Search Results (362)

Research on filial imprinting has yielded insights into a range of behavioural and neurobiological phenomena, and these insights have in turn fed back to elucidate behavioural development. This review will summarize important stages in this progression, with emphasis on the neural mechanisms underlying visual filial imprinting in the domestic chick. Imprinting entails recognition of stimuli, in terms of both form and certain abstract features. A striking property of imprinting is the development of a preference for a stimulus slightly different from one that has become familiar, a property having profound implications for survival. Compelling evidence indicates that the intermediate and medial mesopallium (IMM) in the chick forebrain is a site of memory encoding for imprinting. In addition, processes within the IMM are intimately associated with learning capacity in the absence of specific experience (a predisposition). Electrophysiological, neuroanatomical, pharmacological, biochemical and ablation studies have implicated the IMM in the recognition of individual conspecifics, and recent research has elucidated the underlying neurobiological mechanisms at the cellular and sub-cellular levels. Results from studies of imprinting in chicks have led to the discovery of analogous processes in humans and promise to yield insights into cognitive development in both species. Full article

As a major source of energy consumption and greenhouse gas emissions, the low-carbon retrofitting of existing buildings can help to alleviate climate pressures and increase environmental resilience, and it is an effective and feasible way to achieve the goal of sustainable development. However, the majority of existing urban renewal research focuses on analyzing influencing factors, carbon reduction potential, carbon reduction measures, and the cost-effectiveness of carbon reduction. There is a lack of systematic review and synthesis of theoretical and technological research in carbon reduction and retrofitting. Using the systematic literature review method following the PRISMA protocol, we screened 244 peer-reviewed articles at the title/abstract level and conducted an in-depth analysis of 77 studies that met the PICOS-based inclusion criteria. This review summarizes the evolution of low-carbon retrofit technologies for existing buildings over the past five decades, and identifies climate change pressures, policies and regulations, stakeholder demand, and technological innovation as the primary drivers for decarbonizing existing buildings. These drivers, which are also influenced by technological capabilities, retrofit markets, and inherent building characteristics, drive both building decarbonization and broader SDGs such as climate mitigation. Focusing on the drivers of decarbonization, technological development, and retrofit benefits in retrofitting existing buildings, this review presents an inventory-based technical pathway framework. We identified key issues in the decarbonization and retrofitting of buildings and developed this framework to guide decarbonization and retrofitting practices. The framework can provide tech-driven retrofit guidance for researchers, policymakers, and administrators. Full article

Text summarization involves analyzing large amounts of text, selecting the salient text features, and arranging them coherently. The graph-based TextRank and statistical topic modeling are unsupervised approaches for generating an extractive synopsis. Deep learning models are supervised, data-driven, and pre-trained on a huge corpus of data, making a significant contribution to automatic text summarization systems. Despite grammatical correctness and coherence, deep learning-based summarization systems are prone to factual inconsistency. This has hindered the applicability of transformer-based summarizers, particularly in critical domains where misleading summarization systems can lead to severe consequences due to their significant social impact. This work proposes an ingenious hybrid hierarchical approach that combines unsupervised approaches, such as the TextRank algorithm and Latent Dirichlet Allocation (LDA)-based summaries, with contemporary transformer-based language models. When validated on three benchmark summarization datasets, empirical results prove that our hybrid hierarchical transformer-based approach mitigates the factual inconsistency problem inherent in abstractive summarization. The improved summary consistency score of the abstractive summaries generated with our multilevel hybrid approach, in comparison to the fine-tuned baseline transformer-based language models, increases trust in transformer-based summarizers. Full article

Communicating the design and results of agent-based models (ABMs) to subject matter experts is challenging, which hinders participation and limits trust in simulation-based decision support. Large language models (LLMs) can communicate ABMs as textual summaries, thus complementing traditional disclosure through statistical and visualization techniques. While prior work translated the structure of conceptual models into narratives via LLMs, our extension covers the dynamics of simulation models via an automated simulation-to-text method that extracts contextual information from NetLogo ABMs, performs repeated simulations, and generates narrative descriptions (including the model’s purpose, parameters, and simulation dynamics) using mutimodal LLMs. Furthermore, four summarization algorithms spanning abstractive and extractive methods provide shorter reports. Using Design-of-Experiments methods over three peer-reviewed ABMs, state-of-the-art multimodal LLMs from 2026 (Gemini 3.1 Pro, Qwen 3.5, Kimi K2.5, Claude Opus 4.6) and different prompt elements (e.g., roles, examples, generating insights, statistical analyses), we compare our results with several reference reports (e.g., from associate professors). We find that report quality is determined mainly (i.e., up to 34% of the variance) by the summarization algorithm and its interaction with the LLM, with abstractive summarizers (BART, T5) producing more coherent and readable reports, while Claude Opus 4.6 is the most robust LLM. Full article

