Search Results (214)

In bioinformatics, pathway analyses are used to interpret biological data by mapping measured molecules with known pathways to discover their functional processes and relationships. Pathway analysis has become an essential tool for interpreting large-scale omics data, translating complex gene sets into actionable experimental insights. However, issues inherent to pathway databases and misinterpretations of pathway relevance often result in “pathway fails,” where findings, though statistically significant, lack biological applicability. For example, the Tumor Necrosis Factor (TNF) pathway was originally annotated based on its association with observed tumor necrosis, while it is multifunctional across diverse physiological processes in the body. This review broadly evaluates pathway analysis interpretation, including embedding-based, semantic similarity-based, and network-based approaches to clarify their ideal use-case scenarios. Each method for interpretation is assessed for its strengths, such as high-quality visualizations and ease of use, as well as its limitations, including data redundancy and database compatibility challenges. Despite advancements in the field, the principle of “garbage in, garbage out” (GIGO) shows that input quality and method choice are critical for reliable and biologically meaningful results. Methodological standardization, scalability improvements, and integration with diverse data sources remain areas for further development. By providing critical guidance with contextual examples such as TNF, we aim to help researchers align their objectives with the appropriate method. Advancing pathway analysis interpretation will further enhance the utility of pathway analysis, ultimately propelling progress in systems biology and personalized medicine. Full article

To enhance the accuracy and reliability of large language models (LLMs) in regulatory question-answering tasks, this study addresses the complexity and domain-specificity of regulatory texts by designing a retrieval-augmented generation (RAG) testing framework. It proposes a dimensionality reduction-based semantic similarity measurement method and a retrieval optimization approach leveraging information reasoning. Through the construction of the technical route of the intelligent knowledge management system, the semantic understanding capabilities of multiple mainstream embedding models in the text matching of financial regulations are systematically evaluated. The workflow encompasses data processing, knowledge base construction, embedding model selection, vectorization, recall parameter analysis, and retrieval performance benchmarking. Furthermore, the study innovatively introduces a multidimensional scaling (MDS) based semantic similarity measurement method and a question-reasoning processing technique. Compared to traditional cosine similarity (CS) metrics, these methods significantly improved recall accuracy. Experimental results demonstrate that, under the RAG testing framework, the mxbai-embed-large embedding model combined with MDS similarity calculation, Top-k recall, and information reasoning effectively addresses core challenges such as the structuring of regulatory texts and the generalization of domain-specific terminology. This approach provides a reusable technical solution for optimizing semantic matching in vertical-domain RAG systems, particularly for MDSs such as law and finance. Full article

Background: The rapid integration of generative AIs, such as ChatGPT, into industrial, process, and construction management introduces both operational advantages and emerging cognitive risks. While these models support task automation and safety analysis, their internal architecture differs fundamentally from human cognition, posing interpretability and trust challenges in high-risk contexts. Methods: This study investigates whether architectural design elements in Transformer-based generative models contribute to a measurable divergence from human reasoning. A methodological framework is developed to examine core AI mechanisms—vectorization, positional encoding, attention scoring, and optimization functions—focusing on how these introduce quantifiable “distances” from human semantic understanding. Results: Through theoretical analysis and a case study involving fall prevention advice in construction, six types of architectural distances are identified and evaluated using cosine similarity and attention mapping. The results reveal misalignments in focus, semantics, and response stability, which may hinder effective human–AI collaboration in safety-critical decisions. Conclusions: These findings suggest that such distances represent not only algorithmic abstraction but also potential safety risks when generative AI is deployed in practice. The study advocates for the development of AI architectures that better reflect human cognitive structures to reduce these risks and improve reliability in safety applications. Full article

