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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 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]

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]

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]

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]

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 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]

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]

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 article is devoted to analysis of emergent properties of the system of binary oppositions in the genetic code ensemble. The epochal model of the double helix of DNA by Watson and Crick showed that the multiple reproduction of genetic information on DNA strands uses the ancient principle "like begets like" based on the simple complementarity in pairs of nucleobases. Each of these pairs is built on the binary opposition "purine-pyrimidine". But the system of DNA n -plet alphabets and genetic coding is much richer in types of binary oppositions, which also have some coding meanings related to this principle. The article contains the results of the application of the author's "method of hierarchy binary stochastics" (HBS-method) to the analysis of the quasi-stochastic organization of binary sequences of hydrogen bonds in genomic single-stranded DNAs. This analysis revealed hidden probability rules related to dichotomous fractal-like probability trees. The relationship between inherited bodily dichotomies in living organisms and the discovered probability dichotomies in information sequences of genomic DNAs is discussed. The encoding properties of molecular binary oppositions in the DNA nucleotide system allows the algorithmic construction of (2 n ∗2 n )-matrices of probabilities of n -plets in these binary sequences, which are matrix representations of 2 n -dimensional hyperbolic numbers. Connections of these multidimensional numbers with some inherited physiological phenomena and deep neural networks are noted. A unified algebra-numeric certification of the DNAs of genomes and genes - based on these multidimensional numerical systems - is proposed. [ABSTRACT FROM AUTHOR]

• Decision support based on clinical guidelines is important in medicine. • For comorbid patients, guideline interactions along time must be detected. • We provide a temporal framework supporting both preferences and probabilities. • We provide temporal reasoning and query answering facilities. • Temporal reasoning with preferences and probabilities provides a flexible support. Knowledge-based decision support systems have a long tradition within the medical area. In particular, in the last decades, many Computer-Interpretable Guidelines (CIG) systems have been built to provide evidence-based and knowledge-based support to physicians. Since CIGs are, by definition, devoted to the management of specific diseases, the treatment of comorbid patients constitutes a challenging task in the area, involving (i) the detection of the possible interactions between (the effects of) the actions recommended by multiple CIGs (one for each disease of the patient), (ii) the management of such interactions and, finally, (iii) the conciliation of (the recommendations of) different CIGs. This paper focuses on issue (i) above, and specifically, on an innovative approach to support interaction detection along the temporal dimension. Practically, interactions can only occur between effects that intersect in time. Therefore, interaction detection involves the representation of temporal information (temporal constraints), and temporal reasoning (to propagate such constraints). Additionally, query answering facilities are important to support physicians in the investigation of the results of temporal reasoning. Current CIG approaches that face such issues take into account only "crisp" temporal constraints, i.e., they consider all temporal constraints as equally probable\preferred. However, preferences about the temporal constraints between CIGs actions may be available, as well as knowledge about the probabilistic distribution of the effects of CIGs actions in time. Considering such additional pieces of information can provide crucial advantages, in term of the flexibility and informativeness of the support provided by the CIG system to physicians. In this paper, we propose the first homogeneous approach to represent and reason with (propagate) temporal constraints with both preferences and probabilities . We ground our approach on the widely-used Simple Temporal Problem (STP) framework, which supports temporal reasoning on temporal constraints about possible distances between events. We extend (i) the representation formalism to associate preferences and\or probabilities to the possible distances, and (ii) the operations to propagate the constraints to combine also preferences and probabilities. We also (iii) provide an experimental evaluation of our approach, and (iv) propose a wide range of query-answering supports, to facilitate physicians in the analysis of the results of temporal reasoning in general, and in the temporal detection of possible interactions in particular. Finally, (v) we also show how such a temporal framework is integrated in GLARE-SSCPM, a CIG system to treat comorbid patients, and show the advantages of our approach considering a running example. [ABSTRACT FROM AUTHOR]

