Language barriers exacerbate health disparities among minorities. This study highlights the efficacy of simple interventions in mitigating language barriers, optimizing clinic efficiency, and enhancing preparedness for surgical procedures in Spanish-speaking (SS) females diagnosed with breast cancer in a safety net community hospital. A bilingual questionnaire was given to English and SS patients during their initial clinic visit. Additionally, a bilingual handout on surgical options and postoperative care was provided to selected surgical patients. Among the 41 patients studied, those who completed the questionnaire had an average encounter time of 27.06 minutes, compared to 45.26 minutes for those who did not. For SS patients, the average encounter time with the questionnaire was 34.07 minutes versus 46.54 minutes without it. The average cost of a medical interpreter was $17.04 with the questionnaire, versus $23.27 without. All SS patients who received the educational handout expressed greater satisfaction and understanding of their treatment plans and were less likely to require additional pre-surgery visits. This study demonstrated how the simple implementation of a translated questionnaire can reduce interpretation cost, encounter time, and improve clinic flow. Furthermore, utilizing a bilingual surgical educational handout enhances patient understanding, surgery readiness and reduces unnecessary additional visits.

This survey provides a comprehensive overview of in-context learning (ICL) in large language models (LLMs), a phenomenon where models can adapt to new tasks without parameter updates by leveraging task-relevant information within the input context. We explore the definition and mechanisms of ICL, investigate the factors contributing to its emergence, and discuss strategies for optimizing and effectively utilizing ICL in various applications. Through a systematic review of recent literature, we first clarify what ICL is, distinguishing it from traditional fine-tuning approaches and highlighting its unique characteristics. We then delve into the underlying causes of ICL, examining theories ranging from implicit meta-learning during pre-training to the emergence of task vectors in LLMs. The survey also covers various approaches to enhance ICL performance, including prompt engineering techniques, demonstration selection strategies, and methods for improving generalization across diverse tasks. Additionally, we discuss the limitations and challenges of ICL, such as its sensitivity to demonstration ordering and potential biases. By synthesizing findings from numerous studies, we aim to provide researchers and practitioners with a clear understanding of the current state of ICL research, its practical implications, and promising directions for future investigation. This survey serves as a valuable resource for those seeking to leverage ICL capabilities in LLMs and contributes to the ongoing discourse on the remarkable adaptability of these models.

Only languages with abundant resources are given the opportunity to build speech recognition systems, whereas low resource languages are disregarded. This project attempts to create a system that can normally convert speech to text in the Myanmar (Burmese) language, a language part of low resource languages. The Burmese language is extremely intricate and is made up of vowels, consonants, tones, and stress. There are 10 numbers, 12 vowels, and 33 letters in the Myanmar alphabet. Deep learning allows for the evolution of numerous algorithms while eradicating ignorance and even low-resource languages may be turned into speech-to-text systems with a small quantity of data. The procedures required to construct a successful speech recognition system that can assist both the corporate sector and society will be covered in this project, including data collecting, preprocessing, modeling, prototyping, assessment, and ultimately deployment.

In the dynamic landscape of E-commerce, the proliferation of user-generated queries regarding products highlights the critical need for advanced automatic question answering (AQA) systems. These systems are instrumental in harnessing the vast array of information available online to deliver immediate and informative responses to potential buyers. Recognizing the complexity of such queries, which often require synthesis of reviews, product specifications, and responses to similar questions, we introduce a sophisticated model, the Comprehensive E-commerce Answer Generation System (CEAGS). This model adeptly navigates the challenges posed by irrelevant data and sentiment ambiguity in user-generated content. Our approach leverages a dual-phase process of relevance determination and sentiment clarity, setting the stage for a transformative response generation mechanism. Empirical analyses reveal the superiority of CEAGS, with our relevance assessment framework outstripping existing models by a notable margin in precision metrics, and our answer generation module achieving unprecedented gains in content preservation and coherence. Notably, CEAGS marks a pioneering contribution to the E-commerce domain by integrating disparate information sources to formulate responses that are not only accurate but also contextually rich and user-centric.

This paper will argue that memory from early childhood underlies many practices and beliefs that we commonly refer to as “religious”. The consciousness of young children does not yet have certain features that characterizes adult consciousness. This paper will concentrate on four of these: (I) a reality that is recognizable; (II) a sense of temporal duration; (III) a sense of self; (IV) an experience of the world that is deeply affected by our acquaintance with (a) language. Absence of these features presumably characterizes the consciousness of infants. It also often characterizes mystical experiences. The paper will argue that the human tendency to engage in so-called religious practices and beliefs makes most sense on the assumption that adults somehow preserve the memory of their state of being in childhood.

This paper examines the potential of large language models, specifically ChatGPT, BingChat, and Bard, in enhancing the work of teachers in various educational settings. These models, powered by cutting-edge artificial intelligence, offer a wide range of applications that can aid educators in curriculum development, personalized instruction, student engagement, and administrative tasks. By harnessing the capabilities of these language models, teachers can not only streamline their workload but also improve the overall quality of education. This paper explores the ways in which these models can empower educators and revolutionize teaching practices.

The daVinci Surgical Robot has revolutionized minimally invasive surgery by enabling greater accuracy and less invasive procedures. However, the system lacks advanced features and autonomy necessary for it to function as a true partner. To enhance its usability, we introduce the implementation of a ChatGPT-based natural language robot interface. Overall, our integration of a ChatGPT-enabled daVinci Surgical Robot has potential to expand the utility of the surgical platform by supplying a more accessible interface. Our system can listen to the operator speak and, through the ChatGPT-enabled interface, translate the sentence and context to execute specific commands to alter the robot’s behavior or to activate certain features. For instance, the surgeon could say (even in Spanish) “please track my left tool” and the system will translate the sentence into a specific track command. This specific error-checked command will then be sent to the hardware which will respond by controlling the camera of the system to continuously adjust to center the left tool in the field of view. We have implemented many commands including “Find my tools” (tools that are not in the field of view) or start/stop recording that can be triggered based on a natural conversational context. Here, we present details of our prototype system, give some accuracy results, and explore its potential implications and limitations. We also discuss how artificial intelligence tools (such as ChatGPT) of the future could be leveraged by robotic surgeons to reduce errors and enhance the efficiency and safety of surgical procedures and even ask for help.

This paper aims to provide an overview of the complete process in the development of a Domain-Specific Language (DSL). It explains the construction steps such as preliminary research, language implementation, and evaluation. Moreover, it provides details for different key components which are commonly found in the DSLs such as the abstraction layer, DSL metamodel, and the applications. It also explains the general limitations related to the Domain-Specific Languages for Workflows.

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

Countless disasters have resulted from climate change, causing severe damage to infrastructure and the economy. These disasters have significant societal impacts, necessitating mental health services for the millions affected. To prepare for and respond effectively to such events, it is crucial to understand people's emotions and the life incidents they experience both before and after a disaster strikes. In this case study, we mined the emotions and life incidents expressed on Twitter during the 2017 Hurricane Harvey. Between August 20 and August 30, we collected a dataset of approximately 400,000 public tweets related to the storm. Using a BERT-based model, we predicted the emotions associated with each tweet. Following emotion prediction, we applied various natural language processing techniques to analyze the content of the tweets, aiming to uncover the 'topics' discussed alongside specific 'emotions.' To efficiently identify these topics, we utilized the Latent Dirichlet Allocation (LDA) technique for topic modeling, which allowed us to bypass manual content analysis and extract meaningful patterns from the data. However, rather than stopping at topic identification, we further refined our analysis by integrating Graph Neural Networks (GNN) and Large Language Models (LLM). The GNN was employed to generate embeddings and construct a similarity graph of the tweets, which was then used to optimize clustering. This step ensured that related tweets were accurately grouped together based on their content and context. Subsequently, we used an LLM to automatically generate descriptive names for each event cluster. This advanced approach allowed us to identify and label life incidents automatically, providing a nuanced understanding of the key issues faced by the community during the hurricane. The results revealed significant emotional shifts as the hurricane reached its most devastating phase. These shifts coincided with pressing concerns and demands from the population, such as evacuation plans, animal safety, safety updates, public policy, help requests, flood impacts, and the spread of fake news. By combining GNN and LLM for automatic event name prediction, our study not only mapped out the emotions but also accurately categorized the specific life incidents that were most relevant during the disaster, offering critical insights for disaster preparedness and response strategies.

Sentiment analysis is a technique used to understand the publics’ opinion towards an event, product, or organization. For example, positive or negative opinion or attitude towards electric vehicle (EV) brands. This provides companies with valuable insight about the public's opinion of their products and brands. In the field of natural language processing (NLP), transformer models have shown great performances over the traditional machine learning algorithms. However, these models have not been explored extensively in the EV domain. EV companies are becoming signif-icant competitors in the automotive industry and are projected to cover up to 30% of the United States light vehicle market by 2030 [1]. In this study, we present a comparative study of large language models (LLMs) including bidirectional encoder representations from transformers (BERT), robustly optimized BERT approach and generalized autoregressive pretraining for language un-derstanding using Lucid motors and Tesla motors YouTube datasets. Results evidenced LLMs like BERT and her variants are off-the-shelf algorithms for sentiment analysis, specifically, when fi-ne-tuned. Furthermore, our findings presents the need for domain adaptation whilst utilizing LLMs. Finally, the experimental results showed that RoBERTa achieved consistent performance across the EV datasets with a F1 score of at least 92%.

Large language models (LLMs) excel in providing natural language responses that sound authoritative, reflect knowledge of the context area, and can present from a range of varied perspectives. Agent Based Models and Simulation consist of simulated agents that interact within a simulated environment to explore societal, social, and ethical, among other, problems. Agents generate large volumes of data over time and discerning useful and relevant content is an onerous task. LLMs can help in communicating agents’ perspectives on key events by providing natural language narratives. However, these narratives need to be factual, transparent, and reproducible. To this end, we present a structured narrative prompt for sending queries to LLMs. Chi-square tests and Fisher’s Exact tests are applied to assess statistically significant difference in sentiment scores of the narrative messages between simulation generated narratives, ChatGPT-generated narratives, and real tweets. The narrative prompt structure effectively yields narratives with the desired components from ChatGPT. This structure is expected to be extensible across LLMs. In 14 out of 44 categories, ChatGPT generated narratives which has sentiment scores that were not discernibly different, in terms of statistical significance (alpha level of 0.05), from the sentiment expressed in real tweets. Three outcomes are provided: (1) a list of benefits and challenges for LLMs in narrative generation; (2) a structured prompt for requesting narratives of a LLM based on simulated agents’ information; and (3) an assessment of statistical significance in the sentiment prevalence of the generated narratives compared to real tweets. This indicates significant promise in the utilization of LLMs for helping to connect simulated agent’s experiences with real people.

In this paper we analyse spatial variation in Japanese dialectal lexicon by assembling a set of methodologies using theories in variationist linguistics and GIScience, and tools used in historical GIS. Based on historical dialect atlas data, we calculate a linguistic distance matrix across survey localities. The linguistic variation expressed through this distance is contrasted with several measurements, based on spatial distance, utilised to estimate language contact potential across Japan, historically and at present. Further, administrative boundaries are tested for their separation effect. Measuring aggregate association within linguistic variation can contrast previous notions of dialect area formation by detecting continua. Depending on local geographies in spatial subsets, great circle distance, travel distance and travel times explain a similar proportion of the variance in linguistic distance despite the limitations of the latter two. While they explain the majority, two further measurements estimating contact have lower explanatory power: least cost paths modelling contact before the industrial revolution, based on DEM and seafaring, and a linguistic influence index based on settlement hierarchy. Historical domain boundaries and present day prefecture boundaries are found to have a statistically significant effect on dialectal variation. However, the interplay of boundaries and distance is yet to be identified. We claim that a similar methodology can address spatial variation in other digital humanities, given a similar spatial and attribute granularity.

Background: Mental synthesis is the conscious purposeful process of synthesizing a novel mental image from objects stored in memory. In our everyday use of language, we rely on mental synthesis to communicate an infinite number of images with a finite number of words. In typical children, the timeline of mental synthesis acquisition is highly correlated with an increasing vocabulary. Children with ASD, on the other hand, may learn hundreds of words but never acquire mental synthesis. In these individuals, tests assessing vocabulary comprehension may fail to demonstrate the profound deficit in mental synthesis and the resulting inability to understand flexible syntax and spatial prepositions. Objective: We developed a 20-question parent-reported evaluation tool designed to quantitatively assess mental synthesis ability and to serve as a complimentary scale for Autism Treatment Evaluation Checklist (ATEC). Results: Internal reliability was good (Cronbach’s alpha > .9), and the MSEC exhibited adequate test-retest reliability after a three- and nine-months follow up period. The MSEC results positively correlated with the ATEC communication subscale, providing support for construct validity. Moreover, MSEC scores were significantly different for children of different ASD severity levels confirming the known groups validity. Conclusions: This study represents the first step toward the development of an instrument to measure mental synthesis in children with ASD. Although the current empirical evaluation demonstrated strong evidence of excellent psychometric properties, such as validity and reliability, additional studies should be performed to replicate these findings.

