecast values for the main indicators representing the standard of living of the population have been developed. Scientifically based proposals have also been developed to ensure the stability of economic growth and improve the lifestyle of the population.

A univariate time series methodology based on sequence-to-sequence learning for short to midterm wind power production

The biggest wind farm of Turkey is placed at Manisa which is located in the Aegean Region. Electricity is a non-storable commodity for that reason, it is very important to have a strong forecast and model of the potential electricity production to plan the electricity loads. In this study, the aim is to model and forecast electricity production of the wind farms located at Manisa by using a univariate model based on sequence-to-sequence learning. The forecasting range of the study is from short term to midterm. The strength of the proposed model is that; it only needs its own lagged value to make forecasts. The empirical evidences show that the model has high coefficient of variation (R2) in short term and moderate R2 in the midterm forecast. Although in the mid-range forecasts R2 slightly decreases mean squared error and mean absolute error shows that the model is accurate also in the midterm forecasts. The proposed model is not only strong in hourly electricity production forecasts but with a slight modification also in forecasting the minimum, maximum and average electricity production for a fixed range. This study concludes with two fresh and intriguing future research ideas.

Spot Charter Rate Forecast for Liquefied Natural Gas Carriers

Recent maritime legislation demands the transformation of the transportation sector to greener and more energy efficient. Liquified natural gas (LNG) seems a promising alternative fuel solution that could replace the conventional fuel sources. Various studies have focused on the prediction of the LNG price; however, no previous work has been carried out on the forecast of the spot charter rate of LNG carrier ships, an important factor for the maritime industries and companies when it comes to decision-making. Therefore, this study is focused on the development of a machine learning pipeline to address the aforementioned problem by: (i) forming a dataset with variables relevant to LNG; (ii) identifying the variables that impact the freight price of LNG carrier; (iii) developing and evaluating regression models for short and mid-term forecast. The results showed that the general regression neural network presented a stable overall performance for forecasting periods of 2, 4 and 6 months ahead.

Istoriya tandemnoj tekhnologii bitkojn-blokchejn

The rapid development of technology during the last decade of the 20th century and the first two decades of the 21st century, predetermined qualitative changes in the economic life of the global community. The author deliberately highlights the last decade of the 20th century because in 1990-1991 the concept of the World Wide Web has found material embodiment, becoming generally available to the mass user. As the technological foundation of the modern digital economy, author analyzes the digital currency bitcoin and blockchain algorithms through the prism of economic, technological, legal and social practice. The key trends for the modern digital economy are identified and, on their basis, a mid-term forecast of its development is given. Despite the economic subject of scientific analysis, the methods of historical science are used in the work: historical analytics, historical-comparative method, historical-systematical method and retrospective methods.

Green Economy Development Progress in the Republic of Buryatia (Russia).

Under current conditions, the green economy concept has received a comprehensive response in achieving the sustainable development of regions. However, measuring green economic development progress is dynamic, quantitatively characterized by indicators reflecting various aspects. The difficulty lies in a comprehensive environmental sustainability assessment in a context that includes the territory’s environmental, social, and economic factors. The study aimed to assess the progress of the Republic of Buryatia’s (Russia) “green” economic development. The proposed methodology for constructing a composite index is based on five dimensions’ aggregation—resource efficiency, environmental efficiency, environmental quality of life, natural assets, and institutional factors. The composite index helped generalize the complex processes of the region’s environmental–socio–economic development. Its main feature is the reflection of the environmental specificity of the territory. We built a mid-term forecast of the composite and sub-indices, determined their future trend, and assessed the opportunities and conditions for the fastest transition of the Republic of Buryatia to a green economy. The developed composite index is a key tool for regulating green economic development progress, determining prospects, and region management. This paper attempts to fill the gap in a comprehensive assessment of the Republic of Buryatia’s current situation using a composite index.

Stock Price Prediction Based on Data Mining Combination Model

Predicting stock indexes is a common concern in the financial world. This work uses neural network, support vector machine (SVM), mixed data sampling (MIDAS), and other methods in data mining technology to predict the daily closing price of the next 20 days and the monthly average closing price of the future expected daily closing price on the basis of the market performance of stock prices. Additionally, by the mutual ratio of weighted mean square error the study achieves the best prediction result. Combining value investment effectively with nonlinear models, a complete stock forecasting model is established, and empirical research is conducted on it. Results indicate that SVM and MIDAS have good results for stock price forecasting. Among them, MIDAS has a better mid-term forecast, which is approximately 10% higher than the forecast accuracy of the SVM model; Meanwhile, SVM is more accurate in the short-term forecast.

