Negative Transfer Research Articles

Unravelling the La dopant induced physicochemical changes and the electrical behaviour on BaFeO3 under oxidising and reducing atmospheres

Electroceramic oxides like La-doped BaFeO3 garner greater interest owing to their tunability of mixed ionic conduction. However, reports on the effect of La on the microstructural changes, the type of conduction mechanism under different atmospheres and the negative charge transfer influenced by the Fe-O hybridization in Ba1−xLaxFeO3−δ are hardly available. Doping La induces cation vacancies and Fe reduction, creating nanorod decorated surface after the high-temperature treatment. Conductivity studies expose the rate-limiting steps for hydrogen oxidation reaction (HOR) and oxygen reduction reaction (ORR) as the ohmic resistances and Ba segregation effects. Among the samples studied, Ba0.9La0.1FeO3−δ performs better at 800 °C with an activation energy of ∼0.35 eV under both oxidizing and reducing atmospheres. Surface analysis after EIS reveals Fe and O with different oxidation states that enhance the total conductivity. The amount of hydroxyl species retained (23.66% for Ba0.9La0.1FeO3−δ ) and the ratio of the adsorbed to the lattice oxygen (15.33% for Ba0.9La0.1FeO3−δ ) gives insight on HOR and ORR activities. Also, computational studies validated the negative charge transfer mechanism of the samples under thermally assisted oxidation. The results indicate Ba0.9La0.1FeO3−δ as a tuneable electrode for solid oxide cells.

Common syntactic errors in the writing skills of the 3rd year students at Van Lang University

This study aimed to analyze common syntactic errors found in the argumentative essays of third-year English major students at the Faculty of Foreign Languages, Van Lang University, Vietnam. Quantitative and qualitative methods were used to obtain data in this study. The quantitative approach involved counting and calculating the frequency, percentage, and ranking of syntactic errors, while the qualitative method was adopted to explain these errors. The instrument for collecting data was argumentative essays written by 37 third-year English majors enrolling in Writing-5 at Van Lang University. Microsoft Word was used to collect and analyze syntactic errors and sentence structures, while Microsoft Excel was employed to calculate the frequency and rank of these errors and structures. The results found that the five most frequent errors in their essays were at the noun phrase and sentence levels. These errors included run-on sentences, preposition errors, article errors, sentence fragments, and pronoun errors. Through error analysis, it was indicated that the majority of errors made by students were due to the negative transfer from their first language and the literal translation. Based on the findings, language teachers and syllabus designers could use these results as guidance to design appropriate teaching methods and materials.

Bilingual Translation in Spiderman the Movie Storybook

The language transfer can be seen in the lexical choices in a translation. A translator may have different considerations when applying its use in a text since adaptation of context is an essential part to be employed through the translation. Problems with transferring the source language into the target language certainly have an effect on delivering messages within a text. This study aims to analyze the lexical usage in the bilingual translation of the movie storybook Spiderman and the strategies in the translation, including their meaning. The qualitative method used DeepL to assist in the analysis of differences that might come into the translation. In some cases, words translated from English into Indonesian were found non-equivalent, however, the rest of them were understandable. Translation by a more general word strategies were also used to be placed in the story rather than using the specific words and vice versa, paraphrasing using a related word, omission, and neutral words or less expressive words found to correspond the meaning. The equivalence of transferring the source language (SL) into the target language (TL) can be achieved when optional words are found in the translation in which meaning is also taken for consideration within the human translation. The product of translation was presented bilingually to analyze the differences in meaning between the two languages’ context of use. These showed DeepL as one of the machine translations with translation memories using Artificial Intelligence technology which compares the differences and similarities of sense in translation study. They can work together for the analysis to understand the use and results. Keywords: equivalence, non-equivalence, lexical, strategy

Language transfer in L2 academic writings: a dependency grammar approach

Dependency distance (DD) is an important factor in language processing and can affect the ease with which a sentence is understood. Previous studies have investigated the role of DD in L2 writing, but little is known about how the native language influences DD in L2 academic writing. This study is probably the first one that investigates, though a large dataset of over 400 million words, whether the native language of L2 writers influences the DD in their academic writings. Using a dataset of over 2.2 million abstracts of articles downloaded from Scopus in the fields of Arts & Humanities and Social Sciences, the study analyzes the DD patterns, parsed by the latest version of the syntactic parser Stanford Corenlp 4.5.5, in the academic writing of L2 learners from different language backgrounds. It is found that native languages influence the DD of English L2 academic writings. When the mean dependency distance (MDD) of native languages is much longer than that of native English, the MDD of their English L2 academic writings will be much longer than that of English native academic writings. The findings of this study will deepen our insights into the influence of native language transfer on L2 academic writing, potentially shaping pedagogical strategies in L2 academic writing education.