In the rapidly evolving field of natural language processing (NLP), the processing of the Chinese language, with its unique complexities, presents significant challenges, especially in the context of Large Language Models (LLMs) like LLaMA2. These challenges are further exacerbated by the presence of non-standardized text prevalent across digital Chinese content. To address these challenges, this paper proposes a novel hybrid approach that seamlessly integrates deep contextual embeddings with Convolutional Neural Networks (CNNs) to enhance the processing of standardized Chinese text. Our approach involves a multi-stage process wherein deep contextual embeddings are first utilized to capture the nuanced semantic relationships within text. Following this, CNNs are employed to identify and exploit structural and syntactic patterns, facilitating a comprehensive understanding of the text. This hybrid model significantly improves LLaMA2’s efficiency and accuracy across various Chinese text processing tasks by ensuring that both semantic depth and structural nuances are accurately captured. The effectiveness of our model is demonstrated through rigorous testing across several benchmarks, showcasing its superiority in processing Chinese text with enhanced accuracy and speed. This research not only contributes to the advancement of text processing capabilities of LLMs but also opens new avenues for their application in tasks such as automated translation and sentiment analysis.

This study aimed to compare procedural learning skills between Spanish-speaking preschool children (ages 4 years to 4 years, 11 months) with Developmental Language Disorder (DLD) and their typically developing (TD) peers. Using the Serial Reaction Time task, participants (30 DLD and 30 TD children) respond to visual stimuli in a sequenced manner over four blocks, followed by a random order block. The task assessed reaction time (RT) and accuracy. Results showed a significant interaction between group and block for reaction time (RT) and accuracy, with children with DLD exhibiting longer RTs and accuracy deficits across blocks. In contrast, the TD group showed higher RT efficiency and accuracy in the sequential blocks and as expected, decreased performance in the random block according to the experimental manipulation. Overall, the results of this investigation suggest that there was no implicit learning in the DLD group, as indicated by the SRT task paradigms of procedural memory. These findings align with the Procedural Deficit Hypothesis (PDH), suggesting that linguistic deficits in the TD population may derive from a general dysfunction of the procedural memory system domain in the Spanish context.

Highly-skilled migrants and refugees finding employment in low-skill vocations, despite professional qualifications and educational background, has become a global tendency, mainly due to the language barrier. Employment prospects for displaced communities are mostly decided by their knowledge of the sublanguage of the vocational domain they are interested in working. Common vocational domains include agriculture, cooking, crafting, construction, and hospitality. The increasing amount of user-generated content in wikis and social networks provides a valuable source of data for data mining, Natural Language Processing and machine learning applications. This paper extends the contribution of the authors’ previous research on automatic vocational domain identification by further analyzing the results of the machine learning experiments with the domain-specific textual data set, considering 2 research directions: a. predictions analysis and b. data balancing. Wrong predictions analysis and the features that contributed to misclassification, along with correct predictions analysis and the features that were the most dominant, contributed to the identification of a primary set of terms for the vocational domains. Data balancing techniques were applied on the data set to observe their impact on the performance of the classification model. A novel 4-step methodology is proposed in this paper for the first time, consisting of successive applications of SMOTE oversampling on imbalanced data. Data oversampling obtains better results than data undersampling in imbalanced data sets, while hybrid approaches perform reasonably well.

Due to the substantial growth of internet users and its spontaneous access via electronic devices, the amount of electronic contents is growing enormously in recent years through instant messaging, social networking posts, blogs, online portals, and other digital platforms. Unfortunately, the misapplication of technologies has boosted with this rapid growth of online content which leads to the rise in suspicious activities. People misuse the web media to disseminate malicious activity, perform the illegal movement, abuse other people, and publicize suspicious contents on the web. The suspicious contents usually available in the form of text, audio or video, whereas text contents have been used in most of the cases to perform suspicious activities. Thus, one of the most challenging issues for NLP researchers is to develop a system that can identify suspicious text efficiently from the specific contents. In this paper, a Machine Learning (ML)-based classification model is proposed (hereafter called STD) to classify Bengali text into non-suspicious and suspicious categories based on its original contents. A set of ML classifiers with various features has been used on our developed corpus, consisting of 7000 Bengali text documents where 5600 documents used for training and 1400 documents used for testing. The performance of the proposed system is compared with the human baseline and existing ML techniques. The SGD classifier `tf-idf’ with the combination of unigram and bigram features are used to achieve the highest accuracy of 84.57%.

Neuraminidase (NA)-based immunity could reduce the severity of infection caused by the new antigenic variants of influenza viruses. Detection of NA-inhibiting (NI) antibodies simultaneously with antibodies to hemagglutinin (HA) may enhance research of antibody response to influenza vaccines. Methods. The study examined 64 pairs of sera from patients vaccinated in 2018 with seasonal inactivated trivalent influenza vaccines (IIVs) with the formula recommended by WHO for the 2018-2019 influenza season. Antibodies to drift influenza viruses A/Guangdong-Maonan/SWL1536/2019(H1N1)pdm09 and A A/Brisbane/34/2018(H3N2) were studied before vaccination and 21 days after vaccination. To assess NI antibodies, we used the enzyme-linked lectin assay (ELLA) with pairs of reassortant viruses A/H6N1 and A/H6N2, and anti-HA antibodies were detected in hemagglutination inhibition (HI) test. The microneutralization (MN) test was performed in the MDCK cell line with A/H6N1 and A/H6N2 viruses. Results. Seasonal IIVs induce a substantial immune response of NI antibodies to influenza A/H1N1pdm09 and A/H3N2 viruses. A significantly reduced ‘herd’ immunity to drift influenza viruses A/H1N1pdm09 and A/H3N2 was shown, compared to previously circulating strains, but most of all to N2. Seasonal IIVs caused increases in antibodies to homologous and drift viruses, but to drift viruses more often among older patients. The level of NI antibodies to later A/H1N1pdm09 virus in response to IIVs was statistically significantly lower among younger people. After IIV vaccination, the total proportions of individuals with HI antibody levels ≥1:40 and NI antibodies ≥ 1:20 were 32.8% to drift A/H1N1pdm09 virus and 17.2% to drift A/H3N2 virus. Antisera containing HI and NI antibodies exhibit neutralizing properties in vitro against viruses with unrelated HA of H6 subtype. Conclusions. Drift A/H1N1pdm09 and A/H3N2 viruses demonstrated significantly lower reactivity with NI and NI antibodies to early influenza viruses. In response to seasonal IIVs, the level of seroprotection increased, including to drift viruses A, but to a greater extent to A/H1N1pdm09. Based on the data obtained, it can be hypothesized that in the case of a mismatched HA, it will most likely be necessary to vaccinate against the N1-containing influenza virus those who under 60 years of age, and the entire population against virus possessing N2.

Background: Gallstones represent a common gastrointestinal disorder, affecting approximately 10-20% of the global population. Oxidative stress plays a pivotal role in the pathogenesis of gallstones; however, there remains a lack of effective clinical indicators to predict their formation. This study addresses this gap by investigating the relationship between oxidative balance score (OBS) and the incidence of gallstones and gallbladder surgery, utilizing data from the National Health and Nutrition Examination Survey (NHANES) from 2017 to 2020. Methods: This study investigated the association between OBS and the occurrence of gallstones and gallbladder surgery by analyzing data from NHANES for the period 2017–2020, adjusting for potential confounders such as age, sex, race, and comorbidities. Various statistical analyses were applied, including multivariate logistic regression, subgroup analyses, and smoothed curve-fitting models.Results: The results revealed a significant negative correlation between OBS and the risks of both gallstones and gallbladder surgery, with each one-unit increase in OBS associated with a 2.6% reduction in gallstone risk (OR=0.974, 95% CI: 0.958-0.990) and a 3.3% reduction in the risk of gallbladder surgery (OR=0.967, 95% CI: 0.950-0.983). Subgroup analyses confirmed the consistency of these associations across various demographic and health-related factors. Furthermore, a dose-response relationship was observed between higher OBS and a reduced risk of gallbladder surgery.Conclusions: This study demonstrates that a higher OBS is associated with a lower incidence of gallstones and reduced rates of gallbladder surgery, offering new insights into the link between oxidative stress levels and gallstone formation as well as surgical intervention. The findings underscore the potential of OBS as a valuable tool for identifying at-risk populations and informing personalized prevention strategies for gallstone disease.

Background: Non-alcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases worldwide, and its incidence has been rising in parallel with the prevalence of obesity and diabetes. A critical marker for the progression of NAFLD is advanced hepatic fibrosis (AHF), which can result in cirrhosis and hepatocellular carcinoma. This study sought to investigate the potential associations between five nutrition-related indices—the geriatric nutritional risk index (GNRI), prognostic nutritional index (PNI), controlled nutritional score (CONUT), triglyceride-total cholesterol-body mass index (TCBI), and albumin/globulin ratio (AGR)—and the progression of NAFLD and AHF. Methods: This study analyzed the association between five nutrition-related indicators and the occurrence of NAFLD and AHF by utilizing data from the National Health and Nutrition Examination Survey (NHANES) from 2017 to 2020.03. After adjusting for demographic, lifestyle, clinical, and laboratory-related factors, the associations were examined employing various statistical techniques, including multivariate logistic regression, subgroup analysis, and smooth curve fitting analysis. Results: This study included 5,514 subjects, of whom 2,088 were diagnosed with NAFLD and 359 with AHF. After adjusting for potential confounding factors using multivariate logistic regression analysis, the results revealed that GNRI, PNI, and TCBI were positively associated with the incidence of NAFLD, while CONUT and AGR demonstrated negative associations with NAFLD. Additionally, GNRI, CONUT, and TCBI were positively associated with the incidence of AHF, while AGR demonstrated a negative association with AHF.Conclusions: The study demonstrates that nutritional status plays a dual role in the progression of NAFLD and AHF, serving as both a risk and protective factor. These findings offer a scientific foundation for the early identification of high-risk populations and the development of individualized nutritional intervention strategies.

With the development of human-computer interaction, the importance of emotion recognition is increasing. Emotion recognition technology provides practical benefits in various industries such as improving user experience, education, and organizational productivity. In education, emotion recognition can enable real-time understanding of students' emotional states and provide tailored feedback, and in the workplace, by monitoring employees' emotional states, it can improve work performance and job satisfaction. Therefore, multimodal-based research that combines text, speech, and video data for emotion recognition is being conducted in various industries. In this study, we propose an emotion recognition method that combines text and speech data, reflecting the characteristics of the Korean language. For text, KoELECTRA is used to perform embedding, and for speech, MFCC and pitch analysis are used to extract features. Finally, a multimodal transformer model is proposed that combines these two data types to perform emotion recognition. The multimodal transformer model processes text and speech data separately, and then learns the interaction between the two modalities through a Cross-Modal Attention mechanism. Through this, the complementary information from text and speech is effectively combined, improving emotion recognition performance. Experimental results show that the proposed model outperforms single-modality models, achieving a high accuracy of 73.13% and an F1-Score of 0.7344 in emotion classification. This study contributes to the advancement of emotion recognition technology by combining various language and modality data, and higher performance can be expected through the integration of additional modalities in the future.

Cross-lingual sentiment analysis is crucial for understanding and interpreting emotions expressed in text across diverse linguistic contexts. However, cross-lingual sentiment analysis faces challenges such as differences in emotional lexicons, data imbalance, and the need for multi-language sentiment normalization. In this study, we propose a novel approach to address these challenges by leveraging low-resource language training techniques to enhance cross-lingual sentiment analysis. Our method aims to bridge the gap in emotional lexicons by adapting sentiment analysis models to diverse linguistic contexts. Additionally, we tackle issues of data imbalance through innovative data augmentation strategies tailored to each language, and we introduce a multi-language sentiment normalization technique to ensure consistent sentiment interpretation across different languages. Our method achieved state-of-the-art results in twelve languages across four domains, demonstrating superior performance in both text-level and sentence-level sentiment analysis tasks. By conducting comprehensive evaluations across diverse linguistic contexts and domains, we showcase the versatility and effectiveness of our approach in achieving top-tier results in cross-lingual sentiment analysis.

