Search Results (432)

Occupancy is one of the primary contributors to the energy performance gap, defined as the difference between actual and predicted energy usage, in buildings. This paper limits its scope to residential buildings, where occupant-centric consumption often goes unaccounted for in standard energy metrics. This paper starts from the hypothesis that a simple occupant energy efficiency label is needed to capture the essence of occupant behaviour. Such a label would help researchers and practitioners study a wide range of behavioural patterns and may better frame occupant interventions, potentially contributing more than expected to the field. Focusing on the residential sector, this research recognises that the complexity of occupant behaviour and its links to different scientific calculations requires that researchers deal with several intricate factors in their building performance assessments. Moreover, complexity arising from changing attitudes and behaviours—based on building typology, social environment, seasonal effects, and personal comfort levels—further complicates the challenge. Starting with these problems, this paper proposes a framework for an occupant energy labelling (OEL) model to overcome these issues. The contribution of the paper is twofold. Firstly, the literature is reviewed in depth to reveal current research related to occupant behaviour for labelling of humans based on their energy consumption. Secondly, a case study with energy simulations is implemented in the UK, using the CREST tool, to demonstrate the feasibility and potential of OEL. The results show that labelling occupants may help societies reduce building energy consumption by combining insights from energy statistics, surveys, and bills gathered with less effort, and can assist decision-makers in determining the best match between buildings and occupants. While the focus of this study is on residential buildings, future research is recommended to explore the applicability of OEL in office environments, where occupant behaviour and energy dynamics may differ significantly. Full article

Seismic isolation and damping technologies, though extensively used in buildings, are less common in large hydraulic structures, underscoring the importance of researching seismic mitigation methods for these constructions. This research first establishes that saturated sandy soil can act as a damping material through experimental and theoretical analysis. Subsequently, a novel hollow concrete gravity dam containing saturated sandy soil is proposed, utilizing the EOS (equation of state) subroutine for viscous fluids to model the liquefied sand. The findings indicate that the new-type dam exhibits a reduction in displacement of approximately 20% along the flow direction under an 8-degree seismic event compared to conventional gravity dams. This decrease correlates inversely with the characteristic wave speed of the saturated sandy soil, while the energy dissipation capacity of the saturated sandy soil is directly proportional to the soil layer’s thickness. Finally, a small-scale shaking table test revealed that saturated sandy soil effectively reduces displacement and acceleration at the dam crest. These findings were corroborated by numerical simulations, which further substantiated the reliability of both the experimental and simulated data. Utilizing saturated sandy soil for energy dissipation and seismic damping in dams offers cost benefits, high durability, and significant effectiveness, representing a promising direction for the advancement of seismic mitigation in concrete gravity dams. Full article

A new method to discover open-loop, unstable, longitudinal aerodynamic parameters, using a ‘two-stage optimization approach’ for designing optimal inputs, and with an application on the fighter aircraft platform, has been presented. System identification of supersonic aircraft requires formulating optimal inputs due to the extremely limited maneuver time, high angles of attack, restricted flight conditions, and the demand for an enhanced computational effect. A pre-requisite of the parametric model identification is to have a priori aerodynamic parameter estimates, which were acquired using linear regression and Least Squares (LS) estimation, based upon simulated time histories of outputs from heuristic inputs, using an F-16 Flight Dynamic Model (FDM). In the ‘first stage’, discrete-time pseudo-random binary signal (PRBS) inputs were optimized using a minimization algorithm, in accordance with aircraft spectral features and aerodynamic constraints. In the ‘second stage’, an innovative concept of integrating the Fisher Informative Matrix with cost function based upon D-optimality criteria and Crest Factor has been utilized to further optimize the PRBS parameters, such as its frequency, amplitude, order, and periodicity. This unique optimum design also solves the problem of non-convexity, model over-parameterization, and misspecification; these are usually caused by the use of traditional heuristic (doublets and multistep) optimal inputs. After completing the optimal input framework, parameter estimation was performed using Maximum Likelihood Estimation. A performance comparison of four different PRBS inputs was made as part of our investigations. The model performance was validated by using statistical metrics, namely the following: residual analysis, standard errors, t statistics, fit error, and coefficient of determination (R2). Results have shown promising model predictions, with an accuracy of more than 95%, by using a Single Sequence Band-limited PRBS optimum input. This research concludes that, for the identification of the decoupled longitudinal Linear Time Invariant (LTI) aerodynamic model of supersonic aircraft, optimum PRBS shows better results than the traditional frequency sweeps, such as multi-sine, doublets, square waves, and impulse inputs. This work also provides the ability to corroborate control and stability derivatives obtained from Computational Fluid Dynamics (CFD) and wind tunnel testing. This further refines control law design, dynamic analysis, flying qualities assessments, accident investigations, and the subsequent design of an effective ground-based training simulator. Full article

