Subsequent Forecasting Research Articles

Features of the formation of natural-technical systems within the injection areas of the Daldyn kimberlite field (Western Yakutia)

Relevance. Primary diamond deposits mined by PJSC ALROSA in Western Yakutia have widespread watering of varying intensity with sub-permafrost and inter-permafrost strong and very strong brines. Currently, the resulting drainage waters are pumped back into the depths of the permafrost, which leads to the formation of natural-technogenic aquifers that require constant control, monitoring and study. Aim. Identification of the features of the formation of natural-technogenic systems within the injection areas of the Daldyn kimberlite field, determination of the volume of permafrost waters involved in this process, as well as their characteristics to confirm the environmental friendliness of the method of handling drainage brines. Methods. Field work consisted of quarterly routine testing of observation and injection wells. Assessment of technogenic influence, as well as subsequent forecasting of the dynamics of changes in cryohydrogeological conditions and hydrodynamic regime, was carried out using modeling methods in Feflow software. An additional method was analytical balance calculation based on the parameter of changing mineralization. Results and conclusions. The interaction of drainage waters with permafrost waters leads to areal changes in mineralization without significant changes in the dominant anions and cations within the studied objects, forming an equilibrium natural-technogenic system in the peripheral parts relative to background temperature conditions. The volume of formed man-made aquifers will exceed 300 million m3. The process of involving cryogenic waters, despite the significant volumes, has a positive impact on the ecological state of the territory, because in the peripheral parts of the resulting aquifers, equilibrium cryohydrogeological conditions are formed, characteristic of natural cryopegs in the region of study.

Data-Monitoring Solution for Desalination Processes: Cooling Tower and Mechanical Vapor Compression Hybrid System.

The advancement of novel water treatment technologies requires the implementation of both accurate data measurement and recording processes. These procedures are essential for acquiring results and conducting thorough analyses to enhance operational efficiency. In addition, accurate sensor data facilitate precise control over chemical treatment dosages, ensuring optimal water quality and corrosion inhibition while minimizing chemical usage and associated costs. Under this framework, this paper describes the sensoring and monitoring solution for a hybrid system based on a cooling tower (CT) connected to mechanical vapor compression (MVC) equipment for desalination and brine concentration purposes. Sensors connected to the data commercial logger solution, Almemo 2890-9, are also discussed in detail such as temperature, relative humidity, pressure, flow rate, etc. The monitoring system allows remote control of the MVC based on a server, GateManager, and TightVNC. In this way, the proposed solution provides remote access to the hybrid system, being able to visualize gathered data in real time. A case study located in Cartagena (Spain) is used to assess the proposed solution. Collected data from temperature transmitters, pneumatic valves, level sensors, and power demand are included and discussed in the paper. These variables allow a subsequent forecasting process to estimate brine concentration values. Different sample times are included in this paper to minimize the collected data from the hybrid system within suitable operation conditions. This solution is suitable to be applied to other desalination processes and locations.

Forecasting carbon price with attention mechanism and bidirectional long short-term memory network

To improve the precision of carbon price forecasting, our study aims to propose a novel hybrid forecasting model which integrates recurrent neural networks and attention mechanisms. First, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) algorithm is employed to decompose carbon prices into several regular intrinsic mode functions (IMFs) and a residual. Second, multiscale entropy is utilized to differentiate and reconstruct these components to reduce cumulative errors in subsequent forecasting. Subsequently, a bidirectional long short-term memory network (Bi-LSTM) equipped with attention mechanisms is used to forecast each reconstructed component. Attention mechanisms identifies crucial sequence elements, assigns different weights to hidden information, and extracts richer information from the series. Finally, the results of all components are integrated to obtain the final forecasting result. Empirical analysis conducted on real datasets from the Guangdong and Hubei carbon markets demonstrates that the proposed hybrid model outperform prevailing mainstream forecasting models in terms of both horizontal and directional forecasting metrics.

