Statistical Operator Research Articles

Swarming procedures to solve the novel perturbed delay third order singular model

The current study shows a novel singular perturbed delay third order model (NSPD-TOM) with its two categories using the conventional Lane-Emden mathematical model. The comprehensive details of the perturbed, shape/delay and singular terms are also provided for both categories of the NSPD-TOM. The numerical results for solving the NSPD-TOM are provided by applying the procedures of artificial neural networks together with the optimizations performances of global swarming and local active set methods. The NSPD-TOM is performed based on the accuracy, substantiation, and authenticity using the achieved and exact results comparison. Moreover, the stochastic procedure is further authenticated by applying the statistical operators to solve the NSPD-TOM.

Unveiling and Unraveling Aggregation and Dispersion Fallacies in Group MCDM

Priorities in multi-criteria decision-making (MCDM) convey the relevance preference of one criterion over another, which is usually reflected by imposing the non-negativity and unit-sum constraints. The processing of such priorities is different than other unconstrained data, but this point is often neglected by researchers, which results in fallacious statistical analysis. This article studies three prevalent fallacies in group MCDM along with solutions based on compositional data analysis to avoid misusing statistical operations. First, we use a compositional approach to aggregate the priorities of a group of DMs and show that the outcome of the compositional analysis is identical to the normalized geometric mean, meaning that the arithmetic mean should be avoided. Furthermore, a new aggregation method is developed, which is a robust surrogate for the geometric mean. We also discuss the errors in computing measures of dispersion, including standard deviation and distance functions. Discussing the fallacies in computing the standard deviation, we provide a probabilistic criteria ranking by developing proper Bayesian tests, where we calculate the extent to which a criterion is more important than another. Finally, we explain the errors in computing the distance between priorities, and a clustering algorithm is specially tailored based on proper distance metrics.

The impact of special exercises (land-water) in developing the functional capabilities and performance of shipwrecked rescuers

The aim of the research is to design specific exercises (land-water) by supporting the development of functional skills and performance of shipwreck rescuers. This was observed through the researcher's work as well as by monitoring drowning incidents using Iraqi Ministry of Health statistics and Babylon Governorate statistics, particularly the high incidence of drowning. Their reasons included the low functional competence of rescuers' body systems and that the rescue operation could require a high level of physical and functional exertion. Likewise, the lack of specific exercises for lifeguards and the lack of use of training aids (legs, pull ropes) in the daily training sessions for lifeguards. Which requires training according to the development in the use of these training methods, which are practiced in the countries of the world in the field of training. As a result, this negatively impacted lifeguard performance and was an element that led to an increase in drowning incidents and then a departure from the goals for which this assistance was set. And explore the impact of using these tools. The researcher designed special ground-water exercises with auxiliary means and conducted daily training units, and they were divided into two groups. The first is experimental and the second controls, and the experimental group has implemented the proposed method prepared by the researcher. And the control group implemented the traditional training curriculum prepared by the official. The researcher conducted the necessary tests before and after the training period, which lasted (8) weeks, with three training units per week. The results were analyzed using the necessary statistical operations, and the researcher concluded that both groups had remarkable development. However, the experimental group was the best in the results, and in light of this, the researcher recommends the need to prepare special exercises (land - water) with special aids for the rescue process to develop the functional capabilities of the body systems and develop the performance of shipwrecked rescuers.