Characterizing end-of-life household vehicles’ generations in China: Spatial-temporal patterns and resource potentials

China is now boasting a huge number of end-of-life vehicles (ELVs) with a low recovery rate, resulting in a waste of resources and severe environmental pollution. Predicting the generation of ELVs in future helps to enhance recycling efficiency by providing a basis for the planning of recycling industry. However, previous research did not provide a detailed, mid-term forecast. Based on a stocks-driven model and the bottom-up extrapolation of in-use stocks, this study characterizes the generation of end-of-life household vehicles (HVs) in China by addressing the detailed spatial-temporal patterns and resource potentials during 2019–2050. The results show that the annual end-of-life HVs in China will continuously increase during 2019–2050, resulting an accumulated 1.48 billion units, among which urban areas will account for 86%, and internal combustion engine vehicles (ICEVs) will take up 80%. Regarding the spatial patterns, eastern region will possess the largest proportion, and wide variations are found among all provinces due to the difference in population size and economic development level, which have important implications for further planning of end-of-life HVs recycling industry. Before 2050, accumulated quantity of all types of metals in end-of-life HVs will approach domestic mine production (in 2019), or even approach the current global mine production, indicating they have a great potential as an indispensable source for domestic resource supply.

Global partnership for sustainable development: challenges and perspectives for Ukraine

Research background: The global concept of sustainable development was elaborated in detail by the UN and the Club of Rome. Development Agenda 2030 and 17 Sustainable Development Goals (SDG) were adopted in 2015 with the main priorities of social development of the world. The monitoring and analysis of the 17 SDG and 169 targets of the new agenda will be carried out through a set of global indicators. Purpose of the article is to develop a system of indicators for monitoring the achieving the SDG in Ukraine. This system will use for estimating the current results and developing mid-term forecast. Methods: The classical econometric methods and models, in particular, the exponential smoothing method was used for estimating and forecasting. Also, the multidimensional ranking and the method of expert assessments were used. The research is carried out based on statistical data (from 2008 to 2018 years) of the State Statistics Service of Ukraine, Ministry of Economic Development and Trade of Ukraine, UN reports. Findings & Value added: The article contains an overview of the essence, components, aims, methodology, results and forecast for achieving the 17th Sustainable Development Goal ”Global Partnership” in Ukraine. The system of individual & integrated indicators for the global partnership was built for evaluating the current results and using for forecast modelling. The model of global partnership and mid-term forecast for Ukraine was built for the paper’s aim achieving. The results showed the main challenges for Ukraine in achieving the 17th SDG soon.

Analytical Review and Mid-Term Forecast of the Development of the Russian Meat Market in the Context of a Pandemic of a New Coronavirus Infection

Currently, the Russian Federation has a difficult economic situation on the meat market. The problems affecting the effective development of this market were especially aggravated in the context of the new coronavirus infection pandemic, and directly affected the food security of the Russian Federation. The authors of the article reviewed the meat products market at the turn of 2019 - 2020, and also presented two options for its development for the medium term - basic and crisis ones. In addition, barriers to the export of meat products to the world market have been identified and directions for their early overcoming have been proposed. The results obtained in the course of this study not only provide an answer to the question about the role of external interventions in the economy of the state, but may become the basis for thinking about the importance of careful planning of long-term studies to assess difficult-to-predict risks, such as a new coronavirus infection, in terms of concerning the food security of the state.

Deep learning approaches to mid-term forecasting of social-economic and demographic effects of a pandemic

The COVID-19 outburst has brought serious demographical, economic, and social impacts. Moreover, in large countries, these consequences can vary from region to region. Therefore, authorities and experts lack the models to predict these various impacts at the regional level. This paper presents deep neural network models to do a mid-term forecast of the COVID-19 effect in the Russian regions. The models are based on the various recurrent and sliding-window architectures and utilize the attention mechanism to consider the indicators of the neighbor regions. These models are trained on various data, including daily cases and deaths, the diseased age structure, transport availability of the regions, and the unemployment rate. The experimental evaluation of the models shows that the demographic and healthcare indicators can significantly improve mid-term economic impact prediction accuracy. We also revealed that the neighboring regions’ data helps predict the pandemic’s healthcare and demographical impact. Namely, we have detected improvement for both the number of infected and the death rate.