Heterogeneous transfer learning considering feature representation and environmental consistency for landslide spatial prediction

ABSTRACT The difficult, time-consuming, and imbalanced acquisition of landslide inventories in complex and heterogeneous large areas often results in limited predictive performance for most statistical landslide spatial prediction methods. Although partial transfer learning methods have produced reliable predictive results and successfully implemented knowledge transfer between data-rich and data-scarce areas, most of these methods generally only extract inadequate environmental features and lack the ability to interpret when, what, and how to effectively transfer knowledge to other regions with limited data. In this paper, a heterogeneous transfer learning method considering feature representations and environmental consistency, which features robustness, similarity, and transferability, is proposed for landslide spatial prediction. Specifically, we trained a stacked autoencoder (SAE) to extract more nonlinear features among environmental factors, and added an environmental similarity criterion to the transfer adaptation boosting (TrAdaBoost) algorithm to minimize feature dissimilarities and avoid negative transfer in different scenarios. To evaluate the robustness of the proposed method, we first selected two target areas (Lushan County and Luding County, China) and a source area (Wenchuan County, China) as case study areas. Then, we directly combined the source area and target area as an additional dataset without considering transfer learning to validate the significance and necessity of the proposed method. The area under the receiver operating characteristic curve (AUC) of the two target regions for the proposed method were 0.920 and 0.972, respectively, which were greater than those of the traditional TrAdaBoost (0.909 and 0.969, respectively), SAE (0.790 and 0.937, respectively), and random forest (0.915 and 0.966, respectively) methods. Furthermore, the AUC values of the SAE (0.851 and 0.900) and random forest (0.890 and 0.935) models based on the expanded datasets were also lower than those of the proposed method. Therefore, the experimental results show that the proposed method can be generalized well due to its efficient utilization and high adaptability. Moreover, the proposed method can not only be applied to emergency rescue and disaster prevention but can also offer a promising way to improve landslide predictions of models with incomplete landslide inventories.

Система обучения категории вида русских глаголов в китайской аудитории

The aim of the study is to propose a system of teaching the category of aspect of Russian verbs to Chinese students. The paper presents a research conducted at the Altai State Pedagogical University, which helped to identify the main problems in the assimilation of types of verbs by Chinese students and to test the developed teaching system, which made it possible to reduce the number of errors in the use of the grammatical category of aspect by three times for each specific Chinese student who took part in the research. The scientific novelty of the study lies in developing the system of lessons to overcome linguistic difficulties in the use of the category of aspect by Chinese students in the framework of teaching Russian as a foreign language. As a result, the teaching system is presented that may be useful for teachers of Russian as a foreign language, methodologists, and linguists interested in studying language transfer, aspect in Russian and Chinese.

A meta-weighted network equipped with uncertainty estimations for remaining useful life prediction of turbopump bearings

Uncertainty quantification and negative transfer characterization are significant challenges in the context of deep learning-based remaining useful life prediction with limited data. Conventional Bayesian methods lack scalability to deep learning algorithms for uncertainty quantification due to intricate network connections and extensive training parameters. Additionally, although transfer learning is a promising way to improve the generalization ability of prediction algorithms, its effectiveness is not always guaranteed when leveraging source data undesirably reduces the prediction performance, which is named negative transfer. This manuscript proposed a meta-weighted neural network equipped with uncertainty estimation to discern in-distribution from out-of-distribution data and an adaptive sample meta re-weighting strategy is designed by specifying the weighting function from prediction loss to sample weight according to the gradient direction. Performance evaluations on cryogenic bearings demonstrate that the proposed algorithm can quantitatively determine the weights of source data based on prediction error, ultimately leading to accurate interval remaining life prediction.

