Search

This article is a continuation of the work published in the previous issue of the journal.1 The main approaches to constructing scenarios for long-term socio-economic development are compared, both structural and morphological. The results of the scenario are verified using the traditional structural method based on the morphological approach, and a system of mutually agreed upon scenarios for long-term socio-economic and scientific and technological development is proposed (based on the structural approach). [ABSTRACT FROM AUTHOR]

North-Eastern (NE), India and its adjoining region is one of the sixth most seismically active regions of the world. In the present investigation, the return period of earthquake and probability of occurrence inferred from Gutenberg–Richter (GR) relation was estimated for NE, India region and its vicinity. When we consider the entire NE, India region and its vicinity, it evidently suggested that the return period of earthquakes of 7 ≤ Mw ≤ 8.6 is short, which ranges from 32.73 to 162.59 years. It was observed that the earthquake occurrence from infinitesimally short interval t~0 for Mw~3.6–4 is embedded with 100% probability. The earthquakes of Mw~4.1–5.3 reach 100% in 10 years. Similarly, Mw~5.4–5.7 reaches to 100% in 20 years. Likewise, Mw~5.8–5.9, 6.0–6.1 and 6.2 reach ~100% in 30, 40 and 50 years, respectively. For large earthquakes of Mw~7.0–8.0, the probability of occurrence reaches >80% in 100 years. This observation strongly indicates that the likelihood of earthquakes occurring in the north-eastern region of India and its surrounding areas tends to increase over time. Further, the region was divided into four zones, namely Block I (26.5–28.5ºN; 89–95ºE), Block II (26.5–28.5ºN; 95–97.5ºE), Block III (23–26.5ºN; 93–97.5ºE) and Block IV (23–26.5ºN; 89–93ºE) based on seismicity and the major tectonic domains of the region. In terms of return period based on GR-relation and stochastic observations, we may conclude that the risk associated with occurrence of earthquake is highest in Block IV, followed by Block III, Block I and Block II respectively. Further, a comparison of the probabilities of earthquake return period considering seismogenic depths along with hypocentral depth data for different blocks was investigated for a comprehensive understanding of seismic occurrences over time. However, overall, the patterns and trends observed remain consistent, emphasizing the seismic activity within each block and its associated return periods. The stochastic observations and findings are elaborately accentuated in the article. [ABSTRACT FROM AUTHOR]

Although familiar with the developments in probability theory of his time, Vilfredo Pareto made little use of this tool in his writings, preferring theoretical constructions based on experimentation and observation. This article attempts to reconstruct Pareto's overall approach to probability by examining his references to the distribution of income, an economic fact that lends itself to probabilistic investigation. The result of this research shows how Pareto alludes to the application of probability to income and social groups, but leaves the task to his followers. JEL classification B31, B4, C1. [ABSTRACT FROM AUTHOR]

The International Society for Animal Genetics (ISAG) currently advocates for a transition towards single nucleotide polymorphism (SNP) markers as a potential alternative for equine parentage verification. To ascertain the efficacy of this transition, it is imperative to evaluate the performance of parentage testing using SNPs in juxtaposition with short tandem repeats (STRs). As per ISAG's recommendation, we used an equine genotyping‐by‐sequencing panel with 144 SNPs for this purpose. Equine parentage is currently realized using 16 microsatellites (STRs) excluding the LEX3 marker. In this study, 1074 horses were genotyped using the 144 SNPs panel, including 432 foals, 414 mares, and 228 stallions, from five different breeds: 293 Arabians, 167 Barbs, 189 Thoroughbreds, 73 Anglo‐Arabians, and 352 Arabian‐Barbs. As a result, two SNPs markers were eliminated from the panel system due to inconsistent amplification across all examined individuals leaving 142 SNPs markers for analysis. A comparative analysis between SNPs and STRs markers revealed that the mean expected heterozygosity was 0.457 for SNPs and 0.76 for STRs, while the mean observed heterozygosity stood at 0.472 for SNPs and 0.72 for STRs. Furthermore, the probability of identity was calculated to be 5.722 × 10−57 for SNPs and 1.25 × 10−15 for STRs markers. In alignment with the Hardy–Weinberg equilibrium in polyploids test, 110 out of the total SNPs were consistent with the Hardy–Weinberg equilibrium in polyploids test (p > 0.05). Employing both SNPs and STRs markers, the mean polymorphic information content was discerned to be 0.351 for SNPs and 0.72 for STRs. The cumulative exclusion probabilities for SNP markers exceeded 99.99%, indicating that the 142 SNPs panel might be adequate for parentage testing. In contrast, when utilizing STRs markers, the combined average exclusion probabilities for one and both parents were determined to be 99.8% and 99.9%, respectively. Our comprehensive study underscores the potential of SNPs in equine parentage verification, especially when compared to STRs in terms of exclusion probabilities. As a corollary, the application of SNPs for parentage verification and identification can significantly contribute to the conservation initiative for the five Moroccan horse breeds. Nonetheless, further research is required to address and replace the deficient SNPs within the panel. [ABSTRACT FROM AUTHOR]

