Search Results (3,203)

Background: The reported data on African universal neonatal hearing screening (UNHS) practices tend to be quite scarce, despite the developments in hearing screening the last two decades. The objective of this systematic review was (a) to identify the most recent (in a 20-year span) literature information about NHS/UNHS programs in Africa and (b) to provide data on the procedures used to assess the population, the intervention policies, and on the estimated prevalence of congenital hearing loss with an emphasis on bilateral hearing loss cases. Methods: Queries were conducted via the PubMed, Scopus, and Google Scholar databases for the time window of 2004–2024. The mesh terms used were “OAE”, “universal neonatal hearing screening”, “congenital hearing loss”, “well babies”, and “Africa”. Only research articles and review papers were considered as good candidates. The standard English language filter was not used, to identify information from non-English-speaking scientific communities and groups. Results: Data from 15 papers were considered, reflecting the neonatal hearing practices of nine African states. No country-wide NHS programs were reported. The various screening realities are implemented within big urban centers, leaving the residents of rural areas unassisted. For the latter, proposals based on tele-medicine protocols have been suggested. The data on HL prevalence are also incomplete, but the available data refer to rates from 3 to 360 subjects per 1000. These data cannot be taken at face value but within the small sample size context in which they were acquired. Regarding the causes of HL, very few data have been reported; consanguinity is the most attributed factor, at least in the Sub-Saharan African states. For the majority of the programs, no data were reported on hearing loss prevalence/incidence or on any strategies to restore hearing. Conclusions: The information on the African neonatal hearing screening are quite scarce, and it is an urgent need to convince audiologists from the African localized programs to publish their hearing screening data. Full article

Background: Distributed graph databases are a promising method for storing and conducting complex pathway queries on large-scale drug knowledge graphs to support drug research. However, there is a research gap in evaluating drug knowledge graphs’ storage and query performance based on distributed graph databases. This study evaluates the feasibility and performance of distributed graph databases in managing large-scale drug knowledge graphs. Methods: First, a drug knowledge graph storage and query system is designed based on the Nebula Graph database. Second, the system’s writing and query performance is evaluated. Finally, two drug repurposing benchmarks are used to provide a more extensive and reliable assessment. Results: The performance of distributed graph databases surpasses that of single-machine databases, including data writing, regular queries, constrained queries, and concurrent queries. Additionally, the advantages of distributed graph databases in writing performance become more pronounced as the data volume increases. The query performance benefits of distributed graph databases also improve with the complexity of query tasks. The drug repurposing evaluation results show that 78.54% of the pathways are consistent with currently approved drug treatments according to repoDB. Additionally, 12 potential pathways for new drug indications are found to have literature support according to DrugRepoBank. Conclusions: The proposed system is able to construct, store, and query a large graph of multisource drug knowledge and provides reliable and explainable drug–disease paths for drug repurposing. Full article

Background/Objectives: Artificial intelligence (AI) and machine learning (ML) are transforming healthcare by enabling predictive, diagnostic, and therapeutic advancements. Pediatric healthcare presents unique challenges, including limited data availability, developmental variability, and ethical considerations. This narrative review explores the current trends, applications, challenges, and future directions of ML in pediatric healthcare. Methods: A systematic search of the PubMed database was conducted using the query: (“artificial intelligence” OR “machine learning”) AND (“pediatric” OR “paediatric”). Studies were reviewed to identify key themes, methodologies, applications, and challenges. Gaps in the research and ethical considerations were also analyzed to propose future research directions. Results: ML has demonstrated promise in diagnostic support, prognostic modeling, and therapeutic planning for pediatric patients. Applications include the early detection of conditions like sepsis, improved diagnostic imaging, and personalized treatment strategies for chronic conditions such as epilepsy and Crohn’s disease. However, challenges such as data limitations, ethical concerns, and lack of model generalizability remain significant barriers. Emerging techniques, including federated learning and explainable AI (XAI), offer potential solutions. Despite these advancements, research gaps persist in data diversity, model interpretability, and ethical frameworks. Conclusions: ML offers transformative potential in pediatric healthcare by addressing diagnostic, prognostic, and therapeutic challenges. While advancements highlight its promise, overcoming barriers such as data limitations, ethical concerns, and model trustworthiness is essential for its broader adoption. Future efforts should focus on enhancing data diversity, developing standardized ethical guidelines, and improving model transparency to ensure equitable and effective implementation in pediatric care. Full article

