Background: People with Down syndrome (DS) have a trisomy of chromosome 21 and marked deficits in verbal memory but relatively preserved visuospatial perception. Verbal memories are related to semantic knowledge. Little is known about the organization of semantic knowledge in people with DS, which was the aim of this study. Aims: The extent of false memory in people with DS was examined. Possible differences in the number of false memories induced by nominal and verbal themes were examined. Methods and Procedures: In the study phase, ten word lists with semantically related associates were presented. In the recognition phase, participants made judgments on whether each word had been represented previously as semantically related lures, old words, and semantically unrelated new words. Outcomes and Results: Differences in the processing of nominal and verbal theme-related lures were observed among people with DS. When processing nominal theme-related lures, people with DS showed delayed false positives. When processing verbal theme-related lures, people with DS showed no differences compared to controls. Similar patterns have been observed in people with DS when processing nominal theme-related associates and verbal theme-related associates. People with DS have delayed recognition of associates. When processing unrelated words, they showed significantly higher false positives than controls for both nominal and verbal eliciting themes. Further analyses of the themes revealed differences in false positives across syntactic categories among groups. Conclusions and Implications: People with DS showed a delayed recognition of associates and deviance in recognizing unrelated words in the nominal and verbal theme categories. People with DS were delayed in terms of nouns, but deviated with verbs in associated and unrelated words. Additionally, people with DS showed distinct patterns in processing five nominal lures and three verbal lures, suggesting that they formed false memories differently based on distinct syntactic categories. Thus, it was concluded that people with DS develop a deviant semantic structure. Category-based interventions for people with DS should be implemented in order to improve their semantic knowledge through lexical connections.

Multilingual classrooms are becoming more common worldwide due to increasing mobility. Teaching foreign languages using the mother tongue in multilingual classrooms has become problematic; some of the significant concerns in Japanese language acquisition – such as the high contextuality of the language, vocabulary and kanji learning – make the direct teaching method confusing for many students, especially at the beginner and elementary levels. This article provides the preliminary results of a study carried out as a component of a more extensive empirical research project on instructional languages in classrooms teaching Japanese as a foreign language. It aims to gather, analyse and synthesise data from a survey conducted at a supranational level. The results of the first two sections of a questionnaire containing closed and open-ended questions were used for a quantitative analysis of the profile of Japanese language instructors with a focus on finding possible connections. The wide geographic distribution of the respondents (274 teachers from fifty-seven countries and regions) made it possible to collect data from a more representative sample and draw meaningful conclusions about the spread of multilingual classrooms that would be impossible in smaller-scale research. The study’s primary results show the proportion of multilingual classrooms among countries in different regions and across different educational stages, the distribution of Japanese language instructors in various educational stages depending on the highest level of formal education attained, and the proportion of part-time instructors working in different academic institutions.

Considering that emotional events diversify as one ages, this study formulates the Salient Episodic Memory Aggregation Hypothesis, which claims that aging induces homogenization of episodic memory and consequently increases the salience of emotional episodes. To test this hypothesis, the study focused on sadness episodic memory and conducted a web-based survey and a combined analysis consisting of natural language processing, machine learning, and network graph analysis. A sample of 2,954 adults aged between their 20s and over 70s participated in this study. They were asked to describe a sad event they had experienced and assess the psychological properties of the recalled episodes. After isolating and lemmatizing episodic words using natural language processing, vocabulary frequency was counted for each generation, and network graph analysis based on machine learning was performed to compare the structures of sadness episodes between generations. As a result, there were mainly two types of episodic words for which frequencies either declined or increased with age. The network structures of episodic words for the older generations possessed higher proximity (shortest path length) to higher concentrations of episodic words. These findings support the current hypothesis, indicating that the nature of sadness episodes differ between generations, and that older people have more aggregated episodic memory structures.

This review paper investigates the influence of gestures on foreign language (FL) vocabulary learning through a series of experiments conducted in our laboratory. The manipulation of the gesture-word relationship was a consistent factor across the studies. Firstly, we examined the impact of gestures on noun and verb learning. The results revealed that participants exhibited better learning outcomes when FL words were accompanied by congruent gestures compared to a no gesture condition. This suggests that gestures have a positive effect on FL learning when there is a meaningful connection between the words and the accompanying gestures. However, in general, the recall of words in conditions where gestures were incongruent or lacked meaning was lower than in the no gesture condition. This indicates that under certain circumstances, gestures may have a detrimental impact on FL learning. We analyzed these findings in terms of their implications for facilitating or interfering with FL acquisition. Secondly, we addressed the question of whether individuals need to physically perform the gestures themselves to observe the effects of gestures on vocabulary learning. To explore this, participants were divided into two experimental groups. In one group, participants learned the words by actively performing the gestures ("do" learning group), while the other group simply observed the gestures performed by others ("see" learning group). The processing of congruent gestures facilitated the recall of FL words in both the "see" and "do" learning groups. However, the interference effect associated with processing incongruent gestures was more pronounced in the "see" learning group than in the "do" learning group. Thus, the performance of gestures appears to mitigate the negative impact that gestures may have on the acquisition of FL vocabulary. In conclusion, our findings suggest that iconic gestures can serve as an effective tool for learning vocabulary in a FL, particularly when the gestures align with the meaning of the words. Furthermore, the active performance of gestures helps counteract the negative effects associated with inconsistencies between gestures and word meanings. Consequently, if a choice must be made, a FL learning strategy in which learners acquire words while making gestures congruent with their meaning would be highly desirable.

Self-sustained learning (SSL) is considered a crucial factor in promoting students’ long-term learning and development in a sustainable society. To deepen the understanding of SSL, especially in the context of learning English as a second language, the current study examined the influence of intercultural communicative skills, language mindset, and positive L2 self on SSL in English learning among 1,238 college students in China. The results showed that students not only had a strong language mindset, positive L2 self, and intercultural communicative skills but also scored higher in long-term self-sustained English learning. Findings from the Structural Equation Modeling indicated that both language mindset and intercultural communicative skills positively predicted positive L2 self and self-sustained English learning. Furthermore, language mindset and intercultural communicative skills had an indirect impact on self-sustained English learning through positive L2 self. These findings suggest that to promote English learners’ SSL, instructors should help facilitate students’ intercultural communicative skills and foster a growth language mindset, as well as positive language learning beliefs.

Among the most popular learning management systems available worldwide is Moodle [1]. This current study examines how learners' attitudes toward English as a foreign language (EFL) and their language proficiency are affected by Moodle's interactive language learning activities. Thirty-three undergraduate students participated in this study. We investigated the effects of engaging language learning exercises that were practiced on Moodle using an experimental re-search design. To find out if the experimental and control groups differed significantly from one another on pre- and post-measures regarding the development of language skills and attitudes toward language classrooms, a number of statistical tests were employed. We conducted data analysis using SPSS software. Results demonstrated that there were differences favoring the ex-perimental group in the language skills developments and attitudes of learners toward the lan-guage classroom. There were no apparent differences in the forming structures, speaking, or listening of the control group's learners. On the other hand, there were notable differences in the overall score, writing and reading skills, and lexical item mastery. Additionally, significant dif-ferences in the language acquisition growth of the experimental and control groups were found using an Independent Samples T-test in the post-test, with the experimental group benefiting.

Sustainable development and education in sustainable development (ESD) are crucial and interconnected topics under wide discussion today. Young adults need to be educated in how to help bring about change. Thus, the dynamic relationship between school and home is important. In education, use could be made of English language classes, especially since English is a global language that enables access to the sustainability literature and facilitates discussion in global conferences and meetings. Consequently, English as a foreign language (EFL) students could gain confidence from being able to understand and use the terminology of, for example, sustainability. In this paper, the use of competence-based language teaching (CBLT) is proposed as a means of introducing sustainability topics into EFL writing classes at a university in Saudi Arabia. Students’ competence in sustainable development is therefore considered based on their ability to write about suggested sustainable development topics, measured on a scale for five dimensions of ESD proposed by Michelsen and Fischer (2017). The results demonstrate that the students possessed competence in sustainable development and intended to participate in its implementation. Thus, recommendations are made for the stakeholders in the education sector to include sustainable development topics as an initial offering in the curricula of various specialties using the CBLT approach.

In the field of Natural Language Processing (NLP), Large Language Models (LLMs) have shown great potential in document-level event extraction tasks, but existing methods face challenges in the design of prompts. To address this issue, we propose an optimization strategy called "Definition-driven Document-level Event Extraction (DDEE)." By adjusting the length of the prompt and enhancing the clarity of heuristics, we have significantly improved the event extraction performance of LLMs. We used data balancing techniques to solve the long-tail effect problem, enhancing the model's generalization ability for event types. At the same time, we refined the prompt to ensure it is both concise and comprehensive, adapting to the sensitivity of LLMs to the style of prompts. In addition, the introduction of structured heuristic methods and strict limiting conditions has improved the precision of event and argument role extraction. These strategies not only solve the prompt engineering problems of LLMs in document-level event extraction but also promote the development of event extraction technology, providing new research perspectives for other tasks in the NLP field.

Background: The effect of velamentous cord insertion (VCI) on perinatal outcomes in twin pregnancies is unclear due to conflicting findings. This retrospective study aimed to examine VCI prevalence and related risk factors in twin pregnancies and its association with adverse perinatal outcomes. Methods: Women with twin pregnancies who delivered between January 2012 and December 2021 in a single tertiary hospital were included. The participants were divided into dichorionic (DC) and monochorionic diamniotic (MCDA) groups, and their maternal and fetal characteristics and VCI rates were compared. Logistic regression models were used to identify risk factors for VCI and VCI-related perinatal outcomes. Results: Among the 694 twin pregnancies included in this study, the VCI rate was significantly higher in MCDA than in DC twins. Body mass index and MCDA twins were significant risk factors for VCI, whereas assisted reproductive technology pregnancy was a significant protective factor against VCI. In DC twins, VCI did not affect perinatal outcomes. Conclusion: In MCDA twins, VCI was a significant risk factor for fetal growth restriction, twin-to-twin transfusion syndrome, and preterm birth at

We conducted a retrospective evaluation of the clinical outcomes and prognostic factors in patients with non-metastatic castration-resistant prostate cancer (nmCRPC) treated with first-line androgen receptor signaling inhibitors (ARSI) in real-world clinical practice in Japan. Between 2012 and 2023, a total of 127 consecutive patients with nmCRPC received ARSI treatment. Overall survival (OS), metastatic-free survival (MFS), and prostate-specific antigen progression–free survival (PSA-PFS) from ARSI initiation were assessed using Kaplan–Meier methodology. Clinical factors associated with OS in nmCRPC were analyzed using the Cox proportional hazards model. Among the patients, 72, 26, 12, and 17 received enzalutamide (ENZ), abiraterone (ABI), apalutamide (APA), and darolutamide (DARO) as first-line therapy. The median OS and MFS for all patients were 79.0 and 42.0 months, respectively. Median PSA-PFS was 27.0, 20.0, 10.0, and 14.0 months for patients treated with ENZ, ABI, APA, and DARO, respectively (p = 0.33). Multivariate analysis revealed that a baseline PSA level ≥3.67 ng/ml at ARSI initiation was significantly associated with poorer OS (p = 0.002). ARSI demonstrated favorable efficacy in nmCRPC patients. There were no significant differences in clinical outcomes among different types of ARSI therapy for nmCRP. Elevated baseline PSA at ARSI initiation was significantly associated with poorer OS.

This critical review delves into the often-overlooked aspect of early childhood language development: the potential impact of exposure to foreign languages. While multilingualism is widely celebrated for its cognitive and cultural benefits, emerging research suggests that early exposure to foreign languages may pose challenges for young children's linguistic development. The review synthesizes findings from 17 studies to explore the complexities surrounding this issue, including linguistic confusion, language delay, reduced vocabulary growth, impaired language processing, and socio-emotional implications. Additionally, mitigating factors and future directions for research and intervention are discussed. By shedding light on this topic, the review aims to inform educators, parents, and policymakers about the nuances of bilingual language acquisition and the importance of providing tailored support to optimize early childhood language development in multilingual contexts.

