Search Results (279)

This study introduces a custom-designed CNN architecture that extracts robust, multi-level facial features and incorporates preprocessing techniques to correct or reduce asymmetry before classification. The innovative characteristics of this research lie in its integrated approach to overcoming facial asymmetry challenges and enhancing CNN-based emotion recognition. This is completed by well-known data augmentation strategies—using methods such as vertical flipping and shuffling—that generate symmetric variations in facial images, effectively balancing the dataset and improving recognition accuracy. Additionally, a Loss Weight parameter is used to fine-tune training, thereby optimizing performance across diverse and unbalanced emotion classes. Collectively, all these contribute to an efficient, real-time facial emotion recognition system that outperforms traditional CNN models and offers practical benefits for various applications while also addressing the inherent challenges of facial asymmetry in emotion detection. Our experimental results demonstrate superior performance compared to other CNN methods, marking a step forward in applications ranging from human–computer interaction to immersive technologies while also acknowledging privacy and ethical considerations. Full article

The accumulation and delayed processing of notices generated during the engineering construction process have a significant impact on project settlement and, thus, project cost. Currently, there is a lack of research on intelligent notice processing. Although large language models (LLMs), such as ChatGPT, have demonstrated exceptional performance in natural language processing, their effectiveness in specific vertical fields, such as construction engineering, is limited due to a lack of specialized training. In light of this, this study proposes a knowledge-augmented language model for intelligently processing design notices in EPC (engineering–procurement–construction) projects. This method consists of the following three key components: database construction, price retrieval, and prompt development. During database construction, exception detection was introduced to ensure data quality, and an appropriate database framework was proposed. The price retrieval module features innovative retrieval rules for improved efficiency and accuracy. Prompt development was based on mainstream methods, which were tailored for this task. The result of processing notices includes cost analysis and claimability judgement. The method achieved promising results in experiments with real project data. Based on these results, the paper discusses the model’s advantages, application scenarios, and input text requirements, providing insights and suggestions for future research. Full article

Background/Objectives: Vertical ridge augmentation remains a critical challenge in implant dentistry for addressing inadequate alveolar bone height. The inlay technique, or sandwich osteotomy, has gained attention for its potential to improve graft vascularization and predictability. This systematic review aimed to evaluate the clinical outcomes of the inlay technique. Methods: A systematic search was conducted in Cochrane Library and Medline databases for studies published from 2015 to 2025 to capture the most recent studies and advancements specifically focusing on the inlay technique. Inclusion criteria encompassed observational and interventional studies, including randomized controlled trials (RCTs) and cohort and case series with a focus on outcomes related to the inlay technique. Key outcomes were extracted and analyzed, including implant survival rates, MBL, vertical bone gain, and surgical complications. Results: Eleven studies involving 352 patients and more than 612 implants were included, with a mean follow-up of 2.27 ± 2.69 years (range: 4 months to 8 years). The implant survival rates ranged from 84.5% to 100%. Mean vertical bone gain varied from 2.69 to 4.4 mm. Complications were fewer with the inlay technique compared to onlay and other grafting methods, with significantly reduced graft-related failures and soft tissue issues. Conclusions: The inlay technique shows good vertical bone augmentation with high implant survival rates and fewer complications compared to other reconstructive techniques. Longer follow-up studies are needed to support its value in managing vertically deficient ridges. Moreover, further studies with extended follow-up are required to evaluate long-term marginal bone loss. Full article

