Search results for: multivariate distribution theory

Authors: Ebru Turgal, Beyza Doganay Erdogan

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Machine learning aims to model the relationship between the response and features. Medical decision-making researchers would like to make decisions about patients’ course and treatment, by examining the repeated measurements over time. Boosting approach is now being used in machine learning area for these aims as an influential tool. The aim of this study is to show the usage of multivariate tree boosting in this field. The main reason for utilizing this approach in the field of decision-making is the ease solutions of complex relationships. To show how multivariate tree boosting method can be used to identify important features and feature-time interaction, we used the data, which was collected retrospectively from Ankara University Chest Diseases Department records. Dataset includes repeated PF ratio measurements. The follow-up time is planned for 120 hours. A set of different models is tested. In conclusion, main idea of classification with weighed combination of classifiers is a reliable method which was shown with simulations several times. Furthermore, time varying variables will be taken into consideration within this concept and it could be possible to make accurate decisions about regression and survival problems.

Keywords: boosted multivariate trees, longitudinal data, multivariate regression tree, panel data

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Authors: Ezgi Nevruz, Kasirga Yildirak, Ashis SenGupta

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Comparing or ranking risks is the main motivating factor behind the human trait of making choices. Cumulative prospect theory (CPT) is a preference theory approach that evaluates perception and bias in decision making under risk and uncertainty. We aim to investigate the aggregate claims of different risk classes in terms of their comparability and amenability to ordering when the impact of risk perception is considered. For this aim, we prioritize the aggregate claims taken as actuarial risks by using various stochastic ordering relations. In order to prioritize actuarial risks, we use stochastic relations such as stochastic dominance and stop-loss dominance that are proposed in the frame of partial order theory. We take into account the dependency of the individual claims exposed to similar environmental risks. At first, we modify the zero-utility premium principle in order to obtain a solution for the stop-loss premium under CPT. Then, we propose a stochastic stop-loss dominance of the aggregate claims and find a relation between the stop-loss dominance and the first-order stochastic dominance under the dependence assumption by using properties of the familiar as well as some emerging multivariate claim distributions.

Keywords: cumulative prospect theory, partial order theory, risk perception, stochastic dominance, stop-loss dominance

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Authors: Ibrahim Suliman Hanaish

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Disaggregation daily rainfall using stochastic models formulated based on multivariate approach (MuDRain) is discussed in this paper. Seven rain gauge stations are considered in this study for different distances from the referred station starting from 4 km to 160 km in Peninsular Malaysia. The hourly rainfall data used are covered the period from 1973 to 2008 and July and November months are considered as an example of dry and wet periods. The cross-correlation among the rain gauges is considered for the available hourly rainfall information at the neighboring stations or not. This paper discussed the applicability of the MuDRain model for disaggregation daily rainfall to hourly rainfall for both sources of cross-correlation. The goodness of fit of the model was based on the reproduction of fitting statistics like the means, variances, coefficients of skewness, lag zero cross-correlation of coefficients and the lag one auto correlation of coefficients. It is found the correlation coefficients based on extracted correlations that was based on daily are slightly higher than correlations based on available hourly rainfall especially for neighboring stations not more than 28 km. The results showed also the MuDRain model did not reproduce statistics very well. In addition, a bad reproduction of the actual hyetographs comparing to the synthetic hourly rainfall data. Mean while, it is showed a good fit between the distribution function of the historical and synthetic hourly rainfall. These discrepancies are unavoidable because of the lowest cross correlation of hourly rainfall. The overall performance indicated that the MuDRain model would not be appropriate choice for disaggregation daily rainfall.

