Search Results (3,223)

Road hazardous material transportation plays a critical role in road traffic management. Due to the dangerous nature of the cargo, hazardous material transportation trucks (HMTTs) have different route selection and driving characteristics compared to traditional freight trucks. These differences lead to unique travel and emission patterns, which in turn affect traffic management strategies and emission control measures. However, existing research predominantly focuses on safety aspects related to individual vehicle behavior, with limited exploration of the broader travel and emission characteristics of HMTTs. To bridge this gap, this study develops a comprehensive framework for analyzing the travel patterns and emissions of HMTTs. The methodology begins by applying a Gaussian mixture distribution model to identify vehicle stop points, eliminating biases associated with subjective settings. Origin–destination (OD) pairs are then determined through stop time clustering, followed by the extraction of travel characteristics using non-negative matrix factorization. Emissions are subsequently calculated based on the identified trip data. The relationship between emissions and land use characteristics is further analyzed using geographically weighted regression (GWR). Crucially, this study leverages data from the BeiDou Satellite Navigation System, focusing on HMTTs operating within Shanghai. The processed data reveal three distinct travel modes of HMTTs, categorized by spatiotemporal patterns: Daytime—Surrounding cities, Early morning—In-city, and Midnight—Scattered. Moreover, unlike other road vehicles, HMTT emissions are heavily influenced by industrial and company-related points of interest (POIs). These findings highlight the significant role of BeiDou Satellite Navigation System data in optimizing HMTT management strategies to reduce emissions and improve overall safety. Full article

The high-speed aviation piston pump plays a vital role in hydraulic systems in the aviation field. Extremely complex force situations happen during running operations due to the coupling between multiple components, as a result of the overall dynamic characteristics being complex and changeable, which brings great difficulties and challenges to its performance optimization. Taking the high-speed aviation piston pump as the research object, a mechanical balance equation of the piston based on the dynamic balance method was proposed. The reaction force of the swashplate and the influence of rotational speed and outlet pressure on it were modeled. Through the balance of the system and the component subsystem, the load of the support bearing of the piston pump under different working conditions is analyzed, as well as the influence of the rotational speed and the outlet pressure on the bearing stiffness by the quasi-static method. In addition, the discrete model of the piston pump spindle and the discrete model of the rotor system are established. The accuracy of the model is verified by the finite element method. The maximum error of the spindle discrete model is 6.13%, and the maximum error of the rotor system discrete model is 15.28%. The transfer matrix analysis shows that the working condition parameters have little effect on the critical speed of the spindle and rotor system, and the outlet pressure has a more significant effect than the speed. The research results provide a theoretical basis and analysis method for the dynamic analysis and structural optimization of the high-speed aviation piston pump. Full article

In this study, we explore various fractional integral properties of R-matrix functions using the Hilfer fractional derivative operator within the framework of fractional calculus. We introduce the $\theta$ integral operator and extend its definition to include the R matrix functions. The composition of Riemann–Liouville fractional integral and differential operators is determined using the $\theta$-integral operator. Additionally, we investigate the compositional properties of $\theta$-integral operators, and we establish their inversion, offering new insights into their structural and functional characteristics. Full article

Rhegmatogenous retinal detachment (RRD) is a sight-threatening condition involving retinal detachment and the accumulation of fluid in the subretinal space. Proliferative vitreoretinopathy (PVR) is a pathologic complication that develops after RRD surgery, and approximately 5–10% of RRD cases develop post-operative PVR. Prolonged inflammation in the wound healing process, epithelial–mesenchymal transition (EMT), retinal pigment epithelial (RPE) cell migration and proliferation, and epiretinal, intraretinal, and subretinal fibrosis are typical in the formation of PVR. RPE cells undergo EMT and become fibroblast-like cells that migrate to the retina and vitreous, promoting PVR formation. Fibroblasts transform into myofibroblasts, which promote fibrosis by overproducing the extracellular matrix (ECM). RPE cells, fibroblasts, glial cells, macrophages, T lymphocytes, and increased ECM production form contractile epiretinal membranes. Cytokine release, complement activation, RPE cells, glial cells, and endothelial cells are all involved in retinal immune responses. Normally, wounds heal within 4 to 6 weeks, including hemostasis, inflammation, proliferation, and remodeling phases. Properly initiated inflammation, complement activation, and the function of neutrophils and glial cells heal the wound in the first stage. In a retinal wound, glial cells proliferate and fill the injured area. Gliosis tries to protect the neurons and prevent damage, but it becomes harmful when it causes scarring. If healing is complicated, prolonged inflammation leads to pathological fibrosis. Currently, there is no preventive treatment for the formation of PVR, and it is worth studying in the future. Full article

