Specific Words Research Articles

Cutaneous Intertriginous Langerhans Cell Histiocytosis in Adults: A Case Report and Systematic Review.

Authors characterized all published adult cases of cutaneous, intertriginous Langerhans cell histiocytosis (LCH) to bring this clinical presentation to the attention of clinicians. We emphasize the morphology, histopathology, immunohistochemical profiles, and genetic mutations associated with these cases. A systematic review of the National Center for Biotechnology Information's PubMed was conducted, utilizing the following specific key words to identify all adult LCH patients with cutaneous intertriginous involvement: "Intertriginous Langerhans," "Vulvar Langerhans," "Genital Langerhans," "Perineal Langerhans," "Perianal Langerhans," "Intergluteal Langerhans," "Inguinal Langerhans," "Axillary Langerhans," and "Inframammary Langerhans." Reports were subjected to strict inclusion criteria: case reports, case series, or meta-analyses documenting case(s) of biopsy-proven LCH with cutaneous, intertriginous involvement in adult patients (>18 years of age at the time of diagnosis). This systematic review identified 1 original and 121 published cases of biopsy-proven, cutaneous, intertriginous LCH in adult patients. Morphology commonly included eroded, ulcerated papules and plaques, and rare presentations demonstrated potential mimickers (hidradenitis suppurativa, deep fungal mycosis, condyloma accuminata). This systematic review encompasses the largest compilation of adult cutaneous intertriginous LCH cases in the medical literature to our knowledge to date. This study identifies an important clinical presentation of this rare, commonly pediatric diagnosis; highlights trends among these cases and important clinical mimickers; and serves as a reminder to clinicians to maintain suspicion for LCH in adult populations, particularly in the setting of intertriginous cutaneous involvement.

Factors Associated with Abusive Head Trauma in Young Children Presenting to Emergency Medical Services Using a Large Language Model

Objectives Abusive head trauma (AHT) is a leading cause of death in young children. Analyses of patient characteristics presenting to Emergency Medical Services (EMS) are often limited to structured data fields. Artificial Intelligence (AI) and Large Language Models (LLM) may identify rare presentations like AHT through factors not found in structured data. Our goal was to apply AI and LLM to EMS narrative documentation of young children to detect AHT. Methods This is a retrospective cohort study of EMS transports of children <36 months of age with a diagnosis of head injury from the 2018–2019 ESO Research Data Collaborative. Non-abusive closed head injury (NA-CHI) was distinguished from AHT and child maltreatment (AHT-CAN) through 2 expert reviewers; kappa statistic (k) assessed inter-rater reliability. A Natural Language Processing (NLP) framework using an LLM augmented with expert derived n-grams was developed to identify AHT-CAN. We compared test characteristics (sensitivity, specificity, negative predictive value (NPV)) between this NLP framework to a Generative Pretrained Transformer (GPT) or n-grams only models to detect AHT-CAN. Association of specific word tokens with AHT-CAN was analyzed using Pearson’s chi-square. Area Under the Receiver Operator Curve (AUROC) and Area Under the Precision-Recall Curve (AUPRC) are also reported. Results There were 1082 encounters in our cohort; 1030 (95.2%) NA-CHI and 52 (4.8%) AHT-CAN. Inter-rater agreement was substantial (k = 0.71). The augmented NLP framework had a specificity and sensitivity of 72.4% and 92.3%, respectively with a NPV of 99.5%. In comparison, the GPT model had a sensitivity of 69.2%, specificity of 97.1% and NPV of 98.4% and n-grams alone had a sensitivity of 53.8%, specificity of 62.0%, NPV of 96.4%. AUROC was 0.91 and AUPRC was 0.52. A total of 44 n-grams and bi-grams were positively associated with AHT-CAN including “domestic,” “various,” “bruise,” “cheek,” “multiple,” “doa,” “not respond,” “see EMS.” Conclusions AI and LLMs have high sensitivity and specificity to detect AHT-CAN in EMS free-text narratives. Words associated with physical signs of trauma are strongly associated with AHT-CAN. LLMs augmented with a list of n-grams may help EMS identify signs of trauma that aid in the detection of AHT in young children.

