Applied Sciences

Compared with the traditional operation mode of emergency vehicles, the mobile emergency vehicle is regarded as a new type of emergency facility carrier with the features of variable locations, flexible mobility, and intelligent decision-making. It can provide an effective solution to reasonably respond to the uncertain risks of sudden disasters. Focusing on meeting the maximum demand for materials and services in disaster areas, this paper proposes a scheduling model of mobile emergency vehicles with dual uncertainty of path and demand. The model, solved by an integer-coding hybrid genetic algorithm, aims to obtain minimum mobile emergency scheduling cost and time by transforming the multi-objective problem into a single-objective problem. The “5.12” Wenchuan earthquake is used as an example to validate the model and solving method. The results show that the model can reduce the impact of uncertain risks and improve the scientific logic of emergency strategies and deployments based on the actual crisis scenario. It benefits from introducing mobile emergency vehicles and optimizing their scheduling process. Full article

The specific work presents an optical and thermal investigation of a hybrid thermo-photovoltaic solar collector with an asymmetrical compound parabolic mirror. Such collectors offer an innovative and sustainable approach to address both the thermal and electrical demands of residents on islands using renewable sources of energy and thus reducing the dependency on fossil fuels. The main goal of this investigation involves an analysis of the prementioned type of solar collector, incorporating an innovative and cost-effective numerical modelling technique aiming to enhance comprehension of its energy and exergy performance. The optical performance of the collector was calculated first with ray tracing for the month of June, and the ideal slope was determined for the same month. After the optical analysis, the energy and exergy performance were both estimated by implementing a novel numerical method in both COMSOL and SolidWorks. Based on the optical analysis, it was determined that the most favorable inclination angle for achieving optimum optical efficiency on the mean day of June is 10°. The thermal analysis, focusing on thermal efficiency, showed a maximum deviation of 5.3% between the two solutions, which indicates the reliability of the method. The collector achieved a maximum thermal efficiency of 58.55% and a maximum exergy efficiency of 16.94%. Full article

On 6 February 2023, two major earthquakes occurred in the Turkish province of Kahramanmaraş. The first earthquake with a magnitude of Mw 7.7 occurred in the center of Kahramanmaras, while the second earthquake with a magnitude of Mw 7.5 occurred in the region of Elbistan. These earthquakes caused heavy damage and loss of life in the affected regions. In particular, the Elbistan region experienced both earthquakes with great severity. Following the earthquakes, damage analyses were carried out on the earthquake-affected structures in this region. In the region, 1045 buildings were destroyed, 2640 buildings were heavily damaged, and 463 buildings were moderately damaged by the earthquakes. In this study, the relation between the material quality and the damage status of the affected buildings in the Elbistan region was investigated. A total of 20 buildings with heavy and moderate damage, built both before and after the year 2000, were selected for analysis. Samples were taken from these buildings, and the compressive strength values of the samples were obtained. Further, in situ experiments featuring the Schmidt and UPV tests were performed in the buildings. The results found that the buildings lacked adequate concrete strength. In particular, the post-2000 structures recorded concrete strength values below the established standard. This study proves the necessity of following established regulations in the design and construction of buildings in earthquake-prone zones, especially with respect to the construction materials used. Full article

Object detection (OD) coupled with active learning (AL) has emerged as a powerful synergy in the field of computer vision, harnessing the capabilities of machine learning (ML) to automatically identify and perform image-based objects localisation while actively engaging human expertise to iteratively enhance model performance and foster machine-based knowledge expansion. Their prior success, demonstrated in a wide range of fields (e.g., industry and medicine), motivated this work, in which a comprehensive and systematic review of OD and AL techniques was carried out, considering reputed technical/scientific publication databases—such as ScienceDirect, IEEE, PubMed, and arXiv—and a temporal range between 2010 and December 2022. The primary inclusion criterion for papers in this review was the application of AL techniques for OD tasks, regardless of the field of application. A total of 852 articles were analysed, and 60 articles were included after full screening. Among the remaining ones, relevant topics such as AL sampling strategies used for OD tasks and groups categorisation can be found, along with details regarding the deep neural network architectures employed, application domains, and approaches used to blend learning techniques with those sampling strategies. Furthermore, an analysis of the geographical distribution of OD researchers across the globe and their affiliated organisations was conducted, providing a comprehensive overview of the research landscape in this field. Finally, promising research opportunities to enhance the AL process were identified, including the development of novel sampling strategies and their integration with different learning techniques. Full article

