Goodness-of-fit Test Research Articles

Norm‐based spectrum sensing for cognitive radios under generalised Gaussian noise

AbstractCognitive radio (CR) systems are configured to dynamically assess the spectrum utilisation and contribute towards an improved spectrum efficiency. Hence, accurate detection of the incumbent signal in a given channel, popularly known as spectrum sensing (SS), is essential for CR. Here, in the domain of SS, the authors introduce a new goodness‐of‐fit test (GoFT) founded on p‐norm of the observations at the receiver node. To capture the heavy‐tailed nature of noise distribution in practical communication channels, the authors utilise generalised Gaussian distribution (GGD) as a noise model. A novel p‐norm detector (PND) and a geometric power detector (GPD) is proposed and corresponding probability density function (PDF) under GGD is derived. Via Monte Carlo simulations, the authors show a match of the derived PDFs with the simulation results. Using Neyman‐Pearson framework the performances of PND and GPD are compared with an existing differential entropy detector (DED), the well‐known energy detector (ED) and joint correlation and energy detector (CED) under GGD noise model. Evaluation of proposed PND and GPD utilising Monte Carlo simulations indicate a superior performance. Further, the experiments employing real‐world data establish superiority of the proposed detectors as compared to existing techniques. The authors derive and implement an optimised threshold for PND, providing further improvement in performance.

The value of non-contrast chest CT in the prediction of myocardial injury in patients with the COVID-19 Omicron variant

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Omicron variant associated myocardial injury seriously affected the patient's health. Chest computed tomography (CT) is an essential imaging diagnostic tool for evaluating lung diseases in these patients, but its value in the diagnosis of myocardial injury remains unknown. The purpose of this study was to evaluate the lung lesions in patients with Omicron infection with or without myocardial injury, and to evaluate the predictive value of non-contrast chest CT in such patients with myocardial injury. We enrolled 122 consecutive hospitalized patients with laboratory-confirmed coronavirus disease 2019 (COVID-19) for non-contrast chest CT examination. These patients were divided into two groups according to whether myocardial injury occurred. Myocardial injury was defined as a Troponin I level above the 99th-percentile upper reference limit (0.04 ng/mL). The imaging manifestations of the patients’ lungs were evaluated. Myocardial CT value, left atrium (LA) size, long diameter of left ventricular (LV), and cardiothoracic ratio (CTR) were recorded. Multivariate logistic analysis was performed to identify the predictive factors associated with myocardial injury. Of 122 patients, 61 patients (50%) had myocardial injury. Compared with patients without myocardial injury, there was worse NYHA class, more critical patients, higher incidence of bronchial meteorology, larger area and percentage of lung lesions, diameters of LA, and lower myocardial CT value in the myocardial injury group (P < 0.05). Troponin I concentration in patients with myocardial injury group showed negative correlation with myocardial CT value (r = − 0.319, P = 0.012). The multivariable logistic regression analysis showed that disease severity status (OR 2.279; 95% CI 1.247–4.165, P = 0.007), myocardial CT value (OR 0.849; 95% CI 0.752–0.958, P = 0.008), neutrophil count (OR 1.330; 95% CI 1.114–1.587, P = 0.002) were independent predictors of myocardial injury. The discrimination of the model was good (C-statistic = 0.845, 95% CI 0.775–0.914) and well calibrated with a Hosmer–Lemeshow test for goodness of fit (P = 0.476). Patients infected with Omicron with myocardial injury had more severe lung disease than those without myocardial injury. Non-contrast chest CT can be a useful method of detection of myocardial injury in Omicron infection patients.