This paper presents a new algorithm for assessing the reliability of three-dimensional (3D) slope stability considering the spatial variability of soil based on the Particle Swarm Optimization (PSO) algorithm. First, a 3D random field is generated using the Karhunen–Loève (K-L) expansion method. Then, the simplified Bishop method of limit equilibrium is coupled with the PSO algorithm to calculate safety factors of the slope. Finally, the failure probability of the slope is determined using the Monte Carlo Simulation method. After validating the rationality of the proposed method through a typical case study, this paper offers an in-depth examination of how soil spatial variability affects the stability of 3D slopes. It is observed that, given identical soil correlation lengths, slope geometric parameters, and failure surface widths, the failure probability is positively correlated with soil spatial variability parameters, while the mean safety factor demonstrates an inverse relationship with these variability parameters. Additionally, the failure probability tends to increase as the soil correlation lengths increase, and it also escalates with the expansion of the failure surface width. In contrast, the mean safety factor exhibits an upward trend with the augmentation of the horizontal correlation length, while it diminishes progressively as the vertical correlation length grows, and it also shows a decline with the widening of the failure surface width. The proposed algorithm significantly improves computational efficiency while ensuring accuracy, making it suitable for the reliability analysis of three-dimensional slopes. Full article

Pavement surface distresses are analyzed by transportation agencies to determine section performance across their pavement networks. To efficiently collect and evaluate thousands of lane-miles, automated processes utilizing image-capturing techniques and detection algorithms are applied to perform these tasks. However, the precision of this novel technology often leads to inaccuracies that must be verified by pavement engineers. Developments in artificial intelligence and machine learning (AI/ML) can aid in the progress of more robust and precise detection algorithms. Deep learning models are efficient for visual distress identification of pavement. With the use of 2D/3D pavement images, surface distress analysis can help train models to efficiently detect and classify surface distresses that may be caused by traffic loading, weather, aging, and other environmental factors. The formation of these distresses is developing at a higher rate in coastal regions, where extreme weather phenomena are more frequent and intensive. This study aims to develop a YOLOv5 model with 2D/3D images collected in the states of Louisiana, Mississippi, and Texas in the U.S. to establish a library of data on pavement sections near the Gulf of Mexico. Images with a resolution of 4096 × 2048 are annotated by utilizing bounding boxes based on a class list of nine distress and non-distress objects. Along with emphasis on efforts to detect cracks in the presence of background noise on asphalt pavements, six scenarios for augmentation were made to evaluate the model’s performance based on flip probability in the horizontal and vertical directions. The YOLOv5 models are able to detect defined distresses consistently, with the highest mAP50 scores ranging from 0.437 to 0.462 throughout the training scenarios. Full article

To investigate the non-stationary characteristics of the wind field at the U-shaped canyon bridge site and its impact on fluctuating wind characteristics, a wind observation tower was installed near a cable-stayed bridge. The Augmented Dickey–Fuller (ADF) test was employed to assess the stationarity of wind speed series, while the discrete wavelet transform (DWT) was applied to reconstruct the time-varying mean wind and analyze its effect on fluctuating wind characteristics. Results indicate that wind speeds in this region exhibit bimodal distribution characteristics, with the Weibull-Gamma mixed distribution model providing the best fit. The proportion of non-stationary samples increases with height. Autocorrelation function (ACF), partial autocorrelation function (PACF) tests, and power spectral density (PSD) analysis determined the optimal wavelet decomposition level for wind speed in this region. Analysis of non-stationary samples using db10 wavelet reconstruction reveals that the stationary wind speed model overestimates turbulence intensity but underestimates the turbulence integral scale. The downwind spectrum deviates from the Kaimal spectrum in both low- and high-frequency bands, whereas the vertical spectrum aligns well with the Panofsky spectrum. The findings demonstrate that the wavelet reconstruction method more accurately captures fluctuating wind characteristics under the complex terrain conditions of this canyon area. Full article