Soil organic matter (SOM) is an essential component of soil fertility that plays a vital role in the preservation of healthy ecosystems. This study aimed to produce an SOM-level map of the Batifa region in northern Iraq. Random forest (RF) and extreme gradient boosting (XGBoost) models were used to predict the SOM spatial distribution. A total of 96 soil samples were collected from the surface layer (0–30 cm) of both cropland and soil areas in Batifa. In addition, remote sensing data were obtained from Landsat 8, including bands 1–7, 10, and 11. Supplementary variables such as the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), brightness index (BI), and digital elevation model (DEM) were employed as tools to predict SOM levels across the region. To evaluate the accuracy of the RF and XGBoost models in predicting SOM levels, statistical metrics, including mean absolute error (MAE), root mean square error (RMSE), and determination coefficient (R²), were used, with 80% of the data used for prediction and 20% for validation. The findings of this study revealed that the XGBoost model exhibited higher accuracy (MAE = 0.41, RMSE = 0.62, and R² = 0.92) in predicting SOM than the RF model (MAE = 0.65, RMSE = 0.96, R² = 0.79). Band 10, DEM, SAVI, and NDVI were identified as the most important predictors for both the models. The methodology employed in this study, which utilizes machine learning models, has the potential to map SOM in similar settings. Furthermore, the results offer significant insights for the stakeholders involved in soil management, thereby facilitating the enhancement of agricultural techniques. Full article

For future requirements, asset management of turnouts needs to rely on data-based assessment tools. These tools must enable the quantification of quality behaviour of turnouts and identify causes of poor behaviour. In this paper, we provide a toolbox addressing these requirements. We use track geometry as the main criterion for quality behaviour in combination with additional indicators, each associated with a different component, to understand turnout performance. The toolbox is applied to five similar turnouts to compare their performance. It is revealed that one of the turnouts performs significantly worse than the others. A deeper analysis can identify worn ballast in several areas of the turnout as the cause of poor performance. Problems in the ballast bed can be attributed to worn insulated rail joints as well as to stiffness changes in the transition areas of the turnout. Full article

Pneumatic tires are used in diverse industries. However, their design is difficult, as it relies on the knowledge of experienced designers. In this paper, we generate images of non-pneumatic tire designs with patterns based on shapes and lines for different generative adversarial network (GAN) models and test the performance of the models. Using OpenCV, 2000 training images were generated, corresponding to spoke, curve, triangle, and honeycomb non-pneumatic tires. The images created for training were used after removing highly similar images by applying mean squared error (MSE) and structural similarity index (SSIM). To identify the best model for generating patterns of regularly shaped non-pneumatic tires, GAN, deep convolutional generative adversarial network (DCGAN), StarGAN v2, StyleGAN v2-ADA, and ProjectedGAN were compared and analyzed. In the qualitative evaluation, the GAN, DCGAN, StarGAN v2, and StyleGAN v2-ADA models distorted the circle shape and did not maintain a consistent pattern, but ProjectedGAN retained consistency in the circle, and the pattern was less distorted than in the other GAN models. When evaluating quantitative metrics, ProjectedGAN performed the best among several techniques when the difference between the generated and actual image distributions was measured. Full article

As blockchain technology plays an increasingly important role in the Internet of Vehicles, how to further enhance the data consensus between the areas of the Internet of Vehicles has become a key issue in blockchain design. The traditional blockchain-based vehicle networking consensus mechanism adopts the double-layer PBFT architecture, through the grouping of nodes for first intra-group consensus, and then global consensus. To further reduce delay, we propose a CRMWSL-PBFT algorithm (C-PBFT) for vehicle networking. Firstly, in order to ensure the security of RSU nodes in the network of vehicles and reduce the probability of malicious nodes participating in the consensus, we propose to calculate the reputation of RSU nodes based on multi-weighted subjective logic (CRMWSL) model. Secondly, in order to ensure the efficiency of blockchain data consensus, we improve the consensus protocol of traditional double-layer PBFT, change the election method of the committee and the PBFT consensus process, and improve throughput by reducing the number of consensus nodes. For the committee, we combine the credibility value and hash method to ensure the credibility of nodes, but also to ensure a certain degree of election randomness. For the PBFT consensus process, the regional committee consensus is carried out first, and then the regional master node carries out the global consensus. Through experimental comparison, we show that the C-PBFT significantly reduces consensus time, network overhead, and is scalable for Internet of Vehicles. Full article

