Search Results (149)

Background: Stress urinary incontinence (SUI) is frequently underrecognized in late pregnancy, with limited tools for effective risk assessment. This study aimed to evaluate the predictive value of clinical and pelvic floor ultrasound parameters for SUI and construct a validated risk model. Methods: Clinical, obstetric, and pelvic floor ultrasound findings were collected from a total of 521 women in late pregnancy who were enrolled in the study. Based on follow-up results, participants were categorized into SUI and non-SUI groups. Logistic regression analyses were used to identify independent risk factors for SUI, which were incorporated into a nomogram. Results: Four independent predictors were identified: vaginal delivery history (odds ratio [OR] = 2.320), bladder neck funneling (OR = 2.349), bladder neck descent (OR = 1.891), and pubococcygeus muscle contraction strain rate (OR < 0.001). The nomogram achieved an AUC of 0.817 (95% CI: 0.770–0.863) in the training set and 0.761 (95% CI: 0.677–0.845) in the test set. Conclusions: The nomogram based on clinical and pelvic floor ultrasound parameters accurately predicts the risk of SUI during late pregnancy, offering a useful tool for early identification and personalized management. Full article

The accurate identification and protection of the lingual nerve during oral surgery are critical to avoid complications such as a loss of taste or sensation and chronic pain. While numerous studies have described the nerve’s anatomy and injury outcomes, no consensus exists on the optimal method to trace its full course. This narrative review systematically examined the literature from 2010 to 2024, using databases like PubMed, MEDLINE, Embase, and Google Scholar. Keywords included “Lingual nerve,” “Course,” “Anatomy,” and “Clinical implications,” combined with Boolean operators. Studies were selected based on defined criteria, and findings were synthesized to highlight key challenges in diagnosing the nerve’s path. This review identifies difficulties at multiple anatomical sites: the foramen ovale, infratemporal fossa, pterygomandibular space, third molar and retromolar regions, premolar/molar areas, floor of the mouth, and anterior gingiva and tongue. Lingual nerve injury, especially during lower third molar surgeries, remains a major concern, often exacerbated by factors like patient age, unerupted teeth, and lingual surgical approaches. Effective prevention hinges on precise anatomical knowledge and meticulous surgical technique. Microsurgical repair remains the primary treatment but often yields unpredictable outcomes. Emerging regenerative therapies show early promise but require further clinical validation. Imaging tools such as magnetic resonance imaging (MRI) and ultrasound may enhance diagnostic accuracy and surgical planning; however, each has limitations in everyday practice. Ultimately, early identification, careful surgical handling, and appropriate imaging support are vital for improving patient outcomes and minimizing the risks of lingual nerve injury. Full article

The accuracy of identifying dynamic characteristics of super-tall buildings under typhoon conditions, as well as their correlation with the vibration amplitude, remains unclear, limiting the effective assessment of the structural performance and optimization of wind-resistant designs. To address this issue, the measured wind-generated vibration responses of Shanghai World Finance Center during the passage of Typhoon “Rumbia” were derived using data obtained from the health monitoring system of a super-tall building in Shanghai. The first and second inherent frequencies, as well as the damping ratio of the structure, were ascertained through the employment of the curve method and the standard deviation method. Based on this, a comparison and analysis were carried out regarding the variation patterns of the first and second inherent frequencies and the damping ratio with reference to the vibration amplitude. Vibration modes were identified using frequency domain analysis. The results of the natural frequency identification were compared to those from the Peak Picking method to see how well the curve method and the standard deviation method worked at finding modal parameters. Ultimately, an assessment of the super-tall building’s performance during the impact of the typhoon was conducted. The results demonstrate that the curve method and the standard deviation method can accurately identify the inherent frequency and damping ratio of the structure, with the curve method revealing a more pronounced regularity of the modal parameters. For the structure, in the horizontal and longitudinal directions, the first and second inherent frequencies exhibit a negative correlation with amplitude, while the damping ratio shows a positive correlation with amplitude. Moreover, as the floor level rises, the vibration modes in both directions of the structure steadily increase. During the impact of Typhoon “Rumbia”, the building’s performance complied with the requirements set by comfort standards. These analytical results not only provide valuable references for the wind-resistant design and vibration control of super-tall buildings but also offer critical support for condition assessment and damage identification within structural health monitoring systems. Full article