This systematic review examines the potential of digital language learning in contributing to students' cognitive gains. The study reviews existing research on the relationship between digital language learning and cognitive benefits, with a focus on enhanced problem-solving skills, memory, and multitasking ability. The findings indicate that higher levels of digital competences correlate positively with language proficiency outcomes, suggesting that participants with greater digital skills achieve better language learning outcomes. Digital technologies offer interactive and engaging methods of learning, which can positively impact retention and understanding. However, the study also acknowledges the limitations of digital foreign language learning, such as the lack of face-to-face interaction and the need for high motivation and discipline. The research questions explored in this study are: (1) Does digital language learning contribute to cognitive gains in foreign language education? and (2) What are the pedagogical implications for cognitive improvement in digital foreign language education? The study employs the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) methodology to identify and analyze relevant research articles. The results of the review suggest that working with printed texts may be more effective for cognitive gains compared to electronic texts. Additionally, implementing more senses through digital language education appears to be beneficial for cognitive gains. The article concludes by discussing the implications of these findings and the need for further research in this area.

Background/Objectives: Women with subclinical hypothyroidism (SCH) were reported to be at an increased perinatal risk. We aimed to investigate the relationship between SCH and perinatal outcomes in singleton pregnancies resulting from assisted reproduction technology (ART). Methods: We retrospectively examined the perinatal outcomes of ART singleton pregnancies in women who underwent thyroid function screening before conception and delivered at our hospital from January 2020 to July 2023. We defined SCH as thyroid-stimulating hormone (TSH) levels <2.5 mU/L and normal free T₄ levels. The patients were divided into three groups: normal thyroid function (group A), SCH without levothyroxine therapy (group B), and SCH with levothyroxine therapy (group C). The risks of preterm birth, preeclampsia, fetal growth restriction, manual placental removal, and blood loss at delivery were compared among the three groups. Results: Out of the 650 ART singleton deliveries, 581 were placed in group A, 34 in group B, and 35 in group C. The preterm birth rate at <34 weeks was significantly higher in group B and significantly lower in group C than in group A. The rate of preterm delivery at <34 weeks increased in correlation with TSH levels. Levothyroxine therapy was the significant preventive factor for preterm birth at <34 weeks. Conclusions: The preterm birth rate before 34 weeks was significantly higher in the SCH group. Levothyroxine therapy is a significant protective factor against preterm birth before 34 weeks. Universal screening for thyroid function and appropriate hormone therapy in pregnant women may help reduce perinatal risks, including preterm birth.

Decision-making is a complex process of selecting an option from the given choices by analyzing the background information like risk, loss, and gain within the alternative options presented. It has been observed in earlier studies that people are prompt to make less rational decisions when choices are given in a language less known to them. Therefore, to understand the effect of languages on decision-making, we have questioned native Hindi speakers in French and English. French being the foreign language, and English as their second language. Thus, this effect of a non-native language brings to light the important role that the native language plays routinely in judgment and decision-making. In this paper, we developed a Neuropsychological assessment to decipher the effects on decision-making between choices when given in foreign language and second language in comparison with the native language of an individual, which is termed as foreign language effect(Fle). We have explored various possible situations to understand the foreign language effect(Fle) in decision-making and does this change translates when the decision is to be made in the second language. Our study concludes that the Foreign language is least affected by the intuitive biases, followed by the second language, and the native language is most affected by it.

Musyakalah is one of the Arabic linguistic styles included under the category of majaz. This style is commonly used in Al-Quran. The Indonesian translation of Al-Quran is a case where many of the figures of speech are translated literally, thereby causing serious semantic problems. Thus, the research problem of this is formulated with the following questions: 1) How many musyakalah ayahs are there in Al-Quran?; 2) How are the musyakalah ayahs translated, literally (harfiyya) or interpretatively (tafsiriyya)?; 3) How many ayahs are translated literally and how many are translated interpretatively?; and 4) Which translated musyakalah ayahs have the potential to raise semantic and theological problems? The corpus in this research consists of all musyakalah ayahs in Al-Quran and their translation to Indonesian published by the Department of Religious Affairs of Indonesia. The research adopted a descriptive-semantic method. The findings of this research are: 1) There are only eleven ayahs in Al-Quran using musyakalah style, namely: Alhasyr ayah 19, Ali Imran ayah 54, Annaml ayah 50, Alanfal ayah 30, Asysyura ayah 40, Albaqarah ayah 15, Almaidah 116, Aljatsiah ayah 34, Attaubah ayah 79, Annisa ayah 142, and Albaqarah 194; 2) The musyakalah ayahs translated literally are: Aljatsiah 34, Almaidah 116, Asysura 40, Annaml 50, and Alhasyr 19, whereas the musyakalah ayahs translated interpretatively are Albaqarah 194, Annisa 142, Attaubah 79, Albaqarah 15, Alanfal 30, and Ali Imran 54; 3) Of the eleven musyakalah ayahs, only Alhasyr ayah 19 that is translated correctly and does not have the potential of creating misinterpretation. Meanwhile, the interpretation of the other four ayahs can potentially cause misinterpretation or are against the basic principles of Islam. The six remaining ayahs are translated interpretatively and thus do not have the potential to be misinterpreted; 4) The findings suggest that musyakalah ayahs are more appropriately translated interpretatively. Therefore, the following is recommended: 1) Considering the different characteristics of Arabic and Indonesian languages, studies on Al-Quran translation into Indonesian should continuously be carried out; 2) In order to avoid misinterpretation, it is better that the translation of musyakalah ayahs uses tafsiriyya (interpretative) approach; 3) The harfiyya (literal) and tafsiriyya (interpretative) approaches should be developed for other styles beside musyakalah..

Cancer treatment is a significant focus in medicine owing to the increasing global incidence. Patients with advanced cancers that do not respond to conventional therapies have limited options and an unfavorable prognosis. Consequently, researchers are investigating complementary approaches to conventional treatments. One such approach is alkalization therapy, which aims to neutralize the acidic tumor microenvironment (TME) by increasing its pH level. The acidic TME promotes inflammation, tumor progression, and drug resistance. Alkalization therapy has been demonstrated to be effective for various cancers. In addition, natural products, such as triterpenoids, parthenolides, fulvic acid, Taxus yunnanensis, and apple pectin have the potential to alleviate symptoms, maintain physical fitness, and improve treatment outcomes of cancer patients through their anti-inflammatory, antioxidant, and anticancer properties. In this review, we focus on the effects of alkalization therapy and natural products on cancer. Furthermore, we present a case series of advanced cancer patients who received alkalization therapy and natural products alongside standard treatments, resulting in long-term survival. We posit that alkalization therapy together with the supplementation of natural products may confer benefits to cancer patients by mitigating the side effects of chemotherapy and complementing standard treatments. However, further research is warranted to validate these clinical findings.

To address the problems of insufficient semantic fusion between text and questions and the lack of consideration of global semantic information encountered in machine reading comprehension models, we proposed a machine reading comprehension model called BERT_hybrid based on BERT and hybrid attention mechanism. In this model, BERT is utilized to separately map the text and questions into the feature space. Through the integration of Bi-LSTM, attention mechanism, and self-attention mechanism, the proposed model achieves comprehensive semantic fusion between text and questions. The probabilities distribution of answers is computed using Softmax. Experimental results on the public dataset DuReader demonstrate that the proposed model achieves improvements in BLEU-4 and ROUGE-L scores compared to existing models. Furthermore, to validate the effectiveness of the proposed model design, we analyze the factors influencing the model’s performance.

Bio-molecular reagents like antibodies required in experimental biology are expensive and their effectiveness, among other things, is critical to the success of the experiment. Although such resources are sometimes donated by one investigator to another through personal communication between the two, there is no previous study to our knowledge on the extent of such donations, nor a central platform that directs resource seekers to donors. In this paper, we describe, to our knowledge, a first attempt at building a web-portal titled Bio-Resource Exchange that attempts to bridge this gap between resource seekers and donors in the domain of experimental biology. Users on this portal can request for or donate antibodies, cell-lines and DNA Constructs. This resource could also serve as a crowd-sourced database of resources for experimental biology. Further, in order to index donations outside of our portal, we mined scientific articles to find instances of donations of antibodies and attempted to extract information about these donations at the finest granularity. Specifically, we extracted the name of the donor, his/her affiliation and the name of the antibody for every donation by parsing the acknowledgements sections of articles. To extract annotations at this level, we propose two approaches – a rule based algorithm and a bootstrapped relation learning algorithm. The algorithms extracted donor names, affiliations and antibody names with average accuracies of 57% and 62% respectively. We also created a dataset of 50 expert-annotated acknowledgements sections that will serve as a gold standard dataset to evaluate extraction algorithms in the future. Contact: [email protected], [email protected] Database URL: http://tonks.dbmi.pitt.edu/brx Supplementary information: Supplementary data are available at Database online.

The master molecular regulators and mechanisms determining longevity and health span include nitric oxide (NO) and superoxide anion radicals (SOR). L-arginine, the NO synthase (NOS) substrate, can restore a healthy ratio between the dangerous SOR and the protective NO radical to promote healthy aging. Antioxidant supplementation orchestrates protection against oxidative stress and damage—L-arginine and antioxidants such as vitamin C increase NO production and bioavailability. Uncoupling of NO generation with the appearance of SOR can be induced by asymmetric dimethylarginine (ADMA). L-arginine can displace ADMA from the site of NO formation if sufficient amounts of the amino acid are available. Antioxidants such as ascorbic acids can scavenge SOR and increase the bioavailability of NO. The topics of this review are the complex interactions of antioxidant agents with L-arginine, which determine NO bioactivity and protection against age-related degeneration.

The master molecular regulators and mechanisms determining longevity and health span include nitric oxide (NO) and superoxide anion radicals (SOR). L-arginine, the NO synthase (NOS) substrate, can restore a healthy ratio between the dangerous SOR and the protective NO radical to promote healthy aging. Antioxidant supplementation orchestrates protection against oxidative stress and damage—L-arginine and antioxidants such as vitamin C increase NO production and bioavailability. Uncoupling of NO generation with the appearance of SOR can be induced by asymmetric dimethylarginine (ADMA). L-arginine can displace ADMA from the site of NO formation if sufficient amounts of the amino acid are available. Antioxidants such as ascorbic acids can scavenge SOR and increase the bioavailability of NO. The topics of this review are the complex interactions of antioxidant agents with L-arginine, which determine NO bioactivity and protection against age-related degeneration.

Transmission of values and religious concepts to children is one of the most important issues in the third millennium and it has drawn varied and different views among experts and scholars in the world. Research specialized in religious literature for children and adolescents create new capacities in the presentation of religious concepts to the group. Plans have been considered to transfer values and religious concepts in the curricula of primary school children in the group in Iran. It is one of the topics that the authors note to the introduction of the minutiae of religion in the first three elementary grades. In this study, the collection and analysis methods providing content related to the minutiae of religion in reading books the first till third sections of the years 2013-2015. In addition, the plan includes aspects of other branches of religion in these books on information collected from text books that collected and classified. The result is that "definitely good and forbidding the evil" and "prayer" have the highest frequency of applications in the selected books. Further branches made of branches of religion in these books, represents the values of religious, moral and social as well.

In this study, we provide the Lie symmetries and a classification of plane symmetric static space-times. Based on the invariance of the Lagrange equations of plane symmetries static space-time systems under the transformation Lie group, we provide the Lie symmetry determination equation, Lie symmetry theorem, and the conserved quantity of the systems; by utilizing the Lie symmetry method to solute the system, we give complete classification of the plane symmetric static space-times systems. The research results indicate that using the Lie symmetry method to study plane symmetric static spacetime can identify a series of conserved quantities that exist in the system; Discovered 5 basic Lie symmetries in the system; When the metric parameters of the system are appropriately selected, the plane symmetric static spacetime can have 6, 7, 8, 9, 11, and 17 Lie symmetric symmetries and conserved quantities.

This paper proposes a metaphysical framework for distinguishing between human and machine intelligence. It posits two identical deterministic worlds -- one comprising a human agent and the other a machine agent. These agents exhibit different information processing mechanisms despite their apparent sameness in a causal sense. Providing a conceptual modeling of their difference, this paper resolves what it calls “the vantage point problem” – namely, how to justify an omniscient perspective through which a determinist asserts determinism from within the supposedly deterministic universe.