Keywords: rainfall disaggregation, multivariate disaggregation rainfall model, correlation, stochastic model

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Authors: Irfan Ahmad Afip

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This main research objective is to accurately identify the possibility for a Leptospirosis outbreak severity of a certain area based on its input features into a multivariate regression model. The research question is the possibility of an outbreak in a specific area being influenced by this feature, such as social demographics and hydrometeorological extremes. If the occurrence of an outbreak is being subjected to these features, then the epidemic severity for an area will be different depending on its environmental setting because the features will influence the possibility and severity of an outbreak. Specifically, this research objective was three-fold, namely: (a) to identify the relevant multivariate features and visualize the patterns data, (b) to develop a multivariate regression model based from the selected features and determine the possibility for Leptospirosis outbreak in an area, and (c) to compare the predictive ability of multivariate regression model and machine learning algorithms. Several secondary data features were collected locations in the state of Negeri Sembilan, Malaysia, based on the possibility it would be relevant to determine the outbreak severity in the area. The relevant features then will become an input in a multivariate regression model; a linear regression model is a simple and quick solution for creating prognostic capabilities. A multivariate regression model has proven more precise prognostic capabilities than univariate models. The expected outcome from this research is to establish a correlation between the features of social demographic and hydrometeorological with Leptospirosis bacteria; it will also become a contributor for understanding the underlying relationship between the pathogen and the ecosystem. The relationship established can be beneficial for the health department or urban planner to inspect and prepare for future outcomes in event detection and system health monitoring.

Keywords: geographical information system, hydrometeorological, leptospirosis, multivariate regression

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Authors: Boukhris Lahouari, Bouchetara Mostefa

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The active safety of a vehicle is mainly influenced by the properties of the installed braking system. With the increase in road traffic density and travel speeds, increasingly stringent requirements are placed on the vehicle's behaviour during braking. The achievable decelerations are limited by the physical aspect characterized by the coefficient of friction between the tires and the ground. As a result, it follows that an optimized distribution of braking forces becomes necessary for a better use of friction coefficients. This objective could only be achieved if sufficient knowledge is available on the theory of vehicle dynamics during braking and on current standards for the approval of braking systems. These will facilitate the development of a braking force calculation algorithm that will enable an optimized distribution of braking forces to be achieved. Operating safety is conditioned by the requirements of efficiency, progressiveness, regularity or fidelity of a braking system without obviously neglecting the recommendations imposed by the legislator.

Keywords: brake force distribution, distribution diagram, friction coefficient, brake by wire

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Authors: Boukhris Lahouari, Bouchetara Mostefa

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The active safety of a vehicle is mainly influenced by the properties of the installed braking system. With the increase in road traffic density and travel speeds, increasingly stringent requirements are placed on the vehicle's behaviour during braking. The achievable decelerations are limited by the physical aspect characterized by the coefficient of friction between the tires and the ground. As a result, it follows that an optimized distribution of braking forces becomes necessary for a better use of friction coefficients. This objective could only be achieved if sufficient knowledge is available on the theory of vehicle dynamics during braking and on current standards for the approval of braking systems. This will facilitate the development of a braking force calculation algorithm that will enable an optimized distribution of braking forces to be achieved. Operating safety is conditioned by the requirements of efficiency, progressiveness, regularity or fidelity of a braking system without obviously neglecting the recommendations imposed by the legislator.

Keywords: brake force distribution, distribution diagram, friction coefficient, brake by wire

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Authors: H. Aissa, L. Mouzai, M. Bouhadef

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The effects of down slope steepness on soil splash distribution under a water drop impact have been investigated in this study. The equipment used are the burette to simulate a water drop, a splash cup filled with sandy soil which forms the source area and a splash board to collect the ejected particles. The results found in this study have shown that the apparent mass increased with increasing downslope angle following a linear regression equation with high coefficient of determination. In the same way, the radial soil splash distribution over the distance has been analyzed statistically, and an exponential function was the best fit of the relationship for the different slope angles. The curves and the regressions equations validate the well known FSDF and extend the theory of Van Dijk.

Keywords: splash distribution, water drop, slope steepness, soil detachment

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Authors: Linh Nguyen Tung, Anh Truong Viet, Nghien Nguyen Ba, Chuong Trinh Trong

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This paper proposes a reconfiguration methodology based on a continuous genetic algorithm (CGA) and particle swarm optimization (PSO) for minimizing active power loss and minimizing voltage deviation. Both algorithms are adapted using graph theory to generate feasible individuals, and the modified crossover is used for continuous variable of CGA. To demonstrate the performance and effectiveness of the proposed methods, a comparative analysis of CGA with PSO for network reconfiguration, on 33-node and 119-bus radial distribution system is presented. The simulation results have shown that both CGA and PSO can be used in the distribution network reconfiguration and CGA outperformed PSO with significant success rate in finding optimal distribution network configuration.

