Search Results (121)

In this paper, a pattern synthesis method of uniform linear and concentric elliptical antenna arrays using the Kepler optimization algorithm (KOA) is proposed. The KOA, which utilizes Kepler’s laws to predict the position and velocity of planets at arbitrary times, is first applied to deal with the optimization problems of linear and elliptical antenna arrays. Radiation patterns with high gain and low sidelobe levels (SLLs) are synthesized by optimizing the critical parameters (amplitude, phase, and rotation) of the linear arrays. Moreover, a concentric elliptical array is designed to demonstrate the capability of the KOA framework to solve complex problems and achieve the desired performance. In order to accurately consider mutual coupling between the elements, the full-wave method of moments (MoM) is used to calculate the radiation characteristics of the arrays in the optimization method. The effectiveness of the proposed method is proved by four typical examples. The results show that, compared with the butterfly optimization algorithm (BOA), Harris hawks optimization (HHO), and crayfish optimization algorithm (COA), the proposed method possesses high gain and SLL suppression capabilities, which makes it suitable for various array types. Full article

Products with complex structures are structurally intricate and involve multiple professional fields and engineering construction elements, making it difficult for a single contractor to independently develop and manufacture such complex structural products. Therefore, during the research, development, and production of complex products, collaboration between manufacturers and suppliers is essential to ensure the smooth completion of projects. In this process, a complex supply chain network is often formed to achieve collaborative cooperation among all project participants. Within such a complex supply chain network, issues such as delayed delivery, poor product quality, or low resource utilization by any participant may trigger the bullwhip effect. This, in turn, can negatively impact the delivery cycle, product cost, and quality of the entire complex product, causing it to lose favorable competitive positions such as quality advantages and delivery advantages in fierce market competition. Therefore, this paper firstly explores the mechanism of complex product manufacturing and the supply network of complex product manufacturing, in order to grasp the inherent structure of complex product manufacturing with a focus on identifying symmetrical properties among supply chain nodes. Secondly, a complex product supply chain network model is constructed with the Graphical Evaluation and Review Technique (GERT), incorporating symmetry constraints to reflect balanced resource allocation and mutual dependencies among symmetrical nodes. Then, from the perspective of supply chain, we focus on identifying the shortcomings of supply chain suppliers and optimizing the management cost of the whole supply chain in order to improve the quality of complex products, delivery level, and cost saving level. This study constructs a Restricted Grey GERT (RG-GERT) network model with constrained outputs, integrates moment-generating functions and Mason’s Formula to derive transfer functions, and employs a hybrid algorithm (genetic algorithm combined with non-linear programming) to solve the multi-objective optimization problem (MOOP) for joint optimization of delivery time, quality, and cost. Empirical analysis is conducted using simulated data from Y Company’s aerospace equipment supply chain, covering interval parameters such as delivery time [5–30 days], cost [40,000–640,000 CNY], and quality [0.85–1.0], validated with industry-specific constraints. Empirical analysis using Y Company’s aerospace supply chain data shows that the model achieves a maximum customer satisfaction of 0.96, with resource utilization efficiency of inefficient suppliers improved by 15–20% (p < 0.05) after secondary optimization. Key contributions include (1) integrating symmetry analysis to simplify network modeling; (2) extending GERT with grey parameters for non-probabilistic uncertainty; (3) developing a two-stage optimization framework linking customer satisfaction and resource efficiency. Full article

