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Activation of microglia by classical inflammatory mediators can convert astrocytes into a neurotoxic A1 phenotype in a variety of neurological diseases . Development of agents that could inhibit the formation of A1 reactive astrocytes could be used to treat these diseases for which there are no disease-modifying therapies. Glucagon-like peptide-1 receptor (GLP1R) agonists have been indicated as potential neuroprotective agents for neurologic disorders such as Alzheimer's disease and Parkinson's disease . The mechanisms by which GLP1R agonists are neuroprotective are not known. Here we show that a potent, brain-penetrant long-acting GLP1R agonist, NLY01, protects against the loss of dopaminergic neurons and behavioral deficits in the α-synuclein preformed fibril (α-syn PFF) mouse model of sporadic Parkinson's disease . NLY01 also prolongs the life and reduces the behavioral deficits and neuropathological abnormalities in the human A53T α-synuclein (hA53T) transgenic mouse model of α-synucleinopathy-induced neurodegeneration . We found that NLY01 is a potent GLP1R agonist with favorable properties that is neuroprotective through the direct prevention of microglial-mediated conversion of astrocytes to an A1 neurotoxic phenotype. In light of its favorable properties, NLY01 should be evaluated in the treatment of Parkinson's disease and related neurologic disorders characterized by microglial activation.

As a significant percentage of disasters and fatal accidents still occur in the construction sector, it is legally obligatory to conduct workplace risk assessments to avoid accidents and enhance safety. Identifying harmful and hazardous elements is crucial to discern the distinctive characteristics of potential accidents. However, conventional risk-assessment approaches, which rely on the skills and experience of safety managers, may overlook important factors, leading to inconsistencies in the procedures employed across different sites. Such unstructured safety knowledge reduces accessibility and utility, increases reliance on individual skills, and renders information management inefficient. Recently, the focus has shifted from efficient data storage to obtaining valuable knowledge tailored to specific use-cases. Knowledge-graph-based systems integrate and manage the relationships between knowledge entities, thereby enhancing the development of knowledge bases. Research on automatically extracting and managing predefined knowledge from various forms of data through natural language processing (NLP) is ongoing. This study proposes a novel method that uses NLP and graph models to automatically extract predefined knowledge from unstructured construction data and build an entity-relationship-based risk-assessment knowledge base. We developed an entity-name recognition and keyword-extraction engine that defines the core knowledge related to construction safety and risk assessments. This engine can automatically extract predefined knowledge from unstructured data by learning from NLP data. The extracted risk-assessment knowledsge was used to create a knowledge base, and its efficiency and effectiveness were validated through comparisons with existing methods. The results of this study are significant because they lay the foundation for an automatic knowledge-management system for construction safety and risk assessment, offering both practical and academic contributions to the field of construction safety.

The non-invasive photodynamic therapy has been limited to treat superficial tumours, primarily ascribed to poor tissue penetration of light as the energy source. Herein, we designed a long-circulating hydrophilized titanium dioxide nanoparticle (HTiO 2 NP) that can be activated by ultrasound to generate reactive oxygen species (ROS). When administered systemically to mice, HTiO 2 NPs effectively suppressed the growth of superficial tumours after ultrasound treatments. In tumour tissue, the levels of proinflammatory cytokines were elevated several fold and intense vascular damage was observed. Notably, ultrasound treatments with HTiO 2 NPs also suppressed the growth of deeply located liver tumours at least 15-fold, compared to animals without ultrasound treatments. This study provides the first demonstration of the feasibility of using HTiO 2 NPs as sensitizers for sonodynamic therapy in vivo .

Many studies have been conducted to define the critical success factors (CSFs) for off-site construction (OSC) activation, but there has been a lack of identification of the relationship with the identified CSFs. However, it is necessary to clearly identify the hierarchy and relationships with the success factors in order to develop specific strategies for OSC activation. This work presents a study that was conducted to identify the CSFs for OSCs and establish the relationships of the identified CSFs for OSC. First, 20 CSFs for OSCs were identified through prior study reviews related to CSFs for OSC. Next, the interpretive structural modeling (ISM), which has advantages in developing an understanding of complex relationships, was leveraged in order to analyze the relationships between 20 CSFs for OSC to derive a hierarchical model consisting of seven levels. The CSFs for OSC were classified into four groups using MICMAC analysis, which is useful for classifying factors by the strength of the relationship with factors based on driving power and dependence power. This proposed model can be used as a basis for developing management measures for OSC project success.