Mutual dislocation of seismogenic faults during strong earthquakes will result in a large relative displacement on both sides of the fault. It is of great significance to explore the influence of the collision effect between the main beam and the transverse shear key on the seismic response of the bridge under fault dislocation. In this paper, a series of cross-fault ground motions with different ground permanent displacements are artificially synthesized using a hybrid simulation method. Based on the contact element theory, the Kelvin–Voigt model is used to simulate the lateral collision effect. The effect of lateral collision on the seismic response of the continuous girder bridge is compared from the two aspects of fault dislocation position and fault dislocation degree. On this basis, the analysis of lateral collision parameters is carried out with the aim of reasonably regulating the seismic response of the structure. The results show that, compared with the near-fault bridge, the influence of lateral collision on the cross-fault bridge is stronger. The amplification of the bending moment of the central pier and the limitation of the bearing displacement are five times and two times, respectively, for the near-fault bridge. When the fault has a large dislocation, the weak point of the structural damage is the bending failure of the pier bottom and the residual torsion after the earthquake. The collision parameters of conventional bridges will aggravate the bending moment demand of the pier bottom of cross-fault bridges and limit their bearing displacement too much. Therefore, by appropriately reducing the collision stiffness and increasing the initial gap, the internal force and displacement response distribution of the cross-fault bridge structure can be more reasonable. The study in this paper has reference significance for seismic analysis of cross-fault bridges with transverse shear keys. Full article

The continuous development of the industry of composite materials and epoxy resins requires the development of components that modify these systems. It is extremely beneficial to modify functionality by using one or two substances instead of modifying only one system parameter. Typically, this end-use will determine the key parameters of the resin system that should be modified and the modification systems designed as such. In this study, we introduce novel systems utilizing ionic liquids, strategically designed to concurrently alter multiple system parameters, including: (i) flexibility, (ii) crosslinking density, and (iii) fire resistance. The following techniques were used in the research: (i) Differential Scanning Calorimetry (DSC), (ii) Thermogravimetric Analysis (TGA), (iii) Dynamic Mechanical Analysis (DMA) and (iv) fire performance tests (UL-94, Limiting Oxygen Index and Mass loss type cone calorimetry (MLC)) to show as much dependence of material parameters on the type of modifying additive as possible. Both the cured resin and the curing process as well as a single-layer composite reinforced with carbon fiber were tested. The results show that properly designed ionic liquids are able to perform many functions in the composite material and simultaneously affect several parameters, both by lowering and increasing them. In addition, they can exhibit activity in the field of flame-retardant composites. Full article

Knowledge graphs (KGs) play a crucial role in many applications, such as question answering, but incompleteness is an urgent issue for their broad application. Much research in knowledge graph completion (KGC) has been performed to resolve this issue. The methods of KGC can be classified into two major categories: rule-based reasoning and embedding-based reasoning. The former has high accuracy and good interpretability, but a major challenge is to obtain effective rules on large-scale KGs. The latter has good efficiency and scalability, but it relies heavily on data richness and cannot fully use domain knowledge in the form of logical rules. We propose a novel method that injects rules and learns representations iteratively to take full advantage of rules and embeddings. Specifically, we model the conclusions of rule groundings as 0–1 variables and use a rule confidence regularizer to remove the uncertainty of the conclusions. The proposed approach has the following advantages: (1) It combines the benefits of both rules and knowledge graph embeddings (KGEs) and achieves a good balance between efficiency and scalability. (2) It uses an iterative method to continuously improve KGEs and remove incorrect rule conclusions. Evaluations of two public datasets show that our method outperforms the current state-of-the-art methods, improving performance by 2.7% and 4.3% in mean reciprocal rank (MRR). Full article

