Background Knowledge Research Articles

Creating a foreign language environment by organizing extracurricular activities in a non-linguistic university as one of the ways of implementing Stephen Krashen’s natural approach to foreign language acquisition

   The relevance of the study is predetermined by the insufficiency of the number of foreign language classes in a non-language university for the effective acquiring of communicative competences by students and the fulfillment of educational and profession orientation goals in the classroom. Our hypothesis is that the creation of a language environment by organizing extracurricular activities of cultural, educational and professional value is an effective way of organizing the learning process, corresponding to the theory of natural language acquisition by «exposure to comprehensibleand compelling input».   The aim of the study is to describe a model of organizing such a language environment and its advantages in the framework of Stephen Krashen's hypotheses. In our research we use the methods of monitoring, literature analysis, synthesis and deduction.   As a result of our work, we have come to the conclusion that a diverse language environment created by organizing various additional activities is an excellent means of forming communicative competences of students throughout the period of study at the university, it contributes to the formation of professional motivation and background knowledge, as well as to the upbringing of a citizen who is not only proud of his country but is also able to present it decently at the international level.

Hypoglossal Nerve Neuropathies—Analysis of Causes and Anatomical Background

The hypoglossal nerve is the last, and often neglected, cranial nerve. It is mainly responsible for motor innervation of the tongue and therefore the process of chewing and articulation. However, tumors, aneurysms, dissections, trauma, and various iatrogenic factors such as complications after surgeries, radiotherapy, or airway management can result in dysfunction. Correct differential diagnosis and suitable treatment require a thorough knowledge of the anatomical background of the region. This review presents the broad spectrum of hypoglossal neuropathies, paying particular attention to these with a compressive background. As many of these etiologies are not common and can be easily overlooked without prior preparation, it is important to have a comprehensive understanding of the special relations and characteristic traits of these medical conditions, as well as the most common concomitant disorders and morphological traits, influencing the clinical image. Due to the diverse etiology of hypoglossal neuropathies, specialists from many different medical branches might expect to encounter patients presenting such symptoms.

Research on the Application of Improved Apriori Algorithm in Sport-Adaptive Online Teaching System Under Big Data Environment

Each student in an adaptive education system has significant differences in knowledge background, ability level and cognitive style. Therefore, to build an adaptive teaching system, it is necessary to establish an operable, reasonable and individualized student model by clarifying students’ abilities and differences. The improved Apriori algorithm under big data is the most classic association rule algorithm, which is generated by a set of candidates, and it uses the iterative method of hierarchical search to traverse a set of frequency items in the transaction database. After finding the set of frequency items, select the association according to the trust rules. This paper studies how to apply the improved Apriori algorithm to an adaptive online education system in a big data environment. An evolutionary algebra is taken with mean fit of 80, population size of 20, mean fit of 0.28, population size of 60, mean fit of 0.26, population size of 80, mean fit of 0.25, mean population size of 0.25. The population size is 200, and the average fitting is 0.24. The larger the error, the smaller the error between each indicator of the test paper and the corresponding value specified by the user. The improved Apriori algorithm in the big data environment has designed five themes of rule mining, which are mainly used for class management: class linkage, class category linkage, student basic information linkage, lecture and basic information linkage, and lecture mode linkage. They play the role of teaching assistants with a specific role.

The Concept of Sign

Purpose: This paper is trying to show that the meaning of the sign has become a kind of mystery and has not yet been analyzed in a convincing way.   Theoretical Framework: In this article, a model will be mentioned that can turn this mystery into a problem and then propose a solution for this problem. This article has theoretical aspect, and proceeds with a hypothesis that can maintain its efficiency for all kinds of signs.   Result and Discussion: In this article, the author tried to explained three hypotheses. First, all the sentences that form in linguistic context will perceive based on sentence or sentences from background knowledge combined with sentences from situational context and the message. Second, the concept of unites in sentence shapes by proposition. Third, the assumption introduced in this article expand in all kinds of signs, whether symbol, icon or index. In another word, the concept of sign, from whatever kinds, forms by some sentences.   Value/Originality: Each sign, from whatever kind, must signify something other than itself. This signification process links the form of a sign to its meaning and involved us in a problem that has always been taken for granted. What is taken for granted in the meantime is the ‘meaning’ of the sign, which has never been explicitly investigated.

