Mathematical Model For Prediction Research Articles

A bilevel data-driven method for sewer deposit prediction under uncertainty

Deposit accumulation is one of the predominant causes of sewer blockage and overflow. Nevertheless, the traditional detection methods are costly and time-consuming, and the accuracy of the mathematical models for deposit prediction is usually affected by some uncertain factors (e.g., pipe properties and flow velocity of water). This paper proposes a framework of global sensitivity analysis (GSA) to identify the most sensitive indicators for sewer deposit prediction by (i) developing a data-driven bilevel (i.e., catchment level and segment level) model to map the relation between input and output indicators and (ii) employing three different GSA methods, namely, the Morris method, Sobol method, and Borgonovo index method to identify the indicators as important or unimportant (insensitive). The results show that the likelihood of combined sewer overflow occurrences (LCSOO), pipe age (PA), and pipe material (PM) are influential parameters for the thickness of deposits. Here, we pay close attention to the most influential parameters, which can help improve forecast prediction accuracy.

Admission neutrophil-to-lymphocyte ratio to predict 30-day mortality in severe spontaneous basal ganglia hemorrhage.

Spontaneous intracerebral hemorrhage (ICH) usually occurs in the basal ganglia and is highly lethal and disabling. The aim of this study was to evaluate the predictors of 30-day mortality in patients with severe spontaneous basal ganglia hemorrhage. This retrospective study included patients with severe basal ganglia intracerebral hemorrhage treated in the Third Affiliated Hospital of Soochow University from 2012 to 2018. Demographic, clinical, laboratory and neuroradiological data were collected. The short-term prognosis was evaluated and divided into death within 30-days and survival over 30-days. We studied the factors affecting the prognosis of patients with severe intracerebral hemorrhage, analyzed the parameters related to neutrophil-to-lymphocyte (NLR) at admission, and evaluated the predictive effect of NLR on 30-day mortality. A total of 105 patients was included in this retrospective study. The 30-day death group had a larger hematoma, a higher probability of ventricular hemorrhage, a higher ICH score and a lower Glasgow Coma Scale (GCS) score on admission. Meanwhile, the patients in the death group had higher White blood cells (WBC) counts, neutrophil counts, NLRs and C-reactive protein (CRP) levels. The risk factors for 30-day death were related to the ICH volume, GCS score, ICH score, WBC count, neutrophil count, NLR and CRP. The univariate receiver operating characteristic (ROC) curve of the risk factors showed that the NLR had the best prediction performance. Mathematical predictive models for ICH patients showed that the model with NLR had better prediction accuracy. The NLR is expected to be a potential biomarker for predicting the prognosis of patients with severe basal ganglia hemorrhage.

Introducing a design procedure for Archimedes Screw Turbine based on optimization algorithm

Archimedes screw turbines have gained popularity in generating power from flowing water. One aspect that hasn't been considered yet is optimizing those parameters that are considered assumptions in the prediction models. In this research, a simple procedure was introduced to design an appropriate Archimedes screw turbine for a given site using a prediction model and an optimization model. First, the mathematical efficiency prediction model of Archimedes screw turbines was developed and validated with experimental and real-world Archimedes screw turbine data. Then, the grey wolves optimization algorithm was developed to be used as an actual engineering application, sensitively analyzed, and coupled with the prediction model. The prediction models were evaluated. The model convergence was investigated, and then, the optimization parameters were found to get maximum efficiency. The results revealed that the optimum number of iterations and grey wolves are 200 and five, respectively. Also, the best values for the inner diameter to the outer diameter ratio, the pitch to the outer diameter ratio, tilt angle, and blade number were found in ranges: 0.43–0.56, 1–1.2, 20–22.5, and 2–4, respectively.