Thermal barrier coatings (TBCs) and air film-cooling technology have been extensively utilized in nickel-based, single-crystal turbine blades to enhance their heat resistance. However, structural complexity and material property mismatches between layers can affect residual stresses and potentially lead to coating failure. In this study, a three-dimensional finite element model with atmospheric plasma-spraying thermal barrier coatings (APS-TBCs) deposited on air-cooled, nickel-based, single-crystal blades was established to investigate residual stress character under centrifugal load, considering the effect of temperature, crystal orientation deviation angle, oxide layer thickness, and the number of cycles. The results show that when the centrifugal load is increased from 300 MPa to 700 MPa, the absolute value of the residual stress at the crest of the interface between Top Coat (TC) and Thermally Grown Oxide (TGO) increases by only 8.5%, whereas in the region of compressive to tensile stress conversion, residual stress decreases by 100.9%. As the crystal orientation deviation angle increases, the absolute value of the residual compressive stress increases and the absolute value of the residual tensile stress decreases, but the performance is more special in the valley region, where the absolute value of the residual stress increases with the increase in the deviation angle. Special attention is required, as the increase in temperature leads to a rise in the absolute value of residual stress. For example, at the trough of the TC–TGO interface, when the temperature increases from 910 °C to 1100 °C, the residual stress increases by 9.8%. The effect of the number of cycles on residual stress is relatively weak. For instance, at the wave crest of the TC–TGO interface, the residual stress differs by only 0.6 MPa between one cycle and three cycles. The effect of oxide layer thickness on residual stress in the TBCs after a single cycle is nonlinear. When the oxide layer thickness is 0, 4, and 7 μm, the residual stress undergoes a transition between tensile and compressive directions at different locations. The exploration of these results has yielded some valuable laws that can provide a reference for the study of the damage mechanism of TBCs, as well as a guide for the optimization of nickel-based turbine blades in the manufacturing and use processes. Full article

Light-polymerizing reline materials offer improved chairside workability compared to conventional auto-polymerizing reline materials, addressing the partial denture (RPD) incompatibility caused by residual ridge resorption owing to long-term use. This study evaluates the fitting accuracy of relined materials by combining conventional fitting tests with three-dimensional (3D) measurements for detailed analysis. Light-polymerizing reline material (HikariLiner^®, Tokuyama, Tokyo, Japan, LP) and auto-polymerizing material (Rebase III^®, Tokuyama, AP) were used. The gaps formed between the relined denture base and the simplified edentulous model were evaluated. The displacement and deviation of the experimentally relined RPDs on the partially edentulous models were analyzed using 3D data superimposition. In the edentulous model, the gaps at all measurement points were significantly smaller for the AP than in the LP. Moreover, the alveolar ridge crest gap was significantly larger than that at other sites. In the partial denture model, the RMS values at the residual ridge crest were significantly lower for the AP. The evaluation method using 3D scanning and comparison was suitable for a detailed fit analysis. Further improvements in the scanning accuracy may enhance future assessments. Therefore, the evaluation method using 3D scanning and comparison was suitable for effectively analyzing the fit of relines, necessitating further accuracy improvements. Full article