Keywords: distribution network reconfiguration, particle swarm optimization, continuous genetic algorithm, power loss reduction, voltage deviation

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Authors: Bezhan Ghvaberidze

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A fuzzy multi-objective facility location-selection problem (FLSP) under uncertain and extreme environments based on possibility theory is developed. The model’s uncertain parameters in the q-rung orthopair fuzzy values are presented and transformed in the Dempster-Shaper’s belief structure environment. An objective function – distribution centers’ selection ranking index as an extension of Dempster’s extremal expectations under discrimination q-rung orthopair fuzzy information is constructed. Experts evaluate each humanitarian aid from distribution centers (HADC) against each of the uncertain factors. HADCs location problem is reduced to the bicriteria problem of partitioning the set of customers by the set of centers: (1) – Minimization of transportation costs; (2) – Maximization of centers’ selection ranking indexes. Partitioning type constraints are also constructed. For an illustration of the obtained results, a numerical example is created from the facility location-selection problem.

Keywords: FLSP, multi-objective combinatorial optimization problem, evidence theory, HADC, q-rung orthopair fuzzy set, possibility theory

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Authors: Thrivikraman Aswathi, S. Advaith

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As there can be cases where the use of real data is somehow limited, such as when it is hard to get access to a large volume of real data, we need to go for synthetic data generation. This produces high-quality synthetic data while maintaining the statistical properties of a specific dataset. In the present work, a generative adversarial network (GAN) is trained to produce multivariate time series (MTS) data since the MTS is now being gathered more often in various real-world systems. Furthermore, the GAN-generated MTS data is fed into a transformer-based deep learning architecture that carries out the data categorization into predefined classes. Further, the model is evaluated across various distinct domains by generating corresponding MTS data.

Keywords: GAN, transformer, classification, multivariate time series

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Authors: Balasaheb Bansode, L. Ladusingh

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The public distribution system is an important component of food security. It is a massive welfare program undertaken by Government of India and implemented by state government since India being a federal state; for achieving multiple objectives like eliminating hunger, reduction in malnutrition and making food consumption affordable. This program reaches at the community level through the various agencies of the government. The paper focuses on the accessibility of PDS at household level and how the present policy framework results in exclusion and inclusion errors. It tries to explore the sanctioned food grain quantity received by differentiated ration cards according to income criterion at household level, and also it has highlighted on the type of corruption in food distribution that is generated by the PDS system. The data used is of secondary nature from NSSO 68 round conducted in 2012. Bivariate and multivariate techniques have been used to understand the working and consumption of food for this paper.

Keywords: calories intake, entitle food quantity, poverty aliviation through PDS, target error

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Authors: Igor Vladimirovich Kuzminov

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The proposed article is an alternative version of the Theory of Relativity. The version is based on the concepts of classical Newtonian physics and does not deny the existing calculation base. The proposed theory completely denies Einstein's existing Theory of Relativity. The only thing that connects these theories is that the proposed theory is also built on postulates. The proposed theory is intended to establish the foundation of classical Newtonian physics. The proposed theory is intended to establish continuity in the development of the fundamentals of physics and is intended to eliminate all kinds of speculation in explanations of physical phenomena. An example of such speculation is Einstein's Theory of Relativity (E).