Large-language-model (LLM) APIs demonstrate impressive reasoning capabilities, but their size, cost, and closed weights limit the deployment of knowledge-aware AI within biomedical research groups. At the other extreme, standard attention-based neural language models (SANLMs)—including encoder–decoder architectures such as Transformers, Gated Recurrent Units (GRUs), and Long Short-Term Memory (LSTM) networks—are computationally inexpensive. However, their capacity for semantic reasoning in noisy, open-vocabulary knowledge bases (KBs) remains unquantified. Therefore, we investigate whether compact SANLMs can (i) reason over hybrid OpenIE-derived KBs that integrate commonsense, general-purpose, and non-communicable-disease (NCD) literature; (ii) operate effectively on commodity GPUs; and (iii) exhibit semantic coherence as assessed through manual linguistic inspection. To this end, we constructed four training KBs by integrating ConceptNet (600k triples), a 39k-triple general-purpose OpenIE set, and an 18.6k-triple OpenNCDKB extracted from 1200 PubMed abstracts. Encoder–decoder GRU, LSTM, and Transformer models (1–2 blocks) were trained to predict the object phrase given the subject + predicate. Beyond token-level cross-entropy, we introduced the Meaning-based Selectional-Preference Test (MSPT): for each withheld triple, we masked the object, generated a candidate, and measured its surplus cosine similarity over a random baseline using word embeddings, with significance assessed via a one-sided t-test. Hyperparameter sensitivity (311 GRU/168 LSTM runs) was analyzed, and qualitative frame–role diagnostics completed the evaluation. Our results showed that all SANLMs learned effectively from the point of view of the cross entropy loss. In addition, our MSPT provided meaningful semantic insights: for the GRUs (256-dim, 2048-unit, 1-layer): mean similarity $\left({\mu }_{sts}\right)$ of 0.641 to the ground truth vs. 0.542 to the random baseline (gap 12.1%; $p<{10}^{-180}$). For the 1-block Transformer: ${\mu }_{sts}=0.551$ vs. $0.511$ (gap 4%; $p<{10}^{-25}$). While Transformers minimized loss and accuracy variance, GRUs captured finer selectional preferences. Both architectures trained within <24 GB GPU VRAM and produced linguistically acceptable, albeit over-generalized, biomedical assertions. Due to their observed performance, LSTM results were designated as baseline models for comparison. Therefore, properly tuned SANLMs can achieve statistically robust semantic reasoning over noisy, domain-specific KBs without reliance on massive LLMs. Their interpretability, minimal hardware footprint, and open weights promote equitable AI research, opening new avenues for automated NCD knowledge synthesis, surveillance, and decision support. Full article

Unsupervised representation learning can train deep learning models to formally express the semantic connotations of objects in the case of unlabeled data, which can effectively realize the expression of the semantics of geospatial entity categories in application scenarios lacking expert knowledge and help achieve the deep fusion of geospatial data. In this paper, a method for the semantic representation of the geospatial entity categories (denoted as feature embedding) is presented, taking advantage of the characteristic that regions with similar distributions of geospatial entity categories also have a certain level of similarity. To construct the entity category embedding, a spatial proximity graph of entities and an adjacency matrix of entity categories are created using a large number of geospatial entities obtained from OSM (OpenStreetMap). The cosine similarity algorithm is then employed to measure the similarity between these embeddings. Comparison experiments are then conducted by comparing the similarity results from the standard model. The results show that the results of this model are basically consistent with the standard model (Pearson correlation coefficient = 0.7487), which verifies the effectiveness of the feature embedding extracted by this model. Based on this, this paper applies the feature embedding to the regional similarity task, which verifies the feasibility of using the model in the downstream task. It provides a new idea for realizing the formal expression of the unsupervised entity category semantics. Full article

Background/Objectives: The use of standardized assessment tools within the nursing care process is a globally established practice, widely recognized as a foundation for evidence-based evaluation. Accurate translation is essential to ensure their correct and consistent clinical use. While effective, traditional procedures are time-consuming and resource-intensive, leading to increasing interest in whether artificial intelligence can assist or streamline this process for nursing researchers. Therefore, this study aimed to assess the translation’s quality of nursing assessment scales performed by ChatGPT 4.0. Methods: A total of 31 nursing rating scales with 772 items were translated from English to Italian using two different prompts, and then underwent a deep lexicometric analysis. To assess the semantic accuracy of the translations the Sentence-BERT, Jaccard similarity, TF-IDF cosine similarity, and Overlap ratio were used. Sensitivity, specificity, AUC, and AUROC were calculated to assess the quality of the translation classification. Paired-sample t-tests were conducted to compare the similarity scores. Results: The Maastricht prompt produced translations that are marginally but consistently more semantically and lexically faithful to the original. While all differences were found to be statistically significant, the corresponding effect sizes indicate that the advantage of the Maastricht prompt is slight but consistent across all measures. The sensitivity of the prompts was 0.929 (92.9%) for York and 0.932 (93.2%) for Maastricht. Specificity and precision remained for both at 1.000. Conclusions: Findings highlight the potential of prompt engineering as a low-cost, effective method to enhance translation outcomes. Nonetheless, as translation represents only a preliminary step in the full validation process, further studies should investigate the integration of AI-assisted translation within the broader framework of instrument adaptation and validation. Full article