In the evolving landscape of digital marketing, cross-channel attribution modeling plays a crucial role in understanding and optimizing the customer journey across various touchpoints. As consumers interact with brands through multiple channels—such as social media, email, search engines, and display ads—accurately attributing conversions to the right channels is vital for optimizing marketing strategies and budgets. However, the advent of stringent privacy regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), has significantly impacted the data collection and analysis processes in digital marketing. This paper explores the challenges and opportunities presented by cross-channel attribution modeling in the age of heightened privacy awareness. It examines how traditional data-driven attribution models, which often rely on tracking individual user behavior, are being adapted or replaced in response to privacy concerns and regulatory requirements. The paper discusses the emergence of privacy-preserving techniques, such as aggregated data analysis, differential privacy, and the use of anonymized data, which aim to balance the need for accurate attribution with the protection of consumer privacy. Furthermore, the paper highlights the role of first-party data and the growing importance of consent management in the collection and utilization of consumer information. It also investigates how marketers are leveraging advancements in artificial intelligence and machine learning to enhance attribution models in a privacy-conscious world. The study concludes by offering best practices for businesses seeking to navigate the complexities of cross-channel attribution in the context of evolving privacy regulations, emphasizing the need for transparency, compliance, and ethical data handling. This research provides valuable insights for marketers, data scientists, and policymakers on how to effectively manage the interplay between accurate attribution and privacy, ensuring that marketing efforts remain effective while respecting consumer rights.

We propose an innovative approach called SMPTC3, designed specifically to enhance security and privacy in cross-chain transaction verification. This approach addresses multi-chain, multi-participant information exchange and large-scale cross-chain transfers, resisting various types of malicious attacks. We leverage the verifiability of cross-chain transactions based on smart contracts and innovatively transform transaction information into confidential sets, organizing them into quadratic secret polynomials. By utilizing secret sharing and random distribution techniques, we construct a secure multiparty computation method, tailored for cross-chain transactions. To enhance the efficiency of cross-chain transactions, we introduce cross-chain batch processing technology, grouping inter-chain transactions into cross-chain transaction sets. Unlike traditional distributed notary technologies, SMPTC3 designates honest participants as a cross-chain notary group, reducing the time required for redundant signature confirmations and significantly lowering the possibility of malicious notaries. Theoretical analysis and empirical experiments demonstrate that SMPTC3 is highly efficient in addressing cross-chain transaction security issues.

The microstructure evolution during the cold rolling and subsequent annealing of Alloy 800H was investigated. Two distinct rolling methods, unidirectional rolling and cross-rolling, were intro-duced. Results show that better ductility was observed in the unidirectional rolling than in cross-rolling, whilst a higher volume fraction of large deformed grains was observed in the cross-rolled plates than in the unidirectionally rolled plates. Recrystallization occurred faster in plates from unidirectional rolling than from cross-rolling.

Bismaleimide (BMI) is a high-performance thermosetting resin widely used in the aerospace, automotive, and mechanical and electronics industries due to its chemical and mechanical stability. It also has the capability of being used as an ink in polyjets used for 3D printing evident from thinner mellamide FTIR bands and faster curing rates as observed from TGA-DSC studies.

Classical Hodgkin lymphoma (cHL) is a highly curable disease (70-80%), even though long-term toxicities, drug resistance, and predicting clinical responses to therapy are major challenges in cHL treatment. To solve these problems, we characterized two cHL cell lines with acquired resistance to doxorubicin, KM-H2dx and HDLM-2dx (HRSdx), generated from KM-H2 and HDLM-2 cells, respectively. HRSdx cells developed cross-resistance to vinblastine, bendamustin, cisplatin, dacarbazine, gem-citabine, brentuximab vedotin (BV), and γ-radiation. Both HDLM-2 and HDLM-2dx cells had an intrinsic resistance to BV but not to the drug MMAE. HDLM-2dx acquired cross-resistance to caelyx. HRSdx cells had in common: decreased CD71, CD80, CD54, cyt-ROS, HLA-DR, DDR1, CD44; increased Bcl-2, CD58, COX2, CD26, CCR5, invasive capability; increased CCL5, TARC, PGE2, TGF-β, and capability to hijack monocytes. In HRSdx cells, less sensitive to DNA damage and oxidative stress, the efflux drug transporters MDR1 and MRP1 were not up-regulated, and doxorubicin accumulated in cytoplasm rather than in nucleus. Both autophagy inhibitor chloroquine and extracellular vesicles (EVs) release inhibitor GW4869 enhanced doxorubicin activity and counteracted doxorubicin resistance. In conclusion, this study identifies common modulated antigens in HRSdx cells, the associated cross-resistance patterns, and new potential therapeutic options to enhance doxorubicin activity and overcome resistance.

The need for cross-modal retrieval (CMR) among users is growing because of the swift advancements in computer vision and natural language processing. Promoting home operating systems is greatly aided by the openKylin home operating system. It is focused on enhancing the functionality of the operating system with safe and dependable operating technologies. Practical implications abound for integrating computer vision research into home operating systems. A multimodal retrieval approach based on openKylin is presented to improve the usability of text search graphs, audio, and video. The overview of pertinent domestic and international research is the main topic of this article. This technological advancement. It then offers details on the design, implementation procedure, and final implementation outcomes for demonstration. The works of natural operations show that this strategy has excellent accuracy and performance.

Aryl chlorides and bromides in the presence of NHC palladium hydroxo dimers of the type [{Pd(µ-OH)Cl(NHC)}2] (where NHC = IPr, SIPr, IMes, SIMes) undergo an efficient and selective Sonogashira cross-coupling with terminal acetylenes. For the coupling of 4-chlorotoluene with phenylacetylene TON = 560000 was observed. The procedure enables high-throughput and selective synthesis of a broad spectrum of unsymmetrically substituted 1,2-diarylacetylenes. Nearly a quantitative amount of HCl was detected within cross-coupling products. Water plays the role of the base and is regenerated in the process so that it does not have to be used in stoichiometric amounts (relative to the limiting reactant). Generation of the active Pd(0) complex proceeds via ethanol oxidation.

Here, we found higher percentage of CD8+ T cells in piglets immunized with CVC1302-adjuvanted inactivated FMDV vaccine. We wonder whether promoted cellular immunity induced by CVC1302-adjuvanted inactivated FMDV vaccine due to the promoted antigen cross-presentation efficiency of OVA by DC mainly via the cytosolic pathways, demonstrated by enhanced levels of lysosomal escape of OVA in DC, loaded with OVA and CVC1302. Higher levels of ROS and significant enhanced elevated lysosomal pH in DC facilitated the lysosomal escape of OVA. Significant enhanced CTL activities were observed in mice immunized with OVA-CVC1302. Overall, CVC1302 increased the cross-presentation of exogenous antigens and the cross-priming of CD8+ T cells by alkalizing the lysosomal pH and facilitating the lysosomal escape of antigen. These studies shed new light on the development of immunopotentiators to improve the cellular immunity induced by vaccine.

For a long time, the transition to adulthood was seen as a troubled period where good development was synonymous with the absence of problems. Positive Youth Development (PYD) emerged as a holistic and strength-based perspective that focuses on the fact that young people may have the internal and external resources for healthy and successful development through five dimensions (5C’s) that empower them: Perceived Competence, Confidence, Character, Connection, and Caring. The aim of this study was to examine the relationship between the global PYD factor (and the 5C’s) and risk behaviors, in addition to analyzing gender differences. This study showed the results of a cross-sectional study of 1044 emerging adults from 11 Spanish universities in 2021. Data collection was performed by applying an online self-report measure. The results showed that the Character was protective against substance abuse, mainly in women, while the connection was related to the participation of betting money and online betting in men. Caring was protective against money bets in the men’s sample. However, controversial results were found regarding Perceived competence, which had a positive association with substance abuse, money bets, and drunk driving. It seems that high levels of Perceived competence, rather than objective competence, were associated with engagement in various risk behaviors, especially in the absence of other C´s. These results are discussed regarding the promotion of PYD to improve successful development and may suggest the need to foster healthy lifestyles and prevent risky behaviors in youth through the 5C's promotion programs within the university context.

The pion-argon cross-section measurement is crucial to understand effects such as final-state interactions, which account for a large source of systematic uncertainty in neutrino oscillation experiments. ProtoDUNE-SP, with its charged particle beam data, can provide such experimental constraints. This paper elaborates the methodology to measure the cross-section on large-scale LArTPCs like ProtoDUNE-SP. We use the 1 GeV MC sample to demonstrate the analysis procedures. The cross-sections for pion kinetic energy ranging from 350 MeV to 950 MeV are measured on the MC. The consistency of the MC results with its input values serves as validation of the method and the procedures, which we will later use to perform measurements on the data sample.

Economic globalisation has promoted the extensive circulation of commodities around the world, and international trade has increasingly been a strong booster for economic growth in China. As an foundation for national economy, the shift of agricultural products (AP) from production to distribution has become the focus of agricultural development, and in recent years, the international trade of agricultural products in China has been the highest in the world. While, cross-border e-commerce (CBE), as one of the important modes of agricultural products circulation, has brought new opportunities for China's fresh agricultural products to carry out online import and export trade under the favourable policies continuously introduced internationally. This research analyses status of APCBE in Yunnan Province, China, and propose strategies for developing CBE of Yunnan AP.

In the field of vision-based robot grasping, effectively leveraging RGB and depth information to accurately determine the position and pose of a target is a critical issue. To address this challenge, we propose a tri-stream cross-modal fusion architecture for 2-DoF visual grasp detection. This architecture facilitates the interaction of RGB and depth bilateral information and is designed to efficiently aggregate multiscale information. Our novel modal interaction module (MIM) with spatial-wise cross-attention algorithm adaptively captures cross-modal feature information. Meanwhile, the channel interaction modules (CIM) further enhance the aggregation of different modal streams. In addition, we efficiently aggregate global multiscale information through a hierarchical structure with skipping connections. To evaluate the performance of our proposed method, we conduct validation experiments on standard public datasets and real robot grasping experiments. We achieve the image-wise detection accuracy of 99.4% and 96.7% on Cornell and Jacquard datasets respectively. The object-wise detection accuracy reaches 97.8% and 94.6% on the same datasets. Furthermore, physical experiments using the 6-DoF Elite robot demonstrate a success rate of 94.5%. These experiments highlight the superior accuracy of our proposed method.

Background: Cardiovascular disease (CVD) has been one of the leading causes of death and disability-adjusted life years lost worldwide. Blood pressure, lipid, and cholesterol are good predictors of CVD risk and correspond upon age and physical fitness. However, few studies have explored the variation trend of CVD risk factors across different populations upon age and their muscle strength. Objective: to analysis the variation tendency of CVD risk factors in blood according to age and relative grip strength among different populations. Method: 25363 participants were recruited in this cross-sectional study and 24709 were included in the analysis. A logistic regression and a Bayesian probabilistic analysis based on Markov Chain Monte Carlo (MCMC) Modeling is conducted to build probability prediction models of hypertension, hyperlipidemia, and hypercholesterolemia according to age, relative grip strength, body weight conditions, and physical activity levels. Results: 1) age might be the main influence factor of hypertension, which is regarded as one of the primary CVD risk factors. However, although keeping a high level of physical activity might have positive effect on preventing hypertension because that individuals with normal body weight and higher physical activity shows a lower probability of being diagnosed with hypertension, it might could not prevent individuals from getting hypertension with age. 2) After 60, individuals of normal body weight seem more likely to have hyperlipidemia than those are overweight or obese. 3) Larger relative grip strength might not be able to offset the negative effects of obesity, overweight and physical inactivity on hyperlipidemia. 4) The probability of getting hypercholesterolemia varies less with age and relative grip strength. Conclusion: Body weight management and keeping high levels of physical activity are recommended at any age. It might benefit to increase some bodyweight after 60 years old.

