L2 attitude and motivation of secondary, undergraduate, and postgraduate ESL learners in India

Second language (L2) attitude and motivation-related studies focusing on differences caused by age have mostly highlighted the temporal dimension of L2 attitude and motivation. Age-related L2 motivation studies have also been gainfully employed at comparisons between L2 learners of different age groups recruited from different L2 learning environments. Such studies have not, however, attempted an analysis of the L2 attitudinal and motivational differences that may exist among L2 learners within a closer age range, e.g., 18 to 25 years. This article presents the findings of an L2 attitude and motivation survey, using a modified version of Dӧrnyei et al. (2006) and Ryan (2005), conducted among secondary, undergraduate, and postgraduate English as a second language (ESL) learners (N210) in India. It primarily presents a comparative analysis of the L2 attitudinal and motivational constructs of integrativeness, instrumentality, cultural interest, linguistic self-confidence, and L2 anxiety attested in the sample. Additionally, it offers a description of the correlation between the five L2 attitudinal and motivational constructs concerning the different ESL groups. As the ESL learners across the academic levels demonstrated ESL motivation more on the side of instrumentality, they also reported linguistic self-confidence more in the familiar environment of an L2 classroom than outside of it. Since better motivational strategies enhance learner dedication to the learning of a certain L2, an elaborated understanding of the specific differences in L2 attitude and motivation within this important age range should help design more useful and effective L2 pedagogical methods.

Estimation of 1-km Resolution All-Sky Instantaneous Erythemal UV-B with MODIS Data Based on a Deep Learning Method

Although ultraviolet-B (UV-B) radiation reaching the ground represents a tiny fraction of the total solar radiant energy, it significantly affects human health and global ecosystems. Therefore, erythemal UV-B monitoring has recently attracted significant attention. However, traditional UV-B retrieval methods rely on empirical modeling and handcrafted features, which require expertise and fail to generalize to new environments. Furthermore, most traditional products have low spatial resolution. To address this, we propose a deep learning framework for retrieving all-sky, kilometer-level erythemal UV-B from Moderate Resolution Imaging Spectroradiometer (MODIS) data. We designed a deep neural network with a residual structure to cascade high-level representations from raw MODIS inputs, eliminating handcrafted features. We used an external random forest classifier to perform the final prediction based on refined deep features extracted from the residual network. Compared with basic parameters, extracted deep features more accurately bridge the semantic gap between the raw MODIS inputs, improving retrieval accuracy. We established a dataset from 7 Surface Radiation Budget Network (SURFRAD) stations and 1 from 30 UV-B Monitoring and Research Program (UVMRP) stations with MODIS top-of-atmosphere reflectance, solar and view zenith angle, surface reflectance, altitude, and ozone observations. A partial SURFRAD dataset from 2007–2016 trained the model, achieving an R2 of 0.9887, a mean bias error (MBE) of 0.19 mW/m2, and a root mean square error (RMSE) of 7.42 mW/m2. The model evaluated on 2017 SURFRAD data shows an R2 of 0.9376, an MBE of 1.24 mW/m2, and an RMSE of 17.45 mW/m2, indicating the proposed model accurately generalizes the temporal dimension. We evaluated the model at 30 UVMRP stations with different land cover from those of SURFRAD and found most stations had a relative RMSE of 25% and an MBE within ±5%, demonstrating generalization in the spatial dimension. This study demonstrates the potential of using MODIS data to accurately estimate all-sky erythemal UV-B with the proposed algorithm.

Sleep late? Pre-reform institutional embeddedness and entrepreneurial reinvestment of private firms in China’s transition economy

Purpose This study aims to introduce an important temporal dimension to the research on institution and entrepreneurship in the transition period. This study develops the concept of pre-reform institutional embeddedness, and explores its impact on entrepreneurial reinvestment of private firms in China’s transition economy. Design/methodology/approach The authors used secondary data of a nationally representative sample of China’s private firms collected in the early days of the institutional transition period and applied ordinary least squares regressions and the Baron and Kenny approach to test the theoretical model. Findings Pre-reform institutional embeddedness has a negative impact on entrepreneurial reinvestment of private firms in the transition period. This relationship is mediated by guanxi-induced employment, such that pre-reform institutional embeddedness promotes guanxi-induced employment, which in turn discourages a private firm to reinvest. Additionally, the negative impact of guanxi-induced employment on entrepreneurial reinvestment is reduced when decentralization of decision-making is used. Practical implications First, entrepreneurs should be aware of pre-reform institutional embeddedness’ negative influence on firms’ risk-taking abilities and incentives. Private firms already constrained by this connection could alleviate the negative impacts through a widespread delegation of decision-making authority. Second, policymakers should be cautious about improper government-business relationships, which may discourage private firms from fully pursuing entrepreneurial growth opportunities. Originality/value This paper makes theoretical contributions to the literature on entrepreneurial reinvestment, embeddedness perspective of entrepreneurship and imprinting theory.