The visual nature of Sign Language datasets raises privacy concerns that hinder data sharing, which is essential for advancing deep learning (DL) models in Sign Language recognition and translation. This study evaluated two anonymization techniques, realistic avatar synthesis and face swapping (FS), designed to anonymize the identities of signers, while preserving the semantic integrity of signed content. A novel metric, Identity Anonymization with Expressivity Preservation (IAEP), is introduced to assess the balance between effective anonymization and the preservation of facial expressivity crucial for Sign Language communication. In addition, the quality evaluation included the LPIPS and FID metrics, which measure perceptual similarity and visual quality. A survey with deaf participants further complemented the analysis, providing valuable insight into the practical usability and comprehension of anonymized videos. The results show that while face swapping achieved acceptable anonymization and preserved semantic clarity, avatar-based anonymization struggled with comprehension. These findings highlight the need for further research efforts on securing privacy while preserving Sign Language understandability, both for dataset accessibility and the anonymous participation of deaf people in digital content. Full article

Despite advancements in divergence and distance measures across fuzzy set extensions, the development of such measures for Globular T-Spherical Fuzzy Sets (G-TSFSs) remains significantly unexplored. Existing approaches often fall short in capturing the rich semantics and high-dimensional uncertainty that G-TSFSs represent, limiting their utility in complex decision environments. This study is motivated by the need to fill this critical gap and advance decision science through more expressive and structurally aligned tools. This paper introduces a suite of novel divergence measures (Div-Ms) specifically formulated for G-TSFSs, a powerful tool for capturing uncertainty in multi-criteria group decision-making (MCGDM) under complex conditions. These Div-Ms serve as the foundation for developing new distance measures (Dis-Ms) and similarity measures (SMs), where both Dis-Ms and SMs are symmetry-based and their essential mathematical properties and supporting theorems are rigorously established. Leveraging these constructs, we propose a robust G-TSF-TOPSIS framework and apply it to a real-world problem, selecting optimal solar energy systems (SESs) for a university context. The model integrates expert evaluations, assuming equal importance due to their pivotal and complementary roles. A sensitivity analysis over the tunable parameter (ranging from 4.0 to 5.0 with an increment of 0.2) confirms the robustness and stability of the decision outcomes, with no changes observed in the final rankings. Comparative analysis with existing models shows superiority and soundness of the proposed methods. These results underscore the practical significance and theoretical soundness of the proposed approach. The study concludes by acknowledging its limitations and suggesting directions for future research, particularly in exploring adaptive expert weighting strategies for broader applicability. Full article