This paper examines the challenges associated with the efficient planning and operation of an E-grocery delivery system using Autonomous Delivery Robots (ADR) during unforeseen events. The primary objective is to minimize unfulfilled customer demands rather than focusing solely on cost reduction, considering the humanitarian aspect. To address this, a two-echelon vehicle routing problem is formulated, taking into account stochastic service times and demands. Two models, namely a deterministic model and a chance-constraint model, are employed to solve this problem. The results demonstrate that the chance-constraint model significantly reduces unmet demands compared to the deterministic model, particularly when the delivery deadline has a broad time window and the ADR/van speed ratio is moderate. Full article

With global climate warming, the frequency and intensity of typhoons are increasing, highlighting the significance of studying storm surges for coastal engineering disaster mitigation. In this study, we assessed the predictive capabilities of the new ERA5 reanalysis model and the traditional Fujita−Takahashi model for storm surges. We found that the traditional Fujita−Takahashi model, utilizing a prelandfall typhoon wind field, exhibited higher accuracy in storm surge predictions, while the ERA5 reanalysis model, employing a postlandfall wind field, demonstrated superior performance. By considering the strengths and weaknesses of both wind field models and analyzing the impact of Typhoon In-fa (2021) on the East China Sea, we determined the influence of this typhoon on storm surge heights along the eastern coastal region. These research findings provide valuable insights for the development of effective protection strategies, offering valuable references for coastal resilience planning. Full article

In this paper, a simulation model for Selective Laser Melting (SLM) technology is established to simulate the additive manufacturing process of a turbine impeller for an aerospace engine. By utilizing the simulation model, variations in laser power and scanning speed are employed to obtain simulated results of thermal deformation for the turbine impeller under different laser power and scanning speed conditions. The results indicate that the thermal deformation of the component increases with the augmentation of laser power, decreases with the escalation of scanning speed, and eventually stabilizes. Based on the relationship between thermal deformation and energy, the energy utilization efficiency of the SLM process under different conditions is calculated. The findings demonstrate that, within a certain range of power, the synergistic effect of laser power and scanning speed allows for an increase in energy utilization efficiency and a reduction in processing time while ensuring the mechanical performance of the formed parts. Consequently, this approach proves effective in lowering production costs for complex components based on SLM technology. Full article

Incipient motion has been a topic of investigation by researchers, engineers and scientists for more than a century. The main approach for studying sediment entrainment has been the static approach that uses temporal and spatial averaged flow parameters like bed shear stress and stream power to link them indirectly to sediment entrainment. Recent research outputs have shed light on the important role of turbulent fluctuations in the sediment transport process. It is suggested that the approach of using temporal and spatial averaged parameters fails to account for the dynamic and probabilistic nature of the entrainment process, as inherited by flow turbulence. This has led to the introduction of the only dynamic criteria in the literature for studying sediment entrainment, namely the impulse and energy criteria. These criteria take into account both the magnitude and duration of the turbulent flow event used for assessing the conditions that can result in sediment entrainment. In light of this, this work aims to assess whether there is a trend in terms of the type of flow structures that occur in sequence before and after the occurrences of the flow impulses that have resulted in the coarse particle’s entrainment. To achieve this, we conducted a well-controlled laboratory experiment to investigate the incipient motion of a 7 cm diameter instrumented particle. Five runs of the experiment were performed at flowrates close to the threshold of motion. The instrumented particle was equipped with micro-electro-mechanical sensors (MEMS) to accurately measure its inertial dynamics and detect motion. The sensors recorded entrainment events, and these events were stochastically linked to the impulses occurring for the tested flow conditions. Quadrant analysis was used to investigate the type of flow structures that occurred before, during and after the occurrence of quadrant events with an impulse above the critical impulse. The findings herein associate coarse particle entrainments with energetic impulses linked primarily to sweep events (Q₄) and secondarily, sequence of sweeps (Q₄) and ejections (Q₁). Full article

