Search Results (10,346)

This text investigates the bending/buckling behavior of multi-layer asymmetric/symmetric annular and circular graphene plates through the application of the nonlocal strain gradient model. Additionally, the static analysis of multi-sector nanoplates is addressed. By considering the van der Waals interactions among the layers, the higher-order shear deformation theory (HSDT), and the nonlocal strain gradient theory, the equilibrium equations are formulated in terms of generalized displacements and rotations. The mathematical nonlinear equations are solved utilizing either the semi-analytical polynomial method (SAPM) and the differential quadrature method (DQM). Also, the available references are used to validate the results. Investigations are conducted to examine the effect of small-scale factors, the van der Waals interaction value among the layers, boundary conditions, and geometric factors. Full article

Background: This article introduces SC-Phi2, a fine-tuned StarCraft II small language model. Small language models, like Phi2, Gemma, and DistilBERT, are streamlined versions of large language models (LLMs) with fewer parameters that require less computational power and memory to run. Method: To teach Microsoft’s Phi2 model about StarCraft, we create a new SC2 text dataset with information about StarCraft races, roles, and actions and use it to fine-tune Phi-2 with self-supervised learning. We pair this language model with a Vision Transformer (ViT) from the pre-trained BLIP-2 (Bootstrapping Language Image Pre-training) model, fine-tuning it on the StarCraft replay dataset, MSC. This enables us to construct dynamic prompts that include visual game state information. Results: Unlike the large models used in StarCraft LLMs such as GPT-3.5, Phi2 is trained primarily on textbook data and contains little inherent knowledge of StarCraft II beyond what is provided by our training process. By using LoRA (Low-rank Adaptation) and quantization, our model can be trained on a single GPU. We demonstrate that our model performs well at build order prediction, an important StarCraft macromanagement task. Conclusions: Our research on the usage of small models is a step towards reducing the carbon footprint of AI agents. Full article

This paper examines Portuguese literary texts in which fire is explicitly included in the narrative. These texts include descriptions of various occurrences and uses of fire and their intertwined social and ecological effects. They shed light on the origins, actors, practices, and impacts of fire, and they reveal past perceptions of fire, namely the role fire played in social processes and in the making of landscapes. It becomes evident that in literary texts, fire is not merely a physical element but also a powerful symbolic force of life, death, and transformation. Furthermore, the literary landscapes of different regions describe fire in distinct ways, reflecting particular geographical, social, and political contexts. Full article

The past few decades have witnessed an enormous research growth in the field of natural language processing. In this regard, numerous machine learning (ML) algorithms have been applied in different sub-domains of NLP such as speech recognition, text classification, sentiment analysis, etc. Furthermore, their performances have been evaluated using diverse performance metrics. However, a comparative analysis of various ML algorithms in the aforementioned field is a feasible research area to explore. This may efficiently guide future research to precisely focus on the improvement of those particular algorithms that have been found to be more effective based on previous research. Thus, this article provides a comparative analysis regarding the application and effectiveness of different ML algorithms in the field of NLP. Additionally, it highlights the future research direction to be adopted for enhancing the ability of the natural language processing domain. Full article

Background/Objective: Muscle synergy analysis based on machine learning has significantly advanced our understanding of the mechanisms underlying the central nervous system motor control of gait and has identified abnormal gait synergies in stroke patients through various analytical approaches. However, discrepancies in experimental conditions and computational methods have limited the clinical application of these findings. This review seeks to integrate the results of existing studies on the features of muscle synergies in stroke-related gait abnormalities and provide clinical and research insights into gait rehabilitation. Methods: A systematic search of Web of Science, PubMed, and Scopus was conducted, yielding 10 full-text articles for inclusion. Results: By comprehensively reviewing the consistencies and differences in the study outcomes, we emphasize the need to segment the gait cycle into specific phases (e.g., weight acceptance, push-off, foot clearance, and leg deceleration) during the treatment process of gait rehabilitation and to develop rehabilitation protocols aimed at restoring normal synergy patterns in each gait phase and fractionating reduced synergies. Conclusions: Future research should focus on validating these protocols to improve clinical outcomes and introducing indicators to assess abnormalities in the temporal features of muscle synergies. Full article

