Statistical Parameters Research Articles

Role of Substrate on the Fractal Characteristics of Nanostructure Surfaces of Electrodeposited Nickel Thin Films

This study investigates the structure, morphological, and roughness properties of electrodeposited nickel thin films grown on different substrates, that is, silicon, copper, and gold. X‐ray diffraction analysis reveals that the Ni coatings exhibit a face‐centered‐cubic phase, regardless of the substrate used, although the texture is mainly influenced by the substrate. Atomic force microscopy images show that larger grains are obtained with gold substrates, while smaller ones are observed with copper and silicon substrates, in agreement with scanning electron microscopy. The results demonstrate that both topological and fractal characteristics of the Ni thin films are significantly influenced by the type of substrate. Statistical parameters are quantified to compare the surface morphology of the different samples. Fractal analysis reveals that the fractal dimensions of all surfaces range between 2 and 3, indicating self‐affinity. Fractal succolarity and lacunarity are measured to assess the penetration of a liquid into the surface and the distribution of gaps in the Ni film surfaces, respectively. Minkowski functionals are utilized for topological analysis to characterize the internal structure of the Ni thin films. The observed differences in roughness characteristics provide evidence that the type of substrate affects the nucleation and growth of surface features during electrodeposition.

Sarcopenia and Dinapenia in Patients with Ulcerative Colitis (Cross-Sectional Observational Study)

Background. Sarcopenia is a generalised, progressive decline in skeletal muscle mass, strength and performance leading to reduced quality of life, increased risk of falls, hospitalisation and mortality. Primary sarcopenia results from age-related changes in muscle tissue. In certain cases, sarcopenia develops secondary as a consequence of diseases including ulcerative colitis (UC), which is associated with a systemic inflammatory process, malabsorption syndrome, restriction of patients’ physical activity and nutrient deficiencies. The term “dynapenia” or “probable sarcopenia” has been proposed to describe the decrease in muscle strength. Studies have demonstrated that muscle strength is a more relevant marker for predicting adverse outcomes because its measurement is more practical, whereas muscle mass is technically difficult to measure. Aims — to increase the effectiveness of the diagnosis of dinapenia, to assess the prevalence and risk factors of its formation in patients with UC. Methods. A single-centre, observational cross-sectional study included 80 UC patients. To identify factors associated with the development of dinapenia, patients with dinapenia were compared with patients without dinapenia. The investigated parameters in patients: sex, age, body mass index, peculiarities of the course of the disease, ongoing treatment, concomitant pathology, peculiarities of nutrition, nutritional deficiency, malabsorption syndrome, bad habits, psychological stress and sleep duration, physical activity, laboratory indicators of inflammation, myokine-adipokine profile. Statistical parameters were calculated using Statistica 10.0.1011.0 programme. Results. Dinapenia is present in 32.5% of UC patients. Risk factors for the formation of dinapenia in UC patients include female sex (p = 0.0003); nutritional insufficiency (p = 0.021); low physical activity (p = 0.010); artificial feeding in infancy (p = 0.024); inflammation, namely C-reactive protein, autoantibodies of class G against double-stranded DNA (p = 0.006; p = 0.002 respectively). Patients with UC with dinapenia significantly more often gave a positive answer to the questionnaire question “Does daily activity cause you a lot of stress?” (p = 0.048). In the group of UC patients with dinapenia, the level of cortisol in evening saliva was significantly higher than in the comparison group (p = 0.005). Conclusions. Sarcopenia and UC are multifactorial conditions with common developmental mechanisms that can burden each other. Dynamometry is not technically difficult and is justified from an economic point of view. The timely detection of dinapenia and its correction will additionally affect the mechanisms of UC pathogenesis in order to improve the quality of life and prognosis of patients.

A Qualitative Review of the Air Rescue One Rural Search and Rescue Program in British Columbia, Canada.

