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..

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.

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.

This article presents a new input device for spastic patients and others with similar symptoms. The sensor consists of a disc used to determine positions and can replace standard joysticks in medical devices such as electric-powered wheelchairs. Using a standard joystick while operating a powered wheelchair can result in dangerous situations when spastic movements occur and extremities cramp making them uncontrollable. To avoid this, a disc was developed that can be controlled with any body part. By shifting weight (x- and y-axis) the disc can tilt in any direction creating a proportional output signal and can also be pushed down in the center (z-axis) to open a menu on a screen for example. When spasms occur it is impossible for users to get stuck on the input device because the disc is flat and can be mounted within a control panel. Body parts coming into contact with the disc would merely slide across the disc without triggering unintentional actions. The sensor presented here is also adaptive and can be adjusted to fit a user’s strength and range of motion. This proposal aims to develop an input device that enables spastic patients to operate sensitive systems safely.

Recent improvements in deepfake creation have made deepfake videos more realistic. Moreover, open-source software has made deepfake creation more accessible, which reduces the barrier to entry for deepfake creation. This could pose a threat to the people’s privacy. There is a potential danger if the deepfake creation techniques are used by people with an ulterior motive to produce deepfake videos of world leaders to disrupt the order of countries and the world. Therefore, research into the automatic detection of deepfaked media is essential for public security. In this work, we propose a deepfake detection method using upper body language analysis. Specifically, a many-to-one LSTM network was designed and trained as a classification model for deepfake detection. Different models were trained by varying the hyperparameters to build a final model with benchmark accuracy. We achieved 94.39% accuracy on the deepfake test set. The experimental results showed that upper body language can effectively detect deepfakes.

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.

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.

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 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.

While some research supports utilizing plain radiography for measuring biomechanical alignment of the spine for prognosis and treatment, there are contrasting viewpoints regarding both the value and utilization of these procedures in conservative care. Evaluation of both conservative and non-conservative approaches to spinal care revealed vast differences in radiographic utilization and interpretation between orthopedic surgeons, primary care physicians, chiropractic physicians, and physical therapists, which may account for the different viewpoints and rationale in the literature. A research summary is provided regarding the various methods of spinal analysis utilized by these professions to help determine if there is any unique diagnostic or prognostic value associated with radiographic utilization that could assist in advancing conservative spinal assessment and treatment. Understanding any unique value provided through biomechanical assessment utilizing plain radiography of the spine established in the literature may help chiropractic physicians determine the appropriate use of radiographic procedures in clinical practice and how to coordinate efforts with other conservative and non-conservative professions to improve spinal healthcare.

The objective of this study is to determine the relationship between body condition score (BCS) and body carcass fat (BCF) of ewes at two key stages of the production cycle. Data were collected from 88 Coopworth ewes including BCS measurements manually evaluated by the farm manager, BCF measurements determined by computerized tomography (CT) and body parameter measurements automatically determined by an image processing application. Artificial neural network (ANN) statistical analysis method was used to develop a relationship between the body parameters and the BCF with an r2=0.96 and RMSE=1.01 using all top and side variables.

The Ayers Rock of Australia which is known for its charming colors at different time is actually the Celestial Body Ring of the planet Earth that implies the great Celestial Body Love happened on Earth long long ago. The Tasmania island of Australia which is known for the World Heart is actually in the state of face to face with the main island of Australia, which implies the image spokesperson for the inner core (the heart) of the planet Earth. In other words, with the outline of the main island of Australia, the basic key information of the inner core of the planet Earth could be easily obtained by deducing logically, just like the similarity between the fists and heart of human body. Similar to the mechanism of the blood circulation dominated by the heart of human body, it is dominated and motivated by the inner core of the Earth which forms the circulation of mantle magma between the crust and the inner core, and what leads to the formation of expanding and active area on the equatorial and middle-lower latitude area and the silent and convergent area on the North and South Pole central area. Therefore, the mantle magma whirlpools under the crust on the North and South Pole are supposed to exist logically. The walking sideways of the crabs just implies the celestial body language of the motive trend of the main island of Australia based on their similar outline.

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.

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.

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.

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

Lean body mass (LBM) is correlated with powerlifting performance in athletes competing in different bodyweight classes. However, it remains unknown whether changes in LBM are correlated with performance changes in powerlifters preparing for a competition. Aim of the study was to investigate the changes in LBM and performance in powerlifters preparing for a competition. Eight male powerlifters (age 31.7±9.8years, height 1.77±0.06m, weight 99.2±14.6kg) and three female powerlifters (age 32.7±16.3years, height 1.54±0.06m, weight 66.6±20.9kg), participated in the study. Athletes followed individualized periodized training programs for 12 weeks aiming to maximize their performance for the national championship. Maximum strength (1-RM) in squat, bench press, and deadlift, body composition, grip strength, anaerobic power, quadriceps’ cross sectional area and vastus lateralis muscle architecture were measured before and after the training period. Significant increases were found after the training period in squat (5.8±7.0%, P

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.

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.

The assortment of low-cost 3D printers for “in-practice” use, e.g., for clear aligner therapies, is ever increasing. To address concerns about the accuracy of orthodontic models produced on such printers when cost efficient modes of 3D printing are employed, this study examined the effect of print model body type and print angulation on accuracy. Six printing-configuration groups were included: 2 model types (solid or hollow shell) combined with 3 print angles (0°, 70°, or 90°) with 10 models/group; all models were printed with 100-µm layer thickness using a digital light processing-based 3-dimensional printer. Eleven selected structures and distances were measured on the printed models with a digital microscope and compared to the same measures on a digitized master model. The clinically-acceptability range was set at ±0.25 mm difference from the master model for single tooth measurements (intra-tooth) and ±0.5 mm for cross-arch measurements (inter-tooth). For individual measurements across all models, 98% fell within clinical acceptability. For mean measurements within each model group, only canine height for the shell-0° model had a mean difference (0.26 mm ± 0.03) outside the clinically acceptable range for intra-tooth measurements. Standard deviations for all intra-tooth measurements were within 0.07 mm. While none of the mean inter-tooth measurements exceeded the acceptability range, the standard deviations were larger (0.04 to 0.30 mm). Accuracy of orthodontic models for clear aligner therapies was not impacted beyond the clinically acceptable range when altering model body type and print angulation to improve efficiency of 3D printing. These findings suggest greater flexibility of the practitioner to alter print settings to address time and cost efficiency in various clinical scenarios and still maintain clinically acceptable model accuracy.

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.

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.}

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.

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.

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.

Urinary tract diseases are common in cats, and often require surgical reconstruction. Here, to explore the possibility of urinary tract reconstruction in cats using in-vivo tissue architecture (iBTA), biosheets fabricated using iBTA technology were implanted into the feline bladder and the regeneration process was histologically evaluated. The biosheets were prepared by embedding molds into the dorsal subcutaneous pouches of six cats for 2 months. A section of the bladder wall was removed, and the biosheets were sutured to the excision site. After 1 and 3 months of implantation, the biosheets were harvested and evaluated histologically. Implantable biosheets were formed with a success rate of 67%. There were no major complications following implantation, including tissue rejection, severe inflammation, or infection. Urinary incontinence was also not observed. Histological evaluation revealed the bladder lumen was almost entirely covered by urothelium after 1 month, with myofibroblast infiltration into the biosheets. After 3 months, the urothelium became multilayered, and mature myocytes and nerve fibers were observed at the implantation site. In conclusion, this study showed that tissue reconstruction using iBTA can be applied to cats, and that biosheets have the potential to be useful in both the structural and functional regeneration of the feline urinary tract.

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.

Carotenoids are common and diverse organic compounds with various functional roles in ani-mals. Except for certain aphids, mites, and gall midges, all animals acquire necessary carotenoids only through their diet. The House fly (Musca domestica) is a cosmopolitan pest insect that populates diverse habitats. Its larvae feed on organic substrates that may vary in carotenoid composition according to their specific content. We hypothesized that the carotenoid composition in the adult House fly’s body would reflect its composition in the larval feed. We reared house fly larvae on substrates that differed in carotenoid composition and characterized the carotenoid composition of the emerging adults. Our results indicate that the carotenoid composition of adult House flies is related to the carotenoid composition in its natal substrate, but does not directly reflect it. We suggest that these findings may be developed to identify sources of House fly in-festations. We also recommend that care should be taken when considering the rearing substrates of House fly larvae used for animal feed.