The contemporary challenge in remote sensing lies in the precise retrieval of increasingly abundant and high-resolution remotely sensed images (RS image) stored in expansive data warehouses. The heightened spatial and spectral resolutions, coupled with accelerated image acquisition rates, necessitate advanced tools for effective data management, retrieval, and exploitation. The classification of large-sized images at the pixel level generates substantial data, escalating the workload and search space for similarity measurement. Semantic-based image retrieval remains an open problem due to limitations in current artificial intelligence techniques. Furthermore, on-board storage constraints compel the application of numerous compression algorithms to reduce storage space, intensifying the difficulty of retrieving substantial, sensitive, and target-specific data. This research proposes an innovative hybrid approach to enhance the retrieval of remotely sensed images. The approach leverages multilevel classification and multiscale feature extraction strategies to enhance performance. The retrieval system comprises two primary phases: database building and retrieval. Initially, the proposed Multiscale Multiangle Mean-shift with Breaking Ties (MSMA-MSBT) algorithm selects informative unlabeled samples for hyperspectral and synthetic aperture radar images through an active learning strategy. Addressing the scaling and rotation variations in image capture, a flexible and dynamic algorithm, modified Deep Image Registration using Dynamic Inlier (IRDI), is introduced for image registration. Given the complexity of remote sensing images, feature extraction occurs at two levels. Low-level features are extracted using the modified Multiscale Multiangle Completed Local Binary Pattern (MSMA-CLBP) algorithm to capture local contexture features, while high-level features are obtained through a hybrid CNN structure combining pretrained networks (Alexnet, Caffenet, VGG-S, VGG-M, VGG-F, VGG-VDD-16, VGG-VDD-19) and a fully connected dense network. Fusion of low- and high-level features facilitates final class distinction, with soft thresholding mitigating misclassification issues. A region-based similarity measurement enhances matching percentages. Results, evaluated on high-resolution remote sensing datasets, demonstrate the effectiveness of the proposed method, outperforming traditional algorithms with an average accuracy of 86.66%. The hybrid retrieval system exhibits substantial improvements in classification accuracy, similarity measurement, and computational efficiency compared to state-of-the-art scene classification and retrieval methods. Full article

We present a new framework to study the stability of semantic projections based on word embeddings. Roughly speaking, semantic projections are indices taking values in the interval $[0,1]$ that measure how terms share contextual meaning with the words of a given universe. Since there are many ways to define such projections, it is important to establish a procedure for verifying whether a group of them behaves similarly. Moreover, when fixing one particular projection, it is important to assess whether the average projections remain consistent when replacing the original universe with a similar one describing the same semantic environment. The aim of this paper is to address the lack of formal tools for assessing the stability of semantic projections (that is, their invariance under formal changes which preserve the underlying semantic context) across alternative but semantically related universes in word embedding models. To address these problems, we employ a combination of statistical and AI methods, including correlation analysis, clustering, chi-squared distance measures, weighted approximations, and Lipschitz-based estimators. The methodology provides theoretical guarantees under mild mathematical assumptions, ensuring bounded errors in projection estimations based on the assumption of Lipschitz continuity. We demonstrate the practical applicability of our approach through two case studies involving agricultural terminology across multiple data sources (DOAJ, Scholar, Google, and Arxiv). Our results show that semantic stability can be quantitatively evaluated and that the careful modeling of projection functions and universes is crucial for robust semantic analysis in NLP. Full article

In order to address the problem of Unmanned Aerial Vehicles (UAVs) being difficult to locate in environments without Global Navigation Satellite System (GNSS) signals or with weak signals, this paper proposes a localization method for UAV aerial images based on semantic topological feature matching. Unlike traditional scene matching methods that rely on image-to-image matching technology, this approach uses semantic segmentation and the extraction of image topology feature vectors to represent images as patterns containing semantic visual references and the relative topological positions between these visual references. The feature vector satisfies scale and rotation invariance requirements, employs a similarity measurement based on Euclidean distance for matching and positioning between the target image and the benchmark map database, and validates the proposed method through simulation experiments. This method reduces the impact of changes in scale and direction on the image matching accuracy, improves the accuracy and robustness of matching, and significantly reduces the storage requirements for the benchmark map database. Full article

Background: Systematic reviewers face a growing body of biomedical literature, making early-stage article screening increasingly time-consuming. In this study, we assessed six large language models (LLMs)—OpenHermes, Flan T5, GPT-2, Claude 3 Haiku, GPT-3.5 Turbo, and GPT-4o—for their ability to identify randomized controlled trials (RCTs) in datasets of increasing difficulty. Methods: We first retrieved articles from PubMed and used all-mpnet-base-v2 to measure semantic similarity to known target RCTs, stratifying the collection into quartiles of descending relevance. Each LLM then received either verbose or concise prompts to classify articles as “Accepted” or “Rejected”. Results: Claude 3 Haiku, GPT-3.5 Turbo, and GPT-4o consistently achieved high recall, though their precision varied in the quartile with the highest similarity, where false positives increased. By contrast, smaller or older models struggled to balance sensitivity and specificity, with some over-including irrelevant studies or missing key articles. Importantly, multi-stage prompts did not guarantee performance gains for weaker models, whereas single-prompt approaches proved effective for advanced LLMs. Conclusions: These findings underscore that both model capability and prompt design strongly affect classification outcomes, suggesting that newer LLMs, if properly guided, can substantially expedite systematic reviews. Full article