A Dynamic Hybrid Cryptosystem Using Chaos and Diffie–Hellman Protocol: An Image Encryption Application

Images with sensitive content require encryption for storage and transmission. Symmetric schemes can cipher them, while an asymmetric cryptosystem can distribute the secret key safely. For this reason, we propose a dynamic hybrid cryptosystem, which ciphers images and transfers its private keys. It has a symmetric algorithm that applies the Lorenz equations for generating different boxes and permutations in every encryption process and round. Since the secret key concatenates two private numbers, an asymmetric algorithm is included for its key distribution. The proposal uses the Diffie–Hellman protocol with ElGamal for obtaining a seed and building 128 strings. Then, the SHA-512 is applied in each of them a number of times associated with the secret key value in its blockchain representation. The resultant strings are concatenated to conform to the public key. Finally, the tests indicate that the cryptosystem resists differential, linear, algebraic, and brute-force attacks. Its cipher quality is high according to the entropy, correlation, DFT, NPCR, UACI, AC, texture analysis, and goodness of fit test. Additionally, occlusion, additive, multiplicative, and the proposed χ2 noise attacks are simulated on encrypted images. Finally, the sharpness loss is measured with the Similarity Parameter and improved with a filter 5 × 5.

External validation of NTCP-models for radiation pneumonitis in lung cancer patients treated with chemoradiotherapy

PurposeNormal tissue complication probability (NTCP) models can be used to estimate the risk of radiation pneumonitis (RP). The aim of this study was to externally validate the most frequently used prediction models for RP, i.e., the QUANTEC and APPELT models, in a large cohort of lung cancer patients treated with IMRT or VMAT. [1–2] Methods and materialsThis prospective cohort study, included lung cancer patients treated between 2013 and 2018. A closed testing procedure was performed to test the need for model updating. To improve model performance, modification or removal of variables was considered. Performance measures included tests for goodness of fit, discrimination, and calibration. ResultsIn this cohort of 612 patients, the incidence of RP ≥ grade 2 was 14.5%. For the QUANTEC-model, recalibration was recommended which resulted in a revised intercept and adjusted regression coefficient (from 0.126 to 0.224) of the mean lung dose (MLD),. The APPELT-model needed revision including model updating with modification and elimination of variables. After revision, the New RP-model included the following predictors (and regression coefficients): MLD (B = 0.250), age (B = 0.049, and smoking status (B = 0.902). The discrimination of the updated APPELT-model was higher compared to the recalibrated QUANTEC-model (AUC: 0.79 vs. 0.73). ConclusionsThis study demonstrated that both the QUANTEC- and APPELT-model needed revision. Next to changes of the intercept and regression coefficients, the APPELT model improved further by model updating and performed better than the recalibrated QUANTEC model. This New RP-model is widely applicable containing non-tumour site specific variables, which can easily be collected.

Diagnostic Value of Clinical Parameters for the Prediction of Osteoporosis in Menopause Iranian Women

Background: Osteoporosis might result in an increased risk of bone fracture. Diagnosis of osteoporosis results in proper treatment and reduction of fracture rate. Objectives: This study aimed to construct a predictive model of osteoporosis case finding in Iranian women. Methods: A prospective diagnostic value study was designed, enrolling 317 asymptomatic women 50 years old or more referred for screening, at the Imam Hossein Medical Center, Tehran, Iran, for two years. The data was collected with the census method. A questionnaire including risk factors was completed, and bone mass densitometry (BMD) was done by the dual-energy X-ray absorptiometry (DXA) method in all cases. According to standard curves, bone density of the femur and lumbar spine clarified osteoporosis status for each person. In the first step, univariate analysis with osteoporosis as the main outcome did use the chi-squared test, independent sample t-test. In the next step, factors with a P-value of less than 0.2 were included in the multivariate logistic model, and a predictive model was constructed. The goodness of fit test was applied to assess the model building. The area under the curve (AUC) calculated for the model and the best cut-point for risk of menopause according to the Youden index were determined. The significance level was set at 0.05 for statistical analysis. Statistical analyses did use the program SSPS version 17.0 (SPSS, Inc., Chicago, IL, USA). Results: In 317 cases of the present study, the mean age of the population was 52.46 years old. Ninety-nine (%31.2) of these asymptomatic women revealed osteoporosis on the BMD test. Age and family history of osteoporosis were risk factors, and BMI, parity, and menopause age were protective factors for osteoporosis. Constructed model of osteoporosis prediction was as follows: (age × 0.149) + (family history × 0.963) - (BMI × 0.088) - (menopause age × 0.097) - (parity × 0.80). Optimal cutoff = 0.336 based on Youden method was chosen to predict the risk of osteoporosis. Conclusions: BMD test in Iran in more than 50 years old might find positive osteoporotic cases in at least 23.8%. BMD test in Iran in more than 50 years old might find positive osteoporotic cases in at least 23.8%. A model of osteoporosis probability constructed based on age, family history, menopause age, and parity in the present study can predict women at risk of osteoporosis.