EXAMINE THE LIBYAN HEALTH LAW'S SUFFICIENCY IN HOLDING HEALTH PRACTITIONERS ACCOUNTABLE AND REDUCING ERRORS

The current study aims to conduct a review of the adequacy of Libyan health law to hold people accountable and reduce the errors of health practitioners, and the study population was represented by "all employees of Tripoli Medical Hospital and Zintan Court of First Instance," and the sample number reached 302 health practitioners. The researcher used the descriptive analytical method, and she attempted to answer the research's main question, which is "How a health practitioner can hold people accountable?" The researcher took into account the scientific criteria in selecting the sample, and the exploratory study was conducted to ensure the validity and stability of the study tool, in which he relied on the scientific questionnaire, and the researcher used the appropriate statistical operations relying on the SPSS programme in the process of analysing the results, also relying on the discussion of the results on the scientific, academic, and field references of the researcher related to the subject of the study. The results of the current study found that the participants in the study have confirmed that custom and habit are sometimes taken into account in addressing the errors of health practitioners, and these results obtained the highest degree of acceptance among the participants, while they were followed by the participants' assertion that there are internal regulations and laws in the Ministry of Health on which to base dealing with cases of medical errors, while the results showed that there is a practise of investigating cases of health practitioners' errors regardless of their cause from the participants' point of view, and who They further affirmed that the existing Libyan health law is not sufficient to punish the practises of health workers when they err and that accountability mechanisms are not accepted and satisfied. The results on the axis of the mechanism of assisting the Libyan health law in reducing medical errors also showed the importance of educating health practitioners in scientific seminars or educational lectures about the duties, obligations, and regulations imposed by the Libyan health law and the need to work on developing laws periodically in line with the developments and problems that occur in this field to protect the patient from medical errors and the law.

ELEMENTARY SCHOOL BUILDINGS CONDITION ASSESSMENT: CASE OF PRIMORJE-GORSKI KOTAR COUNTY (CROATIA)

This study deals with the condition examination of elementary school buildings in Primorje-Gorski Kotar County, Croatia. For schools to be functional and of high quality, adequate maintenance is mandatory. Therefore, school maintenance programs need information on the building's condition based on systematic and comprehensive assessments as a primary input. This study aims to establish a framework for assessing the condition of school buildings, based on which information can be obtained quickly and practically regarding the condition of all critical facility elements. To this end, a database on schools and their conditions was created. The conditions of school buildings in terms of damage and defects and their causes were determined. Descriptive and inferential statistical operations were performed on the collected data. A condition assessment of the buildings was performed using a questionnaire survey among the principals, based on the school buildings division model developed in this study; the model presents building elements and a systematic visual condition rating scale. According to the available data, this is Croatia's first condition assessment on elementary school buildings. The main results show that on average, the examined schools are in good condition. Moreover, according to the results, the school building condition is most affected by dilapidation and lack of financial resources.

Severity assessment of wheat stripe rust based on machine learning.

The accurate severity assessment of wheat stripe rust is the basis for the pathogen-host interaction phenotyping, disease prediction, and disease control measure making. To realize the rapid and accurate severity assessment of the disease, the severity assessment methods of the disease were investigated based on machine learning in this study. Based on the actual percentages of the lesion areas in the areas of the corresponding whole single diseased wheat leaves of each severity class of the disease, obtained after the image segmentation operations on the acquired single diseased wheat leaf images and the pixel statistics operations on the segmented images by using image processing software, under two conditions of considering healthy single wheat leaves or not, the training and testing sets were constructed by using two modeling ratios of 4:1 and 3:2, respectively. Then, based on the training sets, two unsupervised learning methods including K-means clustering algorithm and spectral clustering and three supervised learning methods including support vector machine, random forest, and K-nearest neighbor were used to build severity assessment models of the disease, respectively. Regardless of whether the healthy wheat leaves were considered or not, when the modeling ratios were 4:1 and 3:2, satisfactory assessment performances on the training and testing sets can be achieved by using the optimal models based on unsupervised learning and those based on supervised learning. In particular, the assessment performances obtained by using the optimal random forest models were the best, with the accuracies, precisions, recalls, and F1 scores for all the severity classes of the training and testing sets equal to 100.00% and the overall accuracies of the training and testing sets equal to 100.00%. The simple, rapid, and easy-to-operate severity assessment methods based on machine learning were provided for wheat stripe rust in this study. This study provides a basis for the automatic severity assessment of wheat stripe rust based on image processing technology, and provides a reference for the severity assessments of other plant diseases.