Keywords: the theory of relativity, postulates of the theory of relativity, criticism of Einstein's theory, classical physics

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Authors: Hang Yang, Jichao Li, Kewei Yang, Tianyang Lei

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Multivariate time series anomaly detection is a significant research topic in the field of data mining, encompassing a wide range of applications across various industrial sectors such as traffic roads, financial logistics, and corporate production. The inherent spatial dependencies and temporal characteristics present in multivariate time series introduce challenges to the anomaly detection task. Previous studies have typically been based on the assumption that all variables belong to the same spatial hierarchy, neglecting the multi-level spatial relationships. To address this challenge, this paper proposes a multi-scale spatial and unified temporal feature fusion network, denoted as MSUT-Net, for multivariate time series anomaly detection. The proposed model employs a multi-level modeling approach, incorporating both temporal and spatial modules. The spatial module is designed to capture the spatial characteristics of multivariate time series data, utilizing an adaptive graph structure learning model to identify the multi-level spatial relationships between data variables and their attributes. The temporal module consists of a unified temporal processing module, which is tasked with capturing the temporal features of multivariate time series. This module is capable of simultaneously identifying temporal dependencies among different variables. Extensive testing on multiple publicly available datasets confirms that MSUT-Net achieves superior performance on the majority of datasets. Our method is able to model and accurately detect systems data with multi-level spatial relationships from a spatial-temporal perspective, providing a novel perspective for anomaly detection analysis.

Keywords: data mining, industrial system, multivariate time series, anomaly detection

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Authors: Daniel Nkrumah

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The Magic Bullet theory was a popular media effect theory that defined the power of the mass media in altering beliefs and perceptions of its audiences. However, following the People's Choice study, the theory was said to have been disproved and was supplanted by the Two-Step Flow Theory. This paper examines the relevance of the Magic Bullet theory in Africa and establishes whether it is still relevant in Africa's media spaces and societies. Using selected cases on the continent, it adopts a grounded theory approach and explores a new theoretical model that attempts to enforce an argument that the Two-Step Flow theory though important and valid, was ill-conceived as a direct replacement to the Magic Bullet theory.

Keywords: magic bullet theory, two-step flow theory, media effects, african media

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Authors: Islem Kedidi, Rihab Bedoui Bensalem, Faysal Manssouri

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In traditional models of insurance data, the number and size of claims are assumed to be independent. Relaxing the independence assumption, this article explores the Vine copula to model dependence structure between multivariate frequency and average severity of insurance claim. To illustrate this approach, we use the Wisconsin local government property insurance fund which offers several insurance protections for motor vehicles, property and contractor’s equipment claims. Results show that the C-vine copula can better characterize the multivariate dependence structure between frequency and severity. Furthermore, we find significant dependencies especially between frequency and average severity among different coverage types.

Keywords: dependency modeling, government insurance, insurance claims, vine copula

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Authors: Wanhyun Cho, Soonja Kang, Sanggoon Kim, Soonyoung Park

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We present probabilistic multinomial Dirichlet classification model for multidimensional data and Gaussian process priors. Here, we have considered an efficient computational method that can be used to obtain the approximate posteriors for latent variables and parameters needed to define the multiclass Gaussian process classification model. We first investigated the process of inducing a posterior distribution for various parameters and latent function by using the variational Bayesian approximations and important sampling method, and next we derived a predictive distribution of latent function needed to classify new samples. The proposed model is applied to classify the synthetic multivariate dataset in order to verify the performance of our model. Experiment result shows that our model is more accurate than the other approximation methods.

Keywords: multinomial dirichlet classification model, Gaussian process priors, variational Bayesian approximation, importance sampling, approximate posterior distribution, marginal likelihood evidence

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Authors: Manel Youssef, Lotfi Belkacem

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This paper employs multivariate long memory GARCH models to simultaneously estimate mean and conditional variance spillover effects between oil prices and different financial markets. Since different financial assets are traded based on these market sector returns, it’s important for financial market participants to understand the volatility transmission mechanism over time and across these series in order to make optimal portfolio allocation decisions. We examine weekly returns from January 1, 2003 to November 30, 2012 and find evidence of significant transmission of shocks and volatilities between oil prices and some of the examined financial markets. The findings support the idea of cross-market hedging and sharing of common information by investors.