In the ever-evolving world of veterinary care, the occurrence of bone fractures in canines poses a common and complex problem, especially in extra-small breeds and dogs that are less than 1 year old. The objective of this research is to fill a gap in predicting the risk of canine bone fractures. A machine learning method using a random forest classifier was constructed. The algorithm was trained on a dataset consisting of 2261 cases that included several factors, such as canine age, gender, breed, and weight. The performance of the algorithm was assessed by examining its capacity to forecast the probability of fractures occurring. The findings of our study indicate that the tool has the capability to provide dependable predictions of fracture risk, consistent with our extensive dataset on fractures in canines. However, these results should be considered preliminary due to the limited sample size. This discovery is a crucial tool for veterinary practitioners, allowing them to take preventive measures to manage and prevent fractures. In conclusion, the implementation of this prediction tool has the potential to significantly transform the quality of care in the field of veterinary medicine by enabling the detection of patients at high risk, hence enabling the implementation of timely and customized preventive measures. [ABSTRACT FROM AUTHOR]

Gold prices have been of major interest for a lot of investors, analysts, and economists. Accordingly, a number of different modeling approaches have been used to forecast gold prices. In this manuscript, the geometric Brownian motion approach, used in the pricing of numerous types of assets, is used to forecast the prices of gold at yearly, monthly, and quarterly frequencies. This approach allows for simulating one-period-ahead prices and the associated probabilities. The expected prices obtained from the simulated prices and probabilities are found to provide reliable forecasts when compared with the observed yearly, monthly, and quarterly prices. [ABSTRACT FROM AUTHOR]

Denić (2018, 2019, To appear) observes that the availability of distributive inferences—for sentences with disjunction embedded in the scope of a universal quantifier—depends on the size of the domain quantified over as it relates to the number of disjuncts. Based on her observations, she argues that probabilistic considerations play a role in the computation of implicatures. In this paper we explore a different possibility. We argue for a modification of Denić's generalization, and provide an explanation that is based on intricate logical computations but is blind to probabilities. The explanation is based on the observation that when the domain size is no larger than the number of disjuncts, universal and existential alternatives are equivalent if distributive inferences are obtained. We argue that under such conditions a general ban on 'fatal competition' (Magri 2009a,b, Spector 2014) is activated, thereby predicting distributive inferences to be unavailable. [ABSTRACT FROM AUTHOR]

We identify and analyze three caveats that may arise when analyzing the linguistic abilities of Large Language Models. The problem of unlicensed generalizations refers to the danger of interpreting performance in one task as predictive of the models’ overall capabilities, based on the assumption that because a specific task performance is indicative of certain underlying capabilities in humans, the same association holds for models. The human-like paradox refers to the problem of lacking human comparisons, while at the same time attributing human-like abilities to the models. Last, the problem of double standards refers to the use of tasks and methodologies that either cannot be applied to humans or they are evaluated differently in models vs. humans. While we recognize the impressive linguistic abilities of LLMs, we conclude that specific claims about the models’ human-likeness in the grammatical domain are premature.

In the ever-evolving world of veterinary care, the occurrence of bone fractures in canines poses a common and complex problem, especially in extra-small breeds and dogs that are less than 1 year old. The objective of this research is to fill a gap in predicting the risk of canine bone fractures. A machine learning method using a random forest classifier was constructed. The algorithm was trained on a dataset consisting of 2261 cases that included several factors, such as canine age, gender, breed, and weight. The performance of the algorithm was assessed by examining its capacity to forecast the probability of fractures occurring. The findings of our study indicate that the tool has the capability to provide dependable predictions of fracture risk, consistent with our extensive dataset on fractures in canines. However, these results should be considered preliminary due to the limited sample size. This discovery is a crucial tool for veterinary practitioners, allowing them to take preventive measures to manage and prevent fractures. In conclusion, the implementation of this prediction tool has the potential to significantly transform the quality of care in the field of veterinary medicine by enabling the detection of patients at high risk, hence enabling the implementation of timely and customized preventive measures.

In this perspective review, we assess fundamental flaws in Darwinian evolution, including its modern versions. Fixed mutations 'explain' microevolution but not macroevolution including speciation events and the origination of all the major body plans of the Cambrian explosion. Complex, multifactorial change is required for speciation events and inevitably requires self-organization beyond what is accomplished by known mechanisms. The assembly of ribosomes and ATP synthase are specific examples. We propose their origin is a model for what is unexplained in biological evolution. Probability of evolution is modeled in Section 9 and values are absurdly improbable. Speciation and higher taxonomic changes become exponentially less probable as the number of required, genetically-based events increase. Also, the power required of the proposed selection mechanism (survival of the fittest) is nil for any biological advance requiring multiple changes, because they regularly occur in multiple generations (different genomes) and would not be selectively conserved by the concept survival of the fittest (a concept ultimately centered on the individual). Thus, survival of the fittest cannot 'explain' the origin of the millions of current and extinct species. We also focus on the inadequacies of laboratory chemistry to explain the complex, required biological self-organization seen in cells. We propose that a 'bioelectromagnetic' field/principle emerges in living cells. Synthesis by self-organization of massive molecular complexes involves biochemical responses to this emergent field/principle. There are ramifications for philosophy, science, and religion. Physics and mathematics must be more strongly integrated with biology and integration should receive dedicated funding with special emphasis for medical applications; treatment of cancer and genetic diseases are examples. [ABSTRACT FROM AUTHOR]