Flocs, visible particles formed in sugar-sweetened beverages, reduce clarity and consumer acceptance of products. Their presence can be caused not only by different types of trace impurities in the sugar but also by interactions among beverage components. In this review, scientific reports on acid beverage flocs (ABFs) and alcohol flocs are summarized, the main pathways for their formation are described, and practical options for detecting them and preventing their formation in beverages are compiled. Using Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 and related guidance, literature searches of Scopus, Web of Science (WoS), PubMed, Food Science and Technology Abstracts (FSTA), CAB Abstracts, and International Commission for Uniform Methods of Sugar Analysis (ICUMSA) resulted in the inclusion of 56 studies. In various types of beverages, complexes formed between proteins (Ps) and polyphenols (PPs) often initiate haze and floc formation, while polysaccharides (dextran, pectin, and starch), silica or silicates, and inorganic ions influence charge balance, particle bridging, and floc growth rate. Ethanol in alcohol beverages can further destabilize colloids and promote aggregation. For beet sugars, saponin–protein interactions are a likely pathway for the formation of ABF, but the available evidence is not consistent. In cane sugars, the reported roles of proteins, polysaccharides, silica, and starch in floc formation vary considerably between studies. For quality assurance, ICUMSA floc tests (GS2-40 and GS2-44) should be complemented by turbidity or haze measurement and colloid characterization such as light scattering, ζ–potential, and infrared IR-based analytical methods supported by chemometrics. Risk mitigation works best as a two-level strategy that combines impurity removal during sugar production and stabilization steps in beverage formulation and storage, including the use of clarification agents and control of pH, temperature, ionic strength, and oxygen exposure. Standardized reporting and validation of rapid predictors against ICUMSA benchmarks remain essential. Full article

Background: Hepatitis E virus (HEV) is an increasingly recognized cause of acute viral hepatitis in Thailand as the burden of hepatitis A, B, and C has declined. HEV is a positive-sense RNA virus in the family Hepeviridae with three major open reading frames encoding replication proteins (ORF1), the capsid protein (ORF2), and an accessory protein involved in viral egress (ORF3). Unlike highly endemic regions where genotypes 1 and 2 are linked to waterborne outbreaks, infections in Thailand are reported mainly as sporadic cases associated with zoonotic transmission, most commonly genotype 3. Objectives: This review summarizes the epidemiology, transmission routes, and public health implications of HEV infection in Thailand. Methods: Peer-reviewed studies on HEV seroprevalence, molecular epidemiology, and transmission in Thailand were identified through PubMed using combinations of the keywords “HEV” and “Thailand”. Two investigators independently screened titles, abstracts, and full texts. Eligible studies were synthesized qualitatively. Results: Earlier studies suggested low population exposure, but more recent evidence indicates substantial cumulative risk. A nationwide survey among blood donors reported anti-HEV IgG seroprevalence of about 30%, with geographic variation and increasing prevalence with age. Detection of HEV RNA in pigs, slaughterhouse environments, and retail pork products, together with links to raw or undercooked pork consumption, supports pigs as the principal reservoir and foodborne exposure as an important route. Transfusion-associated infection has also been documented. Conclusions: In Thailand, HEV infection is linked mainly to zoonotic and foodborne transmission involving genotype 3. Stronger surveillance, food safety measures, and risk-based blood safety policies are needed. Full article