Rural emergency prehospital care in British Columbia is conducted primarily by the British Columbia Ambulance Services or ground search and rescue volunteers. Since 2014, the volunteer Air Rescue One (AR1) program has provided helicopter emergency winch rescue services to rural British Columbia. The aim of this research was to describe the activity of the AR1 program and to make recommendations to improve future operations. Data were collected retrospectively from September 2014 to May 2021, and parameters of emergency callout statistics from the organization's standard operating guidelines, rescue reports, and interviews were summarized and reviewed. Of 152 missions within the study period, 105 were medically related rescues involving trauma or cardiac events. Snowmobiling, mountain biking, and hiking were the most common activities requiring rescue. The 38 medical callouts that were not completed by AR1 were reviewed for contributing factors. Response time varied due to the vast service area, but median time from request to takeoff was 55 min (interquartile range 47-69 min), and median on-scene time was 21 min (interquartile range 11-33 min). AR1 provides advanced medical care into British Columbia's remote and difficult-to-access areas, minimizing delays in treatment and risk to patients and responders. Callout procedures should be streamlined enabling efficient AR1 activation. Collection of medical and flight information should be improved with standardized documentation, aiding in internal education and future research into the program's impact on emergency prehospital care. Future directions for improvement of care include the possibility of introducing portable ultrasound technology.

Statistical Parameters Extracted from Radar Sea Clutter Simulated under Different Operational Conditions.

A complete framework of predicting the attributes of sea clutter under different operational conditions, specified by wind speed, wind direction, grazing angle, and polarization, is proposed for the first time. This framework is composed of empirical spectra to characterize sea-surface profiles under different wind speeds, the Monte Carlo method to generate realizations of sea-surface profiles, the physical-optics method to compute the normalized radar cross-sections (NRCSs) from individual sea-surface realizations, and regression of NRCS data (sea clutter) with an empirical probability density function (PDF) characterized by a few statistical parameters. JONSWAP and Hwang ocean-wave spectra are adopted to generate realizations of sea-surface profiles at low and high wind speeds, respectively. The probability density functions of NRCSs are regressed with K and Weibull distributions, each characterized by two parameters. The probability density functions in the outlier regions of weak and strong signals are regressed with a power-law distribution, each characterized by an index. The statistical parameters and power-law indices of the K and Weibull distributions are derived for the first time under different operational conditions. The study reveals succinct information of sea clutter that can be used to improve the radar performance in a wide variety of complicated ocean environments. The proposed framework can be used as a reference or guidelines for designing future measurement tasks to enhance the existing empirical models on ocean-wave spectra, normalized radar cross-sections, and so on.

A probabilistic framework for external pitting corrosion growth modelling for buried steel pipelines considering soil properties

This paper introduces a novel framework for developing reliable probabilistic predictive corrosion growth models for buried steel pipelines using pipeline inspection data. The framework adopts a power-law function of time model formulation, accounting for nonconstant damage growth rates, and considers the correlation between defect depth and length growth models. The proposed framework explicitly incorporates local influential soil properties in the model formulation; thus, it requires no segmentation and homogenous defect growth assumption and provides defect-specific growth models. The framework is applicable regardless of the availability of matched or non-matched defect data. For corrosion initiation time estimation, two different approaches are proposed: one is to use a Poisson process to account for defect occurrence, which can also predict newly generated defects since the last inspection, and the other is to use multivariate linear regression of soil and pipe properties. The statistics of unknown model parameters are assessed using a Bayesian updating framework in which the model error can be incorporated. The proposed framework is applied using two different sets of data: one set of inline inspection (ILI) data and one set of field excavation data. A case study is conducted, where time-dependent system reliability of an in-service pipeline is assessed considering small leak and burst failure modes using the developed defect growth models. The impact of the growth model accuracy on the probability of failure is investigated, and the importance analysis is performed to identify the most influential random variables to the probability of failure.