Mathematical model for prediction of cargo flow during the construction of the railway line Uzbekistan - Kyrgyzstan – China

The purpose of this work is to study a mathematical model for predicting freight traffic during the construction of a new railway line Uzbekistan - Kyrgyzstan - China, taking into account the considered economic factors. The proposed algorithm for constructing a predictive mathematical model of freight traffic during the construction of a new Uzbekistan-Kyrgyzstan-China railway line will make it possible to choose technical solutions that determine the operation of the Uzbekistan railway in the future, as well as the best options for strengthening its capacity in the future.

Optimization of process parameters for cutting force minimization in dry turning of aluminium 6063 using taguchi approach

This study investigated the turning of 6063 aluminium alloy for minimizing the cutting forces using taguchi method. The turning process is done at low feed rates and depth of cuts for finish turning condition in the present study. Under these cutting conditions, minimum cutting force is produced hence, the objective of the study is to optimize the turning parameters for cutting force minimization with good surface finish. All 27 experiments were performed in dry condition in line with sustainable machining aspects. All responses were optimized individually. Optimal parameter was (v3-f1-d1) for minimizing all the responses. Experiment number 19 produces the optimum value of cutting force (CF) 21 N, feed force (FF) 21.22 N and surface roughness (SR) 0.9546 µm found through taguchi method. The prediction mathematical model was established for all the responses for finding the optimal turning parameter. The R2 value for all the responses in the present study is almost equal to unity which shows the good prediction accuracy. The parameter depth of cut is greatly affecting the cutting force with 54.22 % and feed force with 90.97 % respectively. The variation in the surface roughness was mainly affected by feed rate with 73.04 % contribution. From the multi-response optimization also, the optimum parameter was found as the same as that of (v3-f1-d1). The experimental and predicted results show good agreement hence; there is an enhancement in the quality characteristics of the output values.

EVALUATION OF MODELS FOR THE PREDICTION OF HIGHER HEATING VALUE OF BIOMASS BASED ON PROXIMATE ANALYSIS

Biomass is a renewable and sustainable source of energy with little greenhouse gas emissions. The higher Heating value (HHV) of biomass is a significant parameter that is used in characterising fuel quality, class and type for energy application systems. Experimental determination of HHV is expensive, takes time and not always available. This brings the need for mathematical models for HHV prediction. In this research, proximate analysis and HHV of ten common biomass samples in Nigeria were determined. The biomass considered included rice husk, rice straw, corn cob, woodchips, groundnut shell, desert date, coconut shell, palm kernel, millet straw and sugarcane bagasse. Eight linear and five non-linear correlations with good performance from the literature were employed for predicting the biomass’ HHV from proximate analysis data. The performance of the models was tested using statistical indicators. Model M1 and M7 were the best among all the tested models with average absolute error (AAE), average bias error (ABE), and root mean square error (RSME) of 3.8389%, 2.5002% and 0.8780 MJ/kg; and 3.8918%, 2.2301% and 0.8701 MJ/kg respectively. Other models also correlated relatively well with the experimental HHV with low error, though, some are good for specific biomass only. This research identifies the best models that have high accuracy and can be used for the prediction of the higher heating value of biomass samples from proximate analysis.

Mathematical model of bone grafting risk prediction in the oral cavity and success factors

Mathematical model of bone grafting risk prediction in the oral cavity and success factors

Prediction of Intracranial Temperature Through Invasive and Noninvasive Measurements on Patients with Severe Traumatic Brain Injury.