Background and Objectives: Bladder cancer is one of the most common malignancies of the urinary tract and poses a significant clinical challenge due to its biological heterogeneity and high rates of recurrence and progression. Urothelial carcinoma represents the predominant histological subtype, ranging from non-muscle-invasive disease with relatively favorable outcomes to aggressive muscle-invasive and metastatic forms associated with poor prognosis. Accurate diagnosis, staging, prognostic stratification, and assessment of treatment response are therefore essential for optimal patient management. The objective of this review is to summarize and critically evaluate the current evidence on the role of positron emission tomography/computed tomography (PET/CT) in bladder cancer, with particular emphasis on [¹⁸F]FDG PET/CT and non-FDG radiotracers. Materials and Methods: A narrative review of the available literature was performed, focusing on clinical studies, review articles, and guideline documents addressing the use of PET/CT in bladder cancer. The literature search included articles published between 2000 and 2025, while earlier landmark studies were selectively included if considered historically important for understanding the development of PET/CT imaging in bladder cancer. The initial search yielded over 500 records; after screening titles and abstracts, more than 100 articles were selected for full-text evaluation. The analyzed evidence encompasses the clinical applications of [¹⁸F]FDG PET/CT and alternative radiotracers, including choline-, acetate-, methionine-, and sodium fluoride-based tracers, and fibroblast activation protein inhibitors (FAPI), across different stages of disease and clinical settings. Results: Conventional imaging modalities, such as computed tomography and magnetic resonance imaging, provide important anatomical information but remain limited in the evaluation of lymph node involvement, early metastatic disease, treatment response, and disease recurrence. Despite limitations related to physiological urinary excretion, [¹⁸F]FDG PET/CT has demonstrated clinical value in selected scenarios, particularly for staging, prognostic assessment, detection of recurrence, and response evaluation. To overcome FDG-related constraints, several non-FDG radiotracers have been investigated. Among these, FAPI PET/CT has emerged as a promising modality due to its ability to target the tumor stroma, potentially improving lesion detectability and tumor-to-background contrast. Conclusions: This review summarizes and critically evaluates current evidence on the role of PET/CT in bladder cancer, with a focus on [¹⁸F]FDG PET/CT and non-FDG radiotracers. The discussed studies highlight their applications in primary diagnosis, staging, prognostic assessment, detection of recurrence, and evaluation of treatment response, as well as their respective advantages and limitations. Furthermore, potential future directions for PET/CT imaging in clinical practice are outlined, emphasizing the need for further research to clarify the optimal use of established and emerging radiotracers. Full article

Tinea incognito is an atypical form of dermatophytosis caused by previous use of topical or systemic immunosuppressive therapy, most often corticosteroids. Modification of the clinical presentation frequently leads to diagnostic delay and misdiagnosis, especially in patients with concomitant chronic inflammatory skin diseases such as psoriasis. We present a narrative review of the literature on tinea incognito in patients with psoriasis during immunosuppressive therapy. We screened 386 abstracts and included 16 comparable case reports focusing on tinea incognito occurring in patients with psoriasis or during antipsoriatic treatment. The review summarizes clinical presentations, diagnostic challenges, and therapeutic approaches reported in the literature. Additionally, we present a clinical case of a 27-year-old man with a long history of plaque psoriasis treated with methotrexate and cyclosporine. The patient developed rapidly progressive skin lesions with pustular features and further deterioration despite systemic antipsoriatic therapy. Initial mycological examinations were negative. Histopathological examination revealed a chronic purulent perifollicular inflammatory process with extension into the subcutaneous tissue. The correct diagnosis was confirmed after a repeat skin biopsy with periodic acid–Schiff and Grocott staining and fungal culture of the skin tissue, which revealed Trichophyton rubrum. The review highlights that clinical features are often nonspecific and may overlap with inflammatory dermatoses. This underscores the need for a high index of clinical suspicion for fungal infection in atypical or refractory psoriatic lesions. It also emphasizes the importance of repeated mycological and histopathological examinations to achieve an accurate diagnosis, avoid inappropriate escalation of immunosuppression, and enable timely antifungal treatment. Full article

With the rapid growth of e-commerce, recommender systems have been widely adopted across diverse online services by presenting products aligned with user preferences. Moreover, review-based recommender systems have been studied to alleviate the sparsity of interaction data. However, many studies directly use full review texts, which may contain redundant semantics or noise that is irrelevant to recommendations, thereby degrading data quality and recommendation performance. To address this limitation, this study proposes summarized reviews fusion for adaptive recommendation (SuReFAR), which predicts ratings by summarizing reviews into key information using a multi-summarization strategy. Specifically, SuReFAR utilizes TextRank and bidirectional and auto-regressive transformers (BART) to generate extractive and abstractive summaries of user and item review sets, respectively. Subsequently, we apply an attention mechanism to emphasize salient information within each summary representation and fuse multiple summary representations by adaptively controlling their contributions through a gated multimodal unit (GMU) to predict ratings. We conducted experiments on Amazon and Yelp review datasets, demonstrating that the proposed model consistently outperforms baseline models and captures user preferences more effectively via personalized summary representations. Full article

