Application of Sarima Models in Modelling and Forecasting Monthly Rainfall in Nigeria

Application of SARIMA model in modelling and forecasting monthly rainfall in Nigeria was considered in this study. The study utilizes the Nigerian monthly rainfall data between 1980-2015 obtained from World Bank Climate Portal. The Box-Jenkin’s methodology was adopted. SARIMA (2,0,1) (2,1,1) [12] was the best model among others that fit the Nigerian rainfall data (1980-2015) with maximum p-value from Box-Pierce Residuals Test. The study forecasts Nigeria’s monthly rainfall from 2018 through 2042. It was discovered that the month of April is the period of onset of rainfall in Nigeria and November is the period of retreat. Based on the findings, Nigeria will experience approximately equal amount of rainfall between 2018 to 2021 and will experience a slight increase in rainfall amount in 2022 to about 1137.078 (mm). There will be a decline of rainfall at 2023 to about 1061 (mm). Rainfall values will raise again to about 1142.756 (mm) in 2024 and continue to fluctuate with decrease in variation between 2024 to 2042, then remain steady to 2046 at approximately 1110.0 (mm). Nigerian Government should provide a more mechanized and drier season farming methods to ease the outage of rainfall in future that may be caused due to natural (or unpredictable) variation.

The unpredictable Buridan's ass: Failure to predict decisions in a trivial decision-making task

In order to better examine seemingly unpredictable variation that appears in decision-making studies, I had people chose between two options that had no features or consequences to distinguish them. 100 users of Mechanical Turk completed 200 binary choices, and I examined the accuracy with which statistical models could predict the choices. Across three different conceptualizations of the prediction problem and a variety of models ranging from logistic regression to neural networks, I obtained at best modest predictive accuracy. Predicting trivial choices may actually be more difficult than predicting meaningful choices. These strongly negative results appear to place limits on the predictability of human behavior.

Homoeologous recombination is recurrent in the nascent synthetic allotetraploid Arachis ipaënsis × Arachis correntina4x and its derivatives

Genome instability in newly synthesized allotetraploids of peanut has breeding implications that have not been fully appreciated. Synthesis of wild species-derived neo-tetraploids offers the opportunity to broaden the gene pool of peanut; however, the dynamics among the newly merged genomes creates predictable and unpredictable variation. Selfed progenies from the neo-tetraploid Arachis ipaënsis × Arachis correntina (A. ipaënsis × A. correntina)4x and F1 hybrids and F2 progenies from crosses between A. hypogaea × [A. ipaënsis × A. correntina]4x were genotyped by the Axiom Arachis 48 K SNP array. Homoeologous recombination between the A. ipaënsis and A. correntina derived subgenomes was observed in the S0 generation. Among the S1 progenies, these recombined segments segregated and new events of homoeologous recombination emerged. The genomic regions undergoing homoeologous recombination segregated mostly disomically in the F2 progenies from A. hypogaea × [A. ipaënsis × A. correntina]4x crosses. New homoeologous recombination events also occurred in the F2 population, mostly found on chromosomes 03, 04, 05, and 06. From the breeding perspective, these phenomena offer both possibilities and perils; recombination between genomes increases genetic diversity, but genome instability could lead to instability of traits or even loss of viability within lineages.

Hierarchical Inferences Support Systematicity in the Lexicon

Language exhibits striking systematicity in its form-meaning mappings: Similar meanings are assigned similar forms. Here we study how systematicity relates to another well-studied phenomenon, linguistic regularization, the removal of unpredictable variation in linguistic variants. Systematicity is ultimately a property of classes of form-meaning mappings, each member of which can be acted upon independently by linguistic regularization. Both are supported by a cognitive bias for simplicity, but this leaves open the question of how they interact to structure the lexicon. Using data from a recent artificial gesture learning experiment by Verhoef, Padden, and Kirby(2016), we formalize cognitive biases at the item level and the language level as inductive biases in a hierarchical Bayesian model. Simulated data from models that lack either one of those biases show how both are necessary to capture subjects' systematicity preferences. Our results bring conceptual clarity about the relationship between regularization and systematicity and promote a multi-level approach to cognitive biases in artificial language learning and language evolution.

Unpredictable movement as an anti-predator strategy

Prey animals have evolved a wide variety of behaviours to combat the threat of predation, and these have been generally well studied. However, one of the most common and taxonomically widespread antipredator behaviours of all has, remarkably, received almost no experimental attention: so-called ‘protean’ behaviour. This is behaviour that is sufficiently unpredictable to prevent a predator anticipating in detail the future position or actions of its prey. In this study, we used human ‘predators’ participating in 3D virtual reality simulations to test how protean (i.e. unpredictable) variation in prey movement affects participants' ability to visually target them as they move (a key determinant of successful predation). We found that targeting accuracy was significantly predicted by prey movement path complexity, although, surprisingly, there was little evidence that high levels of unpredictability in the underlying movement rules equated directly to decreased predator performance. Instead, the specific movement rules differed in how they impacted on targeting accuracy, with the efficacy of protean variation in one element depending on the values of the remaining elements. These findings provide important insights into the understudied phenomenon of protean antipredator behaviour, which are directly applicable to predator–prey dynamics within a broad range of taxa.

When regularization gets it wrong: children over-simplify language input only in production

Children tend to regularize their productions when exposed to artificial languages, an advantageous response to unpredictable variation. But generalizations in natural languages are typically conditioned by factors that children ultimately learn. In two experiments, adult and six-year-old learners witnessed two novel classifiers, probabilistically conditioned by semantics. Whereas adults displayed high accuracy in their productions – applying the semantic criteria to familiar and novel items – children were oblivious to the semantic conditioning. Instead, children regularized their productions, over-relying on only one classifier. However, in a two-alternative forced-choice task, children's performance revealed greater respect for the system's complexity: they selected both classifiers equally, without bias toward one or the other, and displayed better accuracy on familiar items. Given that natural languages are conditioned by multiple factors that children successfully learn, we suggest that their tendency to simplify in production stems from retrieval difficulty when a complex system has not yet been fully learned.

Endogenous sources of variation in language acquisition

A fundamental question in the study of human language acquisition centers around apportioning explanatory force between the experience of the learner and the core knowledge that allows learners to represent that experience. We provide a previously unidentified kind of data identifying children’s contribution to language acquisition. We identify one aspect of grammar that varies unpredictably across a population of speakers of what is ostensibly a single language. We further demonstrate that the grammatical knowledge of parents and their children is independent. The combination of unpredictable variation and parent–child independence suggests that the relevant structural feature is supplied by each learner independent of experience with the language. This structural feature is abstract because it controls variation in more than one construction. The particular case we examine is the position of the verb in the clause structure of Korean. Because Korean is a head-final language, evidence for the syntactic position of the verb is both rare and indirect. We show that (i) Korean speakers exhibit substantial variability regarding this aspect of the grammar, (ii) this variability is attested between speakers but not within a speaker, (iii) this variability controls interpretation in two surface constructions, and (iv) it is independent in parents and children. According to our findings, when the exposure language is compatible with multiple grammars, learners acquire a single systematic grammar. Our observation that children and their parents vary independently suggests that the choice of grammar is driven in part by a process operating internal to individual learners.