Hybrid L2 Research Articles

Tracing the dynamics of motivational self-constructs across the lifespan: Tales from senior learners of English

In the context of an ageing and increasingly more independent learning population, a narrative inquiry into the life stories of motivated and still active senior language users remains a promising yet unexplored area. This case study forms part of a PhD research (Darnault, 2023) on the motivational dynamics of lifelong foreign language learning (FLL) individuals. We recorded the retrospective stories of 3 exceptionally motivated French senior learners of English, aged 65 to 80, from childhood to their current learning experience. Our triangulated and multimodal approach elicited written, oral and visual data. An inductive thematic analysis first highlighted the emergence of motivational peaks, subsequently followed by the examination of clusters of self-constructs within participants’ individual time frames in light of the L2 Motivational Self System (L2MSS) framework. Results showed that upon retirement all three learners integrated language learning into their daily routine, incorporating it as an integral part of their identity and broader sense of self outside L2 domain-specific motivational constructs. A particularly unyielding hybrid L2 self emerged in later years, drawing from the combination of ought-to, ideal and anti-ought-to selves that prevailed with different degrees of intensity and interaction according to life periods.

Achieving Joint Attention and Understanding of Task Responsibilities in Synchronous Hybrid L2 Classroom Group Work

Abstract This article addresses the need to better understand interactional asymmetries, challenges, and solutions in implementing synchronous hybrid language teaching. We investigate video-recorded peer interactions in a higher education language teaching context in which a student uses a telepresence robot, a remotely moveable videoconferencing tool, to participate in small-group task work in L2 English together with students who are physically located in the language classroom. Drawing on multimodal conversation analysis, we examine how the geographically dispersed peer group achieves, maintains, and repairs their joint attention on task-relevant learning materials as they are accomplishing a task, and how this kind of referential interactional work enables their co-operation as a group. Based on the analysis, we argue that in synchronous hybrid learning there is a need to reflexively adjust interactional practices to secure an intersubjective understanding of learning tasks and their progressivity. The findings also suggest that sensory and interactional asymmetries should be taken into account when developing and implementing synchronous hybrid learning environments that aim at equality of opportunities regardless of the participation mode.

Diabetes Prediction Algorithm Using Recursive Ridge Regression L2

At present, the prevalence of diabetes is increasing because the human body cannot metabolize the glucose level. Accurate prediction of diabetes patients is an important research area. Many researchers have proposed techniques to predict this disease through data mining and machine learning methods. In prediction, feature selection is a key concept in preprocessing. Thus, the features that are relevant to the disease are used for prediction. This condition improves the prediction accuracy. Selecting the right features in the whole feature set is a complicated process, and many researchers are concentrating on it to produce a predictive model with high accuracy. In this work, a wrapper-based feature selection method called recursive feature elimination is combined with ridge regression (L2) to form a hybrid L2 regulated feature selection algorithm for overcoming the overfitting problem of data set. Overfitting is a major problem in feature selection, where the new data are unfit to the model because the training data are small. Ridge regression is mainly used to overcome the overfitting problem. The features are selected by using the proposed feature selection method, and random forest classifier is used to classify the data on the basis of the selected features. This work uses the Pima Indians Diabetes data set, and the evaluated results are compared with the existing algorithms to prove the accuracy of the proposed algorithm. The accuracy of the proposed algorithm in predicting diabetes is 100%, and its area under the curve is 97%. The proposed algorithm outperforms existing algorithms.

A refined discretized timer-dependent Lyapunov functional for impulsive delay systems

A new type of discretized Lyapunov functional is introduced for stability and hybrid L2×l2-gain analysis of linear impulsive delay systems. This functional consists of three parts. The first part is the conventional discretized functional for delay-independent stability analysis. The second part is the looped-functionals-like functional for exploiting the internal structure inside impulse intervals, in which the decision matrix functions are approximated by piecewise linear matrix functions. The third part is a discretized adjustive factor, which makes the proposed Lyapunov functional continuous along the system trajectories. Thanks to this continuity, the resultant criteria for exponential stability and finite hybrid L2×l2-gain are expressed in terms of a combination of the continuous and discrete parts of the system. It is shown through numerical examples that the accuracy of the stability test increases with the increase of the partition number on impulse intervals, and the convergence rate is faster than that of the previous discretized functionals-based approach.