Mathematical education in Greece is constantly evolving in the pursuit of optimal learning outcomes for students despite their cognitive differences. This study seeks to gain insight into the use of new technologies in teaching probability theory and the gender differences in the comprehension of probability theory. To this end, a survey was conducted involving 500 students of the Department of Statistics and Insurance Science at the University of Piraeus. The respective questionnaire involves questions of self-reported results and employs the Likert scale to obtain the students' perceptions. Our data demonstrate no difference among the genders regarding the use of new technologies or their performance (i.e., the test scores) in the relevant courses. [ABSTRACT FROM AUTHOR]

This study seeks to identify the ontological and epistemological foundations of linguistic theory, in light of recent developments in scientific practice that takes language as its object. It requires a reconsideration of the existential concept of language and raises the issue of the existence of ontological and epistemological constants in linguistic theory. Taking into consideration the difference in the nature of linguistic sound and meaning. The approach adopted is historical and comparative, examining the major ontological and epistemological milestones. At the ontological level, the study notes that René Descartes linked the existence of language to mental existence, but the theory of relativity and statistical theory have transformed language into a virtual digital system, calling into question its ontological framework. Not to mention the fact that language is no longer merely a mental product, as evidenced by the existence of automatically generated linguistic texts. Epistemologically, the verification of linguistic knowledge has evolved, moving from logical proof to experimentation, and is now tied to utility and added value, with the virtual simulation of artificial intelligence as the environment for experimentation and proof. [ABSTRACT FROM AUTHOR]

The study uses numerical weather prediction models to predict the occurrence of heavy convective rainfall associated with the passage of the African Easterly Wave (AEW) during the period 23–26 August 2017 over Nigeria. Fraction skill score (FSS) and method for object‐based diagnostic evaluation (MODE) verification techniques were applied to verify how well the models predict the high‐impact event and to demonstrate how these tools can support operational forecasting. Ensemble model forecasts at a convective scale from UK Met Office Unified Model (MetUM) and a one‐way nested weather research and forecasting (WRF) model were compared with the integrated multisatellite retrievals for global precipitation measurement (IMERG GPM). The purpose is to examine skills of improved model resolution and ensemble in reproducing rainfall forecasts on useful scales and how the skill varies with spatial scale. WRF 2 and 6 km model forecasts show comparable skill at smaller grid scales. The skill of MetUM improves dramatically when the verification statistics are applied to the ensemble mean of the binary fields of the individual member forecast. The object‐based analysis reveals a similar structure as observed, although displaced eastwards. Most improvement occurred for heavier rainfall events associated with the passage of the AEW. WRF 6 km compares reasonably well with WRF 2 km in terms of shape and structure of rainfall underscoring the ability of the model to reasonably represent convection at 6 km horizontal resolution. The ensemble members in MetUM explicitly reproduce convection at 4 km resolution but are displaced at about 166 km behind observed rainfall. [ABSTRACT FROM AUTHOR]

In recent years, brain tumors have become one of the most common fatal diseases. Despite the existence of an important number of research studies on tumors, the proportion of research on predicting the growth of tumors remains insufficient due to the intricate nature of this research domain. Therefore, the presence of any application able to predict the growth of the tumor may have a role in eliminating the tumor by finding the appropriate treatment for it before it grows. This paper investigates tumor growth and presents a technique for tumor growth prediction based on the Discrete Time Markov Chain (DTMC) and K-Nearest Neighbor (KNN) algorithms. The design and development of this technique consists of a proposition of a stochastic model of tumor progression. This is followed by an extension of the mode to several cases that allow the derivation of new cases based on the study of predictive probabilities. The aim of this paper is to develop a model based on the KNN and DTMC algorithms that can classify tumors and predict the future state based on the current state of the tumor without the knowledge of the past state. In other words, all relevant information about the past and the present that would be useful in making predictions is available in the current state. In terms of performance evaluation metrics, the results show that the proposed method exceeds the existing methods with 97.65% accuracy, 71.65% specificity and 99.087% sensitivity. [ABSTRACT FROM AUTHOR]

Copyright of Al-Zaytoonah University of Jordan Journal for Human & Social Studies is the property of Al-Zaytoonah University of Jordan and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