Neural dynamics of social verb processing: an MEG study.

Human vocabularies include specific words to communicate interpersonal behaviors, a core linguistic function mainly afforded by social verbs (SVs). This skill has been proposed to engage dedicated systems subserving social knowledge. Yet, neurocognitive evidence is scarce, and no study has examined spectro-temporal and spatial signatures of SV access. Here, we combined magnetoencephalography and time-resolved decoding methods to characterize the neural dynamics underpinning SVs, relative to nonsocial verbs (nSVs), via a lexical decision task. Time-frequency analysis revealed stronger beta (20 Hz) power decreases for SVs in right fronto-temporal sensors at early stages. Time-resolved decoding showed that beta oscillations significantly discriminated SVs and nSVs between 180 and 230 ms. Sources of this effect were traced to the right anterior superior temporal gyrus (a key hub underpinning social conceptual knowledge) as well as parietal, pre/motor and prefrontal cortices supporting nonverbal social cognition. Finally, representational similarity analyses showed that the observed fronto-temporal neural patterns were specifically predicted by verbs' socialness, as opposed to other psycholinguistic dimensions such as sensorimotor content, emotional valence, arousal, and concreteness. Overall, verbal conveyance of socialness seems to involve distinct neurolinguistic patterns, partly shared by more general sociocognitive and lexicosemantic processes.

Words to Avoid During Wide-awake Local Anesthesia No Tourniquet Surgery to Enhance Patient Experience.

In the evolving landscape of ambulatory surgery, wide-awake local anesthesia no tourniquet (WALANT) surgery has emerged as a preferred approach due to its efficacy, cost-effectiveness, and patient satisfaction. This paradigm shift places the patient at the center of intraoperative communication, requiring a significant change in the dialogue within the operating room (OR). Traditional conversations, which often exclude the unconscious patient, must evolve to accommodate and prioritize the psychological comfort of the conscious patient. This article examines the impact of language, tone, and conversation content on the patient experience during WALANT. We propose a communication framework that emphasizes empathy, reassurance, and patient inclusion to reduce anxiety and increase patient compliance. By analyzing common OR dialogues and their potential impact on awake patients, we identify specific phrases and words that may exacerbate patient stress or discomfort. Alternatives are suggested to foster a more positive and inclusive environment. Our recommendations are based on extensive clinical experience and supported by relevant literature, highlighting the critical role of mindful communication in improving clinical outcomes and patient satisfaction in WALANT.

Word Vector Representation of Latin Cuengh Based on Root Feature Enhancement

The Latin Cuengh is a kind of language used in China’s minority areas. Due to its complex pronunciation and semantic system, it is difficult to spread widely. To deal with and protect this language further, this paper considers using the current word vector representation technology to study it. Word vector representation is the basic method and an important foundation of current research on natural language processing. It relies on a large number of data resources and is obtained through the paradigm of pre-training and feature learning. Due to the extreme lack of Latin Cuengh corpus resources, it is very difficult to obtain word vectors by relying on a large amount of data training. In this study, we propose a word vector representation method that combines the root features of Latin Cuengh words. Specifically, while training and learning the Latin Cuengh language corpus, this method uses the special word roots in the Latin Cuengh language to modify the training process, which can enhance the expression ability of the root features. The method uses the mask method based on BERT to mask the word roots after word segmentation and predict the masked word roots in the output layer of the model to obtain a better vector representation of Latin Cuengh words. The experimental results show that the word vector representation method proposed in this paper is effective and has the ability to express Latin Cuengh semantics. The accuracy rate of words semantic is nearly 2% points higher than that of BERT representation, and the judgment of the semantic similarity of words is more accurate.