Assessment of menstrual hygiene management practice and associated factors among prisoners in South Nation Nationalities and peoples region, Ethiopia

IntroductionMenstrual hygiene management practice is the requirements and necessities; such as the use of a sanitary pad or clean and mushy absorbents, sufficient washing of the genital area, proper disposal of the used absorbents, and other special needs for the women during menstruation. ObjectiveTo assess menstrual hygiene management practice and associated factors among prisoners in south Nation Nationalities and Peoples Region, Ethiopia, 2022. MethodsAn institution-based cross-sectional study was conducted from May 1 to July 30/2022. The total sample size was 605 and a simple random sampling technique was used to select prison institutions and women. The data were collected through face-to-face interviews. Data were entered using Epi data 4.6 version and analyzed by using SPSS version 26 software. Multicollinearity was checked and the goodness of fit test was done by using the Hosmer Lemeshow model of goodness fit test. Univariate analysis was done and variables with p value less than 0.25 were taken to bivariate logistic regression analysis. Adjusted odds ratio with the 95% confidence interval was considered and statistical significance was at a p-value less than 0.05 in bivariate logistic regression analysis. ResultThe prevalence of menstrual hygiene management practice was 50.6% (95% CI 47.3–54.4). Age 19–29 years old [AOR: 5.03, 95% CI 1.73–14.62], educational status; not formally educated [AOR: 0.05, 95% CI 0.02–0.13], educational status; primary level [AOR: 0.17, 95% CI 0.07–0.39], previous occupation; student [AOR: 2.56, 95% CI 1.06–6.21], previous occupation; a private employee [AOR: 4.11, 95% CI 1.48–11.42], previous occupation; government employee [AOR: 3.46, 95% CI 1.18–10.14], absence of support from family [AOR: 0.14, 95% CI 0.08–0.24] and absence of work engagement in prison [AOR: 0.44, 95% CI 0.25–0.78] were associated with MHMP. ConclusionIn this study, about five from ten women in prison practice menstrual hygiene management. Age, educational status, previous occupation, support from family, and work engagement in prison were important risk factors for MHMP. Therefore, support from family and engaging the women to work in prison institutions may increase the MHMP in prison institutions.

Comparison of the performance of different wind speed distribution models applied to onshore and offshore wind speed data in the Northeast Brazil

This paper aims to evaluate the performance presented by different distribution models when applied to onshore and offshore wind speed data and determine which distribution has the best fit in each location. The onshore data were measured at two stations located in the Northeast Brazil and the offshore data were measured by two ocean buoys located in the South Atlantic. Five probability distributions were used to model wind speed, namely: Weibull, Nakagami, extended generalized Lindley, generalized gamma and generalized extreme value. The probability distribution parameters were estimated using maximum likelihood, modified maximum likelihood and the method of multi-objective moments. In addition, three goodness of fit tests were used to select the distribution model that best fits a region’s wind speed data, namely: Kolmogorov–Smirnov test, Deviation of Skewness and Kurtosis and the Akaike Information Criterion. The results of these tests were aggregated into a single performance metric called “total error”. In both onshore locations, the extended generalized Lindley and generalized gamma were, in general, superior to the other distributions. In the offshore wind data from the ocean buoys, the extended generalized Lindley, generalized gamma and Weibull showed the best fit in adjusting the histogram. The generalized extreme value distribution presented the worst fit for all four locations. According to the results, there is no single distribution model that is more appropriate to fit a set of wind speed data, and it is necessary to carry out a study to know which of the available distributions is the most appropriate for any particular dataset.