Background: New insecticides with novel modes of action such as neonicotinoids have recently been recommended for public health use by WHO. Resistance monitoring of such novel insecticides requires a robust protocol to monitor the development of resistance in natural populations. In this study, we comparatively used three different solvents to assess the susceptibility of malaria vectors to neonicotinoids across Africa.Methods: Mosquitoes were collected from May to July 2021 from three agricultural settings in Cameroon (Njombe-Penja, Nkolondom, and Mangoum), the Democratic Republic of Congo (Ndjili-Brasserie), Ghana (Atatam), and Uganda (Mayuge). Using the CDC bottle test, we compared the effect of three different solvents (ethanol, acetone, acetone+MERO) on the efficacy of neonicotinoids against Anopheles gambiae s.l. In addition, TaqMan assays were used to genotype key pyrethroid-resistant markers in An. gambiae and to evaluate potential cross-resistance between pyrethroids and clothianidin.Results: Lower mortalities were observed for all populations when using absolute ethanol or acetone alone as solvent (11.4- 51.9% mortality for Nkolondom, 31.7- 48.2% for Mangoum, 34.6- 56.1% for Mayuge, 39.4- 45.6% for Atatam, 83.7- 89.3% for Congo and 71.05- 95.9% for Njombe pendja) compared to acetone + MERO for which 100% mortality was observed for all the populations. Synergist assays (PBO, DEM and DEF) revealed a significant increase of mortality suggesting that metabolic resistance mechanisms are contributing to the reduced susceptibility. A negative association was observed between the L1014F-kdr mutation and clothianidin resistance with a greater frequency of homozygote resistant mosquitoes among the dead than among survivors (OR=0.5; P=0.02). However, the I114T-GSTe2 was in contrast significantly associated with a greater ability to survive clothianidin with a higher frequency of homozygote resistant among survivors than other genotypes (OR=2.10; P=0.013). Conclusions: This study revealed a contrasted susceptibility pattern depending on the solvents with ethanol/acetone resulting in lower mortality, thus possibly overestimating resistance, whereas the addition of MERO consistently increased the efficacy of neonicotinoids in terms of percentage mortalities and time to final mortality. The addition of MERO could however prevent the early detection of resistance development. We therefore recommend monitoring susceptibility using both acetone alone and acetone+MERO (8-10µg/ml for clothianidin) to capture the accurate resistance profile of the mosquito populations.

The vibrational excitation cross section of a diatomic molecule by positron impact is obtained using wavepacket propagation techniques. The dynamics was carried on a two-dimensional potential energy surface which couples a hydrogen-like harmonic oscillator to a positron via a spherically symmetric correlation polarization potential. The cross section for the excitation of the first vibrational mode is in good agreement with previous reports. Our model suggests that a positron couples to the target vibration by responding instantly to an interaction potential which depends on the target vibrational coordinate.

This paper presents a study on the development of the cross-curricular learning outcome (CCLO) "Ethical, environmental and professional responsibility" by the students of different Bache-lor’sDegrees taught at Universitat Politècnica de València. The work and development of this learning outcome entails great complexity, given the double dimension of responsibility that it involves. At the end of their training at the university, students are expected to show ethical, en-vironmental, and professional responsibility towards themselves and others. Interviews have been conducted with lecturers who work and assess this outcome in their subjects, most/all of them related to science and engineering. The objective was to identify the learning approach used at the different subjects to guarantee the acquisition of this CCLO by the students. A focus group has also been carried out with students to determine the importance they give to this learning outcome, and to know their degree of satisfaction with the training received. The methodology used to obtain the data from lecturers and students and to process the information to get a precise diagnosis is fully described in the paper. Results are satisfactory to some extent: most of the lecturers carry out appropriate activities and most of students achieve the expected proficiency level. Finally, recommendations are given to improve the development of this cross-curricular learning outcome.

Light absorption in single nanowires (NWs) is one of the most crucial factors for photovoltaic applications. In this paper, we carried out a detailed investigation of light absorption in single rectangular NWs (RNWs). We show that the RNWs exhibit improved light absorption compared with the square NWs (SNWs), which can be attributed to the symmetry-breaking structure that can increase the light path length by increasing the vertical side and the enhanced leaky mode resonances (LMRs) by decreasing the horizontal side. We found that the light absorption in silicon RNWs can be enhanced by engineering the horizontal and vertical sides, the photocurrent is significantly increased by 276.5% or 82.9% compared with that of the SNWs with the same side length as the horizontal side of 100 nm or the vertical side of 1000 nm, respectively. This work provides an effective way for designing high-efficiency single NW photovoltaic devices based on the symmetry breaking from the SNWs to RNWs.

Background: Physical activity is recommended to mitigate the incidence of obesity, but delivery of community wide initiatives is cumbersome. The challenges met by such programs are magnified when implementation transpires in the remote villages of Alaska. To overcome the difficulty of this challenge in the Arctic, the Skiku/AK Nordic cross-country skiing program was developed. The objective of this study was to evaluate whether access to Skiku/AK Nordic program would promote physical activity levels that met the daily recommendations for physical activity in Alaska Native children. Methods: Eight children (4 females and 4 males; 10±2 years/age) were recruited from Kaktovik, Alaska for participation in this study. Expert coaches and staff provided one week of cross-country ski instruction and access to ski equipment. Physical activity was monitored using ActiGraph GT3X+ accelerometers. Data collected from the devices was then downloaded and analyzed using ActiLife software. Results: The participants expended ~586 calories/day devoted to physical activity. Light and moderate physical activity was 68±38 minutes/day and 447±248 minutes/day, respectively. Conclusions: Delivery of the Skiku/AK Nordic program promoted favorable levels of physical activity in Alaska Native children. Further research is needed to assess the longitudinal and seasonal effectiveness of the Skiku/AK Nordic program.

Light trapping in single nanowires (NWs) are of vital importance for photovoltaic applications. However, circular NWs (CNWs) can limit its light-trapping ability due to high geometrical symmetry. In this work, we present a detailed study of light trapping in single NWs with an elliptical cross-section (ENWs). We demonstrate that the ENWs exhibit significantly enhanced light trapping compared with the CNWs, which can be ascribed to the symmetry-broken structure that can orthogonalize the direction of light illumination and the leaky mode resonances (LMRs). That is, the elliptical cross-section can simultaneously increase the light path length by increasing the vertical axis and reshape the LMR modes by decreasing the horizontal axis. We found that the light absorption can be engineered via tuning the horizontal and vertical axes, the photocurrent is significantly enhanced by 374.0% (150.3%, 74.1%) or 146.1% (61.0%, 35.3%) in comparison with that of the CNWs with the same diameter as the horizontal axis of 100 (200, 400) nm or the vertical axis of 1000 nm, respectively. This work advances our understanding of how to improve light trapping based on the symmetry breaking from the CNWs to ENWs and provides a rational way for designing high-efficiency single or self-assembled NW photovoltaic devices.

Purinergic receptors (P2X) are ATP-gated ion channels with an elusive role in the CNS. While the P2X2 and P2X4 subtypes are widely expressed in most neurons (i.e. at the edge of the postsynaptic densities of excitatory synapses), the direct contribution of P2X to synaptic transmission is uncertain. Several P2X have been shown to participate in receptor cross-talk: an interaction where one receptor (e.g. P2X2) influences the activity of another (e.g. GABA or AMPA receptors.) In this study, we tested for interactions between P2X2 or P2X4 and N-methyl-D-aspartate receptors (NMDARs). Using two-electrode voltage-clamp electrophysiology experiments in X. laevis oocytes, we demonstrate that both P2X2 and P2X4 interact with NMDARs in an inhibitory manner. When investigating the molecular domains responsible for this phenomenon, we found that the P2X2 c-terminus (CT) could interfere with P2X2 and P2X4 interactions with NMDARs. We also report that 11 distal CT residues on the P2X4 facilitate the P2X4-NMDAR interaction, and that a peptide consisting of these P2X4 CT residues (11C) can disrupt the interaction between NMDARs and P2X2 or P2X4. Collectively, these results provide new evidence for the modulatory nature of P2X2 and P2X4, suggesting they might play a more nuanced role in the CNS.

In wideband radar systems, the performance of motion parameters estimation can significantly affect the performance of object detection and the quality of inverse synthetic aperture radar (ISAR) imaging. Although the traditional motion parameters estimation methods can reduce the range migration (RM) and Doppler frequency migration (DFM) effects in ISAR imaging, the computational complexity is high. In this paper, we propose a new fast non-searching motion parameters estimation method based on cross-correlation of adjacent echoes (CCAE) for wideband LFM signals. A cross-correlation operation is carried out for two adjacent echo signals, then the motion parameters can be calculated by estimating the frequency of the correlation result. The proposed CCAE method can be applied directly to the stretching system, which is commonly adopted in wideband radar systems. Simulational results demonstrate that the new method can achieve better estimation performances, with much lower computational cost, compared with existing methods. The experimental results on real radar data sets are also evaluated to verify the effectiveness and superiority of the proposed method compared to the state-of-the-art existing methods.

Sentiment analysis has emerged as a vital application of Natural Language Processing (NLP), enabling the extraction of subjective information from textual data. This study conducts a comparative analysis of various machine learning algorithms employed in sentiment analysis, including traditional models such as Naïve Bayes, Support Vector Machines (SVM), and Decision Trees, as well as contemporary techniques such as Random Forest, Gradient Boosting, and deep learning approaches like Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks. Using a comprehensive dataset sourced from social media platforms and product reviews, we evaluate the performance of these algorithms based on accuracy, precision, recall, and F1-score. Our findings highlight the strengths and weaknesses of each algorithm in handling sentiment classification tasks, emphasizing the influence of feature extraction techniques, such as Bag of Words and Word Embeddings, on model performance. The results indicate that while deep learning models generally outperform traditional algorithms, the choice of algorithm should be tailored to the specific context and requirements of the analysis. This study contributes to the ongoing discourse on the efficacy of machine learning methods in NLP, offering insights that can guide researchers and practitioners in selecting appropriate algorithms for sentiment analysis tasks.

This study examines the impact of prompt engineering on large language models (LLMs), focusing on a comparison between multitasking and single-task prompts. Specifically, we explore whether a single prompt handling multiple tasks — such as Named Entity Recognition (NER), sentiment analysis, and JSON output formatting — can achieve similar efficiency and accuracy to dedicated single-task prompts. The evaluation uses a combination of performance metrics to provide a comprehensive analysis of output quality. Experiments were conducted using a selection of open-source LLMs, including LLama3.1 8B, Qwen2 7B, Mistral 7B, Phi3 Medium, and Gemma2 9B. Results show that single-task prompts do not consistently outperform multitasking prompts, highlighting the significant influence of the model’s data and architecture on performance.

Although large language models demonstrate impressive capabilities, they sometimes generate irrelevant or nonsensical text, or produce outputs that deviate from the provided source input—an occurrence commonly referred to as hallucination. To mitigate this issue, we introduce a novel decoding method that incorporates both factual and hallucination prompts(DFHP), utilizing a contrastive output distribution to highlight the disparity in output probabilities between model predictions influenced by factual prompts and those affected by hallucination prompts. Experiments on both multiple-choice and text generation tasks show that our approach significantly enhances the factual accuracy of large language models without requiring additional training.

In the digital age, social media platforms have become a significant source of both information and misinformation, presenting challenges and opportunities for threat intelligence. Natural Language Processing (NLP) has emerged as a powerful tool for extracting actionable insights from the vast amounts of unstructured text generated on these platforms. This paper explores the application of NLP techniques to enhance social media threat intelligence, focusing on methodologies for detecting and analyzing threats such as disinformation, cyberbullying, and extremist content. We examine various NLP approaches, including sentiment analysis, topic modeling, and entity recognition, and their effectiveness in identifying and mitigating potential risks. The paper also addresses the challenges associated with processing social media data, such as dealing with slang, context, and multilingual content. By leveraging NLP, organizations can improve their ability to monitor and respond to emerging threats in real-time, ultimately enhancing their overall security posture. The findings suggest that while NLP offers significant benefits, it must be complemented by human expertise and ethical considerations to ensure accurate and responsible threat assessment.

Limiting training data in low-resource languages is a barrier to Natural Language Processing (NLP). Regardless, languages with thousands of users worldwide require improved NLP capabilities. The Table-to-Text task—typically develops natural language descriptions using data tables, tests model reasoning abilities—although it is especially tough in multi-language settings. System output frequently lack credit to their underlying data. Intermediate planning strategies—which include Question-Answer (QA) blueprints—improving summarization tasks by presenting related QA pair prior to verbalization. Therefore, this study analyses whether QA blueprints improve multilingual Table-to-Text output attribution. An enlarged multilingual dataset, encompassing African languages, contains QA blueprints that were produced and filtered heuristically. This dataset is used to fine-tune sequence-to-sequence model (transformers)—both with and without blueprints. Two configurations are evaluated—English reference blueprints with language target verbalization and modified blueprints. These results show that English-only models benefit from blueprints, whereas multi language models cannot. Errors in machine-translating blueprints provide challenges, as do models that rely on their generated blueprints.

As Large Language Models (LLMs) increasingly accommodate larger inputs, context windows spanning hundreds of thousands or even millions of tokens are touted as promising for a wide array of applications. However, the potential decay in reasoning ability for larger inputs may compromise their utility. This study introduces a new benchmark called Find the Origin, which progressively tests the efficacy of LLMs on a simple intellectual task as the size of the context window increases. The test, conducted on 14 different LLMs for comparative analysis, demonstrated that reasoning ability is dependent on input size. Additionally, three independent tests were performed with the GPT-4 Turbo model to demonstrate its reasoning degradation in different contexts as input size increases.

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.

