Yoxas/statistical_literacy huggingface dataset:
In this paper, a new bio-inspired metaheuristic algorithm called Giant Armadillo Optimization (GAO) is introduced, which imitates the natural behavior of giant armadillo in the wild. The fundamental inspiration in the design of GAO is derived from the hunting strategy of giant armadillos in moving towards prey positions and digging termite mounds. The theory of GAO is expressed and mathematically modeled in two phases: (i) exploration based on simulating the movement of giant armadillos towards termite mounds, and (ii) exploitation based on simulating giant armadillos' digging skills in order to prey on and rip open termite mounds. The performance of GAO in handling optimization tasks is evaluated in order to solve the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that GAO is able to achieve effective solutions for optimization problems by benefiting from its high abilities in exploration, exploitation, and balancing them during the search process. The quality of the results obtained from GAO is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that GAO presents superior performance compared to competitor algorithms by providing better results for most of the benchmark functions. The statistical analysis of the Wilcoxon rank sum test confirms that GAO has a significant statistical superiority over competitor algorithms. The implementation of GAO on the CEC 2011 test suite and four engineering design problems show that the proposed approach has effective performance in dealing with real-world applications.
In this paper, a new bio-inspired metaheuristic algorithm called the Lyrebird Optimization Algorithm (LOA) that imitates the natural behavior of lyrebirds in the wild is introduced. The fundamental inspiration of LOA is the strategy of lyrebirds when faced with danger. In this situation, lyrebirds scan their surroundings carefully, then either run away or hide somewhere, immobile. LOA theory is described and then mathematically modeled in two phases: (i) exploration based on simulation of the lyrebird escape strategy and (ii) exploitation based on simulation of the hiding strategy. The performance of LOA was evaluated in optimization of the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that the proposed LOA approach has high ability in terms of exploration, exploitation, and balancing them during the search process in the problem-solving space. In order to evaluate the capability of LOA in dealing with optimization tasks, the results obtained from the proposed approach were compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that LOA has superior performance compared to competitor algorithms by providing better results in the optimization of most of the benchmark functions, achieving the rank of first best optimizer. A statistical analysis of the performance of the metaheuristic algorithms shows that LOA has significant statistical superiority in comparison with the compared algorithms. In addition, the efficiency of LOA in handling real-world applications was investigated through dealing with twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems. The simulation results show that LOA has effective performance in handling optimization tasks in real-world applications while providing better results compared to competitor algorithms.
In this paper, a new human-based metaheuristic algorithm called Technical and Vocational Education and Training-Based Optimizer (TVETBO) is introduced to solve optimization problems. The fundamental inspiration for TVETBO is taken from the process of teaching work-related skills to applicants in technical and vocational education and training schools. The theory of TVETBO is expressed and mathematically modeled in three phases: (i) theory education, (ii) practical education, and (iii) individual skills development. The performance of TVETBO when solving optimization problems is evaluated on the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that TVETBO, with its high abilities to explore, exploit, and create a balance between exploration and exploitation during the search process, is able to provide effective solutions for the benchmark functions. The results obtained from TVETBO are compared with the performances of twelve well-known metaheuristic algorithms. A comparison of the simulation results and statistical analysis shows that the proposed TVETBO approach provides better results in most of the benchmark functions and provides a superior performance in competition with competitor algorithms. Furthermore, in order to measure the effectiveness of the proposed approach in dealing with real-world applications, TVETBO is implemented on twenty-two constrained optimization problems from the CEC 2011 test suite. The simulation results show that TVETBO provides an effective and superior performance when solving constrained optimization problems of real-world applications compared to competitor algorithms.
In this paper, an enhanced binary version of the Rat Swarm Optimizer (RSO) is proposed to deal with Feature Selection (FS) problems. FS is an important data reduction step in data mining which finds the most representative features from the entire data. Many FS-based swarm intelligence algorithms have been used to tackle FS. However, the door is still open for further investigations since no FS method gives cutting-edge results for all cases. In this paper, a recent swarm intelligence metaheuristic method called RSO which is inspired by the social and hunting behavior of a group of rats is enhanced and explored for FS problems. The binary enhanced RSO is built based on three successive modifications: i) an S-shape transfer function is used to develop binary RSO algorithms; ii) the local search paradigm of particle swarm optimization is used with the iterative loop of RSO to boost its local exploitation; iii) three crossover mechanisms are used and controlled by a switch probability to improve the diversity. Based on these enhancements, three versions of RSO are produced, referred to as Binary RSO (BRSO), Binary Enhanced RSO (BERSO), and Binary Enhanced RSO with Crossover operators (BERSOC). To assess the performance of these versions, a benchmark of 24 datasets from various domains is used. The proposed methods are assessed concerning the fitness value, number of selected features, classification accuracy, specificity, sensitivity, and computational time. The best performance is achieved by BERSOC followed by BERSO and then BRSO. These proposed versions are comparatively assessed against 25 well-regarded metaheuristic methods and five filter-based approaches. The obtained results underline their superiority by producing new best results for some datasets.