Using Excel Solver’s Parameter Function in Predicting and Interpretation for Kinetic Adsorption Model via Batch Sorption: Selection and Statistical Analysis for Basic Dye Removal onto a Novel Magnetic Nanosorbent

Magnetic nanosorbents efficiently capture substances, particularly basic dyes, and can be easily recovered using a magnetic field in water treatment. Adsorption is a cost-effective and highly efficient method for basic dye removal. This study compared eight nonlinear kinetic adsorption models using Microsoft Excel 2023, which provided a detailed analysis and statistical results comparable to advanced programs like MATLAB and OriginPro. The Fractal Like-Pseudo First Order (FL-PFO) model showed the best fit for the kinetic adsorption model, closely predicting experimental data at 33.09 mg g−1. This suggests that the diffusion rate of basic dye within the magnetic nanosorbent pores is a crucial factor. The statistical parameters confirmed the suitability of these kinetic adsorption models for describing the observed behavior. Overall, Microsoft Excel emerged as a reliable tool for predicting adsorption behavior using various kinetic models for basic dye removal, offering a wide range of functions for diverse applications, including environmental monitoring and modeling. Corrected Akaike’s information criterion was used to determine the optimal model. It found the lowest AICcorrected value of about −3.8479 for the FL-PFO kinetic model, while the Elovich kinetic adsorption model had the highest AICcorrected value of 29.6605. This indicates that the FL-PFO kinetic model effectively correlated the kinetic data. It can be concluded that Microsoft Excel’s accessibility, familiarity, and broad range of capabilities make it a valuable resource for many aspects of environmental engineering.

Machine learning models for the elastic-critical buckling moment of sinusoidal corrugated web beam

The torsional stiffness of I-beams with sinusoidal corrugated web is higher than that of flat web beams and the accuracy of the available hand-calculation methods to determine the elastic critical lateral-torsional buckling moment depends on the geometrical parameters of the beam and the web corrugation. This study proposes different machine learning models to determine the elastic lateral-torsional buckling moments of corrugated web beams. Various machine-learning algorithms such as Decision Tree, Random Forests, Gradient Boosting, Support Vector Regression, Catboost, and Deep Neural Network were employed to develop and train for predicting the elastic-critical lateral-torsional buckling moments of I-beams with corrugated web. An extensive dataset with 2250 pieces was constructed using linear buckling analyses on full-shell finite element models to determine the elastic-critical buckling moment of simply supported beams with sinusoidal web corrugation. Based on the statistical parameters of the predicted and test data, the accuracy and safety assessment of the different machine learning models are examined. The accuracy of the available hand-calculation methods is also investigated. The results of the parametric study showed that the overall performance of the different machine learning models is promising, although, not all are directly suited for the described problem.

Anaerobic Abilities Students Using Indirect Tests: Fatigue Index and Peak Power

Background: Most human movements are supported by its functional capabilities of the anaerobic type, which is dominated by submaximal and maximal sports activities performed in a short time. Functional capabilities of the anaerobic type of man participate in most of his movement activities. Conditioned on adequate and extremely good interaction and functioning of the cardiovascular and respiratory systems, morphological status, metabolic processes, muscle structure, nutritional status, physical activity, etc. Purpose: The research was conducted in the conditions of the so-called field testing with the primary goal of assessing the acute state of anaerobic abilities of physical and sports students, by calculating the fatigue index (FI). Methods: The sample included 80 students of Physical and Sports Education, different geographical regions, average weight 77.88±8.49kg, age 20-23 years. The Running Anaerobic Sprint Test (RAST) was used to assess FI, and the basic statistical parameters were calculated. Results: The average value of the student's leg power is 573.89 watts (378.42 watts vs. 817.99watts), and the recorded average (FI) of 8.35 watts/sec (3.97watts/sec. vs. 16.93watts/sec.). Conclusions: The obtained results confirmed the average values of anaerobic capacity which are suitable for the examined population of subjects, while the maximal the value of FI suggests a weaker state of the anaerobic capacity of the individuals (less tolerance to lactates).

Individual Management of Physical Activity of Tourism and Recreation Students Through Mobile Applications

Abstract Introduction. The impact of mobile applications on the physical activity of Tourism and Recreation students in Krakow has not been the subject of in-depth research so far. The aim of this study is to identify the areas of management, scope of use and motivation for using mobile applications during physical activity by students of Tourism and Recreation. The theoretical framework for motivation analysis is the Achievement Goal Theory. Material and methods. A quantitative approach was adopted, surveys were conducted among students of Tourism and Recreation in Krakow. The basic parameters of descriptive statistics, coefficient of variation V, and correlation coefficients were calculated. Results. Students of Tourism and Recreation use mobile applications while running, cycling and aerobics. The most important functionalities for students are those that support processes of controlling the performance of their physical activity. The results show that, with regard to the Achievement Goal Theory, stronger motivation comes from aspects falling into the Goal category. Conclusions. In the theoretical aspect, the results deepened the understanding of the processes of individual management of students’ physical activity; in the methodological aspect, they allowed the researchers to conclude that the Achievement Goal Theory is suitable for analysing the motivation to use mobile applications during physical activity, while in the practical aspect, the results indicated the possibility of improving the quality of students’ physical activity through individual management with the efficient use of mobile applications.