This paper explores the role of open-source large language models in IoT cybersecurity world. The threats of malicious activity on the Internet and the loss of private information are very real and lead to serious consequences. The purpose of this paper is to investigate how open source-large language models can help to defend against the growing threat of cyber-crimes. We conducted our experiments in two directions. The first one is a security assistant that helps with cybersecurity best practices advices. The second one is a how large language model can simulate a vulnerable IoT system. For both types of experiments, the interactive mode of operation of the language model is used. In the context of the cybersecurity research, a major advantage of the locally installed open-sourced large language models is that they do not share sensitive data with a remote system in a cloud. The paper concludes by discussing the potential impact of open-source large language models on cybersecurity research and recommends future research directions.

Word Sense Disambiguation (WSD) stands as a persistent challenge within the Natural Language Processing (NLP) community. While various NLP packages exist, the Lesk algorithm in the NLTK library, a widely recognized tool, demonstrates suboptimal accuracy. Conversely, the application of deep neural networks offers heightened classification precision, yet their practical utility is constrained by demanding memory requirements. This research paper introduces an innovative method addressing WSD challenges by optimizing memory usage without compromising state-of-the-art accuracy. The presented methodology facilitates the development of WSD system that seamlessly integrates into NLP tasks, resembling the functionality offered by the NLTK library. Furthermore, this paper advocates treating the BERT language model as a gold standard, proposing modifications to manually annotated datasets and semantic dictionaries such as WordNet to enhance WSD accuracy. The empirical validation through a series of experiments establishes the effectiveness of the proposed method, achieving state-of-the-art performance across multiple WSD datasets. This contribution represents advancement in mitigating the challenges associated with WSD, offering a practical solution for integration into NLP applications.

The exploration and understanding of sentiments and emotions through textual analysis are foundational to advancements in natural language processing (NLP). While sentiment analysis, which involves discerning the polarity of text, has been extensively explored, the nuanced detection of complex emotions within textual content is still an emerging area of study. In this paper, we introduce EmoLeverage, a model leveraging Transformer architecture and enhanced with a novel Fusion Adapter module. This model aims to deepen emotion detection capabilities by drawing insights from fundamental sentiment analysis tasks, focusing exclusively on textual data. EmoLeverage sets a new standard in the field by demonstrating that deep, nuanced understanding of emotions is achievable through text alone, challenging and surpassing existing methodologies in emotion identification on key datasets. This is a significant step forward, considering the complexity of human emotions and the subtleties involved in their expression through language. By concentrating on textual input, EmoLeverage offers a promising avenue for applications where audio or visual data is not available, highlighting the versatility and potential of text-based emotional analysis. Our approach illustrates the untapped potential of leveraging sophisticated NLP techniques to enhance the detection and comprehension of a wide array of emotions from text. The success of EmoLeverage in outperforming conventional models in emotion recognition tasks underscores the model's effectiveness in parsing and understanding the intricate expressions of human emotions through linguistic cues alone. This achievement paves the way for more accurate, efficient, and nuanced emotion detection tools, potentially transforming various sectors by providing deeper insights into human emotional states through text.

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.

Children with hearing loss have been found to have significantly more behavioral and emotional challenges than their typically hearing peers, though these outcomes are variable at the individual level. Working memory deficits have been found to relate to executive functioning and overall emotion regulation, leading to behavior challenges. Language development is essential for development of social relationships and communicating one’s needs - this may lead to distress when children cannot communicate effectively. Based on prior findings in children with hearing loss and their typically hearing peers, working memory and language skills were hypothesized to be related to parent and teacher report of socioemotional functioning. Participants were 35 children with hearing loss (66% female, M = 5.17 years old, SD = ±1.97) whose language, working memory, and socioemotional functioning were evaluated during the course of treatment and educational planning. Bivariate analyses indicated that working memory was related to a number of socioemotional domains (e.g., functional communication, atypicality, withdrawal), as were language scores (e.g., social skills, inattention). The direction of these associations was such that stronger working memory and language skills were related to more regulated socioemotional functioning. A call to action of the current study includes more education with regard to profiles and presentations of children with hearing loss, and an early focus on socioemotional learning to foster the development of regulatory skills.

Language bias stands as a noteworthy concern in Visual Question Answering (VQA), wherein models tend to rely on spurious correlations between questions and answers for prediction. This prevents the models from effectively generalizing, leading to a decrease in performance. To address this bias, we propose a novel modality fusion collaborative de-biasing algorithm (CoD). In our approach, bias is considered as the model’s neglect of information from a particular modality during prediction. We employ a collaborative training approach to facilitate mutual modeling between different modalities, achieving efficient feature fusion and enabling the model to fully leverage multi-modal knowledge for prediction. Our experiments on various datasets, including VQA-CP v2, VQA v2, and VQA-VS, using different validation strategies, demonstrate the effectiveness of our approach. Notably, employing a basic baseline model resulted in an accuracy of 60.14% on VQA-CP v2.

Our natural language approach concerns syntactic analysis using a dedicated Javascript library - wink-nlp - and semantic analysis based on Prolog programming language, facilitated by another Javascript library - tau-prolog - that allows defining logical programs, declaring rules and checking for goals inside Javascript language. Firstly, our program splits the original text into sentences, than into tokens and identifies each part of the sentence, dynamically maps entities into Prolog rules, then check the spelling accordingly to the Definite Clause Grammar (DCG) by querying the pre-defined program for initial goals (the sentence itself). Basically, we let the parser infer its own rules from the syntactic point of view, then check the grammar from a semantic perspective against the DCG inside the same work flow or pipeline of steps.The provided article combine the usage of wink-nlp and tau-prolog packages for natural language processing (NLP) and understanding (NLU).

This publication delves into the intricate world of human communication, extending beyond the realm of spoken and written language to explore the profound influence of non-verbal communication. Focusing on body language—comprising gestures, facial expressions, postures, and movements—it reveals the pivotal role it plays in conveying emotions, intentions, and psychological states. The rapidly evolving fields of neuroscience and cognitive science are investigating the neural mechanisms and cognitive processes that underpin the interpretation and utilization of these non-verbal cues. Notably, the amygdala's involvement in processing emotional facial expressions has provided a neurological foundation for our emotional understanding in non-verbal communication. Concurrently, cognitive psychology sheds light on how perception, attention, and memory influence our interpretation of non-verbal cues and communication strategies.In our modern, technologically advancing society characterized by global interconnectivity and cultural diversity, the significance of non-verbal communication, especially in digital and virtual contexts, is paramount. The transformation of non-verbal expression into digital symbols like emoticons and emojis raises questions regarding its ability to maintain authenticity and emotional understanding. This publication endeavors to comprehensively explore the fundamental role of body language and non-verbal communication in contemporary society. By integrating insights from psychology, neuroscience, social sciences, and digital technologies, it aims to elucidate the underlying mechanisms guiding non-verbal communication. Additionally, it delves into the implications of non-verbal communication across diverse domains, including interpersonal communication, the workplace, politics, advertising, and the virtual world.The research presented herein strives to offer a thorough understanding of interpersonal dynamics, relationships, and social behavior, with the goal of promoting the development of more effective, conscious, and ethically guided communication strategies. Ultimately, this analysis aspires to inspire further interdisciplinary research and reflection, addressing emerging challenges and fully leveraging the potential of non-verbal communication in modern society. This, in turn, guides our path towards a communication landscape that is more inclusive, authentic, and empathetic.

Abstract: Background: Restless Legs Syndrome /Willis-Ekbom disease (RLS/WED) has occasionally -but not consistently- been associated with cognitive, and most notably, language and executive impairment. The present study was conducted to investigate the cognitive trajectories of older individuals with RLS/WED. Methods: Participants were drawn from the randomly selected, older (>64 years), population-based HELIAD cohort. Individuals without dementia, with available neuropsychological evaluations at baseline and follow-up were considered for potential eligibility. A comprehensive assessment examining five principal components of cognition (memory, visuo-spatial ability, attention, executive function, and language) was administered to the partici-pants. Generalized estimating equations analysis was used to examine the unadjusted and adjusted (for critical factors and covariates) effects of RLS/WED on cognition over time. Results: A total of 1003 predominantly female, older (72.9 ±4.9 years) partici-pants, with follow-up evaluations after a mean of 3.09 ±0.85 years and without demen-tia at baseline and follow-up were included in the present study. Among them, 81 were diagnosed with RLS/WED at baseline. Global cognition, memory, attention, executive and visuo-perceptual skills did not differ between those with and without RLS/WED. However, the RLS/WED group performed worse on language at baseline by 0.249 of a standard deviation, while demonstrating a mitigated language decline over time, by 0.063 of a standard deviation. The unadjusted models yielded similar results. Conclu-sions: Our findings were indicative of a baseline language disadvantage among older individuals with RLS/WED, but the initial discrepancy tends to dissolve over time.

Teaching and learning a new language is a complex process that requires more than just memorizing vocabulary and grammar rules. Immersing oneself in the target language's culture and history is essential for achieving fluency and accuracy. In recent years, researchers have explored the potential of music as a tool for language learning. This paper discusses the benefits of using music in language learning, including improved listening comprehension, pronunciation and fluency, and cultural understanding. The theoretical framework of sociocultural theory, cognitive load theory, and task-based language learning is presented to support the use of music in language learning. This paper also provides practical recommendations for learners to incorporate music into their language learning routine, as well as resources for finding music in the target language, such as online music platforms and language learning apps. The article concludes by encouraging language learners to incorporate music into their language learning routine and providing resources for finding music in the target language, such as online music platforms and language learning apps. Overall, this paper demonstrates that music can be a powerful and enjoyable tool for enhancing language learning outcomes.

Recent advancements in Natural Language Processing (NLP), particularly in Large Language Models (LLMs), associated with deep learning-based computer vision techniques, have shown substantial potential for automating a variety of tasks. One notable model is Visual ChatGPT, which combines ChatGPT’s LLM capabilities with visual computation to enable effective image analysis. The model’s ability to process images based on textual inputs can revolutionize diverse fields. However, its application in the remote sensing domain remains unexplored. This is the first paper to examine the potential of Visual ChatGPT, a cutting-edge LLM founded on the GPT architecture, to tackle the aspects of image processing related to the remote sensing domain. Among its current capabilities, Visual ChatGPT can generate textual descriptions of images, perform canny edge and straight line detection, and conduct image segmentation. These offer valuable insights into image content and facilitate the interpretation and extraction of information. By exploring the applicability of these techniques within publicly available datasets of satellite images, we demonstrate the current model’s limitations in dealing with remote sensing images, highlighting its challenges and future prospects. Although still in early development, we believe that the combination of LLMs and visual models holds a significant potential to transform remote sensing image processing, creating accessible and practical application opportunities in the field.

This paper explores how Artificial Intelligence (AI) can help reduce the communication gap and streamline the distribution of resources. It uses an exploratory case study to examine the current state of the communication gap and the potential opportunities AI can bring to close it. The paper will discuss the current state of the communication gap, the current methods of resource distribution, and the potential opportunities AI can bring to reduce the communication gap. It will also explore the potential implications of AI on resource distribution and the communication gap. The paper will review the existing literature on the communication gap, resource distribution, and AI. It will then focus on the opportunities AI can bring to the communication gap. It will analyze the use of machine learning, natural language processing, and other AI-based technologies to reduce the communication gap. It will also examine the potential implications of AI on resource distribution, such as developing more efficient distribution channels, increasing transparency, and reducing costs. Finally, the paper will analyze the impact of AI on the communication gap and resource distribution. It will discuss the potential challenges and benefits of using AI, such as the need for data security, the potential for bias, and the limitations of AI technology. The paper will also explore the potential for AI to improve the distribution of resources and reduce the communication gap. Overall, this paper provides an exploratory case study on the opportunities of how AI can contribute to reducing the communication gap and streamlining the distribution of resources. It discusses the potential implications of AI on the communication gap and resource distribution, and it reviews the potential challenges and benefits of using AI. The paper provides an in-depth analysis of the potential opportunities of using AI to reduce the communication gap and streamline the distribution of resources.

Retweet prediction is an important task related to different problems such as information spreading analysis, the automatic detection of fake news, social media monitoring, etc. In this study we explore the possibilities of retweet prediction based on heterogeneous data sources. In order to classify the tweet according to the amount of retweets, we combine features extracted from the multilayer network and the text. More specifically, we introduce a multilayer framework that proposes the multilayer network representation of Twitter. This formalism captures different users' actions and complex relationships as well as other key properties of communication on Twitter. We select a set of local network measures from each layer and construct a set of multilayer network features. In addition, we adopt a BERT-based language model, namely Cro-CoV-cseBERT to capture high-level semantics and structure of tweets as a set of text features. Then, we train six machine learning (ML) algorithms: random forest, multilayer perceptron, light gradient boosting machine, category embedding model, neural oblivious decision ensembles and attentive interpretable tabular learning model in the task of retweet prediction. We compare the performance of all six algorithms in three different setups (i) using only text features, (ii) using only multilayer network features and (iii) using both sets of features. We evaluate all setups in terms of standard evaluation measures i.e. precision, recall, F1-score and accuracy. For this task, we first prepare and use an empirical dataset of 199,431 tweets in the Croatian language posted during the period between January 1, 2020 and May 31, 2021. Our results indicate that by integrating multilayer network features with text features the prediction model would perform better than using just one set of features.