Efficient Multi-Source Anonymity for Aggregated Internet of Vehicles Datasets

The widespread use of data makes privacy protection an urgent problem that must be addressed. Anonymity is a traditional technique that is used to protect private information. In multi-source data scenarios, if attackers have background knowledge of the data from one source, they may obtain accurate quasi-identifier (QI) values for other data sources. By analyzing the aggregated dataset, k-anonymity generalizes all or part of the QI values. Hence, some values remain unchanged. This creates new privacy disclosures for inferring other information about an individual. However, current techniques cannot address this problem. This study explores the additional privacy disclosures of aggregated datasets. We propose a new attack called a multi-source linkability attack. Subsequently, we design multi-source (k,d)-anonymity and multi-source (k,l,d)-diversity models and algorithms to protect the quasi-identifiers and sensitive attributes, respectively. We experimentally evaluate our algorithms on real datasets: that is, the Adult and Census datasets. Our work can better prevent privacy disclosures in multi-source scenarios compared to existing Incognito, Flash, Top-down, and Mondrian algorithms. The experimental results also demonstrate that our algorithms perform well regarding information loss and efficiency.

Prompt-Enhanced Generation for Multimodal Open Question Answering

Multimodal open question answering involves retrieving relevant information from both images and their corresponding texts given a question and then generating the answer. The quality of the generated answer heavily depends on the quality of the retrieved image–text pairs. Existing methods encode and retrieve images and texts, inputting the retrieved results into a language model to generate answers. These methods overlook the semantic alignment of image–text pairs within the information source, which affects the encoding and retrieval performance. Furthermore, these methods are highly dependent on retrieval performance, and poor retrieval quality can lead to poor generation performance. To address these issues, we propose a prompt-enhanced generation model, PEG, which includes generating supplementary descriptions for images to provide ample material for image–text alignment while also utilizing vision–language joint encoding to improve encoding effects and thereby enhance retrieval performance. Contrastive learning is used to enhance the model’s ability to discriminate between relevant and irrelevant information sources. Moreover, we further explore the knowledge within pre-trained model parameters through prefix-tuning to generate background knowledge relevant to the questions, offering additional input for answer generation and reducing the model’s dependency on retrieval performance. Experiments conducted on the WebQA and MultimodalQA datasets demonstrate that our model outperforms other baseline models in retrieval and generation performance.

Assessing disorganized attachment in Mexican individuals with psychosis-risk symptoms: initial psychometric evaluation of a Spanish version of the revised Psychosis Attachment Measure

ABSTRACT Background Knowledge of the role of attachment in psychosis would be enhanced by research in low- and middle-income countries and increased availability of measures of disorganized attachment. The Psychosis Attachment Measure is a widely-used self-report questionnaire recently expanded to incorporate the assessment of disorganized attachment. The present study examined the psychometric properties of a Spanish-language adaptation of the revised Psychosis Attachment Measure (PAM-R) in Mexican individuals reporting psychosis-risk symptoms. Methods The sample comprised 264 individuals with a positive screen for high risk for psychosis, drawn from a more extensive general population study. Participants completed a battery of questionnaires via an online survey. Results An exploratory factor analysis of the 23 items yielded three factors interpreted as representing anxious, avoidant, and disorganized attachment. Four items had poor loadings on all factors and were not retained. The dimensions demonstrated adequate internal consistency, but convergent validity was only supported for the anxious and disorganized attachment dimensions. Disorganized attachment showed medium-sized associations with childhood maltreatment, attenuated positive symptoms, and symptom-related distress. Discussion The findings provide initial support for using the disorganized subscale in the Mexican context and for continuing the examination of the measure to further determine its research and clinical utility in Spanish-speaking populations.

Power needs knowledge: CEO knowledge background and goodwill impairment

ABSTRACT We investigate whether and how CEO knowledge background affects goodwill impairment and whether it mitigates investors’ risk assessment of goodwill impairment. We employ CEOs’ educational, financial, and overseas backgrounds to represent their knowledge background. The results, based on a sample of Chinese firms, show that CEOs with a higher level of knowledge background are more likely to test and record a goodwill impairment in a timely manner. In addition, we find that the negative effect of goodwill impairment on abnormal stock returns exists only in firms whose CEOs have a weaker knowledge background. The results of our cross-sectional tests and additional analyses are consistent with our main inference that a higher level of CEO knowledge background facilitates a deeper understanding of goodwill, which mitigates cognitive bias and thereby increases CEOs’ willingness to record a goodwill impairment in a timely manner and ensure investors’ trust.