Background/Objectives: Virtual autopsy (virtopsy) is a new domain of research for interdisciplinary teams of radiologists and forensic specialists. This scoping review aims to underline the current state-of-the-art research using combined imaging modalities. Methods: We searched the PubMed database using the term virtopsy for articles that are available in free full text, indexed in the Medline Database, and published in English. The query returned 49 articles on this subject that have been published since 2002. Results: The main imaging modalities used for postmortem imaging were computed tomography (PMCT), angiography (PMCTA), magnetic resonance imaging (PMMRI), and ultrasonography (PMUS). PMCT is highly effective for detecting complex osseous injuries, tracing bullet trajectories, or identifying characteristic findings in drowning cases. PMCTA is valuable for evaluating vascular lesions, particularly in natural death cases. PMMRI is superior in analyzing soft tissues, including brain and spinal structures, cerebrospinal fluid, microbleeds, and laryngohyoid lesions, and identifying cardiomyopathies in young individuals. PMUS serves as an alternative, and its portability also allows for use in forensic settings. One specific situation observed was the increased number of studies published about virtopsy during the COVID-19 pandemic. Another aspect is the increased focus on this alternative to conventional autopsy in the regions where maneuvering of the deceased is limited according to cultural and social customs. Conclusions: We underline the advantages and limitations of each imaging modality used for virtopsy. Further studies need to be developed in order to gather supplementary data regarding the use of these imaging modalities in the new era of artificial intelligence in medicine. Full article

This study investigates the implementation of LLM agents in smart city management, leveraging both the inherent language processing abilities of LLMs and the distributed problem solving capabilities of multi-agent systems for the improvement of urban decision making processes. A multi-agent system architecture combines LLMs with existing urban information systems to process complex queries and generate contextually relevant responses for urban planning and management. The research is focused on three main hypotheses testing: (1) LLM agents’ capability for effective routing and processing diverse urban queries, (2) the effectiveness of Retrieval-Augmented Generation (RAG) technology in improving response accuracy when working with local knowledge and regulations, and (3) the impact of integrating LLM agents with existing urban information systems. Our experimental results, based on a comprehensive validation dataset of 150 question–answer pairs, demonstrate significant improvements in decision support capabilities. The multi-agent system achieved pipeline selection accuracy of 94–99% across different models, while the integration of RAG technology improved response accuracy by 17% for strategic development queries and 55% for service accessibility questions. The combined use of document databases and service APIs resulted in the highest performance metrics (G-Eval scores of 0.68–0.74) compared to standalone LLM responses (0.30–0.38). Using St. Petersburg’s Digital Urban Platform as a testbed, we demonstrate the practical applicability of this approach to create integrated city management systems with support complex urban decision making processes. This research contributes to the growing field of AI-enhanced urban management by providing empirical evidence of LLM agents’ effectiveness in processing heterogeneous urban data and supporting strategic planning decisions. Our findings suggest that LLM-based multi-agent systems can significantly enhance the efficiency and accuracy of urban decision making while maintaining high relevance in responses. Full article

This paper presents a systematic review on reinforcement learning approaches for combinatorial optimization problems based on real-world industrial applications. While this topic is increasing in popularity, explicit implementation details are not always available in the literature. The main objective of this paper is characterizing the agent–environment interactions, namely, the state space representation, action space mapping and reward design. Also, the main limitations for practical implementation and the needed future developments are identified. The literature selected covers a wide range of industrial combinatorial optimization problems, found in the IEEE Xplore, Scopus and Web of Science databases. A total of 715 unique papers were extracted from the query. Then, out-of-scope applications, reviews, surveys and papers with insufficient implementation details were removed. This resulted in a total of 298 papers that align with the focus of the review with sufficient implementation details. The state space representation shows the most variety, while the reward design is based on combinations of different modules. The presented studies use a large variety of features and strategies. However, one of the main limitations is that even with state-of-the-art complex models the scalability issues of increasing problem complexity cannot be fully solved. No methods were used to assess risk of biases or automatically synthesize the results. Full article

This paper presents a novel image fusion method designed to enhance the integration of infrared and visible images through the use of a residual attention mechanism. The primary objective is to generate a fused image that effectively combines the thermal radiation information from infrared images with the detailed texture and background information from visible images. To achieve this, we propose a multi-level feature extraction and fusion framework that encodes both shallow and deep image features. In this framework, deep features are utilized as queries, while shallow features function as keys and values within a residual cross-attention module. This architecture enables a more refined fusion process by selectively attending to and integrating relevant information from different feature levels. Additionally, we introduce a dynamic feature preservation loss function to optimize the fusion process, ensuring the retention of critical details from both source images. Experimental results demonstrate that the proposed method outperforms existing fusion techniques across various quantitative metrics and delivers superior visual quality. Full article