Keywords: oil prices, stock indices returns, oil volatility, contagion, DCC-multivariate (FI) GARCH

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Authors: Malika Kharouf, Aly Chkeir, Khac Tuan Huynh

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Detecting change points in time series data is a challenging problem, especially in scenarios where there is limited prior knowledge regarding the data’s distribution and the nature of the transitions. We present a method designed for detecting changes in the covariance structure of high-dimensional time series data, where the number of variables closely matches the data length. Our objective is to achieve unbiased test statistic estimation under the null hypothesis. We delve into the utilization of Random Matrix Theory to analyze the behavior of our test statistic within a high-dimensional context. Specifically, we illustrate that our test statistic converges pointwise to a normal distribution under the null hypothesis. To assess the effectiveness of our proposed approach, we conduct evaluations on a simulated dataset. Furthermore, we employ our method to examine changes aimed at detecting frailty in the elderly.

Keywords: change point detection, hypothesis tests, random matrix theory, frailty in elderly

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Authors: Farouk Metiri, Halim Zeghdoudi, Mohamed Riad Remita

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Credibility theory is an experience rating technique in actuarial science which can be seen as one of quantitative tools that allows the insurers to perform experience rating, that is, to adjust future premiums based on past experiences. It is used usually in automobile insurance, worker's compensation premium, and IBNR (incurred but not reported claims to the insurer) where credibility theory can be used to estimate the claim size amount. In this study, we focused on a popular tool in credibility theory which is the Bayesian premium estimator, considering Lindley distribution as a claim distribution. We derive this estimator under entropy loss which is asymmetric and squared error loss which is a symmetric loss function with informative and non-informative priors. In a purely Bayesian setting, the prior distribution represents the insurer’s prior belief about the insured’s risk level after collection of the insured’s data at the end of the period. However, the explicit form of the Bayesian premium in the case when the prior is not a member of the exponential family could be quite difficult to obtain as it involves a number of integrations which are not analytically solvable. The paper finds a solution to this problem by deriving this estimator using numerical approximation (Lindley approximation) which is one of the suitable approximation methods for solving such problems, it approaches the ratio of the integrals as a whole and produces a single numerical result. Simulation study using Monte Carlo method is then performed to evaluate this estimator and mean squared error technique is made to compare the Bayesian premium estimator under the above loss functions.

Keywords: bayesian estimator, credibility theory, entropy loss, monte carlo simulation

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Authors: Arne Fagerstrom, Gary Cunningham, Fredrik Hartwig

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In this paper, a theory of sustainable enterprises, sustainable enterprise theory (SET), is developed. The sustainable enterprise theory can only be a valid theory if knowledge about life and nature is complete. Knowledge limitations should not stop enterprises from doing business with a goal of better long-term life on earth. Life demands stewardship of the resources used during one’s lifetime. This paper develops a model influenced by (the classical) enterprise theory and resource theory that includes more than money in the business activities of an enterprise. The sustainable enterprise theory is then used in an analysis of accountability and in discussions about sustainable businesses.

Keywords: sustainable business, sustainability reporting, sustainable values, theory of the firm

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Authors: Leila Jafari, Viliam Makis, G. B. Akram Khaleghei

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In this paper, we present a maintenance model of a two-unit series system with economic dependence. Unit#1, which is considered to be more expensive and more important, is subject to condition monitoring (CM) at equidistant, discrete time epochs and unit#2, which is not subject to CM, has a general lifetime distribution. The multivariate observation vectors obtained through condition monitoring carry partial information about the hidden state of unit#1, which can be in a healthy or a warning state while operating. Only the failure state is assumed to be observable for both units. The objective is to find an optimal opportunistic maintenance policy minimizing the long-run expected average cost per unit time. The problem is formulated and solved in the partially observable semi-Markov decision process framework. An effective computational algorithm for finding the optimal policy and the minimum average cost is developed and illustrated by a numerical example.

Keywords: condition-based maintenance, semi-Markov decision process, multivariate Bayesian control chart, partially observable system, two-unit system

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Authors: Pham Van Nha, Le Cam Binh

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Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm. PSO was inspired by the natural behavior of birds and fish in migration and foraging for food. PSO is considered as a multidisciplinary optimization model that can be applied in various optimization problems. PSO’s ideas are simple and easy to understand but PSO is only applied in simple model problems. We think that in order to expand the applicability of PSO in complex problems, PSO should be described more explicitly in the form of a mathematical model. In this paper, we represent PSO in a mathematical model and apply in the multivariate data classification. First, PSOs general mathematical model (MPSO) is analyzed as a universal optimization model. Then, Model of Optimal Centroids (MOC) is proposed for the multivariate data classification. Experiments were conducted on some benchmark data sets to prove the effectiveness of MOC compared with several proposed schemes.