Enhancing property and activity prediction and interpretation using multiple molecular graph representations with MMGX

Graph Neural Networks (GNNs) excel in compound property and activity prediction, but the choice of molecular graph representations significantly influences model learning and interpretation. While atom-level molecular graphs resemble natural topology, they overlook key substructures or functional groups and their interpretation partially aligns with chemical intuition. Recent research suggests alternative representations using reduced molecular graphs to integrate higher-level chemical information and leverages both representations for model. However, there is a lack of studies about applicability and impact of different molecular graphs on model learning and interpretation. Here, we introduce MMGX (Multiple Molecular Graph eXplainable discovery), investigating the effects of multiple molecular graphs, including Atom, Pharmacophore, JunctionTree, and FunctionalGroup, on model learning and interpretation with various perspectives. Our findings indicate that multiple graphs relatively improve model performance, but in varying degrees depending on datasets. Interpretation from multiple graphs in different views provides more comprehensive features and potential substructures consistent with background knowledge. These results help to understand model decisions and offer valuable insights for subsequent tasks. The concept of multiple molecular graph representations and diverse interpretation perspectives has broad applicability across tasks, architectures, and explanation techniques, enhancing model learning and interpretation for relevant applications in drug discovery.

Bayesian Regression Facilitates Quantitative Modeling of Cell Metabolism.

This paper presents Maud, a command-line application that implements Bayesian statistical inference for kinetic models of biochemical metabolic reaction networks. Maud takes into account quantitative information from omics experiments and background knowledge as well as structural information about kinetic mechanisms, regulatory interactions, and enzyme knockouts. Our paper reviews the existing options in this area, presents a case study illustrating how Maud can be used to analyze a metabolic network, and explains the biological, statistical, and computational design decisions underpinning Maud.

Multi-Scale Simulation of Complex Systems: A Perspective of Integrating Knowledge and Data

Complex system simulation has been playing an irreplaceable role in understanding, predicting, and controlling diverse complex systems. In the past few decades, the multi-scale simulation technique has drawn increasing attention for its remarkable ability to overcome the challenges of complex system simulation with unknown mechanisms and expensive computational costs. In this survey, we will systematically review the literature on multi-scale simulation of complex systems from the perspective of knowledge and data. Firstly, we will present background knowledge about simulating complex systems and the scales in complex systems. Then, we divide the main objectives of multi-scale modeling and simulation into five categories by considering scenarios with clear scale and scenarios with unclear scale, respectively. After summarizing the general methods for multi-scale simulation based on the clues of knowledge and data, we introduce the adopted methods to achieve different objectives. Finally, we introduce the applications of multi-scale simulation in typical matter systems and social systems.

Sustainable procurement of medical devices in an international context - Part 3

Background and Objectives This article is the third in a series of three manuscripts published in this journal. It aims to describe how sustainable procurement of medical devices (MDs) can be implemented in operational projects in the context of developing countries. It also further details how the biomedical/clinical engineer lead (BCEL) in charge of technical support during the MD procurement process can apply sustainability principles and concepts of value-based procurement. Material and Methods Based on the authors’ experience of more than 20 years in procurement projects and implementation of MDs, the role of the BCEL will be developed from a theoretical point of view with the description of the second and third pillars of a sustainable purchase following the needs assessment: the assessment of existing conditions along with local capacities and the evaluation of the use conditions during the lifetime of the medical equipment. The application of these principles in operational projects will be further discussed by analyzing literature and lessons learned from projects implemented in developing countries. Results/Proposal The BCEL has a key role in the sustainable procurement of MDs to design the technical specifications of the goods, related services, and post-sales conditions to maximize the benefit of the investment. As the specialist can analyze the local existing conditions and capacities while ensuring efficient use of the MDs during their lifespan, they can contribute to a sustainable implementation of MDs in developing countries.   The BCEL shall also be able to analyze the local and international markets to find all possible technological solutions that meet the needs, local conditions, and capacities and ensure quality use during the lifespan of the purchased MD. The BCEL shall have competencies in identifying all the risks related to the use of the MD from the safety risks linked to its installation, use, and maintenance to the sustainability risks linked to obtaining the conditions that guarantee the use of the device and maintaining them as long as possible. Examples of these conditions include the presence of qualified and trained users, availability of maintenance and consumable budgets, availability and maintenance of infrastructure conditions (access, electrical power, water, drainage, medical gasses, etc.), and last but not least, presence of patients requiring a diagnosis or treatment using the purchased MD who were identified during the evaluation of the first pillar: a sound needs assessment. Conclusion As an evolution of the BCEL’s traditional biomedical and clinical engineering work, he/she shall assume the responsibility to guarantee the sustainability of the MD purchase. This quality assurance and control role is achieved by a sound theoretical background knowledge based on the three sustainable procurement pillars: the needs, existing and lifetime use conditions assessments, the analysis of the local and international markets, and a broad understanding of sustainability risks.