The brain's temperature measurements (TB) in patients with severe brain damage are important, in order to offer the optimal treatment. The purpose of this research is the creation of mathematical models for the TB's prediction, based on the temperatures in the bladder (TBL), femoral artery (TFA), ear canal (TΕC), and axilla (TA), without the need for placement of intracranial catheter, contributing significantly to the research of the human thermoregulatory system.The research involved 18 patients (13 men and 5 women), who were hospitalized in the adult intensive care units (ICU) of Larissa's two hospitals, with severe brain injury. An intracranial catheter with a thermistor was used to continuously measure TB and other parameters. The TB's measurements, and simultaneously one or more of TBL, TFA, TEC, and TA, were recorded every 1h.To create TB predicting models, the data of each measurement was separated into (a) model sample (measurements' 80%) and (b) validation sample (measurements' 20%). Multivariate linear regression analysis demonstrated that it is possible to predict brain's temperature (PrTB), using independent variables (R2 was TBL=0.73, TFA=0.80, TEC=0.27, and TA=0.17, p<0.05). Significant linear associations were found, statistically, and no difference in means between TB and PrTB of each prediction model. Also, the 95% limits of agreement and the percent coefficient of variation showed sufficient agreement between the TB and PrTB in each prediction model.In conclusion, brain's temperature prediction models based on TBL, TFA, TEC, and TA were successful. Its determination contributes to the improvement of clinical decision-making.

Analysis on Prediction Methods for Formation Pore Pressure under Multiple Pressure Formation Mechanisms

There is abnormal fluid pressure widely distributed in Yinggehai-Qiongdongnan Basin, with abnormal zones extending from different depths, and the formation causes of abnormal pressure here are complicated. It is one of the three HTHP offshore regions in the world at present. Hence, drilling in this region is very difficult. It is discovered in drilling practices in recent years that, due to the multiple formation mechanisms of abnormal formation pressure in this region, the conventional pressure prediction model based on under-compaction theory is insufficient to explain the abnormal high pressure suddenly occurring in the middle shallow layer in diapiric structure of Yinggehai-Qiongdongnan Basin, which causes very large deviation of pore pressure prediction before drilling. Based on the basic principle of improved Fillippone method and with the three-dimensional high precision seismic velocity field of geologic modeling inversion, the authors build a three dimensional pore pressure model of target block, extract the pressure slice of horizon and across the target well, analyze the other source pressure transmission possibly caused by formation connectivity, and import them into the mathematical model of pore pressure prediction, so as to attain the purpose of improving the precision of pore pressure prediction. This method has been well applied in pre-drilling pressure prediction for the HTHP block in the South China Sea, reaching a prediction precisi on above 95%.

Analysis of COVID-19 mathematical model for predicting the impact of control measures in Rwanda

This paper shows the impact of control measures on the predictive COVID-19 mathematical model in Rwanda through sensitivity analysis of the basic reproduction number R0. We have introduced different levels of the control measures in the model, precisely, 90%, 80%, 60%, 40%, 20%, 0% and studied their effects on the variation of the model variables. The results from numerical simulations reveal that the more the adherence to the control measures at the percentage of 90%, 80%, 60%, 40%, 20%, 0%, the more the number of COVID-19 cases, hospitalized and deaths reduces which indicates the reduction of the spread of the pandemic in Rwanda. Moreover, It was shown that the transition rate from the infectious compartment is very sensitive to R0 as the increase/decrease in its value increases/decreases the value of R0 and this leads to the high spread or the containment of the pandemic respectively.

Mathematical model estimation and prediction application of Covid-19 infection in Indonesia using Levenberg-Marquardt Algorithm based on Python

COVID-19 was originally diagnosed in Wuhan, China, in late December 2019; it subsequently expanded internationally, affecting around 7 million people and caused 300,000 deaths by May 2020. An Application that could surveillance the Covid-19 in Indonesia is needed. Therefore, we proposed a prediction application for the COVID-19 pandemic situation in Indonesia by referring to public compliance with surveillance policies using the SPCIRD model. The Levenberg Marquardt curve fitting method was chosen because it is simple and produces a reasonably good model. According the result of questionnaire and black box testing our application performed really well. The result of our SPCIRD model with Levenberg-Marquardt optimization achieved R2 with the score -1.248 in active cases, -0.235 in recovered and -3.982 in death for 281 number of iterations. We are also achieved 93.3% that agree and very agree with the application usability to understand and to predict pattern from COVID-19. It was also had 83.3\\% respondent that satisfy with the prototype.