In this paper, Brownian dynamics simulation (BDS) studies are executed to demonstrate the distinctive influences of the extent of confinement on the collapsing mechanism and kinetics of a flexible hydrophobic polymer chain in a poor solvent. The collapsing behavior is quantified by the time of collapse, which below a critical dimension of the confinement (h(c)), encounters a drastic reduction with a further strengthening in the degree of confinement. For dimensions greater than this critical one, the collapse occurs through the well-known hydrodynamic interaction (HI) controlled multiple-globule-mediated mechanisms. However, for channel dimensions less than this critical one, the collapse mechanism is drastically altered. Under such circumstances, the collapse gets predominantly controlled by the confinement effects (with negligible contribution of the HIs) and occurs via the formation of a single central globule. This central globule rapidly engulfs the noncondensed polymer segments, and in the process largely hastens up the collapsing event. Under such circumstances, the collapse time is found to decrease linearly with decrements in the channel height. On the contrary, for channel heights greater than h(c), the multiple-globule-mediated collapse is characterized by a collapse time that shows an exponential dependence on the channel height, rapidly attaining a state in which the confinement effect becomes inconsequential and HIs dictate the entire collapsing behavior. We further propose detailed arguments based on physical reasoning as well as free energy estimations to conclusively support the qualitative and quantitative nature of influences of the confinement on the polymer collapse.
In this paper, the multi-objective optimal power flow (MOOPF) problem optimization objectives focus on four optimization objectives: generation cost, emission, real power loss and voltage deviation (VD). Three renewable energy sources with successful industrial applications, including wind energy, solar energy, and tidal energy are introduced. Renewable energy supply is uncertain, so Weibull distribution probability, lognormal probability and Gumbel probability are used to calculate the instability and intermittency of wind energy, solar energy and tidal energy, respectively. The inclusion of four energy supplies on the IEEE-30 test system and the consideration of renewable energy reserves and penalty cost calculation improve the realism of the model. In order to obtain the control parameters that minimize the four optimization objectives, a named multi-objective pathfinder algorithm (MOPFA) based on elite dominance and crowding distance was proposed to solve this multi-objective optimization problem. Simulation results show the feasibility of the model, and MOPFA can get more evenly distributed Pareto front and provide more diverse solutions. A compromise solution was selected by the fuzzy decision system. Comparison with the recently published literature also shows that the proposed model can effectively reduce emissions and other indicators. In addition, the statistical test results show that MOPFA's multi-objective optimization performance ranks first. In solving this complex optimization problem, results show the MOPFA is superior to other multi-objective algorithms in optimization accuracy and speed.
In this paper, we aim to both borrow information from existing units and incorporate the target unit's history data in time series forecasting. We consider a situation when we have time series data from multiple units that share similar patterns when aligned in terms of an internal time. The internal time is defined as an index according to evolving features of interest. When mapped back to the calendar time, these time series can span different time intervals that can include the future calendar time of the targeted unit, over which we can borrow the information from other units in forecasting the targeted unit. We first build a hierarchical state space model for the multiple time series data in terms of the internal time, where the shared components capture the similarities among different units while allowing for unit-specific deviations. A conditional state space model is then constructed to incorporate the information of existing units as the prior information in forecasting the targeted unit. By running the Kalman filtering based on the conditional state space model on the targeted unit, we incorporate both the information from the other units and the history of the targeted unit. The forecasts are then transformed from internal time back into calendar time for ease of interpretation. A simulation study is conducted to evaluate the finite sample performance. Forecasting state-level new COVID-19 cases in United States is used for illustration.