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

Evolutionary perspectives have generated many questions and some answers in the study of human health and disease. The field of evolutionary medicine, and related analytics of evolutionary psychiatry and evolutionary psychology have extended and expanded the way health disorders are viewed by searching for why humans, as a species, are vulnerable to certain pathological conditions. The search is organized into four domains that apply proximate and evolutionary explanations to human traits and developmental sequences. This framework opens inquiry to the ontogeny, phylogeny, mechanism, and adaptive significance of human health conditions. In this paper I argue that evolutionary medicine seems to parallel biomedicine in its primarily pathogenic focus. That is, conditions of pain, suffering, and disorder have received the most attention. Some work has used the architecture of evolutionary medicine to take a salutogenic approach, evaluating the proximate and evolutionary explanations of human well-being. I propose that an evolutionary understanding of human well-being requires a survey of emotions and their relationship with neurobiology, language, and culture. My anthropology based, multidisciplinary review of biopsychosocial processes reveals the way evolution has shaped modern human understanding of well-being through sociolinguistic learning processes and thereby our individual experiences of well-being. These insights have the power to contextualize human suffering and flourishing as we progress toward the goal of attenuating the former and expanding the latter.

Many children have suspected delayed language development and need extensive support from parents and the health care team. This study aimed to investigate suspected delayed language development and factors associated with suspected delayed language development among early childhood in Southern Thailand. Children aged 24 to 60 months were recruited as study samples using stratified random sampling conducted in 23 districts and simple randomized seven sections (425 children). Instruments comprised demographic data of the children and families, The preschool temperament questionnaire, and the Language Development Screening questionnaire using developmental surveillance and promotion manual. I was collecting data from July 2020 to January 2021. Data analysis used descriptive statistics and logistic regression. The results showed that 40.9 percent suspected delayed development. Daily screen time exceeding 2 hours per day (A.O.R. = 17.30, 95% CI: 7.35-40.72), and regarding a child's temperament, moderate-to-difficult temperament (A.O.R. = 9.56, 95% CI: 5.12-17.85) were significantly associated with a suspected delay of language development. The study suggested two-way communication and appropriate responses will help develop children's language.

Documenting cultural heritage by using artificial intelligence (AI) is crucial for preserving the memory of the past and a key point for future knowledge. However, modern AI technologies make use of statistical learners that lead to self-empiricist logic, which, unlike human minds, use learned non-symbolic representations. Nevertheless, it seems that it is not the right way to progress in AI. If we want to rely on AI for these tasks, it is essential to understand what lies behind these models. Among the ways to discover AI there are the senses and the intellect. We could consider AI as an intelligence. Intelligence has an essence, but we do not know whether it can be considered “something” or “someone”. Important issues in the analysis of AI concern the structure of symbols -operations with which the intellectual solution is carried out- and the search for strategic reference points, aspiring to create models with human-like intelligence. For many years, humans, seeing language as innate, have carried out symbolic theories. Everything seems to have skipped with the advent of Machine Learning. In this paper, after a long analysis of history, the rule-based and the learning-based vision, we propose KERMIT as a unit of investigation for a possible meeting point between the different learning theories. Finally, we propose a new vision of knowledge in AI models based on a combination of rules, learning and human knowledge.

Deep neural networks are hegemonic approaches to many machine learning areas, including natural language processing (NLP). Thanks to the availability of large corpora collections and the capability of deep architectures to shape internal language mechanisms in self-supervised learning processes (also known as "pre-training"), versatile and performing models are released continuously for every new network design. But these networks, somehow, learn a probability distribution of words and relations across the training collection used, inheriting the potential flaws, inconsistencies and biases contained in such a collection. As pre-trained models have found to be very useful approaches to transfer learning, dealing with bias has become a relevant issue in this new scenario. We introduce bias in a formal way and explore how it has been treated in several networks, in terms of detection and correction. Also, available resources are identified and a strategy to deal with bias in deep NLP is proposed.

This paper presents the current lexical, morphological, syntactic and rule-based machine translation work for Erzya and Moksha that can and should be used in the development of a roadmap for Mordvin linguistic research. We seek to illustrate and outline initial problem types to be encountered in the construction of an Apertium-based shallow-transfer machine translation system for the Mordvin language forms. We indicate reference points within Mordvin Studies and other parts of Uralic studies, as a point of departure for outlining a linguistic studies with a means for measuring its own progress and developing a roadmap for further studies.

Humans have a unique cognitive ability known as relational abstraction, which allows us to identify logical rules and patterns beyond basic perceptual characteristics. This ability represents a key difference between how humans and other animals learn and interact with the world. With current large language models rivaling human intelligence in many regards, we investigated whether relational abstraction was an emergent chat completion capacity in various models. We find that despite their impressive language processing skills, all tested language models failed the relational match-to-sample (RMTS) test, a benchmark for assessing relational abstraction. These results challenge the assumption that advanced language skills inherently confer the capacity for complex relational reasoning. The paper highlights the need for a broader evaluation of AI cognitive abilities, emphasizing that language proficiency alone may not be indicative of certain higher-order cognitive processes thought to be supported by language.

With the influence of Big Data culture on qualitative data collection, acquisition, and processing, it is becoming increasingly important that social scientists understand the complexity underlying data collection and the resulting models and analyses. Systematic approaches for creating computationally tractable models need to be employed in order to create representative, specialized reference corpora subsampled from Big Language Data sources. Even more importantly, any such method must be tested and vetted for its reproducibility and consistency in generating a representative model of a particular population in question. This article considers and tests one such method for Big Language Data downsampling of digitally-accessible language data to determine both how to operationalize this form of corpus model creation, as well as testing whether the method is reproducible. Using the U.S. Nuclear Regulatory Commission's public documentation database as a test source, the sampling method's procedure was evaluated to assess variation in the rate of which documents were deemed fit for inclusion or exclusion from the corpus across four iterations. The findings of this study indicate that such a principled sampling method is viable, thus necessitating the need for an approach for creating language-based models that account for extralinguistic factors and linguistic characteristics of documents.

The retrofitting of existing reinforced concrete (RC) buildings with Cross-Laminated Timber (CLT) panels presents a promising approach for enhancing seismic performance and overall structural resilience. However, effective integration of CLT with existing RC structures poses significant challenges, particularly concerning the design of connections between CLT panels and the RC structure. This paper introduces a novel connection that addresses these challenges by focusing on both structural and architectural considerations. Structurally, the connection is engineered to provide optimal stiffness, strength, and deformation capacity, ensuring robust performance under seismic and dynamic loads. Architecturally, the design incorporates a pre-defined weak component that facilitates easy access and rapid replacement of damaged parts, thereby reducing downtime and maintenance efforts. The proposed connection was evaluated through a series of monotonic and cyclic loading tests, demonstrating its structural efficiency and reliability. The results indicate that the new connection system not only meets the necessary structural requirements but also offers practical benefits for maintenance and repair, contributing to the overall sustainability and resilience of retrofitted RC buildings. This innovative approach represents a significant advancement in the field of structural retrofitting, providing a viable solution for integrating CLT panels into existing RC frameworks.

This study explores the comparative performance of FinBERT, GPT-4, and Logistic Regression for sentiment analysis and stock index prediction using the NGX All-Share Index dataset. By leveraging advanced language models like GPT-4 and FinBERT, alongside a traditional machine learning model, Logistic Regression, we aim to classify market sentiment, generate sentiment score, and predict market price movements. The models were assessed using metrics such as accuracy, precision, recall, F1 score, and ROC AUC. Results indicate that Logistic Regression outperformed both FinBERT and GPT-4, with an accuracy of 81.83% and a ROC AUC of 89.76%. GPT-4 predefined approach exhibited a lower accuracy of 54.19% but demonstrated strong potential in handling complex data. FinBERT, while offering more sophisticated analysis, was computationally demanding and yielded a moderate performance. Hyperparameter optimization using Optuna and cross-validation techniques ensured the robustness of the models. This study highlights the strengths and limitations of these approaches in stock market prediction and presents Logistic Regression as the most efficient model for this task, with FinBERT and GPT-4 showing promise for future exploration.

Background: Chronic diseases are major causes of morbidity, disability, and death worldwide. In many cases, they are potentially preventable and treatable. Glaucoma is one of the leading causes of morbidity and blindness, the early recognition of which can help reduce the adverse outcomes of the disease. Health illiteracy among these patients and the absence of self-management of these diseases are barriers to effective management worldwide. Therefore, the present study aimed to calculate the level of health literacy and activity level of patients’ activation in self-management and to explore the link between them among patients with glaucoma in outpatient clinics. Material and methods: Convenience sampling of patients with glaucoma attending the outpatient ophthalmology clinics of a university hospital in Heraklion-Greece was performed. Convenience sampling was used to recruit 312 participants. Data were collected between November 2023 and May 2024. The Patient Activation Measure-13 (PAM-13) questionnaire, in its Greek version, was used to evaluate patients’ activation level in self-management, whereas the Greek version of the European Health Literacy Survey Questionnaire 16 (HLS-EU-16) was used to assess health literacy. Results: Participants were classified as level 1(disengaged & overwhelmed) level in self-management (= 40.7, SD: ±29.9) and a sufficient health literacy level (= 10.7, SD ±3.7). However, there was no statistically significant relationship between health literacy levels and patient activation in self-management (p=0.602). Conclusions: Glaucoma patients experience challenges with health literacy and self-management activation, though there is no significant relationship between these two parameters. Multifaceted interventions seem essential for enhancing patients’ activation. Further research involving larger sample sizes, multi-center studies, and interventional design is needed.

The rapidly evolving domain of multimodal sentiment analysis is crucial for unraveling the intricate layers of emotional expression in social media content, customer service interactions, and personal vlogs. This research introduces a cutting-edge Advanced Cross-Modal Gating (ACMG) framework that significantly enhances the precision of sentiment analysis by refining the interplay among textual, auditory, and visual modalities. Our approach addresses three foundational aspects of sentiment analysis: (1) Advanced learning of cross-modal interactions, which focuses on extracting and synthesizing sentiment from varied modal inputs, thus providing a holistic view of expressed emotions; (2) Mastery over the temporal dynamics of multimodal data, enabling the model to maintain context and sentiment continuity over extended interactions; and (3) Deployment of a novel fusion strategy that not only integrates unimodal and cross-modal cues but also dynamically adjusts the influence of each modality based on its contextual relevance to the sentiment being expressed. The exploration of these dimensions reveals that the nuanced modeling of cross-modal interactions is crucial for enhancing model responsiveness and accuracy. By applying the ACMG model to two highly regarded datasets—CMU Multimodal Opinion Level Sentiment Intensity (CMU-MOSI) and CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI)—we achieve groundbreaking accuracies of 83.9\% and 81.1\%, respectively. These results represent significant improvements of 1.6\% and 1.34\% over the current state-of-the-art, showcasing the superior performance and potential of our approach in navigating the complexities of multimodal sentiment analysis.

Background/Objectives: Population aging is rapidly increasing, and the importance of preventive medicine has been stressed. Health checkup, diet, and exercise are of paramount importance. This study aimed to evaluate the effectiveness of a personalized dual-task intervention that combined exercise with cognitive tasks in improving physical and cognitive functions among independently living elderly individuals. Methods: Participants were aged >65 years who were mostly independent in their activities of daily living, divided into 2 groups. The group receiving the 20-min robot-assisted session was compared to the group receiving traditional functional restoration training. This randomized trial assessed the impact of this intervention on the 30-s chair stand test score and Montreal Cognitive Assessment-Japanese version score of the participants. Results: Both scores significantly improved in the intervention group, indicating enhanced lower-limb function and cognitive capabilities. Conclusions: These findings suggest that integrating cognitive tasks with physical exercise can be a potent strategy to improve overall well-being in the elderly, offering valuable insights for designing comprehensive preventive health programs tailored to this demographic.