Background: Bariatric surgery (BS) has a significant impact on body composition (BC) and con-sequently may affect established sarcopenic obesity (SO) in candidate patients. The aim of this study is to assess the utility of muscle ultrasound (MUS) of rectus femoris thickness (RFT) for the evalua-tion of BC and skeletal muscle function in patients undergoing BS compared to bioimpedance analysis (BIA), dual-energy X-ray absorptiometry (DEXA) and dynamometry. On the other hand, we aim to demonstrate how MUS of RFT correlates better with the quality of life (Qol) in this population, likely because it enables the detection of regional quadriceps muscle sarcopenia, in comparison to the other mentioned methods. Methods: This is a prospective pilot study including 77 subjects (64.9% female, mean age: 53.2 ± 8.67 years) who underwent BS. Handgrip strength was measured using a dynamometer, fat-free mass index (iFFM) was assessed by BIA, appendicular muscle index (AMI) was calculated using DEXA and RFT was measured by MUS. Moreover, Homeostasis Model Assessment of Insulin Resintance (HOMA-IR) was calculated. All these measurements were conducted 1 month prior to BS and at the 12-month follow-up. QoL was as-sessed using the Moorehead-Ardelt questionnaire. Results: The mean BMI decreased by 12.95 ± 3.56 kg/m2 (p=0.001). Firstly, we observed positive correlation pre-surgery between HOMA and: RFT (r=0.27, p=0.02), iFFM (r=0.36, p=0.001), AMI (r=0.31, p=0.01) and dynamometer (r=0.26, p=0.02). In addition, we found correlation between RFT and iFFM (pre-surgical: r=0.31, p=0.01; post-surgical: r=0.25, p=0.05) and between RFT and lower extremities AMI post-surgical (r=0.27, p=0.04). Secondly, we observed significant reductions in iFFM, AMI and RFT (p=0.001), but not with dynamometer (p=0.94). Finally, a tendency to a positive correlation between QoL question-naire and RFT post-surgery (r=0.23, p=0.079) was observed. Conclusions: Our results suggest that RFT measured by MUS for the evaluation of SO and the follow-up after BS of these patients. Moreover, RFT can provide relevant information about regional sarcopenia and probably has an accurate correlation with QoL in comparison with the other methods.

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.

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.

With the rapid growth of large language models (LLMs), there is increasing demand for memory and computation in LLMs. Recent efforts on post-training pruning of LLMs aim to reduce the model size and computation requirements, yet the performance is still sub-optimal. In this paper, we present a plug-and-play solution for post-training pruning of LLMs. The proposed solution has two innovative components: 1) Relative Importance and Activations (RIA), a new pruning metric that jointly considers the weight and activations efficiently on LLMs; and 2) Channel Permutation, a new approach to maximally preserve important weights under N:M sparsity. The two proposed components can be readily combined to further enhance the N:M semi-structured pruning of LLMs. Our empirical experiments show that RIA alone can already surpass all existing post-training pruning methods on prevalent LLMs, e.g., LLaMA ranging from 7B to 65B. Furthermore, N:M semi-structured pruning with channel permutation can even outperform the original LLaMA2-70B on zero-shot tasks, together with practical speed-up on specific hardware. Our code is available at: https://github.com/biomedical-cybernetics/Relative-importance-and-activation-pruning

The commercial aviation industry is constantly evolving, driven by technological innovation and the growing demand for more efficient and environmental friendly transportation solutions. In this context, the design and development of next-generation aircraft is a crucial task, with the goal of reducing environmental impact and improving flight efficiency while continuing to ensure passenger comfort and safety. This paper aims at evaluating the technical feasibility as well as economic and environmental viability of a narrow body aircraft powered by hydrogen for direct combustion. Notably, the paper offers innovative insights regarding the update of literature models for conceptual design, in order to consider the impact of hydrogen fuel on the sizing loop at aircraft level. Moreover, it also focuses on the evaluation of operating costs for the aforementioned aircraft class, highlighting the required model updates and considerations to properly match the effect on costs of the introduction of the innovative fuel. Ultimately, a preliminary analysis of upstream sustainability of the concept, considering fuel life cycle, is provided, in order to understand the overall competitiveness, not only in terms of monetary resources but also of environmental impact, beyond the pure operating life of the airplane.

Background: The effect of COVID-19 infection, versus the indirect effect of the pandemic on body composition remains unclear. This study investigates long-term changes in body composition in COVID–19 survivors compared to a contemporary control group. Method: This is a prospective study involving adults who had a pre-pandemic whole-body DXA scan (DXA#1) performed between 2017 and 2019. Participants were asked to return for a repeat whole-body DXA scan (DXA#2) after the pandemic. Detailed data were collected including medical and COVID history. Inflammation markers and fasting lipids were measured. For those participants who had a COVID-19 infection between the 2 DXAs, DXA#2 was acquired at least one year after COVID-19 infection. Results: Overall, 160 adults were enrolled; 32.5% females, 51.8% non-white, with mean age of 43.2 years. Half (n=80) of the participants had documented COVID infection between their 2 DXA scans (COVID+ group), and the other half never had COVID. COVID-negative participants had an increase in annualized trunk fat (g) [922.5 vs. 159.7; P=0.01], total fat (g) [1564.3 vs. 199.9; P=0.2], and LBM (g) [974.9 vs. -64.5; P=0.0002], when compared to COVID+ group. However, among the COVID+ group, no differences were seen in annualized trunk fat, total fat mass, or LBM between those with PASC and without (p>0.05). Conclusion: During the pandemic, both COVID-19-survivors and COVID-negative groups exhibited increases in weight, and total and trunk fat, likely associated with pandemic-linked lifestyle modifications. However, only COVID-19-survivors had a decline in lean body mass over the same period, regardless of PASC symptoms.

Hormone changes across women's menstrual cycle may lead to changes in perception of chemical signals and hormonal responses to these cues. The aim of the present study was to determine the role of menstrual cycle phase on the effects of extracts of male axillary secretions (EMAS) in women. We tested healthy reproductive age and premenopausal women (n=29). EMAS/control solution was applied once in two hours either in the follicular or luteal phase, while collecting saliva samples for LH and cortisol monitoring. LH and cortisol concentrations were analyzed using EIA technique. PANAS and VAS were used to assess the mood. We observed a significant increase in the number of LH peaks (p=0.0447) and their amplitude (p=0.0469) when EMAS was applied in the follicular phase. The same application in the luteal phase lowered the amplitude of LH peaks (p=0.0382). EMAS application increases salivary cortisol level in reproductive age women relative to premenopausal women (p=0.0032). PANAS revealed changes in positive and negative affect after EMAS application. Our data indicate the significance of the menstrual cycle phase for EMAS effects on LH secretion, mood, but not on cortisol secretion in women.

Suicide is a public health problem influenced by a variety of biological, psychological, social, and environmental factors. Recognizing the warning signs at each stage is crucial for prevention and helping those at risk. In this context, body awareness, especially interoceptive body awareness, emerges as a potentially relevant and underexplored factor. In this study, binary logistic regression and linear regression analyses were used to examine the relationship between interoceptive body awareness and suicidal orientation in Colombian university students. In addition, the bootstrap technique was used to resample and estimate the distribution of the data. The results support the idea that greater interoceptive awareness may protect against suicidal orientation by improving emotional regulation. An inverse relationship was found between interoceptive awareness and suicidal ideation. These findings are consistent with previous literature on the importance of body awareness for emotional well-being. Further longitudinal research is needed to explore this relationship in depth.

Despite the recognized influence of body mass on combat techniques, the relationship between body segment mass (BSM) and combat moves remains unexplored. This study aimed to examine the relationship between striking arm mass (SAM), kicking leg mass (KLM), and body mass (BM) with the dynamic forces of the direct punches (DP), palm strikes (PS), elbow strikes (ES), front kicks (FK), and countermovement jumps (CMJ) performance. Sixteen male military cadets (22.3 ± 1.8 years, 181.4 ± 7.0 cm, 82.1 ± 8.5 kg) performed combat techniques, with performance measured by using a force plate and body segment mass assessed by Dual-energy X-ray absorptiometry. Spearman's correlation analysis, the Wilcoxon test, and Cohen's d were applied. Results indicated the relationship between KLM or BM and FK impulse (r = 0.64, p = 0.01; r = 0.52, p = 0.04, respectively) and CMJ impact force (r = 0.80, p ≤ 0.01; r = 0.70, p ≤ 0.01, respectively). Moreover, FK peak and impact forces were moderately correlated with CMJ jump height (r = 0.74, p ≤ 0.01; r = 0.77, p ≤ 0.01). The highest relationship was found between KLM and FK impulse; however, the difference in variability explained by KLM versus body mass is only 12%. This suggests that knowledge of BSM did not provide a significantly better estimate of the dynamic forces of the punches and FK than BM.