In this paper, I provide a close examination of early Christian biographical sources through the heuristic lens of “home studies”, tracing a thread from the New Testament Gospels to martyrdom texts, the apocryphal Acts literature, Eusebius’ biography of Origen in his Church History, and finally, Athanasius’ Life of Antony. I demonstrate that the lens of home allows us to see that in each of these discrete groups of texts, Christ’s call to discipleship is understood to redefine and reconstitute the meaning of home and relatedly, family: to be “home” required a great deal of displacement and mobility as one forsook one’s biological family and household for the sake of obedience to the call of Christ. I argue that three topics, typically examined separately, are fruitfully brought together through the lens of home: (1) the shaping of ancient Christian identity formation, as expressed by the characters’ use of familial language to identify other members of the early Christian movement; (2) the mobile nature of the person who joins the movement, providing insights about the mobility and travel of many of its members; and (3) ancient Christian eschatological thought concerning the final dwelling of Christ-believers in some form of otherworldly home after death. Full article

Sentiment classification plays a crucial role in evaluating user feedback. Today, online media users can freely provide their reviews with few restrictions. User reviews on social media are often disorganized and challenging to classify as positive or negative comments. This task becomes even more difficult when dealing with large amounts of data, making sentiment classification necessary. Automating sentiment classification involves text classification processes, commonly performed using deep learning methods. The classification process using deep learning models is closely tied to text representation. This step is critical as it affects the quality of the data being processed by the deep learning model. Traditional text representation methods often overlook the contextual meaning of sentences, leading to potential misclassification by the model. In this study, we propose a novel fusion text representation model, GloWord_biGRU, designed to enhance the contextual understanding of sentences for sentiment classification. Firstly, we combine the advantages of GloVe and Word2Vec to obtain richer and more meaningful word representations. GloVe provides word representations based on global frequency statistics within a large corpus, while Word2Vec generates word vectors that capture local contextual relationships. By integrating these two approaches, we enhance the quality of word representations used in our model. During the classification stage, we employ biGRU, considering the use of fewer parameters, which consequently reduces computational requirements. We evaluate the proposed model using the IMDB dataset. Several scenarios demonstrate that our proposed model achieves superior performance, with an F1 score of 90.21%. Full article

Abbott Elementary, the Emmy-award winning mockumentary-style comedy television show, chronicles the day-to-day efforts of a group of teachers in a Philadelphia public elementary school who, despite the odds stacked against them, are determined to help their students succeed. With humor and heart, the show also uncovers some of the larger policy issues at play in U.S. urban public education, such as teacher shortages, school funding, and school choice. This essay describes the development of an interdisciplinary Honors seminar for undergraduate students at a large, public university in the Midwest U.S. that used episodes of Abbott Elementary as a central curricular text to support students in analyzing current policy issues in urban education. Drawing on Giroux’s concept of media as public pedagogy, this essay details how the course design employed a critical intersectional multiculturalism cultural studies approach to create opportunities for students to engage in a multiperspectival analysis, including (1) an understanding of political culture, (2) textual analysis, and (3) audience reception. Implications and recommendations are made for selecting and integrating popular culture media as curricular text in interdisciplinary coursework. Full article

Recent studies have shown that, due to redundancy, some heads of the Transformer model can be pruned without diminishing the efficiency of the model. In this paper, we propose a constrained optimization algorithm based on Hebbian learning, which trains specific layers in the Transformer architecture in order to enforce diversification between the different heads in the multi-head attention module. The diversification of the heads is achieved through a single-layer feed-forward neural network that is added to the Transformer architecture and is trained with the proposed algorithm. We utilize the algorithm in three different architectural variations of the baseline Transformer model. In addition to the diversification of the heads, the proposed methodology can be used to prune the heads that capture redundant information. Experiments on diverse NLP tasks, including machine translation, text summarization, question answering and large language modeling, show that our proposed approach consistently improves the performance of baseline Transformer models. Full article