This Special Issue of Humanities comes at a time when the viability of the humanities are challenged on numerous fronts. On the one hand, the humanities face material threats as the politics of austerity continues throughout Europe and the United States, diminishing public support and making profit margin and “job creation” the primary measure of value or the basis of state university funding decisions. On the other, the humanities face conceptual, theoretical and ethical challenges, as the emergence of post-racial and post-humanist discourses signal what Foucault called “a change in the fundamental arrangements of knowledge.” The defining boundaries of constructs such as “race” and “human” have been radically called into question, challenging us to rethink the classificatory systems that found hierarchical relationships between, for example, the “fully human” and sub-human or non-human others. Despite dominant nations’ professed commitment to a universal human rights paradigm, racialized identities are still often the targets of disenfranchisement and dehumanization, while the exploitation and destruction of the natural world continues in the name of “progress” and profits.

With the widespread Application of Virtual Reality (VR) in education, optimizing foreign language learning in VR has become a focal point of research. This paper introduces a comprehensive theoretical framework (VR-CCL) based on constructivism and cognitive load theory to enhance foreign language learning in VR. Through a literature review, we explore the applications of VR in education, foreign language learning theories, and prior works on technology-assisted language learning. We further detail the three main components of the VR-CCL framework and validate its effectiveness through two case studies: Duolingo VR and Rosetta Stone VR. Finally, we discuss the strengths and limitations of the framework and its implications for educators and developers.

Bioremediation of crude oil-contaminated soil in Kuwait was evaluated using the biowashing pilot reactor system, whose components included 3 biowashing reactors; an oil separator, a hydrocyclone, and a dissolved air flotation. The biowashing pilot reactor system was fed with hemoglobin, a cheap and rich nutrient source containing carbon and nitrogen for bacterial growth. The initial total petroleum hydrocarbons (TPH) concentration was about 84,000 mg/kg soil. The initial TPH concentration decreased to 38,000 mg/kg soil on day 1. The degradation extents of TPH were 55%, 91%, and 96% on days 1, 3, and 5. The first-order rate constant for TPH degradation rate was 0.6820.0004/day. The initial unresolved complex mixture (UCM) concentration was 78,000 mg/kg soil. The degradation extents of UCM were 53%, 91%, and 98% on days 1, 3, and 5. Then, the degradation extents of individual components of total polycyclic aromatic hydrocarbons (PAH), alkylated PAH, and n-alkanes were measured for 5 days. 16S rRNA gene copy number was measured during 5 days for bacterial population estimation. Although there was a day delay in the accretion of the number of copies, the number increased from day 2 to day 5. The present study suggests that the biowashing pilot reactor system with a capacity of 200 L is efficient for TPH degradation.

African swine fever (ASF) continues to spread in Africa, Europe, Asia, and the island of Hispaniola increasing the need to develop more streamlined and highly efficient surveillance and diagnostic capabilities. One way to achieve this is by further optimization of already established standard operating procedures to remove bottlenecks for high-throughput screening. Real-time polymerase chain reaction (RT-PCR) is the most sensitive and specific assay available for the early detection of ASF, but it requires high quality nucleic acid extracted from the samples. Whole blood from live pigs and spleen tissue from dead pigs are the preferred samples for ASF RT-PCR. Whole blood can be used as is in nucleic acid extraction, but spleen tissues require an additional homogenization step. In this study, we compared the homogenates and swabs prepared from 52 spleen samples collected from pigs experimentally inoculated with highly and moderately virulent ASF virus strains. The results show that not only are the spleen swabs more sensitive when executed with a low-cell-count nucleic acid extraction procedure followed by RT-PCR assays, they also increase the ability to isolate ASFV from positive spleen samples. Swabbing is a convenient, simpler, and less time-consuming alternative to tissue homogenization. Hence, we recommend spleen swabs over tissue homogenates for high-throughput detection of ASFV by real-time PCR.

Glaucoma is one of the primary causes of blindness worldwide. Canal opening surgery, a type of minimally invasive glaucoma surgery (MIGS) applied in cases of mild to moderate glaucoma, has gained increasing popularity in recent years due to its efficacy in reducing intraocular pressure, its safety profile, the simplicity of its technique, and the reduced likelihood of compromised vision. Nevertheless, the existing body of histopathological studies remains insufficient for a comprehensive understanding of post-surgical wound healing. Consequently, debates persist among researchers regarding the mechanism through which Schlemm’s canal opening surgery reduces intraocular pressure, as well as the surgical techniques that may impact the outcomes and the factors influencing surgical success. As the history of MIGS is relatively short and lacks sufficient systemic reviews or meta-analysis evaluating the influence of individual factors, this review was conducted to illuminate disparities in the researchers’ opinions at the current stage of research.

This study establishes a multivariate function model for natural human load carrying walking in some typical scenarios like college equipments and materials relocation for students. For classified materials relocation needs and constraints, we obtain the relationship between walking speed and load weight for single person, as well as the time cost for different round trips. By establishing an integer programming model with the minimum total transportation time cost and quality assurance as the objective function and the requirements of negative weight and speed as the constraints conditions, we reach the optimal item allocation methods considering time cost and shelf life were obtained. We discover that there is an approximate linear relationship between the change in natural walking speed and travel time when the load is small, thus obtaining the time cost of student transportation under different round-trip situations. The Monte Carlo simulation algorithm is used to obtain the optimal allocation scheme that meets the efficiency and quality requirements. The analysis methods and results can be used as guidance for task scheduling optimization for material relocation in educational organizations as well as commercial agencies.

The metabolites secreted by probiotics or released after their lysis are called postbiotics. They provide physiological benefits to the host, preventing the colonisation of pathogens by improving the intestinal environment for beneficial commensal bacteria, which reduces the incidence of digestive disorders and improve the immune system. The aim of this work was to evaluate the addition of postbiotics to dairy cow rations during the transition period on nutrient digestibility, composition, and milk yield. The effects of two postbiotics were evaluated in twelve Friesian cows from 30 days before calving to two months of lactation. The animals were randomly allocated to the treatments control (CT) and supplemented with postbiotics (PC and PR). Feeding was ad libitum with 60/40 of forage/concentrate ratio. Daily feed intake and milk production were recorded individually throughout the study. Two digestibility balances were performed, one before parturition and one after parturition. Colostrum was sampled at first milking and milk was sampled weekly. Data were analysed using a mixed mode in R software. The results suggest that postbiotic supplementation in late gestation and early lactation increase the voluntary intake of dry matter, especially in the PR treatment, with higher apparent total tract digestibility of dry matter, organic matter and neutral detergent fiber. Both treatments including postbiotics induced an increase in colostral immunoglobulin concentration. Milk production of cows receiving the PC treatment was the highest, with high fat and protein yields and a higher persistence of the production curve throughout the lactation.

This paper examines the use of multisensory virtual reality (VR) as a novel approach in psychological therapy for autobiographical memory retrieval with benefits for cognitive enhancement, stress reduction etc. The research in this paper was done under Romanian Arut Research Grant no. 27/09/10/2023 ”Intelligent VR system for treating autobiographical/episodic memory deficits".  By simulating realistic multisensory experiences, VR technology offers an immersive environment that surpasses traditional therapeutic methods in effectiveness. Previous studies demonstrate improved outcomes in treating various psychological conditions (affective disorders and PTSD, specific phobias and age-related cognitive decline). Technological advancements in VR, such as olfactory integration cand contribute to the realism and therapeutic potential of these environments, the integration of various physical stimuli with VR holds promising potential psychological therapies and highlighting the need for further interdisciplinary research. In this pilot study we tested the efficacy of a new system for triggering autobiographical memory retrieval. For this we used images combined with odors in a congruent manner and offering participants the chance to interact with the VR environment by using 2 virtual hands. We evaluated the efficacy of this system using qualitative methods with emphasis on the evaluation of the emotions associated with memory recollection and the ease of triggering memories. All participants in our pilot study experienced intense emotions related to childhood or adolescence and the pleasant feelings they had during the experiment persisted even after it ended. This is an advancement to what exists currently and original research elements for our paper.

Large-span post-tensioned (PT) beams play a crucial role in maximizing the benefits of post-tensioning techniques. Bonded and unbonded systems are prevalent, with the latter being more widespread in the United States. While bonded systems are advantageous for creating long spans when multiple tendons are grouped in ducts, limited literature exists on their demolition. In a case study, this paper addresses the unique challenge of demolishing large-span bonded post-tensioned beams due to a building's functional change. Emphasizing insights for engineers, it explores the use of cutting and dismantling methods, considering the presence of prestressed cables. The demolition process is distinctive due to the presence of numerous pre-stressed cables along the beams, necessitating a specialized and cautious cutting approach. This is accomplished through the use of a drilling technique that selectively distresses the tendons, ensuring they are not all affected simultaneously. An intriguing observation discussed in this paper pertains to the occurrence of horizontal cracks accompanied by loud sounds following the drilling process, offering insights from the design perspective of PT systems. The paper details an innovative method for safely demolishing large-span bonded PT beams, using ground-penetrating radar and computer models to navigate structural complexities and ensure nearby structures' safety.

"Mem u Zin" holds a paramount status in Kurdish literature, celebrated for its profound exploration of love. Authored by the esteemed Kurdish poet and writer Ahmet Khani in the 17th century, this epic masterpiece represents the pinnacle of literary achievement within the Kurdish cultural milieu. By conducting a concise analysis of "Mem u Zin," our aim was to shed light on the underlying factors that contribute to the perceived separation of Kurdish society from perfection. We provided a comprehensive view of the linguistic obstacles encountered by the Kurds in this exploration.

A novel hypothesis concerning language evolution is advanced. It posits that languages have evolved as a means of binding individuals to a group, as well as for defining those groups. This means that language evolution has to be considered on the level of groups and not only on the level of individuals. This hypothesis helps to explain the huge diversity of human languages, as well as their complexity. Perhaps more importantly, it explains why adults lose the ability to learn languages with the ease that children possess.

The formation of hydrogen sulfide (H₂S) in petroleum reservoirs by anaerobic microbial activity is called biogenic souring of reservoirs and is a risk in the oil and gas industry as the compound is extremely toxic, flammable and corrosive. In this paper, we present a workflow and the tools to assess biogenic souring from a pragmatic engineering perspective. The retention of H₂S in the reservoir due to the reactions with iron-bearing rock minerals (e.g. siderite) is shown in a theoretical approach here and supported with literature data. Cases are given for two fields under secondary (waterflooding) and tertiary flooding with microbial enhanced oil recovery (MEOR). The use of the Monte Carlo method as a numerical modelling tool to incorporate uncertainties in the measured physical/chemical/ biochemical data is demonstrated as well. A list of studies conducted with different chemicals alone or in combination with various biocides to mitigate biogenic souring provides an overview of potential inhibitors as well as possible applications. Furthermore, the results of static and dynamic inhibition tests using molybdate are presented in more detail due to its promising mitigation ability. Finally, a three-step workflow for the risk assessment of biogenic souring and its possible mitigation is presented and discussed.

This literature review delves into the realm of fault detection techniques for transmission lines, aiming to provide a comprehensive overview of recent advancements and key trends in the field. Employing a structured approach, the review synthesizes a plethora of research spanning from 2019 to 2024, sourced from diverse databases including IEEE Xplore, ScienceDirect, ResearchGate, Scopus, Litmaps, and Google Scholar. The methodology encompasses a systematic literature search protocol, stringent inclusion and exclusion criteria, meticulous data extraction, and a multi-dimensional analysis framework. The literature review uncovers a spectrum of fault detection methodologies, ranging from traditional signal processing techniques like Discrete Wavelet Transform and phase angle-based methods to cutting-edge deep learning algorithms such as Capsule Networks and Convolutional Neural Networks. Insights gleaned from the review underscore the critical importance of fault detection in maintaining the reliability, safety, and efficiency of power grids, highlighting its role as a frontline defense against widespread outages and equipment damage. Key findings from the review shed light on the efficacy of different fault detection approaches, showcasing their strengths and limitations across diverse system conditions. Furthermore, the review identifies common trends and challenges, including the need for real-world validation, scalability, adaptability to various network configurations, and cybersecurity considerations. This literature provides valuable insights and recommendations for future research endeavors in fault detection for transmission lines. By embracing advancements in both traditional and emerging techniques, researchers can continue to enhance the resilience and dependability of power transmission systems, ensuring their ability to withstand evolving challenges and safeguard critical infrastructure.