Colorectal cancer is a leading type of cancer worldwide and a major contributor to cancer fatalities, and liver metastasis is the most likely distant metastasis in colorectal cancer patients. Classifying and predicting whether liver metastasis occurs in colorectal cancer patients can help doctors timely determine the progress of the disease and form a more reasonable treatment plan, which results in a better prognosis for patients. In this paper, using the Surveillance, Epidemiology, and End Results database, selecting both symmetric and asymmetric features, we extracted the disease-related data of 40,870 patients who were pathologically diagnosed with colorectal cancer from 2010 to 2015 and classified and modeled whether the patients developed liver metastasis to show the symmetry of this study. A total of six deep learning models were utilized, and hyperparameter optimization was performed on the models using the Crested Porcupine Optimizer. The best-performing model was selected and model interpretation was performed to explore the features that affect whether patients develop liver metastasis. Among the six deep learning models selected, the FT-Transformer model, which was hyperparameter optimized by the Crested Porcupine Optimizer, performed the best, with an accuracy of 0.945, with a 95% confidence interval (CI) of [0.942, 0.952], and an AUC of 0.949, with a 95% CI of [0.942, 0.957]. This study can help doctors make medical decisions, detect patients with liver metastases of colorectal cancer earlier, monitor the indicators that have a significant impact on the occurrence of liver metastasis in patients, and use timely surgical treatment, radiotherapy, chemotherapy, and other corresponding therapeutic interventions to improve the survival rate of patients. Full article

High-impedance fault detection poses significant challenges for distribution network maintenance and operation. We propose a dual-path neural network for high-impedance fault detection. To enhance feature extraction, we use a Gramian Angular Field algorithm to transform 1D zero-sequence voltage signals into 2D images. Our dual-branch network simultaneously processes both representations: the CNN extracts spatial features from the transformed images, while the GRU captures temporal features from the raw signals. To optimize model performance, we integrate the Crested Porcupine Optimizer (CPO) algorithm for the adaptive optimization of key network hyperparameters. The experimental results demonstrate that our method achieves a 99.70% recognition accuracy on a dataset comprising high-impedance faults, capacitor switching, and load connections. Furthermore, it maintains robust performance under various test conditions, including different noise levels and network topology changes. Full article

Polymeric materials made from renewable sources that can biodegrade in the environment are attracting considerable attention as substitutes for petroleum-based polymers in many fields, including additive manufacturing and, in particular, Fused Deposition Modelling (FDM). Among the others, poly(hydroxyalkanoates) (PHAs) hold significant potential as candidates for FDM since they meet the sustainability and biodegradability standards mentioned above. However, the most utilised PHA, consisting of the poly(hydroxybutyrate) (PHB) homopolymer, has a high degree of crystallinity and low thermal stability near the melting point. As a result, its application in FDM has not yet attained mainstream adoption. Introducing a monomer with higher excluded volume, such as hydroxyvalerate, in the PHB primary structure, as in poly(hydroxybutyrate-co-valerate) (PHBV) copolymers, reduces the degree of crystallinity and the melting temperature, hence improving the PHA printability. Blending amorphous poly(lactic acid) (PLA) with PHBV enhances further PHA printability via FDM. In this work, we investigated the printability of two blends characterised by different PLA and PHBV weight ratios (25:75 and 50:50), revealing the close connection between blend microstructures, melt rheology and 3D printability. For instance, the relaxation time associated with die swelling upon extrusion determines the diameter of the extruded filament, while the viscoelastic properties the range of extrusion speed available. Through thoroughly screening printing parameters such as deposition speed, nozzle diameter, flow percentage and deposition platform temperature, we determined the optimal printing conditions for the two PLA/PHBV blends. It turned out that the blend with a 50:50 weight ratio could be printed faster and with higher accuracy. Such a conclusion was validated by replicating with remarkable fidelity high-complexity objects, such as a patient’s cancer-affected iliac crest model. Full article