In low-permeability sandstone reservoirs (LPSR), impermeable interlayers significantly challenge carbon capture, utilization, and storage (CCUS) and enhance oil recovery (CO₂-EOR) processes by creating complex, discontinuous flow units. This study aims to address these challenges through a comprehensive multi-faceted approach integrating geological and microscopic analyses, including core analysis, reservoir petrography, field emission-scanning electron microscopy (FE-SEM), energy dispersive spectroscopy (EDS), and well-logging response analysis, and utilizing three-dimensional (3D) geological modeling. The current comprehensive investigation systematically characterizes interlayer types, petrophysical properties, thickness, connectivity, and their spatial distribution in the reservoir unit. Numerical simulations were conducted to assess the sealing efficiency and the impact of various interlayer materials on CO₂ flooding over a 10-year period. Results indicate the presence of petrophysical and argillaceous interlayers, with optimal sealing occurring in petrophysical barriers ≥ 4 m and argillaceous barriers ≥ 1.5 m thick. CO₂ leakage occurs through preferential pathways that emerge in a side-to-middle and bottom-to-top direction in interbeds, with multidirectional pathways showing greater leakage at the bottom compared to the upper side within barriers. Increased interlayer thickness constraints CO₂ breakthrough but reduces vertical flooding area and production ratio compared to homogeneous reservoirs. Augmented interbed thickness and area mitigate CO₂ breakthrough time while constraining gravity override and dispersion effects, enhancing horizontal oil displacement. These novel findings provide crucial insights for optimizing CCUS-EOR strategies in LPSR, offering a robust theoretical foundation for future applications and serving as a key reference for CO₂ utilization in challenging geological settings of LPSR worldwide. Full article

Background: Post-extraction ridge resorption is an inevitable phenomenon that cannot be eliminated but is significantly reduced using additional surgical techniques known as socket preservation. They aim to create favorable conditions for implant placement and prosthetic restoration. This study aims to assess the effect of socket sealing (SS) with free gingival grafts on the vertical resorption of socket walls at the premolar and molar regions over 3 months. Methods: This randomized two-arm controlled trial with parallel groups (1:1 allocation) was conducted at the Department of Oral Surgery, Medical University-Varna, Bulgaria, from 27 June 2022 to 20 April 2023. Forty patients aged 30–65 were equally and randomly allocated to the SS or the control groups. Atraumatic tooth extraction was performed. In the control group, the socket was left on secondary wound healing. In the SS group, the socket orifice was “sealed” with an FGG harvested from the hard palate or maxillary tuberosity. Results: Data analysis demonstrated that SS with an FGG is a successful method for reducing the post-extraction resorption of the socket walls. In addition, this study confirms that the thickness of the buccal wall is a significant factor in its vertical resorption. Conclusions: Socket sealing with an FGG is a valuable method that eliminates the need for flap reflection and compensates for the soft tissue deficit when immediate implant placement or bone augmentation is required. Further research is necessary to determine the role of different factors influencing bone resorption and compare the effect of different socket preservation methods. Full article

Background: Alveolar ridge resorption following tooth loss poses a significant challenge for successful dental implant placement. In cases of severe atrophy, bone augmentation is required to restore sufficient bone volume. This technical note outlines a detailed, stepwise surgical protocol for horizontal and vertical alveolar ridge augmentation using customized titanium mesh. Materials and Methods: The procedure includes precise mesh fitting, autologous bone grafting, and the application of bioactive agents to promote bone regeneration. Emphasis is placed on the technique’s feasibility, predictability, and the critical steps necessary for preventing complications. Results: The use of customized mesh ensures stability and improved bone regeneration outcomes, enabling clinicians to achieve successful implant placement even in severely atrophic ridges. Conclusions: The described protocol has demonstrated predictable results in both clinical and radiographic evaluations, offering an effective solution for complex bone augmentation cases. Full article

Parkinson’s disease (PD) is a neurological disorder that severely affects motor function, especially gait, requiring accurate diagnosis and assessment instruments. This study presents Dense Multiscale Sample Entropy (DM-SamEn) as an innovative method for diminishing feature dimensions while maintaining the uniqueness of signal features. DM-SamEn employs a weighting mechanism that considers the dynamic properties of the signal, thereby reducing redundancy and improving the distinctiveness of features extracted from vertical ground reaction force (VGRF) signals in patients with Parkinson’s disease. Subsequent to the extraction process, correlation-based feature selection (CFS) and sequential backward selection (SBS) refine feature sets, improving algorithmic accuracy. To validate the feature extraction and selection stage, three classifiers—Adaptive Weighted K-Nearest Neighbors (AW-KNN), Radial Basis Function Support Vector Machine (RBF-SVM), and Multilayer Perceptron (MLP)—were employed to evaluate classification efficacy and ascertain optimal performance across selection strategies, including CFS, SBS, and the hybrid SBS-CFS approach. K-fold cross-validation was employed to provide improved evaluation of model performance by assessing the model on various data subsets, thereby mitigating the risk of overfitting and augmenting the robustness of the results. As a result, the model demonstrated a significant ability to differentiate between PD patients and healthy controls, with classification accuracy reported as ACC [CI 95%: 97.82–98.5%] for disease identification and ACC [CI 95%: 96.3–97.3%] for severity assessment. Optimal performance was primarily achieved through feature sets chosen using SBS and the integrated SBS-CFS methods. The findings highlight the model’s potential as an effective instrument for diagnosing PD and assessing its severity, contributing to advancements in clinical management of the condition. Full article