English Borrowings in Economic Discourse (Based on Modern Russian Trade Advertising)

Currently, the spheres of the economy are expanding, which is reflected in the more frequent use of borrowed economic vocabulary in discourse. There are a number of ways to effectively influence the recipient of information. The role of advertising in this case plays an important role. It should be noted here that in the language of everyday communication, speech types combine various kinds of borrowings. It is they that penetrate into the discourse, are fixed in the lexical and semantic system of the language. The role of economic discourse in the creation of trade advertising should be noted. The renewal of compatibility and the appearance of figurative meanings in common economic terms have become a linguistic feature of the modern era.In the economic discourse we are studying, numerous possibilities of language are used to achieve goals, that is, various techniques, special words, expressions and sentences. Such a language has an impact on people, their relationships and behavior. In turn, commercial advertising, as one of the branches of economic discourse, usually consists of simple sentences. The purpose of commercial advertising is to fix the expression chosen for a particular brand in the memory of a person who wants to purchase this product. Obviously, simple uncomplicated sentences are easier to remember. Advertising should essentially be as concise, clear, and transparent as possible. Therefore, most of them are singlepart sentences.Discourse is a text in a certain communicative situation. In addition to the original words, there are also loanwords in it, which should be distinguished from terms and neologisms. The former can be borrowed and indigenous, inherent only in one particular language. A necessary requirement for the term, as well as its dignity, is its unambiguity. As for borrowed words, synonymy is quite acceptable when switching from one language to another. At the same time, becoming fixed in one language and replenishing its lexical fund, borrowed vocabulary becomes familiar to its speakers, it becomes part of any stylistic layer and thus no longer perceived as a neologism.In our case, most of these are anglicisms, which have an unusual structure and play an important role in the economic discourse reflecting commercial advertising. From the economists’ point of view, advertising is a kind of a marker of a product being advertised for sale, and for linguists it is a specific form of language functioning. But it is from these two positions that advertising with native and borrowed words attract attention. The analysis of English borrowings functioning in Russian economic discourse is the objective of this article.It dictates the formulation and solution of the following tasks: 1) to determine the criteria for the expediency of using English-language borrowings in the Russian language; 2) to identify ways of interpreting the meanings of borrowed words. The article analyzes variations of borrowed lexemes of words and their interpretation in connection with the division into separate categories.

Endophoric Signposting: A Contrastive Study of Textual References in L2 Czech Master's Theses and Native English Academic Writing

The aim of this study is to contribute to cross-cultural and cross-disciplinary discourse analysis, shedding light on English L2 learners’ metadiscursive practices. Focusing on a specialised learner corpus of English-medium Master’s theses written by Czech university students, the research explores the occurrence of endophoric markers and their characteristics. To enable cross-linguistic and cross-cultural comparison, a reference corpus of representative L1 English academic discourse was compiled. A new taxonomy used here, which draws on Hyland’s list of endophoric markers (2005) and an extensive literature review, categorises endophoric markers into three groups: purely directional, markers using specific words, and a category combining the first two. The study investigates the occurrence and function of these markers, focusing on anaphoric, cataphoric, and non-directional references. It also aims to identify differences in the usage of endophoric markers in English between L2 novice writers and experienced L1 academic writers, providing insights into trends and patterns in the employment of endophoric markers in academic writing in a way that accounts for disciplinary and linguistic factors. The results reveal higher endophoric marker frequencies in the Master’s Thesis Corpus, suggesting distinctive discourse patterns among Czech learners using English as an L2, with a predominance of specific endophoric markers and notable cross-disciplinary variation.

Effect of Title Length and Word Count on Views: Evidence from Microsoft Excel videos on YouTube

This study examines the influence of title length and the number of words in titles on the view counts of YouTube videos, with a focus on optimizing title characteristics to enhance viewer engagement. Using a dataset of 12,466 Microsoft Excel tutorial videos published between January 2006 and November 2023, the analysis identifies significant patterns in title characteristics that affect viewership. The results reveal a bell-shaped relationship, with both mean and median view counts peaking at specific title lengths and word counts, emphasizing the importance of striking a balance between informativeness and brevity. Furthermore, the study highlights the critical role of visual information (character count) compared to semantic information (word count) in driving video engagement. By providing actionable insights, this research offers practical, cost-efficient guidelines for creators to optimize their video titles, ultimately contributing to more effective content strategies on digital platforms.