Lidar error in complex terrain - case study based on two measurement campaigns

Nowadays, lidar devices are increasingly being used to determine wind potential. The decision to use a lidar device or a meteorological measuring mast depends, among other things, on the topology of the terrain. In flat terrain lidar profilers are sufficiently accurate compared to previously used measuring masts. Due to their measuring principle, however, there are differences in measuring accuracy depending on the terrain. This deviation compared to a measurement with a measuring mast or with anemometers attached to it is referred to as lidar error. However, there are still uncertainties regarding the measurement accuracy of lidar profilers, especially in complex terrain.Among other things, the size of the lidar error in relation to terrain complexity or terrain type is unclear. The lidar error can also differ for different lidar devices with different measuring principles. In order to investigate this in more detail, various lidar devices are compared in this work with regard to their measurement uncertainty in complex terrain. For this purpose, measurement data from two measurement campaigns lasting several months were analyzed. In both measurement campaigns, the lidar devices are compared with data from a measuring mast.There are clear differences between the lidar devices. These are type-dependent, wind speed-dependent and also wind direction-dependent. The latter is due in particular to different terrain topographies from the different cardinal directions. These differences are analyzed with the help of suitable statistical parameters and then evaluated in the context of current scientific research.

Decoding the molecular Symphony: Unravelling neurologically crucial GSK-3 inhibition through 2D QSAR modelling with MLR, PLS, and ANN approaches

This study aimed to decipher the encoded information within the molecular structure of a dataset of GSK-3β inhibitors through a comprehensive quantity structural-activity relationship (QSAR) investigation employing traditional physicochemical descriptors. Various statistical techniques were applied, encompassing linear methods such as Multiple Linear Regression (MLR) and Partial Least Squares (PLS), along with non-linear approaches such as Artificial Neural Networks (ANN). Rigorous validation using diverse statistical tools confirmed the models’ precision and predictability. The model exhibited exceptional statistical relevance, as evidenced by standard parameters: S-value (0.37), F-value (37.17), r (0.93), r2 (0.855), and r2CV (0.78). They assessed predictive power and robustness by involving specific statistical parameters. Key descriptors analyzed, including Verloop L (subs 2), Lipole Z component (whole molecule), and VAMP Dipole Z component, offered crucial insights into their respective contributions. This analysis implies that targeted modifications in the substitution pattern potentially enhance GSK-3β inhibitory activity significantly. The established model clarified how bioactivity depends on structure and offered valuable recommendations for creating new compounds with better inhibitory activity profiles against the GSK-3β enzyme.

Biomechanical improvements in gait following medial pivot knee implant surgery

BackgroundTotal knee replacements are used to improve function and reduce pain in patients with advanced osteoarthritis. The medially stabilising implant is designed to mimic a healthy knee. This study aims to provide a comprehensive analysis of the kinematics and kinetics of a medially stabilising knee implant, comparing it to a healthy control group, as well as to its pre-operative state and the contralateral limb. MethodsSixteen total knee replacement patients and ten healthy participants were recruited. Patients underwent testing 4–6 weeks before surgery and repeated the same tests 12 months after surgery. Healthy participants completed the same tests at a single time point. All participants completed three walking trials: kinematics was captured with eight cameras; kinetics with in-ground force plates. Subject-specific musculoskeletal models were developed in OpenSim. Inverse kinematics and inverse dynamics were used to determine gait parameters. Joint angles and joint moments were evaluated using Statistical Parametric Mapping. Patient-reported outcome measures were also collected at both time points. FindingsSpatiotemporal results indicate significant differences in velocity and step length between pre-operative patients and control participants. Differences are observed in the adduction angles between the contralateral and ipsilateral limbs pre-operatively. Postoperatively, there was an increase in the 1st peak flexion moment, reduced adduction moment and reduced internal rotation moment. In PROMs, patients all report improvements in pain levels and high satisfaction levels following surgery. InterpretationsFollowing medial stabilising total knee arthroplasty, patients displayed improved clinical parameters and joint moments reflecting a shift towards more normal, healthy gait.