BACKGROUND AND OBJECTIVES: One of the most important issues that insurance companies face when introducing new insurance products is how to price such products. In this article, in addition to introducing the increased annuity product, the payments of this product for cancer insureds, taking into account the probability of death or survival have been calculated for use in the country’s insurance industry. METHODS: This research is of a quantitative type and in it, the amount of annuity insurance payment increased for the insured who are cancer patients, for a single net insurance premium, is calculated using two different approaches to adjust the probability of death. Also in this research the data of the American Cancer Society and the list of technical profit rates announced in the supplement of Regulation No. 68 of the Supreme Council of Insurance, as well as the announced data table of the Iranian life insurance at the year 2021, were used. FINDINGS: In this article, the effect of adjusting the mortality probability using “adjustment multiplier” and “age rating” approaches on the amount of annuity payouts for insureds of different ages were investigated. It is expected that with an increase in the adjustment factor, which means an increase in the insured’s health problems, the life expectancy will decrease and the amount of pension payment will increase, which the findings of the article also confirm this issue. In addition, based on the relative survival assessment of 5 years and with the amount of pension payment for the insured, the life expectancy using the adjustment factor and the increased amount of pension payment for the 65-year-old insured with various cancers of the breast, colon, lung and skin with Standard health status was compared. Based on the findings of this research, local skin cancer and local breast cancer with a 5-year survival probability of 99% have the lowest pension payment increase of 2.13%. On the other hand, diffuse lung cancer and diffuse colon cancer with survival probability of 8 and 15 percent, have the highest increase in pension payments, i.e. 413.57 and 309.47 compared to standard pension payments. The fuzzy payout of annuity for the insured with local skin cancer and local breast cancer was obtained the interval [161/04, 167/88], and the payout of age rating approach with the value of 167/34 is also included in this interval. It should be noted that, in general, the payout of the “age rating” approach is higher than the “adjustment multiplier” approach. CONCLUSION: If a person has a certain health condition, the mortality and survival probability, as well as life expectancy, will be different from the standard state. For this purpose, an adjustment factor is considered for the mortality probability, and the annuity payout will be adjusted accordingly. In this article, the amount annuity payouts for the insured with different types of cancer was investigated, compared and analyzed based on two different adjustment mortality probabilities approaches. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic revealed that many countries have failed to provide the general population with the cognitive tools to thoroughly understand and cope with a global health crisis. While scientists and leaders worldwide have struggled to discover ways to contain the spread of the virus, this difficult task has become overwhelming due to the limited ability of many citizens to grasp the urgency of the situation. Although in today's digitized world we have endless access to data and more ways to represent information and statistics than ever before, numerous incidents have demonstrated that the frequent misapprehension of data can cause confusion rather than clarity. This opinion paper examines how issues such as the misunderstanding of large quantities, fractions, probabilities, and mathematical modeling may be affecting the way people view the current pandemic. Finally, we also discuss how numeracy can act as a protective factor against motivated reasoning, which often affects how we consume information related to the pandemic. [ABSTRACT FROM AUTHOR]

Avoiding loss is a crucial, adaptive guide to human behavior. While previous developmental research has primarily focused on gaining rewards, less attention has been paid to loss processing and its avoidance. In daily life, it is often unknown how likely an action will result in a loss, making the role of uncertainty in loss processing particularly important. By using functional magnetic resonance imaging, we investigated the influence of varying outcome probabilities (12%, 34%, and 67%) on brain regions implicated in loss processing (ventral striatum (VS), anterior insula (AI)) by comparing 28 adolescents (10-18 years) and 24 adults (22-32 years) during the anticipation of potential monetary loss. Overall, results revealed slower RTs in adolescents compared to adults with both groups being faster in the experimental (monetary condition) vs. control trials (verbal condition). Fastest RTs were observed for the 67% outcome probability in both age groups. An age group outcome probability interaction effect revealed the greatest differences between the groups for the 12% vs. the 67% outcome probability. Neurally, both age groups demonstrated a higher percent signal change in the VS and AI during the anticipation of potential monetary loss versus the verbal condition. However, adults demonstrated an even greater activation of VS and AI than adolescents during the anticipation of potential monetary loss, but not during the verbal condition. This may indicate that adolescents differ from adults regarding their experience of avoiding losing monetary rewards. [ABSTRACT FROM AUTHOR]

Copyright of Quetes Litteraires is the property of Katolicki Uniwersityt Lubelski Jana Pawla II, Wydzial Nauk Humanistycznych, Institut Filologii Romaj and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article presents a study based on evaluating different techniques to automatically recognize the basic emotions of people with Down syndrome, such as anger, happiness, sadness, surprise, and neutrality, as well as the statistical analysis of the Facial Action Coding System, determine the symmetry of the Action Units present in each emotion, identify the facial features that represent this group of people. First, a dataset of images of faces of people with Down syndrome classified according to their emotions is built. Then, the characteristics of facial micro-expressions (Action Units) present in the feelings of the target group through statistical analysis are evaluated. This analysis uses the intensity values of the most representative exclusive action units to classify people's emotions. Subsequently, the collected dataset was evaluated using machine learning and deep learning techniques to recognize emotions. In the beginning, different supervised learning techniques were used, with the Support Vector Machine technique obtaining the best precision with a value of 66.20%. In the case of deep learning methods, the mini-Xception convolutional neural network was used to recognize people's emotions with typical development, obtaining an accuracy of 74.8%. [ABSTRACT FROM AUTHOR]

Entropy is the concepts from the science of information must be used in the situation where undefined behaviors of the parameters are unknown. The behavior of the casual parameters representing the processes under investigation is a problem that the essay explores from many angles. The provided uniformity criterion, which was developed utilizing the maximum entropy of the metric, has high efficiency and straightforward implementation in manual computation, computer software and hardware, and a variety of similarity, recognition, and classification indicators. The tools required to automate the decision-making process in real-world applications, such as the automatic classification of acoustic events or the fault-detection via vibroacoustic methods, are provided by statistical decision theory to the noise and vibration engineer. Other statistical analysis issues can also be resolved using the provided uniformity criterion.