Opportunistic networks, an evolution of mobile Ad Hoc networks (MANETs), offer decentralized communication without relying on preinstalled infrastructure, enabling nodes to route packets through different mobile nodes dynamically. However, due to the absence of complete paths and rapidly changing connectivity, routing in opportunistic networks presents unique challenges. This paper proposes a novel probabilistic routing model for opportunistic networks, leveraging nodes' meeting probabilities to route packets towards their destinations. The model dynamically builds routes based on the likelihood of encountering the destination node, considering factors such as last meeting time and acknowledgment tables to manage network overload. Additionally, an efficient message detection scheme is introduced to alleviate high overhead by selectively deleting messages from buffers during congestion. Furthermore, the proposed model incorporates cross-layer optimization techniques, integrating optimization strategies across multiple protocol layers to maximize energy efficiency, adaptability, and message delivery reliability. Through extensive simulations, the effectiveness of the proposed model is demonstrated, showing improved message delivery probability while maintaining reasonable overhead and latency. This research contributes to the advancement of opportunistic networks, particularly in enhancing connectivity and efficiency for Internet of Things (IoT) applications deployed in challenging environments.

The satellite Earth observation (EO) sector is burgeoning with hundreds of commercial satellites being launched each year, delivering a rich source of data that could be exploited for societal benefit. Data streams from the growing constellation of commercial satellites are of variable quality, limiting the potential for their combined use in science applications that need long time-series data from multiple sources. The quality of calibration performed on optical sensors onboard many satellite systems is highly variable due to calibration methods, sensor design, mission objective, budget, or other operational constraints. A small number of currently operating well-characterised satellite systems with onboard calibration, such as Landsat-8/9, Sentinel-2, and planned future missions like the NASA Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder, the European Space Agency (ESA) Traceable Radiometry Underpinning Terrestrial and Helio Studies (TRUTHS), and LIBRA from China, are considered benchmarks for optical data quality due to their traceability to international measurement standards. This paper describes the concept of a space-based transfer calibration radiometer called the Satellite Cross-Calibration Radiometer (SCR) that would enable the calibration parameters from satellites such as Landsat-8/9, Sentinel-2, or other benchmark systems to be transferred to a range of commercial optical EO satellite systems while in orbit. A description of the key characteristics of the SCR to successfully operate in orbit and transfer calibration from Reference systems to Client systems is presented. A system like the SCR in orbit could complement SI-Traceable satellites (SITSats) to improve data quality and consistency and facilitate the interoperable use of data from multiple optical sensor systems for delivering higher returns on the global investment in EO.

Financial stress can have significant implications for individuals, businesses, asset prices and the economy as a whole. This study examines the nonlinear structure and dynamic changes in the multifractal behavior of cross-correlation between the financial stress index (FSI) and four well-known commodity indices, namely Commodity Research Bureau Index (CRBI), Baltic Dry Index (BDI), London Metal Index (LME) and Brent Oil prices (BROIL), using multifractal detrended cross correlation analysis (MFDCCA). For analysis, we utilized daily values of FSI and commodity index prices from 16th June 2016 to 9th July 2023. The following are the most important empirical findings: (I) All of the chosen commodity market indices show cross correlations with the FSI and have notable multifractal characteristics. (II) The presence of power law cross-correlation implies that a noteworthy shift in FSI is likely to coincide with a considerable shift in the commodity indices. (III) The multifractal cross-correlation is highest between FSI and Brent Oil (BROIL) and lowest with LME. (IV) The rolling windows analysis reveals a varying degree of persistency between FSI and commodity markets. The findings of this study have a number of important implications for commodity market investors and policymakers.

The cost effective design of tidal turbines requires a good estimation of the loading cycles and their extrema that are related to the unsteady fluctuations of the current velocity. Apart from the ambient turbulence, the main source of velocity fluctuations is the presence of surface waves. In the present study, we analyse the effect of waves propagating against the current on the performance and the loads of a twin vertical axis tidal turbine by an experimental approach at a 1/20 scale. Overall, the results show little or no effect of the waves on the average power and loads compared to the condition with current only, but a significant impact on their standard deviation that rises linearly with the waves amplitude. The drag, lift and pitching moment show extended ranges up to 7.5 times higher and extreme values exceedance by 60 to 100 % with irregular waves compared to the condition without waves. Those load and power fluctuation increase are fully due to the presence of waves as the coherence function between the rotor torque or the loads and the velocity spectra exceed 0.5 on the whole wave frequency bandwidth. The results also reveal a rotational sampling of the waves by the rotors that had also been observed on horizontal axis turbines. From a structural design point of view, the authors recommand to conduct tests in irregular wave conditions as the loads range and extreme values are 1.5 to 2 times higher than those encountered with regular waves of the same significant height and period.

(1) Background: We aimed to identify factors associated with the presence of apical lesions (AL) on panoramic radiographs and to evaluate the predictive value of the identified factors. (2) Methodology: Panoramic radiographs from 1071 patients (age: 11-93 a, mean: 50.6 a ± 19.7 a) with 27532 teeth were included. Each radiograph was independently assessed by five experienced dentists for AL. A range of shallow machine learning algorithms (logistic regression, k-nearest neighbor, decision tree, random forest, support vector machine, adaptive and gradient boosting) were employed to identify factors on patient and tooth level associated with AL and to predict AL. (3) Results: AL were detected in 522 patients (48.7%) and 1133 teeth (4.1%), whereas males showed a significantly higher prevalence than females (52.5%/44.8%; p<0.05). Logistic regression found that an existing root canal treatment was the most important risk factor (adjusted Odds Ratio 16.89; 95% CI: 13.98-20.41), followed by the tooth type ‘molar’ (2.54; 2.1-3.08) and the restoration with a crown (2.1; 1.67-2.63). Associations between factors and AL were stronger and accuracy higher when using less complex models like decision tree (F1 score: 0.9 (0.89-0.9)). (4) Conclusions: The presence of AL was higher in root-canal treated teeth, those with crowns and molars. More complex machine learning models did not outperform less complex ones.

Chaphamaparvovirus (ChPV) is an ancient virus that has been detected in a variety of hosts. In this study, based on the phylogenetic analysis and the adaptability of ChPV to multiple hosts, we evaluated the potential of feline (FeChPV) and canine ChPV (CaChPV) for cross-species transmis-sion. Phylogenetic analysis showed that FeChPV and CaChPV were closely related. Notably, two strains of ChPVs isolated from domestic cats and 2 from dogs clustered together with CaChPVs and FeChPVs, respectively, suggesting the stringent boundaries between canine and feline ChPV may be broken. Further analysis revealed that CaChPV and FeChPV were more adapted to dogs than to cats, strongly suggesting the possibility of unidirectional or bidirectional transmission be-tween dogs and cats. Mutation analysis identified several shared mutations in cross-species-transmissible strains that were not located within immune epitopes. Furthermore, the VP struc-tures of FeChPV and CaChPV exhibited a high degree of similarity across both cross-species-transmissible and non-cross-species-transmissible strains. However, definitive experimental evi-dence is lacking, and its capacity for cross-species transmission should be approached with cau-tion and elucidated in further studies.

This study investigates the applications of reconfigurable intelligent surfaces (RIS) in heterogeneous downlink networks (HetNet). Due to the network densification, the small cell base station (SBS) interferes with the macrocell users (MUE). In this paper, we utilize RIS to mitigate cross-tier interference in an HetNet via directional beamforming by adjusting the phase shift of the RIS. We consider an RIS-assisted HetNet consisting of multiple SBS nodes and MUEs that utilize both direct paths and reflected paths. Therefore, the aim of this study is to maximize the sum rate of all MUEs by jointly optimizing the transmit beamforming of the macrocell base station (MBS) and the phase shift of the RIS. An efficient RIS reflecting coefficients-based optimization (RCO) is proposed based on a successive convex approximation approach. Simulation results are provided to show the effectiveness of the proposed scheme in terms of its sum rate in comparison with the scheme HetNet without RIS and the scheme HetNet with RIS but with random phase shifts.

This research was conducted with the aim of knowing how to compare the financial performance of the oil and gas trading industry with cross-sectional analysis techniques. The type of research conducted in this research is qualitative research with research objects at several companies, namely PT Radiant Utama Interinsco Tbk, PT Elnusa Tbk, PT AKR Corporindo Tbk, and PT Aneka Gas Industri Tbk. The data used in this study comes from secondary data, namely the internet in the form of company financial reports for the 2019-2021 period obtained from the Indonesia Stock Exchange. The results of this study indicate that PT Radiant Utama Interinsco Tbk has the best financial performance on inventory turnover ratio and average age, PT Elnusa Tbk has good inventory performance on current ratio, quick ratio, DAR, and DER., PT AKR. Corporindo Tbk has performed well on the ratios of TIE, FCC, Dividend Payout, Dividend Yield, and PER. PT Aneka Gas Industri Tbk has performed well on the ratio of Receivables Turnover Ratio and Average Age of Receivables.

Introduction: This study explores exposure to misinformation, COVID-19 risk perception, and confidence towards the government as predictors to negative attitudes toward the COVID-19 vaccine. Methods: A cross-sectional survey was carried out from 30 June to 30 August 2021 involving 775 respondents. The survey instrument for the questionnaire is an adaptation from various different studies consisting of five main variables: 1) misinformation about vaccination; 2) risk perception toward COVID-19; 3) attitudes toward the vaccination programme; 4) intention to get vaccinated; and 5) public confidence in the government in executing the vaccination programme. Results: The results of this study indicate that higher exposure to misinformation led to higher levels of negative attitudes toward the COVID-19 vaccine. When the perceived risk of COVID-19 infection was high, mistrust of vaccine benefits was low but there were also higher worries about the future effects of the vaccine. Confidence in government was associated with lower negative attitudes toward the COVID-19 vaccine. Conclusion: The results of this study may help develop an understanding of negative attitudes toward vaccinations in Malaysia and its contributing factors.

Objective: The aim of this study was to provide a new machine learning method to determine temporal events and inner-cycle parameters (e.g., cycle, poles and skis contact and swing time) in cross-country roller ski skating on the field, using a single deported inertial measurement unit (IMU). Methods: The developed method is based on long short-term memory neural networks to detect poles and skis initial and final contact with the ground during the cyclic movements. Eleven athletes skied four laps of 2.5 km at low and high intensity using skis with two different rolling coefficients. They were equipped with IMUs attached to the upper back, lower back and to the sternum. Data from force insoles and force poles were used as reference system. Results: The IMU placed on the upper back provided the best results, as the LSTM network was able to determine the temporal events with an accuracy ranging from 49 to 55 ms and the corresponding inner-cycles parameters were calculated with a precision of 63 to 68 ms. The method detected 95% of the events for the poles and 87% of the events for the skis. Conclusion: The proposed LSTM method provides a promising tool for assessing temporal events and inner-cycle phases in roller ski skating showing the potential of using a deported IMU to estimate different spatio-temporal parameters of human locomotion.

This article aims to identify the current development of cross-border tourism between Vietnam and China. The paper analyzes the perception and strategy of cross-border tourism development in the two countries, especially in the context of China’s implementation of the “Belt and Road Initiative” (BRI). The article emphasizes that Vietnam and China have recently made efforts to promote cross-border tourism development. At the same time, Vietnam and China see cross-border tourism development as a significant part of comprehensive border cooperation between the two countries. However, the development plans for cross-border tourism between the two countries are still mainly on the desk, not yet implemented effectively. Thus, its achievements are relatively modest. Besides, the article emphasizes that the interests of the people living in the border area, especially the Vietnam side, are almost “forgotten” in the development of cross-border travel between the two countries. Besides, the article also analyzes the challenges that the two sides are facing, especially the Vietnam side, in promoting the development of cross-border tourism between the two countries.

Background: Dengue is the most common arthropod-borne sickness worldwide, impacting at least 50 million people each year. The dengue virus has four primary serotypes. Infection with one serotype confers homotypic immunity but not heterologous immunity, and secondary infections may be more severe. Although blood transfusions and organ donations have also been observed, the Aedes aegypti mosquito is the primary vector for the transmission of dengue. Infection causes a continuum of clinical illness, from asymptomatic infection to dengue fever, DHF, and dengue shock syndrome (DSS).Aim: To assess the presence of anti DENV IgG and anti DENV IgM antibodies specific to the four dengue serotypes in blood donor service donors and the importance of pre-donation screening in routine blood collection procedures.Method: 3 mL of peripheral venous blood from 507 blood donors was collected in tubes with BD vacutainer gel tube for serum separation after epidemiological records were reviewed. After that, serum was separated and tests were performed by SD Bioline Dengue Duo. Participants in the study completed a social and epidemiological questionnaire that contained information such as age, gender, and dengue diagnosis.Result: Out of the 507 blood samples that were taken, 473 (93.3%) came from male blood donors, while the remaining 34 (6.7%) belonged to female blood donors. The ratio of males to females is 13.91 to 1. The age range is 18–60 years, and the mean and standard deviation are both 27.7 and 6.5. 183 of the 507 samples produced anti DENV IgG positivity, while 324 did not. The ratio of positive to negative was 1.25:2.Conclusion: According to the findings of this study, quantitative methods for determining the presence of anti-dengue antibodies or detecting the dengue virus in blood donors in endemic areas should be devised in order to ensure the quality of blood transfusions.