Dwell‐time‐dependent conditions for exponential stability and hybrid L2 × l2‐gain of linear neutral time‐delay systems with impulsive effects

AbstractThis article considers the problems of achieving exponential stability and finite hybrid L2 × l2‐gain with respect to continuous and discrete disturbances for linear neutral time‐delay systems with impulsive effects. In the framework of descriptor system representation, a piecewise timer‐dependent Lyapunov functional is introduced to analyze the double impacts of state‐delay and impulse‐dwell‐time on stability of the underlying system. This functional depends on the state over an impulse interval and the state over a delay interval. Its construction is based on a delay‐fractioning scheme which ensures all delay subintervals contains at most one impulse instant. The relations among the augmented state variables are explored by using the impulse‐timer functions and the Newton–Leibniz formula. It is proved that the positive definiteness property of the functional at nonimpulse instants is not necessary for ensuring stability. The hybrid L2 × l2‐gain analysis is carried out by jointly using the introduced functional and a discrete‐time Lyapunov function. The obtain criteria for exponential stability and hybrid L2 × l2‐gain are expressed in terms of linear matrix inequalities. Their effectiveness is verified through two numerical examples.

A deep-learning-based unsupervised model on esophageal manometry using variational autoencoder

High-resolution manometry (HRM) is the primary method for diagnosing esophageal motility disorders and its interpretation and classification are based on variables (features) from data of each swallow. Modeling and learning the semantics directly from raw swallow data could not only help automate the feature extraction, but also alleviate the bias from pre-defined features. With more than 32-thousand raw swallow data, a generative model using the approach of variational auto-encoder (VAE) was developed, which, to our knowledge, is the first deep-learning-based unsupervised model on raw esophageal manometry data. The VAE model was reformulated to include different types of loss motivated by domain knowledge and tuned with different hyper-parameters. Training of the VAE model was found sensitive on the learning rate and hence the evidence lower bound objective (ELBO) was further scaled by the data dimension. Case studies showed that the dimensionality of latent space have a big impact on the learned semantics. In particular, cases with 4-dimensional latent variables were found to encode various physiologically meaningful contraction patterns, including strength, propagation pattern as well as sphincter relaxation. Cases with so-called hybrid L2 loss seemed to better capture the coherence of contraction/relaxation transition. Discriminating capability was further evaluated using simple linear discriminative analysis (LDA) on predicting swallow type and swallow pressurization, which yields clustering patterns consistent with clinical impression. The current work on modeling and understanding swallow-level data will guide the development of study-level models for automatic diagnosis as the next stage.

A structure prior weighted hybrid [formula omitted]–[formula omitted] variational model for single infrared image intensity nonuniformity correction

Infrared (IR) image nonuniformity correction is of critical importance to visual perception and subsequent applications. A range of methods have been extensively studied, whereas they either fail to achieve visually natural results, or only exhibit high performance for a specified type of nonuniform scene (e.g., Gaussian-shaped aero-thermal radiation-induced intensity nonuniformity). In the present study, a novel variational framework is proposed, reasonably considering the inherent characteristics both of the latent IR image and bias field, as an attempt to correct IR image intensity nonuniformity. In other words, given the gradient correlation between the degraded image and the bias field, a structure prior weighted ℓ2-norm regularizer is substituted to constrain the bias field. Moreover, under the global sparseness of latent clear IR image, a ℓp-regularized term is employed to accurately restore the latent image, as well as eliminating texture noise. To efficiently optimize this hybrid ℓ2–ℓp variational model, both iteratively re-weighted least square and alternating minimization schemes are adopted to address the mentioned non-convex problem. According to the experiments of several simulated and real IR datasets, the feasibility of the proposed algorithm and its ability to fit different nonuniform conditions compared with five existing methods are demonstrated.

Delay-dependent stability and hybrid [formula omitted]-gain analysis of linear impulsive time-delay systems: A continuous timer-dependent Lyapunov-like functional approach

This paper presents a new approach for analyzing delay-dependent stability and hybrid L2×l2-gain performance of linear impulsive delay systems. The new approach is inspired by the delay-partitioning method, the timer-dependent Lyapunov functional method, and the looped-functional method. In the delay-partitioning framework, a new type of timer-dependent Lyapunov functional is constructed, which depends on the partition on impulse intervals and also on impulse dynamics. Different from the previous discontinuous Lyapunov functionals, the introduced Lyapunov functional is continuous along the trajectories of the considered impulsive delay system. Consequently, two different problems of exponential stability and hybrid L2×l2-gain performance are tackled by using the same class of Lyapunov functionals. It is shown that the positive definiteness of this Lyapunov functional inside impulse intervals is not necessary for proving exponential stability. By use of new integral inequalities based techniques, delay-dependent criteria for exponential stability and finite hybrid L2×l2-gain are established in terms of linear matrix inequalities. Numerical examples are provided to illustrate the efficiency of the new approach.