Transfer learning based hybrid model for power demand prediction of large-scale electric vehicles

Accurately predicting the power demand of large-scale electric vehicles (EVs) is one of the key tasks of power grid operation optimization. However, this task is difficult to complete due to insufficient data and high randomness of power demand. To address this issue, this paper proposes a transfer learning based hybrid method for power demand prediction of large-scale EVs. Firstly, the linear trend of power demand is extracted by Multiple Linear Regression (MLR), and the nonlinear residual error is obtained by removing the trend from the original data. Secondly, the residual error is predicted via the bidirectional long short-term memory network (BiLSTM). Meanwhile, transfer learning is employed to improve the prediction accuracy of BiLSTM. The BiLSTM is pre-trained using the residual error data from building energy consumption and fine-tuned by the residual error data from EV power demand. Finally, the extracted trend of large-scale EVs’ power demand and the predicted residual errors are combined to obtain the final predicted results. To validate the proposed method, the power demand of a real EV charging station is predicted using the proposed method, and fifteen models are taken for comparison (including the most popular data-driven models). The experimental result indicates that the proposed method can improve the predictive performance by at least 14.23% compared with the comparative models in terms of symmetric mean absolute percentage error, effectively avoids negative transfer results, and significantly enhance the confidence level.

Thermodynamic and spectroscopic investigation of the molecular interactions among vitamin B5, calcium salt, and cyclodextrins in aqueous media

The pharmaceutical industry aims to enhance drug development processes by gaining a deeper understanding of the physiochemical properties of drug molecules. This study provides a comprehensive analysis of the volumetric and compressibility properties of the drug, calcium-D-pantothenate (CDP) in aqueous solutions of cyclodextrins (β-CD/HP-β-CD). By measuring density and speed of sound, we have evaluated various parameters, including apparent molar volume, compressibility, and their corresponding infinite dilution parameters. by measuring density and speed of sound. In addition to this, positive transfer parameters such as ΔtrVs,φo and ΔtrKs,φo of solute provide insights into solute-co-solute and solute–solvent interactions. Also, Hepler’s constant offers valuable information on the structure-breaking characteristics of the studied drug in cyclodextrin medium. The decrease in molar conductance of binary system on addition of cyclodextrin supports our theoretical observation from volumetric and compressibility studies. Positive values of activation energy favours spontaneity and solubilization process. Furthermore, the 1H NMR study demonstrates that the β-CD molecule interacts more strongly with CDP molecules as compared to HP-β-CD.

Analyzing Mother Tongue Interference in Turkish EFL Students' Written Products: A Lexical and Syntactical Perspective

It is clear that languages vary in numerous aspects and are considered to be dynamic and evolving entities. It is evident that each language possesses distinct characteristics, and the way it interacts with another language over the extensive process of language acquisition is of great interest to many experts in the subject of Second Language Acquisition. Language learning is a continuous process that involves exposure to various elements. Despite extensive research, the idea of language transfer remains enigmatic as studies have not fully elucidated it. The objective of this study is to identify the errors that arise due to the influence of the first language on the learning of written English by Turkish learners of English as a foreign language in a formal environment. The objective is to examine the linguistic domains impacted by transfer and to analyze the consequences of this transfer on the educational process. The additional objective is to ascertain whether a correlation exists between the rates of error and the levels of proficiency in the second language (L2). The research questions are addressed through the utilization of qualitative and quantitative data analysis approaches. A total of 252 students were included in this study after excluding the 16 non-native Turkish speakers out of the initial 268 participants. The study's findings indicate that negative transfer is seen in both lexical and syntactical language categories. Empirical evidence has demonstrated a strong correlation between certain error kinds and the competence levels of students. Moreover, further specific recommendations have been proposed to enhance the discipline of Second Language Acquisition (SLA). The constraints of this study have also been incorporated.

A Copula Based Unsupervised Domain Adaptation for Image Classification

In this paper, we present an unsupervised domain adaptation algorithm for image classification using principal component analysis (PCA) and Gaussian copula function alignment. The motivation of the proposed algorithm stems from the idea of CORAL algorithm which extracts domain invariant features by aligning the correlation structure between a source and a target domain. However, it suffers from the fact that highly skewed marginal distributions happen to distort the correlation structure so that it may cause a negative transfer. Therefore we utilize a copula function that enables us to analyze separately the dependency structure and the marginals by Sklar’s theorem. In particular, we propose to align the Gaussian copula correlations in the copula feature spaces instead of aligning the correlation matrices in the original space. Considering the extremely skewed distribution of SURF image features in Office-Caltech10 data set we apply PCA first in order to extract some skewness-mitigated principal features and then derive copula features to align for domain adaptation by using CORAL idea with Gaussian copula correlation matrices. The proposed method showed a good classification accuracy when applied to image classification problem in an unsupervised domain adaptation setting.

PARADIGMATIC SOUND INTERFERENCE IN THE ENGLISH SPEECH OF TURKOPHONES

This study explores language interference in Turkophone speakers learning English, emphasizing the impact of phonetic and phonological discrepancies on second language acquisition. It highlights the need for a nuanced teaching approach that incorporates phonetic training and practical language application to address the unique challenges faced by learners from linguistically diverse backgrounds.