Having valid and reliable tools that help health professionals to assess fear in children undergoing medical procedures is essential to offer humanized and quality of care in the paediatric population. The aim of this study was to develop the cross-cultural adaptation and the evaluation of the psychometric properties of the Spanish version of the “Child Medical Fear Scale” in its shortened version (CMFS-R). The design consisted of two phases, first of cross-cultural adaptation and second of the psychometric validation of the CMFS-R with a sample of 262 children from Spain. Confirmatory factor analysis was conducted to assess construct validity and the Cronbach alpha and the adjusted item-total score correlation coefficients were performed to study reliability. The results confirmed internal consistency and construct validity of the Spanish version of the CMFS-R, indicating that the scale has an acceptable level of validity and reliability. Therefore, this study brings a new version of the scale to assess fear related to medical procedures for use in the Spanish paediatric population.

The uptake of buildings employing cross-laminated timber (CLT) assemblies and designed to Passivhaus standard has accelerated internationally over the past two decades due to several factors including design responses to the climate crisis by decarbonising the building stock. Structural CLT technology and the voluntary Passivhaus certification both show measurable benefits in reducing energy consumption, while contributing to durability and indoor comfort. However, there is a general lack of evidence to support a fast uptake of these technologies in Australia. This paper responds to the compelling need of providing quantitative data and adoption strategies, it explores their combined application as a potential pathway for climate-appropriate design of energy-efficient and durable mass timber envelope solutions for subtropical and tropical Australian climates. Hygrothermal risk assessments of interstitial condensation and mould growth of CLT wall assemblies inform best-practice design of mass timber buildings in hot and humid climates. This research found that the durability of mass timber buildings located in hot and humid climates may benefit from implementing the Passivhaus standard to manage interior conditions. The findings also suggested that climate-specific design of the wall assembly is critical for mass timber buildings, in conjunction with excellent stormwater management practices during construction and corrosion protection for metallic fasteners.

Two ligands, 1, 3-bis(4-carboxyphenyl)imidazolium chloride and 4, 4՛-bipyridine, were employed to prepare nickel and zinc azolium based MOFs, 1 and 2 by the mixed ligand solvothermal approach. The positively charged azolium moieties in the imidazolium linker resulted in a charged environment in the as-synthesized frameworks. As a result, 1 and 2 demonstrated preferential adsorption of CO2 over methane molecules in the gas phase adsorption due to the higher quadruple moment of CO2, which interacts more with the positively charged frameworks. Besides, in aqueous media, 1 and 2 exhibited incredible adsorption efficiency for anionic dyes. In the following, MOF 1 was metallated at the carbene site of the azolium linker to generate the novel heterogeneous catalyst 1-Pd, which was successfully applied for Sonogashira and Suzuki-Miyaura coupling reactions without losing its activity after three cycles.

We report on the successful preparation of wet dressings hydrogels based on Chitosan-Poly(N-Vinyl-Pyrrolidone)-Poly(ethylene glycol)-Poly(acrylic acid) and Poly(ethylene oxide) by e-beam cross-linking in weakly acidic media, to be used for rapid healing and pain release of infected skin wounds. The structure and compositions of hydrogels investigated according to sol-gel and swelling studies, network parameters, as well as FTIR and XPS analyses showed the efficient interaction of the hydrogel components upon irradiation, maintaining the bonding environment while the cross-linking degree increasing with the irradiation dose and the formation of a structure with the mesh size in the range 11-67 nm. Hydrogels with gel fraction above 85% and the best-swelling properties in different pH solutions were obtained for hydrogels produced with 15 kGy. The hydrogels are stable in the simulated physiological condition of an infected wound and show appropriate moisture retention capability and the water vapor transmission rate up to 272.67 g m-2 day-1, to ensure fast healing. The hydrogels proved to have a significant loading capacity of ibuprofen (IBU), being able to incorporate a therapeutic dose for the treatment of severe pains. Simultaneously, IBU was released up to 25% in the first 2h, having a release maximum after 8h.

Mass crises are disruptive to people's mental health. The study aimed to explore mental distress during COVID-19 quarantine in a sample of university workers in Brazil. The survey included sets of questions about demographics, health, and support, an open question about major concerns, and the Clinical Outcome Routine Evaluation (CORE-OM), a measure of mental distress. 407 professionals participated in the study: mean age of 40 years (SD = 11.2), mostly female (67.8%), married (64.8%) and fulfilling social distancing to avoid COVID-19 infection (99%). Using the Consensual Qualitative Research for simple qualitative data (CQR-M) the main areas of concern were grouped into six domains, as follows: Work, Health, Isolation, Personal life and routine, Social environment, and Future. Many responses were multiple. They form categories indicating specific concerns within these domains. Quantitative data were analyzed by identifying the simple effects of potential predictors of mental distress. The results indicated medium effects of help with household chores, psychiatric treatment, age and physical exercise. Having someone available to listen was the only variable with a large effect in reducing mental suffering. The hybrid approach showed that the psychological experience during the pandemic is quite multifaceted and complex pointing new clues for public mental health.

This paper examined the multifractal properties of six acknowledged agro-meteorological parameters, such as reference evapotranspiration (ET0), wind speed (U), incoming solar radiation (SR), air temperature (T), air pressure (P), and relative air humidity (RH) of five stations in California, USA. The investigation of multifractality of datasets from stations with differing terrain conditions: Dagget, Bakersfield, Santa Maria, Los Angeles and San Diego using the Multifractal Detrended Fluctuation Analysis showed the existence of a long term persistence and multifractality irrespective of the location. The scaling exponents of SR and ET0 time series are found to be higher for stations with higher altitudes. Subsequently, this study proposed using the novel multifractal cross correlation (MFCCA) method to examine the multiscale-multifractal correlations properties between ET0 and other investigated variables. MFCCA could successfully capture the scale dependent association of different variables and the dynamics in the nature of their associations from seasonal to multi-annual time scale. The multifractal exponents of pressure and relative air humidity are consistently lower than the exponents of ET0, irrespective of station location. This study found that joint scaling exponent was nearly the average of scaling exponents of individual series in different pairs of variables. Additionally, the α-values of joint multifractal spectrum were lower than the α values of both of the individual spectra, validating two universal properties in the mutifractal cross correlation studies for agro-meteorological time series. The temporal evolution of cross-correlation showed similar pattern for all pair-wise associations involving ET0, except for the RH-ET0 link.

In this project, we use a statistical multiple regression to study the impact of eight various predictors (relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution) to estimate the cooling load energy efficiency of residential buildings. We try to analyze and visualize the effect of each predictor with each of the response variable using different classical statistical analysis tools used in describing linear models, in such a way so that we can find out the most strongly related predictor variables. Before starting all of this, we use the idea of model selection by stepwise regression technique and compare the AIC of these models and identified a better model between all of them. Then, we compare a classical linear regression approach by simulations on 768 diverse residential buildings show that we can predict CL with low mean absolute error. By using ANOVA we determine variation in the different residuals. Also, we use non constant variance test to verify it. Furthermore, we check leverage and influence points as well as outliers as well as determined cook distance for influential points. By taking box cox transformation and weights, we also introduce WLS technique to fit the model for better results and did all type of important analysis to understand the energy efficiency. Finally, we show 5-fold cross validation to verify our model.

Recently deep cross-modal hashing networks have received increasing interests due to its superior query efficiency and low storage cost. However, most of existing methods concentrate less on hash representations learning part, which means the semantic information of data cannot be fully used. Furthermore, they may neglect the high-ranking relevance and consistency of hash codes. To solve these problems, we propose a Self-Attention and Adversary Guided Hashing Network (SAAGHN). Specifically, it employs self-attention mechanism in hash representations learning part to extract rich semantic relevance information. Meanwhile, in order to keep invariability of hash codes, adversarial learning is adopted in the hash codes learning part. In addition, to generate higher-ranking hash codes and avoid local minima early, a new batch semi-hard cosine triplet loss and a cosine quantization loss are proposed. Extensive experiments on two benchmark datasets have shown that SAAGHN outperforms other baselines and achieves the state-of-the-art performance.

Zika virus (ZIKV) is a flavivirus that originated in Africa but emerged in Latin America in 2015. In this region, other flaviviruses such as Dengue (DENV), West Nile, and Yellow Fever Virus (YFV) also circulate, allowing for possible antigenic cross-reactivity to impact viral infections and immune responses. Studies have found antibody mediated enhancement between DENV and ZIKV, but the impact of YFV antibodies on ZIKV infection has not been fully explored. ZIKV infections cause congenital syndromes, such as microcephaly, necessitating further research into ZIKV vertical transmission through the placental barrier. Recent advancements in biomedical engineering have generated co-culture methods that allow for in vitro recapitulation of the maternal: fetal interface. This study utilized a transwell assay, which is a co-culture model utilizing human placental syncytiotrophoblasts, fetal umbilical cells, and a differentiating embryoid body to replicate the maternal: fetal axis. To determine if cross reactive YFV vaccine antibodies impact the pathogenesis of ZIKV across the maternal fetal axis, maternal syncytiotrophoblasts were inoculated with ZIKV or ZIKV incubated with YFV vaccine anti-sera, and viral load was measured 72 hours post inoculation. The data show that the impact of YFV on ZIKV replication is cell line dependent. In differentiating embryoids, the presence of YFV antibodies enhanced ZIKV infection. Since viral pathogenesis, and the impact of antigenic cross-reactive antibodies, is cell line specific at the maternal-fetal axis, this suggests there may be discreet mechanisms that impact congenital ZIKV infections.

Background: Public misconception and demand for the indication of antibiotics could lead to inappropriate prescribing and consumption. Successful treatment can only be achieved when the public and industrial users have knowledge on antibiotic use and resistance. This survey is aimed to assess antibiotic usage and knowledge regarding antibiotics and antimicrobial resistance (AMR) among undergraduate students of Universiti Brunei Darussalam (UBD), public university located in Brunei Darussalam. Methods: A cross-sectional study was performed using a self-administered questionnaire. The questionnaire was adapted from the World Health Organization (WHO) Antibiotic Resistance, Multi-country public awareness survey distributed online. Students at UBD were invited to participate in the online survey through internal email. The questionnaire consisted of 5 sections: demographic information, antibiotic usage, knowledge on antibiotics, antibiotic resistance (AMR) and use of antibiotics in agriculture. Data were analyzed descriptively and appropriate inferential statistics was used accordingly. Cronbach’s alpha was also done to determine the internal consistency. The section on antibiotic use and knowledge showed good internal consistency of Cronbach’s alpha 0.66 and 0.86 respectively. Research ethics approval was obtained from the PAPRSB Institute of Health Sciences, Universiti Brunei Darussalam (UBD). Results: A total of 145 students returned the complete questionnaire. The result of the study found that 50% of the students had good level of knowledge of antibiotic and antimicrobial resistance with a mean total knowledge score of 11.4 out of 14. Respondents reported the use of antibiotic in the past (69%). Many of the students could identify the use of antibiotics for the treatment of bacterial infection. However, there were also students who incorrectly thought that antibiotics can be used for cold and flu (43%) and fever (41%). Moreover, 76% of the respondents mistakenly believed that antibiotic resistance is the result of the body becoming resistant to antibiotics. Only 12% of the respondents were found to have poor knowledge in the study. Conclusions: Misconceptions in regards to the use of antibiotics for conditions related to viral illnesses was noticed among the respondents in our study. Thus, improving knowledge on antibiotics is crucial to address those beliefs.

Since the COVID-19 caused by SARS-CoV-2 break out in Wuhan China from Dec. 2019, it has spread to hundreds of countries up to now. Scientists from all over the world have paid tremendous efforts to research and try to control the disease. Previous studies suggested that some of the wild animals could be intermediate hosts between humans and origination of SARS-CoV-2, and some companion animals of humans can be infected by SARS-CoV-2, which raised our curiosity about cross-infection of SARS-CoV-2 between animals and humans. Thus, we select some kinds of animals that might have contact with humans to estimate the susceptibility to SARS-CoV-2 in different animals by evolutionary analysis of their receptors for SARS-CoV-2. The results show that some companion animals of the Felidae family like the cat has a higher infection possibility while the species of the Rodent family like the rat and mouse having close contact with humans show an opposite result, which consist with recent animal experiments and researches. These should raise concerns about cross-infection between human and companion animals or animals having close contact with humans which might turn animals into depositaries of the coronavirus even after control of SARS-CoV-2 spreading and cause second or more waves of infections after social reopening. Another side of our results stands by the opinion that bioinformatic analysis can be consistent with experiments in some respects so that we can prevent unnecessary sacrifice of laboratory animals in future experiments.