In recent decades, few-shot object detection in SAR imagery has gained prominence as a major research focus. The unique imaging mechanism of SAR causes the model to suffer from foreground–background imbalance and inaccurate extraction of class prototypes for novel class instances. Therefore, we propose an innovative few-shot object detection algorithm for SAR images via context-aware and robust Gaussian flow representation. First, we design the Context-Aware Enhancement module to address the foreground–context imbalance problem by refining representative support features into fine-grained prototypes, which are deeply fused with query features based on the prototype matching paradigm. Second, we devise the Manifold Class Distribution Estimation module to address the difficulty of class distribution estimation and the fluctuation of class centers of the sparse novel class. Furthermore, we formulate the Category-Balanced Difference Aggregation module to model the relationship between the base class and the novel class, addressing the sensitivity of the model to the variance of the novel class instances. Finally, we design the Cosine Decoupling Module so that the aggregated features are executed only on the classification branch without affecting the precise localization of the target. Experiments based on SAR-AIRcraft-1.0 and the self-constructed MSAR-AIR dataset indicate that the fine-grained detection and identification performance of the novel class of airplanes can reach 32.90% and 55.26%, respectively, in the 10-shot and 50-shot cases. In addition, our method enables cross-domain detection for different scenarios and sample types and exhibits excellent generalization performance in data-sparse scenarios. Full article

Understanding sports presents a fascinating challenge for Natural Language Processing (NLP) due to its intricate and ever-changing nature. Current NLP technologies struggle with the advanced cognitive demands required to reason over complex sports scenarios. To explore the current boundaries of this field, we extensively evaluated mainstream and emerging large models on various sports tasks and addressed the limitations of previous benchmarks. Our study ranges from answering simple queries about basic rules and historical facts to engaging in complex, context-specific reasoning using strategies like few-shot learning and chain-of-thought techniques. Beyond text-based analysis, we also explored the sports reasoning capabilities of mainstream video language models to bridge the gap in benchmarking multimodal sports understanding. Based on a comprehensive overview of main-stream large models on diverse sports understanding tasks, we presented a new benchmark, which highlighted the critical challenges of sports understanding for NLP and the varying capabilities of state-of-the-art large models on sports understanding. We also provided an extensive set of error analyses that pointed to detailed reasoning defects of large model reasoning which model-based error analysis failed to reveal. We hope the benchmark and the error analysis set will help identify future research priorities in this field. Full article

This study advances AI-powered news delivery by introducing an innovative chatbot capable of providing personalized news summaries and real-time event analysis. This approach addressed a critical gap identified through a comprehensive review of 52 AI chatbot studies. Unlike prior models limited to static information retrieval or predefined interactions, this chatbot harnesses generative AI and real-time data integration to deliver a dynamic and tailored news experience. Its unique architecture combines conversational AI, robotic process automation (RPA), a comprehensive news database (989,432 reports from 2342 sources spanning 27 October 2023 to 30 September 2024), and a large language model (LLM). Within this architecture, LLM generates dynamic queries against the News database for obtain tailored News for the users. Hence, this approach interprets user intent, and delivers LLM-based summaries of the fetched tailored news. Empirical testing with 35 users across 321 diverse news queries validated its robustness in navigating a combinatorial classification space of 53,916,650 potential news categorizations, achieving an F1-score of 0.97, recall of 0.99, and precision of 0.96. Deployed on Microsoft Teams and as a standalone web app, this research lays the foundation for transformative AI applications in news analysis, promising to revolutionize news consumption and empower a more informed citizenry. Full article

Face verification and identification traditionally follow a symmetric matching approach, where the same model (e.g., ResNet-50 vs. ResNet-50) generates embeddings for both gallery and query images, ensuring compatibility. However, real-world scenarios often demand asymmetric matching, especially when query devices have limited computational resources or employ heterogeneous models (e.g., ResNet-50 vs. SwinTransformer). This asymmetry can degrade face recognition performance due to incompatibility between embeddings from different models. To tackle this asymmetric face recognition problem, we introduce the Learnable Anchor Embedding (LAE) model, which features two key innovations: the Shared Learnable Anchor and a Light Cross-Attention Mechanism. The Shared Learnable Anchor is a dynamic attractor, aligning heterogeneous gallery and query embeddings within a unified embedding space. The Light Cross-Attention Mechanism complements this alignment process by reweighting embeddings relative to the anchor, efficiently refining their alignment within the unified space. Extensive evaluations of several facial benchmark datasets demonstrate LAE’s superior performance, particularly in asymmetric settings. Its robustness and scalability make it an effective solution for real-world applications such as edge-device authentication, cross-platform verification, and environments with resource constraints. Full article