Hydraulic tomography (HT) based on the successive linear estimator (SLE) offers the high-resolution characterization of aquifer heterogeneity but conventionally requires prolonged pumping to achieve steady-state conditions, limiting its applicability in contamination-sensitive or low-permeability settings. This study bridged theoretical and practical gaps (1) by identifying spatial periodicity (hole effect) as the mechanism underlying divergences in steady-state cross-correlation patterns between random finite element method (RFEM) and first-order analysis, modeled via an oscillatory covariance function, and (2) by validating a novel short-term sampling strategy for SLE-based HT using field experiments at the University of Göttingen test site. Utilizing early-time drawdown data, we reconstructed spatially congruent distributions of hydraulic conductivity, specific storage, and hydraulic diffusivity after rigorous wavelet denoising. The results demonstrate that the short-term sampling strategy achieves accuracy comparable to that of long-term sampling strategy in characterizing aquifer heterogeneity. Critically, by decoupling SLE from steady-state requirements, this approach minimizes groundwater disturbance and time costs, expanding HT’s feasibility to challenging environments. Full article

The digital transformation of enterprises constitutes a crucial element of modernization processes in the countries of the European Union, supporting the achievement of sustainable development goals through increased innovation, economic efficiency and the development of high-tech sectors and knowledge-intensive services. In empirical analyses, multidimensional statistical analysis methods were applied, with particular emphasis on linear ordering, classification methods and regression analysis. Ten monitoring indicators adopted by the European Union constitute the basis for assessing the digital transformation advancement of enterprises. The study classified EU countries into three groups according to the level of digital transformation: low, medium and high. Based on the employment structure in high-tech manufacturing, medium–high-tech manufacturing and knowledge-intensive services, four distinct types of employment structures were identified. A positive relationship was found between the level of digital transformation and the share of employment in knowledge-intensive services, while no significant relationship was observed for the other sectors. The study shows that the EU countries are clearly differentiated both in terms of the level of digital transformation of enterprises and the structure of employment in high-tech manufacturing, medium–high-tech manufacturing and knowledge-intensive services. A higher level of digital transformation supports the growth of employment in knowledge-intensive services, while its impact on high-tech and medium–high-tech manufacturing remains limited. Full article

Developing control systems for high aspect ratio aircraft can be challenging due to the flexibility of the structure involved in the control loop design. A model-based approach can be straightforward to tune the control system parameters and, to this aim, a reliable aircraft flexible model is mandatory. This paper aims to present the approach pursued to design a control strategy considering the flexible aircraft simulator in the loop. Once the elastic model for the longitudinal dynamics has been set up, genetic algorithms are used to determine-together with a Linear Quadratic Regulator controller—a logic to improve the dynamic behaviour whilst encountering a gust. A relatively low order elastic model is developed for the dynamics in the longitudinal plane, including both rigid body and elastic degrees of freedom defined in a vehicle-fixed reference frame. The rigid body degrees of freedom and the associated states are the same as those of the rigid vehicle, whilst the additional states represent the elastic degrees of freedom. Modal characteristics are calculated from a finite element model of the aircraft using a commercial code, with the weight distribution added as lumped masses on grid points, while the aerodynamic rigid properties are described with a nonlinear database. Using the 2-D strip theory and neglecting the unsteady effects, the aeroelastic stability derivatives, i.e., elastic influence coefficients, are computed to superimpose the elastic effects on the rigid body degrees of freedom and vice versa. The flexible dynamics is compared to the rigid one in order to highlight the relevant changes in the aircraft modes. Following is herein proposed a control strategy combining genetic algorithms and Linear Quadratic Regulator controller to reduce the load factor, also considering the oscillation amplitude due to a deterministic gust encountered in a predefined flight condition. Full article

Alongside technological advancement, there is a growing need for materials that are easier to obtain and process and that offer multiple uses, thereby reducing environmental impact. Such materials are generally subject to mechanical, resistance and fatigue studies, often without considering their thermal properties, which could potentially expand the range of applications for the studied compound. The current study aims to analyze possible fluctuations and deviations from linearity in temperature flow curves, as well as their impact on the conductivity coefficient. These studies are conducted on a new type of panel made of fiberglass, a low-cost material with significant recycling potential, using foam elements recycled from packaging insulations and a cement biding mixture. This study considers the time variation of the different thermal coefficients and the temperature curves obtained from the experimental measurements. These data are analyzed and used to simulate heat variation in order to observe the heat flux fluctuations within the plate. The results suggest that the proposed composite plate can serve as an alternative to classical insulating panels. Full article