Demographers and other social scientists often study effect heterogeneity (defined here as differences in outcome-predictor associations across groups defined by the values of a third variable) to understand how inequalities evolve between groups or how groups differentially benefit from treatments. Yet answering the question "Is the effect larger in group A or group B?" is surprisingly difficult. In fact, the answer sometimes reverses across scales. For example, researchers might conclude that the effect of education on mortality is larger among women than among men if they quantify education's effect on an odds-ratio scale, but their conclusion might flip (to indicate a larger effect among men) if they instead quantify education's effect on a percentage-point scale. We illuminate this flipped-signs phenomenon in the context of nonlinear probability models, which were used in about one third of articles published in Demography in 2018-2019. Although methodologists are aware that flipped signs can occur, applied researchers have not integrated this insight into their work. We provide formal inequalities that researchers can use to easily determine if flipped signs are a problem in their own applications. We also share practical tips to help researchers handle flipped signs and, thus, generate clear and substantively correct descriptions of effect heterogeneity. Our findings advance researchers' ability to accurately characterize population variation. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is revolutionizing micro and macro-aided decision-making processes across various sectors and disciplines worldwide. This paper uses the probability technique to theoretically forecast AI-aided integration program potential to offer accelerated, concrete and individualized integration support for new and 'old' resident immigrants throughout their stay in Finland. The algorithms are base on various taxonomies and topologies for individualized self-paced, life-long situation-specific integration support. This action research article theoretically reviews two high-quality peer-reviewed publications on AI in aging through systematic reviews and meta-analyses, combining results from multiple impact evaluation studies to construct arguments and draw conclusions for the use of AI in accelerating new migrants' integration in Finland. Data is analyzed qualitatively and quantitively to deduce realistic predictions. This article uses grounded theory to test theories that underscore the role and impact of AI in accelerating integration. This article presents the foundation on which future integration programs will be implemented. [ABSTRACT FROM AUTHOR]

A strong foundation in early number concepts is crucial for students' future success in statistics. Despite its importance in statistics, many first-year students struggle to comprehend the normal distribution due to a lack of basic number sense. Students get confused about the order and magnitude of negative z-scores on a standard normal curve or when problems about normally distributed random variables are presented in word questions which involve phrases that indicate inequalities. As a result, students shade wrong areas on the bell-shaped curve when they have to calculate probabilities for normally distributed variables. Visual representations such as the number line can support students' development of quantitative literacy or number sense by helping them create a mental representation of the order and magnitude of numbers as well as inequalities. Based on a comprehensive investigation of evidence demonstrating this weakness, this experienced-based perspective proposes a framework that demonstrates how the number line can be used as a powerful teaching tool to promote students' conceptual understanding about the normal distribution. The framework illustrates with authentic examples how the number line relates to the horizontal axis of the normal and standard normal curve and how it can be used to address erroneous quantitative reasoning when students are required to calculate probabilities for normally distributed variables. To determine if the number line teaching intervention affects student performance, the researcher adopted a non-equivalent pretest-posttest design with two intact classes. The results of the analysis show that students who received the intervention performed significantly better in the posttest. [ABSTRACT FROM AUTHOR]

Data analysis is one of the most popular fields of mathematics and includes statistics and probability. These two mathematical domains are some of the most well-known, influencing everyday life and the various sciences. Their teaching lays the foundation for primary education and culminates in secondary education. Probability and statistics are necessary for today and the future of several professions. This research attempts to highlight the multidisciplinary character of these two disciplines through the textbooks of primary education in Greece and Singapore. It aims to highlight the dependence of mathematics teaching on interdisciplinarity through textbooks. The textbook analysis was chosen because books offer varied learning opportunities. The researchers selected the books, partaking in the comparative analysis. After defining the basic principles dividing lines for the differentiation of the exercises, the analysis was conducted. It included two stages. In the first stage, the activities of the books were examined in their framework application. Then, their interdisciplinary character was accentuated in the scientific field. The results reveal a substantial dependence of data analysis on interdisciplinarity. More interesting is that the distribution of interdisciplinary exercises is prevalent in the scientific milieus.

The rising cases of floods and the onset of drought in different parts of Nigeria require urgent attention particularly because Nigeria accommodates the largest population in Africa, hence any negative climate impact on it can easily ripple into other African regions. To understand the risk factors that drive these extreme events, we study the bivariate extreme cases of monthly precipitation and temperature observations over a period of 116 years (1901–2016). This is the first paper providing bivariate extreme value analysis of data in Nigeria. The mean rainfall and temperature variables exhibit interrelationships such as dry-cold and wet-cold associations. We further investigate whether these relationships are present at the tails by making use of the annual minimum rainfall-annual minimum temperature and annual maximum rainfall-annual minimum temperature pairs. Their extreme dependence structures are also quantified by applying the parametric bivariate extreme value models. Our results show that the compound extremes of dry-cold and wet-cold conditions exhibit a zero to weak extreme dependence at varying quantile levels. A much stronger dependence structure is present between the annual maximum rain and the total volume of rainfall. By considering both independent and dependent probability assumptions, we show that the former may lead to an underestimation of the risks associated with existing climatic hazards. The implications of these results are highlighted throughout the paper. [ABSTRACT FROM AUTHOR]

