Search Results (362)

As a national treasure, architectural heritage carries multiple value dimensions such as history, technology, art, and culture. With the increasing demand for architectural heritage protection and utilization, the traditional static digital model of architectural heritage based on geometric expression can no longer meet the practical application of multi-stage and multi-level scenarios. To this end, this paper proposes a value-chain-driven multi-level digital twin model of architectural heritage. Based on the three-stage logic of protection, management, and dissemination of value-chain classification, it integrates four types of models: geometry, physics, rules, and behavior. Combined with different hierarchical application levels, the digital model of architectural heritage is refined into a VCLOD (Value-Chain-Driven Level of Detail) detail hierarchy system to achieve a unified expression from spatial form restoration to intelligent response. Through the empirical application of three typical scenarios: the full-area guided tour of the Forbidden City, the exhibition curation of the central axis and the preventive protection of the Meridian Gate, the model shows the following specific results: (1) the efficiency of tourist guidance is improved through real-time personalized path planning; (2) the exhibition planning and visitor experience are improved through dynamic monitoring and interactive management of the exhibition environment; (3) the predictive analysis and preventive protection measures of structural safety are realized, effectively ensuring the structural safety of the Meridian Gate. The research results provide a theoretical basis and practical support for the systematic expression and intelligent evolution of digital twins of architectural heritage. Full article

There is an increasing need to understand the long-term dynamics and quality of SARS-CoV-2 immune memory—both humoral and cellular—particularly with emerging variants. This study aimed to evaluate immune durability and variant-specific modulation through a longitudinal analysis of individuals with diverse SARS-CoV-2 exposure histories, over two years after infection and/or vaccination. The study involved assessing anti-spike IgG and IgA levels over time and analyzing their relationship with neutralizing activity against both ancestral and Omicron SARS-CoV-2 variants. Persistence of T cell responses was evaluated using intracellular cytokine staining (ICS) and activation-induced marker (AIM) assays. Anti-S IgG levels remained stable over time and increased after each immune stimulation, suggesting cumulative immune memory. Neutralizing capacity correlated strongly with IgG levels, showing long-term stability for pre-Omicron variants, but a moderate decline for Omicron. CD4⁺ and CD8⁺ T cell responses persisted across all groups, largely unaffected by Omicron mutations. However, cytokine profiles revealed subtle, variant-dependent changes. These findings underscore the durability of cellular immunity and the comparatively reduced robustness of Omicron-specific humoral responses. Such insights are crucial for understanding long-term protection against evolving SARS-CoV-2 variants and guiding public health strategies. Full article

In the field of structural engineering, accurate identification of modal damping ratio is the key to structural dynamic response analysis. In order to accurately identify the modal damping ratio of the structure, this study proposes a method to identify the first-order modal damping ratio of the structure by analyzing the free attenuation response of the acceleration signal. By intercepting the free attenuation section from the structural dynamic response output, the amplitude is extracted, and the logarithmic estimation slope of the amplitude is fitted by the least square method to establish a theoretical model for identifying the first-order modal damping ratio. The results show that the method has high accuracy and good stability when the modal damping ratio is in the range of 0.00500~0.06400, and different nodes have little effect on the accuracy of identification. When the modal damping ratio is in the range of 0.06400~0.07000, the accuracy of the method is relatively low and the stability is relatively poor, but it is still within the acceptable range. When the damping ratio is greater than 0.07000 or less than 0.00500, the accuracy may be reduced. In order to further verify the effectiveness of the method, it is applied to the damping identification of a steel arch bridge project. The dynamic response of the bridge under random excitation and El Centro seismic wave excitation is analyzed by using the recommended value and identification value of the first-order damping ratio. The results show that the method can accurately and reliably identify the first-order modal damping ratio, which is significantly different from the empirical modal damping ratio. The identified modal damping ratio can more accurately describe the dynamic response of the structure after long-term use, while the recommended value is not applicable. This method can be applied to the modal damping ratio identification of other structural types, which reflects that the modal damping ratio identification method proposed in this study has certain engineering significance. It is worth noting that the accuracy of identification will be reduced when the modal damping ratio is less than 0.00500 or more than 0.07000, and it may not even be applicable if the modal damping ratio is too small or too large. This method has higher requirements for acceleration signals. In engineering, it may be affected by noise and other factors, resulting in reduced identification accuracy. In practical engineering, it is necessary to improve the identification accuracy of first-order modal damping ratio by changing the interception point of the free attenuation section of the acceleration signal and the screening of the amplitude. Full article