Disentangling the effects of the built environment on urban vitality at the scale of community life circles is crucial for informing precise urban planning and design, particularly in the context of urban renewal. However, studies examining the complex relationships and spatial heterogeneity in these effects remain limited, hindering the identification of built environment characteristics that may generate sustainable benefits. Therefore, this study took Xi’an, a typical high-density city in Northwest China, as an example. The eXtreme Gradient Boosting (XGBoost) model and the SHapley Additive exPlanations (SHAP) method were utilized to reveal threshold effects and spatial correlations between the built environment and community life circles’ vitality across varying buffer zones. The results show that (1) there is a significant spatial correlation between the built environment and the core–periphery structure of community life circles’ vitality. (2) Indicators, such as facility accessibility, the floor area ratio, intersection density, and the residential land use ratio, contribute significantly to community life circles’ vitality. (3) While the micro-built environment and socio-economic factors show limited contributions, their collaboration with the macro-built environment can enhance their individual effects, highlighting the necessity of taking them into account together. These findings provide new insights into supporting community life circles’ vitality through urban planning and design. Full article

Artistic masterpieces are mostly collected in museums located in the center of urban areas, which are prone to heavy traffic. Traffic-induced vibrations can represent a significant hazard for museum objects, due to the repeated nature of the excitation and the brittle, pre-damaged condition of the artifacts. This is the case of the Sarcophagus of the Spouses, displayed at the National Etruscan Museum of Villa Giulia in Rome. Vibrations on the floor of the room are measured by means of velocimeters, highlighting substantial vertical amplitudes and recommending the design of an isolation system. For its design, the dynamic identification of the statue is essential, but the use of contact or laser sensors is ruled out. Therefore, a recent technique that magnifies the micromovements present in digital videos is used and the procedure is validated with respect to constructions where the dynamic identification was available in the literature. In the case of the Sarcophagus, identified frequencies are satisfactorily compared with those of a finite element model. The recognition of the dynamic characteristics shows the method’s potential while using inexpensive devices. Because costs for cultural heritage protection are usually very high, this simple and contactless dynamic identification technique represents an important step forward. Full article

The lack of a testing framework for various indoor positioning technologies brings huge challenges to the systematic and fair evaluation of positioning systems, which greatly hinders the development and industrialization of indoor positioning technology. In order to solve this problem, this article refers to international standards, such as ISO/IEC 18305, and uses the China Electronics Standardization Institute’s rich experience in indoor positioning technology research and testing to build a universal positioning performance testing and evaluation framework. First, this paper introduces the experimental environment in detail from the aspects of the coordinate system definition, test point selection, building type definition, motion mode definition, and motion trajectory setting. Then, this paper comprehensively measures performance evaluation indicators from dimensions such as the accuracy index, relative accuracy, startup time, fault tolerance, power consumption, size, and cost. Finally, this paper elaborates on the testing methods and processes of positioning precision, accuracy, relative accuracy, floor identification, indoor–outdoor distinction, latency, relative accuracy, success rate, and movement speed tests. Full article