Timely, region-based geo-maps like choropleths are essential for smart city applications like traffic monitoring and urban planning because they can reveal statistical patterns in geotagged data. However, because data overloading is brought on by the quick inflow of massive geospatial data, creating these visualizations in real time presents serious difficulties. This paper introduces ApproxGeoMap, a novel system designed to efficiently generate approximate geo-maps from fast-arriving georeferenced data streams. ApproxGeoMap employs a stratified spatial sampling method, leveraging geohash tessellation and Earth Mover’s Distance (EMD) to maintain both accuracy and processing speed. We developed a prototype system and tested it on real-world smart city datasets, demonstrating that ApproxGeoMap meets time-based and accuracy-based quality of service (QoS) constraints. Results indicate that ApproxGeoMap significantly enhances efficiency in both running time and map accuracy, offering a reliable solution for high-speed data environments where traditional methods fall short. Full article

Knowledge graph (KG) reasoning aims to obtain new knowledge based on existing data. Utilizing large language models (LLMs) through in-context learning for KG reasoning has become a significant direction. However, existing methods mainly extract in-context triples by manually defined standards (such as the neighbors that are directly linked with the query triple), without considering whether they are useful for LLM reasoning. Furthermore, the triples beyond the neighbors can also provide important clues for reasoning. Therefore, it is necessary to extract more useful in-context triples of LLMs for KG reasoning. This paper proposes a rule-and-reinforce in-context triple extraction method to enhance the in-context learning of LLMs for KG reasoning. First, we collect the in-context triples specific to each query triple with the guidance of logical rules, and a neural extractor is pre-trained by the collected triples. Subsequently, the feedback of LLMs is collected as rewards to further optimize the extractor, where the policy gradient is utilized to encourage the extractor to explore more useful triples that yield higher rewards. The experimental results on five different knowledge graphs demonstrate that the proposed method can effectively improve the reasoning performance of LLMs. Compared to the traditional reasoning method AnyBURL, the greatest improvement is 0.147 on Hits@10, FB15k-237. Full article

Background/Objectives: The aim of this study is to investigate the side effects of two techniques of rapid maxillary expansion—Surgically Assisted Rapid Palatal Expansion (SARPE) and Miniscrew-Assisted Rapid Palatal Expansion (MARPE)—in adult patients, to guide the selection of the most cost-effective clinical treatment plan. Methods: The review protocol was developed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis—extension for Scoping Reviews (PRISMA-ScR) guidelines. Eligibility criteria were defined based on the study objectives. The research team formulated a PICO question to identify relevant studies in the literature. Five databases were queried: MEDLINE (via PubMed), Scopus, Cochrane Library, Web of Science, and Embase. Additionally, a manual search was conducted. Results: The computer-assisted search identified 746 articles, of which only 26 fully met the inclusion criteria and were included in the scoping review. Among the included studies, 11 were retrospective, 12 were prospective, and 3 were randomized clinical trials. SARPE was evaluated in 21 studies, MARPE in 4 studies, and 1 article reported complications associated with both techniques. The side effects described in the studies were synthesized and categorized into five groups: expansion failure, asymmetric expansion, dentoalveolar issues, surgical complications, and appliance-related problems. Conclusions: The results indicate that both techniques involve risks. The most reported adverse effects were dentoalveolar and surgical in nature. Dentoalveolar side effects, such as dental tipping, were predominantly associated with the MARPE technique, while surgical complications were more commonly observed with the SARPE technique. Patient age is crucial for treatment choice as well as proper design and planning of the expansion device. Consequently, careful patient selection and treatment planning are essential to minimize the side effects of maxillary expansion in adult patients. Full article

To address current issues in natural language spatiotemporal queries, including insufficient question semantic understanding, incomplete semantic information extraction, and inaccurate intent recognition, this paper proposes NL2Cypher, a DeBERTa (Decoding-enhanced BERT with disentangled attention)-based natural language spatiotemporal question semantic conversion model. The model first performs semantic encoding on natural language spatiotemporal questions, extracts pre-trained features based on the DeBERTa model, inputs feature vector sequences into BiGRU (Bidirectional Gated Recurrent Unit) to learn text features, and finally obtains globally optimal label sequences through a CRF (Conditional Random Field) layer. Then, based on the encoding results, it performs classification and semantic parsing of spatiotemporal questions to achieve question intent recognition and conversion to Cypher query language. The experimental results show that the proposed DeBERTa-based conversion model NL2Cypher can accurately achieve semantic information extraction and intent understanding in both simple and compound queries when using Chinese corpus, reaching an F1 score of 92.69%, with significant accuracy improvement compared to other models. The conversion accuracy from spatiotemporal questions to query language reaches 88% on the training set and 92% on the test set. The proposed model can quickly and accurately query spatiotemporal data using natural language questions. The research results provide new tools and perspectives for subsequent knowledge graph construction and intelligent question answering, effectively promoting the development of geographic information towards intelligent services. Full article