A parametric and feasibility study for data sampling of the dynamic mode decomposition: range, resolution, and universal convergence states

Scientific research and engineering practice often require the modeling and decomposition of nonlinear systems. The dynamic mode decomposition (DMD) is a novel Koopman-based technique that effectively dissects high-dimensional nonlinear systems into periodically distinct constituents on reduced-order subspaces. As a novel mathematical hatchling, the DMD bears vast potentials yet an equal degree of unknown. This effort investigates the nuances of DMD sampling with an engineering-oriented emphasis. It aimed at elucidating how sampling range and resolution affect the convergence of DMD modes. We employed the most classical nonlinear system in fluid mechanics as the test subject—the turbulent free-shear flow over a prism—for optimal pertinency. We numerically simulated the flow by the dynamic-stress Large-Eddies Simulation with Near-Wall Resolution. With the large-quantity, high-fidelity data, we parametrized and identified four global convergence states: Initialization, Transition, Stabilization, and Divergence with increasing sampling range. Results showed that Stabilization is the optimal state for modal convergence, in which DMD output becomes independent of the sampling range. The Initialization state also yields sufficient accuracy for most system reconstruction tasks. Moreover, defying popular beliefs, over-sampling causes algorithmic instability: as the temporal dimension, n, approaches and transcends the spatial dimension, m (i.e., m < n), the output diverges and becomes meaningless. Additionally, the convergence of the sampling resolution depends on the mode-specific dynamics, such that the resolution of 15 frames per cycle for target activities is suggested for most engineering implementations. Finally, a bi-parametric study revealed that the convergence of the sampling range and resolution are mutually independent.

Environmental Stability and Long-term Variability of Harpacticoid Copepod Assemblages

Harpacticoid copepods of the Chernaya Bay (White Sea) intertidal zone were collected in 45 surveys carried out from spring to autumn over a 25-year period (1996-2020) at three sites that differed in sediment properties. There were no significant long-term trends or seasonal cycles in total abundance. Regarding the species composition, the differences between sites were the most important source of variability over the whole period while the fine-scale (within-habitat) variability was low. Epibenthic species prevailed in fine silty sand, both burrowing and epibenthic species prevailed in medium sand, and interstitial and burrowing species prevailed in coarse sand. A comparison of the data on harpacticoid assemblages from a number of geographically remote loci corroborated the generality of this pattern. In the temporal dimension, the structure of each community was stable until the early 2000s, when the proportion of epibenthic, burrowing and interstitial species changed following changes in sediment properties (increasing siltation at sandy sites and decreasing siltation at the silty site). At each site, there was an increasing long-term trend in diversity (both in total richness and in expected species number). This increase was particularly apparent at sandy sites because of the appearance of epibenthic species. We suggest that sediment composition is the key factor determining the composition of harpacticoid assemblages in space and time. The “ecomorphological profile”, i.e., the proportion of species with different lifestyle and morphological traits, is a useful and informative indicator for describing and typifying these assemblages.

Thinking across Frames – Temporally Extended Consciousness and the Animation Timeline

This article explores ways in which animation production technologies (including pre-cinema, film, and digital tools) have evolved as a system that abstracts time, primarily through its spatialization. This abstraction necessitates certain assumptions about the nature of time, including its linearity and directionality. Animation technologies have evolved so as to support various modes of temporally extended consciousness; an animator’s craft thinks and works through time. Embedded within digital production technologies, the animator is faced with a new philosophical instrument: the animation timeline. The main timeline utility in most animation software adopts a linear, mechanical model of time with the individual frame as the base unit. However, digital animation timeline can also complicate the spatialized temporal dimension, as the timeline is also embedded within animated objects in motion paths and other interface elements . As animated objects always exist through time, not merely within individual frames, the animation software tools for working with time both confine and unlock opportunities for working with time.