Staggered floor frame structures with good spatial adaptability are widely used in large-space civil buildings such as conference halls and terminal buildings. However, the short columns formed by staggered floor slabs significantly affect load transfer, which is unfavorable to the seismic performance of the structure. To address this issue, based on a practical project, this paper establishes a finite element analysis model, sets up steel-tube-reinforced concrete (ST-RC) columns at staggered floors to improve the insufficient ductility of short columns, and adopts the dynamic time–history analysis method combined with performance-based evaluation methods to study the effects of different seismic input angles (0°, 30°, 60°, 90°) on the seismic performance of local staggered floor frame structures at both the overall and member levels. The research results show that at the overall level, the fourth floor of the staggered floor frame structure is the weak floor, and the most unfavorable seismic input angle is 60°; additionally, at the member level, the damage of each member meets the performance objectives. Frame beams are more severely damaged under 0° and 90° seismic input, frame columns are more severely damaged under 30° and 60° seismic input, and the damage degree of ST-RC columns is similar in the four directions. As energy-dissipating members, frame beams have a significantly higher proportion of nonlinear strain energy than frame columns and ST-RC columns, which can effectively consume a large amount of seismic energy and enable the structure to retain more safety reserves. Therefore, for irregular buildings such as staggered floor frame structures that are prone to damage due to insufficient ductility of short columns, setting ST-RC columns at staggered floors can effectively reduce structural damage. The adoption of evaluation methods at both the overall structural and member levels enables a comprehensive understanding of the damage status of staggered floor structures. Full article

The integration of machine learning (ML) into industrial automation is fundamentally reshaping how manufacturing systems are monitored, inspected, and optimized. By applying machine learning to real-time sensor data and operational histories, advanced models enable proactive fault prediction, intelligent inspection, and dynamic process control—directly enhancing system reliability, product quality, and efficiency. This review explores the transformative role of ML across three key domains: Predictive Maintenance (PdM), Quality Control (QC), and Process Optimization (PO). It also analyzes how Digital Twin (DT) and Edge AI technologies are expanding the practical impact of ML in these areas. Our analysis reveals a marked rise in deep learning, especially convolutional and recurrent architectures, with a growing shift toward real-time, edge-based deployment. The paper also catalogs the datasets used, the tools and sensors employed for data collection, and the industrial software platforms supporting ML deployment in practice. This review not only maps the current research terrain but also highlights emerging opportunities in self-learning systems, federated architectures, explainable AI, and themes such as self-adaptive control, collaborative intelligence, and autonomous defect diagnosis—indicating that ML is poised to become deeply embedded across the full spectrum of industrial operations in the coming years. Full article

The Buades Gallery (1973–2003) was not merely a commercial space in Madrid. In the history of art in Spain, it served as a professional and political node for Spanish conceptualism, an art form which, due to its idiosyncrasies, required its own channels of distribution. This article seeks to examine the trajectory of Mercedes Buades in alignment with this movement, re-evaluating her role from a feminist perspective and highlighting the importance of certain agents who have traditionally been invisibilised. To this end, a theoretical approach is adopted, following the sociology of art and the social history of art, paying particular attention to the contributions of Enrico Castelnuovo, Pierre Bourdieu and Núria Peist. These frameworks enable an analysis of the role of the gallerist as a structuring agent within the artistic field, capable of generating symbolic capital and establishing dynamics of production, circulation and consumption in the context of post-Franco Spain, a country that lacked a consolidated museum infrastructure at the time. Even so, Mercedes Buades established a model of gallery practice that, beyond its commercial dimension, contributed decisively to the symbolic configuration of contemporary art in Spain and formed part of a network of artistic visibility that promoted experimental art. Full article