The automatic identification and three-dimensional reconstruction of house plans has emerged as a significant research direction in intelligent building and smart city applications. Three-dimensional models reconstructed from two-dimensional floor plans provide more intuitive visualization for building safety assessments and spatial suitability evaluations. To address the limitations of existing public datasets—including low quality, inaccurate annotations, and poor alignment with residential architecture characteristics—this study constructs a high-quality vector dataset of raster house plans. We collected and meticulously annotated over 5000 high-quality floor plans representative of urban housing typologies, covering the majority of common residential layouts in the region. For architectural element recognition, we propose a key point-based detection approach for walls, doors, windows, and scale indicators. To improve wall localization accuracy, we introduce CPN-Floor, a method that achieves precise key point detection of house plan primitives. By generating and filtering candidate primitives through axial alignment rules and geometric constraints, followed by post-processing to refine the positions of walls, doors, and windows, our approach achieves over 87% precision and 88% recall, with positional errors within 1% of the floor plan’s dimensions. Scale recognition combines YOLOv8 with Shi–Tomasi corner detection to identify measurement endpoints, while leveraging the pre-trained multimodal OFA-OCR model for digital character recognition. This integrated solution achieves scale calculation accuracy exceeding 95%. We design and implement a house model recognition and 3D reconstruction system based on the WebGL framework and use the front-end MVC design pattern to interact with the data and views of the house model. We also develop a high-performance house model recognition and reconstruction system to support the rendering of reconstructed walls, doors, and windows; user interaction with the reconstructed house model; and the history of the house model operations, such as forward and backward functions. Full article

Marine cobalt-rich crusts, extensively used in industries such as aerospace, automotive, and electronics, are crucial mineral resources located on the ocean floor. To effectively exploit these valuable resources, underwater imaging is essential for real-time detection and distribution mapping in mining areas. However, the presence of suspended particles in the seabed mining environment severely degrades image quality due to light scattering and absorption, hindering the effective identification of the target objects. Traditional image processing techniques—including spatial and frequency domain methods—are ineffective in addressing the interference caused by suspended particles and offer only limited enhancement effects. This paper proposes a novel underwater image restoration method that combines polarization imaging and homomorphic filtering. By exploiting the differences in polarization characteristics between suspended particles and target objects, polarization imaging is used to separate backscattered light from the target signal, enhancing the clarity of the cobalt crust images. Homomorphic filtering is then applied to improve the intensity distribution and contrast of the orthogonal polarization images. To optimize the parameters, a genetic algorithm is used with image quality evaluation indices as the fitness function. The proposed method was compared with traditional image processing techniques and classical polarization imaging methods. Experimental results demonstrate that the proposed approach more effectively suppresses backscattered light, enhancing the clarity of target object features. With significant improvements in image quality confirmed by several no-reference quality metrics, the method shows promise as a solution for high-quality underwater imaging in turbid environments, particularly for deep-sea mining of cobalt-rich crusts. Full article

Aircraft hangars are essential in the aviation industry, providing crucial maintenance and protection for aviation assets. However, constructing these upper structures involves significant safety risks. Due to the complexity of upper structure construction, it is vital to prioritize safety to prevent workplace accidents. Ensuring construction safety is not only crucial for operational efficiency but also aligns with several Sustainable Development Goals (SDGs), such as Decent Work and Economic Growth (SDG 8) and Industry, Innovation, and Infrastructure (SDG 9). This study assesses the safety risks associated with hangar construction using activity-based failure modes and effects analysis (FMEA). A mixed-method approach is adopted, incorporating insights from five construction safety experts and data from 100 individuals directly involved in the upper structure construction of the spaceframe hangar. Descriptive data analysis was employed to establish the foundation for computing risk priority numbers (RPNs) using the FMEA technique. Three primary activities were identified as having extremely high risks: workers falling from heights during the lifting and erection of the space frame, workers falling from heights during basement excavation while installing floor slab formwork, and workers falling from heights during the casting of floor slabs. These activities present safety risks with RPN values ranging from 64 to 100, including incidents of workers falling from heights and being struck by materials. This study serves as a crucial reference for formulating construction safety plans that encompass risk identification, assessment, and control measures. The findings provide essential insights into various safety hazards in construction projects, particularly those related to military infrastructure. By identifying and assessing these risks, the research facilitates the development of more effective and comprehensive safety protocols. Implementing the recommended control measures ensures a proactive approach to mitigating potential accidents and injuries. Consequently, this research contributes to academic knowledge and enhances safety standards and practices within the construction industry. Full article