The purpose of this study was to establish the association between total and segmental body composition (BC) variables and anaerobic performance and to create optimal models that best predict such performance in CrossFit® (CF) athletes. Fifty athletes, 25 males and 25 females (age: 33.26 ± 6.81 years; body mass: 72.57 ± 12.17 kg; height: 169.55 ± 8.71 cm; BMI: 25.06 ± 2.31 kg·m−2) were recruited to participate and underwent BC analysis using dual-energy X-ray absorptiometry (DXA) and an all-out laboratory test on a cycle ergometer (Wingate) to determine their anaerobic performance. The results show a significant correlation between BC values and performance, ranging from moderate (r = -0.34, p = 0.015) to near-perfect (r = 0.96, p < 0.01). Furthermore, the created performance prediction models exhibited predictive capacities ranging from 19% (p = 0.017) to 93% (p < 0.001). All prediction models were created using total or segmental lean mass variables, excluding others. BC and performance variables found significant differences between males and females. The findings demonstrate that BC variables are crucial indicators of anaerobic performance in CF athletes. Professionals responsible for athlete performance should use this information to monitor athletes and design training programs.

In a trip planning service, travelers first set a starting point, a destination, and a sequential list of specific points of interest types (e.g., museums, restaurants, and parks). Based on this information, the service searches the spatial database to customize the best travel itinerary for the tourist. However, in previous studies, planners only considered the time factor when designing the optimal route, and failed to fully consider the quality of each point of interest. In this study, we specifically leveraged the capabilities of large language model to parse and respond to complex travel-related user queries. To apply large language model to route planning, we fine-tuned the model to understand geotagging and user travel preferences. We have introduced a novel graph search algorithm combined with large language model output, which optimizes the route search process to provide optimal travel recommendations by taking into account various factors such as distance length, budget constraints, and popularity of tourist attractions. In addition, we have integrated real-time traffic data and historical travel data to further improve the prediction accuracy and application usefulness of the model. In the experimental validation phase, we designed a series of benchmarks to compare the performance of the system with traditional algorithms and other machine learning-based route planning methods. Experimental results show that our model has a significant improvement compared with traditional methods in improving the speed and accuracy of path selection.

Bigeye grunt Brachydeuterus auritus is an important economic species in the waters off Sierra Leone in West Africa, with great ecological value. Based on the survey data of bottom trawl nets from 6 voyages from 2019 to 2021, the body weight-length relationship (WLR), as well as their relative fatness, were studied in depth using the method of body length and its corresponding mean body weight. This study fitted the WLR of bigeye grunt by gender, calculated its fullness, and analyzed the influence of environmental factors. The results showed that month and gender had a significant impact on body length and weight distribution (P<0.01). There was a highly significant negative correlation (P<0.01) between the condition factor (a) and the allometric factor (b) in the WLR, and salinity significantly affects b (P<0.05). The (lga)/b ratio was less affected by environmental factors which may be related to the body size and density of the fish. The difference in b between male and female populations was extremely significant (P<0.01). The relative fatness had significant seasonal variation characteristics, with highly significant effects of sex and body length (P<0.01). With increasing body length, the relative fatness showed two types of changes, continuously increasing and first increasing then decreasing, with salinity and b significantly affecting the type of change (P<0.05) and depth having a highly significant effect (P<0.01). The bigeye grunt off the coast Sierra Leone exhibited significant seasonal growth characteristics, with significant differences in growth characteristics between male and female individuals. This study suggested that salinity may be the main factor affecting its growth and development. In addition, this study hypothesised that the peak breeding season may occur during the dry season, beginning in December and ending before April of the following year. The study provides updated and more comprehensive insights into the biology and ecology of the bigeye grunt offshore Sierra Leone, West Africa, which can provide scientific basis for its resource assessment, development and utilization, and resource management.

Objective: To analyse the association between maternal risk factors such as age, body mass index (BMI) and cigarette smoking, with perinatal outcomes. Materials and Methods: This a retrospective analysis from prospectively collected data performed at Hospital Universitario de Torrejón (Madrid, Spain), between September 2017 and December 2019. All pregnant women with singleton pregnancies and non-malformed live fetuses attending their routine ultrasound examination at 11+0 to 13+6 weeks' gestation were invited to participate. Association between preeclampsia, preterm birth, gestational diabetes mellitus (GDM), small for gestational age (SGA) or fetal growth restricted (FGR) neonates and type of delivery according to maternal age, BMI, and cigarrete smoking was studied. Logistic mixed models were used to analyze the data. Results: 1921 patients were included in the analysis. Women of 40 years old had a significantly higher risk of having GDM (Odds Ratio (OR) 1.61, 95% Confidence Interval (CI) 1.08 to 2.36), and SGA neonates (OR 1.54, 95% CI 1.00 to 2.37). Women with a BMI <18 had an increased rate of birth SGA and FGR neonates (OR 3.28, 95% CI 1.51 to 7.05, and OR 3.73, 95% CI 1.54 to 8.37, respectively), whereas women with a BMI ≥35 had a higher risk of GDM (OR 3.10, 95% CI 1.95 to 4.89). Smoking increased the risk of having SGA and FGR neonates (OR 1.83, 95% CI 1.36 to 2.46, and OR 1.91, 95% CI 1.29 to 2.78). Conclusions: Advanced maternal age, low or high BMI, and smoking status are significant risk factors for pregnancy complications. Both clinicians and society should concentrate their efforts on addressing these factors to enhance reproductive health.

The anthropometric features offer an overall image of the nutritional status. The investigated groups were aged 19 and 21. The anthropometric measurements of height and weight were recorded following standard procedures. The objectives of this study were to evaluate the harmonious body development, the nutrition status and the body composition. The evolution of the stature increases until 20 years after which stagnation follows. The body mass index of subjects was found to be normal. The body fat may vary over time in subjects depending on age and gender. Body water and body muscle mass content increased with age. The harmony of body development was assessed by means of the Amar index. Regarding the constitutional index, the women subjects belong to the leptosome type and the male subjects belong to the picnic type. The status of the investigated subjects is within the average stature.

This purpose was to compare the ability of body shape index (ABSI) and body roundness index (BRI) with waist circumference (WC), body mass index (BMI), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR) and body adiposity index (BAI) to predict metabolic risk. The cross-sectional study was conducted in 17,360 Chinese subjects (were aged 18-95 years old) who escaped cardiovascular disease (CVD) or diabetes. Biochemical and anthropometric variables were measured by trained staff. Receiver operating characteristic curve (ROC) and optimal cutoff values of obesity indices were recruited to compare the predictive ability for metabolic risk factors. The mean age of subjects was 53.7(13.1) years, 41.6 % were males. Within young group (<60 years), the areas under the curve (AUC) demonstrated that WC, BMI, WHR, WHtR and BRI were able to similarly predict high metabolic risk in males (0.74 vs. 0.74 vs. 0.73 vs. 0.73 vs. 0.73) and in females (0.73 vs. 0.73 vs. 0.71 vs. 0.73 vs. 0.73), while the approximate predictive ability were only acquired in males (0.73 vs. 0.73 vs. 0.70 vs. 0.73 vs. 0.73) within elder group (≥60 years). The optimal cut-off values of BRI for high metabolic risk were calculated in males (<60 y: 3.49 vs. ≥60 y: 3.46) and females (<60 y: 3.47 vs. ≥60 y: 3.60). Meanwhile, BRI displayed a strong prediction to elevated BP and elevated TG in males (AUC = 0.64; AUC = 0.70) and to elevated BP, elevated TG and elevated SUA in females (AUC = 0.67; AUC = 0.69; AUC =0.70). BRI was able to similarly predict high metabolic risk compared to WC, BMI, WHR, WHtR and BAI, while ABSI was not. Moreover, BRI revealed specific predictive ability for elevated BP, elevated TG and elevated SUA.

Major (exo)planetary and satellite bodies seem to concentrate at intermediate areas of the radial 1 distributions of all the objects orbiting in each (sub)system. We show that angular-momentum transport during secular evolution of (exo)planets and satellites necessarily results in the observed intermediate accumulation of the massive objects. We quantify the ‘middle’ as the mean of mean motions (orbital angular velocities) when three or more massive objects are involved. Radial evolution of the orbits is expected to be halted when the survivors settle near mean-motion resonances and angular-momentum transfer between them ceases (gravitational Landau damping). This dynamical behavior is opposite in direction to what has been theorized for viscous and magnetized accretion disks, in which gas spreads out and away from either side of any conceivable intermediate area. We present angular momentum transfer simulations in few-body systems, and we calculate the tidal dissipation timescales and the physical properties of the mean tidal field in planetary and satellite (sub)systems.