Large language model (LLM) processing, with natural language as its core, carries out information retrieval through intelligent Q&A. It has a wide range of application scenarios and is commonly considered a kind of generative AI. However, when LLMs handle generation tasks, the results generated by fundamental LLMs with an insufficient comprehensive performance, specifically in the vertical domain, are often inaccurate due to a poor generalization ability, resulting in the so-called “hallucination” phenomenon. To solve these problems, in this study, an enhanced retrieval scheme for LLM processing was developed, named the BM-RAGAM (BM25 retrieval-augmented generation attention mechanism), by constructing a vectorized knowledge base, utilizing a hybrid joint retrieval strategy of keyword matching through searching and a semantic-enhanced association with an attention mechanism and taking ocean-front- and eddy-related knowledge in oceanography as an example. This scheme realized accurate word-based matching with the BM25 algorithm and text generation through a semantic-enhanced association using RAG, and it was used to construct a vector database of the text knowledge on ocean fronts and eddies. The output was compared and analyzed with the fundamental LLM of Qwen2-72B using the proposed scheme, and an ablation experiment was conducted. The results show that the proposed scheme greatly reduced hallucination generation in the process of text generation, making its outputs more interpretable. Full article

Fluid–structure interaction introduces critical failure modes due to varying stiffness and changing contact states in rotor-stator systems. This is further aggravated by stress fluctuations due to shaft impact with a fixed stator when the shaft rotates. In this paper, the investigation of imbalance and rotor-stator contact on a rotating shaft was carried out in viscous fluid. The shaft was modelled as a vertical elastic rotor system based on a vertically oriented elastic rotor operating in an incompressible medium. Implicit representation of the rotating system including the rotor-stator contact and the hydrodynamic resistance was formulated for the coupled system using the energy principle and the Navier–Stokes equations. Additionally, the monolithic approach included an implicit strategy of the rotor-stator fluid interaction interface conditions in the solution methodology. Advanced time-frequency methods, such as Hilbert transform, continuous wavelet transform, and estimated instantaneous frequency maps, were applied to extract the vibration features of the dynamic response of the faulted rotor. Time-varying stiffness due to friction is thought to be the main reason for the frequency fluctuation, as indicated by historical records of the vibration displacement, whirling orbit patterns of the centre shaft, and the amplitude–frequency curve. It has also been demonstrated that the augmented mass associated with the rotor and stator decreases the natural frequencies, while the amplitude signal remains relatively constant. This behaviour indicates a quasi-steady-state oscillatory condition, which minimises the energy fluctuations caused by viscous effects. Full article

Integrating wind turbines within urban environments, either as building-mounted units or standalone installations, represents a valuable step toward sustainable city development. Vertical axis wind turbines (VAWTs) are commonly favored in these settings due to their ability to handle turbulent winds; however, they generally exhibit lower energy conversion efficiency compared to horizontal axis wind turbines (HAWTs). Selecting optimal urban or suburban locations with favorable wind conditions opens the possibility of deploying HAWTs, leveraging their higher efficiency even at comparable wind speeds. This paper presents a methodology for designing highly efficient HAWTs for urban use, supported by computational fluid dynamics (CFD) analyses to produce power curves and evaluate the energy conversion efficiency of both bare and augmented turbine designs. Differing from prior studies, this work also incorporates a detailed economic analysis, examining how reductions in the Levelized Cost of Energy (LCOE) enhance the cost-effectiveness of small-scale distributed wind systems. The findings offer insights into the technical and economic viability of small-scale HAWT configurations for distributed energy generation across diverse urban locations with varying wind profiles. Full article