Project schedule monitoring and controlling are critical challenges of construction project management that are not adequately implemented, likely due to the predominance of earned value management and the lack of utilizing technology, such as BIM tools. Unlike earned value, earned duration management (EDM) was developed, which includes several indices to track schedule progress and measure the performance of a schedule. The goal of this research was to establish a decision support system to track and monitor construction project activities during construction, with better performance and accuracy. A survey was conducted and distributed among ten site engineers, selected from different construction sites. The survey asked the site engineers about the possible durations of certain activities; based on their answers, the authors started the proposed system. In this study, we aimed to develop a decision support system (DSS), which combines BIM with EDM to help calculate probabilistic total project duration, visually detecting critical activities, monitoring visually risky activities subjected to delay and visually categorizing the accuracy of estimated duration for delayed activities. [ABSTRACT FROM AUTHOR]

Bayesian learning has been successfully used in many fields to make decision or sense the outcome of causes or influences of events. However, the relations between causes and observed events are more complicated than the Bayesian inference can learn, like prediction in some psychology experiments, which should consider human experience and the interdependence of the events. This paper raises Bayesian learning to intelligent learning by putting events in a network to analysis the complicated criterions and interdependences among events with the individual probability judgments. Finally, the results of experiment used by intelligent learning illustrate more complex relations than Bayesian learning. [ABSTRACT FROM AUTHOR]

A major hypothesis about conditionals is the Equation in which the probability of a conditional equals the corresponding conditional probability: p(if A then C) = p(C|A). Probabilistic theories often treat it as axiomatic, whereas it follows from the meanings of conditionals in the theory of mental models. In this theory, intuitive models (system 1) do not represent what is false, and so produce errors in estimates of p(if A then C), yielding instead p(A & C). Deliberative models (system 2) are normative, and yield the proportion of cases of A in which C holds, i.e., the Equation. Intuitive estimates of the probability of a conditional about unique events: If covid-19 disappears in the USA, then Biden will run for a second term, together with those of each of its clauses, are liable to yield joint probability distributions that sum to over 100%. The error, which is inconsistent with the probability calculus, is massive when participants estimate the joint probabilities of conditionals with each of the different possibilities to which they refer. This result and others under review corroborate the model theory. [ABSTRACT FROM AUTHOR]

Intensity-duration-frequency (IDF) equations have important applications in several engineering areas such as urban drainage designs, hydrological modeling, and soil conservation projects. This study analyzes the annual maximum series and fits IDF equations for 44 rainfall stations in Alagoas State, Brazil. We adjusted parameters of the Gumbel distribution (GD) and the Generalized Extreme Value (GEV) distribution. The fitting of the observed data to the probability distributions, as well as the selection of the best distribution, were based on the Kolmogorov-Smirnov and Anderson-Darling tests at a 5% significance level. The GEV distribution with parameters obtained by the L-moments method was considered the best in 73% of rainfall stations. The estimated IDF equations showed a good fit, with determination coefficients above 0.991. The maximum rainfall intensities have spatial variation following the climatic zones of the state. The fitted equations allow estimating rainfall intensities from 5 minutes to 24 hours with a return period of 2 to 100 years, and standard error of less than 6.83 mm h-1.

Data analysis is one of the most popular fields of mathematics and includes statistics and probability. These two mathematical domains are some of the most well-known, influencing everyday life and the various sciences. Their teaching lays the foundation for primary education and culminates in secondary education. Probability and statistics are necessary for today and the future of several professions. This research attempts to highlight the multidisciplinary character of these two disciplines through the textbooks of primary education in Greece and Singapore. It aims to highlight the dependence of mathematics teaching on interdisciplinarity through textbooks. The textbook analysis was chosen because books offer varied learning opportunities. The researchers selected the books, partaking in the comparative analysis. After defining the basic principles dividing lines for the differentiation of the exercises, the analysis was conducted. It included two stages. In the first stage, the activities of the books were examined in their framework application. Then, their interdisciplinary character was accentuated in the scientific field. The results reveal a substantial dependence of data analysis on interdisciplinarity. More interesting is that the distribution of interdisciplinary exercises is prevalent in the scientific milieus. [ABSTRACT FROM AUTHOR]

The notion focus affinity refers to the likelihood that a grammatical role (e.g., subject, object) is the narrow focus of a sentence. There is evidence that such differences exist at the level of distributional data. For example, subjects are less often focal and more often topical as compared to direct objects. However, it is not obvious whether focus affinity is genuinely related to grammatical roles. Focus affinity could be an epiphenomenon of various other linguistic and pragmatic processes or their interactions (e.g., direct objects are more often indefinite, less often animate, more often new than subjects). In our controlled experimental study, we investigate the focus affinity of three adjunct types (instruments, locatives, depictive secondary predicates) in Spanish and provide evidence that these grammatical roles indeed differ with respect to focus affinity. Depictives show the highest degree of focus affinity, followed by instruments and finally locatives. These effects are robust and stable even if we account for a number of possible alternative explanations. Thus, we suggest that focus affinity is a property sufficiently closely associated with grammatical roles to justify more attention both in theoretical linguistic and psycholinguistic literature. • Focus affinity is an understudied information structural concept. • Focus affinities of three adjunct types have been experimentally investigated. • Focus hierarchy: Depictives > Instruments > Locatives. • Differences between grammatical roles are robust even if we consider alternative explanations (e.g., probabilities). [ABSTRACT FROM AUTHOR]