An Analysis of Influencing Factors on Bilingual Reading of Second Language Learners

Proficiency in second-language (L2) reading is an important skill essential in an increasingly globalized world. Previous research has often addressed disparate elements influencing L2 reading proficiency without aggregating them into a comprehensive framework. This paper seeks to fill this gap by systematically collecting and examining the various factors that impact the ability to read in a second language. The primary objective of this research is to compile an exhaustive list of factors affecting L2 reading and to analyze their respective influences. By categorizing these factors into external and internal dimensions, this study aims to provide a holistic view of the ecosystem within which L2 reading comprehension develops and operates. Employing a qualitative literature review methodology, this research meticulously synthesizes existing studies and theoretical perspectives on L2 reading. The factors influencing L2 reading proficiency are divided into two broad categories: external and internal. External factors encompass educational contexts, socioeconomic influences, the role of technological tools and digital literacy, as well as cultural and contextual elements, and the characteristics of reading materials. Internal factors are identified as cognitive processes, the effects of language transfer and interference, and the roles of motivation and attitude in L2 reading. Each factor is analyzed for its unique contribution and interplay with other elements within the L2 reading process.

Few-shot satellite image classification for bringing deep learning on board OPS-SAT

Bringing artificial intelligence on board Earth observation satellites unlocks unprecedented possibilities to extract actionable items from various image modalities at the global scale in real time. This is of paramount importance nowadays, as downlinking large amounts of imagery is not only prohibitively expensive but also time-consuming. However, building deep learning solutions that could be deployed on board an edge device is challenging due to the limited manually-annotated satellite datasets and hardware constraints of an edge device. This paper addresses these challenges through harnessing a blend of data-centric and model-centric approaches to build a well-generalizing yet efficient and resource-frugal deep learning model for multi-class satellite image classification in the few-shot learning settings. This integrated strategy is formulated to enhance classification performance while accommodating the unique demands of an image analysis chain on board OPS-SAT, a nanosatellite operated by the European Space Agency. The experiments performed over a real-world dataset of OPS-SAT images delves into the interactions between data- and model-centric techniques, underscores the significance of synthesizing artificial training data and emphasizes the value of ensemble learning. However, they also caution against negative transfer in domain adaptation. This study sheds light on effective model training strategies and highlights the multifaceted challenges inherent in deep learning for practical Earth observation, contributing insights to the field of satellite image classification within the constraints of nanosatellite operations. To ensure reproducibility of our study, the implementation is available at https://github.com/ShendoxParadox/Few-shot-satellite-image-classification-OPS-SAT.

Effect of particle size versus surface charge on the brain targeting behavior of elastic nanovesicles: In-vitro characterization, comparison between I-optimal and D-optimal statistical optimization and in-vivo pharmacokinetic evaluation

Rasagiline mesylate (RM) is a potent monoamine oxidase-B inhibitor that suffers from extensive hepatic metabolism, low bioavailability, and low brain distribution. Thus, this study aimed to formulate an intranasal nanovesicular system with high RM encapsulation (EE%) and enhanced delivery to the brain tissues. In this perspective, negatively and positively charged RM-transfersomal vesicles were prepared, characterized and the formulation parameters were statistically optimized as per two different response surface methodologies namely, I-optimal and D-optimal design. Although both statistical designs acted almost similarly in terms of experimental response analysis; the I-optimal design might be functioning better in terms of formulations’ optimization and response prediction with higher desirability factors. Consequently, the optimized negative transfersomes (particle size: 213 nm, EE%:62.55 %) and positive transfersomes (particle size: 515 nm, EE%:70.9 %) suggested by the I-optimal design were furtherly formulated, characterized for their in-vitro release, elasticity, transmission electron microscope and pH. Finally, both systems were administered intranasally and compared to an intravenous RM solution where the optimized positive transfersomes has shown significantly higher brain targeting efficiency, positive direct transfer percentage values and longer brain mean residence time. These results suggested that the surface charge of the formulated vesicles could be more effective factor -than the particle size-in enhancing the brain bioavailability of the administered drugs.