Beyşehir Gölü Populasyonundaki Sudak (Sander Lucioperca Linnaeus, 1758) Balıklarının Vücut Uzunluğu ile Canlı Ağırlıkları Arasındaki İlişkilerin Matematiksel Modellerle İncelenmesi

The aim of this study is to examine the relationship between the total length and standard lengths with live weight of the freshwater pikeperch growing in the Beyşehir Lake population using four mathematical models. 50 female fish materials of marketable size limit age were used. The data obtained were divided into two groups: estimation (70%) and test (30%). The applied models are exponential decay, exponential and Wood models, and in such research generally preferred power model. R2, R2adj, RMSE, AIC, BIC and MAPE values were used as goodness of fit tests and comparison criteria of the models. According to the study results, the total length gives more accurate estimates than a standard length in the estimating live weight. As a result, it was concluded that other models might provide better results than the power model, and they were suggested to be used in estimating the live weight of pikeperch fish.

Performance Analysis of Public Transport in Gianyar Regency

The performance of an urban public transport plays an important role in accommodating population mobility and realizing sustainable transportation. The performance of public transport can be influenced by several factors, including the quality of public transport services themselves and the accessibility of the roads they pass. Service quality concerns arrival frequency, waiting time, travel time and so on. Road accessibility is influenced by the presence of side barriers, such as parking vehicles on the road, vehicles in and out, slow vehicles, and so on. This study analyzes the exogenous latent variables of Service Quality and Road Accessibility that affect the endogenous variables of Public Transportation Performance in Gianyar Regency, especially on the Batubulan-Ubud Terminal Route. The method of collecting community perception data is by distributing 250 questionnaires to respondents who live along the Batubulan-Ubud Terminal Route Corridor. The data analysis method used Statistical Equation Modeling (SEM) AMOS. Validity and reliability tests were carried out using the confirmatory factor analysis (CFA) method. Based on the results of the analysis, it was found that the quality of public transportation services had a significant positive effect, with p value = 0.18 &lt;0.05, path coefficient 0.049. Road accessibility also has a significant positive effect, with p value = 0.035 &lt; 0.05, coefficient. line 0.225. The Goodness of Fit test also fulfills parameters such as: Chi-square count 66.27 &lt; Chi-square table 81.381; Probability 0.332 0.05; CMIN/DF 1.069 2.00; GFI 0.961 0.960; AGFI 0.943 0.90; TLI 0.995 0.950; CFI 0.995 0.95; RMSEA 0.000 0.08 all meet. The performance of public transportation on the Batubulan-Ubud Terminal Route is considered very bad due to poor service quality and road accessibility.

Intensity-Duration-Frequency equations for Rio Grande do Sul - Brazil, based on stationary rainfall series

Heavy rainfall information is essential for environmental studies and water engineering. This study therefore aimed to adjust Intensity-Duration-Frequency (IDF) equations for 247 locations in the Rio Grande do Sul (RS) using stationary rainfall series. Mann-Kendall’s test was applied to identify the temporal trends in the Annual Maximum Daily Rainfall (AMDR) series of 271 rain gauges in RS. The Kappa, Generalized Extreme Value (GEV), Gumbel, two-parameters Log-Normal and three-parameters Log-Normal probabilistic distributions were adjusted to the AMDR series without significant temporal trend. The best distribution fit was given by Anderson-Darling’s test, so the AMDR was discretized up to 5 minutes. IDF equations coefficients were adjusted in RStudio, using Nash-Sutcliffe’s Coefficient and the Root-Mean-Square Error to evaluate them. In conclusion: the most suitable distributions for the AMDR were the multiparametric Kappa and GEV; the IDF equations coefficients adjustment was classified as “excellent”; coefficients a and b varied across the RS and are correlated with the AMDR and geographical positions; and the c and d coefficients were practically constant.&#x0D; Keywords: goodness of fit test, Kappa probabilistic distribution, trends test.