This study presents a systematic literature review on the environmental impact of industrial applications of Rare Earth Elements (REEs), particularly those classified as Critical Raw Materials (CRMs), such as Neodymium alloys. These materials are key components of permanent magnets (PMs) used in electrical machines, including automotive applications, wind turbine generators, and various consumer electronics. A structured methodology began with a comprehensive search across multiple scientific databases utilizing primary and secondary keywords. Studies were selected through a multi-step process, including screening by title, abstract, and full-text review, ensuring the inclusion of relevant and high-quality research. This approach allowed for a rigorous and reproducible assessment of the literature. The review was conducted to address two central issues: the main environmental impacts of using rare earths in permanent magnets for electric motors, and the role of recycling and reuse strategies in reducing them. The review summarizes current knowledge on the life cycle environmental impacts of REEs, from extraction to end-of-life management, highlighting opportunities and challenges in recycling and reuse. While recycling can partially reduce environmental impact, significant gaps remain in efficiency and large-scale feasibility. The literature also emphasizes the substantial impacts of REEs in permanent magnets, including resource depletion, energy use, and emissions. Overall, the study highlights the need to integrate environmental considerations into the design and management of REE-containing systems and identifies research gaps to support more sustainable and efficient materials management. Full article

Background/Objectives: Myocardial injury after non-cardiac surgery (MINS) is a common and serious postoperative complication. Its largely asymptomatic course hampers early recognition, highlighting the importance of systematic biomarker monitoring. The aim of this review is to summarize current evidence on the diagnosis, risk factors, and management of MINS, with a focus on otolaryngology, where intraoperative hypotensive techniques may increase risk. Methods: A basic science review was conducted using PubMed, Embase, and the Cochrane Library (2005–2025). From 2712 records, 30 studies met the inclusion criteria after removing duplicates, screening titles/abstracts, and full-text assessment. These studies formed the basis for the final analysis. Results: Observational studies and reviews identify perioperative troponin monitoring as the diagnostic gold standard. However, no evidence-based management guidelines exist, and otorhinolaryngology-specific data remain rare but not entirely absent. Troponin elevation in the early postoperative period reliably predicts adverse outcomes. While MINS is well documented in vascular and orthopedic surgery, evidence in otolaryngology is limited. Controlled hypotension in procedures such as functional endoscopic sinus surgery or head and neck tumor resection may further elevate risk. Conclusions: MINS is an underrecognized complication with major prognostic significance. The lack of standardized management and the absence of large otolaryngology cohorts underscore an urgent need for targeted research and specialty-specific guidelines and support the justification for integrating existing evidence into otolaryngologic practice. Full article

Transformer-based models face persistent challenges in long-document summarization due to fixed input-length constraints. Hybrid approaches address this limitation by applying extractive preprocessing to select salient sentences for downstream abstractive summarization. However, many unsupervised extractive methods, including TextRank and LexRank, rely on heuristic graph centrality and often struggle to preserve semantic coherence or control redundancy. This paper proposes GravRank, an unsupervised and deterministic extractive summarization framework that models sentence importance as an emergent property of pairwise semantic interactions governed by a softened Plummer potential. Sentences are embedded in a shared semantic space, and a global energy function is defined over all sentence pairs using a softened interaction kernel. This formulation jointly encodes relevance and redundancy within a single scoring function, avoiding iterative graph propagation, supervised training, and post hoc diversity filtering. The deterministic extractive output is used as input to a BART-based abstractive summarization model, forming a hybrid pipeline for long and semantically dense documents. Experiments on the BillSum, PubMed, and GovReport datasets show that GravRank improves over classical unsupervised baselines, remains competitive with recent extractive methods, and yields a competitive result in downstream abstractive summarization when combined with BART. Full article