Sarcasm detection in text poses significant challenges for traditional sentiment analysis, as it often requires an understanding of context, word meanings, and emotional undertones. For example, in the sentence “I totally love working on Christmas holiday”, detecting sarcasm depends on capturing the contrast between affective words and their context. Existing methods often focus on single-embedding levels, such as word-level or affective-level, neglecting the importance of multi-level context. In this paper, we propose SAWE (Sentence, Affect, and Word Embeddings), a framework that combines sentence-level, affect-level, and context-dependent word embeddings to improve sarcasm detection. We use pre-trained transformer models SBERT and RoBERTa, enhanced with a bidirectional GRU and self-attention, alongside SenticNet to extract affective words. The combined embeddings are processed through a CNN and classified using a multilayer perceptron (MLP). SAWE is evaluated on two benchmark datasets, Sarcasm Corpus V2 (SV2) and Self-Annotated Reddit Corpus 2.0 (SARC 2.0), outperforming previous methods, particularly on long texts, with a 4.2% improvement on F1-Score for SV2. Our results emphasize the importance of multi-level embeddings and contextual information in detecting sarcasm, demonstrating a new direction for future research. Full article

Image–text matching is a fundamental task in the multimodal research field, connecting computer vision and natural language processing by aligning visual content with corresponding textual descriptions. Accurate matching is critical for applications such as image captioning and text-based image retrieval yet remains challenging due to the differences in data modalities. This paper addresses these challenges by proposing a robust image–text matching model inspired by Contrastive Language–Image Pre-training (CLIP). Our approach employs the Vision Transformer (ViT) model as the image encoder and Bidirectional Encoder Representations from Transformers (Bert) as the text encoder, integrating these into a shared vector space to measure semantic similarity. We enhance the model’s training efficiency using the LiT-tuning paradigm to optimize learning through a cosine decay strategy for dynamic adjustment of the learning rate. We validate our method on two benchmark datasets, WuKong and Flickr30k, demonstrating that our model achieves superior performance and significantly improves key evaluation metrics. The results underscore the model’s effectiveness in achieving accurate and robust image–text alignment. Full article

Introduction: Currently, progesterone is notably absent from conventional feminizing hormone therapies for transgender women. Anecdotal reports indicate the potential for health advantages following the incorporation of progesterone into treatment regimens. The primarily female hormone, progesterone naturally surges in women during the menstrual luteal phase. When administered exogenously, it may expedite bodily changes that are pivotal for gender transition. Progesterone holds promise as a potential remedy for various health conditions prevalent in the transgender woman population. Methods: This narrative review synthesizes existing literature and presents a comprehensive account of the administration of exogenous progesterone in transgender women. A literature search was conducted using the PubMed, Embase, ScienceDirect, and ResearchGate databases. The following keywords were used in the search: progesterone, transgender, breast neoplasms, lactation, prostate, testicular neoplasms, and thrombosis. These terms were combined using Boolean operators. The results of the initial search were screened by three independent reviewers based on their relevance to the topic under study. Results: A total of 104 studies were initially identified as meeting the criteria for inclusion. Following an assessment based on the contents of the title, abstract, and full text, 39 studies were deemed eligible for inclusion. A critical examination of health outcomes was conducted across key sections, including breast development, mental health, lactation, cancer risk (breast and prostate), thrombosis, and nervous and other systems. Discussion: The use of progesterone in the transgender woman population is a topic that has yet to be sufficiently researched. The limited sample size, short follow-up periods, and lack of randomization restrict the potential for achieving a robust scientific evidence base. In order to gain a fuller understanding of this topic, findings from studies on contraception, hormone replacement therapy, and animal models were considered. Conclusions: Progesterone may have a beneficial effect on the bodies of transgender women without significant adverse health effects. Further investigation through well-designed studies is recommended. Randomized controlled trials that include various dosages, broad and long-term effects, and precise demographics are needed. There is an immediate need for more knowledge to create appropriate patent and clinical practice guidelines. Full article

