Search Results (1,989)

Background: Cortical tubers (CTs) are hallmark brain lesions in tuberous sclerosis complex (TSC), historically considered stable in number over time; prior literature has correlated overall CT burden on magnetic resonance imaging (MRI) with disease severity. As longitudinal imaging studies assessing CTs’ evolution over time are lacking, we aim to investigate temporal changes in CTs—both in number and signal—on MRI in a cohort of pediatric TSC patients. Methods: A retrospective single-center analysis was conducted on 57 pediatric TSC patients who underwent longitudinal MRI studies in a 10-year span. Required MRI sequences included volumetric unenhanced T1-weighted, SWI, T2w and/or FLAIR. CTs were evaluated by two neuroradiologists and classified into five subtypes (A, B, C1, C2, D) according to signal characteristics. Statistical comparison was performed using t-tests. Results: Paired t-test analysis demonstrated a significant longitudinal increase in the overall number of CTs, rising from 16.11 ± 12.43 at baseline to 18.77 ± 13.29 at follow-up (mean difference = −2.67, 95% CI [−3.94, −1.39]; t (56) = 4.19; p < 0.0001), corresponding to a moderate effect size (Cohen’s d ≈ 0.56). When stratified by age, patients <2 years—representing the incompletely myelinated subgroup—showed a more pronounced increase in CT burden, from 19.46 ± 15.21 to 24.17 ± 15.75 (mean difference = −4.71, 95% CI [−7.37, −2.04]; t (23) = 3.65; p = 0.0013; d ≈ 0.75). In contrast, patients aged ≥2 years demonstrated a smaller but still significant increase, from 13.67 ± 9.45 to 14.85 ± 9.64 (mean difference = −1.18, 95% CI [−2.08, −0.28]; t (32) = 2.68; p = 0.0115; d ≈ 0.46). Direct comparison between the two subgroups using Welch’s two-sample t-test confirmed that the mean CT count in patients <2 years was significantly higher than in those ≥2 years (mean difference = 3.53 ± 1.36; t = 2.59; df = 28.4; p = 0.0075), with a large effect size (Cohen’s d ≈ 0.78). Type C1-C2 tubers evolved from pre-existing earlier-stage lesions, while most newly visible CTs over time were type A-B. Type D tubers remained rare and derived from earlier-stage CTs. Conclusions: Contrary to previous assumptions, CTs in pediatric TSC showed a tendency to increase in number and evolve in signal over time, thus challenging the notion of stability and suggesting dynamic behavior. Incomplete myelination in early infancy may impact MRI CTs detection by reducing contrast with surrounding brain tissue, potentially leading to their underestimation/misidentification. Full article

With the transformation of the global energy structure, offshore wind power is developing on a large scale, and the efficient and safe installation of offshore converter platforms has become a key technological bottleneck. Based on the elastic force–gravity similarity principle, a 1:65 model scale was adopted. A physical model of the offshore converter station platform was constructed, and the accuracy of the numerical simulation was validated by comparison with the physical model tests. This study investigates the dynamic response of the offshore converter platform and converter valve equipment during the float-over installation and mating process. The structural dynamic responses at key positions were analyzed. The results indicate that, due to the slender and flexible structure of the converter valve equipment, the Z-direction acceleration at the top is about 20% higher than that at the bottom. Moreover, the stress and strain at the bottom connection with the deck are higher than those at the top. The Y-direction acceleration at the edge foundation beam of the platform module is 47% higher than that at the mid-span position. The vibration frequency of the foundation beam on the first floor with the converter valve arranged is increased by 15%. When the jacket piles are subjected to impact, the mid-span response is 25% higher than that at the edges, showing characteristics of “strong at mid-span and weak at the edges”. Full article