Despite the recognized influence of maximum or acceleration strength on combat techniques and countermovement jump (CMJ) performance, the relationship between body segment weights and combat techniques remains unexplored. This study aimed to examine the impact of body segment weights, including striking arm weight (SAW), kicking leg weight (KLW), and trunk weight (TW), on the dynamics of direct punches (DP), palm strikes (PS), elbow strikes (ES), front kicks (FK), and CMJ performance. Sixteen male military cadets (22.3±1.8 years, 181.4±7.0 cm, 83.0±8.1 kg) performed combat techniques, with performance measured by using a force plate and body segment weights assessed by Dual-energy X-ray absorptiometry. Spearman's correlation analysis was applied. Results indicated a strong correlation between KLW and FK impulse (r = 0.64, p ≤ 0.01) and CMJ impact force (r = 0.80, p ≤ 0.01). SAW showed moderate correlations with DP impulse (r = 0.53, p ≤ 0.05) and ES impulse (r = 0.54, p ≤ 0.05). Additionally, FK peak and impact forces were strongly correlated with CMJ jump height (r = 0.74, p ≤ 0.01; r = 0.77, p ≤ 0.01), and FK peak force moderately correlated with CMJ peak velocity (r = 0.63, p ≤ 0.05). These findings suggest targeted arm strength training could enhance striking abilities, while improvements in leg acceleration strength may directly boost kicking performance and vertical jump capabilities, which are crucial in combat scenarios.

The rapid advancement of technology has revolutionized language learning, introducing innovative methods that depart from traditional instructional approaches. This study employs a mixed-methods research design to examine the impact of chatbot-based language learning support, adaptive learning algorithms, and virtual reality language immersion on the language learning proficiency and self-regulated learning skills of English as a Foreign Language (EFL) learners. The research design includes quantitative analysis of language proficiency scores and qualitative exploration of learner experiences with the technological interventions. The theoretical implications of this study are rooted in constructivist and socio-cultural learning theories, which underpin the design and implementation of the technological interventions. The triangulation of qualitative and quantitative data revealed that the participants' positive perceptions of the effectiveness of chatbot-based language learning support were supported by the quantitative results, with the variable "chat-bot based support" demonstrating a substantial mean difference compared to other groups. This convergence of findings reinforces the positive influence of chatbot-based support on language learning outcomes and highlights the importance of integrating theoretical perspectives with empirical evidence to gain a comprehensive understanding of the impact of technological interventions on language learning outcomes for EFL learners. The study's findings provide insights into the potential of these technological interventions to optimize language learning outcomes for EFL learners and promote autonomous learning behaviors.

Vaccines decrease morbidity and mortality. Nevertheless, their benefits depend on public response. During COVID-19, vaccine hesitancy and refusal were rampant, threatening public health. A thorough understand-ing of opponents' arguments is required to address the diffusion of unreliable information on social media and prevent vaccine hesitancy from developing into vaccine refusal. Accordingly, the article examines vac-cine opponents' justifications on social media. Textual content analysis of reader comments on three health-related Israeli Facebook pages was conducted. Data collection encompassed the Israeli COVID-19 vaccination period from October 2020 to May 2022. The comments were analyzed according to the health beliefs model (HBM). We found that vaccine opponents were characterized by low perceptions of the severity of the disease combined with high perceptions of the damages of the vaccine; low perceived benefits of vac-cine compliance; vaccine hesitancy and fear along with public distrust as barriers to change; and call for ac-tion to resist the vaccine and spread related anti-establishment views on the web. As vaccination hesitancy was found to develop into public distrust in the state systems and escalate into conspiracy beliefs and an-ti-vaccination activism, its early detection may prevent future vaccine resistance.

Structured science summaries or research contributions using properties or dimensions beyond traditional keywords enhances science findability. Current methods, such as those used by the Open Research Knowledge Graph (ORKG), involve manually curating properties to describe research papers' contributions in a structured manner, but this is labor-intensive and inconsistent between the domain expert human curators. We propose using Large Language Models (LLMs) to automatically suggest these properties. However, it's essential to assess the readiness of LLMs like GPT-3.5, Llama 2, and Mistral for this task before application. Our study performs a comprehensive comparative analysis between ORKG's manually curated properties and those generated by the aforementioned state-of-the-art LLMs. We evaluate LLM performance through four unique perspectives: semantic alignment and deviation with ORKG properties, fine-grained properties mapping accuracy, SciNCL embeddings-based cosine similarity, and expert surveys comparing manual annotations with LLM outputs. These evaluations occur within a multidisciplinary science setting. Overall, LLMs show potential as recommendation systems for structuring science, but further finetuning is recommended to improve their alignment with scientific tasks and mimicry of human expertise.

(1) The purpose of this study was to compare the percentages of quarterly change for each of the variables analysed (height, weight, body fat percentage and muscle mass) as a function of the quarter in a sample of adolescent football players. (2) The sample consisted of young male football players of different categories belonging to a high-performance football academy, (more than 5 years in football and have spent 1 full year at the academy). (3) Two-way repeated measures ANOVA revealed for the anthropometric variable, P_value

This paper investigates the periodic and quasi-periodic orbits in the symmetric collinear four-body problem through a variational approach. In Section 3, we analyze the conditions under which homographic solutions minimize the action functional. We demonstrate that these solutions for four equal masses arranged in a linear configuration are indeed the minimizers of the action functional and compute the minimum value of the action functional for these solutions. Additionally, we employ numerical experiments using Poincaré sections to explore the existence and stability of periodic and quasi-periodic solutions within this dynamical system. Our results provide a deeper understanding of the variational principles in celestial mechanics and reveal complex dynamical behaviors that are crucial for further studies in multi-body problems. The findings have significant implications for theoretical research and practical applications in astrodynamics and space mission planning.

Farmers are continually looking for new reliable, objective and non-invasive methods for evaluation of ewe condition. Live weight (LW) and body condition score (BCS) are used by farmers as a basis to determine the condition of the animal. Body composition is an important aspect of monitoring animal condition. The body composition is the amount of fat, lean and bone; knowing the amount of each is important because the information can be used for better strategic management interventions. Experiments were conducted to establish the relationship between body composition and body parameters, at key life’s stages (weaning and pre-mating), using measurements automatically determined by an image processing ap-plication at Lincoln University sheep farm for 88 Coopworth ewes. Computerized Tomography technology was used to develop relationship with body parameters and a subset was used to validate the predicted model. Multivariate linear regression (MLR), artificial neural network (ANNs) and regression tree (RT) statistical analysis methods were evaluated to determine their efficacy to predict body fat, lean and bone. The results showed a correlation between fat, lean and bone determined by CT and the fat, lean, bone weight estimated by live weight and body parameters calculated using the image processing application with R2 values of 0.90 for fat, 0.72 for lean and 0.50 for bone using ANNs statistical model. From these results, farmers can utilize accurate measures of fat which will enhance nutritional and management practices.

(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.

The incidence of obesity which leads to insulin resistance (IR) and metabolic disorder increases in developing countries including Indonesia. Male adult has higher risk to have abdominal obesity than female which is associated with cardiometabolic disorders. Several anthropometric measurements have been proposed to predict IR. The aim of this study was to investigate whether body mass, body mass index (BMI), waist circumference (WC), body fat percentage (BF) or visceral fat percentage (VF) could become a better predictor of IR in healthy young male adult. Total of 140 healthy young male adults ranging from 18-25 years were recruited in the study. Insulin resistance was measured by calculating Homeostatic Model Assessment for Insulin Resistance (HOMA-IR). Subjects with HOMA-IR value >75th percentile with cut off 3.75 were defined as IR. Anthropometric measurements included body weight, BMI, WC were performed whereas BF and VC were measured by bioelectrical impedance analysis (BIA). IR had significant strong correlation with body weight, BMI, WC, BF and VF. The area under curve of body mass, BF, VF were greater than WC and BMI. Anthropometric measurements correlated strongly with IR but body weight, BF, VF have stronger correlation than WC and BMI in healthy young male adult.

Background/Objectives: This study looks at how a kinematic chain exercise regimen that targets the lower, core, and upper body affects university shot put participants' shoulder muscle strength and throwing efficiency. This study fills an apparent research void on shot put training approaches by presenting a comprehensive kinematic chain workout program. It was anticipated that this method would improve performance the most, considering the complex biomechanical require-ments of the sport. Methodology: Athletes, seventy in age (19.87 ± 1.31 years), were assigned into two groups at random: experimental (n = 35) and control (n = 35). While the control group carried on with their usual training, the experimental group participated in an 8-week kinematic chain training program, pre- and post-training evaluations were carried out to evaluate shot put throwing ability, shoulder muscle strength, and participant satisfaction with the exercise regimen. Results: demonstrated that, when compared to the control group, the athletes in the kinematic chain program had a significantly improved throwing distance (p = 0.01) and shoulder muscle strength (p = 0.01). Furthermore, there was a significant difference (p = 0.005) in the athletes' satisfaction levels with the workout program among those in the experimental group. This implies that a kinematic chain-focused strategy improves performance and emphasizes the significance this type of training is for shot put athlete performance. Conclusion: As an improvement forward in sports science, the study's finding promotes the inclusion of kinematic chain workouts in shot put training programs and calls for more research on kinematic chain-based approaches in a variety of sports.