In order to study the influence of pile end sediment on the bearing characteristics of bored piles, the on-site bearing capacity test was conducted on a single pile. A mathematical model of bearing capacity and the settlement response of a single pile considering sediment effects and a finite element model of a single pile with pile end sediment were established. In addition, the influence of sediment thickness on the bearing capacity of bored piles was systematically analyzed. The results show that the compaction of sediment at the pile end could significantly improve the ultimate bearing capacity of the single pile. Compared with the single pile that did not consider the compaction of the sediment at the pile end, the load required to reach the ultimate bearing capacity of the pile after compaction of the sediment increases by 900 KN. The settlement of the pile under a maximum vertical load increases with an increase in the thickness of the sediment. The influence of sediment thickness on axial force transmission is mainly reflected in the linear to nonlinear transformation of axial force distribution from low to high during the process of load. The slight decrease in axial force at the bottom of the pile could also be caused by the increase in the thickness of sediment. The increase in sediment layer thickness means that the transfer efficiency of the pile end resistance decreases. However, with an increase in load, the compression effect of the pile end sediment becomes obvious, which will further change the distribution of load between the pile side resistance and the pile end resistance. Full article

The generation of an earthquake and a tsunami is a coupled process of radiating seismic waves and exciting tsunamis, and the two types of waves are simultaneously recorded by ocean-bottom pressure sensors. In order to constrain the earthquake source and evaluate the tsunami hazards, it is necessary to separate the tsunami waves. It is traditional to apply a low-pass filter such that the seismic waves are filtered and the tsunami waves remain. However, filtering may also cause distortion of the tsunami waves. In this study, we first use the finite-element method to simulate the generation of seismic and tsunami waves and show that the coupling is a linear superposition of the two waves. We then propose a new method to extract the tsunami waves. First, a low-pass filter with relatively high cutoff frequency that does not affect the tsunami waves is adopted, so that only tsunami waves and low-frequency seismic waves remain. The low-frequency seismic waves satisfy a theoretical equation $p=\rho ha$ (p pressure, $\rho$ water density, h water depth, and a seafloor vertical acceleration), and they can be predicted and removed by utilizing the records of ocean-bottom acceleration. We demonstrate the procedure by numerical simulations and show that the method successfully extracts clean tsunami signals, which is important for earthquake source characterization and tsunami hazard assessment. Full article

This work presents a nano-watt digital output temperature sensor featuring a supply-insensitive, self-biased current source. Second-order temperature dependencies of the MOS diode are canceled to produce a stable reference and a linear temperature-sensitive voltage. The sensor integrates a sensing unit, voltage-controlled differential ring oscillators, and a low-power frequency-to-digital converter, utilizing a resistor-less design to minimize power and area. The delay element in the ring oscillator reduces stage count, improving noise performance and compactness. Fabricated in 65 nm CMOS, the sensor occupies 0.02 mm² and consumes 60 nW at 25 °C and 0.8 V. Measurements show an inaccuracy of +1.5/−1.6 °C from −20 °C to 120 °C after two-point calibration, with a resolution of 0.2 °C (rms) and a resolution FoM of 0.022 nJ·K⁻². Consuming 0.55 nJ per conversion with a 9.2 ms conversion time, the sensor was tested in a battery-less wireless sensor node, demonstrating its suitability for wireless sensing systems. Full article