A Comprehensive Review of Atmospheric Air Pollutants Assessment Around Landfill Sites

Air pollutants generated as a result of operations of landfill sites pose a threat not only the environment but also to human life. This work focuses on comprehensive review of atmospheric air pollutants around landfill sites with a view of identifying areas where future studies can be conducted. Environmental and health effects of air pollutants within the vicinity of landfill sites and the ways of minimizing the level of the air pollutants were presented. Previous works carried out by scholars for the past two decades were critically examined. Mathematical models for prediction of gaseous pollutants for landfill sites and assessment of human health risk due of inhalation of poisonous gases from landfill sites were discussed. Amongst conclusions made were: (1) Further studies on health impacts of particulate matters (PMs) within the vicinity of landfill sites should focus on low-income countries (LIC) especially in Nigeria which has been perceived as the capital poverty of the world. (2) Developing countries have not been practicing Circular Municipal Solid Waste Management System (CMSW) due to some militating factors hence further works should look into how the militating factors can be surmounted and provide way forward for the implementation of CMSW in developing countries. (3) More works still need to be conducted especially in temperate region to mechanistically explain the positive correlation between PMs and Coronavirus disease. (4) Future works should dive into the cost and economic implications of assessing atmospheric air pollutants within the vicinity of landfill sites for policy making decisions.

Scheduling Dosage of Proton Pump Inhibitors Using Constrained Optimization With Gastric Acid Secretion Model

Dosage schedule of the Proton Pump Inhibitors (PPIs) is critical for gastric acid disorder treatment. In this paper, we develop a constrained optimization based approach for scheduling the PPIs dosage. In particular, we exploit a mathematical prediction model describing the gastric acid secretion, and use it within the optimization algorithm to predict the acid level. The dosage of the PPIs which is used to enforce acid level constraints is computed by solving a constrained optimization problem. Simulation results show that the proposed approach can successfully suppress the gastric acid level with less PPIs intake compared with the conventional fixed PPIs dosage regimen, which may reduce the long-term side effects of the PPIs.

Prediction of strain distribution during the plane strain tensile test based on artificial neural networks

In this study, finite element method was used to study the effects of various notch geometries on the strain field distributions during the plane strain tensile test for cold-rolled steel (DC01). The artificial neural network approach (ANN) and the response surface methodology (RSM) were adopted to develop the mathematical prediction models applied in the optimization procedure. The strain state was expressed by self-defined metrics, namely, the Plane Strain State Index (PSSI) and the Homogeneity Index (HI) were predicted by changing the notch angle (X degree), notch width (d mm), and notch length (c mm). The Quadratic mathematical models obtained by the RSM, and ANN presented the evolution of PSSI, and HI based on (X, d, and c). The results show that the ANN method provides more precise results compared to those of the RSM approach.

Centennial Assessment of Greenhouse Gases Emissions of Young and Old Hydroelectric Reservoir in Mediterranean Mainland

The quantities of emitted greenhouse gases (GHG) from hydroelectric reservoirs around the world, especially in warm latitudes, have tarnished the green credentials of hydroelectric energy. The accurate measurement and evaluation of the emitted GHG, which is a critical element in climate change policy, present a significant challenge. Many works have been focused on GHG emissions assessment from reservoirs around the world, but there are no available data concerning reservoirs in Mediterranean countries. In this study, emitted GHG from a young and an old in line water reservoirs, located in Mediterranean mainland, were collected using a “static floating chamber”. Methane and carbon dioxide concentrations were measured and their specific emission rates were calculated. The data were used for the development of a mathematical prediction model, proposed for GHG emission assessment. Statistical correlation of GHG emissions and water physicochemical characteristics led to the interpretation of the biochemical processes taking place into the reservoirs. Furthermore, the impact of reservoir’s morphology on GHG emissions was recorded, enhancing its importance on decision making for "green" energy investments. The results of this study indicate that artificial reservoirs in Mediterranean mainland generally have lower GHG emissions than those located in tropical and subtropical climatic zones and higher than those in continental subarctic zones and approach the highest values of CO2 emissions and the mean CH4 emissions recorded in continental climatic zones. The applied methodology provides a comprehensive tool for the long term (centennial) emissions assessment and the assessment of reservoirs impacts on the environment.