The construction sector plays a key role in the growth of developing countries but faces major environmental challenges, such as greenhouse gas emissions and resource depletion. Life Cycle Assessment (LCA) is an essential tool for evaluating these impacts and promoting sustainable choices. However, its effective application is limited by the lack of local databases. This study introduces a systematic framework (LOCAL-LCID₂) for creating local Life Cycle Inventory (LCI) databases for developing countries. Its application is demonstrated in Burkina Faso’s (BF) context through a comparative LCA of commonly used materials, covering the cradle-to-gate stage. The methodology follows seven steps: (1) identification of materials, (2) data collection, (3) analysis of material and energy flows, (4) development of LCI database, (5) structuring the database using SimaPro 9.6.0, (6) calculation of environmental impacts via ReCiPe 2016 Midpoint, and (7) uncertainty analysis using the pedigree matrix and Monte Carlo simulation. The materials are categorized into two main groups (imported and locally produced) with five subcategories: materials for roofs, walls/structures, floors, openings, and others. The results show that for wall materials, concrete blocks have the highest Global Warming Potential (GWP), with 88.3% of CO₂ emissions attributed to cement, implying an urgent need to optimize cement use and explore alternative binders for sustainable construction. Stabilized earth blocks show intermediate GWP at 65% of concrete block emissions, while straw-stabilized adobe demonstrates the lowest environmental impact, suggesting significant potential for reducing construction’s carbon footprint through traditional material optimization. The importation of steel sheets and ceramic tiles shows high GWP due to their energy-intensive production processes and long-distance transport (4 to 40% of emissions), highlighting opportunities to reduce impacts through local manufacturing and optimization of supply chains. The diversification of BF’s energy mix through clean energy imports from neighboring countries decreases GWP by 26.9%, indicating that regional energy partnerships and renewable energy investments are key pathways for minimizing environmental impacts related to energy consumption in the construction industry. Finally, the uncertainty analysis reveals the need for primary data updates in the current LCI database, highlighting both data quality enhancement opportunities and future research perspectives for industrial process assessment. The methodological framework equips decision-makers in developing countries with tools to implement sustainable construction practices through strategic material selection and regional resource optimization. Full article

In this paper, the idea of a radar based on orthogonal frequency division multiplexing (OFDM) is applied to 5G NR Positioning Reference Signals (PRS). This study demonstrates how the estimation of the communication channel using the PRS can be applied for the identification of objects moving near the 5G NR receiver. In this context, this refers to a 5G NR base station capable of detecting a high-speed train (HST). The anatomy of a 5G NR frame as a sequence of OFDM symbols is presented, and different PRS configurations are described. It is shown that spectral analysis of time-varying channel impulse response weights, estimated with the help of PRS pilots, can be used for the detection of transmitted signal reflections from moving vehicles and the calculation of their time and frequency/Doppler shifts. Different PRS configurations with varying time and frequency reference signal densities are tested in simulations. The peak-to-noise-floor ratio (PNFR) of the calculated radar range–velocity maps (RVM) is used for quantitative comparison of PRS-based radar scenarios. Additionally, different echo signal strengths are simulated while also checking various observation window lengths (FFT lengths). This study proves the practicality of using PRS pilots in remote sensing; however, it shows that the most dense configurations do not provide notable improvements, while also demanding considerably more resources. Full article

The rising complexity of construction projects and the industry’s commitment to sustainable practices have driven the extensive adoption of Building Information Modeling (BIM) technology. A core function of BIM is the early identification and resolution of clashes during the design phase, which serves to mitigate costly rework and delays in the construction process. This study presents an advanced method for classifying and prioritizing hard clashes between structural components and mechanical, electrical, and plumbing (MEP) systems. Employing the Best-Worst Method (BWM), this research assigned specific weights to structural and MEP elements based on expert evaluations. Six parameters were incorporated into this prioritization framework: the weights determined by the BWM, outputs from Navisworks software (v2021), the ratio of MEP volume to floor volume, the functional purpose of each floor, and the number of adjacent elements. A custom-developed plugin for Autodesk Navisworks integrated these parameters, enabling real-time automated clash prioritization. Clashes were ranked by criticality through a calculation involving the six parameters, which enhanced the efficiency of clash detection by optimizing time and cost considerations during the design phase. Case study results indicate that beams and columns represent the most critical structural elements, while ducts are identified as the most significant MEP elements. The proposed method substantially improves clash detection and prioritization efficiency and accuracy, yielding considerable benefits in project management and resource allocation. Full article