This paper aims to investigate the feasibility of machine learning methods for the vulnerability assessment of buildings and structures. Traditionally, the seismic performance of buildings and structures is determined through a non-linear time–history analysis, which is an accurate but time-consuming process. As an alternative, structural responses of buildings under earthquakes can be obtained using well-trained machine learning models. In the current study, machine learning models for the damage classification of RC buildings are developed using the datasets generated from numerous incremental dynamic analyses. A variety of earthquake and structural parameters are considered as input parameters, while damage levels based on the maximum inter-story drift ratio are selected as the output. The performance and effectiveness of several machine learning algorithms, including ensemble methods and artificial neural networks, are investigated. The importance of different input parameters is studied. The results reveal that well-prepared machine learning models are also capable of predicting damage levels with an adequate level of accuracy and minimal computational effort. In this study, the XGBoost method generally outperforms the other algorithms, with the highest accuracy and generalizability. Simplified prediction models are also developed for preliminary estimation using the selected input parameters for practical usage. Full article

This study is based on the whole gene resequencing data of 162 individuals from 16 chicken breeds. We calculated the historical effective population size (Ne), differentiation time and genetic hybridization degree of the population to understand its historical dynamics, in order to provide a theoretical basis for the scientific protection and utilization of the germplasm resources of Baicheng Oil Chicken (BCY). The main results are as follows: using SMC++ and fastsimcoal2 software, respectively, we estimated Ne of BCY at 46,066 in the past and inferred a divergence time of 428–548 years ago. D-statistical analysis revealed a ~7% genetic introgression from White Leghorn chicken (LH) to BCY. Notably, infiltration genes such as CTNNAL1 (potentially influencing egg production) and RARX (possibly associated with fat deposition) were identified. These findings provide insights into BCY’s demographic history and support its genetic conservation and utilization. Full article

The risk of non-human primate (NHP) malaria transmission to humans is increasing, with Plasmodium knowlesi and Plasmodium cynomolgi emerging as significant zoonotic threats, particularly in Malaysia. While P. knowlesi is well-documented, P. cynomolgi infections in humans remain underreported, largely due to diagnostic challenges. Routine microscopy and standard molecular diagnostic tools often misdiagnose P. cynomolgi infections as P. vivax due to morphological similarities and genetic homology. We report a new case of a human P. cynomolgi infection misdiagnosed as Plasmodium vivax in a 32-year-old male with no prior malaria history or travel to endemic countries. The initial diagnoses made by the microscopy and qPCR conducted by the Kota Bharu Public Health Laboratory in Kelantan identified the infection as P. vivax. However, cross-examination by the Institute for Medical Research (IMR) revealed the presence of mixed-species infection, prompting further analysis. The real-time PCR and sequencing performed at MAPELAB, Spain, confirmed the co-infection of P. vivax and P. cynomolgi. This case highlights the diagnostic limitations in detecting P. cynomolgi, which shares high genetic similarity with P. vivax, leading to potential cross-reactivity and diagnostic inaccuracies. As P. cynomolgi emerges as the second zoonotic malaria species after P. knowlesi capable of infecting humans in Southeast Asia, improved diagnostic methods are urgently needed. Enhanced molecular diagnostics and comprehensive epidemiological studies are essential to elucidate transmission dynamics, assess public health implications, and inform effective malaria control strategies. Full article