Exploring the Disproportionate Impact of COVID-19 in Older Adults in Canada

Introduction: Many patients recovered from Covid-19 without requiring any critical treatment, vulnerable populations such as older people, especially those with comorbidities, are more likely to develop a severe infection and face higher mortality rates. Background: Why are older Adults are Higher Risk? The majority of older adults have comorbid conditions, which are associated with a higher risk for COVID-19. Many live in residential care homes, which have seen the highest number of COVID-19 cases due to tightly shared living spaces. With weakened immune systems, older adults living in poverty face additional challenges in protecting themselves from the virus, as it is difficult for them to comply with public health measures such as social distancing. 32.5% of individuals in the lowest income quartile were hospitalized due to COVID19, compared to only 11.4% of individuals from the highest income quartile. Methods: The data was retrieved from the Canadian MIS Database (CMDB) and the discharge abstract database (DAD). The CMDB contains financial and statistical operations information on public hospitals and regional health authorities across Canada. Case selection is based on COVID-19 diagnosis codes available in the International Statistical Classification of Diseases and Related Health Problems. Results: Elderly faced higher rates of hospitalization during the pandemic. The average length of hospital stay was also higher for older adults—hospitalizations of individuals under age 65 without comorbidity 41,707 with comorbidity 12,372. Hospitalizations of individuals 65 and older without comorbidity were 22,221 with comorbidity 24,731. Higher Hospitalization rates and issues of inequality in the healthcare system globally. As the pandemic progressed, hospitalization increased in number significantly. The average length of stay for individuals younger than age 65 without comorbidity is 7.7 days compared to 17.0 days in patients with comorbidity. The average full length of stay for individuals 65 and older without comorbidity was 13.2 days, While in patients with comorbidity 19.2 days. Older adults had higher in-facility death rates due to COVID-19 hospitalization. The in-facility death rate of individuals younger than 65 without comorbidity is 2.5%, With comorbidity at 12.9%. In-facility death rate of individuals 65 and older without comorbidity is 14.4%, with comorbidity at 23.2%. Conclusion: By analyzing and comparing various hospitalization rates for Canada, the precise data indicate that older adults have been disproportionately impacted by COVID-19. It is now important to determine the underlying structural issues that have caused this inequality to prioritize healthy aging.

Cross effects in spin hydrodynamics: Entropy analysis and statistical operator

We revisit the construction of first-order spin hydrodynamics and find that the constitution relations receive the corrections from the cross effects resulting from spin-orbit coupling. Starting from a routine entropy analysis, we show how to identify cross effects and new cross transport coefficients from the second law of thermodynamics. Interestingly, the conventional transport coefficient heat conductivity $\kappa$ is bounded from below by the product of cross transport coefficients, which means the threshold of heat conduction is changed. With recourse to Zubarev's non-equilibrium statistical operator, we reproduce the construction of first-order spin hydrodynamics and identification of cross effects in a more rigorous way. By seeking the dispersion relations of normal modes, we find that these cross effects suppress the attenuation of sound modes and heat mode appearing in conventional hydrodynamics and also have impacts on the damping of non-hydrodynamic spin modes.

BENCHMARKING OF ANTIBODY TITRES SELECTIONS USING THE RANKING METHOD

Based on the serological monitoring results the humoral immunity level in birds was assessed after the use of three variants of inactivated vaccines against avian influenza, which causative agent is capable of genetic rearrangements and generation of extremely dangerous highly virulent forms. The refore there arise the need for accurate quantitative assessments of antibody titres, their benchmarking against threshold values and statistical analysis of the received results. The serological examinations allow obtaining the selections of antibody titres values, determining the arithmetic or geometric mean values, as well as statistical indicators such as standard deviations, coefficients of variation or standard error of the mean. to the parametric ones, since they are based on the given normal distribution of the elements of a selection. However, this condition is not always met, which leads to the incorrect conclusions, especially when conducting a comparative analysis. Alongside, it should be noted that banchmarking made in a group consisting of more than two selections cannot be made on independent pairs. In this case the special multi-comparative methods are required. The aim of this work was to conduct a comparative analysis of three antibody titres selections. The nonparametric multiple comparison Miller's method was applied, which is designed to detect the essentiality of mean trends differences in selections, regardless of the type of changing variables distribution. According to the method’s algorithm, the entire array of experimental data was integrated into a common rank-ordered (ranked) row, where each variable was assigned a serial number (rank). Respective with the original selections, the variables’ mean ranks were calculated. The mean ranks subtraction results and the necessary statistical operators were determined, which were benchmarked against the table values (the table is attached). Thus, the statistical significance of the discovered results of subtraction was drawn. The method did not require any special mathematical background of the researchers.