China has witnessed extensive construction of underground powerhouses for pumped storage power stations. With the continuous increase in unit capacity, vibration problems have become particularly pronounced. Intense vibrations may not only disrupt the normal operation of hydropower units but also compromise the overall structural safety of the powerhouse. Moreover, in dynamic analyses of powerhouse structures, different parameters exert varying degrees of influence on the results, making it essential to systematically examine their impacts. This study focuses on a large-scale underground powerhouse, establishing a three-dimensional finite element model of Unit #1 to investigate its dynamic characteristics and parametric sensitivity. Through modal and harmonic response analyses, the effects of key parameters—including the zone of surrounding rock, elastic modulus of surrounding rock, dynamic elastic modulus of concrete, and damping ratio—were systematically evaluated. Results indicate that an expanded surrounding rock zone reduces natural frequency and increases dynamic displacement, with a zone twice the span length offering an optimal balance between accuracy and computational efficiency. Increasing the elastic modulus of the surrounding rock raises the natural frequency and slightly reduces displacement, while having a limited impact on dynamic stress. The dynamic elastic modulus of concrete shows a square-root relationship with natural frequency and an inverse correlation with dynamic displacement. The damping ratio has negligible influence on natural frequency, dynamic displacement, and dynamic stress. These findings provide a theoretical basis and practical guidance for parameter selection in the dynamic analysis of underground powerhouse structures, enhancing the reliability of numerical simulations. Full article

The integration of Soft Open Points (SOPs) into distribution networks has been an essential method for enhancing operational flexibility and efficiency. But simultaneous optimization of network reconfiguration and SOP scheduling constitutes a difficult mixed-integer nonlinear programming (MINLP) problem that is likely to suffer from premature convergence with standard metaheuristic solvers, particularly in large power networks. This paper proposes a novel hybrid algorithm, hTLBO–CO, which synergistically integrates the exploitative capability of Teaching–Learning-Based Optimization (TLBO) with the explorative capability of the Cheetah Optimizer (CO). One of the notable contributions of our framework is an in-depth problem formulation that enables SOP locations on both tie and sectionalizing switches with an efficient constraint-handling scheme, preserving topo-logical feasibility through a minimum spanning tree repair scheme. The evolved hTLBO–CO algorithm is systematically validated across IEEE 33-, 69-, and 119-bus test feeders with differential operational scenarios. Results indicate consistent dominance over established metaheuristics (TLBO, CO, PSO, JAYA), showing significant efficiency improvement in power loss minimization, voltage profile enhancement, and convergence rate. Remarkably, in a situation with a large-scale 119-bus power grid, hTLBO–CO registered a significant 50.30% loss reduction in the single-objective reconfiguration-only scheme, beating existing state-of-the-art approaches by over 15 percentage points. These findings, further substantiated by comprehensive statistical and multi-objective analyses, confirm the proposed framework’s superiority, robustness, and scalability, establishing hTLBO–CO as a robust computational tool for the advanced optimization of future distribution networks. Full article

Background: AMR causes a large global health burden, with approximately 4.95 million deaths linked to bacterial AMR in 2019, 1.27 million due to AMR directly. Although Brazil mandated prescriptions for systemic antibiotics in 2010/2011, self-medication and access without prescriptions continue, with community pharmacists playing a vital part in antimicrobial stewardship (AMS). This study examined antibiotic misuse and AMR in Brazil through community pharmacists’ perspectives, emphasising their dual role as professional actors and frontline observers of public behaviour. Methods: We conducted 20 semi-structured interviews with community pharmacists and performed reflexive thematic analysis of their accounts, repeating five independent analytic cycles to confirm thematic robustness. Results: Six themes were consistently identified as recounted by pharmacists in their practice contexts: Access and Self-Medication; Relationships with Healthcare Professionals; Knowledge and Beliefs about Antibiotics; Use and Adherence; Healthcare System Barriers; and Regulation and Enforcement. Pharmacists mentioned regular requests for antibiotics without prescriptions, drug reuse, and significant impact from community, i.e., from relatives, and peers. The common misunderstanding was that antibiotics treat viral illnesses. Structural issues, for instance GP appointment costs and long waits, made patients seek help from pharmacies. Due to regulation being applied inconsistently, pharmacies struggled to refuse unsuitable requests. Conclusions: Framed through pharmacists’ dual vantage as professionals and frontline observers, the findings highlight intertwined factors underpinning inappropriate antibiotic use in Brazil and support a multi-pronged intervention spanning health system strengthening, professional education, economic considerations, and community engagement. Full article