Forecasting carbon price using signal processing technology and extreme gradient boosting optimized by the whale optimization algorithm

AbstractPredicting carbon prices is crucial for the growth of China's carbon trading industry. This paper proposes a residual correction model that considers multiple influencing factors. First, the best historical data and main external factors input by the model are determined by using the partial autocorrelation function and Spearman correlation analysis, and the carbon price forecasting index system is constructed. Second, the whale optimization algorithm (WOA) is utilized to determine the optimal parameters of the extreme gradient boosting (XGBoost), and the WOA‐XGBoost model is built to perform preliminary carbon price forecasts and obtain the residual series. Finally, the carbon price residual series undergoes decomposition into multiple components utilizing the complete ensemble empirical mode decomposition for subsequent forecasting and the aggregation of outcomes. Experiments are conducted to predict two carbon trading markets in Hubei and Guangzhou, and a feature importance analysis is performed. The results indicate that the proposed hybrid model consistently outperforms the comparative models in terms of prediction accuracy. Furthermore, it is revealed that historical carbon prices and European Union carbon prices are the key factors influencing the prediction of carbon market prices.

Методика полевых исследований оползней в Чеченской Республике

Introduction. Landslide engineering and geological surveys usually consist of reconnaissance observations and field work to collect the necessary material and information, and their purpose is to document surface conditions in such a way as to provide the necessary basis for subsequent forecasting of conditions and parameters of landslide geomass movement. At the same time, the main task of identifying a landslide is to determine its boundaries on the earth’s surface and other parameters, including the area of the hearth and the displaced geomass. Materials and мethods. Identification of landslides in the field is an integral part of standard geomorphological cartography. At the same time, an important approach to the study of landslides is the methods of geomorphological inventory of landslides, which are direct methods during which, as a rule, the main characteristics of the morphology of landslides, as well as provoking and predisposing factors, are recorded. Results. It has been established that shallow landslides caused by rains, earthquakes and man-made human activity have become quite frequent within the North Caucasus, serving to summarize the data of previous studies. Discussion. There are more than 120 modern active landslides within the Chechen Republic. Mapping of all landslide displacements on the territory of the Chechen Republic was not carried out, only geological surveys were carried out in certain areas and near settlements in order to prevent emergency situations for the infrastructure of settlements and mining facilities. Due to the inaccessibility of the mountainous terrain and the limited possibilities of using satellite images of high destructibility, there is currently no complete information about the spread of ancient and modern landslides in hard-to-reach areas. Conclusion. However, despite the fact that the special literature on landslide hazards is constantly updated with new data, and with increasing complexity, some methodological issues of landslide research remain unresolved. First, despite the frequent use of statistical models of landslide susceptibility, there remains uncertainty about the sampling system of landslide geomass, the resolution applied and the uncertainty of the model used. Secondly, due to the increase in computing power and the availability of specialized software tools, landslide susceptibility models are sometimes used without proper interpretation of the geomorphological reliability of the results. In addition, there are no reliable estimates of landslide susceptibility for many remote regions (especially mountainous ones). Resume: 1. The article presents the results of research on landslides in the Czech Republic. 2. It has been established that shallow landslides caused by rains, earthquakes and man-made human activity have become quite frequent within the North Caucasus, serving to summarize the data of previous studies. It is shown what ground engineering and geological studies of landslide territories consist of. 3. Future research is needed to prevent landslides in the Shatoi district in the village of Gush-Kert (Chechen Republic).

An adaptive embedding procedure for time series forecasting with deep neural networks

Nowadays, solving time series prediction problems is an open and challenging task. Many solutions are based on the implementation of deep neural architectures, which are able to analyze the structure of the time series and to carry out the prediction. In this work, we present a novel deep learning scheme based on an adaptive embedding mechanism. The latter is exploited to extract a compressed representation of the input time series that is used for the subsequent forecasting. The proposed model is based on a two-layer bidirectional Long Short-Term Memory network, where the first layer performs the adaptive embedding and the second layer acts as a predictor. The performances of the proposed forecasting scheme are compared with several models in two different scenarios, considering both well-known time series and real-life application cases. The experimental results show the accuracy and the flexibility of the proposed approach, which can be used as a prediction tool for any actual application.