The development of Xiongan New Area in Hebei Province, China, as a significant national choice, has considerable strategic significance for the integrated growth of Beijing, Tianjin, and Hebei. This paper proposes a cloud model for the suitability evaluation of the construction of Xiongan New Area based on entropy weight analysis, taking into account the geological conditions, groundwater environment, environmental geological problems, and other factors of the suitability of image city development. According to the research, the suitability evaluation findings for the project building employing the cloud model are in strong accord with those of the traditional model and have some application potential. The evaluation’s findings indicate that the project construction in Xiongan New Area is acceptable, with suitable and relatively suitable sites making up 81.4% of the total area and excellent circumstances for project development, construction, and usage. This study offers helpful direction for Xiongan New Area’s urban land-space design and serves as a useful point of comparison for studies looking at the viability of other deep Quaternary Plain region engineering buildings. Full article

In this paper, the finite-difference time-domain (FDTD) method is derived for electromagnetic simulations in media described by the time-fractional (TF) constitutive relations. TF Maxwell’s equations are derived based on these constitutive relations and the Grünwald–Letnikov definition of a fractional derivative. Then the FDTD algorithm, which includes memory effects and energy dissipation of the considered media, is introduced. Finally, one-dimensional signal propagation in such electromagnetic media is considered. The proposed FDTD method is derived based on a discrete approximation of the Grünwald–Letnikov definition of the fractional derivative and evaluated in a code. The stability condition is derived for the proposed FDTD method based on a numerical-dispersion relation. The obtained numerical results are compared with the outcomes of reference frequency-domain simulations, proving the accuracy of the proposed approach. However, high spatial resolution is required in order to obtain accurate results. The developed FDTD method is, unfortunately, computation and memory demanding when compared to the ordinary FDTD algorithm. Full article

The sudden increase in the operating pressure of nuclear power plants (NPPs) is due to the water hammer phenomenon, which tends to produce a whipping effect that causes serious damage to the pipes and their surroundings. The mechanical response of these pipelines under the influence of such fast fluid transients can be estimated using the fluid–structure interaction (FSI) method. The computational time and expense are predominantly dependent on the number of finite elements developed in the model. Hence, an effective modeling technique with limited and efficient nodes and elements is desired to obtain the closest possible results. A coupled 1D/3D finite element modeling approach using the FSI method is proposed to determine the influence of fast transients on the mechanical pipe whipping behavior of gas pipelines in NPPs. The geometric coupled modeling approach utilizes the presence of both the 3D solid elements and the 1D beam elements sharing a local conjunction. The computational model is modelled for a pipe-to-wall impact test scenario taken from the previously conducted French Commissariat a l’Energie Atomique (CEA) pipe whip experiments. The results of displacement, stresses, and impact velocity at the 3D section featuring the elbow are compared for the change in the 3D solid length varied at the juncture of the elbow. The computed results from the Ansys FSI coupling method using the Fluent and Transient Structural modules provides fair validation with the previously conducted experimental results and correlates with the CEA pipe whip tests on pipe-to-wall impact models. Thus, the 1D/3D coupled modeling approach, which minimizes the area of the solid region by constricting it to the impact area with appropriate contact modeling at the junctures, can be considered in the future for decreasing the computational time and the creation of finite elements. Full article

The prediction of pause fillers plays a crucial role in enhancing the naturalness of synthesized speech. In recent years, neural networks, including LSTM, BERT, and XLNet, have been employed for pause fillers prediction modules. However, these methods have exhibited relatively lower accuracy in predicting pause fillers. This paper introduces the utilization of the RoBERTa model for predicting Chinese pause fillers and presents a novel approach to training the RoBERTa model, effectively enhancing the accuracy of Chinese pause fillers prediction. Our proposed approach involves categorizing text from different speakers into four distinct style groups based on the frequency and position of Chinese pause fillers. The RoBERTa model is trained on these four groups of data, which incorporate different styles of fillers, thereby ensuring a more natural synthesis of speech. The Chinese pause fillers prediction module is evaluated on systems such as Parallel Tacotron2, FastPitch, and Deep Voice3, achieving a notable 26.7% improvement in word-level prediction accuracy compared to the BERT model, along with a 14% enhancement in position-level prediction accuracy. This substantial improvement results in a significant enhancement of the naturalness of the generated speech. Full article