Background/Objectives: There are various challenges in the segmentation of anatomical structures with artificial intelligence due to the different structural features of the relevant region/tissue. The aim of this study was to detect the nasolacrimal canal (NLC) using the nnU-Net v2 convolutional neural network (CNN) model in cone beam-computed tomography (CBCT) images and to evaluate the successful performance of the model in automatic segmentation. Methods: CBCT images of 100 patients were randomly selected from the data archive. The raw data were transferred to the 3D Slicer imaging software in DICOM format (Version 4.10.2; MIT, Massachusetts, USA). NLC was labeled using the polygonal type of manual method. The dataset was split into training, validation and test sets in a ratio of 8:1:1. nnU-Net v2 architecture was applied to the training and test datasets to predict and generate appropriate algorithm weight factors. The confusion matrix was used to check the accuracy and performance of the model. As a result of the test, the Dice Coefficient (DC), Intersection over Union (IoU), F1-Score and 95% Hausdorff distance (95% HD) metrics were calculated. Results: By testing the model, DC, IoU, F1-Scores and 95% HD metric values were found to be 0.8465, 0.7341, 0.8480 and 0.9460, respectively. According to the data obtained, the receiver-operating characteristic (ROC) curve was drawn and the AUC value under the curve was determined to be 0.96. Conclusions: These results showed that the proposed nnU-Net v2 model achieves NLC segmentation on CBCT images with high precision and accuracy. The automated segmentation of NLC may assist clinicians in determining the surgical technique to be used to remove lesions, especially those affecting the anterior wall of the maxillary sinus. Full article

Wind turbines are predominantly situated in remote, high-altitude regions, where they face a myriad of harsh environmental conditions. Factors such as high humidity, strong gusts, lightning strikes, and heavy snowfall significantly increase the vulnerability of turbine blades to fatigue damage. This susceptibility poses serious risks to the normal operation and longevity of the turbines, necessitating effective monitoring and maintenance strategies. In response to these challenges, this paper proposes a novel fault detection method specifically designed for analyzing wind turbine blade noise signals. This method integrates the Tyrannosaurus Optimization Algorithm (TROA) with a support vector machine (SVM), aiming to enhance the accuracy and reliability of fault detection. The process begins with the careful preprocessing of raw noise signals collected from wind turbines during actual operational conditions. The method extracts vital features from three key perspectives: the time domain, frequency domain, and cepstral domain. By constructing a comprehensive feature matrix that encapsulates multi-dimensional characteristics, the approach ensures that all relevant information is captured. Rigorous analysis and feature selection are subsequently conducted to eliminate redundant data, thereby focusing on retaining the most significant features for classification. A TROA-SVM classification model is then developed to effectively identify the faults of the turbine blades. The performance of this method is validated through extensive experiments, which indicate that the recognition accuracy rate is 98.7%. This accuracy is higher than that of the traditional methods, such as SVM, K-Nearest Neighbors (KNN), and random forest, demonstrating the proposed method’s superiority and effectiveness. Full article

This paper shows that at a certain time-point in the analysis procedure, the accuracy of T-spline based isogeometric analysis (IGA) may be substantially improved by increasing the multiplicity of the inner knots up to the polynomial degree. This task can be performed by considering the Bézier extraction operator matrix elementwise, and thus an increased number of updated control points are easily received in the geometrical and computational models. Nevertheless, after the determination of the unique control points, the Bézier elements near the T-junctions may not be well shaped, and thus minor automatic interventions are required to ensure full (i.e., C⁰ and G⁰) compatibility. The improved IGA-based solution may be used as a reference to determine the a posteriori error estimations in the T-spline elements of the domain, and thus may be a useful tool for IGA adaptation. The methodology is shown in BVPs dominated by Laplace–Poisson equations in rectangular and curvilinear domains, while eigenvalues and eigenvectors were extracted in a rectangular acoustic cavity. Full article

For Advanced Driver Assistance Systems (ADASs), lots of researchers have been constantly researching various devices that can become the eyes of a vehicle. Currently represented devices are LiDAR, camera, and radar. This paper suggests one of the operation processes to study radar, which can be used regardless of climate change or weather, day or night. Thus, we propose a simple and easy calibration method for Multi-Input Multi-Output (MIMO) radar to guarantee performance with initial calibration parameters. Based on a covariance matrix, the modified signals of all channels improve performance, reducing unexpected interferences. Therefore, using the proposed coupling matrix, we can reduce unexpected interference and generate accurately calibrated results. To prove and verify the improvement in our method, a practical experiment is conducted with Frequency-Modulated Continuous-Wave (FMCW) MIMO radar, mounted on an automotive. Full article