Keywords: analysis of optimization, artificial intelligence based optimization, optimization for learning and data analysis, global optimization

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Authors: A. Bozkurt, C. Kocatepe, R. Yumurtaci, İ. C. Tastan, G. Tulun

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Efficient use of energy, with the increase in demand of energy and also with the reduction of natural energy sources, has improved its importance in recent years. Most of the losses in the system from electricity produced until the point of consumption is mostly composed by the energy distribution system. In this study, analysis of the resulting loss in power distribution transformer and distribution power cable is realized which are most of the losses in the distribution system. Transformer losses in the real distribution system were analyzed by CYME Power Engineering Software program. These losses are disclosed for different voltage levels and different loading conditions.

Keywords: distribution system, distribution transformer, power cable, technical losses

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Authors: Ambachew Simreteab Gebremedhn

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Context: The paper explores the use of synchronous reference frame theory (SRFT) and instantaneous reactive power theory (IRPT) based control of Unified Power Quality Conditioner (UPQC) for improving power quality in distribution systems. Research Aim: To investigate the performance of different control configurations of UPQC using SRFT and IRPT for mitigating power quality issues in distribution systems. Methodology: The study compares three control techniques (SRFT-IRPT, SRFT-SRFT, IRPT-IRPT) implemented in series and shunt active filters of UPQC. Data is collected under various control algorithms to analyze UPQC performance. Findings: Results indicate the effectiveness of SRFT and IRPT based control techniques in addressing power quality problems such as voltage sags, swells, unbalance, harmonics, and current harmonics in distribution systems. Theoretical Importance: The study provides insights into the application of SRFT and IRPT in improving power quality, specifically in mitigating unbalanced voltage sags, where conventional methods fall short. Data Collection: Data is collected under various control algorithms using simulation in MATLAB Simulink and real-time operation executed with experimental results obtained using RT-LAB. Analysis Procedures: Performance analysis of UPQC under different control algorithms is conducted to evaluate the effectiveness of SRFT and IRPT based control techniques in mitigating power quality issues. Questions Addressed: How do SRFT and IRPT based control techniques compare in improving power quality in distribution systems? What is the impact of using different control configurations on the performance of UPQC? Conclusion: The study demonstrates the efficacy of SRFT and IRPT based control of UPQC in mitigating power quality issues in distribution systems, highlighting their potential for enhancing voltage and current quality.

Keywords: power quality, UPQC, shunt active filter, series active filter, non-linear load, RT-LAB, MATLAB

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Authors: O. P. Popoola, O. A. Alawode, M. O. Olayiwola, A. M. Oladele

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This research work is based on using discriminant analysis to forecast crime rate in Nigeria between 1996 and 2008. The work is interested in how gender (male and female) relates to offences committed against the government, against other properties, disturbance in public places, murder/robbery offences and other offences. The data used was collected from the National Bureau of Statistics (NBS). SPSS, the statistical package was used to analyse the data. Time plot was plotted on all the 29 offences gotten from the raw data. Eigenvalues and Multivariate tests, Wilks’ Lambda, standardized canonical discriminant function coefficients and the predicted classifications were estimated. The research shows that the distribution of the scores from each function is standardized to have a mean O and a standard deviation of 1. The magnitudes of the coefficients indicate how strongly the discriminating variable affects the score. In the predicted group membership, 172 cases that were predicted to commit crime against Government group, 66 were correctly predicted and 106 were incorrectly predicted. After going through the predicted classifications, we found out that most groups numbers that were correctly predicted were less than those that were incorrectly predicted.