As the need for digital transformation (DT) increased in order to improve productivity in the construction industry, the market for building information modeling (BIM), the main technology of DT, gradually expanded. Strategies for promoting BIM have been established and announced in South Korea. Accordingly, the related regulations have been modified and there is continuous investment in BIM. Despite these efforts, BIM adoption has not gone smoothly. This study aims to empirically verify an acceptance model as of 2021 based on the BIM acceptance models proposed by previous studies, and to propose implications by analyzing the significant relationship changes in acceptance models by period. It found a change in the mechanism of accepting BIM over time and derived implications about the causes of changes in connection with the comparative analysis results and the status of BIM-related policy announcements. If promotion strategies are inspected and adoption strategies suitable for each period are established through the regular verification of the BIM acceptance mechanism, we expect that the effectiveness and efficiency of investments in promoting BIM will improve.

Alzheimer’s disease (AD) is the most common cause of age-related dementia. Increasing evidence suggests that neuroinflammation mediated by microglia and astrocytes contributes to disease progression and severity in AD and other neurodegenerative disorders. During AD progression, resident microglia undergo proinflammatory activation, resulting in an increased capacity to convert resting astrocytes to reactive astrocytes. Therefore, microglia are a major therapeutic target for AD and blocking microglia-astrocyte activation could limit neurodegeneration in AD. Here we report that NLY01, an engineered exedin-4, glucagon-like peptide-1 receptor (GLP-1R) agonist, selectively blocks β-amyloid (Aβ)-induced activation of microglia through GLP-1R activation and inhibits the formation of reactive astrocytes as well as preserves neurons in AD models. In two transgenic AD mouse models (5xFAD and 3xTg-AD), repeated subcutaneous administration of NLY01 blocked microglia-mediated reactive astrocyte conversion and preserved neuronal viability, resulting in improved spatial learning and memory. Our study indicates that the GLP-1 pathway plays a critical role in microglia-reactive astrocyte associated neuroinflammation in AD and the effects of NLY01 are primarily mediated through a direct action on Aβ-induced GLP-1R + microglia, contributing to the inhibition of astrocyte reactivity. These results show that targeting upregulated GLP-1R in microglia is a viable therapy for AD and other neurodegenerative disorders.

The Korean domestic market is focused on the introduction of BIM (Building Information Modeling) owing to an influx of investment due to increased interest and mandatory application of BIM. However, the rate of BIM introduction is high, while BIM user proficiency is low. Against these problems, the authors proposed an acceptance model for BIM in construction organizations in 2012. As the number of BIM application cases increases and the number of BIM-trained users increases as time goes on, BIM users’ positive perception of BIM values are expected to increase, which may change the BIM acceptance mechanism. Therefore, we conducted a longitudinal study of the 2012 BIM acceptance model against 2019 data to estimate changes in factors affecting BIM acceptance attitudes as well as the mechanism of the relationships between factors over time spent using the technology. To generalize the results, the respondents were spread across construction sites. The data obtained 119 samples from a sample of experienced users of BIM. We used AMOS 21.0 for hypothesis testing of structural equation modeling (SEM), and the 2019 BIM acceptance model was compared against the 2012 acceptance model using an independent sample t-test. As a result, it was confirmed that the 2012 BIM acceptance model is still suitable for describing the BIM acceptance mechanism of the construction organization, and there was a difference between the 2012 model and the 2019 model. This seems to have changed the mechanism of BIM acceptance by being change perception of BIM users as time goes on. The results of this study can be used to establish a BIM activation strategy for each BIM acceptance stage and are expected to be applicable to establishing a BIM activation strategy for construction organizations or countries with similar BIM acceptance stage.

