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Serving as a fundamental opening section, the research article introduction (RAI) provides an entrance to a certain topic in the exploration of the unknown content of the research. It is a manifestation of rhetorical maneuver and establishes the context, identifies the challenges, and outlines the rationale for the study. Therefore, it is vital to make a well-crafted RAI to catch readers’ attention. However, the disparities in rhetorical moves employed in English linguistics RAIs between non-Scopus-indexed and Scopus-indexed journals have received less scrutiny. The present study aims to investigate the rhetorical move structures in both types of corpora. The non-Scopus and Scopus corpus each contained 50 RAIs. Based on the adapted Swales’ (1990) Create-A-Research-Space (CARS) model, the occurrence frequency of moves and steps was first analyzed, and the relationship between the frequency of moves and steps and the indexing of journals was then explored. The findings showed that the most frequent move in both corpora was Move 1, followed by Move 3 and Move 2. However, the higher number of overall moves in the non-Scopus corpus suggested that there was an overuse of moves by the less experienced writers, and through Chi-square, Move 3 Step 1B (announcing present research) was detected to have a significant relationship with the indexing of journals. The findings of this study pave the way for writers to construct well-organized RAIs and get their papers published in the field of linguistics.

In 2017, the Republic of Kazakhstan began the phased transition of its alphabet from Cyrillic to Latin script. This transition has presented significant challenges to Kazakhstani document examiners, who have yet to develop appropriate methodologies for the analysis of handwriting samples written in the Kazakh language using Latin letters. This study aims to identify distinguishing macro and micro features of letters within Kazakh writing samples produced using the Latin alphabet and determine their frequencies of occurrence and discriminating power indices. Micro features were examined using the four most frequently appearing letters: \"a\", \"y\", \"e\" and \"n\". A comparative analysis of tested Latin letters with those of a similar configuration in Cyrillic demonstrated differences in the number of distinguishing features, as well as in the frequency of occurrence and discriminating power indices of similar features. These results show that separate statistical bases should be used for Latin and Cyrillic letters when analysing handwriting samples based on the frequencies of occurrence of micro and macro writing features.

Research has begun to demonstrate that L2 words can be learned incidentally through watching audio-visual materials. Although there are a large number of studies that have investigated incidental vocabulary learning through reading a single text, there are no studies that have explored incidental vocabulary learning through viewing a single full-length TV program. The present study fills this gap. Additionally, three word-related variables (frequency of occurrence, cognateness, word relevance) and one learner-related variable (prior vocabulary knowledge) that might contribute to incidental vocabulary learning were examined. Two experiments were conducted with Dutch-speaking EFL learners to measure the effects of viewing TV on form recognition and meaning recall (Experiment 1) and meaning recognition (Experiment 2). The findings showed that viewing TV resulted in incidental vocabulary learning at the level of meaning recall and meaning recognition. The research also revealed that learning was affected by frequency of occurrence, prior vocabulary knowledge, and cognateness.

Achieving the desired accuracy in time series forecasting has become a binding domain, and developing a forecasting framework with a high degree of accuracy is one of the most challenging tasks in this area. Combining different forecasting methods to construct efficient hybrid models has been widely reported in the literature regarding this challenge. Various types of hybrid models have been developed and successfully employed to improve forecasting accuracy. The well-known hybrid models can be generally categorized into four classes: (1) preprocessing-based, (2) parameter optimization-based, (3) components combination-based, and (4) postprocessing-based hybrid models. Despite the significant successes of hybrid models, efforts to access more accurate results face continued growth. Hybridization of hybrid models is a novel idea proposed to obtain extreme accuracy in recent literature, in which two or more hybrid classes are combined instead of conjoining the conventional individual forecasting methods. Although, in many publications, the aforementioned classes of hybrid models have been reviewed and analyzed in a wide variety of forecasting fields; no study is conducted to review the hybridization of hybrid models. This paper’s main contribution is to fill this gap and provide classification and comprehensive review of the current endeavors done in the hybridization of hybrid models in time series forecasting areas. Our searches indicate that more than 250 papers have been published in recent years utilizing hybridization of hybrid models. In this paper, these published papers have been classified regarding their different used combination strategies into four main categories, including (1) Hybridization with preprocessing-based hybrid models (HPH), (2) Hybridization with parameter optimization-based hybrid models (HOH), (3) Hybridization with components combination-based hybrid models (HCH) and, (4) Hybridization with postprocessing-based hybrid models (HSH). Each hybridization of the hybrid class is evaluated regarding the usage frequency, specific merits, and limitations. It can be inferred from reviewing articles that the hybridization of the hybrid concept, as a recent advancement in time series forecasting, can significantly improve traditional hybrid models’ accuracy. Furthermore, each category’s research gaps and some future research directions are identified in this paper.

Word frequency is the most important variable in research on word processing and memory. Yet, the main criterion for selecting word frequency norms has been the availability of the measure, rather than its quality. As a result, much research is still based on the old Kučera and Francis frequency norms. By using the lexical decision times of recently published megastudies, we show how bad this measure is and what must be done to improve it. In particular, we investigated the size of the corpus, the language register on which the corpus is based, and the definition of the frequency measure. We observed that corpus size is of practical importance for small sizes (depending on the frequency of the word), but not for sizes above 16–30 million words. As for the language register, we found that frequencies based on television and film subtitles are better than frequencies based on written sources, certainly for the monosyllabic and bisyllabic words used in psycholinguistic research. Finally, we found that lemma frequencies are not superior to word form frequencies in English and that a measure of contextual diversity is better than a measure based on raw frequency of occurrence. Part of the superiority of the latter is due to the words that are frequently used as names. Assembling a new frequency norm on the basis of these considerations turned out to predict word processing times much better than did the existing norms (including Kučera & Francis and Celex). The new SUBTL frequency norms from the SUBTLEXUS corpus are freely available for research purposes from http://brm.psychonomic-journals.org/content/supplemental, as well as from the University of Ghent and Lexique Web sites.