The duality of cohesion and coherence in Ibn Zurayq's poem "Do Not Reproach Him"

This research conducts an in-depth examination of discourse analysis tools specifically cohesion and coherence within the poem "Do Not Reproach Him" by the Baghdadi poet Ibn Zurayq. The research highlights five essential tools such as references, substitution, deletion, conjunction and lexical cohesion while focusing on the concept of cohesion to examine the nuances of textual utterance. Additionally, it integrates coherence theory focusing on elements such as overall structure, titling, background knowledge, intentional title and suggestiveness and adherence to contextual interpretation and context. The results highlight a significant lack of coherent features in the poem particularly the complete lack of proper nouns. Nevertheless, various cohesion strategies assist in achieving a level of textual cohesion and semantic consistency that is considered acceptable. This is facilitated through interpretation that supports the overall harmony of the poem. According to the study, the poet was often dispersed and unbalanced which made the audience sympathetic when they read the poem. Remarkably, this sentiment endures over time, persisting long after the composition of the poem. The study underscores the enduring impact of the poet's portrayal of dispersion and imbalance resonating with readers through the years and offering valuable insights into the lasting emotional and textual resonance of poetic compositions.

Overview of Knowledge Reasoning for Knowledge Graph

Knowledge graphs are large-scale semantic networks that considerably impact knowledge representation. Mining hidden knowledge from existing data, including triplet knowledge reasoning, is a primary objective of knowledge graphs. With the development of Neural Network (NN) and Deep Learning (DL), the interpretability of triplet knowledge reasoning gradually decreases; furthermore, what machines learn is not actual reasoning but digital reasoning shortcuts. To solve this problem, more background knowledge needs to be introduced into knowledge graphs: causal graphs can offer valuable causal logic knowledge for reasoning; temporal quadruples can provide essential temporal distribution details; and commonsense graphs can furnish pertinent commonsense understanding to support reasoning. In recent years, many scholars have incorporated additional background knowledge into knowledge graphs to construct more complex reasoning mechanisms. This paper reviews the basic concepts and definitions of knowledge reasoning and the reasoning methods used for knowledge graphs. Specifically, we dissect the reasoning methods into four categories: triplet reasoning, causal inference, temporal inference, and commonsense reasoning. Finally, we discuss the remaining challenges and research opportunities related to knowledge graph reasoning.

Learning path recommendation with multi-behavior user modeling and cascading deep Q networks

An online learning platform has become an important channel for learners to obtain knowledge due to its easy access and rich resources. In order to meet online learners' short-term needs with frequent changes and long-term learning goals during learning process, this paper focuses on user modeling and learning path recommendation, and we propose a new method for learning path recommendation through multi-behavior user modeling and cascading deep Q networks (cDQN-PathRec). Our model uses a knowledge graph-based multi-behavior transformer architecture for users’ state modeling, in which a learner's knowledge background, learning styles, learning settings, and learning preferences are taken into consideration. We use a cascading DQN with a two-level reward function to help an agent converge towards a balanced overall and local optima and to generate a learning path recommendation. Comprehensive experiments on two real-world online learning datasets demonstrate effectiveness of the proposed cDQN-PathRec method.

Hierarchical Knowledge-Enhancement Framework for multi-hop knowledge graph reasoning

Multi-hop reasoning has gained significant attention in the area of knowledge graph completion, intending to predict missing facts through existing knowledge. However, due to sparsity knowledge and lack of reachable paths for the model to make sensible decisions, most multi-hop reasoning models suffer from degradation performance on the sparse knowledge graphs (KGs). Faced with this challenge, we propose the Hierarchical Knowledge-Enhancement Framework (Hi-KnowE), which improves reasoning performance over sparse KGs by conducting hierarchical dynamic path completion under various background knowledge guidance. Hi-KnowE is based on hierarchical reinforcement learning, breaking the task into high-level and low-level processes. Firstly, we apply high-level policy to reason relations and the relation action space is enlarged under rule guidance. Secondly, tail entities are obtained by low-level policy and semantic knowledge is used to refine the entity action space. The sparsity problem is alleviated by generating reliable information. Moreover, we introduce rule-based inner attention, adopting the overall semantics of relevant rules to assist agent reasoning. This strategy makes the path for the agent more logical and flexible. We further evaluate Hi-KnowE on four sparse datasets extracted from Wikidata and FreeBase. The results outperform the baseline models, demonstrating improvements of up to 1.5% and 1.9% in MRR and Hit@10.