Структура сопутствующих заболеваний в случаях летальных исходов ВИЧ-инфицированных пациентов, содержащихся в исправительных учреждениях ФСИН России

Aim. To study the structure of comorbidities in HIV-infected patients who died in institutions of the penal system. Materials and methods. The material of the study was the data of a retrospective analysis of 515 clinical cases with fatal outcomes from diseases in the institutions of the Federal Penitentiary Service of Russia in the Udmurt Republic from 2004 to October 2020. The study group (group I) consisted of 91 patients from among the persons infected with HIV, the control group (group II) – patients with HIV-negative status (424 patients). Statistical processing of the material was carried out using the methods of variation statistics. Significance was assessed using the Pearson Chi-square test (χ2), Student's t test. During the study, the structure of concomitant diseases in cases of lethal outcomes was studied in dynamics. For the statistical processing of indicators and the implementation of a medium-term forecast (for three periods (years) ahead) of the values of the studied epidemiological indicators, the determination coefficient R2 was used. Results. As part of the in-depth analysis of mortality, it was proved that HIV-infected patients, unlike those with HIV-negative status, significantly more often suffer from diseases of the circulatory system (I00–I99), which were diagnosed on average in 47.1% of cases (χ2=11.557, p<0.001), diseases of the digestive system (K00–K93) diagnosed on average in more than half (58.3%) of clinical cases (χ2=12.829, p<0.001), diseases of the nervous system (G00–G99) diagnosed on average in 73.4% (χ2=74.151, p<0.001), as well as diseases of the urinary system (N00–N99), diagnosed on average in 20.8% of patients (χ2=7.993, p=0.005). With the implementation of the mid-term forecast, an increase in the structure of concomitant pathologies of diseases of the circulatory system (R2=0.4023) and diseases of the digestive system (R2=0.4807) is expected. The share of diseases of the nervous system will remain stably high (R2=0.3242). Conclusion. HIV-infected patients in penitentiary institutions are characterized by polymorbid conditions, which require timely diagnosis and therapy in order to reduce the risk of poor outcomes and disability.

Meteorological and Climate Modelling Services Tailored to Viticulturists

Grape production is likewise inherently interconnected to climate and weather, and, although grapes may grow worldwide, premium wine-grape production occurs in Mediterranean-like climate ranges. Changes in climate and weather patterns are threatening premium wine-grapes, directly affecting the European wine industry. This is because grapevines are extremely sensitive to their surrounding environment, with seasonal variations in yield much higher than other common crops, such as cereals. With a view to making South European wine industry resilient to climate change, VISCA (Vineyards Integrated Smart Climate Application) project has deployed a Climate Service (CS) Decision Support System (DSS) tool that provides to wine producers with well-founded information to be able to apply correctly adaptation strategies on specific grape varieties and locations, and to achieve optimum production results (e.g., yield and quantity). In this paper we show the meteorological, seasonal and climatic models and data sets used to answer the viticulturist needs; from short-term and mid-term forecast to seasonal forecast and climate projections.

Assessment of the prospects for changing the demographic situation in the regions of Russia, taking into account the development of social infrastructure

The current situation in the field of demographic development in Russia is characterized by a combination of many heterogeneous factors, the overwhelming majority of which are negative. Thus, the risk is posed by the aging of the population, changes in reproductive behavior, an increase in the demographic burden, a decrease in real incomes of a significant share of the population, etc. Socio-economic factors and living conditions of the population have a significant impact on demographic processes. The article examines the issues of assessing the impact of the level of development of social infrastructure on demographic processes in the constituent entities of the Russian Federation. It is pointed out that the current situation is characterized by a high degree of differentiation in all key indicators of regional development, including the relationship between the parameters of demographic development and the development of the social infrastructure of the region. An approach to the grouping of regions, taking into account the interval estimation of each of the considered parameters, is proposed. This approach has been tested on three key parameters of population movement – fertility, mortality and migration. The obtained results of the grouping of regions were used in the formation of a set of regression equations describing the dependence of the values of the parameters of population movement on the level of development of social infrastructure and the group numbers assigned to the regions. In the course of the study, rating assessments of the constituent entities of the Russian Federation were obtained from the standpoint of the parameters under consideration and a mid-term forecast of changes in the population growth rate was formed. Thus, the authors have shown that social infrastructure, acting as a component of the general regional infrastructure, with which people directly interact and which, itself being a reflection of the development of the regional economy, directly affects the reproductive and migratory behavior of Russian citizens.