Copyright of Revista Habanera de Ciencias Médicas is the property of Universidad de Ciencias Medicas de La Habana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: Background: Social and material deprivation accelerate the development of multimorbidity, yet the mechanisms which drive multimorbidity pathways and trajectories remain unclear. We aimed to examine the association between health inequality, risk factors and accumulation or resolution of LTCs, taking disease sequences into consideration. Methods: We conducted a retrospective cohort of adults aged 18 years and over, registered between April 2005 and May 2020 in general practices in one inner London borough (n = 826,936). Thirty-two long term conditions (LTCs) were selected using a consensus process, based on a definition adapted to the demographic characteristics of the local population. sThe development and resolution of these LTCs were examined according to sociodemographic and clinical risk factors (hypertension; moderate obesity (BMI 30·0–39·9 kg/m2), high cholesterol (total cholesterol > 5 mmol/L), smoking, high alcohol consumption (>14 units per week), and psychoactive substance use), through the application of multistate Markov chain models. Findings: Participants were followed up for a median of 4.2 years (IQR = 1·8 - 8·4); 631,760 (76%) entered the study with no LTCs, 121,424 (15%) with 1 LTC, 41,720 (5%) with 2 LTCs, and 31,966 (4%) with three or more LTCs. At the end of follow-up, 194,777 (24%) gained one or more LTCs, while 45,017 (5%) had resolved LTCs and 27,021 (3%) died. In multistate models, deprivation (hazard ratio [HR] between 1·30 to 1·64), female sex (HR 1·13 to 1·20), and Black ethnicity (HR 1·20 to 1·30; vs White) were independently associated with increased risk of transition from one to two LTCs, and shorter time spent in a healthy state. Substance use was the strongest risk factor for multimorbidity with an 85% probability of gaining LTCs over the next year. First order Markov chains identified consistent disease sequences including: chronic pain or osteoarthritis followed by anxiety and depression; alcohol and substance dependency followed by HIV, viral hepatitis, and liver disease; and morbid obesity followed by diabetes, hypertension, and chronic pain. Interpretation: We examined the relations among 32 LTCs, taking the order of disease occurrence into consideration. Distinctive patterns for the development and accumulation of multimorbidity have emerged, with increased risk of transitioning from no conditions to multimorbidity and mortality related to ethnicity, deprivation and gender. Musculoskeletal disorders, morbid obesity and substance abuse represent common entry points to multimorbidity trajectories.

In recent years, there have been efforts to promote probabilistic reporting and the use of computational algorithms across several forensic science disciplines. Reactions to these efforts have been mixed—some stakeholders argue they promote greater scientific rigor whereas others argue that the opacity of algorithmic tools makes it challenging to meaningfully scrutinize the evidence presented against a defendant resulting from these systems. Consequently, the forensic community has been left with no clear path to navigate these concerns as each proposed approach has countervailing benefits and risks. To explore these issues further and provide a foundation for a path forward, this study draws on semi-structured interviews with fifteen participants to elicit the perspectives of key criminal justice stakeholders, including laboratory managers, prosecutors, defense attorneys, judges, and other academic scholars, on issues related to interpretation and reporting practices and the use of computational algorithms in forensic science within the American legal system.

This article presents a study based on evaluating different techniques to automatically recognize the basic emotions of people with Down syndrome, such as anger, happiness, sadness, surprise, and neutrality, as well as the statistical analysis of the Facial Action Coding System, determine the symmetry of the Action Units present in each emotion, identify the facial features that represent this group of people. First, a dataset of images of faces of people with Down syndrome classified according to their emotions is built. Then, the characteristics of facial micro-expressions (Action Units) present in the feelings of the target group through statistical analysis are evaluated. This analysis uses the intensity values of the most representative exclusive action units to classify people’s emotions. Subsequently, the collected dataset was evaluated using machine learning and deep learning techniques to recognize emotions. In the beginning, different supervised learning techniques were used, with the Support Vector Machine technique obtaining the best precision with a value of 66.20%. In the case of deep learning methods, the mini-Xception convolutional neural network was used to recognize people’s emotions with typical development, obtaining an accuracy of 74.8%.