The effect of labor and capital on transportation logistics’ development: The case of Ho Chi Minh, Vietnam

The objective of this study was to measure eight variables, which included total labor, female labor, trained labor, high school labor, annual capital, asset and long-term capital, state capital, and abroad capital. A second objective was to assess which variables impact labor productivities, capital productivities, and gross domestic product (GDP) growth in four provinces in the southern key economic region in Vietnam. It also assessed how the GDP growth of transportation logistics in Ho Chi Minh (HCM), the GDP growth of HCM, and the GDP growth of Vietnam are impacted by the above eight variables and how they affect each other. The methodology used included the Cobb-Douglas formulation employed in a structural equation modeling (SEM) analysis and five steps of SEM’s goodness of fit testing. The software used is Stata 17.0. Some remarkable findings are: Firstly, trained labor had a total and direct positive impact on both goods labor productivity and passenger labor productivity. Secondly, state capital had no effect on goods labor productivity, passenger labor productivity, goods capital productivity, and passenger capital productivity. Thirdly, while trained labor positively affected the growth of Tay Ninh Province, state capital negatively affected the GDP growth of Tien Giang Province. Fourthly, trained labor indirectly negatively affected the GDP growth of transportation logistics in HCM. The implications are that, based on the magnitude of the coefficients, the direction of the effects, the number of variables that affect and are affected, and the magnitude of the effects, we believe that trained labor should be decreased while the GDP growth of transportation logistics should be increased in HCM. State capital had a direct and negative impact on GDP growth in Tien Giang Province; our recommendation is that state capital should be reduced in order to increase GDP growth in Tien Giang Province.

The Effect of Artificial Intelligence and Gamification on Online Purchase Intention Mediated by Customer Experience: Study on Indonesian Marketplace Users

Objectives: This study aims to analyze the effect of Artificial intelligence and gamification on online purchase intention, as well as the role of customer experience as a variable that mediates the effect of artificial intelligence and gamification on online purchase intention. The marketing approach in today's era tends to lean towards digital personalization using Artificial intelligence and gamification to improve customer engagement and experience. This is ongoing with the fast rate at which technology has been expanding. The development of artificial intelligence has increased consumer happiness even more, making it even more crucial in the contemporary environment. Also, Gamification has clearly been identified as a method of influencing consumer behavior. The use of game design elements in non-game contexts is known as gamification. Gamification and persuasive technologies have been used for business purposes and to influence customer behavior.Methodology: The method used in this study is a quantitative method using survey research with marketplace users in Indonesia as the object of the research. Data collection which was used is the cross-sectional method by distributing online questionnaires from Google Forms. The research sample was selected using the purposive sampling method in order to obtain 272 respondents from various users (Tokopedia, Shopee, Lazada, Blibli, and Bukalapak users). The data analysis that is used is the path analysis Structural Equation Model (SEM) by SmartPLS 3.0 software. The first stage in data analysis is to recapitulate data on the characteristics of the respondents. The next step is to measure the validity and reliability using discriminant validity, convergent validity, composite reliability, and Cronbach's alpha. Then, to test the inner model, a Goodness of Fit test was carried out through R-Square and significance testing by interpreting path analysis values, T-values, and P-Values.Finding: In this research conducted on marketplace users in Indonesia, it was found that Artificial Intelligence and Gamification are determinants of Customer Experience but these two variables cannot directly support Online Purchase Intention. Further findings indicate that Customer Experience can be an important determining factor for Online Purchase Intention. Customer experience can strengthen the online purchase intention among marketplace users.Conclusion: The result of this study shows that Artificial intelligence and gamification cannot affect online purchase intention directly but artificial intelligence and gamification can affect online purchase intention indirectly through customer experience

Crop Diversification and Sustainability in a Cocoa Agroforestry System in Meme Division South West Region, Cameroon