Interference of Thai Language on Dialectal Variations of Jawi Malay in Narathiwat

The connection between language and culture becomes increasingly evident when the Thai language influences the variations of Jawi Malay dialects in Narathiwat. This research aims to analyze the impact of Thai language interference on the dialectal variations of Malay language in Narathiwat, Thailand, using a sociolinguistic approach. We investigate changes in language, including vocabulary usage, phonological shifts, and social influences in the everyday situations of Narathiwat society. The research methods involve interviews, surveys, text analysis, and observations in everyday contexts. The expected outcomes are to enhance understanding of language dynamics in Narathiwat, which plays a crucial role in preserving cultural and linguistic identities in a multilingual environment. This study has the potential to provide valuable insights into the preservation and maintenance of Malay language in Narathiwat, emphasizing the importance of language and sociolinguistic research in areas with strong language influences and significant cultural blends. This abstract covers the research background, objectives, research methods, research findings, discussion, conclusion, and research implications, ensuring a comprehensive understanding of the scope and relevance of this study

What Does It Take to Play the Piano? Cognito-Motor Functions Underlying Motor Learning in Older Adults.

The acquisition of skills, such as learning to play a musical instrument, involves various phases that make specific demands on the learner. Knowledge of the cognitive and motor contributions during learning phases can be helpful in developing effective and targeted interventions for healthy aging. Eighty-six healthy older participants underwent an extensive cognitive, motoric, and musical test battery. Within one session, one piano-related and one music-independent movement sequence were both learned. We tested the associations between skill performance and cognito-motor abilities with Bayesian mixed models accounting for individual learning rates. Results showed that performance was positively associated with all cognito-motor abilities. Learning a piano-related task was characterized by relatively strong initial associations between performance and abilities. These associations then weakened considerably before increasing exponentially from the second trial onwards, approaching a plateau. Similar performance-ability relationships were detected in the course of learning a music-unrelated motor task. Positive performance-ability associations emphasize the potential of learning new skills to produce positive cognitive and motor transfer effects. Consistent high-performance tasks that demand maximum effort from the participants could be very effective. However, interventions should be sufficiently long so that the transfer potential can be fully exploited.

The Negative Transfer of Chinese Phonology on Students English Accents and Its Impact on Accent Perception

The Negative Transfer of Chinese Phonology on Students English Accents and Its Impact on Accent Perception

Nose of dog, eye of elk, and wolf’s liver: exploring the interconnectedness of Indigenous health and foraging among the Dukha reindeer herders of Mongolia

ABSTRACT Recent studies show that Traditional Ecological Knowledge (TEK) of Indigenous Peoples’ food systems and the transfer of that knowledge to their children are critical to physical and mental health, promote the transfer of language and culture, and further resilience. The aim of this exploratory, medical ecological study was to document TEK about foraging and plant use among the Dukha, a small, semi-nomadic reindeer herding community in Northern Mongolia. Field research was conducted at multiple remote camps in 2019 and 2023. Methods included semi-structured, open-ended interviews (N = 16), photo identification, and participant observation (N = 34 families). Study Informants identified 27 foraged plants. Results placed Dukha foraging practices within a framework illustrating the interconnectedness of their worldview and cultural norms, including the physical and spiritual aspects of the landscape, with health, illness, and curing. Dukha curative knowledge of the flora aligns with both Traditional Mongolian Medicine (TMM) and scientific studies on healing and preventative care. Through foraging and plant use, the Dukha balance relationships with the land and spirits, benefit from better nutrition and physical activity, utilise healing qualities of indigenous flora, and foster the intergenerational transfer of language and traditional knowledge.

Fast and effective molecular property prediction with transferability map.

Effective transfer learning for molecular property prediction has shown considerable strength in addressing insufficient labeled molecules. Many existing methods either disregard the quantitative relationship between source and target properties, risking negative transfer, or require intensive training on target tasks. To quantify transferability concerning task-relatedness, we propose Principal Gradient-based Measurement (PGM) for transferring molecular property prediction ability. First, we design an optimization-free scheme to calculate a principal gradient for approximating the direction of model optimization on a molecular property prediction dataset. We have analyzed the close connection between the principal gradient and model optimization through mathematical proof. PGM measures the transferability as the distance between the principal gradient obtained from the source dataset and that derived from the target dataset. Then, we perform PGM on various molecular property prediction datasets to build a quantitative transferability map for source dataset selection. Finally, we evaluate PGM on multiple combinations of transfer learning tasks across 12 benchmark molecular property prediction datasets and demonstrate that it can serve as fast and effective guidance to improve the performance of a target task. This work contributes to more efficient discovery of drugs, materials, and catalysts by offering a task-relatedness quantification prior to transfer learning and understanding the relationship between chemical properties.