This study aims to investigate the impact of body heat loss on the thermal and aerodynamic conditions in a whole-body cryotherapy chamber. The underlying hypothesis is that the heat generated by the human body alters the thermal and aerodynamic environment inside the cabin. A numerical study was conducted to test this hypothesis to analyze the thermodynamic exchanges between the human body and the cabin during a 3-minute whole-body cryotherapy session. The computational fluid dynamics (CFD) approach was used to study the unsteady heat transfer between the human body and the interior of the cryotherapy cabin. A thermal boundary condition, based on a mathematical model developed from experimental data, was applied to simulate skin cooling kinetics over time. The post-processing of the 3D results, including temperature, velocity fields, and thermal flux maps at the body surface, provided insight into the thermo-convective mechanisms involved in a whole-body cryotherapy session. The study found that body heat loss significantly affects the temperature fields inside the cabin, leading to global modifications of the aeraulic and thermal conditions. These findings suggest that cryotherapy protocols may need to be adjusted or the cabin set temperature optimized to enhance the therapeutic benefits.

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.

Older adults with obesity have an increased risk for cognitive decline and subsequent Alzheimer’s disease. Previous research has focused on the impact of individual nutrients and overall dietary patterns to improve cognitive function. The Brain and Body Health (BB-Health) study aims to demonstrate the feasibility of a multicomponent nutritional supplement (MCNS) and weight loss (WL) intervention for individuals with overweight and obesity who are not consuming a healthy diet. BB-Health is a parallel-group randomized placebo-controlled 2x2 factorial pilot trial comparing the MCNS, WL, and MCNS+WL with a Control group. Eligible participants are 55 to 85 years of age with a body mass index 27.0 to 39.9 kg/m2. Major exclusion criteria include type 2 diabetes, moderate or severe cognitive impairment, and self-reported healthy dietary pattern. The BB-Health feasibility trial is registered as NCT05542199 at clinicaltrials.gov. The BB-Health objectives are to report: 1) recruitment and enrollment, including pace and descriptive statistics of the study sample; 2) adherence to the administered interventions; 3) successful data collection and performance of outcome assessments, including evaluation of change in key outcomes over time that are powered for this pilot study; and 4) summary statistics on outcomes for use in planning a subsequent, larger trial. The outcomes of interest are age-sensitive cognitive assessments, cerebral hemodynamics, and related health parameters. All measurements are collected at baseline, 3-, 6-, and 12-month visits. If the BB-Health protocol demonstrates feasibility, the novel MCNS with and without a parallel WL intervention has potential to be administered in a confirmatory randomized trial to evaluate ability to prevent age-related cognitive decline and improve brain health. The BB-Health Feasibility Study could significantly contribute to our understanding of the influence of diet and weight management on cognitive health

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.

while enjoying a relative positive revival in the digital age, Esperanto and the assessment of its language vitality is often problematic and prone to gross errors, and therefore a theoretical re-flection is required. Unlike other lesser-used languages, Esperanto is intergenerationally trans-mitted mainly outside the family, and so Fishman’s GIDS and subsequent scales such as the EGIDS cannot be applied straightforwardly for language vitality diagnosis and estimation. In particular, it is the social movement with its language activists who guarantee its vitality and developing, for more than a century. A key aspect is the digital domain, where the relatively good positioning of Esperanto does not reflect in a parallel increase in the number of activists. This paper critically assesses the digital language vitality of Esperanto on the basis of its language ide-ology and other sociolinguistic data as a starting point for a discussion to overcome the limits of Blanke’s (2006) scale of language vitality of Esperanto and its rivals. This assessment eventually leads to a more general reflection on the role of ‘coolification’, i.e., the positive effects on language attitudes and development thanks to digital visibility, its limits and the issue of placing it in the context of language vitality in general.

Throughout the eukaryotic tree of life, amoeboid organisms have evolved that aggregate upon starvation and form multicellular fruiting bodies, consisting of a ball of spores atop a stalk. This chapter discusses the remarkable convergent evolution of a stalked fruiting body in these different taxa. It then discusses a well-studied group of aggregative fruiters, the cellular slime molds, in more detail. These organisms exhibit substantial variation in their stalk formation and composition, which allows a better understanding of the evolution, maintenance and possible functions of stalked fruiting bodies, but also points to potential costs and benefits of different types of stalks.

Background: Smad4, a critical tumor suppressor gene, plays a significant role in pancreatic biology and tumorigenesis. Genetic background and sex are known to influence phenotypic outcomes, but their impact on pancreatic weight in Smad4-deficient mice remains unclear. This study investigates the impact of Smad4 deficiency on pancreatic weight in F1 mice from diverse Collaborative Cross (CC) lines, focusing on the influence of genetic background and sex. Methods: F1 mice were generated by crossbreeding female CC mice with C57BL/6J-Smad4tm1Mak males. Genotyping confirmed the presence of Smad4 knockout alleles. Mice were housed under standard conditions, euthanized at 80 weeks, and their pancreatic weights were measured, adjusted for body weight, and analyzed for effects of Smad4 deficiency, sex, and genetic background. Results: The overall population of F1 mice showed a slight but non-significant increase in adjusted pancreatic weights in heterozygous knockout mice compared to wild-type mice. Sex-specific analysis revealed no significant difference in males but a significant increase in adjusted pancreatic weights in heterozygous knockout females. Genetic background analysis showed that lines CC018 and CC025 substantially increased adjusted pancreatic weights in heterozygous knockout mice. In contrast, other lines showed no significant difference or varied non-significant changes. The interplay between genetic background and sex further influenced these outcomes. Conclusion: Smad4 deficiency affects pancreatic weight in a manner significantly modulated by genetic background and sex. This study highlights the necessity of considering these factors in genetic research and therapeutic development, demonstrating the value of the Collaborative cross-mouse population in dissecting complex genetic interactions.

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.

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.

In this work, we show that a late fusion approach to multi-modality in sign language recognition improves the overall ability of the model in comparison to the singular approaches of Computer Vision (88.14%) and Leap Motion data classification (72.73%). With a large synchronous dataset of 18 BSL gestures collected from multiple subjects, two deep neural networks are benchmarked and compared to derive a best topology for each. The Vision model is implemented by a CNN and optimised MLP and the Leap Motion model is implemented by an evolutionary optimised deep MLP topology search. Next, the two best networks are fused for synchronised processing which results in a better overall result (94.44%) since complementary features are learnt in addition to the original task. The hypothesis is further supported by application of the three models to a set of completely unseen data where a multi-modality approach achieves the best results relative to the single sensor method. When transfer learning with the weights trained via BSL, all three models outperform standard random weight distribution when classifying ASL, and the best model overall for ASL classification was the transfer learning multi-modality approach which scored 82.55% accuracy.

Background and Objective: Although whole body-electromyostimulation (WB-EMS) has been shown to favorably improve body composition in several research studies, it has not been confirmed how it affects adipose tissue-derived biomarkers and circumferences of the abdomen and thighs. Materials and Methods: Twenty participants of obese elderly women were randomly classified into an EMS group (EMSG; n = 10) or a non-EMS group (CON; n = 10). The WB-EMS suits used in this study enabled the simultaneous activation of 8 muscle groups with selectable intensities for each phase. Stimulation frequency was selected at 85 Hz, impulse-width at 350 μ seconds, and impulse-rise as a rectangular application. Impulse duration was 6 seconds with a 4-second break between impulses. The EMS sessions were combined with aerobic dancing 3 times a week for 12 weeks. Both groups received the same program; however, the CON did not receive electrical stimuli. Biomarkers and body composition including circumferences of the abdomen and thighs were measured on Week 0 and Week 12. Results: Compared with the CON, 1) a significant effect of the EMS-intervention concerning decreased body weight (p = 0.032) and increased basal metabolic rate (p = 0.029) were evident. The thigh subcutaneous fat and total fat of EMSG were not significantly changed, but those of CON were significantly increased after 12 weeks. 2) The biomarkers of CON either increased or showed no change, although the interleukin (IL)-6 was significantly (p = 0.028) decreased in EMSG. Specifically, C-reactive protein (CRP; p = 0.005) and alpha-fetoprotein (p = 0.050) of CON were significantly increased after 12 weeks. In addition, the IL-6 (p = 0.034), CRP (p = 0.001), carcinoembryonic antigen (p = 0.017), and low density lipoprotein (p = 0.005) of EMSG were lower than those of CON in the end of experiment. 3) Conclusions: The results indicate that EMS intervention improved body composition and reduced negative biomarkers, thus highlighting its effectiveness.