Analysis of English Students’ Listening Anxiety during Classroom Learning at Higher Secondary Level in Karachi

Anxiety plays a pivotal role in poor communication while language learning in general and listening Anxiety in specific is a major cause of poor listening comprehension. The study was designed with the purpose to assess the relation between listening anxiety and the performance of students in a listening comprehension classroom. The study used two investigation tools namely the listening comprehension test and the questionnaire based on various factors that could create or raise anxiety levels experienced by Science students at College Level. 100 Science College English students as subjects were selected for this study. The results show that there exist certain negative correlations both between English language Listening Anxiety and listening test scores. The factors that create anxiety the most are recognized as poor listening habits of students and lack of background knowledge. Based on findings, recommendations are also made.

Promoting Linear Algebraic Reasoning among Students: Affordances and Challenges

As students transition from the mathematics they learn in school years, including their first-year calculus courses, to the first course in linear algebra, they experience discontinuities in their perspective of what mathematics is. Their propensity to continue applying the same habits of learning in the face of this change leads to failure and frustration. The failure manifests itself in the quality of understanding basic concepts as well as in the lack of linear algebraic reasoning. Instructional treatments applied in my teaching experiments to foster students’ ability to reason linear algebraically resulted in mixed success – some of the treatments were successful, others less so. The latter are accounted for by the structural complexity of the subject matter and students’ background knowledge. The pedagogical approaches offered in this paper are oriented within a particular theoretical framework for the learning and teaching of mathematics, called DNR. Reflections and broader implications are addressed through the lenses of this framework.

Chinese Senior High EFL Learners’ Foreign Language Reading Anxiety: Profile and Sources

In comparison with other skill-specific foreign language anxiety, foreign language reading anxiety (FLRA) was a less-researched realm. The purpose of the study was to investigate the general profile and possible sources of FLRA in the under-explored Chinese senior high EFL students. The 60 participants were from 2 high schools in China. The study employed the “explanatory sequential mixed method design” (Creswell & Plano Clark, 2018). First, the quantitative data were collected via the adapted Foreign Language Reading Anxiety Scale (FLRAS) (Lu & Liu, M., 2015), and analyzed with SPSS. Then, based on participants’ scores on FLRAS (adapted), specific responses, and consent to further interviews, 4 subjects were selected as sources of qualitative data. The conclusions were drawn that (1) more than half the learners (78.33%) were generally exposed to little FLRA (M=2.57); (2) possible FLRA sources could be divided into 4 main categories, with overall 16 subsets: (a) individual factors (reading interest, self-expectation, reading strategy use, background knowledge); (b) textual factors (topic, task type, text length, tested vocabulary, grammar, text structure, rhetoric); (c) instructional factors (teaching method, evaluation); (d) situational factors (teacher-student dynamic, parental anticipation, peer pressure). Despite limitations such as limited sample size and scope, absence of further validation testing, and neglect of examination of background variables, the study purveyed valuable suggestions for language educators to enhance strategies addressing FLRA among Chinese EFL senior high learners. These suggestions included considering the impact of text structure and rhetorical devices on FLRA, prioritizing vocabulary instruction, and implementing anxiety-reducing interventions, e.g. the “flipped classroom model” (Gök et al. 2021). Furthermore, the study emphasized the need for further research on the more representative FLRA profile of Chinese young EFL learners, statistical examination of the relevance of various sources to FLRA, investigation of the relationship between background variables and FLRA among Chinese senior high EFL learners, and exploration of the universality and language-specific nature of different FLRA sources.

Strategies of Rendering Realia in Mediated Literary Translation

Literary texts contain a variety of culture-specific elements requiring special background knowledge on the part of translators. In particular, the issue of realia translation poses certain challenges in mediated literary translation, as in this case the translator has to deal with transferring the source message into the target language through an intermediary text. The investigation is aimed at studying the notion of realia and identifying the strategies of their faithful conveyance in translation through an intermediary language. The findings of the article reveal that mediated translation may sometimes complicate the problem of ensuring adequate cross-cultural interaction between the writer and the reader, as the intermediary language inevitably interferes with this process leaving its imprints on the final target text. The corpus selected for this paper comprises the Russian translation of J.D. Salinger’s “The Catcher in the Rye”, the Armenian mediated translation from that Russian version and the Armenian translation from the original. The following methods have been applied in the present investigation: comparative analysis to find out similarities and differences in the translators’ application of various translation strategies, and the method of deductive research to study various transformations made in the process of translation to achieve equivalence in rendering realia.