Optimizing the geodetic networks based on the distances between the net points and the project border

Geodetic networks are important for most engineering projects. Generally, a geodetic network is designed according to precision, reliability, and cost criteria. This paper provides a new criterion considering the distances between the Net Points (NPs) and the Project Border (PB) in terms of Neighboring (N). Optimization based on the N criterion seeks to relocate the NPs as close as possible to PB, which leads to creating shorter distances between NPs or those distances linking NPs with Target Points (TPs) to be measured inside PB. These short distances can improve the precision of NPs and increase the accuracy of observations and transportation costs between NPs themselves or between NPs and TPs (in real applications). Three normalized N objective functions based on L1, L2, and L∞‒norms were formulated to build the corresponding N optimization models, NL1; NL2; and NL∞ and to determine the best solution. Each model is subjected to safety, precision, reliability, and cost constraints. The feasibility of the N criterion is demonstrated by a simulated example. The results showed the ability of NL∞ to determine the safest positions for the NPs near PB. These new positions led to improving the precision of the network and preserving the initial reliability and observations cost, due to contradiction problems. Also, N results created by all N models demonstrate their theoretical feasibility in improving the accuracy of the observations and transportation cost between points. It is recommended to use multi-objective optimization models to overcome the contradiction problem and consider the real application to generalize the benefits of N models in designing the networks.

A new criterion of asymptotic stability for Hopfield neural networks with time-varying delay

The objective of this paper is to analyze the stability of Hopfield neural networks with time-varying delay. For the system to operate in a steady state, it is important to guarantee the stability of Hopfield neural networks with time-varying delay. The Lyapunov-Krasovsky functional method is the main method for investigating the stability of time-delayed systems. On the basis of this method, the stability of Hopfield neural networks with time-varying delay is ana-lysed. It is known that due to such factors as communication time, limited switching speed of various active devices, time delays often arise in various technical systems, which significantly degrade the performance of the system, which can in turn lead to a complete loss of stability. In this regard, a Lyapunov-Krasovsky type delay-product functional was con-structed in the paper, which allows more information about the time delay and reduces the conservatism of the method. Then a generalized integral inequality based on the free matrix was used. A new criterion for asymptotic stability of Hop-field neural networks with time-varying delay, which has less conservatism, was formulated. The effectiveness of the proposed method is illustrated. Thus an asymptotic stability criterion for Hopfield neural networks with time-varying delay was formulated and justified. The expanded Lyapunov-Krasovsky functional is constructed on the basis of delay and quadratic multiplicative functional, and the derivative of the functional is defined by a matrix integral inequality with free weights. The effectiveness of the method is illustrated by a model example.

Sounding of sporadic E layers from CSES radio occultation and comparing with ionosonde measurements

GNSS radio occultation (RO) plays an important role in ionospheric electron density inversion and sounding of sporadic E layers. As the China's first electromagnetic satellite, China Seismo Electromagnetic Satellite (CSES) has collected the RO data from both GPS and BDS-2 satellites since March 2018. In this study, we extracted the carrier to noise density ratio (CNR) data of CSES and calculated the standard deviation of normalized CNR. A new criterion is developed to determine the Es events, that is when the mean value of the absolute value of the difference between the normalized CNR is greater than 3 times of the standard deviation. The statistics show that sporadic E layers have strong seasonal variations with highest occurrence rates in summer season at middle latitudes. It is also found that the occurrence height of Es is mainly located at 90–110 km, and the period of local time 15:00–18:00 is the high incidence period of Es. In addition, the geometric altitudes of a sporadic E layer detected in CSES radio occultation profiles and the virtual heights of a sporadic E layer obtained by the Wuhan Zuo Ling Tai (ZLT) ionosonde show four different space-time matching criterions. Our results reveal that there is a good agreement between both parameters which is reflected in the significant correlation.

Stability analysis of a simple mathematical model for school bullying

<abstract><p>School bullying is a highly concerned problem due to its effect on students' academic achievement. The effect might go beyond that to develop health problems, school drop out and, in some extreme cases, commit suicide for victims. On the other hand, adolescents who continuously bully over time are at risk of becoming involved in gang membership and other types of crime. Therefore, we propose a simple mathematical model for school bullying by considering two variables: the number of victims students and the number of bullies students. The main assumption employed to develop the mathematical model is that school policy bans bullying and expels students who practice this behavior to maintain a constructive educational environment within the school. We show that the model has two equilibrium points, and that both equilibrium points are locally and globally asymptotically stable under certain conditions. Also, we define a threshold parameter with a new criterion called the bullying index. Furthermore, we show that the model exhibits the phenomena of transcritical bifurcation subject to the bullying index. All the findings are supported with numerical simulations.</p></abstract>

Diabetology for primary health care – the use of metformin in the treatment of prediabetes, primary prevention and secondary prevention in type 2 diabetes

The 2021 guidelines on the management of diabetes by the Polish Diabetes Association introduce the new criterion for the diagnosis of diabetes – percentage of glycated hemoglobin HbA1c > 6.5%. This is important, especially now, during the COVID-19 epidemic, for patients with diagnosed pre-diabetes who are already using metformin, as it allows to release the need for an oral glucose tolerance test. The article describes the principles of using metformin in the prevention of type 2 diabetes.