The Reliability of Assessing Gender Minority Status Based on Gender Identity and Sex Assigned at Birth in the African Context: Findings from the HIV Prevention Trials Network 075 Study.

Purpose: This study evaluated the reliability of assessing transgender status in African populations using questions about current gender identification and sex assigned at birth. Methods: Data were obtained from the HIV Prevention Trials Network 075, a study designed to assess the feasibility of recruiting and retaining 400 men who have sex with men (MSM) in a 1-year prospective cohort study in Kenya, Malawi, and South Africa and conducted from 2015 to 2017. We compared responses of 401 participants to questions about gender identity obtained at the screening and enrollment visits and, for a subset of participants, in open interviews 6-9 months after enrollment. Results: One hundred and eleven of the 401 persons reported to identify as female or transgender at either the screening visit and/or the enrollment visit. Of those 111 persons, 42 (37.8%) switched between the two assessment moments from male to female or transgender, or vice versa. Furthermore, most transgender women were sexually attracted to men and identified as gay. In the follow-up interview with a subset of participants, almost all persons categorized as transgender identified as MSM. Conclusion: Categorizing persons as transgender based on self-identified gender and sex assigned at birth has limited reliability in African populations. Possible explanations for the observed inconsistencies in the responses to gender-related questions are discussed. For future studies, an understanding is needed of what specific words and labels such as gender, female, or transgender mean to study participants.

EXPRESS: Naming speed during language production in younger and older adults: Examining the effects of sentence context.

Word retrieval during speech production has been found to slow down with ageing. Usually, words are produced in sentence contexts. The current studies examined how different sentence contexts influence lexical retrieval in younger and older adults. We also examined the potential influence of semantic knowledge and control on sentence-context effects. Study 1 was completed by 48 younger and 48 older adults. They named pictures that were preceded by a matched context (which predicted that specific target word), a mismatched context (predicting another word), a neutral context (that did not predict one specific word), or no context. In comparison to the neutral context, both younger and older adults' word production was faster in matched contexts, suggesting both age groups benefited from sentence contexts facilitating the retrieval of predictable words. Neither age group was slowed down by the mismatched contexts (compared to the neutral contexts), suggesting these contexts did not create (sufficient) interference to hinder lexical retrieval. In Study 2, participants completed measures of semantic knowledge, verbal fluency, semantic control, and inhibition. Older adults showed larger semantic knowledge but poorer inhibition and (on some measures) semantic control than younger adults. However, none of these measures predicted the sentence context effects observed in Study 1. Together, this suggests older adults' lexical retrieval can continue to benefit from sentence contexts predictive of upcoming words during language production.

Neural Signatures of Word Learning During Adult-Child Interactions

Abstract Early language acquisition relies on the successful communication of specific word meanings when speaking or encoding labels. Here, we tested for evidence of this communication within the parallel functional neural activation that occurred for adults and children as unfamiliar objects were labelled by the caregiver. Measuring event-related hemodynamic responses with functional near-infrared spectroscopy (fNIRS) simultaneously recorded from both individuals, we compared cortical responses that were time-locked to spontaneous instances of object labelling during openly structured interactions. Most notably, children’s responses differed between words learned and not learned in the right posterior temporal cortex, with relatively greater positive activation when children were not learning. The findings bear relevance to investigations of coordination between speakers and listeners during word-learning interactions.