Purpose. This article examines the use of English medium instruction (EMI) for teaching Japanese as a foreign language (JFL) by non-native speakers with a focus on multilingual classrooms. The study also explores teachers’ positive and negative beliefs about using EMI in classroom settings for JFL instruction.Methods and procedure. Two hundred sixty-one non-native Japanese language teachers worldwide voluntarily participated in the survey, answering the questionnaire (Google Forms and Jotforms) used to gather data. Both qualitative and quantitative methods were employed. The research instrument was piloted before the main study and was found to be effective and adequate to elicit the desired data. The research questions aimed to identify whether there were any relationships between the use of EMI and the multilingual classroom. Findings. The results showed a clear correlation between the extensive use of EMI and the multilingual character of JFL multilingual classrooms. Translanguaging turned out to be a common practice adopted by non-native Japanese teachers regardless of the primary language of instruction. The article concludes that EMI can be a valuable tool for JFL instruction in multilingual classrooms. Based on the investigation of the teachers’ beliefs, the study results showed a changing positive attitude towards English employed in JFL classrooms. Implications for research and practice. The findings can be used further to investigate EMI in JFL instructional practices in multilingual classrooms, to improve the quality of JFL instruction, and to facilitate the integration of multilingual education into foreign language teaching. Future research can explore the effectiveness of EMI in different JFL contexts and investigate the impact of EMI on students’ language learning outcomes.

The present paper offers a reading of three selected novels by two Anglophone Arab writers Diana Abujaber and Fadia Faqir. Our reading is fundamentally based on a philosophical post-humanist perception of other ethnic minorities as being inferior and un-human. In interpreting the three novels, Arabian Jazz (2003), My Name is Salma (2007) and Willow Trees Don’t Weep (2014), a main concern is to bring to light how Arabs –and Muslims –have been zombified and de-humanized in Western mainstream media and culture based on a biased stigmatization and stereotyping of a large heterogeneous ethnic group wherein religions, traditions, languages and cultures are diverse. Also, a pivotal preoccupation is going to be the exiling journey of the protagonists from their homelands to Western countries, and how these journeys contribute to the post-humanization of the self, the identity and the culture of Arab displaced immigrants.

Perceived peer support is a critical factor in the academic adjustment of higher vocational college students. While previous studies have explored this relationship, few studies have delved into the underlying mechanisms. This study surveyed 9782 students from 35 higher vocational colleges in 13 provinces and cities in China to explore the relationship and mechanisms between perceived peer support and academic adjustment. Drawing on Conservation of Resources theory and Social Support Resource theory, the findings revealed that perceived peer support not only directly predicted the academic adjustment of higher vocational college students, but also indirectly through the independent mediating effect of academic hope and professional identity, as well as the chain-mediating effect of academic hope to professional identity. Additionally, students from vocational high schools showed a more significant increase in academic hope due to perceived peer support compared to those from general high schools, highlighting the moderating effect of educational background. The study provided a new theoretical perspective for understanding the complex mechanisms of academic adjustment in students and offers valuable guidance for educational practice, emphasizing the importance of fostering perceived peer support, academic hope, and professional identity in higher vocational education.

Unmanned Aerial Vehicles (UAVs), commonly known as drones, have experienced a significant surge in both civilian and military applications, owing to their versatility and cost-effectiveness. However, a negligent use or the operation with harmful intent of these aircraft poses a potential risk to public safety and the privacy of individuals. In response to this threat, counter-drone systems have been developed in recent years, including technologies to detect, position, and neutralize these aircraft. This paper presents a review of different existing commercial counter-drone systems, the technologies behind detection, tracking, and classification, as well as soft and hard kill mitigation.

Psychedelics are drugs capable of eliciting profound alterations in the subjective experience of the users, sometimes with long-lasting consequences. Because of this, psychedelic research tends to focus on human subjects, given their capacity to construct detailed narratives about the contents of their consciousness experiences. In spite of its relevance, the interaction between serotonergic psychedelics and language production is comparatively understudied in the recent literature. This review is focused on two aspects of this interaction: how the acute effects of psychedelic drugs impact on speech organization regardless of its semantic content, and how to characterize the subjective effects of psychedelic drugs by analyzing the semantic content of written retrospective reports. We show that the computational characterization of language production is an emergent powerful tool to predict the therapeutic outcome of individual experiences, relate the effects elicited by psychedelics with those associated with other altered states of consciousness, draw comparisons between the psychedelic state and the symptomatology of certain psychiatric disorders, and investigate the neurochemical profile and mechanism of action of different psychedelic drugs. We conclude that researchers studying psychedelics can considerably expand the range of their potential scientific conclusions by analyzing brief interviews obtained before, during and after the acute effects. Finally, we list a series of questions and open problems that should be addressed to further consolidate this approach.

Due to the extraordinary mechanical properties of ultra-high-performance concrete (UHPC), the shear stirrups in UHPC beams could potentially be removed. The aim of this study is to determine the effect of beam height and steel fiber volume content on the shear behavior of non-stirrup UHPC beams under a larger shear span-depth ratio (up to 2.8). Eight beams were designed and fabricated, including six non-stirrup UHPC beams and two comparing stirrup-reinforced normal concrete (NC) beams. The experimental results demonstrated that the volume content of steel fiber could be a crucial factor affecting the ductility, cracking strength, and shear strength of non-stirrup UHPC beams and altering their failure modes. Additionally, the height of the beam had a considerable effect on its shear resistance. This study revealed that the French standard formulae were more accurate for the UHPC beams with larger shear span-depth ratios and PCI-2021 formulae greatly overestimate the shear capacity of UHPC beams with larger shear span-depth ratios. Xu’s formulae were more accurate for the steel fiber-reinforced UHPC beams with larger shear span-depth ratios.

AI tools provide individualized learning experiences tailored to specific language learners' requirements. Artificial intelligence-driven programs can customize lessons, exercises, and feedback by evaluating a learner's progress and determining strengths and shortcomings. This individualized method aids students in concentrating on their areas of weakness, thus improving their total language competency. This narrative literature review aims to gather relevant literature discussing AI tools as supplementary support in aiding a learner's language acquisition. Relevant studies stated that there are a lot of chances to improve learning outcomes when AI tools are used in language acquisition. Teachers who want to give their students more resources and help should consider including these tools in their lessons. To preserve the interpersonal and cultural components of language learning, it is imperative to strike a balance between the application of AI and conventional teaching techniques. AI tools are always accessible, providing assistance to learners whenever needed. This is especially helpful for individuals with unpredictable schedules or restricted access to conventional language learning materials.

This paper proposes the utilization of large language models as recommendations systems for museums. Since the aforementioned models lack the notion of context, they can’t work with temporal information that is often present in recommendations for cultural environments (e.g. special exhibitions or events). In this respect, the current work aims at enhancing the capabilities of large language models through a fine-tuning process that incorporates contextual information and user instructions. The resulting models are expected to be capable of providing personalized recommendations, aligned with user preferences and desires. More specifically, Generative Pre-trained Transformer 4, a knowledge-based large language model is fine-tuned and turned into a context-ware recommendation system, adapting its suggestions based on user input and specific contextual factors such as location, time of visit, and other relevant parameters. The effectiveness of the proposed approach is evaluated through certain user studies, which ensure an improved user experience and engagement within the museum environment.

Currently the integration of Kazakhstan into the world community requires the training of highly qualified specialists fluently speaking and having a good command of foreign language. The study is devoted to the problem of development of foreign language professional skills of students of non-linguistic specialty. The aim of the research is to identify the efficiency of integrated approach use for shaping foreign language communicative skills of non-linguistic specialty students. The methodological basis of the research was communicative approach to foreign language teaching. In the framework of the research scientific and theoretical literary sources on the problem of the development of foreign language professional skills of non-linguistic specialty students were studied and summarized; the analysis of the empirical material obtained in the questioning and testing of students. To determine the level of formation of foreign language professional competence of international relations specialty students we have developed the following components: linguistic, cognitive, pragmatic. In the course of the experimental study, the authors revealed that the use of problem-based methods such as discussion, project and case technologies, debates in the framework of integrated approach to professional foreign language teaching promote students’ motivation increase and contribute to the improvement of their foreign language professional skills. The results of the study can be used in application of CLIL technology for the formation of foreign language professional competence of non-linguistic specialty students at the university.

Within the fields of English for Academic Purposes (EAP) and English for Research Publication Purposes (ERPP), there is an unquestioned orthodoxy that scholars with English as an Additional Language (EAL) are particularly disadvantaged by the pressure to publish in English (though see Kuteeva 2015 and debate between Hyland 2016a, 2016b and Politzer-Ahlesa et al. 2016). In this paper, I challenge this orthodoxy, raising questions about the evidence upon which it is based. Within a framework of ‘verbal hygiene’ (Cameron 1995, 2012), I will argue that the attention accorded to ‘non-nativeness’ may be disproportionate to its significance for publication success. I conclude by proposing some reorientations for researchers and practitioners in the field that centre on broadening the scope to encompass non-linguistic structures of inequity.

The Philippines is an archipelagic country composed of different indigenous cultural communities (ICCs) spread across various islands. Many of these Indigenous Peoples (IP) are marginalized and do not have access to common resources enjoyed in the lowland areas. An initial assessment focusing on the evaluation of their available livelihood and resources was conducted, in order to find common solutions to existing problems. The assessment took place at the office of the tribal council of Matigsalog in barangay Datu Salumay, Marilog District, Davao City from April 11 to 13, 2023. There were about 42 participants in total for Matigsalog tribes (26 men and 16 women) who attended the meeting and there were 15 participants from the Ata tribe (7 men and 8 women). Validation of the study separately took place through a brief presentation of the results before 15 members of the tribe and a hiking visit in their farmlands. The study determined the existing livelihoods in the two areas which are mainly agricultural, farming of crops (rice, corn, cassava, sweet potato) and fruit trees (coconut, banana, durian, jackfruit, pomelo) and the resources as well as the community’s knowledge, systems, and practices with regards to agricultural crops including climate change. Their sustainable farming practices include intercropping, seed-saving, and exchange practices, preserving these crop varieties and ensuring their availability for future planting seasons, no usage of chemical fertilizers and pesticides. To conclude, the tribe’s aspirations and resulting recommendations are summarized, to facilitate more directed and effective governmental assistance.

Objective: To map the literature pertaining to adult Person-Centered Rehabilitation (PCR).Data Sources: Following our previously published scoping review protocol, data were identified through: three major databases, snowball searches and expert consultation.Study Selection: Two independent reviewers identified English-language papers which addressed adult physical rehabilitation and met one or more of the six pre-defined inclusion categories for PCR content. Data Extraction: Two independent reviewers extracted key characteristics of included papers (e.g. aims, methods, participants). Quantitative methods (descriptive statistics, regression analysis) and qualitative content analyses were used to synthesize the results. Data synthesis: Of 5084 unique records initially screened, 145 papers were included: 111 empirical, including 11 systematic reviews. Empirical papers had data from 13498 clients and 3849 providers, in total. Yearly publications grew exponentially from 2000 to 2017 (r²= 0.65; p<0.01). Publications were unevenly distributed by countries (e.g. United States’ publications per population size was 45 times lower than New Zealand’s). Most papers focused in more than one profession, setting-type or health conditions, respectively 57.2%, 66.2%, and 60.7%. Finally, more than half of the empirical papers (53.2%) studied implementation of PCR approaches, including its effect. Conclusion: This scoping review synthesizes key characteristics and publication trends in the PCR literature, which is mainly empirical and growing exponentially over time. Stakeholders can use the identified trends, gaps, and literature map to guide further PCR research, and both clinical and organizational practices. The high prevalence of papers focused on multiple professions, settings-type, or health conditions reinforces the need for developing a transdisciplinary, trans-service model of PCR, which will be derived from a thematic analysis of this body of literature.

Large Language Models (LLMs) have demonstrated promising capabilities to solve complex tasks in critical sectors such as healthcare. However, LLMs are limited by their training data which is often outdated, the tendency to generate inaccurate ("hallucinated") content and a lack of transparency in the content they generate. To address these limitations, retrieval augmented generation (RAG) grounds the responses of LLMs by exposing them to external knowledge sources. However, in the healthcare domain there is currently a lack of systematic understanding of which datasets, RAG methodologies and evaluation frameworks are available. This review aims to bridge this gap by assessing RAG-based approaches employed by LLMs in healthcare, focusing on the different steps of retrieval, augmentation and generation. Additionally, we identify the limitations, strengths and gaps in the existing literature. Our synthesis shows that proprietary models such as GPT-3.5/4 are the most used for RAG applications in healthcare. Also, there is a lack of standardized evaluation frameworks for RAG-based applications. In addition, the majority of the studies do not assess or address ethical considerations related to RAG in healthcare. It is important to account for ethical challenges that are inherent when AI systems are implemented in the clinical setting. Lastly, we highlight the need for further research and development to ensure responsible and effective adoption of RAG in the medical domain.