Midterm Load Forecasting: A Multistep Approach Based on Phase Space Reconstruction and Support Vector Machine

Electrical load forecasting is a vital process for balancing the electricity supply and demand sections. In order to make a precise prediction, many intelligent methods aiming at forecasting future load of different horizons have been proposed in recent years. Regardless of the great progress in this field, yet two major problems widely exist in the field of midterm load forecasting, which are pseudo midterm forecast and divergence of the forecasting error. This article proposes a multistep forecasting approach, mainly based on phase space reconstruction and support vector machine (SVM) methods, to solve these two problems. The relations between the forecasting step in the future and the historical data points that need to be fed into the SVM model are derived, which endue the model with the real ability to forecast adjustable steps of future load without the divergence of error and, therefore, have a notable engineering application significance. The proposed methodology is implemented on the European Network on Intelligent Technologies dataset. Compared with previous methods, the results show that the multistep implemented model is of more accurate prediction and stronger robustness. Moreover, the method is easy to implement and able to be combined with other intelligent methods to get better performance.

Neuro-fuzzy mid-term forecasting of electricity consumption using meteorological data

Forecasting energy consumption is highly essential for strategic and operational planning. This study uses the Adaptive-Neuro-Fuzzy Inference System (ANFIS) for a midterm forecast of electricity consumption. The model comprises of three meteorological variables as inputs and electricity consumption as output. Two ANFIS models with two clustering techniques (Fuzzy c-Means (FCM) and Grid Partitioning (GP) were developed (ANFIS-FCM and ANFIS-GP) to forecast monthly energy consumption based on meteorological variables. The performance of each model was determined using known statistical metrics. This compares the predicted electricity consumption with the observed and a statistical significance between the two reported. ANFIS-FCM model recorded a better mean absolute deviation (MAD), root mean square (RMSE), and mean absolute percentage error (MAPE) values of 0.396, 0.738, and 8.613 respectively compared to the ANFIS-GP model, which has MAD, RMSE, and MAPE values of 0.450, 0.762, and 9.430 values respectively. The study established that FCM is a good clustering technique in ANFIS compared to GP and recommended a comparison between the two techniques on hybrid ANFIS model.

The Mid-Term Forecast Method of F10.7Based on Extreme Ultraviolet Images

The solar radio flux at 10.7cm (F10.7) is a direct monitor and an important indicator of solar variability, and F10.7is commonly used in empirical atmospheric models, ionosphere models, etc. The source regions of F10.7are mainly in the corona above the active regions, and the extreme ultraviolet (EUV) images reflect the coronal thermal structure. In this paper, an index is defined asPSRbased on the intensity values of solar EUV images to represent the coronal contribution to F10.7. The Spearman correlation coefficient between the observed values of F10.7andPSRis 0.85 in 304 Å EUV images. Based on the high correlation, an empirical model is constructed. Combining the EUV data of SDO/AIA and the twin STEREO/EUVI, solar full-disk EUV images can be generated, and the future 27-day values ofPSRcan be calculated. Then, a realistic estimation of F10.7from 1 to 27 days in advance can be provided by the empirical model. Compared to the predictive values of F10.7by the 54th-order autoregressive models in 2012-2013, the error drop-rate of our model is 12.54%, and our method has significant advantages in the upcoming 3 to 27 days’ forecast.

Workplace relocation from suburb to city center: A case study of Rome, Italy

As metropolitan areas are generally characterized by decentralized jobs because of a car-based living, in this paper we focus on the feasibility of a “reverse” workplace relocation. The case study of a firm relocation from Rome’s suburbs to the historic city centre is presented. The new location, where the employees will have to move, is in a restricted traffic zone. A behavioural-based method was developed to forecast the future mode split of the employees of the firm being relocated. Next, the model’s estimates were compared with the results from a focus group interview. This approach is expected to provide reliable estimates of the mode choice, as it combines the standard discrete choice modelling for the mid-term forecast with the results from a focus group interview for the short-term forecast. The results from the discrete choice model overestimated the choice of public transport, whereas from the focus group interview it emerged that park & ride is expected to be the most chosen mode in the short-term. Finally, this case study suggests that a workplace relocation from the suburbs to the center could encourage the employees to lessen car-dependent habits.