Modeling hydrodynamic regime of the technogenic aquifer within the Noyabrsky site of the Aikhal mine

The Noyabrsky site is used for the injection of highly mineralized drainage waters formed during the development of the Aikhal kimberlite pipe (Republic of Sakha (Yakutia), Russia). The water injected into the wells of the site since 2013 has formed a localized intrapermafrost technogenic aquifer. Taking into account the porosity parameters of the formed permafrost reservoirs, as well as the confirmed useful capacity of the site, its operation is planned to continue until 2044 at the very least. Despite relatively small injection volumes (up to 430 m3/day), forecasting dynamical changes in the hydrodynamic regime of the technogenic water horizon is a challenging task, which is of significant practical importance for the provision of environmental and industrial safety. The assessment of the anthropogenic impact, as well as the subsequent forecasting of the hydrodynamic regime dynamics within the Noyabrsky site, were carried out by modeling methods in the Modflow software. The brine lens formed today is characterized by uneven porosity parameters, which is due to the peculiarities of heat and mass transfer when using permafrost for drainage water injection. Based on the results of predictive modeling a conclusion can be derived that the use of the method of Aikhal mine drainage water injection into the permafrost strata at the Noyabrsky site allows to reduce the influence degree of mining and production operations on the geological environment of the studied area through its localization on an intentionally limited, relatively small area, where injection is carried out, as well as on the environment, by preventing drainage brines from entering surface waters until 2044.

МЕТОДОЛОГІЯ ДОСЛІДЖЕННЯ ОСОБЛИВОСТЕЙ СТАНОВЛЕННЯ ТА РОЗВИТКУ ІНСТИТУТУ ЮВЕНАЛЬНОЇ ПРОБАЦІЇ В УКРАЇНІ

In the article, the author considers the methodology of researching the peculiarities of the establishment and development of the juvenile probation institute in Ukraine. It is indicated that the methodology of juvenile probation research should be based on a dialectical approach, which should be represented by the researcher’s rationalism, his ability to draw parallels between different points of view on the same phenomenon and single out the “core”, that can provide an opportunity to establish the universality of the studied phenomenon and its authenticity. From the point of view of dialectical materialism, juvenile probation should be investigated in the context of its effectiveness and timeliness within the existing development and mentality of Ukrainian society, with subsequent forecasting of its transformation over time. It is noted that the legislator’s perception of the forms and limits of human existence, the legal and social needs of the country’s citizens, gives him the opportunity not only to find ways to respond to any illegal actions of individuals, but also to warn them, correcting the deviant tendencies of the offender. That is why, considering juvenile probation through the prism of epistemology, it is possible to reproduce the symbiosis of sensual and rational, preserving the principle of humanism of criminal legislation and rationalism when working with a criminal offender. It is concluded that the methodology of the study of the peculiarities of the formation and development of the juvenile probation institute is a complex system of values, philosophical principles and guidelines on which the legislative representation of the phenomenon of juvenile delinquency and the single unified structure of humane interaction with deviant adolescents are based. To date, this methodology is almost no different from similar ones in the science of criminal law and has the main task of simplifying the process of researching individual phenomena with the subsequent integration of this kind of understanding into legislative initiatives and practices. Key words: methodology, juvenile, juvenile justice, juvenile probation, criminal offense, criminal offender, criminal legislation, repression.

Assessment of the Technical Condition of High-Voltage Insulators during Operation

During the operation process of high-voltage insulators, the characteristics of assessing their technical state are evaluated by using remote contactless monitoring and by subsequent forecasting of their residual life based on the developed set of diagnostic parameters of critical defects. Special attention is paid to the challenges of the practical application for remote contactless monitoring of high-voltage insulators’ current operating state. Measurements of characteristics for partial discharges on high-voltage insulators with various types of critical defects taken by electromagnetic and acoustic sensors are described. Based on the measurements, it was found that the unusual properties of the PD begin to manifest themselves starting from the intensities q ≥ 1.5–2 nC, and their maximum intensity can reach 5–7 nC. Up to PD intensities q ≤ 3 nC, most parameters of PD characteristics measured by electromagnetic and acoustic sensors correspond with an accuracy of 70–90%. It was found that for small defects with sizes d ≤ 300 μm in HVI, the PD intensity does not exceed 100 pC and depends little on the size of the defect. However, with an increase in the size of defects above 0.4–0.6 mm, a sharp rise in the intensity of the emerging VLPD begins.