Due to the advantages of high bearing capacity, small settlement of pile body, and high material utilization rate, rotary drilling thread special-shaped pile (RDTSSP) has been applied in pile foundation engineering at home and abroad. Through the field static load test, the bearing characteristics of the single pile of the rotary drilling screw pile are tested and analyzed. Based on the field-measured data, the stress characteristics of the rotary drilling screw pile are analyzed by FLAC3D6.0 finite difference software, and the pile characteristics affecting the vertical bearing capacity of the rotary drilling screw-shaped pile are studied. The impact of various pile factors, including length, diameter, and the ratio of pile body to screw modulus, as well as the presence of an enlarged bottom, the elastic modulus of the pile, and the ratio of the pile body to soil elastic modulus, on the load-bearing capacity of rotary drilling thread special-shaped pile (RDTSSP) is examined. The results show that with the increase in pile length, the bearing capacity of the screw-shaped pile increases gradually, but when it increases to a certain value, the increased bearing capacity per unit volume decreases gradually. The increase in pile diameter will lead to a decrease in bearing capacity per unit volume, so the smaller pile diameter should be selected in the design to make full use of the material properties. The bottom expansion has little effect on the bearing capacity, but with the increase in the inner diameter of the bottom expansion, the bearing capacity increases gradually, while the bearing capacity per unit volume decreases and the material utilization rate decreases. Enhancing the modulus of a pile modestly boosts its load-bearing capacity, whereas augmenting the elastic modulus ratio between the pile and the surrounding soil substantially amplifies this capacity. The innovation of this study is to propose a new type of rotary drilling thread-shaped pile, which has significant economic and social benefits in engineering applications. Full article

A global navigation satellite system (GNSS) for remotely piloted aircraft systems (RPASs) positioning is essential, thanks to the worldwide availability and continuity of this technology in the provision of positioning services. This makes the GNSS technology a critical element as malfunctions impacting on the determination of the position, velocity and timing (PVT) solution could determine safety issues. Such an aspect is particularly challenging in urban air mobility (UAM) scenarios, where low satellite visibility, multipath, radio frequency interference and cyber threats can dangerously affect the PVT solution. So, to meet integrity requirements, GNSS receiver measurements are augmented/fused with other aircraft sensors that can supply position and/or velocity information on the aircraft without relying on any other satellite and/or ground infrastructures. In this framework, in this paper, the algorithms of a hybrid navigation unit (HNU) for UAM applications are detailed, implementing a tightly coupled sensor fusion between a dual-frequency multi-constellation GNSS receiver, an inertial measurements unit and the barometric altitude from an air data computer. The implemented navigation algorithm is integrated with autonomous fault detection and exclusion of GPS/Galileo/BeiDou satellites and the estimation of navigation solution integrity/accuracy (i.e., protection level and figures of merit). In-flight tests were performed to validate the HNU functionalities demonstrating its effectiveness in UAM scenarios even in the presence of cyber threats. In detail, the navigation solution, compared with a real-time kinematic GPS receiver used as the reference centimetre-level position sensor, demonstrated good accuracy, with position errors below 15 m horizontally and 10 m vertically under nominal conditions (i.e., urban scenarios characterized by satellite low visibility and multipath). It continued to provide a valid navigation solution even in the presence of off-nominal events, such as spoofing attacks. The cyber threats were correctly detected and excluded by the system through the indication of the valid/not valid satellite measurements. However, the results indicate a need for fine-tuning the EKF to improve the estimation of figures of merit and protection levels associated to the navigation solution during the cyber-attacks. In contrast, solution accuracy and integrity indicators are well estimated in nominal conditions. Full article