Analyzing accident reports to absorb past experiences is crucial for construction site safety. Current methods of processing textual accident reports are time-consuming and labor-intensive. This research applied the LDA topic model to analyze construction accident reports, successfully identifying five main types of accidents: Falls from Height (23.5%), Struck-by and Contact Injuries (22.4%), Slips, Trips, and Falls (21.8%), Hot Work & Vehicle Hazards (18.1%), and Lifting and Machinery Accidents (14.2%). By mining the rich contextual details within unstructured textual descriptions, this research revealed that environmental factors constituted the most prevalent category of contributing causes, followed by human factors. Further analysis traced the root causes to deficiencies in management systems, particularly poor task planning and inadequate training. The LDA model demonstrated superior effectiveness in extracting interpretable topics directly mappable to engineering knowledge and uncovering these latent factors from large-scale, decade-spanning textual data at low computational cost. The findings offer transformative perspectives for improving construction site safety by prioritizing environmental control and management system enhancement. The main theoretical contributions of this research are threefold. First, it demonstrates the efficacy of LDA topic modeling as a powerful tool for extracting interpretable and actionable knowledge from large-scale, unstructured textual safety data, aligning with the growing interest in data-driven safety management in the construction sector. Second, it provides large-scale, empirical evidence that challenges the traditional dogma of “human factor dominance” by systematically quantifying the critical role of environmental and managerial root causes. Third, it presents a transparent, data-driven protocol for transitioning from topic identification to causal analysis, moving from assertion to evidence. Future work should focus on integrating multi-dimensional data for comprehensive accident analysis. Full article

This article proposes a multi-agent path planning and decision-making solution for high-tempo field robotic operations, such as search-and-rescue, in large-scale unstructured environments. As a representative example, the subterranean environments can span many kilometers and are loaded with challenges such as limited to no communication, hazardous terrain, blocked passages due to collapses, and vertical structures. The time-sensitive nature of these operations inherently requires solutions that are reliably deployable in practice. Moreover, a human-supervised multi-robot team is required to ensure that mobility and cognitive capabilities of various agents are leveraged for efficiency of the mission. Therefore, this article attempts to propose a solution that is suited for both air and ground vehicles and is adapted well for information sharing between different agents. This article first details a sampling-based autonomous exploration solution that brings significant improvements with respect to the current state of the art. These improvements include relying on an occupancy grid-based sample-and-project solution to terrain assessment and formulating the solution-search problem as a constraint-satisfaction problem to further enhance the computational efficiency of the planner. In addition, the demonstration of the exploration planner by team MARBLE at the DARPA Subterranean Challenge finals is presented. The inevitable interaction of heterogeneous autonomous robots with human operators demands the use of common semantics for reasoning across the robot and human teams making use of different geometric map capabilities suited for their mobility and computational resources. To this end, the path planner is further extended to include semantic mapping and decision-making into the framework. Firstly, the proposed solution generates a semantic map of the exploration environment by labeling position history of a robot in the form of probability distributions of observations. The semantic reasoning solution uses higher-level cues from a semantic map in order to bias exploration behaviors toward a semantic of interest. This objective is achieved by using a particle filter to localize a robot on a given semantic map followed by a Partially Observable Markov Decision Process (POMDP)-based controller to guide the exploration direction of the sampling-based exploration planner. Hence, this article aims to bridge an understanding gap between human and a heterogeneous robotic team not just through a common-sense semantic map transfer among the agents but by also enabling a robot to make use of such information to guide its lower-level reasoning in case such abstract information is transferred to it. Full article

To accurately evaluate the health condition of the cables of a cross-sea cable-stayed bridge under typhoon effects and to improve the efficiency of damage identification, an accurate bridge damage identification method combining convolutional neural network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) is proposed. A numerical model of the cable-stayed bridge was first established in ANSYS. Based on the monitoring data of Super Typhoon Mujigae, a three-dimensional fluctuating wind field was generated by harmonic synthesis. Through transient analysis, the static and dynamic responses of the cable-stayed bridge under typhoon loads were analyzed, and the critical cable locations most susceptible to damage were identified. Subsequently, the acceleration signals of the structural damage states under typhoon were extracted, and the damage-sensitive features were obtained through the Hilbert transform. Finally, an intelligent damage identification model for cable-stayed bridges was established by combining CNN and BiLSTM, and the identification results were compared with those obtained using CNN and BiLSTM individually. The results indicate that the neural network model combining CNN and BiLSTM performs significantly better than either CNN or BiLSTM alone in predicting both the location and degree of damage. Compared with the standalone CNN and BiLSTM models, the proposed hybrid CNN–BiLSTM network improves the accuracy of damage-location identification by 1.6% and 2.42%, respectively, and achieves an overall damage-degree identification accuracy exceeding 98%. The findings of this study provide theoretical and practical support for the intelligent operation and maintenance of cable-stayed bridges in coastal regions. The proposed approach is expected to serve as a valuable reference for evaluating large-span bridge structures under extreme wind conditions. Full article