Application in college practice teaching based on decision tree algorithm

When Chinese colleges and universities use information technology to improve management level, the internal system will set a variety of databases, such as employment information, educational administration information and so on. Although the system accumulates a large amount of data and information when it runs for a long time, the lack of information awareness and science and technology leads to the fact that managers can only obtain the surface information through simple statistical operations and cannot dig the hidden value. This paper mainly discusses decision tree algorithm and association rules, clarifies the application demand and teaching evaluation system of university practice teaching in the new era, and finally studies the application effect of decision tree algorithm in university practice teaching combined with practical cases.

Study the noise effect on the edges and cross correlation of TV images

The noise that associated with TV images is considered to be a very important problem that complicates the developments of these systems. Because of that, it makes the interpretation and analysis of informative images a very difficult process, and lessens their advantages. The most common type of these images are TV images transmitted by antenna work with (VHF, UHF) frequency and Satellite Transmission. Because TV images move through a complicated system in generating the TV or video signal, coding them, transmitting them, and receiving them by decoding and passing with different sets until appearing on screen. This will lead to a loose and fraud for a part of the information sent in the wave. As a result, it will be affected by an additive noise (Gaussian noise) which leads to deforming and destroying the quality of these images.
 This research aimed at studying the effect of the noise influence of this noise on edge as well as study of the relation between the statistical operators that include the mean and standard deviation (µ, σ) of edge’s regions for the components RGB and illumination component L as a function of time and as a function of antenna rotation.
 As well as we studied the relation between (µ, σ) and signal to noise ratio SNR for the cross correlation of edge's regions and for a homogeneous regions as well as for image as a whole once as a function of time and the other as a function of the angle of antenna rotation as in the first.

Intelligent computing with the knack of Bayesian neural networks for functional differential systems in Quantum calculus model

The purpose behind this research is to utilize the knack of Bayesian solver to determine numerical solution of functional differential equations arising in the quantum calculus models. Functional differential equations having discrete versions are very difficult to solve due to the presence of delay term, here with the implementation of Bayesian solver with means of neural networks, an efficient technique has been developed to overcome the complication in the model. First, the functional differential systems are converted into recurrence relations, then datasets are generated for converted recurrence relations to construct continuous mapping for neural networks. Second, the approximate solutions are determined through employing training and testing steps on generated datasets to learn the neural networks. Furthermore, comprehensive statistical analysis are presented by applying various statistical operators such as, mean squared error (MSE), regression analysis to confirm both accuracy as well as stability of the proposed technique. Moreover, its rapid convergence and reliability is also endorsed by the histogram, training state and correlation plots. Expected level for accuracy of suggested technique is further endorsed with the comparison of attained results with the reference solution. Additionally, accuracy and reliability is also confirmed by absolute error analysis.

Flood susceptible prediction through the use of geospatial variables and machine learning methods