Background: Bioelectrical impedance (BIA) and ultrasound (US) have become popular for estimating body fat percentage (BF%) due to their low cost and clinical convenience. However, agreement of these devices with gold standard method still require investigation. The aim was to analyze the agreement between a gold standard %BF assessment method with BIA and US devices. Methods: Twenty-three men (age 30.1 ± 7.7 years, 82.5 ± 14.9 kg, 1.77 ± 0.05 m) underwent Dual Energy X-Ray Absorptiometry (DXA), BIA (tetrapolar) and US (three-site method) %BF assessment. Pearson and concordance correlations were analyzed. T-test compared means between methods, and Bland Altman plots analyzed agreement and proportional bias. Alpha was set at &lt; .05. Results: Pearson coefficients between BIA and US with DXA were high (BIA = 0.94; US = 0.89; both p &lt; .001). Concordance coefficient was high for BIA (0.80) and moderate for US (0.49). BF% measured by BIA (24.5 ± 7.5) and US (19.4 ± 7.0) were on average 4.4% and 9.6% lower than DXA (29.0 + 8.5%), respectively (p &lt; .001). Lower and upper agreement limits between DXA and BIA were -1.45 and 10.31, while between DXA and US were 2.01 and 17.14, respectively. There was a tendency of both BIA (p = .09) and US (p = .057) to present proportional bias and underestimate BF%. Conclusions: Despite the correlation, the mean difference between the methods were significant, and the agreement limits are very wide. This indicate that BIA and US, as measured in this study, have limited potential to accurately measure %BF compared to DXA, especially in individuals with higher body fat.

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.

The monitoring of maternal and child health, using wearable devices made with wireless sensor technologies, is expected to reduce maternal and child death rates. Wireless sensor technologies have been used in wireless sensor networks to enable the acquisition of data for monitoring machines, smart cities, transportation, asset tracking, and tracking of human activity. Applications based on wireless body area network (WBAN) have been used in healthcare for measuring and monitoring of patient health and activity through integration with wearable devices. Wireless sensors used in WBAN can be cost-effective, enable remote availability, and can be integrated with electronic health record (EHR) management systems. Interoperability of WBAN sensor data with other linked data has the potential to improve health for all, including maternal and child health through the improvement of data access, data quality and healthcare access. This paper presents a survey of the state-of-the-art techniques for managing WBAN sensor data interoperability. The findings in this study will provide reliable support to enable policymakers and health care providers to take action to enhance the use of e-health to improve maternal-child health and reduce the mortality rates of women and children.

The disparity between software design and implementation, especially concerning IoT security and forensics, often lacks clarity and precision. Formal languages, rooted in mathematical rules, logic, and symbols, have proven priceless for expressing specifications and verifying system designs. Various semi-formal and formal languages, such as JSON, XML, predicate logic, and regular expressions, along with formal models like Turing machines, cater to specific domains. This paper introduces a novel formal language, named ObTFL (Object Transaction Formal Language), initially developed for IoT forensics and authentication purposes but later extended to encompass general distributed systems. The paper elucidates the syntax and semantics of ObTFL and presents several case studies showcasing its versatility and effectiveness.

Physical laws manifest themselves through the amalgamation of mathematical symbols, numbers, functions, geometries, and relationships. These intricate combinations unfold within a mathematical model devised to capture and represent the ``objective reality'' of the system under examination. In this symbiotic relationship between physics and mathematics, the language of mathematics becomes a powerful tool for describing and predicting the behavior of the physical world. The language used and the associated concepts are in a perpetual state of evolution, mirroring the ongoing expansion of the phenomena accessible to our scientific understanding. In this contribution, written in honor of Richard Kerner, we delve into fundamental, at times seemingly elementary, elements of the mathematical language inherent to the physical sciences, guided by the overarching principles of symmetry and group theory. Our focus turns to the captivating realm of spheres, those strikingly symmetric entities that manifest prominently within our geometric landscape. By exploring the interplay between mathematical abstraction and the tangible beauty of symmetry, we seek to deepen our understanding of the underlying structures that govern our interpretation of the physical world.

The Median American Sign Language Interpretation Software (ASL) Interpretation Software is a web application that is capable of interpreting American Sign Language in real-time, utilizing an internet connection and a primary web camera, complete with basic phrases and letters. Extensive use of Deep Learning and Neural Networks, specifically Convoluted Neural Networks, enables Median to interpret video inputs and generate accurate results directly displayed to the user in text format. The ultimate goal for Median is to have it act as a bridge between hearing people and members of the deaf community, allowing deaf people to communicate with non-signing people using American Sign Language. Furthermore, Median has been designed to benefit people who lack access to a human ASL Translator, as its format as a website allows it to be accessible anywhere at any time, giving increased availability over human interpreters. Median is designed to be a very versatile program with great potential for growth and expansion.

Language is an essential aspect of teaching and learning mathematics. It is necessary for communicating, transmission of concepts and ideas, and formation of meaning of mathematical concepts. In mathematics, besides symbols, which are usually common in different languages, words and expressions are used, which may invoke different concept images to students in various languages. Some words are used in mathematics and in everyday language with different meanings, while others are used only in mathematics or in mathematics and other disciplines in similar but non-identical ways. In Mathematical Analysis, the used vocabulary is gradually enhanced, and the concepts are defined in a more formal way. In the current study, the language used regarding mathematics of change is examined, focusing on rate of change and in relation to misconceptions of students.

Background Children often suffer the nonspecific musculosceletal pain as reported in literature. Aim To determine relationship between body weights with development of musculoskeletal pain and to determine whether growing in body height is associated with musculoskeletal pain in schoolchildren. Subjects/ Methods A prospective longitudinal study included 1315 school children aged 7-14 years (652 boys and 663 girls) and was performed in 13 elementary schools in B&H. Child body height and body weight were measured. The survey of perception of musculoskeletal pain in different body regions of subjects was conducted by adjusted Nordic Musculosceletal Questionnaire (NMQ). Results The highest prevalence of an overweight and obesity in the 10th year 35.7% and the lowest frequency 17.8% in the 14th year was. In the age 14th obesity was’nt found. Boys have more prevalence of overweight. Using logistic regression model, we found that school children with normal BMI were protected with increased body height of acute lower back pain (β= -0.089, 95%CI, -9.730- -0.023, P< 0.049), and increased body height was protector of obese school children of acute upper back pain (β= -0.356, 95%CI, -14.077- -3.878, P< 0.001) and chronic lower back pain (β= -0.356, 95%CI, -14.077- -3.878, P< 0.001). Conclusion Schoolchildren with normal weight more often have had musculosceletal pain than those with overweight or obesity. This can be associated with intense physical growth period in height, especially. The assumption is that the increase in height changes the relationship between excessive BMI and musculoskeletal pain in children of school age.

Background: The transversus abdominis plane (TAP) block has gained increasing importance as a very effective pain relief in for caesarean section. This study aimed to compare bupivacaine (Group B), bupivacaine with dexmedetomidine (Group BD) and bupivacaine with dexamethasone (Group BDx) applied in ultrasound-guided TAP block in the treatment of CS postoperative pain. Material and Method: American Society of Anesthesiologists I and II pregnant women who underwent cesarean section were included in the study. Forty patients were admitted in each group. All patients were applied TAP block with ultrasound guidance by administering the medications of the group in which they were included. Results: Body mass index (BMI) and operation duration were higher in Group B (p*=0.01 and 0.004, respectively). Instant tramadol requirement and amount were higher in Group B at some of follow up times. Patient satisfaction scale were better in Group BD and Group BDx. Conclusion: TAP block significantly reduces pain after cesarean section, prolongs the time to first analgesic request, and reduces the need for additional opioid analgesics. The addition of adjuvant to the TAP block usually improves the postoperative clinical condition. However, some special conditions such as body mass index (BMI) may negatively affect the clinical situation. Comprehensive, standardized, randomized controlled studies are needed to compare results under different conditions.

Sign language recognition technology can help people with hearing impairments to communicate with those who are hearing impaired. At present, with the rapid development of society, deep learning technology also provided certain technical support for sign language recognition work. In sign language recognition tasks, the use of traditional convolutional neural networks to extract spatio-temporal features from sign language videos suffers from insufficient feature extraction, resulting in low recognition rates. Nevertheless, video-based sign language datasets are very large and require a lot of computational resources for training and generalisation must be ensured, which poses a challenge for recognition. In this paper, we have presented a video-based sign language recognition method based on resnet and lstm. As the number of network layers increases, the ResNet network can effectively solve the granularity explosion problem and obtain better time series features. We use the Resnet convolutional network as the backbone model. At the initialisation stage, we obtain sign language features using ResNet; then, the learned feature space is used as the input of LSTM network to obtain long sequence features. The experimental results show that the accuracy of the above model is better than the mainstream model, and it can effectively extract the spatio-temporal features in sign language videos and improve the recognition rate of sign language actions.