Determination of parameters for the formation process of rare events in the economy for their subsequent forecasting

The article presents a method for determining unknown parameters of the process that forms rare events in the economy.The idea behind the study of rare events in economy is to consider these events not just from a statistical point of view, but from the point of view of the processes that form these events, moreover, as well as a process can be an arbitrary algorithm. Such an event generation process will use parameters, which can be static or dynamic. For example, if we consider the consumption process, which forms discrete purchases of an uncontrolled customer, then such parameters can be the maximum stock and a dynamically changing consumption rate. In general, the process can be arbitrary and possess various parameters. The task is to determine these parameters of an unknown process obtaining only a sample of rare events. The idea of the method is to minimize the loss function, which is determined based on the differences between the events generated during the operation of the process and events from the initial sample of observations. Each event, in addition to the time of occurrence, is also characterized by additional information, for example, the purchase volume. We are trying to find out such parameters of such a process that would allow us to get a very similar sample of events. The dynamic parameters of the process are set in the form of cubic splines of a special structure. For an unambiguous determination of each dynamic parameter, a roughness penalty of the corresponding splines is introduced into the objective function. An example of a process and its structure of parameters to be determined is shown. Optimization is performed numerically, based on the Nelder–Mead algorithm, which runs on a grid to determine the global optimum. The process parameters are determined in steps, at the beginning just to get a few events, then the next events. That allows one large optimization task to be divided into a sequence of simple tasks, this significantly reduces the overall complexity. An assumption is described that must be fulfilled for such a technique to be valid. An example of determining unknown parameters is considered on the example of the consumption process. After determining the process parameters, one can proceed to extrapolation of parameters and forecast future events.

Inverse moment methods for sufficient forecasting using high-dimensional predictors

Summary We consider forecasting a single time series using a large number of predictors in the presence of a possible nonlinear forecast function. Assuming that the predictors affect the response through the latent factors, we propose to first conduct factor analysis and then apply sufficient dimension reduction on the estimated factors to derive the reduced data for subsequent forecasting. Using directional regression and the inverse third-moment method in the stage of sufficient dimension reduction, the proposed methods can capture the nonmonotone effect of factors on the response. We also allow a diverging number of factors and only impose general regularity conditions on the distribution of factors, avoiding the undesired time reversibility of the factors by the latter. These make the proposed methods fundamentally more applicable than the sufficient forecasting method of Fan et al. (2017). The proposed methods are demonstrated both in simulation studies and an empirical study of forecasting monthly macroeconomic data from 1959 to 2016. Also, our theory contributes to the literature of sufficient dimension reduction, as it includes an invariance result, a path to perform sufficient dimension reduction under the high-dimensional setting without assuming sparsity, and the corresponding order-determination procedure.

Вопросы преподавания математического анализа в медицинских вузах

The contradiction between the rapid mathematization of health care through the active introduction of modern technologies and methods based on mathematical achievements in the field of medicine and the lack of a system of training medical students corresponding to these scientific successes, which allows them to carry out mathematical modeling of complex physical, chemical and biological processes at the molecular level for the purpose of their analysis and subsequent forecasting, , problems that arise when teaching mathematical analysis in medical Schools, and possible ways to solve them. The solution identified during the research of problematic issues of teaching of mathematical analysis in medical schools is offered by enhancing the quality of learning through the re-allocated for the development of the discipline course hours stimulate the motivation of students, adjusting content and methodical component of teaching and active introduction in educational process of modern information technologies, elimination of excessive mathematical formalism.