Infrared and visible image fusion and denoising via [formula omitted] norm minimization

Most traditional infrared and visible image fusion methods often ignore noise in acquisition or transmission and their performance inevitably decreases in practical applications. To address this problem, a new and effective variational model is proposed for simultaneous image fusion and denoising. In an ℓ2−ℓp norm minimization setting with p=0 and p=1 respectively, the hybrid ℓ2 norm fidelity term is built to preserve image intensity and details from both infrared and visible images. And the nonconvex ℓ0 norm and convex ℓ1 norm sparsity constraints are applied to reduce noise while preserving important image fine features. Furthermore, a computationally efficient numerical algorithm based on half-quadratic splitting iteration is used to solve the complex optimization problem. Experimental results demonstrate that the proposed method can achieve a superior performance compared with existing fusion methods in both subjective and objective assessments.

The effects of hybrid online planning and L2 proficiency on video-based speaking task performance

This study investigated L2 speaking performance under three different types of task-related time pressure, with a control (Control) group narrating a video at normal playing rate, an online planning (OP) group narrating the video at a slowed playing rate, and a hybrid online planning (HOP) group which combined online planning (a slowed playing rate) with content preparedness (through prewatching the video). The results show that the HOP group outperformed the Control group regarding speech accuracy and complexity, suggesting that this form of online planning, with content preparedness, helps improve speech accuracy and complexity. In addition, L2 proficiency significantly predicted speech accuracy, specifically, among all other performance measures. The implications of these findings for language teaching and learning are discussed, particularly their relevance for the way a Conceptualiser-Formulator balance is important, and for the way proficiency can best be mobilised within task performance.

Studies on heterosis in bhendi [Abelmoschus esculentus (L.) Moench

The studies on heterosis in bhendi was conducted with 20 F1­’s developed from 9 parents viz., SKY/DR/RS/107 (L1), 3 (L2), IC 69257 (L3), 307-10-1-II (L4), 608-8-1 (T1), 770 (T2), IC 140880 (T3), 137-10-1,2 (T4) and Tiruchi Local (T5) in a Line x Tester mating design. The hybrids along with parents were evaluated in RBD with three replications. The hybrid L2 x T3 was considered the best as it has recorded higher magnitude of heterobeltiosis for plant height at flowering (17.49 per cent), plant height at final harvest (42.95 per cent), leaf area index (64.21 per cent), stem diameter (82.79 per cent), fruit length (22.98 per cent), fruit girth (6.17 per cent), number of fruits plant-1 (42.70 per cent) and yield plant-1 (57.77 per cent). The maximum heterotic vigour was found in L2 x T2 for fruit weight (47.24 per cent) and number of fruits plant-1 (96.67 per cent).

Hybrid L2 NUCA Design and Management Considering Data Access Latency, Energy Efficiency, and Storage Lifetime

Nonvolatile magnetic RAM (MRAM) offers high cell density and low leakage power. This paper reports on using a 3-D integration technology based on through-silicon vias, to stack disparate memory technologies (e.g., SRAM and MRAM) together onto chip multiprocessors. In this paper, we explore the design of a 3-D stacked nonuniform hybrid SRAM/MRAM L2 cache architecture (NUCA) using the on-chip network to mitigate the interconnection problem. In addition, this paper investigates the problem of partitioning shared SRAM/MRAM hybrid L2 cache and placing cache data into the partitioned 3-D stacked hybrid NUCA for concurrently executing multiple applications in order to improve the system performance in terms of instructions per second while considering the heterogeneous characteristics in interconnection wire delay, memory cell density, memory access latency, and memory power consumption in a 3-D stacked hybrid SRAM/MRAM L2 cache. Experimental results show that the proposed runtime method with a 3-D stacked hybrid L2 cache improves performance by 61%, energy efficiency, i.e., energy-delay product by 53%, and storage lifetime by 15.6 times on average compared with the conventional SRAM-only L2 cache or MRAM-only L2 cache with the similar area.

Runtime Thermal Management for 3-D Chip-Multiprocessors With Hybrid SRAM/MRAM L2 Cache

Nonvolatile memory such as magnetic RAM (MRAM) offers high cell density and low leakage power while suffering from long write latency and high write energy, compared with SRAM. 3-D integration technology using through-silicon vias enables stacking disparate memory technologies (e.g., SRAM and MRAM) together onto chip-multiprocessors (CMPs). The use of hybrid memories as an on-chip cache can take advantage of the best characteristics that each technology offers. However, the inherent high power density and heat removal limitation in 3-D integrated circuits may incur temperature-related problems. In this paper, we propose a runtime thermal management method for CMPs with the 3-D stacked hybrid SRAM/MRAM L2 cache. The proposed method combines dynamic cache management such as resource allocation, way-based power gating, and data migration with dynamic voltage and frequency scaling of processing cores in a temperature- and energy-aware manner. Experimental results show that the proposed runtime method with the 3-D stacked hybrid L2 cache offers up to 107.37% (55.28% on average) performance improvement and 88.47% (47.65% on average) energy efficiency improvement compared with existing thermal management methods with 3-D stacked SRAM-based L2 cache.