Sign language recognition is essential in hearing-impaired people’s communication. Sign language recognition is an important concern in computer vision and has been developed with rapid progress in image recognition technology. However, sign language recognition using a general monocular camera has problems with occlusion and recognition accuracy in sign language recognition. In this research, we aim to improve accuracy by using a 2-axis bending sensor as an aid in addition to image recognition. We aim to achieve higher recognition accuracy by acquiring hand keypoint information of sign language actions captured by a monocular RGB camera and adding sensor assist. To improve sign language recognition, we need to propose new AI models. In addition, the amount of dataset is small because it uses the original data set of our laboratory. To learn using sensor data and image data, we used MediaPipe, CNN, and BiLSTM to perform sign language recognition. MediaPipe is a method for estimating the skeleton of the hand and face provided by Google. In addition, CNN is a method that can learn spatial information, and BiLSTM can learn time series data. Combining the CNN and BiLSTM methods yields higher recognition accuracy. We will use these techniques to learn hand skeletal information and sensor data. Additionally, the 2-axis Bending sensor glove data support training AI model. Using these methods, we aim to improve the recognition accuracy of sign language recognition by combining sensor data and hand skeleton data. Our method performed better than using skeletal information, achieving 96.5% accuracy in Top-1.

The speech-to-song illusion is a type of auditory illusion that the repetition of a part of a sentence would change people’s perception tendency from speech-like to song-like. The study aims to examine how pace, emotion, and language tonality affect people’s experience of the speech-to-song illusion. It uses a between-subject (Pace: fast, normal, vs. slow) and within-subject (Emotion: positive, negative, vs. neutral; language tonality: tonal language vs. non-tonal language) design. Sixty Hong Kong college students were randomly assigned to one of the three conditions characterized by pace. They listened to 12 audio stimuli, each with repetitions of a short excerpt, and rated their subjective perception of the presented phrase, whether it sounded like a speech or a song, on a five-point Likert-scale. Paired-sample t-tests and repeated measures ANOVAs were used to analyze the data. The findings reveal that a faster speech pace could strengthen the tendency of the speech-to-song illusion. Neither emotion nor language tonality show a statistically significant influence on the speech-to-song illusion. This study suggests that the perception of sound should be in a continuum and facilitates the understanding of song production in which speech can turn into music by having repetitive phrases and to be played in a relatively fast pace.

Body Expression (BE) has been defined in the past decades as a discipline within Physical Education (PE) with very particular characteristics and a strong emotional component. In this study, a programme of this discipline was applied to university Physical Activity and Sports Sciences (PASS) students from six consecutive academic years: three prior to and three during the pandemic. A pre-post design was used to determine how the BE programme affected the university students' self-concept (SC). To do so, a questionnaire with a multidimensional approach to this construct was administered, whose dimensions were closely related to the BE programme's characteristics. The results revealed significant improvements in the final SC, compared to the initial SC. Men reported lower SC values than women before the programme implementation, but higher at the end. Therefore, the change was larger in men, so the programme may have had an equalising effect between groups. It was also verified that the pandemic had particularly affected women.

In the weak-field approximation of covariant theory of gravitation the problem of 4/3 is formulated for internal and external gravitational fields of a body in the form of a ball. The dependence of the energy and the mass of the moving substance on the energy of field accompanying the substance, as well as the dependence on the characteristic size of the volume occupied by the substance are described. Additives in the energy and the momentum of the body, defined by energy and momentum of the gravitational and electromagnetic fields associated with the body are explicitly calculated. The conclusion is made that the energy and the mass of the body can be described by the energy of ordinary and strong gravitation, and through the energies of electromagnetic fields of particles that compose the body.

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.

LLMs are a compressed repository of a vast corpus of valuable information on which they are trained. Therefore, this work hypothesizes that LLMs such as Llama, Orca, Falcon, and Mistral can be used for misinformation detection by making them cross-check new information with the repository on which they are trained. Accordingly, this paper describes the findings from the investigation of the abilities of LLMs in detecting misinformation on multiple datasets. The results are interpreted using explainable AI techniques such as LIME, SHAP, and Integrated Gradients. The LLMs themselves are also asked to explain their classification. These complementary approaches aid in better understanding the inner workings of misinformation detection using LLMs and lead to conclusions about their effectiveness at the task.

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.

This study investigates effective strategies for second language education in primary schools, analyzing the efficacy of immersive and interactive methods, digital literacy tools, and mixed-method approaches. The findings reveal significant improvements in language proficiency and student engagement through these methodologies, with immersive and interactive strategies showing substantial benefits in oral proficiency and engagement, digital tools enhancing accessibility and engagement, and mixed-method approaches yielding the highest overall effectiveness. The study underscores the importance of a balanced, flexible approach to language education, integrating traditional and modern methods to cater to diverse learner needs. These strategies not only advance linguistic skills but also contribute to the broader cognitive and emotional development of young learners, emphasizing the critical role of effective language education in preparing students for a globalized world.

In this paper, we introduce Syntax-Enhanced Transformer Model (SET), a groundbreaking approach in the realm of Transformer-based language modeling that seeks to redefine the boundaries of linguistic analysis and comprehension. SET innovatively combine (i) the well-established high-level performance, scalability, and adaptability of traditional Transformers with (ii) a sophisticated analysis of syntactic structures. This synergy is enabled by a novel attention mechanism tailored to parse syntactic nuances and a deterministic process adept at transforming linearized parse trees into meaningful linguistic representations. Our comprehensive experiments reveal that SET significantly advance the field by surpassing existing benchmarks in sentence-level language modeling perplexity. They exhibit exceptional proficiency in tasks that require an acute awareness of syntax, setting new standards for language models in understanding complex linguistic structures. Furthermore, SET demonstrate an enhanced capability to grasp nuanced linguistic patterns that have traditionally been challenging for standard Transformer models. However, our studies also uncover a unique aspect of SET: while they excel in sentence-level tasks, their representation of sentences as singular vectors—owing to the syntactic composition constraints intrinsic to their design—introduces certain limitations in document-level language modeling. This observation points to an intriguing area for future exploration; it suggests the potential need for an alternative or complementary memory mechanism within Transformer models, one that functions independently from, yet in harmony with, syntactic structures. Such a mechanism could be pivotal in enhancing the model's ability to comprehend and process long-form texts effectively. In conclusion, SET mark a significant stride in the journey towards more sophisticated, syntax-aware language models. They offer promising insights into the integration of deep linguistic knowledge with cutting-edge machine learning techniques, potentially opening doors to a new era of natural language understanding and processing.

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.

The language we use to describe the past can have a strong influence on the audience’s interpretation of our story. In our experiment, we explore, using 3 different conditions, how the framing and language of an accident report can affect the audience’s proposed solutions to manage the problems found. We find that the approach used to create an accident report can have a powerful influence on the audience’s decision making. Whether we are describing an accident in a similar manner to a crime, using a systems approach or we are accepting of multiple stories which are not linear or coherent, the methods we use to capture and communicate the story have a profound impact on the actions decided upon by the reader.

In million years, under the pressure of natural selection, hominins acquired vocal learning, music, language, and intense cooperation, thanks to the efficacy of music in enhancing sociality. Thus, early in human evolution music became part of human life, a relevant activity, which required sophisticated perceptual and motor skills. It contributed to developing cultures and history, social bonding, and from the beginning of life strengthens the mother-baby relation while within the mother’s womb. Music existed in all known human cultures, although it varies in rhythmic and melodic complexity. It is art made of sounds capable of arousing emotions, evokes memories, engages multiple cognitive functions, and promotes attention, concentration, stimulates the imagination, creativity, and harmony of movement. Music and language share the same complex neural network. Music changes the chemistry of the brain activating the reward and prosocial systems, altruism, and allowing its use in therapy. This review explores "what" is music and illustrates the neural circuits that allow the production of music and language and those that transduce the sounds perceived by the ear, localize and archive them, allowing to recall them. Interestingly, songbirds share many commonalities with human music:, common neural pathways that shape vocal learning, and how they make sounds.