Cannabis use is decreasing in England and Wales, while demand for cannabis treatment in addiction services continues to rise. This could be partly due to an increased availability of high-potency cannabis. Adults residing in the UK were questioned about their drug use, including three types of cannabis (high potency: skunk; low potency: other grass, resin). Cannabis types were profiled and examined for possible associations between frequency of use and (i) cannabis dependence, (ii) cannabis-related concerns. Frequent use of high-potency cannabis predicted a greater severity of dependence [days of skunk use per month: b = 0.254, 95% confidence interval (CI) 0.161-0.357, p < 0.001] and this effect became stronger as age decreased (b = -0.006, 95% CI -0.010 to -0.002, p = 0.004). By contrast, use of low-potency cannabis was not associated with dependence (days of other grass use per month: b = 0.020, 95% CI -0.029 to 0.070, p = 0.436; days of resin use per month: b = 0.025, 95% CI -0.019 to 0.067, p = 0.245). Frequency of cannabis use (all types) did not predict severity of cannabis-related concerns. High-potency cannabis was clearly distinct from low-potency varieties by its marked effects on memory and paranoia. It also produced the best high, was preferred, and most available. High-potency cannabis use is associated with an increased severity of dependence, especially in young people. Its profile is strongly defined by negative effects (memory, paranoia), but also positive characteristics (best high, preferred type), which may be important when considering clinical or public health interventions focusing on cannabis potency.

This paper claims that a wide variety of grammatical coding asymmetries can be explained as adaptations to the language users’ needs, in terms of frequency of use, predictability and coding efficiency. I claim that all grammatical oppositions involving a minimal meaning difference and a significant frequency difference are reflected in a universal coding asymmetry, i.e. a cross-linguistic pattern in which the less frequent member of the opposition gets special coding, unless the coding is uniformly explicit or uniformly zero. I give 25 examples of pairs of construction types, from a substantial range of grammatical domains. For some of them, the existing evidence from the world’s languages and from corpus counts is already strong, while for others, I know of no counterevidence and I make readily testable claims. I also discuss how the functional-adaptive forces operate in language change, and I discuss a number of possible alternative explanations.

This review article presents evidence for the claim that frequency effects are pervasive in children's first language acquisition, and hence constitute a phenomenon that any successful account must explain. The article is organized around four key domains of research: children's acquisition of single words, inflectional morphology, simple syntactic constructions, and more advanced constructions. In presenting this evidence, we develop five theses. (i) There exist different types of frequency effect, from effects at the level of concrete lexical strings to effects at the level of abstract cues to thematic-role assignment, as well as effects of both token and type, and absolute and relative, frequency. High-frequency forms are (ii) early acquired and (iii) prevent errors in contexts where they are the target, but also (iv) cause errors in contexts in which a competing lower-frequency form is the target. (v) Frequency effects interact with other factors (e.g. serial position, utterance length), and the patterning of these interactions is generally informative with regard to the nature of the learning mechanism. We conclude by arguing that any successful account of language acquisition, from whatever theoretical standpoint, must be frequency sensitive to the extent that it can explain the effects documented in this review, and outline some types of account that do and do not meet this criterion.

There are a number of disruptive mobility services that are increasingly finding their way into the marketplace. Two key examples of such services are car-sharing services and ride-sourcing services. In an effort to better understand the influence of various exogenous socio-economic and demographic variables on the frequency of use of ride-sourcing and car-sharing services, this paper presents a bivariate ordered probit model estimated on a survey data set derived from the 2014–2015 Puget Sound Regional Travel Study. Model estimation results show that users of these services tend to be young, well-educated, higher-income, working individuals residing in higher-density areas. There are significant interaction effects reflecting the influence of children and the built environment on disruptive mobility service usage. The model developed in this paper provides key insights into factors affecting market penetration of these services, and can be integrated in larger travel forecasting model systems to better predict the adoption and use of mobility-on-demand services.

Objective: The aims of this study are to (a) provide reliable estimates of prevalence rates of family polyvictimization by synthesizing the findings from the existing literature, (b) examine the effect sizes of the impact of one type of family victimization on other types of family victimization, and (c) investigate the correlates of family polyvictimization. Method: Databases of literature published on or before April 2018 were searched. A total of 59 publications met the inclusion criteria and were included in the analysis for the combined odds ratios (ORs) to estimate the associations among different types of family victimization; 38 of them (99,956 participants) were used for the calculation of the combined prevalence rates of family polyvictimization. Findings: The overall co-occurrence rates of family victimization were 9.7% among the general population and 36.0% among the clinical population. The combined OR of other type(s) of victimization when one was present was 6.01 (p < .001). Longitudinal studies show that, when a family reported intimate partner violence, the odds of child abuse and neglect within the same family at a later stage was 3.64 (p < .001). Depression and post-traumatic stress disorder were two significant correlates associated with family polyvictimization. Conclusion: Family polyvictimization is prevalent across the world. The high co-occurrence rates and strong associations between different types of victimization on different family members warrant the need for the early detection of victims and effective preventions and interventions using a family approach, instead of treating victims from the same family individually.