Stories as a Stimulus for Dance Teaching and Learning in the Elementary School Context

ABSTRACT Stories are greatly enjoyed by young children, and as such they are engaging stimuli for teaching dance in the elementary school context. Dance is a powerful medium through which literacy learning can be enriched due to its embodied and multisensory methods. There are many ways to work with stories as stimuli in dance education. For example, it is possible to follow the form of a text’s narrative, investigating specific words and the quality of the movement that the text evokes. Furthermore, the book’s mood and atmosphere can be explored, along with the plot, characters, settings, rhythms, and the emotions it evokes. This article explores one book for its creative potential as a dance teaching-learning experience: Mixed: A Colorful Story by Chung. It also offers extension possibilities for the color-focused ideas that are provided and considers supplementary creative dance tasks for five more color-themed books.

TPU-Supported Ti3C2Tx/TiO2/Ru electrospun mat towards flexible and multi-functional sensors for human motion and NH3 detection

Nowadays, the demand for smart wearable devices has been increasing significantly. Integrating multiple functions into a single sensor has become a continuous challenge for researchers. To address these issues, a novel two-dimensional material, MXene (Ti3C2Tx), is now being utilized to fabricate flexible multifunctional sensors. TPU (thermoplastic polyurethane) fiber mats are prepared through electrospinning. Using TPU fiber mats as the substrate, MXene/TiO2/Ru composites are synthesized through a simple and effective method and loaded onto the fiber mats. Experimental tests have revealed that the TPU/MXene/TiO2/Ru flexible sensor exhibits characteristics such as a large stretching range, good durability, high sensitivity, and excellent breathability. The response of the TPU/MXene/TiO2/Ru flexible sensor can reach up to 80.4 k when it is stretched to 120 % of its original length at a stretching speed of 0.5 mm/s. Moreover, this sensor can be used to detect movements in human wrists, arms, faces, legs, fingers, and even vocal cord vibrations, responding to voice recognition through specific word combinations. Furthermore, this sensor exhibits a maximum response of 15.06 % to 100 ppm NH3 at room temperature, accompanied by high selectivity and stability, indicating its excellent multifunctionality in the fields of human health and environmental protection.

Study of U.S. Democratic and Republican Senators' Political Framing Strategies on Abortion Rights Based on Social Identity Theory and Framing Effect

Political framing significantly shapes public opinions and legislative outcomes in the United States, making the understanding of Democratic and Republican senators' strategies concerning abortion rights necessary. This study employs Social Identity Theory and Framing Effect to analyze cognitive and affective language patterns, as well as frequent references to specific word count in senators' speeches, using the Linguistic Inquiry and Word Count (LIWC) program. The findings reveal distinct partisan differences: Democrats frequently emphasize women's rights and personal freedoms, while Republicans focus on morality and the sanctity of unborn life. These divergent framing strategies highlight how party affiliation influences the discourse on this contentious issue. The research, using both qualitative and quantitative approaches, underscores the complexity of political communication and the need for nuanced studies to explore how language reflects and shapes political identities and ideologies.

Uncovering Key Factors That Drive the Impressions of Online Emerging Technology Narratives

Social media platforms play a significant role in facilitating business decision making, especially in the context of emerging technologies. Such platforms offer a rich source of data from a global audience, which can provide organisations with insights into market trends, consumer behaviour, and attitudes towards specific technologies, as well as monitoring competitor activity. In the context of social media, such insights are conceptualised as immediate and real-time behavioural responses measured by likes, comments, and shares. To monitor such metrics, social media platforms have introduced tools that allow users to analyse and track the performance of their posts and understand their audience. However, the existing tools often overlook the impact of contextual features such as sentiment, URL inclusion, and specific word use. This paper presents a data-driven framework to identify and quantify the influence of such features on the visibility and impact of technology-related tweets. The quantitative analysis from statistical modelling reveals that certain content-based features, like the number of words and pronouns used, positively correlate with the impressions of tweets, with increases of up to 2.8%. Conversely, features such as the excessive use of hashtags, verbs, and complex sentences were found to decrease impressions significantly, with a notable reduction of 8.6% associated with tweets containing numerous trailing characters. Moreover, the study shows that tweets expressing negative sentiments tend to be more impressionable, likely due to a negativity bias that elicits stronger emotional responses and drives higher engagement and virality. Additionally, the sentiment associated with specific technologies also played a crucial role; positive sentiments linked to beneficial technologies like data science or machine learning significantly boosted impressions, while similar sentiments towards negatively viewed technologies like cyber threats reduced them. The inclusion of URLs in tweets also had a mixed impact on impressions—enhancing engagement for general technology topics, but reducing it for sensitive subjects due to potential concerns over link safety. These findings underscore the importance of a strategic approach to social media content creation, emphasising the need for businesses to align their communication strategies, such as responding to shifts in user behaviours, new demands, and emerging uncertainties, with dynamic user engagement patterns.