Lipofuscin is indigestible garbage that accumulates in the autophagic vesicles and cytosol of post-mitotic cells with age. Drs. Brunk and Terman postulated that lipofuscin accumulation is the main or at least a major driving factor in aging. They even posited that the evolution of memory is the reason why we get lipofuscin at all, as stable synaptic connections must be maintained over time, meaning that the somas of neurons must also remain in the same locale. In other words, they cannot dilute out their garbage over time through cell division. Mechanistically, their position certainly makes sense given that rendering a large percentage of a post-mitotic cell’s lysosomes useless must almost certainly negatively affect that cell and the surrounding microenvironment. Here, I explore the possibility that the accumulation of lipofuscin may to some extent exacerbate every other kind of age-related damage. I do not think that lipofuscin removal will reverse/prevent all forms of aging, just the major component facing us currently. In this piece, I will review what is known about lipofuscin accumulation from evolutionary and mechanistic standpoints and discuss a method of removing it from single cells in vitro.

A body plan is a stable configuration of characters for a major taxonomic group, such as chordates or arthropods. Despite widespread casual reliance on the concept for guiding comparisons within and between groups, the nature of body plans as well as the biological causes underlying their evolution have remained elusive. This paper proposes an abstract mechanistic model of body plan identity. We hypothesize that body plans are an evolutionary phenomenon that only applies to a relatively small subset of major clades, rather than being associated with each and every so-called “phylum.” Body plans arise in evolution by stepwise accretion, and require a level of developmental complexity that is only found in some animal clades. Further, we suggest that, parallel to the developmental mechanisms controlling character identity, there are “body plan identity mechanisms” (BpIMs) that maintain entire configurations of characters while possessing a mechanistic architecture that is itself stable and traceable through evolutionary change. These BpIMs, we suggest, are entrenched intercellular signaling networks operating between transient embryonic structures that are destined to differentiate into distinct individualized characters. The activity of a BpIM results in a transient long-range integration of the embryo that is highly sensitive to genetic and environmental perturbations, and that can be detected morphologically as a conserved phylotypic stage. This model is illustrated with detailed interpretations of the notochord signaling system and the segment polarity network as candidate BpIMs in vertebrates and arthropods, respectively. We conclude by contrasting the proposed developmental-mechanistic conception of body plans with the phylogenetic notion of ground plans, and sketch the general outlines of an empirical research program on body plan evolution.

The usual equation for both motions of a single planet around the sun and electrons in the deterministic Rutherford-Bohr atomic model is conservative with a singular potential at the origin. When a dissipation is added, new phenomena appear which were investigated thoroughly by R. Ortega and his co-authors between 2014 and 2017, in particular all solutions are bounded and tend to $0$ for $t$ large, some of them with asymptotically spiraling exponentially fast convergence to the center. We provide explicit estimates for the bounds in the general case that we refine under specific restrictions on the initial state, and we give a formal calculation which could be used to determine practically some special asymptotically spiraling orbits. Besides, a related model with exponentially damped central charge or mass gives some explicit exponentially decaying solutions which might help future investigations. An atomic contraction hypothesis related to the asymptotic dying off of solutions proven for the dissipative model might give a solution to some intriguing phenomena observed in paleontology, familiar electrical devices and high scale cosmology

The aim of the study was to assess the impact of two lengths of Nordic walking (NW) training interventions combined with time restricted eating (TRE) on improving body composition parameters, lipid profile, and levels of selected adipokines in women with disrupted body mass. Overweight and obese women (n=55, age: 21-85) were recruited. Four groups were selected: 6 weeks (SG6, n=13) and 12 weeks intervention (SG12, n=13); and two control groups: CON6 (n=13); and CON12 (n=13). The training sessions took place 3 times a week (60 minutes each) and were conducted outdoor under the supervision of professional coach. The training intensity was determined individually. The extended NW program combined with TRE induced a significant weight reduction in SG12 by 1.96 kg (p=0.010) and fat tissue by 1.64 kg (p=0.05). The proposed interventions did not affect LBM, TBW [kg], VFA and lipid profile. The LDL/HDL ratio changed with small size effect. Leptin concentration differed between groups (p=0.006), but not over time. For resistin, the differentiating factor was time (p=0.019, with lower results observed after the intervention). The change in leptin concentration was negatively correlated with its baseline concentration. Extended to 12 weeks, this intervention allows for an improvement in body composition. Neither 6 nor 12 weeks of training and fasting affected the lipoprotein profile.

Over the last fifty years, management plans have become more descriptive with regard to potential sustainability of forest systems, raising questions about the feasibility of implementing management activities and ensuring sustainability of a wide variety of ecosystem services. To assess this issue, we conducted a survey among forest planning and operation research communities to understand their perceptions regarding the potential of a sample of sustainability statements currently used in forest plans to be incorporated into optimization models or other mathematical operations. The results revealed that only a few statements from the sample were deemed to have relatively mature or firm methodologies and data to enable inclusion in modern mathematical models for land use optimization. These statements were mostly related to economic sustainability, offering quantifiable information such as a non-declining flow of wood products over time and limits on the amount of timber harvested per decade. In contrast, sociocultural and, to some extent, ecological statements regarding sustainability were generally perceived to be more difficult to translate into mathematical modeling efforts. Particularly challenging were statements corresponding to sustaining natural or scenic characteristics of a forest. These findings may be attributed to various factors, including a lack of measurable indicators for sustainability and a potential lack of understanding about the modeling components and their interactions with planned management activities.

In bilingual communities, social interactions take place in both single- and mixed-language con-texts. Some of the information shared in multilingual conversations is often required in conse-quent social encounters, like interlocutors’ personal information. In this study we explored whether the autobiographical information provided in a single-language context is better re-membered than in an equivalent mixed-language situation. More than 400 Basque-Spanish bilin-gual (pre-)teenagers were presented with new persons who introduced themselves by either using only Spanish or only Basque, or by inter-sententially mixing both languages. Different memory measures were collected immediately after the initial exposure to the new pieces of information (immediate recall and recognition) and on the day after (delayed recall and recognition). In none of the time points was the information provided in a mixed-language fashion worse remembered than that provided in a strict one-language context. Interestingly, the variability across partici-pants in their sociodemographic and linguistic variables had a negligible impact on the effects. These results are discussed considering their social and educational implications for bilingual communities.

Music and language are hypothesized to share neural resources, particularly at the level of syntax processing. Recent reports suggest that attention modulates this sharing of neural resources, but the time-course of the effects of attention, and the degree to which attention operates similarly on music and language, are yet unclear. In this EEG study we manipulate the syntactic structure of simultaneously presented musical chord progressions and garden-path sentences in a modified rapid serial visual presentation paradigm, while varying top-down attentional demands to the two modalities. The Early Right Anterior Negativity (ERAN) was observed in response to both attended and unattended musical syntax violations. In contrast, an N400 was only observed in response to attended linguistic syntax violations, and a P3 only in response to attended musical syntax violations. Results show that top-down allocation of attention indeed affects the processing of syntax in both music and language, with different neural resources acting upon the two modalities particularly at later stages of cognitive processing. However, the processing of musical syntax at an earlier stage of the perceptual-cognitive pathway, as indexed by the ERAN, is partially automatic, and is strongly indicative of separate neural resources for music and language.

Hemodynamic relevance of differently located stenoses of the internal jugular veins remains undetermined. It particularly concerns nozzle-like strictures in the upper parts of these veins and stenotic jugular valves located at the end of this vein. This study was aimed at understanding flow disturbances caused by such stenoses. Computational fluid dynamics software, Flowsquare+, was used. We constructed 3-dimensional models of venous outflow, comprising two alternative routes: tube representing the internal jugular vein and irregular network representing the vertebral veins. At the beginning of the tube representing the internal jugular vein, differently-shaped and sized short strictures, representing nozzle-like strictures were built in. At the end of this tube, differently-shaped membranes, representing the jugular valve, were built in. With the use of computational fluid dynamics modeling, we studied how these two obstacles influenced the outflow. We found that the most relevant outflow disturbances were evoked by nozzle-like strictures in the upper part of the internal jugular vein that were either small, long or asymmetrically positioned. Very tight stenotic valves and septum-like malformed valve were equally hemodynamically relevant. These findings suggest that both upper and lower strictures of the internal jugular vein can be of clinical significance.

This review paper investigates the intricate relationship between sexuality and body dysmorphia, emphasizing its profound impact on mental health. Through a critical examination of existing literature, this study illuminates the complex psychological, societal, and cultural dynamics at play within this intersection. Special attention is given to the influence of media representations and societal beauty standards in shaping individual perceptions and experiences. Furthermore, the paper discusses various therapeutic interventions designed to address this interplay, underscoring the necessity for mental health practices that are both sensitive and adaptable to the nuances of these issues. Ultimately, this work enriches the discourse on body image and sexual health, offering critical insights for clinical interventions and highlighting areas for further research.