Tao and Ming (name) or Tao and Yan (language or speech) is an inseparable pair of important concepts in traditional Chinese language philosophy. In LaoTze’s opinion, Tao could not be spoken out, and language or speech could not reach Tao’s essence. Yan and Tao are separated from each other, and they are paradoxical. All the confusion derives from Yan regardless of how delicate they are. Tao can be expressed in words, but it will never be expressed through common languages. In this article, Tao is regarded as a human being’s thoughts or intentions and is even considered to be the origin of the real world. Language can express people’s thought, although it has some limitations. There exist some contradictions between the real word and the world expressed in words. To enable people to better understand the existence of the real world from multiple perspectives at the language or linguistic level without being constrained by words, this article studies LaoTze’s linguistic philosophy reflected in his opinion on the relationship between Tao and Yan and makes a comparative analysis of modern Western language philosophers’ stand on language (or speech) and existence.

RNA sequencing techniques, like bulk RNA-seq and Single Cell (sc) RNA-seq, are critical tools for the biologist looking to analyze the genetic activity/transcriptome of a tissue or cell during an experimental procedure. Platforms like Illumina's next-generation sequencing (NGS) are used to produce the raw data for this experimental procedure. This raw FASTQ data must then be prepared via a complex series of data manipulations by bioinformaticians. This process currently takes place on an unwieldy textual user interface like a terminal/command line that requires the user to install and import multiple program packages, preventing the untrained biologist from initiating data analysis. Open-source platforms like Galaxy have produced a more user-friendly pipeline, yet the visual interface remains cluttered and highly technical, remaining uninviting for the natural scientist. To address this, SeqMate is a user-friendly tool that allows for one-click analytics by utilizing the power of a large language model (LLM) to automate both data preparation and analysis (differential expression, trajectory analysis, etc). Furthermore, by utilizing the power of generative AI, SeqMate is also capable of analyzing such findings and producing written reports of upregulated/downregulated/user-prompted genes with sources cited from known repositories like PubMed, PDB, and Uniprot.

Large language models (LLMs) have recently garnered significant attention due to their remarkable ability to assimilate vast amounts of information and effectively process natural language. In healthcare, natural language constitutes a substantial portion of medical data, rendering LLMs highly promising for various healthcare applications. This study seeks to explore the potential of LLMs in healthcare and clinical decision support (CDS), following PRISMA guidelines for reviews. The analysis encompasses 44 LLMs, each influenced by several factors impacting their performance. Notably, the datasets utilized for pretraining and finetuning processes play a crucial role in determining the model’s domain specificity. Furthermore, distinct model architectures are tailored for specific tasks, while prompting strategies are frequently employed to refine and enhance the model’s performance. LLMs exhibit considerable promise for a wide array of healthcare applications. For instance, LLMs possess the potential to handle and analyze medical information efficiently, facilitate contextual understanding among clinicians and patients, as well as automate the documentation of clinical notes and reports. Presently, however, their implementation within the field remains limited. Notable improvements have been witnessed in the performance of current healthcare-oriented LLMs, with some achieving expert-level competence in medical question-answering (MQA). However, these LLMs face prominent challenges, encompassing ethical concerns, issues related to accountability, and a lack of appropriate regulations. Nevertheless, this study reveals numerous promising applications in healthcare where LLMs could significantly augment the efficiency, accessibility, and manageability of healthcare delivery. Addressing the challenges LLMs encounter is essential for their seamless integration into practical healthcare applications. As a relatively new technology, the development of LLMs is still in its early stages, but their potential is evident through this study. Consequently, fostering collaboration among healthcare professionals, developers, regulators, and other stakeholders is imperative to cultivate dependable LLMs that align with the demands of the healthcare sector.

In an era marked by rapid technological progress and global dynamics, the Neo-Language Acquisition and Learning Theory challenges conventional language acquisition paradigms through its conceptualization of 'Grit: The Unyielding Will to Connect,' emphasizing determination, motivation, and adaptability as the primary drivers of language learning, transcending age-related constraints and critical periods. This theory also underscores 'Self-Preservation: Language as the Canvas of Reality,' as language plays a pivotal role in preserving and conveying our individual and collective experiences. Recent global events have underscored the theory's relevance, prompting a reevaluation of language education in response to changing circumstances. Moreover, 'Techno-Training: Language Acquisition and Learning in the Digital Age' is integral to the theory, as it recognizes the influence of technology on modern language education. Beyond traditional methods, these three reshape our fundamental understanding of said theory. Drawing from historical evidence, psychological principles, and contemporary applications, they supported the conceptualization of the theory itself. Overall, the Neo-Language Acquisition and Learning Theory revolutionizes language education, spotlighting the power of motivation, determination, and adaptability to navigate the evolving landscape of language learning and acquisition.

Children's screen time may affect their growth and development. However, different aspects of children and their parents that affect children's screen time has not been investigated in the Japanese context. This study aimed to explore the relationship between relevant factors affecting children's screen time based on their sleep, difficulties, and parental control, among Japanese elementary and junior high school students. A cross-sectional survey was conducted among parents in Japan. Data on screen time duration, parent-child background, strengths and difficulties, sleep variables, and parental control types were collected from 225 households. Path analysis revealed that high Strengths and Difficulties Questionnaire (SDQ) scores (β = 0.167, p = 0.007), sleep duration (β = -0.282, p &lt; 0.001), and parental control (β = -0.205, p &lt; 0.001) were significantly related to children's screen time. Additionally, late parental bedtime negatively correlated with children's sleep duration (β = -0.326, p &lt; 0.001). The findings suggest that children's difficulties, sleep duration, and parental control are associated with children's screen time. This study, together with previous research, provides comprehensive insights to design interventions to decrease screen time in children in the Japanese context.

Transformative education significantly advances society's growth as an essential doorway to fresh language learning chances. It equips people to succeed in a complicated world by encouraging critical thinking, inclusivity, social responsibility, lifelong learning, and international collaboration. Adopting transformative education can result in a more inventive, sustainable, and equal society where everyone can realize their potential and advance society's interests as educational systems change. This paper aims to gather literature regarding how transformative education becomes a door toward new learning opportunities in societal development especially language. Transformative education may produce a generation of knowledgeable, sympathetic, and proactive students who can bring about constructive social change by prioritizing holistic development. Hence, educators should work to foster a learning atmosphere where all students feel supported and respected. Through integrating social justice ideals into the educational process, transformative education equips students to act as change agents and advocates for equity both within and outside their communities.

Chronic oxidative stress in long-distance runners adversely affects conditioning. Therefore, it is important to objectively assess and monitor oxidative stress but measuring oxidative stress can be invasive or require skill to measure. Therefore, this study aimed to verify whether skin autofluorescence (SAF), a noninvasive, rapid, and easily calculable metric for calculating advanced glycation end products (AGEs), is useful as an oxidative stress biomarker. The subjects were 50 young Japanese male long-distance runners (aged 20.2 ± 1.2 years); 35 average male university students (aged 19.8 ± 1.1 years) served as controls. The interactions and relationships between SAF and plasma pentosidine and oxidative stress markers (reactive oxygen metabolite-derived compounds [d-ROMs], biological antioxidant potential [BAP], and the BAP/d-ROMs ratio) in runners were examined, and SAF in the runners and controls was compared. The results suggest that plasma pentosidine in runners is associated with oxidative stress markers and that plasma pentosidine can assess oxidative stress. However, SAF was not validated as an oxidative stress marker because it was not associated with oxidative stress marker. In future, clarifying the factors affecting SAF may clarify the relationship between SAF, plasma pentosidine, and oxidative stress markers.

Nuclear energy, along with renewable and alternative energy sources, is a crucial green energy source. However, the existing literature often overlooks the role of nuclear energy in achieving sustainable development goals. This study analyzes the impact of green technological innovation, nuclear energy consumption, and trade openness on environmental quality in the US, which consumed the most nuclear energy from 1990 to 2019. The ARDL bounds testing approach was applied for its effectiveness in smaller samples, suitable for the data set used in this study, to determine cointegration relationships. Additionally, the Toda-Yamamoto causality test was employed to explore causal links without requiring series stationarity or cointegration. The ARDL cointegration results indicate a significant long-term relationship between CO2 emissions, green technological innovation, nuclear energy consumption, and trade openness. The results suggest that promoting green technological innovation and nuclear energy (although this effect is less certain) can be effective strategies for reducing CO2 emissions, while the impact of trade openness requires careful consideration due to its potential to increase emissions. Green technological innovation has a significant unidirectional causal effect on CO2 emissions. These results will help policymakers design policies to achieve sustainable environmental goals in the US economy.

The determinants of the presence of “Foreign Doctorate Students” among 36 European Countries for the period 2010-2019 are analyzed in this article. Panel Data with Fixed Effects, Random Effects, WLS, Pooled OLS, and Dynamic Panel are used to investigate the data. We found that the presence of Foreign Doctorate Students is positively associated to “Attractive Research Systems”, “Finance and Support”, “Rule of Law”, “Sales Impacts”, “New Doctorate Graduates”, “Basic School Entrepreneurial Education and Training”, “Tertiary Education” and negatively associated to “Innovative Sales Share”, “Innovation Friendly Environment”, “Linkages”, “Trademark Applications”, “Government Procurement of Advanced Technology Products”, “R&D Expenditure Public Sectors”. A cluster analysis was then carried out through the application of the unsupervised k-Means algorithm optimized using the Silhouette coefficient with the identification of 5 clusters. Finally, eight different machine learning algorithms were used to predict the value of the "Foreign Doctorate Students" variable. The results show that the best predictor algorithm is the "Tree Ensemble Regression" with a predicted value growing at a rate of 114.03%.

Effective communication is key for healthcare providers to provide optimal care for patients. Pharmacists’ fluency in a patient’s native language is vital to communicate effectively. Additionally, language concordance improves patients’ trust and ensures health equity. In the United States (US), Hispanics are the largest minority group, but only 36% of the pharmacy schools in the US offer Spanish courses in their curriculum. Conversely, Middle Eastern countries have implemented English as the language of instruction in pharmacy schools, though the native language of the patient population is Arabic. The discrepancy between the language of education and the language used by patients might lead to communication problems, thus limiting a pharmacist’s role in practice. This review aims to describe the efforts of pharmacy schools both in the US and Middle Eastern countries to incorporate second language (Spanish and Arabic, respectively) in their curriculum. Spanish language content has scarcely been introduced into the pharmacy curriculum in the US either as didactic elements (elective courses, lab sessions, modules within a course, or co-curricular programs) or as language immersion experiences (rotations and internships nationally or abroad). In Arabic-speaking countries, an Arabic course was introduced to the pharmacy curriculum to enhance students’ communication skills. This review provides an overview of what has been done in various pharmacy programs to prepare students for adequate multilingual speaking. The findings reveal the need for additional strategies to assess the impact of language courses on student performance and patient experience, as well as language competence in pharmacists and pharmacy students.

Although working memory (WM) is crucial for intellectual abilities, not much is known about its brain underpinnings, especially the structural connectivity. We used diffusion tensor imaging (DTI) to look across the whole brain for the white matter integrity correlates of the individual differences in the reading span (verbal WM capacity during reading) in healthy adults. Right-handed healthy native Russian speakers (N = 67) underwent DTI on a 3T Philips Ingenia scanner. Verbal WM was assessed with the Daneman-Carpenter reading span test (Russian version). Fractional anisotropy maps from each participant were entered into the group tract-based spatial statistics analysis with the reading span as a covariate; the results were TFCE-corrected. After taking into account effects of age, sex, education and handedness, reading span positively correlated with the white matter integrity in multiple sites: the body, the genu and the splenium of corpus callosum; bilateral corona radiata (anterior, posterior, and superior); bilateral superior longitudinal fasciculus; several tracts in the right hemisphere only, including the internal and external capsule; bilateral superior parietal and frontal white matter. Although the left hemisphere is central for verbal processing, we revealed the important role of the right hemisphere white matter for the verbal WM capacity. Our finding indicates that larger verbal working memory span may originate from additional processing resources of the right hemisphere.