Maxillofacial reconstruction surgery involves restoring bones or skeletal structures in areas such as the mouth, jaw, and face using bones like the iliac crest and fibula. This surgery requires a high level of difficulty and precision, necessitating extensive practice and accurate 3D model simulations. However, due to limitations in training environments, opportunities for sufficient practice are restricted, and the precision of simulations may be compromised by the limitations of existing tools. To address these challenges, this paper proposes a maxillofacial reconstruction surgery simulator utilizing virtual reality technology. The proposed method allows users to explore a virtual space through a head-mounted display, where they can visualize, navigate, and manipulate bone models (move and rotate) using the joystick and buttons of a controller, as well as perform resection operations. Additionally, to verify the effectiveness of the simulator, performance evaluation is conducted through frame per second and resource usage analysis, usability testing is performed via questionnaires with dental students, and accuracy validation is carried out for the reconstruction models. The results of each evaluation method are analyzed to confirm the utility and potential of the proposed simulator. Full article

This work aims to establish a numerical model to investigate the wave interaction induced by the motion of a submerged cylindrical wave energy converter. The results show that when the submerged cylinder is in forced sinusoidal heave motion, distinct hollows and humps are produced on the free surface. As the heave amplitude increased from 1 m to 1.8 m, the depth of the hollow increased by 454%, and the height of the hump increased by 370%. Along with strong nonlinear phenomena, the generation of up to the fourth harmonic on the free surface above the submerged body is found, and the highest amplitude of the second harmonic waves reached 68% of the primary frequency. This indicates that the energy distribution of the wave is decomposed and rebalanced, and some energy in the primary frequency accumulates towards higher harmonics. When the submerged cylinder is in forced sinusoidal surge motion, the free surface elevation decreases in a stepwise manner as the wave transitions from crest to trough. As the cylinder pitches, the elevation of the wave trough decreases by 5% compared to when the submerged cylinder remains static. Full article

The dynamic behavior of a concrete arch dam, the Baixo Sabor dam, built in Portugal, is investigated. A numerical model was developed to represent the dam, foundation, and reservoir system. This model was calibrated and validated through comparison with experimental data from forced vibration tests and ambient vibration monitoring. Recently, a low-intensity earthquake occurred in the region and the dam response was recorded by a seismic monitoring system. The previously calibrated numerical model was used to analyze the dynamic response of the dam under this seismic input, employing a dynamic boundary formulation that takes into account the wave propagation in the rock mass, preventing wave reflections. The results obtained show generally good agreement with the experimental measurements at the dam crest and abutments. The analysis methodology and the main issues involved in the modelling of arch dams under seismic action are discussed. Full article

Background and Objectives: Current craniofacial reconstruction surgical methods have limitations because they involve facial deformation. The craniofacial region includes many areas where the mucosa, exposed to air, is closely adjacent to bone, with the maxilla being a prominent example of this structure. Therefore, this study explored whether human neural-crest-derived stem cells (hNTSCs) aid bone and airway mucosal regeneration during craniofacial reconstruction using a rabbit model. Materials and Methods: hNTSCs were induced to differentiate into either mucosal epithelial or osteogenic cells in vitro. hNTSCs were seeded into polycaprolactone scaffold (three-dimensionally printed) that were implanted into rabbits with maxillary defects. Four weeks later, tissue regeneration was analyzed via histological evaluation and immunofluorescence staining. Results: In vitro, hNTSCs differentiated into both mucosal epithelial and osteogenic cells. hNTSC differentiation into respiratory epithelial cells was confirmed by Alcian Blue staining, cilia in SEM, and increased expression levels of FOXJ1 and E-cadherin through quantitative RT-PCR. hNTSC differentiation into bone was confirmed by Alizarin Red staining, increased mRNA expression levels of BMP2 (6.1-fold) and RUNX2 (2.3-fold) in the hNTSC group compared to the control. Four weeks post-transplantation, the rabbit maxilla was harvested, and H&E, SEM, and immunohistofluorescence staining were performed. H&E staining and SEM showed that new tissue and cilia around the maxillary defect were more prominent in the hNTSC group. Also, the hNTSCs group showed positive immunohistofluorescence staining for acetylated α-tubulin and cytokerin-5 compared to the control group. Conclusions: hNTSCs combined with PCL scaffold enhanced the regeneration of mucosal tissue and bone in vitro and promoted mucosal tissue regeneration in the in vivo rabbit model. Full article