In this paper, we describe the measurement of cognitive ability in the China Family Panel Studies (CFPS), especially for verbal skill, mathematical skill, memory, and quantitative reasoning. The available CFPS cognitive measurements can be useful for studies on the importance of cognitive ability in many substantive domains of interest. Using the CFPS data, we show that measures of cognitive ability are clearly related to key demographic and social characteristics, such as age, gender, education, and hukou status. We also illustrate how cognitive ability influences school performance and deviant behaviors among children, income and political capital among adults, and daily functioning among the elderly.
In this paper, we documented the association between specific wordings regarding domestic violence within gender equality laws and women's attitudes towards domestic violence in African countries. To do so, we used data on the longitudinal Demographic and Health Survey conducted between 2003 and 2018, and we empirically conducted a difference-in-differences analysis that captures variations in the country and timing of the inclusion of specific wordings addressing domestic violence in the legislative framework that encompasses the general principle of gender equality. The empirical analysis showed that the inclusion of a specific article criminalizing domestic violence within a country's general gender equality law contributed to a statistically significant and substantial decrease in the likelihood of women deeming wife-beating behaviors justifiable. We further conducted an analysis of heterogeneity by socioeconomic status, as defined by urban status, wealth, literacy, and access to information; we found that the association between laws' wordings and attitudes towards domestic violence appeared statistically significant and sizeable in all socioeconomic groups.
In this paper, we introduce the concepts of sensitivity and specificity to mathematically describe the accuracy of the peer review process. Sensitivity refers to the probability that the final decision for a manuscript would be acceptance, provided the manuscript meets the journal standards required for publication (i.e., true positive rate). Specificity refers to the probability that the final decision would be rejection, provided the work does not meet the standards required for publication (i.e., true negative rate). Therefore, in the peer review process, sensitivity measures the ability to correctly accept manuscripts that meet the required standards (true positives) and specificity measures the ability to correctly reject manuscripts that do not meet those quality standards required for publication (true negatives). Sensitivity and specificity values can inform the editor under what conditions the outcome of a peer review process becomes more precise and, therefore, if this does not occur, when the editor must improve the analysis involved in processing the information received from reviewers' reports. Sensitivity and specificity understood in this way can promote the ethical conduct of peer review processes and improve the validity of manuscript editorial decisions.
In this paper, we provide a re-interpretation of qualitative and quantitative modeling from a representationalist perspective. In this view, both approaches attempt to construct abstract representations of empirical relational structures. Whereas quantitative research uses variable-based models that abstract from individual cases, qualitative research favors case-based models that abstract from individual characteristics. Variable-based models are usually stated in the form of quantified sentences (scientific laws). This syntactic structure implies that sentences about individual cases are derived using deductive reasoning. In contrast, case-based models are usually stated using context-dependent existential sentences (qualitative statements). This syntactic structure implies that sentences about other cases are justifiable by inductive reasoning. We apply this representationalist perspective to the problems of generalization and replication. Using the analytical framework of modal logic, we argue that the modes of reasoning are often not only applied to the context that has been studied empirically, but also on a between-contexts level. Consequently, quantitative researchers mostly adhere to a top-down strategy of generalization, whereas qualitative researchers usually follow a bottom-up strategy of generalization. Depending on which strategy is employed, the role of replication attempts is very different. In deductive reasoning, replication attempts serve as empirical tests of the underlying theory. Therefore, failed replications imply a faulty theory. From an inductive perspective, however, replication attempts serve to explore the scope of the theory. Consequently, failed replications do not question the theory per se, but help to shape its boundary conditions. We conclude that quantitative research may benefit from a bottom-up generalization strategy as it is employed in most qualitative research programs. Inductive reasoning forces us to think about the boundary conditions of our theories and provides a framework for generalization beyond statistical testing. In this perspective, failed replications are just as informative as successful replications, because they help to explore the scope of our theories.
In this paper, we start by reviewing exchangeability and its relevance to the Bayesian approach. We highlight the predictive nature of Bayesian models and the symmetry assumptions implied by beliefs of an underlying exchangeable sequence of observations. By taking a closer look at the Bayesian bootstrap, the parametric bootstrap of Efron and a version of Bayesian thinking about inference uncovered by Doob based on martingales, we introduce a parametric Bayesian bootstrap. Martingales play a fundamental role. Illustrations are presented as is the relevant theory. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.