EVALUATION OF FUNCTIONING COSTS OF AN ENTERPRISE

Introduction. Costs are a necessary attribute and an unconditional driver of any business entity. Not having made an expense it is impossible for a company to obtain and dispose its revenues. Costs accompany operations of an enterprise throughout its existence, so their proper identification, accounting, grouping, distribution, analysis and subsequent forecasting and planning are necessary processes for effective business activity. Purpose. The goal of the article is a thorough study of available methodological approaches to comprehensive analysis of company’s functioning costs, and forthcoming elaboration of proposals for solving the identified problems. Method (methodology). The author uses a number of general scientific research methods, including abstract-theoretical, dialectical methods, induction and deduction, analysis and synthesis, that were exploited to identify and generalize the theoretical and methodological bases for cost analysis and developing a set of key indicators. Results. The article discloses the tasks, features and aspects of business functioning cost diagnostics of industrial and trading enterprises. The existing methodological approaches to the analysis of cost management efficiency in domestic and foreign sources were analyzed. The author proposes to decompose such a research into five aspects: volume and structure of expenses, unit costs, critical output, resource consumption and cost of capital. A comprehensive set of performance indicators was formed and recommendations for their calculation were suggested. These proposals can be used for a profound cost analysis of industrial, commercial, agricultural business entities, enterprises representing such sectors as «Information and Telecommunications», «Professional, scientific and technical activities» and others.

Geostatistical Methods in Water Distribution Network Design - A Case Study

Abstract Modeling of the loads of water supply networks and their subsequent forecasting is an element necessary for making optimum decisions in the process of planning the development and operation of the water supply networks. The results of this modeling are decisive for the selection of the diameters of the pipelines and their arrangement on the water demand area. This study presents the results of estimation of average values of loads for the selected investment variants. The aim of the article is to present the possibility of simulations and analyses of the geostatistical interpolation methods. Data input in the model regarded the fragment of the real water supply network administered by the Municipal Water and Sewerage Company in Warszawa. Results of the computer analyses for the presented investment variants were related to the operating data of the water supply network and the data on water demand for the years 2014-2017 and 2018-2025. The aim of this paper is to present the advantages of GIS for the water supply systems and to prove that using the appropriate IT system, with provision of proper data processing, may lead to decisions which are optimum in view of the established, often very complex criteria.

Reference couple gold deposit — placer as a basis for forecasting of the new gold deposits in Amur river basin

About 1400 placers and several dozens of ore gold deposits are known in the Amur gold-bearing province. Placers are to a large degree worked out, so the future of the province is seen to be in the discovery of the new gold deposits. The paper shows the dependence of the productivity and composition of the native gold placers on the size and formational affiliation of the placer-forming gold deposit. The reference couples have been identified: the gold deposit and the placer formed due to the erosion of its' upper part. For example, the Tokur gold-quartz deposit is a channel-fill placer in Tokur stream, gold-sulphide-quartz deposit Pioneer—the placer in Ulungi river, gold-sulphide-quartz deposit Bamskoe—the placer of Chulbangro river, Berezitovoye gold-polymetallic deposit—placer of the Konstantinovsky stream and gold-silver field Pokrovskoye—placer of Sergeevsky stream. A forecast of new gold deposits of a certain formation has been made according to the parameters of the placer and the composition of the native gold. A similar selection of reference couples of a gold deposit and a placer with the subsequent forecasting of new deposits can be one of the methods for predicting gold deposits in the other gold-bearing provinces.

НАЦІОНАЛЬНА БАЗА ДАНИХ ЗСУВНИХ ПРОЦЕСІВ: ПРИНЦИПИ РОЗРОБКИ, УПРОВАДЖЕННЯ ТА ЗАСТОСУВАННЯ ДЛЯ ОЦІНКИ ЗСУВНОЇ НЕБЕЗПЕКИ РЕГІОНАЛЬНОГО І ЛОКАЛЬНОГО РІВНЯ

The main approaches and principles of the development and formation of the database of lanslide processes within the territory of Ukraine are described. Among natural risks one of the most common are landslide phenomena, which lead to significant social and economic losses. Ukraine is characterized by active landslide processes within different structural-tectonic and landscape-climatic zones, which requires a unified approach to the development of a system of their assessment, inventory and monitoring with the subsequent forecasting, and assessment of the impact on manmade objects. The national landslide database is the most comprehensive source of information on the distribution of landslides in different regions of the country, and the main task is the input, processing, analysis and updating of information on landslides. The national landslide database consists of a series of tables stored and maintained in the relational database Oracle or PostgreSQL (open-source database). Use of ArcGIS10 provides the ability to display spatial information, store positional and non-positional attributive landslide data, and perform spatial analysis and modelling operations with the purpose of further prediction of landslide hazard at the regional and local levels.