Kiswahili Translation in Digital Media Platforms: Bridging Language Gaps in the AI Age

Purpose: As digital media platforms continue to proliferate globally, the need for multilingual content in these platforms has become increasingly apparent. Translation of online content is a necessity for netizens and other digital platform users who seek knowledge and information around a myriad of user-generated content on diverse issues and topics. Being a widely spoken language that goes beyond the borders of East Africa, Kiswahili’s significance in bridging linguistic barriers and facilitating communication and information access for Kiswahili speakers is clear. The purpose of this exploratory research paper was to examine the state of Kiswahili as a language of translation through different technologies across varied translation apps and social media platforms such as Facebook, You Tube, Tik Tok and Netflix. Methodology: The study adopted a descriptive research design. Data collection was done through online data mining tools including API access for web scrapping and manual extraction of content through random search engine searches. The data for the study included 10 sentences, 10 videos and 25 specific word forms such as proverbs, spiritual/religious words, similes and cultural words for food, beauty and family ties. Content analysis for both text and multimedia content such as written sentences, social media posts and videos was done. The study analyzed translation outputs of 14 Kiswahili translation apps in order to establish the status and accuracy of these translations. Of key consideration was the role of AI technology in the translation apps and the effect it has on the translated texts across different apps. The challenges and emerging issues facing Kiswahili translation through digital technologies were also explored. Findings: The findings of this study showed that the Apps used for Kiswahili translation have demonstrated great translation accuracy in basic semantic forms, with an average score of 60% accuracy upon random searches and between 10-30% accuracy for cultural forms and idiomatic expressions. Unique Contribution to Theory, Practice and Policy: The study was guided by the Skopos theory by Hans Vermeer (1978) which was complemented by the Relevance Theory by Dan Sperber and Deidre Wilson (1986). These Apps seem to have the feature of interoperability in that they seem to produce the same results suggesting that the AI supported feature in the apps may be interconnecting the apps during translation. The study advocates for more AI integration translation apps as this will help standardize Kiswahili translation corpus.

FPGA-Based Design of a Ready-to-Use and Configurable Soft IP Core for Frame Blocking Time-Sampled Digital Speech Signals

‘Frame blocking’ or ‘Framing’ is a technique that divides a time-sampled speech or audio signal into consecutive and equi-sized short-time frames, either overlapped or non-overlapped, for analysis. The framing hardware architectures (FHA) in the literature support framing speech or audio samples of specific word size with specific frame size and frame overlap size. However, speech and audio applications often require framing signal samples of varied word sizes with varied frame sizes and frame overlap sizes. Therefore, the existing FHAs must be redesigned appropriately to keep up with the variability in word size, frame size and frame overlap size, as demanded across multiple applications. Redesigning the existing FHAs for each specific application is laborious, prompting the need for a configurable intellectual property (IP) core. The existing FHAs are inappropriate for creating configurable IP cores as they lack adaptability to accommodate variability in frame size and frame overlap size. Therefore, to address these issues, a novel FHA, adaptable to accommodate the desired variability, is proposed. Furthermore, the proposed FHA is transformed into a field-programmable gate array-based soft, ready-to-use and configurable frame blocking IP core using the Xilinx® Vivado™ tool. The resulting IP core is versatile, offering configurability for framing in numerous applications incorporating real-time digital speech and audio systems. This research article discusses the proposed FHA and frame blocking IP core in detail.