Rainfall infiltration is the primary triggering factor for the instability of unsaturated slopes. At present, rainfall-induced landslides are mainly considered to be influenced by the overall infiltration conditions, while few investigations have been conducted on the influence of infiltration boundaries on slope instability. This study proposes a rainfall infiltration method using a discrete element model (DEM), which is based on saturated–unsaturated seepage theory. The influence of three infiltration boundaries on the instability of homogeneous unsaturated soil slopes was studied. The results showed that the infiltration rate of a rainfall-covered slope crest was faster than that of rainfall-covered slope surfaces. A transient saturated zone was formed on the slope surface after a certain duration of rainfall. Rain infiltration boundary conditions significantly impact the saturation distribution, seepage field, failure mode, and failure period. The safety and stability factors for the rainfall-covered slope crest and full area decreased monotonically with the increase in rainfall duration, while there was a brief increase at the initial stage of rainfall before a quick decline for rainfall-covered slope surfaces. This research provides a preliminary exploration of the impact of rainfall boundary conditions on the instability of slopes, offering a reference basis for DEM simulations that consider slope stability under the influence of rainfall infiltration. Full article

An accurate prognosis of the marine lithium-ion battery capacity is significant in guiding electric ships’ optimal operation and maintenance. Under real-world operating conditions, lithium-ion batteries are exposed to various external factors, making accurate capacity prognostication a complex challenge. The paper develops a marine lithium-ion battery capacity prognostic method based on ICPO-Bi-LSTM under dynamic operating conditions to address this. First, the battery is simulated according to the actual operating conditions of an all-electric ferry, and in each charge/discharge cycle, the sum, mean, and standard deviation of each parameter (current, voltage, energy, and power) during battery charging, as well as the voltage difference before and after the simulated operating conditions, are calculated to extract a series of features that capture the complex nonlinear degradation tendency of the battery, and then a correlation analysis is performed on the extracted features to select the optimal feature set. Next, to address the challenge of determining the neural network’s hyperparameters, an improved crested porcupine optimization algorithm is proposed to identify the optimal hyperparameters for the model. Finally, to prevent the interference of test data during model training, which could lead to evaluation errors, the training dataset is used for parameter fitting, the validation dataset for hyperparameter adjustment, and the test dataset for the model performance evaluation. The experimental results demonstrate that the proposed method achieves high accuracy and robustness in capacity prognostics of lithium-ion batteries across various operating conditions and types. Full article

Increased air pollution is driven by anthropogenic pollution emissions and climate change, which pose great challenges to environmental governance. Strengthening the monitoring of regional air quality levels and analyzing the causes of regional pollution is conducive to the management and sustainable development of the regional atmosphere. Functional data obtained on a wavelet basis were used in the fitting of air quality data of Fujian Province, and wavelet decomposition was performed to obtain low-frequency and high-frequency information. While the Fourier basis cannot adaptively adjust the time–frequency window, resulting in the loss of location information of special frequencies, the wavelet basis solves this problem. Functional analysis of variance was utilized for analyzing spatial differences in air pollution characteristics. Furthermore, the study established a multivariate functional linear regression model to explore the impact of meteorological factors and ozone precursor factors. The results indicated that the overall air quality was gradually improving in Fujian Province, but the concentration of ozone was progressively increasing. Air pollution in coastal areas was higher than that in inland areas. The p-values of the functional analysis of variance for energy values and crest values were less than 0.05. Moreover, the energy entropy and kurtosis values were greater than 0.05. There were significant differences of AQI in the fluctuation amplitude and variation characteristics of different cities. The total squared multiple correlation of regression model was above 50% on average. Ozone is currently the most serious pollution factor, mainly affected by wind speed, temperature, NO₂, and CO. In summer, it was principally influenced by VOCs. The findings of this study could act as a reference in exploring the time–frequency characteristics of air quality data and support of air pollution control. Full article