This paper is a first-order interdisciplinary research, in an interstice defined by linguistic statistics and it appeared by extending the statistical way of thinking in the universe of language. The research structure includes an introductory section that states the specific paradigm or the common statistical and linguistic concepts of the investigation, followed by a brief methodological section, simultaneously of a descriptive and applied type, with reference to laws, methods and statistical models possible in the initially quantitative universe and finally qualitative universe of language, to eventually reach the final section dedicated to an attempt to assess entropy and information energy, based on the results of research in its whole. From an evolutionary point of view, after the philologist initially investigates in an exemplary way and with philological accuracy, and the statistician approaches selectively and inferentially, but also with specific statistical rigor, the interdisciplinary point of view of linguistic statistics allows a rapid confrontation and a multiple evaluation of entropy through the information energy method, but also through other equally interesting methods. The justified hope of interdisciplinary thinking of linguistic statistics to optimally capitalize on simultaneously philological and methodological reasoning to validate or invalidate preliminary hypotheses and to allow the expression of conclusions, follow-up analysis, outlines if not an answer, certainly at least several useful questions, which confirms practically and theoretically both the necessity and the veracity of the research in this article, according to the logic of a successful research in a Veblenian way. [ABSTRACT FROM AUTHOR]

Methods for group comparisons using predicted probabilities and marginal effects on probabilities are developed for regression models for binary outcomes. Unlike approaches based on the comparison of regression coefficients across groups, the methods we propose are unaffected by the scalar identification of the coefficients and are expressed in the natural metric of the outcome probability. While we develop our approach using binary logit with two groups, we consider how our interpretive framework can be used with a broad class of regression models and can be extended to any number of groups. [ABSTRACT FROM AUTHOR]

Considering that prehistoric cultures may have had the socio-religious need and technical ability to create accurate geometric patterns across a large landscape, limited ethnographic and archaeologic evidence are reviewed. Simple but accurate land surveying is discussed. Since any set of existing sites at larger scales coincidentally creates accurate three-point alignments and right-angles, the critical research problem attempts to distinguish designed from random geometry. Unpublished patterns involving great kivas in Chaco Canyon and Temple IV at Tikal are tested for probabilities of design. The more expansive third test considers the location of 26 prominent Adena mounds in relation to 32 river confluence points and four highest mountains in a geographic area some 900 × 1200 km, just slightly larger than a Chacoan world. In 14 test boxes modeling the locations of the 26 mounds, 1000 sets of random points replace equal numbers in each box. Each set is searched for numbers of three-point alignments and ninety-degree angles at or under 0.10º accuracy. Chaco and Tikal tests show a strong likelihood of design at these sites; in the Adena, data indicate a high probability that some number of existing patterns were intentionally surveyed. [ABSTRACT FROM AUTHOR]

The usage and adoption of electric vehicles (EVs) have increased rapidly in the 21st century due to the shifting of the global energy demand away from fossil fuels. The market penetration of EVs brings new challenges to the usual operations of the power system. Uncontrolled EV charging impacts the local distribution grid in terms of its voltage profile, power loss, grid unbalance, and reduction of transformer life, as well as harmonic distortion. Multiple research studies have addressed these problems by proposing various EV charging control methods. This manuscript comprehensively reviews EV control charging strategies using real-world data. This review classifies the EV control charging strategies into scheduling, clustering, and forecasting strategies. The models of EV control charging strategies are highlighted to compare and evaluate the techniques used in EV charging, enabling the identification of the advantages and disadvantages of the different methods applied. A summary of the methods and techniques for these EV charging strategies is presented based on machine learning and probabilities approaches. This research paper indicates many factors and challenges in the development of EV charging control in next-generation smart grid applications and provides potential recommendations. A report on the guidelines for future studies on this research topic is provided to enhance the comparability of the various results and findings. Accordingly, all the highlighted insights of this paper serve to further the increasing effort towards the development of advanced EV charging methods and demand-side management (DSM) for future smart grid applications.

Concepts like the 100‐year flood event can be misleading if they are not updated to reflect significant changes over time. Here, we model observed annual maximum daily streamflow using a nonstationary approach to provide the first global picture of changes in: (a) the magnitudes of the 20‐, 50‐, and 100‐year floods (i.e., flows of a given exceedance probability in each year); (b) the return periods of the 20‐, 50‐, and 100‐year floods, as assessed in 1970 (i.e., flows of a fixed magnitude); and (c) corresponding flood probabilities. Empirically, we find the 20‐/50‐year floods have mostly increased in temperate climate zones, but decreased in arid, tropical, polar, and cold zones. In contrast, 100‐year floods have mostly decreased in arid/temperate zones and exhibit mixed trends in cold zones, but results are influenced by the small number of stations with long records, and highlight the need for continued updating of hazard assessments. Plain Language Summary: Here, we provide the first global examination of recent changes in the size, frequency, and probability of extreme river floods using historical river records. Since the 1970s, the 20‐year and 50‐year extreme river floods have mostly increased in temperate zones but decreased in arid, tropical, polar, and cold zones. In contrast, the 100‐year floods have decreased in arid and temperate zones, and show mixed results in cold zones, but at a smaller sample of sites with long records. Descriptions of changes in extreme flooding depend largely on site selection, and are constrained by availability of long‐term data. Overall, our findings highlight the importance of regularly updating flood hazard assessments under nonstationarity. Key Points: We provide the first global nonstationary assessment of changes in the magnitude, return period, and probability of observed extreme floodsWe find increasing 20‐year and 50‐year floods in temperate climate zones but mostly decreases in arid, tropical, polar, and cold zonesFor the 100‐year floods, at a smaller sample of sites, we find decreases in arid and temperate zones and mixed results elsewhere [ABSTRACT FROM AUTHOR]