Lexical inhibition after semantic violations recruits a domain-general inhibitory control mechanism.

Language processing is incremental. As language signals-for example, words in a sentence-unfold, humans predict and activate likely upcoming input to facilitate comprehension. Prediction not only accelerates understanding but also prompts reassessment in the case of prediction error, fostering learning and refining comprehension skills. Therefore, it is paramount to understand what happens when linguistic predictions are violated-for example, when a sentence ends in an unpredicted word. One theory, which we test here, is that the originally predicted word is actively inhibited after semantic violations. Furthermore, we tested whether this purported lexical inhibition process is achieved by a domain-general mechanism-that is, one that also inhibits other processes (e.g., movement). We combined a semantic violation task, in which highly constrained sentences primed specific words but sometimes continued otherwise, with a motoric stop-signal task. Across two experiments, semantic violations significantly impaired simultaneous action-stopping. This implies that lexical and motor inhibition share the same process. In support of this view, multivariate decoding of electroencephalographic recordings showed early overlap in neural processing between action-stopping (motor inhibition) and semantic violations (lexical inhibition). Moreover, a known signature of motor inhibition (the stop-signal P3) was reduced after this initial overlap period, further suggesting the presence of a bottleneck due to shared processing. These findings show that semantic violations trigger inhibitory processing and suggest that this lexical inhibition recruits a domain-general inhibitory control mechanism. This provides a new perspective on long-standing debates in psycholinguistics, extends the range of a well-characterized cognitive control mechanism into the linguistic domain, and offers support for recent neurobiological models of domain-general inhibitory control. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

BiLSTM-Filt: Neural network for radar word segmentation

Radar word extraction is the analysis foundation for multi-function radars (MFRs) in electronic intelligence (ELINT). Although neural networks enhance performance in radar word extraction, current research still faces challenges from complex electromagnetic environments and unknown radar words. Therefore, in this paper, we propose a promising two-stage radar word extraction framework, consisting of segmentation and recognition. To fill the vacancy of radar word segmentation, we establish the mathematical model from the time series analysis viewpoint and design a novel segmentation neural network based on Bi-direction Long Short-Term Memory with a filter module (BiLSTM-Filt). Specific radar word structure characteristics are extracted by training the network and applied for detecting radar words in the pulse train. To further improve segmentation performance, a bounding box regression method is designed to merge information from sub-region structures. Simulation experiments on a typical MFR, Mercury, reveal that the proposed method can outperform the baseline methods within complex electromagnetic environments, containing corrupted environments, various pulse backgrounds, and variable pulse train lengths. Due to the artificial design structure, the proposed method can also make a trial on unknown radar word segmentation.

A Study on the Impact of Blue Light and Its Brightness on Long-Term Memory

The purpose of this study is to evaluate the effect of blue light and its brightness on long-term memory. Blue light was tested under four distinct illuminance conditions ranging from 400 to 1000 lux, and the results were compared with those of red light as a comparison group. A total of 25 individuals, including 16 men and 9 women with a mean age of 22.04 ± 1.74 years, participated in these tests based on a within-subject research design. Initially, each participant was exposed to individual light conditions for 30 min in the morning. Subsequently, they were asked to complete learning memory tasks, which involved memorizing specific words composed of randomly chosen English consonants. Exactly 20 min after these memory tasks, their long-term memory retention was measured through recall. It was found that blue light was superior to red light in terms of long-term memory enhancement; the difference was statistically significant, with a confidence level of 95%. Moreover, brighter light environments were found to affect long-term memory performance more significantly compared to darker light environments. That said, there was no significant difference in interaction effects between light color and brightness. These findings confirm the results of previous studies highlighting the effectiveness of blue light in enhancing memory retention while also demonstrating that the degree of enhancement may vary depending on light brightness.