This paper formalizes the analytic expressions and some properties of the evolution operator that generates the state-trajectory of dynamical systems combining delay-free dynamics with a set of discrete, or point, constant (and not necessarily commensurate) delays, where the parameterizations of both the delay-free and the delayed parts can undergo impulsive changes. Also, particular evolution operators are defined explicitly for the non-impulsive and impulsive time-varying delay-free case, and also for the case of impulsive delayed time-varying systems. In the impulsive cases, in general, the evolution operators are non-unique. The delays are assumed to be a finite number of constant delays that are not necessarily commensurate, that is, all of them being integer multiples of a minimum delay. On the other hand, the impulsive actions through time are assumed to be state-dependent and to take place at certain isolated time instants on the matrix functions that define the delay-free and the delayed dynamics. Some variants are also proposed for the cases when the impulsive actions are state-independent or state- and dynamics-independent. The intervals in-between consecutive impulses can be, in general, time-varying while subject to a minimum threshold. The boundedness of the state-trajectory solutions, which imply the system’s global stability, is investigated in the most general case for any given piecewise-continuous bounded function of initial conditions defined on the initial maximum delay interval. Such a solution boundedness property can be achieved, even if the delay-free dynamics is unstable, by an appropriate distribution of the impulsive actions. An illustrative first-order example is developed in detail to illustrate the impulsive stabilization results. Full article

Numerous optimization problems exist in the design and operation of power systems, critical for efficient energy use, cost minimization, and system stability. With increasing energy demand and diversifying energy structures, these problems grow increasingly complex. Metaheuristic algorithms have been highlighted for their flexibility and effectiveness in addressing such complex problems. To further explore the theoretical support of metaheuristic algorithms for optimization problems in power systems, this paper proposes a novel algorithm, the Boston Consulting Group Matrix-based Equilibrium Optimizer (BCGEO), which integrates the Equilibrium Optimizer (EO) with the classic economic decision-making model, the Boston Consulting Group Matrix. This matrix is utilized to construct a model for evaluating the potential of individuals, aiding in the rational allocation of computational resources, thereby achieving a better balance between exploration and exploitation. In comparative experiments across various dimensions on CEC2017, the BCGEO demonstrated superior search performance over its peers. Furthermore, in dynamic economic dispatch, the BCGEO has shown strong optimization capabilities and potential in power system optimization problems. Additionally, the experimental results in the spacecraft trajectory optimization problem suggest its potential for broader application across various fields. Full article

The pump heating effect of DFB fiber laser is normally ignored due to the short length of the laser cavity. However, by fabricating a phase-shifted FBG on high concentration Er-Yb codoped fiber to obtain a 16 mm long DFB fiber laser, the gradient surface temperature distributions along the active grating with different pump powers were observed. The average surface temperature rose by 16.82 K with a variation of less than 1.11 K, and the position with the highest temperature moved towards the center of the grating by 5.5 mm, when the pump power was increased from 0 mW to 191.6 mW. The transmission spectrum of the active phase-shifted FBG at different pump powers were measured, and an additional drift of the transmission peak in the stopband was testified. It was identified as an equivalent phase shift up to −0.1 π, which was induced by the gradient longitudinal temperature distribution. Considering that the initial phase shift of the grating was about 1.15 π, the increasing chirp of the active grating due to the pump heating could compensate the phase shift deviation from π surprisingly. The experimental results coincided with the simulation results by using the transmission matrix method under the assumption of piecewise-uniform structure for the chirped phase-shifted grating. The modified model of the active phase-shifted FBG reveals the difference between the cool cavity and the hot cavity at different pump powers, which may be used as a self-optimization mechanism for DFB fiber laser operation. Full article

With increased highway mileage, various types and quantities of infrastructure are equipped on the roadside to improve traffic safety and efficiency but also encounter difficulty in asset management. The collected data are separately stored with diverse formats, granularity and quality, causing repeated acquisitions and islands of information coherence. The life-cycle interoperability of infrastructure data are required to support life-cycle application scenarios in sustainable development. This paper analyzes 459 papers and 538 survey questionnaires to obtain the literature and practical digital requirements, including unified classification and standardized formats, linkage from separated data sources, support for data analysis across different scenarios, etc. To satisfy these requirements, an infrastructure digitalization framework is proposed, including road infrastructure and other data, data governance, life-cycle data integration, application scenarios, regulations and standards, and performance assessment. The application scenarios involve four categories—design and construction, maintenance, operation, and highway administration—each of which contains four or five scenarios. Then, the data integration approach is first developed with master data identification and determination of data elements for data interoperation between different application scenarios, using a modified data–process matrix, correlation matrix, and evaluation factors. A data relationship model is adopted to present static and dynamic correlations from the multi-source data. Numerical experiments are implemented with two practical highway administration and maintenance systems to demonstrate the effectiveness of the data integration approach. Master data identification and data element determination are applied to guide life-cycle data interoperation. Full article