Pengesanan Denyutan Pi2 Hampir Masa Nyata Berdasarkan Model Pembelajaran Mesin Terautomatik

The impact of geomagnetic substorms on space weather and various technology on the Earth urges the need to detect the phenomenon. However, the brief period of substorm occurrences makes it more difficult to directly detect them, therefore, their detection by proxy in the form of precursors need to be done. Pi2 pulsations, which is a damped irregular signal in the horizontal component of the geomagnetic field ( as a reliable precursor to substorms. The cessation of Hz), is seen index that previously was the main detection indicator of Pi2 pulsations motivates this study to introduce a new detection method by implementing machine learning. In this study, several features were extracted from geomagnetic field data based on several statistical parameters, impulsive and signal metrics to be used in the development of classification model. The development was performed based on the automated machine learning (AutoML) approach that determines the best algorithm and optimizes the hyperparameters automatically. AutoML tries various algorithms like ensemble, neural network, Naïve Bayes, support vector machine, k-nearest neighbour and binary decision tree that are optimized by Asynchronous Successive Halving Algorithm. An ensemble classification model was determined to be the best performing model with an accuracy of 99.15%. It was then used in the development of near-real tine detection system. The system streams the geomagnetic field data continuously and reports the Pi2 detections on an open cloud repository, The detection system is envisioned to be one of the reference sources to inquire the occurrences of Pi2 pulsations globally.

Soil Fertility Mapping Using GIS in Sirur Sub-Watershed of Kundagol Taluk of Dharwad District, Karnataka, India

Aims: To identify available nutrient constraints in soils and to generate soil fertility maps using remote sensing and GIS of Sirur sub-watershed in northern dry zone of Karnataka. Place and Duration of Study: The study was carried out in the Sirur sub-watershed (4D5B5j) located in Kundagol taluk of Dharwad district, Karnataka during 2022-24. Methodology: Soil samples (601) from Sirur sub-watershed in northern dry zone of Karnataka were drawn at 320 m x 320 m grid interval and assessed for fertility parameters. Analytical data was interpreted and statistical parameters like range, mean, standard deviation and coefficient of variation were calculated for each parameter. Soil fertility maps were prepared for each parameter using interpolation technique. Results: Soils were neutral to alkaline in reaction and non-saline. Soil organic carbon content was low to medium. Available nitrogen and phosphorus were low to medium, available potassium was high and sulphur was medium to high. Regarding available micronutrients, iron, copper and manganese were sufficient in the soils whereas Zn was deficient in about half of the sub-watershed area and B content was medium to high. Conclusion: The major constraints in sub-watershed are low content of OC, poor status of available N, P2O5 and Zn indicating their immediate attention for sustained crop production.

Experimental investigations to assess surface fatigue failure in rolling contact bearing

Rolling element bearings are the most important components in almost all rotating machines. These bearings are often subjected to repetitive load cycles at different operating conditions. Excessive loads, speeds, and improper operating conditions lead to the propagation of defects on their load-bearing surfaces, thereby causing a negative impact on the performance of rotating machines. This paper presents the results of experimental investigations to assess wear propagation in roller bearings using lubricant degradation, vibration, and statistical parameter analysis methods. A roller bearing setup was developed in the laboratory, and the test bearing (NJ 307E) was subjected to fatigue tests over a period of 900 h. The bearing was operated at a speed and radial load of 800 rpm and 1 kN, respectively. The film thickness analysis revealed a transition in lubrication regimes during 600–900 h of operation. Grease structure degradation and oxidation analyses were carried out using scanning electron microscope images and the Fourier transform infrared radiation technique. Further, the vibration signals are extracted from the bearing housing at regular intervals. Using the fast Fourier transform technique, these vibration signals were used to analyze bearing fault frequencies to highlight the faults developed on bearing contact surfaces. The statistical features of vibration signals such as root mean square, kurtosis, and crest factor were used to assess the severity of wear propagated on the bearing contact surfaces. Integrating tribological and vibration parameter analysis techniques provided a reliable assessment of surface fatigue wear propagated on the roller-bearing contact surfaces.