This paper presents the numerical results of a turbulent vortex in wave–current boundary layers, based on Large Eddy Simulations. Rough wall flow problems have always been a research hotspot in the field of fluid mechanics. The turbulent vortex structure within wave–current boundary layers is of great significance for the study of flow characteristics. However, little is known about turbulent vortices in combined wave–current flows. The purpose of this paper is to investigate the differences in the average velocity profile when waves are superimposed on turbulence compared to when waves and turbulence exist independently, and to demonstrate the evolution process of the turbulent vortex structure formed when waves are superimposed on turbulence. The study adopted rough wall simulations and verified the computational results. The findings indicate that under rough wall conditions, stronger secondary flows and turbulent vortex structures are formed within the boundary layer, and an increase in roughness enhances the turbulence intensity within the boundary layer. Additionally, the impact of wall height on the flow structure cannot be overlooked. This paper also presents the evolution process of the turbulent vortex structure within wave–current boundary layers, providing new insights for the study of rough wall flow-related issues. For the interaction of waves and turbulence under rough wall conditions, high-precision numerical discretization schemes are adopted to construct a bottom boundary layer numerical model. This is achieved by summarizing the progress of existing conclusions, understanding the research progress of numerical simulation in the wave–current boundary layer, constructing high-precision numerical discretization schemes, establishing a physical model of the studied problem and abstracting it into a mechanical model, establishing the entire geometric shape and its spatial influence area, performing spatial grid division, adding the initial conditions required for the solution, and selecting the LES algorithm. Full article

Static analysis is a critical methodology for ensuring the quality, security, and safety of embedded, cyber-physical, and electronic software systems, particularly as such systems become increasingly complex and tightly coupled with hardware and real-time constraints. Through a systematic study of the literature, this paper summarizes the State-of-the-Art in static program analysis. We develop a comprehensive taxonomy of fundamental techniques, including model checking, abstract interpretation, data-flow analysis, and symbolic execution, and examine their application in modern analysis tools used in electronic and safety-critical systems. The survey thoroughly reviews applications across key domains, including vulnerability detection, automotive and embedded software verification, smart contract auditing, and AI-enabled electronic systems. We also critically analyze persistent challenges, including tool integration, scalability limitations, and the trade-off between analysis precision and soundness. Finally, by discussing emerging trends and future research directions—such as machine-learning-enhanced analysis and hybrid static–dynamic techniques—this work provides a structured framework to guide future research and industrial practice in the development of reliable electronic systems. Full article

The ocular surface microbiome plays a critical role in maintaining ocular health, preventing infections, and regulating immune responses. Contact lens (CL) wear has been linked to alterations in microbial composition, potentially leading to dysbiosis and increased susceptibility to ocular infections. This review aims to summarize current evidence on the effects of CL use on the ocular microbiome and to discuss strategies to preserve microbial homeostasis. A literature search was conducted in PubMed, Scopus, Web of Science, and Google Scholar for English-language human studies published between January 2005 and January 2025. We included original studies and systematic reviews evaluating the ocular surface bacterial community in contact lens (CL) wearers using either sequencing-based approaches (microbiome; e.g., 16S rRNA gene sequencing/metagenomics) or culture-based methods (microbiota). Two authors screened titles/abstracts and full texts. Overall, 12 studies met the inclusion criteria and were qualitatively synthesized. Across included studies, CL wear was associated with reproducible changes in the ocular surface bacterial community, most commonly a shift toward a skin-like profile and increased detection/relative abundance of opportunistic taxa (e.g., Pseudomonas, Acinetobacter, and Staphylococcus aureus) together with reduced representation of typical ocular commensals in several sequencing-based datasets. Culture-based studies reported increased recovery of opportunistic bacteria from lenses and storage cases, supporting contamination/biofilm-related mechanisms. Lens care solutions and preservatives were reported to modulate bacterial profiles and may contribute to dysbiosis, although evidence remains heterogeneous across study designs and analytic pipelines. CL use is associated with significant alterations in the ocular microbiome, increasing the risk of microbial keratitis and corneal inflammatory events. Strategies to maintain microbial balance, including careful selection of lens care products and development of antimicrobial lenses, may improve ocular surface health in CL wearers. Future longitudinal studies with standardized sampling and analytic workflows are needed to clarify causal links between CL-associated microbial changes and clinical outcomes. Full article