A Pipeline for Graph-Based Monitoring of the Changes in the Information Space of Russian Social Media during the Lockdown

With the COVID-19 outbreak and the subsequent lockdown, social media became a vital communication tool. The sudden outburst of online activity influenced information spread and consumption patterns. It increases the relevance of studying the dynamics of social networks and developing data processing pipelines that allow a comprehensive analysis of social media data in the temporal dimension. This paper scopes the weekly dynamics of the information space represented by Russian social media (Twitter and LiveJournal) during a critical period (massive COVID-19 outbreak and first governmental measures). The approach is twofold: 1) build the time series of topic similarity indicators by identifying COVID-related topics in each week and measuring user contribution to the topic space, and 2) cluster user activity and display user-topic relationships on graphs in a dashboard application. The paper describes the development of the pipeline, explains the choices made and provides a case study of the adaptation to virus control measures. The results confirm that social processes and behavior in response to pandemic-triggered changes can be successfully traced in social media. Moreover, the adaptation trends revealed by psychological and sociological studies are reflected in our data and can be explored using the proposed method.

Time in Grief: How do Bereaved Parents Mentalize It?

In this multimethod study, we examine bereaved parents’ capacity for mentalizing the temporal dimension of their grief. The theoretical assumptions of our study draw on the clinical and anthropological perspectives on the passage of time in grief. Parents’ mentalization of their experience of grief was measured both in the attachment context, using the Adult Attachment Interview (AAI) and using the narrative Child Loss Interview (CLI). We used thematic analysis to code parents’ mentalizing utterances in order to categorize time-related changes during the grieving process. Parents generally mentalize their grief-related experiences at a lower level of reflective functioning than their general attachment experiences. However, a higher general ability to mentalize contributes to a higher level of RF and greater coherence in mentalizing their grief. Parents experience time in grief through oscillation between the past with the deceased child and a restricted form of existence in the present reality.

From space to movement: the walk as an intellectual dynamic in Diderot's La Promenade du sceptique

Diderot's Promenade du sceptique has sometimes been criticised for a somewhat systematic use of allegory, associated with a tripartite division of space summarising and tracing, between the thorns of devotion, the flowers of worldly life, and the chestnut trees of philosophy, the three paths of life available to men. The work's device is nevertheless much more complex and elaborate, and is affected by a diffuse scepticism from within. Open rather than closed, the space and the places of the walk in La Promenade du sceptique are misleadingly the support of an analogical understanding of the world. The writing of the walk becomes a metaphor for the intellectual dynamic that doubts and searches. Movement in its temporal dimension, as a concrete experience, challenges the conception of a geometric space, mocks deism as well as idealism, and opens the way to the experimental method and the materialist hypothesis.

Epävarmuudesta arvokkaaseen lopputulokseen - serendipisyys tiedonhankintakäyttäytymisessä

A serendipitious event in everyday life is common: it means unexpected information that yields some unintended information and potential value later on. Serendipity as a word has been around for hundreds of years. As a studied concept it is rather recent. Serendipity is not just the unexpected information or experience but rather the ability to recognize and do something with it. Serendipitious discovery of information is different from purposive or known item search as it is more complicated and lasts much longer. The discovery of information by chance or accident is still looking it’s explicit place in models and frameworks of information behaviour. It is still not clear what constitutes the core of the research area of serendipity in information behaviour.The qualities of interaction among people, information, and objects differ in physical vs. digital environments. The bisociation, a creative association between different peaces of information may be computer supported.This article presents an overview of the research study of serendipity in information seeking behaviour. We explore serendipity mainly in the digital information environment. As a setting for our study we use six main drivers of serendipity research relating to digital enviroments presented in McCay-Peet and Toms (2017). The drivers are: 1. Theoretical understanding of the phenomenon of serendipity, 2) physical vs digital, 3) information overload, 4) filter bubbles, 5) user experience, and 6) user strategies.A new refined temporal model of information encountering by Erdelez and Makri (2020) is also presented in this article. The model presents a framework for better understanding of the temporal dimension of the information acquisition. At a macro level the model positions information encountering within contextual factors related for user, information, task and environment related characteristics.