The Foreign Exchange rate is very much crucial for determining the economic health level of the country. The foreign exchange rate provides financial stability, enhances purchasing power and allows global trades. This rate usually fluctuates due to the market forces which control the supply and demand of the currency. Nominal and relative inflation and income level have a substantial effect on determining the exchange rates. Government measures, international situations, natural disasters or any unexpected situation like Covid-19, Rohingya crisis etc. can affect the exchange rates. Besides this, the interaction between the factors can create different reasoning to affect the market. This study tries to identify some factors with relevant examples.

Natural language Processing (NLP) has significantly transformed in the last decade, especially in the field of Language Modeling. Large Language Models (LLMs) have achieved SOTA performances on Natural Language Understanding (NLU) and Natural Language Generation (NLG) tasks by learning language representation in self-supervised ways. This paper provides a comprehensive survey to capture the progression of advances in Language Models. In this paper, we examine the different aspects of Language Models, which started with a few million parameters but have reached the size of a trillion in a very short time. We also look at how these LLMs transitioned from task-specific to task-independent to task-and-language-independent architectures. This paper extensively discusses different pre-training objectives, benchmarks, and transfer learning methods used in LLMs. It also examines different fine-tuning and In-Context learning techniques used in downstream tasks. It also explores how LLMs can perform well across many domains and datasets if sufficiently trained on a large and diverse dataset. Next, it discusses how, over time, the availability of cheap computational power and large datasets have improved LLM’s capabilities and raised new challenges. As part of our study, we also inspect LLMs from the lens of scalability to see how their performance is affected by the model’s depth, width, and data size. Lastly, we provide an empirical comparison of existing trends and techniques and a comprehensive analysis of where the field of LLM currently stand.

Recommender Systems (RecSys) are crucial in managing information overload and enhancing user satisfaction across various digital platforms, including e-commerce and entertainment. Evolving from traditional models to Deep Neural Networks (DNNs) and, more recently, Large Language Models (LLMs), these systems leverage sophisticated algorithms to analyze user behaviors and preferences. LLMs, such as GPT-4, are trained on extensive datasets to comprehend and generate natural language, significantly advancing their ability to deliver personalized recommendations. This tutorial explores the transformative impact of LLMs on RecSys, discussing their development, application in handling complex datasets, and the integration of contextual insights. Real-world examples illustrate how LLMs enhance recommendation accuracy and user experience, highlighting challenges and future directions in the field.

Despite the growing capabilities of large language models, concerns exist about the biases they develop. In this paper, we propose a novel, automated mechanism for debiasing through specified dataset augmentation in the lens of bias producers that can be useful in a variety of industries, especially ones that are “restricted” and have limited data. We consider that bias can occur due to intrinsic model architecture and dataset quality. The two aspects are evaluated using two different metrics we created. We show that our dataset augmentation algorithm reduces bias as measured by our metrics.

Background: The Victorian era in Great Britain was a historical period that brought about profound changes for humanity. The confluence of various factors fostered the development of numerous scientific disciplines. In the field of health sciences, Florence Nightingale played a central role. Following her training as a nurse and her role as superintendent during the Crimean War, she made decisive contributions to establishing a public health network. Methods: This is a retrospective historical heuristic and hermeneutic study analyzing documentation of Nightingale's contributions to public health from around 1890 to 1900. The historical method was used to collect, classify, and analyze primary and secondary sources relevant to the study's objectives. Results: Nightingale supported the development of the concept of “health at home” by training women to implement healthy measures that contributed to the well-being of families and communities. Through the training and development of a formal “Health Missioners program”, she provided the necessary guidance to improve the health of rural communities across the English countryside. Conclusion: The role of Health Missioners has persisted, as health visitors continue to visit homes in the United Kingdom. Therefore, the contribution of Florence Nightingale to public health is unquestionable. This study was not registered.

This paper discusses corporate taxes and Foreign Direct Investment (FDI) in Nigeria. It is a review of literature that attempts to explore and give a reality on the response of FDI to corporate taxes. This paper shows in its introductory phase a prior discussion of the terms corporate tax and FDI and also elements of both terms and classifications. It follows with issues such as the effects of corporate taxes on FDI. This paper discusses the rhetoric whether reduced corporate taxes and other tax incentives has an effect on the inflow of FDI in Nigeria. To answer this question, the paper utilised information gathered from secondary sources including books, newspapers publications, working papers and research findings from studies. This paper shows that from surveyed empirical studies corporate taxes may have an important role in attracting FDI and have a rising effect on the development of the country, and as such recommends that tax incentives should be employed as a means to attracting FDI, a cost benefit analysis should be employed on the types of corporate taxes to determine the benefit from the perceived FDI inflow, in addition a review of the current tax policies should be carried out as they seem not to respond to the current economic situation.

The rapid advancement of large language models (LLMs), such as ChatGPT, has revolutionized natural language processing, enabling these models to generate coherent and contextually relevant text. These capabilities hold significant potential for various applications, particularly in the medical field. LLMs can assist healthcare professionals by providing accurate information, supporting diagnostic processes, and enhancing medical education. However, the implementation of LLMs in clinical settings faces several barriers, including technical limitations, ethical concerns, regulatory constraints, and practical challenges. Ensuring accuracy, reliability, and interpretability is crucial, along with addressing biases, ensuring data privacy, and establishing ethical guidelines. Future research should focus on improving training techniques, developing explainable AI methods, and creating efficient, resource-saving models. Expanding applications through domain-specific models and multi-modal integration can further unlock the potential of LLMs. By addressing these challenges and prioritizing responsible development, LLM technology can be safely and effectively integrated into various domains, driving innovation and improving outcomes. This paper explores the current state of LLM technology, its medical applications, barriers to implementation, and directions for future research and development

In this study the impact of terrorist attacks on exchange rate is estimated. Particularly, the study focuses on Turkish terrorist attacks and its implication on Turkish lira versus pound sterling exchange rate. In order, to find the causal effect the study employed Autoregressive distributive lag (ARDL) bound testing approach as an estimation technique. Accordingly, the analysis reveals that terrorist attack has a negative impact on the exchange rate in both short and long-run. However, the negative effect of terrorism tends to be small in both the short-run and long-run. More precisely, terrorist attack depreciates the exchange rate between Turkish lira and pound sterling by approximately 0.00072 in the next trading day. The long-term effect also shows that terrorist attack depreciates the exchange rate on average by 0.00212.

Nutritional management of depression has long been discussed due to the perceived benefit of a nutritional product having less side effects than pharmaceutical agents. Candidate nutrients for managing depression include Vitamin D, B-vitamins, tryptophan, branch chain amino acids, probiotics, omega-3 fatty acids, folate/methylfolate (aka Vitamin B9), and s-adenosylmethionine. This paper reviews three nutrients which have significant scientific support for the management of depression and have evidence of low levels in subjects with depression, and that correction of the nutritional deficiency provides clinical benefit. We present epidemiological evidence, mechanistic explanation and a review of interventional studies for these nutrients. Finally, relevant nutritional guidelines are presented with their conclusion for the role of each nutrient in the management of depression.

Over recent years, the Ghanaian economy has struggled to find its feet on the ground despite rising public debt and unending inflows of foreign aid. Against this backdrop, this study employs the Vector Error Correction Model (VECM) estimation technique on data from 1970 to 2020 to test the usefulness of the debt overhang hypothesis and the dependency theory in the special case of Ghana. The results confirm evidence of the debt overhang hypothesis and the center-periphery wisdom of the dependency theory in Ghana. The findings depict that an increase in external debt stock and total debt service on external debt have both short and long-run growth-limiting effects on the Ghanaian economy. Similarly, foreign aid catalyzes growth only in the short run and later suppresses rather than stimulates economic growth in Ghana over the long run. The study recommends that harnessing domestic resources, maintaining fiscal discipline by cutting down unproductive expenditures, enhancing an effective tax system, and promoting institutional capabilities to counteract corruption and openness to trade are better ways to fast-track growth development in Ghana.

Sentiment analysis has become an important tool for understanding public opinion across languages and domains. Recently, there has been an increase in the number of studies on sentiment analysis in low-resource languages such as Kazakh. This is important to ensure that modern text analysis technologies are accessible to all users, regardless of their language background. The aim of the study is to create a sentiment analysis model for analyzing texts in Kazakh. As part of this work, we aim to use fine-tuning techniques on our own dataset for already existing models, thus improving their accuracy and efficiency for analyzing Kazakh language texts. This paper presents a manually collected dataset "KazIntTelCom" from the city information service 2GIS, consisting of user reviews, manually annotated by the authors taking into account the polarity of sentiment (i.e. negative positive or neutral). This dataset was used to fine-tune two pre-trained multi-lingual Transformer-based sentiment analysis models taken from the HuggingFace platform. The distillBERT and XLM-RoBERTa models were used for tuning. Also, the models were tested on the dataset "KazSAnDRA". The results show that accurate tuning even on a relatively small dataset gives a significant increase in performance, which is confirmed by an increase in the accuracy index by 20%-30%. In addition, false misses and false detections are analyzed, which allows us to identify directions for further improvement of the models. The contribution of this work, in addition to the dataset, is the analysis of model errors, which will help future developers to make more accurate settings of hyperparameters of training for sentiment analysis in Kazakh. These results are important for natural language processing and their adaptation to low-resource languages, promoting more inclusive and equitable access to modern analytical tools. Thus, this study demonstrates the effectiveness of the Transformers architecture for sentiment analysis in Kazakh and opens new opportunities for further model improvement.

The purpose of the study was to provide support in the decision-making process for architects and engineers regarding large-spans structures for the presentation of archaeological sites in situ - construction of roofs, shelters, and bridges. We examined existing practice and analyzed their engineering classifications looking for a pattern in their application regarding sustainability and relation between type of cultural heritage site and type of applied large span structure. Contemporary engineering structures at built heritage sites create a sharp contrast between old and new. A presentation of cultural heritage in situ requires an understanding of heritage theory and internationally accepted doctrine which exceeds common engineering education. Nevertheless, application of large-span structures, which often take advantages of state-of-art construction-technologies nowadays, is also an aesthetical statement that affects the appearance of the site. Therefore, we gave an overview of the theoretical background of aesthetical issues and the overall ethics of the decision-making process in such sensitive cases. Within the framework of heritage-presentation, engineering and architectural issues, and selected case studies, we concluded in favor of the application of large-span structures under certain conditions.

This research presents the Cross-Cultural Intelligent Language Learning System (CILS), a novel approach integrating Artificial Intelligence (AI) into language education to enhance cross-cultural communication. CILS utilizes advanced AI technologies to provide adaptive, personalized learning experiences that cater to the unique linguistic and cultural backgrounds of each learner. By dynamically adjusting content and methodology, CILS significantly improves linguistic proficiency and cultural understanding, essential for effective global interactions. The implementation of CILS in platforms such as Busuu and HelloTalk has demonstrated marked improvements in engagement and communication skills among learners. Empirical studies validate the system's effectiveness in real-world settings, showing enhanced learner performance and increased intercultural competence. Additionally, the Technology Acceptance Model (TAM) applied confirms that the usability and perceived usefulness of AI-driven systems strongly influence learner acceptance and sustained use. This study not only underscores the potential of AI in transforming language education but also highlights the critical role of cultural sensitivity in designing educational technologies.

Despite the lack of evidence that suggests hemodiafiltration (HDF) offers a better survival outcome than standard hemodialysis (HD), the number of patients initiating HDF in Japan continues to rise. This study examined the temporal change in the number of HDF incidents, evaluated factors associated with all-cause mortality, and compared the mortality risk and survival time of patient on HDF with patients receiving standard HD in three sets of 2-year cohorts. The primary analyses included the insurance claims data of 460 HDF patients and propensity score-matched 903 standard HD patients who initiated dialysis therapy between April 1, 2012 and March 31, 2018. Patient follow-up was censored at the time of death or the end of the 2-year study period. The influence of comorbidities on all-cause mortality was investigated, and the survival outcomes between HDF and standard HD patient groups throughout cohorts were compared. The number of HDF patients was increasing throughout cohorts, but the proportions of mortality cases across cohorts slowly decreased. Dementia status increases the mortality risk of patients regardless of dialysis modality. The mortality risk among HDF patients with dementia was more significant than HD patients. Adjusting for all covariates, we observed that HDF patients had a reduced mortality risk and longer survival time than patients on standard HD. This study supports the notion that HDF reduces all-cause mortality compared with standard HD in an incident dialysis population in Fukuoka Prefecture, Japan.