The diversification of crops and trees in cocoa agroforestry plays a major role in ecosystem goods and services. This study investigated the effects of crop diversification in a cocoa agroforestry farms. The study is aimed at identifying crops, crops preference and profitability in a cocoa agroforestry. Primary data were collected with the use of structured questionnaires and field observations. A sample size of 118 respondents was identified for the study. Forty (40) farmers each were interviewed in Matoh and Bole while 38 farmers interviewed in Njombe Mbonge. The data were analyzed using IBM SPSS Statistics 21. Descriptive statistics were used and Chi square (X2) Goodness-of–fit test was performed to check for association at . Results showed that 65 % of farmers had farm sizes between 2-5 hectares with majority of the farmers being males (72.8 %). The major crops farmers diversified in cocoa agroforestry were plantain, cocoyam, cassava, maize, bush mango, bitter cola and Njangsang. The preferences for cultivation of these crops were mainly for food, income and shades. All respondents (100%) cultivated cocoa for income (X2 cal=113.5, x2tab=7.81). NTFPs were cultivated mainly for income with 61.0 %, for Njangsang (X2 cal=25.1, x2tab=7.81), 37.3 % for Bitter cola (X2cal=17.8, x2tab=7.81) and 47.5 % For Bush mango (X2cal=28.5, x2tab=7.81). An average net farm income (NFI) of 187699.8 FCFA and 238252.9 FCFA was obtained per hectare just for cocoa only and cocoa + diversified crops respectively. A net profit margin of 0.49 and 0.54 were obtained for cocoa only and cocoa + diversified crops respectively. This study recommends that diversifying cocoa with crops and some economic trees will yield better income and improved biodiversity.

A structural equation model of stakeholder roles in the implementation of green construction strategies in the Indian construction industry

The construction industry is one of the leading contributors to greenhouse gas emissions into the atmosphere. Hence, the construction industry needs to start implementing green construction practices. However, various barriers are faced by the stakeholders of the industry which are restricting the shift to green construction. The 12 most frequently mentioned factors in the literature were used to prepare a questionnaire instrument. Responses were received from various stakeholders of the industry on the importance of the mentioned barriers on a five-point Likert scale. A structural equation model was developed attributing the 12 barriers to the 4 responsible stakeholders like Government& Statutory bodies, the Construction industry, Researchers & Academia, Consumers & clients through detailed observation, and experts’ opinions. A confirmatory factor analysis was performed on the model using the SPSS AMOS package, to validate the goodness of fit of the developed model. ‘Limited professionals with proficiency in green building techniques and strategies’, ‘Lack of awareness of the need for green construction’ and ‘Financial & economic feasibility issues’ were found to be the three most influential factors. The survey data was proved reliable through Bartlett’s test of sphericity, Kaiser-Meyer-Olkin test and other goodness of fit tests.

Estimation of wind characteristics at different topographical conditions using doppler remote sensing instrument-a comparative study using optimization algorithm.

This study uses novel evolutionary algorithms and computational techniques to analyze wind potential on flat, complex coastal, and offshore sites utilizing mast as well as remote sensing data. The wind data were recorded using remote sensing technique and conventional technique. The optimum Weibull parameters are estimated using nine methods. The genetic algorithm, particle swarm optimization, and TLBO algorithms are compared and evaluated. The goodness of fit test, such as root mean square error test (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and chi-square test (X2), is used to evaluate the accuracy of the selected methods. Parameter estimates are used to compute wind densities. The TLBO and PSO algorithms outperformed genetic algorithms in terms of efficiency. This research compares remote sensing measurements to cup anemometer measurements.

Impact of interactivity on learning outcome in online learning settings: Ordinal logit model

The study aimed to investigate the relationship between content, teaching, and LMS platform, and student achievement in online classes. To accomplish this, the study drew upon the transactional distance theory and the community of inquiry framework. Using log data from the LMS platform, the study analyzed the impact of student-faculty interaction, student-content interaction, and student-systems interaction on learning outcomes. The sample consisted of online classes offered in the Department of International Marketing at B University in Korea during the spring semester of 2022. The study specifically targeted classes with 40 or more students to ensure an adequate sample size. The researchers used parallelism tests to confirm the validity of the research model, followed by a goodness of fit test with an ordinal Logit model. The results indicated that all three factors of interaction (content, teaching, and platform) had a significant positive effect on learning outcomes, while student-Zoom interaction did not. The interaction between learners and content was found to be the most important predictor of learning achievement, suggesting that student engagement with course materials plays a crucial role in their academic success. The study’s findings have important implications for educators who are interested in improving online teaching and learning. The study recommends expanding the empirical analysis of the research model from the existing structural equation or multiple regression model to the ordinal logit model to provide a more comprehensive understanding of the impact of these factors on student achievement. Overall, the study highlights the importance of considering the complex interaction between content, teaching, and platform in designing and delivering effective online courses that promote student engagement and achievement.