Recent advances in laser scanning systems have enabled the acquisition of 3D point cloud representations of scenes, revolutionizing the fields of Architecture, Engineering, and Construction (AEC). This paper presents a novel pipeline for the automatic generation of 3D semantic models of multi-level buildings from indoor point clouds. The architectural components are extracted hierarchically. After segmenting the point clouds into potential building floors, a wall detection process is performed on each floor segment. Then, room, ground, and ceiling extraction are conducted using the walls 2D constellation obtained from the projection of the walls onto the ground plan. The identification of the openings in the walls is performed using a deep learning-based classifier that separates doors and windows from non-consistent holes. Based on the geometric and semantic information from previously detected elements, the final model is generated in IFC format. The effectiveness and reliability of the proposed pipeline are demonstrated through extensive experiments and visual inspections. The results reveal high precision and recall values in the extraction of architectural elements, ensuring the fidelity of the generated models. In addition, the pipeline’s efficiency and accuracy offer valuable contributions to future advancements in point cloud processing. Full article

Background: Effective prevention and treatment of pelvic floor dysfunction (PFD) necessitates the identification of lesions within the complex pelvic floor muscle (PFM) groups associated with various symptoms. Here, we developed a multi-region pelvic floor muscle functional diagnosis system (MPDS) based on an inflatable stretchable electrode array, which aids in accurately locating areas related to PFD. Methods: Clinical diagnostic experiments were conducted on 56 patients with postpartum stress urinary incontinence (PSUI) and 73 postpartum asymptomatic controls. MPDS collects pelvic floor electromyography from all participants. By assessing EMG parameters such as activation time differences (ATD) and using Jensen–Shannon (JS) divergence to verify, with the aim of locating target muscle groups with functional abnormalities. Results: Clinical test results showed that by observing the AT sequence of the PSUI group and the control group, muscle groups with functional abnormalities in the Pubococcygeus muscle (PC) and Puborectalis muscle (PR) regions could be preliminarily diagnosed. In the assessment of regional muscle contribution values based on JS divergence, it was verified that the contribution values of rapid contraction in the PC and PR regions of the PSUI group were relatively lower compared to those of the control group, which correlated with urinary control dysfunction. Conclusions: These experiments demonstrate that the MPDS helps in accurately locating target muscle groups with functional abnormalities, showcasing its potential in precise assessment of complex muscle groups such as PFM, which may improve diagnostic precision and reliability. Full article

This study addresses the complex hydrogeological conditions and frequent inrush water incidents in the Donghuantuo coal mine by proposing a novel spectral tracing technique aimed at rapidly and accurately identifying the sources of inrush water. Through the analysis of electrical data from the Donghuantuo mine, the electrical characteristics of the mine floor were examined. Systematic sampling of water from the primary aquifers within the mining area was conducted, followed by detailed spectral measurements, resulting in the establishment of a spectral database for inrush water sources in the Donghuantuo mine. The chaotic sparrow search optimization algorithm (CSSOA) was employed to optimize the key parameters of the random forest (RF) model, leading to the development of the CSSOA-RF spectral tracing identification model. This model demonstrated outstanding classification performance in the test set, achieving an accuracy of 100%. This research offers a novel, more accurate, and reliable method for identifying the sources of inrush water, facilitating the rapid identification of sources in coal-bearing regions of North China and reducing disaster losses. Although the geological structure of the study area is relatively simple, the research achieved significant results in identifying both single and mixed water sources. However, further validation and optimization are needed for its applicability in more complex geological conditions. The findings of this study provide crucial technical support for safe mining operations and hold significant reference value for water hazard prevention in similar regions. Full article