Smart cities offer new opportunities for urban governance and sustainable development. However, at the current stage, the construction and development of smart cities generally exhibit a technology-driven tendency, neglecting real resident demand, which contradicts the “human-centric” principle. Traditional top-down methods of demand collection struggle to capture the dynamics and heterogeneity of public demand. At the same time, government service platforms, as one dimension of smart city construction, have accumulated massive amounts of user-generated data, providing new solutions for this challenge. This paper aims to construct a big data-driven analytical framework for dynamically identifying and accurately forecasting core resident demand. The study uses Xi’an City, Shaanxi Province, China, as a case study, utilising user messages from People.cn spanning 2011 to 2023. These messages cover various domains, including urban construction, healthcare, education, and transportation, as the data source. The People.cn message board is China’s most significant nationwide online political platform. Its institutionalised feedback mechanism ensures data content focuses on highly representative specific grievances, rather than the broad emotional expressions on social media. The study employs user messages from People.cn from 2011 to 2023 as its data source, encompassing urban construction, healthcare, education, and transportation. First, a large language model (LLM) was used to preprocess and clean the raw data. Subsequently, the BERTopic model was applied to identify ten core demand themes and construct their monthly time series, thereby overcoming the limitations of traditional methods in short-text semantic recognition. Finally, by integrating variational mode decomposition (VMD) with support vector machines (SVMs), a hybrid demand forecasting model was established to mitigate the risk of overfitting in deep learning when forecasting small-sample time series. The empirical results show that the proposed LLM-BERTopic-VMD-SVM framework exhibits excellent performance, with the goodness-of-fit (R2) on various demand themes ranging from 0.93 to 0.96. This study proposes an effective analytical framework for identifying and forecasting resident demand. It provides a decision-support tool for city managers to achieve proactive and fine-grained governance, thereby offering a viable empirical pathway to promote the transformation of smart cities from technology-centric to human-centric. Full article

To investigate the thermal effects of solar radiation on annular steel structures with non-uniform spans, this study implemented a methodology combining numerical simulation and monitoring. Electronic strain gauges and temperature monitoring points were installed at mid-span of three lifting segments (TS1–3) of “Sky Hall” project to simultaneously record thermal stress and temperature data. The data of temperature was imported into Midas-GEN, where structural thermal stresses were computationally generated through a simplified non-uniform temperature field model. Comparatively analysis showed the following: (1) Thermal stress shows a strong linear correlation with temperature increase, with a Pearson correlation coefficient of r = 0.989; (2) Constraint intensity is a critical factor affecting the magnitude of thermal stress in annular structures—TS3 with lower constraint density exhibits better deformation compatibility, leading to effective stress dissipation (stress increase of 6 MPa per 1 °C rise), while TS1 under strong constraint conditions shows limited deformation capacity, resulting in significantly intensified stress concentration (with 18 MPa increase per 1 °C rise); (3) The variation trends of simulation and monitoring results are highly consistent, though significant deviations exist in some members (the peak monitored stress was 2.31 times the simulated value) due to factors such as structural geometry, material properties, member dimensions, constraint conditions, and the simplified non-uniform temperature field model; (4) According to the most unfavorable combination specified in the Standard for Design of Steel Structures (GB 50017-2017), the design stress value is 203.5 MPa, which is quite less than the yield stress, thus meeting the safety requirement. Full article

A deflection prediction approach based on an adaptive MEC–RBF neural network was developed in this study. By dynamically optimizing the centres, widths, and weights of the RBF network, the proposed method substantially increases the prediction accuracy, and it achieves an R² of 0.9789 and an RMSE of 1.4978 on the training dataset. It effectively resolves the stability challenges that are associated with nonlinear construction conditions in traditional models. An orthogonal experimental design analysis revealed that the girder block length and the cantilever-to-span length ratio (d/L) were the most influential factors that affected deflection, whereas the effects of uniformly distributed loads and temperature were negligible, thereby providing a sound basis for parameter simplification. The application of the model to the Hannan Yangtze River Bridge yielded a maximum discrepancy of only 5.56 mm (17.7% error rate) between the predicted and measured values, thus demonstrating its practical engineering reliability. By innovatively integrating intelligent optimization techniques with neural networks, this approach overcomes the limitations in terms of real-time responsiveness and long-term stability of conventional methods and offers an efficient and reliable technical tool for the control of large-scale bridge construction. Full article