An overview of the chemical compounds forming the rare smectic T phases is cited with references to the historical context. Thermodynamics (transition temperatures, enthalpies) along with the factors (stereochemical constraints, electrostatic interactions, aliphatic chain stacking, intermolecular forces) contributing to the adoption of tetragonal scaffolds are also discussed. Characteristic optical microscopy textures and x-rays diffraction patterns are presented. In parallel, a comparison of the geometrical parameters such as distances between atoms, molecular areas, volumes, and lattice parameters with the closest two-dimensional and three-dimensional organizations, is performed.

One of the non-linear phenomena that affect high bandwidth and long reach communication systems is the non-linear phenomenon called four-wave mixing (FWM). Unfortunately, the simulation of such systems aiming to obtain their design parameter limitations require more time as the number of channels increases. In this paper, we propose a new high-performance computational model to obtain optimal design parameters in a multi san Dense Wavelength Division Multiplexing (DWDM) system, limited by FWM and the intrinsic Amplified Spontaneous Emission (ASE) noise of optical amplifiers employed in each segment. The simulation in this work provides a complete optical design characterization and compares the efficiency and speed improvement of the proposed parallelization model versus a previous sequential model. Additionally, an analysis of the computational complexity of parallel model is presented, where two parallel implementations are used. First, Open Multi−Processing (OpenMP), based on the use of a central, multi-core processing unit is used and secondly the Compute Unified Device Arquitecture (CUDA), which is based on the use of graphics processing unit. Results show that parallelism improves to up to 40 times the performance of the simulation when nested parallelization with CUDA is used, over de sequential method and up to 6 times compared with the implementation with OpenMP using 12 processors. Within our parallel implementation, it is possible to simulate with an increased number of channels, that was unpractical in the sequential simulation.

(1) Background: Attention Deficit Hyperactivity Disorder (ADHD) and Developmental Language Disorder (DLD) are neurodevelopmental disorders that affect various areas of chil-dren’s development, such as language. There is an emerging idea that ADHD is characterised by the presence of language difficulties, which could be explained by the high comorbidity between ADHD and DLD. The purpose of this study is to analyse the empirical evidence of language com-petence in children aged 6–17 years old, diagnosed with ADHD and/or DLD. (2) Method: Fifteen studies with experimental designs were identified from Scopus, PsycINFO and WoS databases, reporting on language skills in children diagnosed with ADHD and/or DLD. Studies relating ex-ecutive functioning to language skills in this target population were also included. (3) Results: The literature is heterogeneous and different components of language are also examined. Even though the results are contradictory they convincingly demonstrate that there are overlapping symptoms between ADHD and DLD, such as language skills and executive functions. (4) Conclusions: the differences in the samples limit the generalisability of the results. Therefore, this review highlights the importance of considering language skills when designing individualised interventions for the population with ADHD and DLD, both in comorbidity and in isolation.

This study introduces a novel computational approach for testing conditional independence (BB CI test) within causal Directed Acyclic Graphs (DAGs), leveraging Bayesian non-parametric bootstrap and machine learning techniques. Our method offers an alternative for validating the assumptions underpinning causal DAGs. Through simulation studies and an industrial case analysis, we demonstrate the test procedure in accurately assessing conditional independence, comparing it with the Generalized Covariance Measure (GCM) test. Our findings suggest that the BB CI test is advantageous in scenarios where existing methods may falter due to violations of model assumptions. This research contributes to the causal inference literature by providing a computational tool for researchers and practitioners to validate causal models.

The pursuit of Artificial Intelligence (AI) that emulates human cognitive processes is a cornerstone of ethical AI development, ensuring that emerging technologies can integrate seamlessly into societal frameworks requiring nuanced understanding and decision-making. Zero-Shot Instance Retrieval (ZSIR) stands at the forefront of this endeavour, potentially providing a robust platform for AI systems, particularly large visual language models, to demonstrate and refine cognition-aligned learning without the need for direct experience. In this paper, we critically evaluate current cognition alignment methodologies within traditional zero-shot learning paradigms, using visual attributes and word embedding generated by large AI models. We propose a unified similarity function that quantifies the cognitive alignment level, bridging the gap between AI processes and human-like understanding. Through extensive experimentation, our findings illustrate that this similarity function can effectively mirror the visual-semantic gap, steering the model towards enhanced performance in zero-shot instance retrieval. This work not only benchmarks the cognition alignment of AI, but also sets a new precedent for the development of visual language models attuned to the complexities of human cognition.}

Bridge load testing is crucial in evaluating bridge load-carrying capacity and construction quality. However, conventional techniques have many limitations such as the need for direct contact and low acquisition frequency. This study provides a comprehensive examination of the static and dynamic load tests of a multi-span cable-stayed bridge utilizing a ground-based radar inter-ferometer (GB-radar). The case study was conducted at the Fifth Nanjing Yangtze River Bridge (FNYRB), which is recognized as the world's first lightweight steel-concrete composite cable-stayed bridge, with two spans measuring 600 m. To enhance measurement accuracy, a method was proposed to detect and recover radar phase jumps caused by vehicle motion. Furthermore, precise geometry projection was employed to acquire vertical displacements from GB-radar which were taken along the line-of-sight (LOS) direction. Moreover, during the static load test, a continuous deformation was observed with a maximum pace of 0.31 mm/min, indicating a post-construction settlement caused by soft soil consolidation. This case study highlights the high sampling frequency, high measurement accuracy, and exceptional weatherproof ability of GB-radar, thereby demonstrating its potential to be an alternative to the structural health monitoring (SHM) system.

Japanese plum tree (Prunus salicina Lindl.) is mainly cultivated in temperate areas of China and some European countries. Certain amounts of wood (from pruning works) are generated every year from this crop of worldwide commercial significance. The main objective of this work was to value this agricultural woody residue, for which the chemical composition of pruning wood extracts from six Japanese plum cultivars was investigated, and the antiproliferative activity of extracts and pure phenolics present in those extracts was measured. For the chemical characterization, total phenolic content and DPPH radical-scavenging assays and HPLC‒DAD/ESI‒MS analyses were performed, the procyanidin (‒)-ent-epicatechin-(2α→O→7,4α→8)-epicatechin (5) and the propelargonidin (+)-epiafzelechin-(2β→O→7,4β→8)-epicatechin (7) being the major components of the wood extracts. Some quantitative differences were found among plum cultivars, and the content of proanthocyanidins ranged from 1.50 (cv. ‘Fortune’) to 4.44 (cv. ‘Showtime’) mg/g of dry wood. Regarding the antitumoral activiy, eight wood extracts and four phenolic compounds were evaluated in MCF-7 cells after 48 h of induction, showing the wood extract from cv. ‘Songold’ and (‒)-annphenone (3) the best antiproliferative activity (IC50: 424 ppm and 405 ppm, respectively).

As one of the main equipment of coal transportation, the belt conveyor with detection system is an important direction for the development of intelligent mine. The occurrences of non-coal foreign objects making contact with belts are common phenomenon in complex production environments and improper human operations. In order to avoid major safety accidents caused by scratches, deviation and breakage of the belt, a foreign object detection method is proposed for belt conveyor in this work. Firstly, a foreign object image dataset is collected and established, and IAT image enhancement module and attention mechanism of CBAM are proposed to enhance image data sample. Moreover, to predict the angle information of foreign objects with large aspect ratios, a rotating decoupling head is designed, and a MO-YOLOX network structure is constructed. Some experiments are carried out with the belt conveyor in the mine intelligent mining equipment laboratory, and the detections of different foreign objects are analyzed. Experimental results show that the accuracy, recall, and mAP50 of the proposed rotating frame foreign object detection method reaches 93.87%, 93.69%, and 93.72%, and the average inference time of foreign object detection is 25 ms.

Ehlers–Danlos syndromes (EDS) are a heterogeneous group of heritable connective tissue disorders (HCTDs) characterized by joint hypermobility, skin hyperextensibility, and tissue fragility. Among the different types, the hypermobile EDS (hEDS) is the most frequent, and includes generalized joint hypermobility as the major diagnostic criterion. Joint hypermobility in hEDS is often associated with pain that not always allow the use of effective pain-reducing treatments. Patients with hEDS always talk about pain using a lot of descriptions. Eighty-nine patients with diagnosis of hEDS were recruited and evaluated. They were asked to write down what grief was to them. The texts were analyzed through LIWC. Correlational analyses were conducted between pain perception and language. A comparison of high/low pain perception and quality of metaphors was carried out. The results showed, depending on the level of pain perception, different language quality is evidenced. The greater the pain the lesser the positive effects and the greater the negative effects and dehumanizing metaphors. Moreover, greater pain seems to be related to a verbal experience of greater isolation and less self-care. In conclusion, the use of metaphors is a useful tool for exploring illness experience and may help clinicians in the rehabilitation program.

Since the beginning of the internet era, there has been an explosion of growth in structured data (such as numbers, symbols, and labels) as well as unstructured data (including images, videos, and text). Efficient and accurate mixed query of these two types of data is a key technology to achieve high-quality information retrieval, and it is also a major challenge that needs to be solved urgently in the industry. In this study, we employ an advanced Transformer model that combines strategies and fine-tuning techniques for multi-task learning. Specifically, the model is first pre-trained on a large-scale, general-purpose dataset to learn different types of data representations and basic language comprehension skills. After that, we fine-tuned the parameters of the model to better suit these specific data processing tasks for specific application scenarios, such as image annotation, video content analysis, and structured data query. At the heart of the model is the self-attention mechanism, which allows the model to automatically emphasize the important parts and ignore irrelevant information when processing the input data. In addition, we have introduced task-specific adaptation layers that are designed to add additional processing power to the original Transformer architecture, such as a semantic analysis layer for unstructured text data and a relational extraction layer for structured data. This combination of general pre-training and task-specific fine-tuning allows the model to flexibly process and integrate information from different data sources, improving processing efficiency and accuracy. Experimental results show that the model performs well in a variety of data processing tasks, significantly improves the accuracy and efficiency of information retrieval, and verifies the strong potential and adaptability of large language models in processing mixed data types.

Recent research has suggested that working memory training interventions may benefit children with Developmental Language Disorder (DLD). The current study investigated a short and engaging adaptive working memory intervention that targeted executive skills and aimed to improve both language comprehension and working memory abilities in children with DLD. Forty-seven 6- to 10-year-old children with DLD were randomly allocated to an executive working memory training intervention (n=24) or an active control group (n=23). A pre-test/intervention/post-test/9-month-follow-up design was used. Outcome measures included assessments of language (to evaluate far transfer of the training) and working memory (to evaluate near transfer of the training). Hierarchical multiple regression analyses controlling for pre-intervention performance and age found group to be a significant predictor of sentence comprehension and of performance on six untrained working memory measures at post-intervention and 9-month follow-up. Children in the intervention group showed significantly higher language comprehension and working memory scores at both time points than children in the active control group. The intervention programme showed potential to improve working memory and language comprehension in children with DLD and demonstrated several advantages: it involved short sessions over a short period; caused little disruption in the school day; and was enjoyed by children.

Interactive Digital Media have become the most important pillars on which contemporary 21st century scenography is based, allowing for the use of many modern digital media as scenographic elements. With the steady development of digital arts, particularly digital animation, it has become a key role as a 21st century in presenting their theatrical visions. My own issue is the use of digital Media as an Interactive scenographic element, as evidenced by the design of several spatial environments within the framework of an idea and a dynamic scenario that interacts with the Several Scenes of the theatrical Performances and is displayed on screen walls or dropped on thematic elements, resulting in a type of interaction between them and the Scenic Design. My project includes instances of models exhibiting the link of designs to theme composition on stage, such as works utilized in plays, musical/motion drama, and event performances . The design used music synced with digital animated media designs as a scenographic element to reinforce the overall visual picture. The study paper examines a theory of my own project, in which I offer a dynamic exhibition of full-back digital settings in rhythm with the music, suited to its movement and the character of the performance, to be employed as a Scenographic element at a Proscenium Theater (12W x 10D x 7H).