The adoption of robots in the construction phase can improve safety by replacing hazardous tasks and enhance productivity by automating repetitive work. Despite these advantages, adoption remains slow, constrained by economic, industrial, institutional, socio-cultural, and technological barriers. Wider acceptance is particularly urgent in construction, where fragmented processes, low profit margins, and safety risks make innovation both necessary and challenging. This study identified 22 critical barriers through a systematic literature review and categorized them into five dimensions. Beyond identification, the study prioritized these barriers using ISM and MICMAC analysis, clarifying which factors are fundamental drivers and which are outcome-related. The results showed that economic drivers occupy the base of the hierarchy and exert the greatest systemic influence, socio-cultural barriers emerge as highly dependent outcomes, and software usability acts as a linkage factor connecting technological immaturity with social acceptance. These findings reveal that barriers are interdependent rather than isolated and underscore the need for a structured prioritization framework. By applying ISM and MICMAC, this study presents a stepwise roadmap that differentiates fundamental drivers from outcome-related constraints, offering academic insights and practical guidance for policymakers to design strategies such as investment incentives, standardization, legal frameworks, and R&D expansion to accelerate adoption.

This study presents a systematic review of ontology–AI integration for construction image understanding, aiming to clarify how ontologies enhance semantic consistency, interpretability, and reasoning in AI-based visual analysis. Construction sites involve highly dynamic and unstructured conditions, making image-based hazard detection and situation assessment both essential and challenging. Ontology-based frameworks offer a structured semantic layer that can complement deep learning models; however, most existing studies adopt ontologies only as post-processing mechanisms rather than embedding them within model training or inference workflows. Following PRISMA 2020 guidelines, a comprehensive search of the Web of Science Core Collection (2014–2025) identified 587 publications, of which 152 met the eligibility criteria, and 16 explicitly addressed construction image data. Topic modeling revealed five functional objectives—regulatory compliance, hazard reasoning, decision support, knowledge reuse, and sustainability—and four primary data modalities: BIM, text, image, and sensor data. Two dominant integration patterns were observed: training-stage and output-stage enhancement. While quantitative performance improvements were modest, qualitative gains were consistent across studies, including reduced false positives, improved interpretability, and enhanced situational understanding. Persistent gaps were identified in standardization, scalability, and real-world validation. This review provides the first structured synthesis of ontology–AI research for construction image understanding and offers an evidence-based research agenda that links observed limitations to actionable directions for semantic AI in construction.

The eruption in Fagradalsfjall Volcano, located in Reykjanes Peninsula, Iceland, from several centuries’ dormant states, occurred for the first time on March 19, 2021. Observations of Fagradalsfjall Volcano were conducted in 2021, and the eruption period lasted for six months until 18 September 2021. Six days pair of interferograms were generated from ninety synthetic aperture radar (SAR) data. Thus, the SAR data will be acquired from the Sentinel-1 satellite from January until December 2021. Time-series measurements were conducted using a combination of persistent scatterer (PS) and distributed scatterer (DS) points to produce denser measurement points (MPs) in the study area. The improved combined scatterers interferometry with optimized point scatterers (ICOPS) algorithm is the time-series method that utilizes both PS and DS MPs and optimizes those combined MPs using a deep learning algorithm over different temporal intervals and using a statistical clustering approach to optimize the MPs spatially. Validation was conducted by comparing the ICOPS result with GPS measurement in Reykjavik. The comparison with the GPS measurement was performed to validate the line-of-sight (LOS) deformation from the ICOPS measurement, which resulted in an RMSE value of about 0.58 cm, which is considered a good correlation. Besides the time-series Interferometry SAR (InSAR) measurement, we used the integrated InSAR and multiple aperture interferometry (MAI) methods to estimate both LOS and along-track surface deformation, respectively, during the Fagradalsfjall, Iceland volcanic eruption. A pair of ALOS-2 data was used between 28 February 2021 and 23 May 2021. The result from the MAI method shows a deformation of approximately ± 2 mm in the azimuth direction around Fagradalsfjall Volcano. The deformation around Fagradalsfjall Volcano was suggested to be due to the activity of the magma reservoir beneath the Earth’s surface, which was formed by dike intrusion. The analysis of the seismicity in Fagradalsfjall was discussed by visualization of the distribution of earthquakes during the deformation occurrence. Further analysis can be conducted by applying multitrack analysis to find the 3D deformation pattern due to the eruption.