This study investigates the use of viscous dampers (VDs) to reduce the vibration of a deepwater offshore platform under joint wind, wave, and earthquake action. A finite element model was established based on the Opensees software (version 3.7.1), incorporating soil–structure interaction simulated by the nonlinear Winkler springs and simulating hydrodynamic loads via the Morison equation. Turbulent wind fields were generated using the von Kármán spectrum, and irregular wave profiles were synthesized from the JONSWAP spectrum. The 1995 Kobe earthquake record served as seismic input. The time-history dynamic response for the deepwater offshore platform was evaluated under two critical scenarios: isolated seismic excitation and the joint action of wind, wave, and seismic loading. The results demonstrate that VDs configured diagonally at each structural level effectively suppress platform vibrations under both isolated seismic and wind–wave–earthquake conditions. Under seismic excitation, the VD system reduced maximum deck acceleration, velocity, displacement, and base shear force by 9.95%, 22.33%, 14%, and 31.08%, respectively. For combined environmental loads, the configuration achieved 15.87%, 21.48%, 13.51%, and 34.31% reductions in peak deck acceleration, velocity, displacement, and base shear force, respectively. Moreover, VD parameter analysis confirms that increased damping coefficients enhance control effectiveness. Full article

Objectives: Pott’s puffy tumor (PPT) is a rare and life-threatening infection of the frontal sinuses, predominantly affecting children but with less frequent reports in adults. Therefore, we present an analysis of one hundred and eighty-one cases of adult patients diagnosed with PPT, along with a description of one of our cases. The purpose of this research is to identify the most common symptoms, predisposing medical history, predominant microorganisms, commonly used antibiotics, treatment options, long-term outcomes, and possible complications in adults. Despite its rarity, PPT has a dynamic course, necessitating familiarization with appropriate treatment methods to improve patient well-being. Methods: Methods involved a systematic search of PubMed, Medline, Google Scholar, Web of Science, EBSCO, and Scopus, following PRISMA guidelines. A total of 122 articles were screened, providing 180 adult patients aged 18 to 86, alongside 1 additional patient treated at our institution, bringing the total to 181 patients. Results: The results showed that the patients ranged from 18 to 86 years of age (mean age of 47 years), with 72.2% being males. The most common symptoms were forehead swelling (74.7%), frontal headache (67%), fever (59.3%), and acute/chronic rhinosinusitis (39.6%). The risk factors associated with its development include sinusitis (49.5%) and previous head trauma (12.6%). Intracranial involvement was found in 38.1% of patients. Streptococcus spp. (19.3%) and Staphylococcus spp. (16.6%) were the most commonly identified pathogens. Surgical intervention was employed in 87.3% of cases, with a mean hospital stay of 23 days. There was no significant difference in hospital stay or rehospitalization rates between those with and without intracranial involvement. Antibiotic therapy was used in 87.3% of cases, with a mean duration of 61 days. A combination of Cephalosporin, Metronidazole, and Nafcillin was the most common empirical antibiotic therapy. The mean follow-up period was 14 months, with a mortality rate of 1.6%. Conclusions: The conclusion highlights the importance of the prompt initiation of empirical antibiotic therapy, followed by targeted treatment based on microbiological cultures. Recognizing that PPT symptoms are not exclusive to pediatric patients but can also affect adults is crucial. PPT warrants further research to optimize its management and outcomes. It is believed that PPT may be more treatable in adults when identified early, which emphasizes the need for PPT recognition among adults. Timely empirical antibiotics based on microbiological results, along with appropriate surgical intervention, are critical for improving outcomes. Multidisciplinary care involving otolaryngologists, neurologists, and infectious disease specialists is essential. Further studies should be developed for the evaluation of diagnostic protocols and long-term management strategies. Full article

This study analyses for the first time the genetic diversity, connectivity, and evolutionary dynamics of the small red scorpionfish (Scorpaena notata) in the Balearic Islands, using two mitochondrial DNA markers: Cytochrome c oxidase subunit I (COI) and the Control Region (CR). Nucleotide diversity of the COI gene was found to be low compared to other commercial fish species, suggesting that fishing may be impacting the population despite being a by-catch species. In contrast, the CR showed higher genetic variability. Demographic history analyses suggest that S. notata underwent a population expansion during the Pleistocene, possibly driven by sea-level changes. Genetic structure analyses (F_st and AMOVA) indicated genetic homogeneity and high connectivity among the Balearic Islands’ population, likely facilitated by its passive dispersion via pelagic eggs and larvae and the oceanographic conditions of the region. Our results suggest that the entire Balearic Islands could be considered as a unique Management Unit, although its potential relation to other nearby areas, such as the Iberian Peninsula, along with the analysis of additional genetic markers, should be addressed in future studies. Full article