ПРОГНОЗУВАННЯ ЧАСОВИХ РЯДІВ СОЦІАЛЬНИХ МЕРЕЖ НА ОСНОВІ НЕЙРОМЕРЕЖЕВОГО ПІДХОДУ

The article presents a predictive model for analyzing the state and behavior of social network objects in dynamics and for building predictions of user behavior. This opens up opportunities for choosing the most promising promotion of objects in social networks, the best placement of advertising, the possibility of creating independent projects. The article proposes an approach to the formalization of the processes of growth/decrease in the popularity of content under the influence of external factors within the framework of a mathematical predictive model based on a recurrent neural network. The purpose of the model is mathematical processing and analysis of stochastic information flows, graphical presentation of forecasts of music trends, changes in the popularity of bloggers, video content, and more. The developed model can be used to predict the growth of reposts in social networks. The list of external and internal variables that affect the behavior of the output data at each moment of time is described. The behavior of time series is studied on the basis of input data obtained from social networks (nomenclature of indicators, number of reposts of researched objects, simulation period). A feature of the model is the ability to work with Big Data arrays, which in most cases have incomplete or "noisy" information. The dependence between input and output is proposed to be specified using a system of constraints and external coefficients that change over time. Control parameters are predictors of the model. Features of building a predictive model are shown, a list of the main limitations of the mathematical model is described. The result of the implementation of the tasks set in the research is a dynamic predictive model that provides an opportunity to create predictions based on such indicators as reposts on social networks, music trends, and the popularity of bloggers. It is proposed to use a recurrent artificial network with a dynamic neuron to predict reposts on selected research objects. The neuron processes input signals and calculates a predictive indicator of the number of reposts. The learning process of the neural network during the simulation period of one month is demonstrated. To reveal the prospects of further development of the model in order to improve the accuracy of forecasts of socio-economic processes.

INFORMATION ON PROGNOSTIC MARKERS OF SEVERE ACUTE BRONCHITIS IN PRESCHOOLERS: A STUDY IN SUMY REGION IN NORTH-EASTERN UKRAINE