Associated probabilities aggregations in multistage investment decision-making

PurposeThe attributes that influence the selection of applicants and the relevant crediting decisions are naturally distinguished by interactions and interdependencies. A new method of possibilistic discrimination analysis (MPDA) was developed for the second stage to address this phenomenon. The method generates positive and negative discrimination measures for each alternative applicant in relation to a particular attribute. The obtained discrimination pair reflects the interaction of attributes and represents intuitionistic fuzzy numbers (IFNs). For the aggregation of applicant's discrimination intuitionistic fuzzy assessments (with respect to attributes), new intuitionistic aggregation operators, such as AsP-IFOWA and AsP-IFOWG, are defined and studied. The new operators are certain extensions of the well-known Choquet integral and Yager OWA operators. The extensions, in contrast to the Choquet aggregation, take into account all possible interactions of the attributes by introducing associated probabilities of a fuzzy measure.Design/methodology/approachFor optimal planning of investments distribution and decreasing of credit risks, it is crucial to have selected projects ranked within deeply detailed investment model. To achieve this, a new approach developed in this article involves three stages. The first stage is to reduce a possibly large number of applicants for credit, and here, the method of expertons is used. At the second stage, a model of improved decisions is built, which reduces the risks of decision making. In this model, as it is in multi-attribute decision-making (MADM) + multi-objective decision-making (MODM), expert evaluations are presented in terms of utility, gain, and more. At the third stage, the authors construct the bi-criteria discrete intuitionistic fuzzy optimization problem for making the most profitable investment portfolio with new criterion: 1) Maximization of total ranking index of selected applicants' group and classical criterion and 2) Maximization of total profit of selected applicants' group.FindingsThe example gives the Pareto fronts obtained by both new operators, the Choquet integral and Yager OWA operators also well-known TOPSIS approach, for selecting applicants and awarding credits. For a fuzzy measure, the possibility measure defined on the expert evaluations of attributes is taken.Originality/valueThe comparative analysis identifies the applicants who will receive the funding sequentially based on crediting resources and their requirements. It has become apparent that the use of the new criterion has given more credibility to applicants in making optimal credit decisions in the environment of extended new operators, where the phenomenon of interaction of all attributes was also taken into account.

SCALING OF SENSATIONS DURING THE PERFORMANCE OF VISUAL TASKS IN RELATION TO HUMAN CENTRIC LIGHTING

Currently, programs for lighting calculation based on computer graphics (CG) allows us to move to a fundamentally new approach to a assessing the quality of lighting. A designing based on illuminance can be complement with designing based on synthetic images or on lighting design. Modern CG programs can calculate the spatial-angular distribution of luminance (SADL). However, to make the assessment of the quality of lighting using SADL a new criterion is needed. This paper considers constructing a physiological model of the visual perception scale based on experimental data and on neural network for simple scene as light source viewed on a uniform background with different luminance levels. Scale based on threshold contrasts of luminance for each sensation can be a fundament of new criterion. The article offers the method of construction the map for each sensation using the example of «discomfort» and «unpleasant» maps that can easily applied in programs.

Data-driven optimization of brittleness index for hydraulic fracturing

Evaluation of brittleness index (BI) is a fundamental principle of a hydraulic fracturing design. A wide variety of BI calculations often baffle field engineers. The traditional value comparison may also not make the best of BI. Moreover, it is often mixed up with the fracability in field applications, thus causing concerns. We, therefore, redefine fracability as the fracturing pressure under certain rock mechanical (mainly brittleness), geological and injecting conditions to clarify the confusion. Then, we propose a data-driven workflow to optimize BIs by controlling the geological and injecting conditions. The machine learning (ML) workflow is employed to predict the fracability (fracturing pressure) based on field measurement. Three representative ML algorithms are applied to average the prediction, aiming to restrict the interference of algorithm performances. The contribution of brittleness on pressure/fracability prediction by error analysis (rather than the traditional method of BI-value comparison) is proposed as the new criterion for optimization. Six classic BI correlations (mineral-, logging- and elastic-based) are evaluated, three of which are optimized for the derivation of a new BI using the backward elimination strategy. The stress ratio (ratio of minimum and maximum horizontal principal stress), representing the geological feature, is introduced into the derived calculation based on the independent variable analysis. The reliability of the new BI is verified by error analyses using data of eight fracturing stages from seven different wells. Approximately 40%~50% of the errors are reduced based on the new BI. The differences among the performances of algorithms are also significantly restrained. The new brittleness index provides a more reliable option for evaluating the brittleness and fracability of the fracturing formation. The machine learning workflow also proposes a promising application scenario of the BI for hydraulic fracturing, which makes more efficient and broader usages of the BI compared with the traditional value comparison.