Music has been shown to increase arousal and attention and even facilitate processing during non-musical tasks, including those related to speech and language functions. Mind wandering has been studied in many sustained attention tasks. Here, we investigated the intersection of these two phenomena: the role of mind wandering while listening to familiar/unfamiliar musical excerpts, and its effects on concurrent linguistic processing. We hypothesized that familiar music would be less distracting than unfamiliar music, causing less mind wandering, and consequently benefit concurrent speech perception. Participants (N = 96 young adults) performed a lexical-semantic congruity task where they judged the relatedness of visually presented word pairs while listening to non-vocal classical music (familiar or unfamiliar orchestral pieces), or a non-music environmental sound clip (control) played in the background. Mind wandering episodes were probed intermittently during the task by explicitly asking listeners if their mind was wandering in that moment. The primary outcome was accuracy and reactions times measured during the lexical-semantic judgment task across the three background music conditions (familiar, unfamiliar, and control). We found that listening to familiar music, relative to unfamiliar music or environmental noise, was associated with faster lexical-semantic decisions and a lower incidence of mind wandering. Mind wandering frequency was similar when performing the task when listening to familiar music and control environmental sounds. We infer that familiar music increases task enjoyment, reduces mind wandering, and promotes more rapid lexical access during concurrent lexical processing, by modulating task-related attentional resources. The implications of using music as an aid during academic study and cognitive tasks are discussed. Full article

With the increasing adoption of digital governance and big data analytics, the quality of government hotline data significantly affects urban governance and public service efficiency. However, existing methods for assessing data record standards focus predominantly on structured data, exhibiting notable inadequacies in handling the complexities inherent in unstructured or semi-structured textual hotline records. To address these shortcomings, this study develops a comprehensive scoring method tailored for evaluating multi-dimensional data record standards in government hotline data. By integrating advanced deep learning models, we systematically analyze six evaluation indicators: classification predictability, dispatch accuracy, record correctness, address accuracy, adjacent sentence similarity, and full-text similarity. Empirical analysis reveals a significant positive correlation between improved data record standards and higher work order completion rates, particularly highlighting the crucial role of semantic-related indicators (classification predictability and adjacent sentence similarity). Furthermore, the results indicate that the work order field strengthens the positive impact of data standards on completion rates, whereas variations in departmental data-handling capabilities weaken this relationship. This study addresses existing inadequacies by proposing a novel scoring method emphasizing semantic measures and provides practical recommendations—including standardized language usage, intelligent analytic support, and targeted staff training—to effectively enhance urban governance. Full article

Does semantic communication require a semantic information theory parallel to Shannon’s information theory, or can Shannon’s work be generalized for semantic communication? This paper advocates for the latter and introduces a semantic generalization of Shannon’s information theory (G theory for short). The core idea is to replace the distortion constraint with the semantic constraint, achieved by utilizing a set of truth functions as a semantic channel. These truth functions enable the expressions of semantic distortion, semantic information measures, and semantic information loss. Notably, the maximum semantic information criterion is equivalent to the maximum likelihood criterion and similar to the Regularized Least Squares criterion. This paper shows G theory’s applications to daily and electronic semantic communication, machine learning, constraint control, Bayesian confirmation, portfolio theory, and information value. The improvements in machine learning methods involve multi-label learning and classification, maximum mutual information classification, mixture models, and solving latent variables. Furthermore, insights from statistical physics are discussed: Shannon information is similar to free energy; semantic information to free energy in local equilibrium systems; and information efficiency to the efficiency of free energy in performing work. The paper also proposes refining Friston’s minimum free energy principle into the maximum information efficiency principle. Lastly, it compares G theory with other semantic information theories and discusses its limitation in representing the semantics of complex data. Full article