Introduction. Respiratory infections, especially in children, are a significant global health concern. Understanding the implications of respiratory infections like acute bronchitis is crucial for devising effective management strategies. These infections, including bronchitis, pneumonia, and influenza, contribute substantially to pediatric hospitalizations. Predicting the severity of acute bronchitis in children is essential for personalized treatment and resource allocation. Developing reliable prognostic tools for acute bronchitis can improve outcomes and optimize healthcare resource utilization. Methods. The study spanned four years (2018–2021) at "Saint Zinaida's Children's Clinical Hospital" and "Primary Health Care Center No. 2" in Sumy City Council. It involved 135 preschool children with acute bronchitis (study group) and 28 healthy children (control). The control group matched the age and gender of the acute bronchitis group. Inclusion criteria comprised parental consent, ages 3–6, and a diagnosis of acute bronchitis; exclusions included parental refusal, ages below 3 or above 7, concurrent somatic or allergic diseases, non-compliance, and diagnoses other than acute bronchitis. Various methods were employed, including clinical, laboratory, instrumental, and statistical analyses. The severity of acute bronchitis was gauged using the BSS-ped clinical tool. Immunological status assessment involved determining cellular immunity indicators via enzyme-linked immunosorbent assay. Hormonal status analysis included thyroid and cortisol levels via enzyme-linked immunosorbent assay. Statistical analysis utilized SPSS 26 and probabilistic modeling based on Bayes' theorem for building prognostic models and assessing risk factors for acute bronchitis. Fisher's criterion determined reliability at a significance level of 0.05, categorizing risk degrees from low to critically high based on a posteriori chances. Results. The study successfully identified key clinical, anamnestic, hormonal, and immunological risk factors for severe acute bronchitis in preschoolers, constructing a predictive mathematical model. Breastfeeding and mixed feeding in infants were not associated with increased severity, contrasting with chronic upper respiratory tract disease and parental habits, notably smoking, linked to heightened severity. Cough severity and auscultatory wheezing, with a BSS-ped score of 4, moderately impacted severe acute bronchitis. An outlined prognostic model confirmed hormonal indicators' influence, particularly elevated reverse triiodothyronine levels, on increased risk. Immune cellular activity, specifically CD8+, CD4+, and CD22+, demonstrated pronounced impacts on severe acute bronchitis in preschoolers. A combined aberration of CD3+ and free triiodothyronine, CD3+ and total triiodothyronine, or CD4+ and free triiodothyronine indicated a critically high risk. The model's reliability was affirmed via ROC analysis, displaying a sensitivity of 91.7 %, specificity of 68.2 %, and an AUC of 0.869, indicating its high quality. Conclusions. In summary, chronic upper respiratory tract disease and parental smoking, particularly when both parents smoke, are significant clinical and anamnestic risk factors for severe acute bronchitis in preschoolers. Cough severity and wheezing on the BSS-ped scale contribute to its development. Hormonal indicators, especially reverse triiodothyronine, display notable impacts, with weaker associations observed for total triiodothyronine and cortisol. Immunological status indicators such as CD22+, CD4+, and CD8+ are also linked to severe acute bronchitis. Combinations of altered CD4+ and free triiodothyronine, CD3+ and free triiodothyronine, CD3+ and total triiodothyronine intensify the risk. When evaluating preschoolers with acute bronchitis, attention to clinical history (chronic upper respiratory disease, parental smoking, severe cough, and pulmonary rales) and specific laboratory parameters (concentration of triiodothyronine, cortisol, and serum levels of CD22+, CD4+, and CD8+) is advisable.

FRACTIONAL CALCULUS OPERATORS–BLOCH–TORREY PARTIAL DIFFERENTIAL EQUATION–ARTIFICIAL NEURAL NETWORKS–COMPUTATIONAL COMPLEXITY MODELING OF THE MICRO–MACROSTRUCTURAL BRAIN TISSUES WITH DIFFUSION MRI SIGNAL PROCESSING AND NEURONAL MULTI-COMPONENTS