Floods are one of the most perilous natural calamities that cause property destruction and endanger human life. The spatial patterns of flood susceptibility were assessed in this study using six applied machine learning (ML) models including Decision Tree (DT), Random Forest (RF), Multilayer Perceptron Neural Network (MLP-NN), Adaptive Boosting (AdaBoost), Logistic Regression (LR), and Support Vector Machines (SVM). A flood inventory map was provided using 180 flood points and 180 non-flood points. Both flood and non-flood locations were randomly partitioned into training and testing datasets with the ratio of 70:30. Sixteen various meteorological, hydrological, and geospatial variables were considered in the flood susceptibility assessment. The multi-collinearity analysis was performed to measure the intensity of correlations between the variables and determine whether factors can be included in the flood susceptibility assessment. From a total of sixteen variables, thirteen were chosen based on the multi-collinearity study. The five statistical indices Receiver Operating Characteristics curve (ROC), Jaccard index, F1 score, Overall Accuracy (OA), and Kappa coefficient (K) were used to evaluate models’ performance. Regarding the results of accuracy criteria, the RF model had the best predictive potential. Flood conditioning factors were reclassified using Quantile classification method and the importance of each class from a given flood conditioning factor was evaluated using the Frequency Ratio (FR) method. Additionally, the spatial relationship between the flood conditioning factors and the flood susceptibility map was investigated using the correlation matrix based on the Spearman correlation analysis. Findings of the correlation matrix revealed that the most important flood conditioning factors, in order of importance, were lithology, drainage density, distance from rivers, soil, rainfall, land cover, and Normalized Difference Moisture Index (NDMI). The flood susceptibility map, as the principal output of this study, was produced in five classes ranging from very low to very high susceptibility. The flood susceptibility map provided using the RF model (i.e., the model with the highest accuracy) indicated that the flood susceptibility is very high in 19.73% of the study area, high in 15.49%, moderate in 16.15%, low in 18.72%, and very low in 29.91%. Findings of this research might be an invaluable resource for better management of flooding since it specifically emphasized the primary causes of flooding as well as locations of heightened flood susceptibility.

Quantum local-equilibrium approach to dissipative hydrodynamics.

The macroscopic hydrodynamic equationsare derived for many-body systems in the local-equilibrium approach, using the Schrödinger picture of quantum mechanics. In this approach, statistical operators are defined in terms of microscopic densities associated with the fundamentally conserved quantities and other slow modes possibly emerging from continuous symmetry breaking, as well as macrofields conjugated to these densities. Functional identities can be deduced, allowing us to identify the reversible and dissipative parts of the mean current densities, to obtain general equationsfor the time evolution of the conjugate macrofields, and to establish the relationship to projection-operator methods. The entropy production is shown to be nonnegative by applying the Peierls-Bogoliubov inequality to a quantum integral fluctuation theorem. Using the expansion in the gradients of the conjugate macrofields, the transport coefficients are given by Green-Kubo formulas and the entropy production rate can be expressed in terms of quantum Einstein-Helfand formulas, implying its nonnegativity in agreement with the second law of thermodynamics. The results apply to multicomponent fluids and can be extended to condensed matter phases with broken continuous symmetries.

MEYER WAVELET NEURAL NETWORKS PROCEDURES TO INVESTIGATE THE NUMERICAL PERFORMANCES OF THE COMPUTER VIRUS SPREAD WITH KILL SIGNALS

This study shows the design of the Meyer wavelet neural networks (WNNs) to perform the numerical solutions of the spread of computer virus with kill signals, i.e. SEIR-KS system. The optimization of the SEIR-KS system is performed by the Meyer WNNs together with the optimization through the genetic algorithm (GA) and sequential quadratic (SQ) programming, i.e. Meyer WNNs-GASQ programming. A sigmoidal-based log-sigmoid function is implemented as an activation function, while 10 numbers of neurons work with 120 variables throughout this study. The correctness of the proposed Meyer WNNs-GASQP programming is observed through the comparison of the obtained and reference numerical solutions. For the consistency and reliability of the Meyer WNNs-GASQ programming, an analysis based on different statistical procedures is performed using 40 numbers of independent executions. Moreover, the use of different statistical operators like mean, median, minimum, standard deviation and semi-interquartile range further validates the correctness of the Meyer WNNs-GASQ programming for solving the SEIR-KS system.