Evaluation of spring flows using recession flow analysis techniques

ABSTRACT This study delves into the analysis of recession characteristics of spring base flow, focusing on the Hill campus spring (elevation of approximately 2,150 m) and Fakua spring (elevation of approximately 1,850 m) in the Tehri Garhwal district of Uttarakhand, India. Spanning from January 1999 to December 2004, discharge data from G.B. Pant University of Agriculture and Technology's Hill campus are employed. The research employs an automated, objective-based method to generate master recession curves (MRCs) and categorize them into early, intermittent, and late recession segments. Statistical parameters and low-flow indices from flow duration curves (FDCs) are utilized for flow assessment, while base flow indices (BFIs) are analyzed using the Web-based Hydrograph Analysis Tool (WHAT). The findings highlight the importance of water storage strategies during the rainy season for the sustainable utilization of spring water. The study suggests that the techniques applied are equally applicable for analyzing river flow recession characteristics, emphasizing the broader implications for integrated water resources planning and management in mountainous regions.

Adsorption efficacy of acetone with zeolitic imidazolate frameworks–8: a mechanistic and kinetic insight

ABSTRACT The zeolitic imidazolate framework-8 (ZIF-8) has been investigated for the dynamic adsorption of acetone from the gas phase. In this study, ZIF-8 was synthesised using a cost-effective and environmentally friendly method and characterised using SEM, XRD, BET, and FTIR. The synthesised ZIF-8 possessed a BET surface area of 1212 m2. g−1 and a 0.64 µm particle size. The dynamic adsorption efficacy of acetone on the thermally pretreated ZIF-8 was investigated in a gas matrix under different operational factors. Increasing the acetone concentration through the adsorbate inlet from 300 to 800 ppm enhanced the adsorption capacity from 114 to 162 mg. g−1, while increasing flow rates from 100 to 200 mL. min−1 diminished the adsorption capacity from 120 to 114 mg. g−1. Additionally, increasing the relative humidity from 30 to 70% diminished the adsorption capacity from 130 to 87 mg. g−1. The acetone adsorption kinetics on ZIF-8 were investigated by the Elovich equation, pseudo-first order (PFO), and pseudo-second order (PSO) models. The statistical parameters indicated that the Elovich equation model is most suitable for representing the kinetic behaviour. In conclusion, ZIF-8 could be introduced as a sorbent with suitable potential for adsorbing acetone from the air in various environmental and occupational settings.

Structural concrete measurements: New distributed approach for standard specimens

Concrete structures are widely used in construction because of their many advantages, including high corrosion resistance, durability, low operating costs, and the ability to design any geometry. However, they also have some disadvantages, such as their high dead weight and susceptibility to cracking. Although this material is widely used globally, new types of concrete, such as high-strength or lightweight aggregate-based, are still being developed. This requires constant control and testing in both laboratory and field conditions. Concrete is a heterogeneous material due to the influence of aggregate grains and local discontinuities, voids or pores. Given these characteristics, it is clear that most conventional diagnostic solutions, such as spot foil strain gauges or extensometers, are unable to accurately analyse these localised effects as they average local strains over the measurement base. In contrast, distributed fibre optic sensing (DFOS) enables strain readings to be taken with millimetre geometric resolution, significantly extending the structural assessment capability. The article describes research on standard concrete cylinders equipped with surface-mounted optical fibres for simultaneous measurements of distributed strains in both the longitudinal direction (compression zone) and circumference direction (tension zone). The novelty of the proposed approach, apart from the way of arranging the sensing fibres on the specimens, lies mainly in the new diagnostic approach that allow (1) the evaluation of the homogeneity of the concrete using statistical parameters, (2) the identification of microcracking and (3) the identification of near-to-failure state based on Poisson’s ratio analysis. Twenty cylindrical specimens were laboratory tested using reference techniques to validate the performance of the DFOS-based system and to provide data for statistical evaluation. The results indicate the possibility of detecting the approaching damage even without knowledge of the material properties of the specimen or the initial stress level at the moment of starting the measurements. The discussion also covered future research directions and potential improvements.