FLOWER COLOR VARIABILITY IN DOUBLE AND THREE-WAY HYBRIDS OF ORNAMENTAL PEPPERS

The species and varieties of peppers can be discriminated by morphological traits of fruit and especially by the flowers. Hybridization is very important in plant evolution as a source of new genetic combinations and as a mechanism of speciation. The aim of this work was to describe the variations in flower color in double and three-way hybrids of ornamental chili pepper. The manual crosses were made in bud flowers at pre-anthesis stage. When the flowers were open, the color of corolla, anther, and filament were recorded in each of the twelve intra and interspecific hybrids. The percentages of each flower type in the F1 generation were calculated. The hybrid L2 x HS1 was incompatible. There was increased variation in flower color in each hybrid with a gradient between white and purple flower color. The cross L5 x HS1 was the only one with 100% of white flower. This variability found in the double and three-way hybrids significantly increase opportunities to develop unique ornamental pepper cultivars with different color of the flowers. The variations in flower color made the plants more attractive to the consumers and can be used in the Capsicum breeding program of the Universidade Federal de Vicosa and Universidade Federal da Paraiba, Brazil.

Being ‘neutral’? English pronunciation among Norwegian learners

ABSTRACTThis study investigates the pronunciation of English among Norwegian adolescents by applying sociolinguistic methods in a second language context. Results from an auditory analysis of seven phonological variables show a blended use of linguistic features from American English and British English, with some additional pronunciations, forming a hybrid and variable L2 accent. The participants’ pronunciation aligns with their self‐expressed target accents. However, American English is the dominant pronunciation, suggesting influence from imported American media. Although the majority of learners aim towards a native accent, a large minority report a wish to avoid native accents and use a neutral variety of English. The variability in the local appropriation of English reflects the transitional status of English in Norway, and contributes to the increasing diversity in the development of English as a global language. The results have implications for English language educators, who must meet the needs of proficient learners in transitional second language environments.

Individual factors and successful learning in a hybrid course

Abstract What personality factors make for a successful hybrid L2 learning experience? While previous studies have examined online learning in comparative terms (i.e. Which format is better: in class or hybrid?), this study examines certain personality and cognitive factors that might define the ideal hybrid language learner. All informants studied introductory Spanish with multimedia materials supported by synchronous chat (video, voice, text). Personality and cognitive traits were probed using the Big Five Inventory scale (BFI 1 ) and the Shipley Institute of Living scale (SILS 2 ), respectively. The results were correlated with course outcomes and learner preferences for online, chat, or in-class activities. Exit interviews were conducted with an eye to offering a richer understanding of how hybrid students viewed online learning. The quantitative data revealed that conscientiousness (per BFI) had a significant, positive correlation with final grades. Low-verbal learners (per SILS) registered a definite preference for working with online materials, as opposed to learning in class or chatting online. The results suggest that students who are conscientiousness learners perform well within the hybrid-learning environment; low-verbal learners, in particular, value the online materials which create the possibility to work online at one's own pace.

Combining Ability for Field Emergence, Kernel Quality Traits, and Certain Yield Components in Sweet Corn (Zea mays L.)

ABSTRACT Sweet corn (Zea mays L.), a specialty corn, has poor field emergence, stand establishment, and low yield compared to field corn, and the latter would be beneficial in the improvement of these traits. Seven field corn and four sweet corn inbred lines were used in a line-by-tester mating design for the analysis of combining ability and heterosis for field emergence, kernel quality traits, and some yield components. Field corn and sweet corn lines differed significantly in flowering, green-ear traits, yield, total soluble solids, and biochemical traits. Field corn inbreds (L5 and L6) and tester T4 emerged as good combiners for early maturity, whereas L2 was a good combiner for yield and sugar content. Testers T2 and T4 were good general combiners for early maturity, green-ear length, total sugar, and non-reducing sugar. Sweet corn recorded higher contents of sugar components and less starch compared with the field corn. Among crosses, L1 × T2, L6 × T4, and L3 × T2 were best specific combiners for early maturity, field emergence, and fresh ear weight, whereas hybrid L7 × T2 had better specific combining ability for grain yield, total soluble solids (TSS), total as well as non-reducing sugars. Further, hybrid L2 × T3 had high per se performance for fresh ear weight (246.67 g) and total sugar (15.49%). Such elite combinations with more fresh harvest yield as well as sugar content can be advanced to select sweet corn lines with improved plant stand and yield, and field corn lines with enhanced sugar content.