ПЕРСПЕКТИВИ СТВОРЕННЯ СТАТИСТИЧНИХ СЕРВІСІВ ЗАГАЛЬНОГО КОРИСТУВАННЯ ДЛЯ ЕКОНОМІЧНИХ ДОСЛІДЖЕНЬ

The excessive popularity of service-oriented architecture implementations has led to considera-tion of the application of this approach in the production of organizations engaged in processing statis-tical and economic information for analytical research. This is due to the possibility of reusing services, each of which is designed to perform specific statistical functions, and the availability of design solu-tions for compiling these functions into a program for implementing a statistical task solution, using orchestration as a basis for dynamic service calls.The article proposes an approach to developing external statistical services as a set of software products for solving analytical tasks by groups of independent researchers who do not have sufficient financial support and for whom the use of externalstatistical services will provide the opportunity to conduct in-depth analysis of their research results. The proposed approach assumes that such a service should have its typical data storage structure with fixed types and attributes, to which the researcher imports their own data, selects a calculation execution scheme, initializes their execution, and receives results. Taking into account this storage scheme, the authors have identified the main groups of metadata necessary to describe the implementation of the statistical process. A statistical business process typi-cally consists of a set of procedures that implement certain statistical operations and data transfor-mations, and the provided set of metadata allows for the realization of the process goal and activity.Issues that require further in-depth research for the practical implementation of the proposed idea have been identified. The use of service-oriented architecture for building statistical processes allows for standardizing the approach by unifying the functions of individual services. The availability of pos-sibilities for practical implementation of software products in a service-oriented architecture using a cloud environment in modern IT industry serves as a guide for further research direction.

Breast Cancer Detection System using Deep Learning Based on Fusion Features and Statistical Operations

Breast Cancer Detection System using Deep Learning Based on Fusion Features and Statistical Operations

A p-Adic Model of Quantum States and the p-Adic Qubit

We propose a model of a quantum N-dimensional system (quNit) based on a quadratic extension of the non-Archimedean field of p-adic numbers. As in the standard complex setting, states and observables of a p-adic quantum system are implemented by suitable linear operators in a p-adic Hilbert space. In particular, owing to the distinguishing features of p-adic probability theory, the states of an N-dimensional p-adic quantum system are implemented by p-adic statistical operators, i.e., trace-one selfadjoint operators in the carrier Hilbert space. Accordingly, we introduce the notion of selfadjoint-operator-valued measure (SOVM)-a suitable p-adic counterpart of a POVM in a complex Hilbert space-as a convenient mathematical tool describing the physical observables of a p-adic quantum system. Eventually, we focus on the special case where N=2, thus providing a description of p-adic qubit states and 2-dimensional SOVMs. The analogies-but also the non-trivial differences-with respect to the qubit states of standard quantum mechanics are then analyzed.

Pipeline Risk Assessment Using GIS

Long-distance energy pipelines are subject to risks of repeated hazards and posing pipeline safety problems. Hazards that may attack the pipelines are environmental and human activities. In this study, the risks of hazards on pipeline were assessed using Geographic Information System (GIS) as research on pipeline risk assessment using GIS is quite limited. Satellites help to monitor pipelines from space. The study spatially analyzes the risks that a pipeline encounters and the Kurdistan oil pipeline from Taq Taq oil field to Peshkhabur was used as a case study. Six criteria including distance to cities and villages, rivers, roads, slopes, and temperature in cold and hot weather were considered. Weight is given to each criterion; a maximum of 37.5% for human activities and a minimum weight of 12.5% for slope. The calculations were carried out spatially rather than through statistical operations. Three sets of maps were obtained for each criterion with different units. Then the maps were overlayed to represent a single map and the units were standardized using Fuzzy membership. The results show the risk level of each criterion along the 270 km length of the Kurdistan national pipeline.

Geochemical functions Add-In to MS Excel for geochemical and mineralogical data processing

«Geochemical functions» is a new free Add-In to MS Excel for geochemists and mineralogists. It implements useful functions for routine operations in geochemical and mineralogical calculations: abundance normalization, calculations of empirical formula coeffi cients from chemical and EMPA analyses, statistical operations with partial and unequal data.