Investigating the Behavior and Pattern of Optimal Water Consumption in Continuous Water Distribution Networks

Due to the complexity of water distribution systems and the large scale of decision-making in their analysis, design, operation and maintenance, the need to use optimization methods in upgrading and calibrating the model has become more important than in the past. The most important issue in the simulation modeling of these systems is the consistency between the calculated and measured data. The review of records showed that in the absence of the results of unaccounted water studies in the network, the use of recommended and empirical statistical methods is still needed as one of the main elements in model calibration. The current research was carried out with the aim of improving calibration methods, investigating the effects of different patterns of unaccounted water based on the calculated water consumption pattern and determining the optimal pattern of unaccounted water within the scope of the water distribution network covered by the number of 6 ground reservoirs. Due to the fact that the exact determination of unaccounted water was not available, and on the other hand, the model calibration was affected by the amount and behavior of unaccounted water, so the difference in water production and all measured uses was considered as unaccounted water and its different behaviors in the network, it was investigated according to the behavior (pattern) of water consumption. 3 different hypotheses were evaluated based on the behavior of the network (reservoir output flow rate in the hydraulic model) with the actual behavior of the reservoir output (according to the data and statistics of the tank output meter). The comparison of three statistical and graphic parameters of pressure distribution showed that the use of the inverse model option of the customers' consumption was not considered as a water model, for calibrating the hydraulic model of the distribution network, more acceptable limits for the closeness of the predicted values to the recorded values of the hourly output reservoirs provided. The confirmation of the hypothesis of the model according to consumption as the pattern of unaccounted water for the studied network indicated that the amount of unaccounted water (unlicensed and unregistered consumers) changed along with the amount of consumption. The present research-applied method in obtaining the results can be a very effective help in designing and optimizing distribution networks for urban water and sewage management organizations (especially in big cities).

Экологическая стабильность и пластичность сортов горчицы сарептской яровой селекции ВНИИМК

Analysis of ecological plasticity and stability must be a base in the development of new genotypes of spring brown mustard. The purpose of the research was a comparative characteristic of spring brown mustard cultivars bred at the V.S. Pustovoit All-Russian Research Institute of Oil Crops by calculated statistic parameters of adaptability, ecological plasticity, stability, stress-resistance, and genetic flexibility in the central zone of the Krasnodar region in 2018–2022. We studied commercial (Nika, Yunona, Gorlinka, Galateya) and promising (VN11/18, VN-27/18, VN-9/18, and VN-25/18) cultivars. The indicators of ecological plasticity and stability were calculated basing on seed yield of spring brown mustard cultivars for five years by a methodology of Eberhart S.A., Rassell W.A. as reworded by Zykin V.А. To calculate a coefficient of a linear regression, we determined environment indexes, which characterized variability of conditions of the cultivar cultivation. The stress-resistance and genetic flexibility were determined by an equation of Rossille A.A., Hamblin J. as presented by Goncharenko А.А. The potential productivity and adaptability were revealed by a methodology of Zhivotkov L.А. The weather conditions in 2020 and 2021 were the most favorable, environmental index had the highest positive meaning and was equal to 1.74 and 1.82 under the highest average yield of brown mustard, which was equal to 1.92 and 1.93 t/ha, respectively. The weather conditions of 2018 and 2022 were less favorable for estimation of brown mustard samples (Ij = -1.71 and -2.00). A comparative analysis of brown mustard cultivars bred at the V.S. Pustovoit All-Russian Research Institute of Oil Crops by parameters of ecological plasticity and stability in the Krasnodar region showed the cultivars Yunona, VN-27/18, and VN-11/18 had the highest plasticity, and Yunona and Galateya demonstrated the highest stability and adaptability to declining of cultivation conditions. Thus, the cultivars Yunona and Galateya were characterized with high yield, plasticity, stability, genetic flexibility, and adaptability. In contrast conditions, the promising cultivar VN-9/18 had high indicators of resistance to stress factors, yield, and adaptability coefficient.