Tissue-like phantoms are widely used as a model for mimicking the optical properties of live tissue. This paper presents the results of a diffusion reflection method as well as fluorescence lifetime imaging microscopy measurements of fluorescein-conjugated gold nanorods in solution as well as inserted in solid tissue-imitating phantoms. A lack of consistency between the fluorescence lifetime results of the solutions and the phantoms raises a question about the ability of tissue-like phantoms to maintain the optical properties of inserted contrast agents.

Ground-level ozone (O3) increases in the northeastern part of Eurasia, where larch species are dominant trees and have been planted intensively. The Japanese larch (Larix kaempferi; hereafter larch) is classified as the tree species most of its sensitive to O3 based on data from long-shoot needles of seedlings. This criterion should be reconsidered based on O3 uptake, which is strongly depended on stomatal conductance (Gs) of adult trees through the difference in relevant needle traits. Because Gs is closely correlated with photosynthetic activity, we measured the in situ seasonal and yearly change in photosynthetic function, needle mass per area (LMA) and nitrogen (N) content of both short- and long-shoot needles of the canopy of larch trees over successive 3 years. No difference was observed in the in light saturated photosynthetic rate at ambient CO2 (Psat) between needles of hetero types of shoot in the latter part of the growing season, but clear differences were found within a specific year, indicating that seasonal changes in climatic factors determine Psat in the long term. The Psat-N relations differed significantly between the needle types. However, Psat at saturated CO2 (Pmax)-N was quite similar, implying that the CO2 diffusion difference between short- and long-shoots is responsible for the differing Psat -N. In conclusion, seasonal and yearly variations in photosynthetic capacity are mainly determined mainly by climatic variations, whereas shoot type determined the traits of photosynthetic N utilization as well as Gs regulation.

The real systems are often complex, nonlinear, and noisy in various areas including mathematics, natural science, and social science. We present the symplectic entropy (SymEn) measure as well as an analysis method based on SymEn to estimate the nonlinearity of the complex system by analyzing the given time series. The SymEn estimation is a kind of entropy based on symplectic principal component analysis (SPCA) which represent organized but unpredictable behaviors of systems. The key to SPCA is to preserve the global submanifold geometrical properties of the systems through symplectic transform in the phase space, which is a kind of the measure-preserving transforms. The capability of preserving the global geometrical characteristics makes the SymEn a test statistic to detect the nonlinear characteristics in several typical chaotic time series and the stochastic characteristic in the Gaussian white noise. The results are in agreement with findings in the approximate entropy (ApEn), the sample entropy (SampEn) and the fuzzy entropy (FuzzyEn). Moreover, the SymEn method is also used to analyze the nonlinearities of the real signals (including the EEG signals for ASD and healthy subjects, and the sound and vibration signals for the mechanical systems). The results indicate that the SymEn estimation can be taken as a measure for describing the nonlinear characteristics in the data collected from the natural complex systems.

This research critically examines the dichotomies in urban public space and discussions on city evolution over the past two centuries. The original discourse identified amplifies the importance that inclusivity needs to address issues deeper than physical accessibility. The growing diversity and changes seen in urban public open spaces are followed throughout history along a timeline of important socio-political and planning injectors in the Western world. These public spaces are the important spine connecting all the private and semi-private parts of the city. Urban public spaces have seen population changes due to globalisation, resulting in mobility and changes due to the different needs of its diverse user base. In this paper, a rigorous quasi-chronological investigation is presented. Firstly, the context of the evolution of space is established. Secondly, a closer focus on inclusion research provides a thorough analysis of the utopian city plans, the development of Haussmann's Paris and the later plans of the Centrist and the Decentrist. Thirdly, a critical and analytical understanding of the effect these precursors have had on the city's public spaces, and the hierarchies it established in a nuanced manner are made explicit. The culmination of this research process is the identification of outcomes associated with inclusivity. Findings are established across three significant themes—a) Understanding public space as social space, b) Components of inclusivity, and c) Values of inclusivity including accessibility, sense of place, conviviality and resilience. Key insights and guidance for inclusivity as a key constituent of social sustainability are the unique contribution.

Keywords: Endodontic microsurgery; Root canal treatment; Isthmus preparation; Short-term outcomes

A compositional data vector is a special type of multivariate observation in which the elements of the vector are non-negative and sum to a constant, usually taken to be unity. A compositional data does not have zero and only retains relative information. Furthermore, comparisons can only be made between compositional data of the same component. The relevant sample space is the standard simplex. A simplex space is a space that is a generalized form of a triangle. For compositional data, many of the operations defined in Euclidean space are meaningless. Microbiome analyzes have become popular in recent years. Operational Taxonomic Units (OTU) and Amplicon Sequence Variants (ASVs) used in microbiome analysis are one type of compositional data. The microbiome data are counts of different species within a sample and it is compositional. Although there is an order of bacterial abundance within a sample, there is no order of bacterial abundance between samples. Firmicutes and Bacteroidetes, the major phyla in the colon, have been observed in humans worldwide. Gut microbiome analyses often use the Firmicutes/Bacteroidetes ratio and principal coordinates analysis (PCoA). Alpha and beta diversities are used as indicators of bacterial flora diversity. However, misinformation is pervasive in the human microbiome literature and analysis. There is a lot of misunderstanding regarding compositional data and its analysis. An attempt was made to demonstrate how to analyze using the “National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN JMD) (Public Data).” The results showed that PCoA did not work, and principal component analysis (PCA) was useful for analyzing the gut microbiome relative diversity.

The recently defined and yet rather new topic of Healthy aging is gathering more attention on the global stage. With world’s population getting older - it is rapidly becoming very important to develop and maintain functional abilities in older age and develop mechanisms to protect senior population from chronic diseases. One of the most effective components, as well as, one may call - process involved in, and strongly associated with aging is recently discovered and Nobel prize awarded - nitric oxide as a signaling molecule, which followed by later discoveries showed to have a positive metabolic, immune and anti-inflammatory effects. One of the most debated topics of the last decade in the scientific community is nitrates, one of the pathways involved in nitric oxide production. Thus, the objective of this study was to evaluate effect of different potassium nitrate concentrate supplementation on Drosophila melanogaster longevity. 0,5-3% potassium nitrate medium was analyzed on the life span and motor function in different groups consisting of 100 females fruit flies each. In this assay, female fly species supplemented with potassium nitrate diet showed life span increase by 18.6% and 5.1% with 1% and 2% KNO3- respectively with a positive impact on locomotor function. In conclusion, we found that low concentration of potassium nitrate medium increased lifespan and locomotor function in Drosophila melanogaster.

Digital recruitment systems have revolutionized the hiring paradigm, imparting exceptional efficiencies and extending the reach for both employers and job seekers. This investigation scrutinizes the efficacy of classical machine learning methodologies alongside advanced large language models (LLMs) in aligning resumes with job categories. Traditional matching techniques, such as Logistic Regression, Decision Trees, Naïve Bayes, and Support Vector Machines, are constrained by the necessity of manual feature extraction, limited feature representation, and performance degradation, particularly as dataset size escalates, rendering them less suitable for large-scale applications. Conversely, LLMs like GPT-4, GPT-3, and LLAMA, adeptly process unstructured textual content, capturing nuanced language and context with greater precision. We evaluate these methodologies utilizing two datasets comprising resumes and job descriptions to ascertain accuracy, efficiency, and scalability. Our results reveal that while conventional models excel with structured data, LLMs significantly enhance the interpretation and matching of intricate textual information. This study highlights the transformative potential of LLMs in recruitment, offering insights into their application and future research avenues.

A digital twin is a virtual representation that serves as the real-time digital counterpart of a physical object or system. Recent advancements have extended the concept of digital twins to humans, incorporating complex biological data such as DNA (Predictiv Care, Inc.) and immune system profiles. These sophisticated models go beyond mere pictorial representations, offering a more holistic digital reflection. However, a significant gap remains. The current human digital twin models are not capable of learning one’s preferences. In this pilot study, we introduce Diginality, a chatbot powered by HyperCLOVA X, a Large Language Model (LLM) developed by NAVER, Inc. Diginality, learns one’s preferences with custom training from data collected by interview-style questions on the user’s topic of interest. Our findings demonstrate that Diginality successfully answers one’s preferences, thereby adding a new dimension to the concept of human digital twins. This work represents a pioneering step towards creating a more comprehensive and psychologically nuanced human digital twin.

Internship programs are crucial for developing language skills because they provide real-world experience, exposure to different cultures, and chances to network with industry professionals. This continuous exposure facilitates confidence-building, fluency improvement, and language barrier-breaking. As a result, internships offer exceptional experiential learning opportunities for language learning and competency development. This review focuses on how internship programs pave the way for interns to have language proficiency skills that will prepare them for the real-life situations of our competitive world. The real-world situations that internship programs offer allow language learners to practice and apply their skills, making them essential for language proficiency. A stronger emphasis on internships in language instruction will better prepare students for the increasingly international world in which language competence is essential.

The proliferation of deep learning in natural language processing (NLP) has led to the development and release of innovative technologies capable of understanding and generating human language with remarkable proficiency. Atinuke, a Transformer-based neural network, optimises performance across various language tasks by utilising a unique configuration. The architecture interweaves layers for processing sequential data with attention mechanisms to draw meaningful affinities between inputs and outputs. Due to the configuration of its topology and hyperparameter tuning, it can emulate human-like language by extracting features and learning complex mappings. Atinuke is modular, extensible, and integrates seamlessly with existing machine learning pipelines. Advanced matrix operations like softmax, embeddings, and multi-head attention enable nuanced handling of textual, acoustic, and visual signals. By unifying modern deep learning techniques with software design principles and mathematical theory, the system achieves state-of-the-art results on natural language tasks whilst remaining interpretable and robust.

Natural language processing model (NLP) are used in chatbots to understand user input, interpret its meaning, and generate conversational responses to provide immediate and consistent assistance. This reduces problem-solving time and staff workload and increases user satisfaction. There are both rule-based chatbots, which use decision trees and are programmed to answer specific questions, and self-learning chatbots, which can handle more complex conversations through continuous learning about data and user interactions. However, only a few chatbots have been developed specifically for the Italian language. This work proposes an NLP model to develop a powerful and efficient Italian QA (Question Answering) chatbot that is easy to use for Italian Public Administration (PA). The proposed model is based on BERT (Bidirectional Encoder Representations from Transformer) architecture, where an Encoder/Decoder module and a Highway Network module have been added to perform more efficient filtering of input text. The Italian version of the Stanford Question Answering Dataset (SQuAD-IT) is used to test the proposed model. The proposed model is one of the first models developed for Italian language-specific chatbots.

This paper addresses the grammatical challenges associated with the development of clause complexity, focusing on the performance of a group of monolingual Spanish-speaking schoolchildren with Specific Language Impairment/Developmental Language Disorder (SLI/DLD) in a longitudinal corpus of oral narrative samples. The study examines the presence of interclause relations of subordination and equivalence (hypotaxis and parataxis) in language samples of two groups: an experimental group made up of 24 schoolchildren with SLI/DLD and a control group made up of 24 schoolchildren with typical development (TD). The results show that while both groups use parataxis as the most common relation between clauses in all school grades, there is a significant decrease in paratactic relations and a significant increase in hypotactic relations from first to fourth grade of primary education. Although the development patterns are highly similar, the SLI/DLD group shows greater difficulties in mastering more complex (hypotactic) relations in fourth grade compared to the control group, indicating that they are less sophisticated in the use of these types of complex relations. These findings suggest that focused support on the most complex structures is needed towards the fourth grade of primary education, given the demands of the school academic register from 6 and 7 years of age, and the potential problems that the development of clause complexity can cause in school-age children.

Micro-level language policy and planning (LPP) primarily concerns local actors' decision-making on matters in relation to language(s) and its users. Despite a growing body of literature focusing on micro language planning in educational settings, there is a scarcity of research examining early childhood education settings such as micro-level LPP context for young English language learners. By adopting a case study approach, the present study examined the educators' enactment of agency in micro-planning the English language education policy (LEP) in one Chinese kindergarten and the associated factors shaping their agency. Our study revealed that the sustainable implementation of the kindergarten English LEP depended on the principal, native English-speaking teachers, and the Chinese assistant teachers' different degrees of agency. Also, the research findings indicated an array of contextual and individual factors nested in a hierarchical structure that facilitated, guided, and constrained the educators' agency in a role-and circumstance-dependent manner. This study contributes to the pertinent literature by casting nuanced light on the different educators' contributions to the micro-level LPP against a national policy that does not endorse early-year English language education.