Commercial complexes, as major sources of urban energy consumption and carbon emissions, face urgent demands for efficiency improvement under the “dual-carbon” strategy. This paper develops a Building Information Modeling (BIM)-enabled life-cycle energy management framework to address fragmented monitoring, weak coordination, and data silos inherent in traditional approaches. Methodologically, a structured literature review was conducted to identify inefficiencies and draw lessons from global practices. An enhanced Delphi method was then applied to refine 12 key evaluation indicators spanning six dimensions—policy, economic, social, technological, environmental, and compliance—which were subsequently integrated into a BIM platform. This integration enables real-time energy monitoring, multi-system diagnostics, and cross-phase collaboration across the design, construction, and operation stages. An empirical case study of the Zhongjian Plaza project in Shanghai demonstrates that the proposed framework not only enhances energy efficiency and reduces life-cycle costs, but also improves user comfort while aligning with both domestic green building standards and international sustainability targets. Overall, the study provides a replicable methodology and practical reference for the smart and low-carbon operation of large-scale commercial complexes, thereby offering strategic insights for advancing sustainable urban development. Full article

Contemporary surveys in the search for extraterrestrial intelligence (SETI) typically make one-off “spot scans” across the sky to search planetary systems for narrow-band radio signals that would indicate the presence of intelligent life. Spot scans may span a duration of seconds to minutes in order to observe a large number of targets with limited resources, but such a strategy does not necessarily consider the timing of exactly when to listen for extraterrestrial signals. Several ideas for possible time markers were suggested in the first few decades of SETI, such as the use of recurrent supernovae, gamma ray bursts, or pulsars as a way of establishing directionality and attracting attention toward an extraterrestrial beacon. Civilizations in binary systems might even choose the points of periastron and apastron in its host system to send transmissions to other single-star civilizations. However, all of these timing considerations were developed prior to the age of exoplanets, which enables a more detailed assessment of targets suitable for SETI. This paper suggests SETI strategies for circumbinary and circumprimary planets based upon the timing of orbital events in such systems. Events such as orbital extremes could represent a logical time marker for extraterrestrial civilizations to transmit, if they desire to be detected. Likewise, a transiting binary pair with inhabited planets around each star could yield maximum detectability of leakage radiation when both stars eclipse within our field of view. As planets in binary systems continue to be discovered, limited-duration SETI surveys should selectively target such systems based upon the occurrence of reasonable time markers. Full article

Underground power pipelines are often encased in box culverts and buried in soil. When foundation pit excavation involves such existing pipelines, the buried box culverts can become partially suspended, risking excessive vertical deformation and requiring effective in situ protection. This study proposed analytical methods to calculate the vertical deformation of large-span box culverts under both unprotected and protected conditions. A case study of the 112 m suspended power box culverts at Yunnan Road Station on Nanjing Metro Line 5 is presented, where the methods are applied to determine the maximum allowable unsupported span and to formulate specific support and suspension protection schemes, which include a number of protection points and their spacing. Validation through ABAQUS modeling shows strong agreement among theoretical predictions, numerical simulations, and field measurements. Parametric analysis further demonstrated that the height, width, and modulus of the reinforced soil around the buried section all have a significant influence on the deformation control effectiveness. This study provides a combined theoretical framework and practical design guidelines for deformation control of large-span suspended box culverts in engineering applications. Full article

Large Language Models (LLMs) are increasingly proposed to personalize healthcare delivery, yet their real-world readiness remains uncertain. We conducted a systematic literature review to assess how LLM-based systems are designed and used to enhance patient engagement and personalization, while identifying open challenges these tools pose. Four digital libraries (Scopus, IEEE Xplore, ACM, and Nature) were searched, yielding 3787 studies; 16 met the inclusion criteria. Most studies, published in 2024, span different types of motivations, architectures, limitations and privacy-preserving approaches. While LLMs show potential in automating patient data collection, recommendation/therapy generation, and continuous conversational support, their clinical reliability is limited. Most evaluations use synthetic or retrospective data, with only a few employing user studies or scalable simulation environments. This review highlights the tension between innovation and clinical applicability, emphasizing the need for robust evaluation protocols and human-in-the-loop systems to guide the safe and equitable deployment of LLMs in healthcare. Full article