National parks serve as critical practical sites for advancing the concept of “harmonious coexistence between humans and nature” and hold a strategic role in establishing global ecological security barriers. Scholars and decision-makers have expressed significant interest in rigorous assessments of the recreational resource value in national parks. This paper focuses on the Three-River-Source National Park, examining the characteristics and components of its recreational resource value through the lens of human–environment relationship theory. Analysis spans dimensions of geological and geomorphological value, ecological service value, historical and cultural value, and aesthetic landscape value. By extracting visitor comments rich in vocabulary related to their perceptions, this study compares variations in resource values and the “resource value–visitor perception” synergy within Three-River-Source National Park, employing text analysis, semantic network analysis, and coordination analysis methods. The findings reveal that (1) Visitor perceptions of recreational resource value display a clear hierarchy, with aesthetic landscape value (43.6%) ranking highest, followed by geological and geomorphological value (26.7%), historical and cultural value (19.3%), and ecological service value (10.4%), showing significant variation among categories; the vocabulary across these value types exhibits a pronounced long-tail distribution. (2) The recreational resource value in the park forms a distinct core centered on prominent attractions, accompanied by patterns of vocabulary aggregation and dispersion. (3) Visitors demonstrate strong synergy in their perception of geological and aesthetic value, weaker perception regarding historical and cultural value, and a relatively narrow understanding of ecological service value. This research enhances public comprehension of the recreational resource value of national parks and provides a scientific foundation for the conservation and sustainable use of recreational resources in national parks, advancing the realization of their recreational functions. Full article

This study investigates the effectiveness of a proposed version of Meta’s LLaMA 3 model in detecting fake claims across bilingual (English and Romanian) datasets, focusing on a multi-class approach beyond traditional binary classifications in order to better mimic real-world scenarios. The research employs a proposed version of the LLaMA 3 model, optimized for identifying nuanced categories such as “Mostly True” and “Mostly False”, and compares its performance against leading large language models (LLMs) including Open AI’s ChatGPT versions, Google’s Gemini, and similar LLaMA models. The analysis reveals that the proposed LLaMA 3 model consistently outperforms its base version and older LLaMA models, particularly in the Romanian dataset, achieving the highest accuracy of 39% and demonstrating superior capabilities in identifying nuanced claims, over all the compared large language models. However, the model’s performance across both languages highlights some challenges, with generally low accuracy and difficulties in handling ambiguous categories by all the LLMs. The study also underscores the impact of language and cultural context on model reliability, noting that even state-of-the-art models like ChatGPT 4.o and Gemini exhibit inconsistencies when applied to Romanian text and more than a binary true/false approach. Full article

The text-to-SQL task aims to convert natural language questions into corresponding SQL queries based on a given database schema. Previous models that rely on graph neural networks often struggle to accurately capture the complex grammatical relationships present in these questions, leading to poor performance when generating queries for longer requests. To address these challenges, we propose RGISQL, which integrates refined grammatical information extracted from the question and employs segmentation processing to effectively manage long queries. Additionally, RGISQL minimizes the complexity of edge embeddings by reducing the coupling within graph neural networks. By utilizing grammatical dependency trees, RGISQL is better equipped to capture the inherent structure and grammatical rules of questions. This refined grammatical information offers additional contextual and semantic cues for the model, thereby enhancing both its generalizability and interpretability. Furthermore, we dynamically assess the importance of different edges based on the graph structure, which helps reduce the coupling of edge embeddings and further improves the model’s performance. Multiple sets of experiments conducted on the Spider and Spider-Syn datasets demonstrate that RGISQL outperforms other baselines, achieving the best results in both datasets. Full article