In this paper, we suggest that causal inference methods could be efficiently used in Quantitative Structure-Activity Relationships (QSAR) modeling as additional validation criteria within quality evaluation of the model. Verification of the relationships between descriptors and toxicity or other activity in the QSAR model has a vital role in understanding the mechanisms of action. The well-known phrase "correlation does not imply causation" reflects insight statistically correlated with the endpoint descriptor may not cause the emergence of this endpoint. Hence, paradigmatic shifts must be undertaken when moving from traditional statistical correlation analysis to causal analysis of multivariate data. Methods of causal discovery have been applied for broader physical insight into mechanisms of action and interpretation of the developed nano-QSAR models. Previously developed nano-QSAR models for toxicity of 17 nano-sized metal oxides towards E. coli bacteria have been validated by means of the causality criteria. Using the descriptors confirmed by the causal technique, we have developed new models consistent with the straightforward causal-reasoning account. It was proven that causal inference methods are able to provide a more robust mechanistic interpretation of the developed nano-QSAR models.
In this project, we reviewed our experience in minimal access paediatric surgery (MAPS) at Greys Hospital over the last decade. This information would provide an overview of our experience and would enable us to identify areas where we can strengthen our surgical service and our training programme. All surgical patients are captured in the hybrid electronic medical registry (HEMR). All patients aged 18 years or less who underwent a MAPS procedure between 2012 and 2021 were reviewed. Data collected included demographic information, type of surgery, nature of the surgery (elective or emergency), organ system operated on, whether trainees or consultants performed the surgeries and the morbidities and mortalities experienced. Statistical analysis included linear regression and ANOVA, which was performed using Jamovi software. A total of 1 328 MAPS procedures were performed on 994 patients over nine years. There were 359 female and 635 male patients. There was a steady increase in the number of cases performed per year. The age of the patients ranged from one day of life to 18 years, with a median of 8 years. The multiple linear regression results indicated a very strong collective significant effect between the courses performed, the number of consultants, and the MAPS cases performed. The ANOVA test for the individual factors was not statistically significant, but there was a very strong combined correlation with an r-value of 0.87 and a p-value of 0.014 using the overall model test. The consultants' training also directly impacted on the teaching and training of registrars, with progressively more cases being performed by trainees over the years. Postoperative morbidity was reported in 40 patients. The morbidity rate was three per cent. There were no mortalities. It is feasible to deliver MAPS to children in our environment. A comprehensive quality improvement strategy has yielded satisfying results. The increased use of MAPS has resulted in a general transfer of skills to junior staff. Ongoing efforts to support the rollout of MAPS in children are warranted.
In this retrospective observational study, we have performed a comparative analysis of the demographic, clinical and epidemiological characteristics of the HCWs affected with SARS-CoV-2 infection during first two waves in India. The overall prevalence of SARS-CoV-2 infection among HCWs was found to be 15.24% (14.20-16.33) and 23.38% (22.14-25.65) during first and second waves respectively. The second wave showed an adjusted odds ratio of 0.04(0.02-0.07) and 2.09(1.49-2.93) for hospitalization and being symptomatic, respectively. We detected significantly higher level of C-reactive protein (CRP) among admitted HCWs during the second wave (5.10 -14.60 mg/dl) as compared to the first wave (2.00 - 2.80 mg/dl). Our study found the relative risk of SARS-CoV-2 reinfection among HCWs during the second wave to be 0.68 [0.57-0.82, p < 0.001)]. Although, the prevalence of SARS CoV-2 infection and risk of being symptomatic was higher during second wave, the risk of hospitalization was less when compared with the first wave.
In this review article, we sought to elucidate how the social determinants of health, including socioeconomic status, education, neighborhood or physical environment, access to healthcare, and race/ethnicity, affect the likelihood of receiving immunotherapy, a novel and expensive treatment for melanoma.&nbsp; Methods: The PubMed database was queried up to May 2023, for studies pertaining to health disparities in melanoma, including studies examining the utilization of immunotherapy agents for the treatment of melanoma across various social determinants of health. Disparities in the utilization of immunotherapy exist across various social determinants. A total of 10 studies were found to report on disparities in receipt of immunotherapy. These studies reported an association between insurance status, education level, socioeconomic status, as well as proximity to a cancer research center, and a lower likelihood of receiving immunotherapy. As the number of novel immunotherapy drugs grows, it is important to understand the various disparities affecting the delivery of immunotherapy across social determinants. The findings from this study can help to drive public health policy aimed at addressing inequities in the treatment of melanoma as well as other cancers.&nbsp; &nbsp; J Drugs Dermatol. 2024;23(5):311-315. doi:10.36849/JDD.7803.