In the seismic analysis of underground structures, the traditional integral response displacement method may misestimate far-field constraint effects because of the empirical placement of cutoff boundaries, leading to inaccuracies and a significant increase in computational costs. This study proposes a cutoff boundary distance criterion of three times the structural size, effectively eliminating boundary reflection errors. Furthermore, by introducing artificial boundary conditions that simulate the static resistance of a semi-infinite foundation and the far-field constraint effects, an improved integral response deformation method considering the far-field effect is developed. Verification based on the dynamic time history method (implemented with the Abaqus/Explicit solver) confirms that the computational error of the proposed method is essentially controlled within 5%, and compared with that of the traditional method, the number of mesh elements and nodes is reduced by 80%~90%. This advancement provides a computationally efficient and highly accurate solution for the seismic analysis of large-scale underground structures, making it highly valuable for practical engineering applications. Full article

Cognitive sensors are embedded in home appliances and other surrounding devices to create a connected, intelligent environment for providing pervasive and ubiquitous services. These sensors frequently create massive amounts of data with many redundant and repeating bit values. Cognitive sensors are always restricted in resources, and if careful strategy is not applied at the time of deployment, the sensors become disconnected, degrading the system’s performance in terms of energy, reconfiguration, delay, latency, and packet loss. To address these challenges and to establish a connected network, there is always a need for a system to evaluate the contents of detected data values and dynamically switch sensor states based on their function. Here in this article, we propose a reinforcement learning-based mechanism called “Adaptive Scheduling in Cognitive IoT Sensors for Optimizing Network Performance using Reinforcement Learning (ASC-RL)”. For reinforcement learning, the proposed scheme uses three types of parameters: internal parameters (states), environmental parameters (sensing values), and history parameters (energy levels, roles, number of switching states) and derives a function for the state-changing policy. Based on this policy, sensors adjust and adapt to different energy states. These states minimize extensive sensing, reduce costly processing, and lessen frequent communication. The proposed scheme reduces network traffic and optimizes network performance in terms of network energy. The main factors evaluated are joint Gaussian distributions and event correlations, with derived results of signal strengths, noise, prediction accuracy, and energy efficiency with a combined reward score. Through comparative analysis, ASC-RL enhances the overall system’s performance by 3.5% in detection and transition probabilities. The false alarm probabilities are reduced to 25.7%, the transmission success rate is increased by 6.25%, and the energy efficiency and reliability threshold are increased by 35%. Full article

This study established a dynamic impact simulation system for a coral limestone cement composite subjected to bidirectional stress confinement conditions by using a coupled method of continuous medium FDM (a coupled continuum-discontinuum approach integrating finite difference continuum modeling (FDM) and the discrete element method (DEM) granular analysis), and verified its accuracy through indoor experiments. The study first conducted dynamic mechanical performance tests on reef limestone concrete using an SHPB experimental device, exploring the effects of the strain-rate governed high-rate response, energy evolution, and failure modes. Subsequently, an FDM-DEM coupled model was used to simulate the impact-induced behavior of concrete at multiaxial stress conditions and constraint conditions, analyzing the strain-rate dependent performance of concrete exposed to biaxial monotonic loading. Test outcomes indicate that the increase in strain rate significantly enhanced the dynamic peak stress, and the collapse behavior shifted from type I to type II. As static loading in the σ₂ direction increased, the dynamic peak stress in the σ₁ direction decreased, while the dynamic peak stress in the σ₂ direction increased, indicating that the constraint stress in the σ₂ direction had an inhibitory effect on the sample’s failure. Through the time-history monitoring and analysis of cracks, it was found that the internal crack growth rate accelerated as the stress increased, while the crack growth tended to stabilize when the stress decreased. Additionally, this study explored the effect of stress constraints on the fragmentation patterns, revealing changes in the failure modes and crack distributions of the sample under different stress states, providing a theoretical basis and technical support for island and reef construction and engineering protection. Full article