Different risks are associated with the operation and maintenance of wind farms in cold climate regions, mainly due to the harsh weather conditions that wind farms experience in that region such as the (i) increased stoppage rate of wind turbines due to harsh weather conditions, (ii) limited accessibility to wind farms due to snow cover on roads, and (iii) cold stress to workers at wind farms. In addition, there are risks that are caused by wind farms during their operation, which impact the surrounding environment and community such as the (iv) risk of ice throw from wind turbines, (v) environmental risks caused by the wind farms, and (vi) social opposition risk to installing wind farms in cold climate regions, such as the Arctic. The analysis of these six risks provides an overall view of the potential risks encountered by designers, operators, and decision makers at wind farms. This paper presents a methodology to quantify the aforementioned risks using fuzzy logic method. At first, two criteria were established for the probability and the consequences of each risk; with the use of experts' judgments, membership functions were graphed to reflect the two established criteria, which represented the input to the risk analysis process. Furthermore, membership functions were created for the risk levels, which represented the output. To test the proposed methodology, a wind farm in Arctic Norway was selected as a case study to quantify its risks. Experts provided their assessments of the probability and consequences of each risk on a scale from 0–10, depending on the description of the wind farm provided to them. Risk levels were calculated using MATLAB fuzzy logic toolbox and ranked accordingly. Limited accessibility to the wind farm was ranked as the highest risk, while the social opposition to the wind farm was ranked as the lowest. In addition, to demonstrate the effects of the Arctic operating conditions on performance and safety of the wind farm, the same methodology was applied to a wind farm located in a non-cold-climate region, which showed that the risks ranked differently. [ABSTRACT FROM AUTHOR]

• New approach of comparing rainfall and discharge extremes in probabilistic framework. • Extreme rainfall elasticity of floods is higher in dry than in wet catchments. • High elasticities are related to highly skewed distributions of runoff coefficients. The aim of this paper is to explore how rainfall mechanisms and catchment characteristics shape the relationship between rainfall and flood probabilities. We propose a new approach of comparing intensity-duration-frequency statistics of maximum annual rainfall with those of maximum annual streamflow in order to infer the catchment behavior for runoff extremes. We calibrate parsimonious intensity-duration-frequency scaling models to data from 314 rain gauges and 428 stream gauges in Austria, and analyze the spatial patterns of the resulting distributions and model parameters. Results indicate that rainfall extremes tend to be more variable in the dry lowland catchments dominated by convective rainfall than in the mountainous catchments where annual rainfall is higher and rainfall mechanisms are mainly orographic. Flood frequency curves are always steeper than the corresponding rainfall frequency curves with the exception of glaciated catchments. Based on the proposed approach of combined intensity-duration-frequency statistics we analyze elasticities as the percent change of flood discharge for a 1% change in extreme rainfall through comparing rainfall and flood quantiles. In wet catchments, the elasticities tend to unity, i.e. rainfall and flood frequency curves have similar steepness, due to persistently high soil moisture levels. In dry catchments, elasticities are much higher, implying steeper frequency curves of floods than those of rainfall, which is interpreted in terms of more skewed distributions of event runoff coefficients. While regional differences in the elasticities can be attributed to both dominating regional rainfall mechanisms and regional catchment characteristics, our results suggest that catchment characteristics are the dominating controls. With increasing return period, elasticities tend towards unity, which is consistent with various runoff generation concepts. Our findings may be useful for process-based flood frequency extrapolation and climate impact studies, and further studies are encouraged to explore the tail behavior of elasticities. [ABSTRACT FROM AUTHOR]

In this study, we report the lowest energy structure of bare Cu13 nanoclusters as a pair of enantiomers at room temperature. Moreover, we compute the enantiomerization energy for the interconversion from minus to plus structures in the chiral putative global minimum for temperatures ranging from 20 to 1300 K. Additionally, employing nanothermodynamics, we compute the probabilities of occurrence for each particular isomer as a function of temperature. To achieve that, we explore the free energy surface of the Cu13 cluster, employing a genetic algorithm coupled with density functional theory. Moreover, we discuss the energetic ordering of isomers computed with various density functionals. Based on the computed thermal population, our results show that the chiral putative global minimum strongly dominates at room temperature. [ABSTRACT FROM AUTHOR]