Keywords: discriminant analysis, DA, multivariate analysis of variance, MANOVA, canonical correlation, and Wilks’ Lambda

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Authors: Arunas Burinskas, Manuela Tvaronavičienė

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Although the new trade theories, unlike the theories of an industrial organisation, see the structure of the market and competition between enterprises through their heterogeneity according to various parameters, they do not pay any particular attention to the analysis of the market structure and its development. In this article, although we relied mainly on models developed by the scholars of new trade theory, we proposed a different approach. In our simulation model, we model market demand according to normal distribution function, while on the supply side (as it is in the new trade theory models), productivity is modeled with the Pareto distribution function. The results of the simulation show that companies with higher productivity (lower marginal costs) do not pass on all the benefits of such economies to buyers. However, even with higher marginal costs, firms can choose to offer higher value-added goods to stay in the market. In general, the structure of the market is formed quickly enough and depends on the skills available to firms.

Keywords: market, structure, simulation, heterogenous firms

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Authors: Luigi Delle Rose, Oliver Fischer, Ahmed Hammad

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In this article, we study the prospects of the proposed Large Hadron electron Collider (LHeC) in the search for heavy neutral scalar particles. We consider a minimal model with one additional complex scalar singlet that interacts with the Standard Model (SM) via mixing with the Higgs doublet, giving rise to an SM-like Higgs boson and a heavy scalar particle. Both scalar particles are produced via vector boson fusion and can be tested via their decays into pairs of SM particles, analogously to the SM Higgs boson. Using multivariate techniques, we show that the LHeC is sensitive to heavy scalars with masses between 200 and 800 GeV down to scalar mixing of order 0.01.

Keywords: beyond the standard model, large hadron electron collider, multivariate analysis, scalar singlet

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Authors: Abd El Hady N. Ebraheim

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This paper introduces a new generalization of the two parameter Weibull distribution. To this end, the quadratic rank transmutation map has been used. This new distribution is named exponentiated transmuted Weibull (ETW) distribution. The ETW distribution has the advantage of being capable of modeling various shapes of aging and failure criteria. Furthermore, eleven lifetime distributions such as the Weibull, exponentiated Weibull, Rayleigh and exponential distributions, among others follow as special cases. The properties of the new model are discussed and the maximum likelihood estimation is used to estimate the parameters. Explicit expressions are derived for the quantiles. The moments of the distribution are derived, and the order statistics are examined.

Keywords: exponentiated, inversion method, maximum likelihood estimation, transmutation map

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Authors: K. A. Adepoju, O. I. Shittu, A. U. Chukwu

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In this paper, a new four-parameter generalized version of the Fisher Snedecor distribution called Beta- F distribution is introduced. The comprehensive account of the statistical properties of the new distributions was considered. Formal expressions for the cumulative density function, moments, moment generating function and maximum likelihood estimation, as well as its Fisher information, were obtained. The flexibility of this distribution as well as its robustness using cancer remission time data was demonstrated. The new distribution can be used in most applications where the assumption underlying the use of other lifetime distributions is violated.

Keywords: fisher-snedecor distribution, beta-f distribution, outlier, maximum likelihood method

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Authors: Ma Chaoyang

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This article starts from the perspective of environmental justice theory and analyzes the accessibility and regional equity of park green spaces in the central urban area of Chengdu in 2022 based on the improved Gaussian 2SFCA analysis method and Gini coefficient method. Then, according to the relevant analysis model, it further explores the correlation between the spatial distribution of park green spaces and the socio-economic conditions of residents in order to provide a reference for the construction and research of Chengdu's park city under the guidance of fairness and justice. The results show that: (1) Overall, the spatial distribution of parks and green spaces in Chengdu shows a significantly uneven distribution of extreme core edge, with a certain degree of unfairness; that is, there is an environmental injustice pattern. (2) The spatial layout of urban parks and green spaces is subject to strong guiding interference from the socio-economic level; that is, there is a high correlation between housing prices and the tendency of parks. (3) Green space resources Gini coefficient analysis shows that residents of the three modes of transportation in the study area have unequal opportunities to enjoy park and green space services, and the degree of unfairness in walking is much greater than that in cycling and cycling.

Keywords: parks and green spaces, environmental justice, two step mobile search method, Gini coefficient, spatial distribution

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