Dry masonry is a type of construction that is nowadays used to a limited extent in the construction sector, including the housing sector. A lack of codified computational methods enabling engineers to design consciously is one of the factors limiting the development of dry walls. This article presents results from testing an innovative solution for dry masonry made of medium-size elements with expanded perlite aggregate. Material with this type of aggregate has a low bulk density (390 ± 10% kg/m³), which allows the production of large blocks and significantly reduces the value of the thermal conductivity coefficient λ = 0.084 ± 0.003 W/m·K. The results obtained were used to determine material parameters for designing a structure mainly exposed to vertical load. The important practical significance of the presented research results from the lack of provisions, specifications or standards allowing for the design, calculation and construction of dry masonry; it is not possible to analyse the behaviour of this type of structure and to design it consciously and safely. The presented research is therefore an important source of information on mechanical parameters essential for the design of structures and provides tools for this. As a result of the tests of nine panels, the mean compressive strength was determined (1.085 N/mm²), and then the procedure of “design assisted by testing” implemented into Eurocode was used to determine characteristic (0.873 N/mm²) and design compressive strength (0.565 N/mm²). Using the relationships σ-ε, an attempt was made to identify material models for the linear and non-linear analysis of the structure and for designing cross-sections. The material models were made considering increased non-linear deformations of a structure under low stresses (the compression toe) which are true and typical for dry masonry. A specific deformability of dry masonry, slightly different to that in masonry structures joined together with mortar, also affects the reduction factor for load-bearing capacity due to second-order effects. Reduction factors determined from true non-linear deformations were lower than the values specified by EC6 for masonry structures. Full article

In cloud-based Distributed Acoustic Sensing (DAS) sensor data management, we are confronted with two primary challenges. First, the development of efficient storage mechanisms capable of handling the enormous volume of data generated by these sensors poses a challenge. To solve this issue, we propose a method to address the issue of handling the large amount of data involved in DAS by designing and implementing a pipeline system to efficiently send the big data to DynamoDB in order to fully use the low latency of the DynamoDB data storage system for a benchmark DAS scheme for performing continuous monitoring over a 100 km range at a meter-scale spatial resolution. We employ the DynamoDB functionality of Amazon Web Services (AWS), which allows highly expandable storage capacity with latency of access of a few tens of milliseconds. The different stages of DAS data handling are performed in a pipeline, and the scheme is optimized for high overall throughput with reduced latency suitable for concurrent, real-time event extraction as well as the minimal storage of raw and intermediate data. In addition, the scalability of the DynamoDB-based data storage scheme is evaluated for linear and nonlinear variations of number of batches of access and a wide range of data sample sizes corresponding to sensing ranges of 1–110 km. The results show latencies of 40 ms per batch of access with low standard deviations of a few milliseconds, and latency per sample decreases for increasing the sample size, paving the way toward the development of scalable, cloud-based data storage services integrating additional post-processing for more precise feature extraction. The technique greatly simplifies DAS data handling in key application areas requiring continuous, large-scale measurement schemes. In addition, the processing of raw traces in a long-distance DAS for real-time monitoring requires the careful design of computational resources to guarantee requisite dynamic performance. Now, we will focus on the design of a system for the performance evaluation of cloud computing systems for diverse computations on DAS data. This system is aimed at unveiling valuable insights into performance metrics and operational efficiencies of computations on the data in the cloud, which will provide a deeper understanding of the system’s performance, identify potential bottlenecks, and suggest areas for improvement. To achieve this, we employ the CloudSim framework. The analysis reveals that the virtual machine (VM) performance decreases significantly the processing time with more capable VMs, influenced by Processing Elements (PEs) and Million Instructions Per Second (MIPS). The results also reflect that, although a larger number of computations is required as the fiber length increases, with the subsequent increase in processing time, the overall speed of computation is still suitable for continuous real-time monitoring. We also see that VMs with lower performance in terms of processing speed and number of CPUs have more inconsistent processing times compared to those with higher performance, while not incurring significantly higher prices. Additionally, the impact of VM parameters on computation time is explored, highlighting the importance of resource optimization in the DAS system design for efficient performance. The study also observes a notable trend in processing time, showing a significant decrease for every additional 50,000 columns processed as the length of the fiber increases. This finding underscores the efficiency gains achieved with larger computational loads, indicating improved system performance and capacity utilization as the DAS system processes more extensive datasets. Full article