The Analysis of Repurchase Intention in Online Transaction of a Marketplace, Shopee (A Case Study: Customers of Fashion Products in Indonesia)

The emergence of shopping online promotes the development of a marketplace in Indonesia. Shopee is one of the biggest marketplaces which always develops its features to increase its customer satisfaction, keeping in mind that many competitors are offering similar services. The variables of e-service quality, engagement, and e-trust are used to examine the repurchase intention of the customers. The method used in this research is a quantitative method with purposive sampling. The testing used in this research is hypothesis testing, which is done by Goodness of Fit Testing using AMOS 26. The result obtained shows that the quality of e-service influence the engagement, the quality of e-service also influence the e-trust, engagement influence the e-trust, engagement influence the repurchase intention, and e-trust influence the repurchase intention.

Visible to Near-Infrared Reflectance Spectroscopy of Asteroid (16) Psyche: Implications for the Psyche Mission's Science Investigations.

The NASA Psyche mission will explore the structure, composition, and other properties of asteroid (16) Psyche to test hypotheses about its formation. Variations in radar reflectivity, density, thermal inertia, and visible to near-infrared (VNIR) reflectance spectra of Psyche suggest a highly metallic composition with mafic silicate minerals (e.g., pyroxene) heterogeneously distributed on the surface in low abundance (<10 vol.%). The Psyche spacecraft's Multispectral Imager is designed to map ≥80% of the surface at high spatial resolution (≤20m/pixel) through a panchromatic filter and provide compositional information for about ≥80% of the surface using seven narrowband filters at VNIR wavelengths (∼400-1,100nm) and at spatial scales of ≤500m/pixel. We analyzed 359 reflectance spectra from samples consistent with current uncertainties in Psyche's composition and compared them to published reflectance spectra of the asteroid using a chi-square test for goodness of fit. The best matches for Psyche include iron meteorite powder, powders from the sulfide minerals troilite and pentlandite, and powder from the CH/CBb chondrite Isheyevo. Comparison of absorption features support the interpretation that Psyche's surface is a metal-silicate mixture, although the exact abundance and chemistry of the silicate component remains poorly constrained. We convolve our spectra to the Imager's spectral throughput to demonstrate preliminary strategies for mapping the surface composition of the asteroid using filter ratios and reconstructed band parameters. Our results provide predictions of the kinds of surface compositional information that the Psyche mission could reveal on the solar system's largest M-type asteroid.

A Robust Test for Elliptical Symmetry

Most signal processing and statistical applications heavily rely on specific data distribution models. The Gaussian distributions, although being the most common choice, are inadequate in most real world scenarios as they fail to account for data coming from heavy-tailed populations or contaminated by outliers. Such problems call for the use of Robust Statistics. The robust models and estimators are usually based on elliptical populations, making the latter ubiquitous in all methods of robust statistics. To determine whether such tools are applicable in any specific case, goodness-of-fit (GoF) tests are used to verify the ellipticity hypothesis. Ellipticity GoF tests are usually hard to analyze and often their statistical power is not particularly strong. In this work, assuming the true covariance matrix is unknown we design and rigorously analyze a robust GoF test consistent against all alternatives to ellipticity on the unit sphere. The proposed test is based on Tyler's estimator and is formulated in terms of easily computable statistics of the data. For its rigorous analysis, we develop a novel framework based on the exchangeable random variables calculus introduced by de Finetti. Our findings are supported by numerical simulations comparing them to other popular GoF tests and demonstrating the significantly higher statistical power of the suggested technique.

Advancing Covid-19 Data Modeling: Introducing a Neutrosophic Extension of Ramous Louzada Distribution

In this research, we introduce a neutrosopic extension of the Ramous Louzada Distribution called the Inverse Ramous Louzada Distribution. We delve into several mathematical properties of this distribution, including the Survival function, Hazard Rate function, cumulative Hazard Rate function, and estimation technique. Moreover, we conduct a comparative analysis between the Inverse Weibull distribution and the traditional Ramous Louzada Distribution, which are two widely used distributions. Our aim is to assess the performance of the developed model through Maximum Likelihood Estimation (MLE), Standard Error (SE), and Goodness of Fit tests.