Chitosan-based biosorbents are particularly valuable in environmental applications, such as wastewater treatment for contaminant removal. However, several challenges remain in optimizing their production and performance related to improving adsorption efficiency, stability, scalability, cost, and sustainable sourcing for large-scale applications. The removal of Methylene Blue (MB) and Orange 16 (O16) from aqueous solutions was studied using a biosorbent derived from the waste biomass of the brewing industry, specifically Saccharomyces pastorianus immobilized into chitosan. The biosorbent (obtained by a straightforward entrapment technique) was characterized using Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Analysis (EDAX) to evaluate its structural properties. The biosorption behavior toward organic contaminants, specifically a cationic and an anionic dye, was investigated. Key operational factors that influenced the biosorbent’s efficiency were examined, including the initial dye concentration, dye type, pH of the aqueous solution, and the amount of biosorbent used. These factors were evaluated during the initial stage of the biosorption studies to assess their impact on the overall performance and effectiveness of the biosorbent in removing the dyes from aqueous solutions. Using this eco-friendly biosorbent, the biosorption capacities obtained using the Langmuir isotherm model were 212.77 mg/g in the case of MB dye and 285.71 mg/g in the case of O16 mg/g, and the results confirmed that the biosorption process is based on a physical mechanism as suggested by the energy values of the process, E, obtained using the DR model: the obtained values of 6.09 kJ/mol (MB dye) and 7.07 kJ/mol (O16 dye) suggest a process based on electrostatic interaction bonds. These results indicate that residual biomass of Saccharomyces pastorianus, as a byproduct of a biotechnological process, can be exploited as a biosorbent by immobilization in an organic matrix (chitosan) for the retention of polluting organic species from the aqueous environment present in aqueous solutions in moderate concentrations. Full article

Emulsified acid treatments present an innovative and environmentally sustainable alternative to conventional hydrochloric acid (HCl) methods in enhancing oil recovery. This study investigates the application of a stable emulsified acid formulation in matrix acidizing operations to improve injectivity in four wells within the Qusahwira Field. Compared to traditional 15% HCl treatments, the emulsified acid demonstrates deeper acid penetration and retardation effect leading to enhanced injection rate. By delivering deep worm holing effects against calcium carbonate formation, this dual-phase system enhances injectivity by 14 times while minimizing the environmental and material impacts associated with spent acid volumes. The methodology integrates advanced neural network modeling to predict stimulation outcomes based on 15 operational and reservoir factors. This model reduces the trial-and-error approach, cutting operational costs and time for carbonate reservoir. Field trials reveal significant improvements in injection pressure and a marked reduction in circulation pressure during stimulation, underscoring the treatment’s efficiency. Developed in a Superior Abu Dhabi laboratory, the emulsified acid achieves high-temperature stability (200 °F) and deep acid penetration, further reducing the ecological footprint of acid stimulation by enhancing operational precision and reducing chemical use. This paper highlights a sustainable approach to optimizing reservoir productivity, aligning with global efforts to minimize environmental impacts in oil recovery processes. Full article

Sparse modular multilevel converters (SMMCs) are a new type of lightweight high-voltage large-power AC/AC converter that significantly reduces the number of components compared to modular multilevel matrix converters (M3Cs). This study proposes a fault ride through a control strategy for SMMC to address the issues of arm energy imbalances and valve-side overvoltage, which occur during asymmetric faults on the low-frequency side. First, we establish models of the energy deviation of the arms under asymmetric short-circuit faults on the low-frequency side of SMMC. We also study the influence mechanism of the control strategies on the arm energy imbalance during faults. On this basis, an arm energy balancing strategy based on zero-sequence voltage injections combined with AC voltage control is proposed; this can achieve arm energy balance and suppress the negative sequence current and overvoltage of the SMMC. Finally, we construct a simulation model of an offshore wind power low-frequency transmission system based on the SMMC. The simulation results show that the proposed energy balance strategy can realize the stable operation of the low-frequency transmission system (LFTS) under asymmetric faults on the low-frequency side, that the maximum capacitor voltage deviation during the fault does not exceed 10% and that capacitor voltage returns to normal 0.25 s after the fault occurs. Full article