Fractional calculus and fractional-order calculus are arranged in lineage as regards the mathematical models with complexity-theoretical tenets capable of capturing the subtle molecular dynamics by the integration of power-law convolution kernels into time- and space-related derivatives emerging in equations concerning the Magnetic Resonance Imaging (MRI) phenomena to which the fractional models of diffusion and relaxation are applied. Endowed with an intricate level of complexity and a unique physical and structural scaffolding at molecular and cellular levels with numerous synapses forming elaborate neural networks which entail in-depth probing and computing of patterns and signatures in individual cells and neurons, human brain as a heterogeneous medium is constituted of tissues with cells of different sizes and shapes, distributed across an extra-cellular space. Characterization of the unique brain cells is sought after to unravel the connections between different cells and tissues for accurate, reliable, robust and optimal models and computing. Accordingly, Diffusion Magnetic Resonance Imaging (DMRI), as a noninvasive and experimental imaging technique with clinical and research applications, provides a measure related to the diffusion characteristics of water in biological tissues, particularly in the brain tissues. Compatible with these aspects and beyond the diffusion coefficients’ measurement, DMRI technique aims to exceed the spatial resolution of the MRI images and draw inferences from the microstructural properties of the related medium. Thus, novel tools become essential for the description of the biological (organelles, membranes, macromolecules and so on) and neurological (axons, dendrites, neurons and so forth) tissues’ complexity. Mathematical model-based computational analyses with multifaceted methods to extract information from the DMRI with SpinDoctor into neuronal dynamics can provide quantitative parametric instruments in order to reflect the tissue properties focusing on the precise link between the tissue microstructure and signals acquired by employing advanced medical imaging technologies. Coalesced with accurate neuron geometry models as well as numerical DMRI simulations, a novel extended and multifaceted predictive mathematical model based on SpinDoctor and Bloch–Torrey partial differential equation (BTPDE) with the Caputo fractional-order derivative (FOD) with three-parameter [Formula: see text] Mittag-Leffler function (MLF) has been proposed and developed in our study by extending for the application on Brain Neuron Spin Unit dataset with the relevant multi-stage application-related steps. The feedforward neural networks (FFNNs) with BFGS Quasi-Newton equation, as one of the artificial neural network (ANN) algorithms, are applied on BTPDE with Caputo fractional-order derivative for the neurons and their algorithmic complexity is computed by building a BTPDE with Caputo FOD Neuron model based on different fractional orders. The fractional-order degree of the proposed and developed model is applied in relation to their corresponding complexity degrees. Consequently, experimentation and observations from the simulation-driven FFNN (with BFGS Quasi-Newton equation) learning scheme applied to the Bloch–Torrey PDE–Caputo FOD with MLF Neuron model (named as FFNN–BTPDE–CFODMLF Neuron model) proposed in this study, are made. Thus, by investigating whether the mathematical models based on the accurate neuron geometry models obtained can be optimized by comparing the errors in order to define the order parameter and identify to what optimal extent the errors are in relation to the prediction results with a particular focus on the neuron model, we have been able to estimate and predict brain microstructure through DMRI, accentuating mathematical and medical contributions based on the exploitation and corroboration of powerful modeling as well as computational capabilities.

Economic features of services and energy cost for engineering systems

There are the results of some municipal services actual specific cost for residential apartment building. These services consist of cold water supply system; drainage and sewage systems; electricity; collection and disposal of municipal solid waste system. Authors did not indicate the specific operating cost for hot water supply and heating systems because their cost was constant. Research period: from 1st quarter of 2018 year to the 2nd quarter of 2022 year. This period was acute phase of Covid-19 pandemic period. Authors have written the dynamics changes in the cost of considered services. Researchers have presented additional factors which affect to the unit cost of municipal services in this paper. There is a brief overview of management companies types which could be in municipal service system. Also it has been considered their capabilities and limitations in creating tariffs process. Authors have identified different trends in the engineering service unit cost changing process. This article could be interested for management companies and tariffs planning departments of public municipal service system in different parts of Russian Federation. Results which were presented in this paper could be used in predictive mathematical models of municipal services economical parts in critical as pandemic or other emergency situations.

Predicting the permeability of the near-bottomhole zone during wave impact

The research reveals that during selection of a method to increase oil recovery it is necessary to take into account rheological features of fluid movement through the formation, effect of capillary forces and heterogeneity of reservoir properties of the productive formation in thickness and along the bedding. Low-frequency wave impact, which is used to increase production in oil fields, is considered. At low-frequency impact new fractures appear and existing fractures in rocks increase in size. The greatest increase in porosity and permeability of rocks occurs at an impact frequency up to 10 Hz. Dynamics of oscillation amplitude during wave's movement in saturated porous medium is studied in the paper: essential attenuation of amplitude occurs at distance up to 1 m from borehole axis. With increase of frequency from 1 to 10 Hz the intensity of amplitude's attenuation decreases. The technology was tested on a well in Perm region (Russia). The actual permeability value was 50 % higher than the predicted value. According to the results of hydrodynamic investigations processing, it was noted that the greatest increase of permeability took place near the wellbore, while away from the wellbore axis permeability remained almost unchanged. In order to refine the mathematical model for prediction of wave impact on rock permeability it is necessary to take into account interconnection of pore space structure, change of adhesion layer, as well as to study transfer of particles during vibration.