Hardware-software complex for acoustic monitoring of meteorological fields in the atmospheric boundary layer

In the present paper, a hardware-software complex (HSC) intended for monitoring of lower layers of the atmosphere and subsequent forecasting of meteorological situations in a limited territory is described. The measuring network comprises remote and local meteorological measuring instruments distributed over the limited territory. The remote means of sounding are acoustic radars (sodars) and temperature profilometers providing measurements of atmospheric parameters in the atmospheric boundary layer. The local instruments for measuring meteorological parameters in the near-ground layer of the atmosphere are ultrasonic meteorological stations. The key feature of the hardware-software complex is the application of multirotor aeromobile platforms (MAP) to the verification of data of remote meteorological measurements (RMP) in the atmospheric boundary layer to improve data quality.

Detection of the interaction between the quality of public administration and the level of socioeconomic development in the region

The article proposes methodological support for detection of the interaction between the quality of public administration and the level of socio-economic development in the region. In order to determine the integral indicator of the level of socio-economic development and creating a regional ranking, a complex of initial indicators is formed in the context of such important components as: economic efficiency; financial self-sufficiency; foreign economic cooperation and investment development; development of small and medium enterprises, infrastructure; labor market efficiency; openness of the authorities and accessibility to administrative services; social protection and security; availability and quality of services in the areas of education, health care, rational nature management; renewable energy and energy efficiency; quality of the environment. The term-sets of linguistic variables of the quality of public administration for the correct use of the fuzzy set method are substantiated. The importance of construction of existing trend models (linear, power, exponential, logarithmic) for each region is proved in order to determine the connection between the indicators and the subsequent forecasting. The feature of the proposed methodological approach is the use of diverse analytical tools, namely: taxonomic analysis, the theory of fuzzy sets, the trend projection method. The use of this approach will help not only to determine the level of influence of the quality of the application of the relevant public administration tools in order to facilitate the adoption of future management decisions in the context of ensuring the effective socio-economic development of the region, but also to assess unambiguously the current situation through reducing the series of indicators of socio-economic development of region to the single one indicator

MATHEMATICAL MODELLING IN FORECASTING THE EFFICIENCY OF TREATMENT OF CHRONIC HEPATITIS C IN CHILDREN

The study developed a model to predict the response to therapy of children with chronic hepatitis C (CHC) based on a system of multivariate analysis. The mathematical model allows timely correction of treatment, significantly increasing the number of early virologic responses and reducing the probability of recurrence of the disease. Aim -optimization of treatment prior to the onset of therapy with subsequent forecasting of the effectiveness of therapy based on multivariate regression analysis. Materials and methods. The study included 116 patients at the age of 3-18 years with CHC in the replicative phase. Children with CHC were divided into 2 groups of 58 patients. Observations in children with CHC were made using clinical, biochemical, immunological and instrumental methods of research, which helped to obtain the most complete information about children with chronic viral hepatitis C. In the course of the conducted multiple regression analysis, we selected the most significant indicators for the establishment of the resulting mathematical model. Results. The method of forecasting response to therapy in chronic hepatitis C in children, developed on the basis of multivariate regression analysis and mathematical modelling of the most important immunological and biochemical parameters, is clinically efficient and justified.

Inverse Moment Methods for Sufficient Forecasting Using High-Dimensional Predictors

We consider forecasting a single time series using a large number of predictors in the presence of a possible nonlinear forecast function. Assuming that the predictors affect the response through the latent factors, we propose to first conduct factor analysis and then apply sufficient dimension reduction on the estimated factors, to derive the reduced data for subsequent forecasting. Using directional regression and the inverse third-moment method in the stage of sufficient dimension reduction, the proposed methods can capture the non-monotone effect of factors on the response. We also allow a diverging number of factors and only impose general regularity conditions on the distribution of factors, avoiding the undesired time reversibility of the factors by the latter. These make the proposed methods fundamentally more applicable than the sufficient forecasting method in Fan et al. (2017). The proposed methods are demonstrated in both simulation studies and an empirical study of forecasting monthly macroeconomic data from 1959 to 2016. Also, our theory contributes to the literature of sufficient dimension reduction, as it includes an invariance result, a path to perform sufficient dimension reduction under the high-dimensional setting without assuming sparsity, and the corresponding order-determination procedure.