A novel graphene-based composite, 5-methyl-1,3,4-thiadiazol-2-amine (MTA) covalently functionalized graphene oxide (GO-MTA), was rationally developed and used for the selective sorption of Ga³⁺ from aqueous solutions, showing a higher adsorption capacity (48.20 mg g⁻¹) toward Ga³⁺ than In³⁺ (15.41 mg g⁻¹) and Sc³⁺ (~0 mg g⁻¹). The adsorption experiment’s parameters, such as the contact time, temperature, initial Ga³⁺ concentration, solution pH, and desorption solvent, were investigated. Under optimized conditions, the GO-MTA composite displayed the highest adsorption capacity of 55.6 mg g⁻¹ toward Ga³⁺. Moreover, a possible adsorption mechanism was proposed using various characterization methods, including scanning electron microscopy (SEM) equipped with X-ray energy-dispersive spectroscopy (EDS), elemental mapping analysis, Fourier transform infrared (FT-IR) spectroscopy, and X-ray photoelectron spectroscopy (XPS). Ga³⁺ adsorption with the GO-MTA composite could be better described by the linear pseudo-second-order kinetic model (R² = 0.962), suggesting that the rate-limiting step may be chemical sorption or chemisorption through the sharing or exchange of electrons between the adsorbent and the adsorbate. Importantly, the calculated q_e value (55.066 mg g⁻¹) is closer to the experimental result (55.60 mg g⁻¹). The well-fitted linear Langmuir isothermal model (R² = 0.972~0.997) confirmed that an interfacial monolayer and cooperative adsorption occur on a heterogeneous surface. The results showed that the GO-MTA composite might be a potential adsorbent for the enrichment and/or separation of Ga³⁺ at low or ultra-low concentrations in aqueous solutions. Full article

Traditional pneumatic tyres are prone to puncture or blowout and other safety hazards. Non-pneumatic tyres use a high-strength, high-toughness support structure to replace the “airbag body” structure of pneumatic tyres, which is made of fibre skeleton materials and rubber laminated layers, thus effectively avoiding the problems of blowout and air leakage. However, discontinuous spokes undergo repeated bending deformation when carrying loads, which leads to energy loss, of which the rolling resistance of non-pneumatic tyres is one of the main sources of energy loss. This paper focuses on the study of gradient honeycomb non-pneumatic tyres. Firstly, a finite element model was established, and the accuracy of the model was verified by numerical simulation and stiffness tests. Secondly, the order of the effect of different spoke thicknesses on rolling resistance was obtained through orthogonal test analysis of four-layer honeycomb spoke thicknesses. Then, four optimized design variables were selected in combination with the spoke angles, and the effects of the design variables on rolling resistance were analyzed in detail by means of the Latin hypercube experimental design. Finally, the response surface model was established, and the non-linear optimization model was solved by the EVOL optimization algorithm considering the tyre stiffness limitations so that the rolling resistance was minimized. The results of the study laid down theoretical and methodological guidance for the design concept and technological innovation of low rolling resistance comfort non-pneumatic tyres. Full article

Building façade color plays a key role in shaping urban image, enhancing urban vitality, and optimizing citizens’ living experience. Moreover, colors can influence people’s perception of space, but the multiple interrelationships between color elements and users’ color evaluation and visual perception have not yet been thoroughly studied. In order to explore the relationships between color elements and visual perception and subjective comfort, this study discusses the matching relationship between color and the comfort of a residential building façade from three levels of color hue, lightness, and saturation by combining eye movement technology and a subjective evaluation. Taking Anshan San Village in Shanghai as the research object, the relationship model between color elements, visual perception, and subjective comfort is established through correlation analysis, linear regression, and multifactor regression analysis, and the correlation between single elements and multiple elements is explored from multi-dimensional perspectives. It aims to assess the operability of eye movement data as a measure of the visual comfort with the residential building façade color through multiple indicators and to explore and establish a new correlation assessment framework between the three attributes of façade color and users’ visual comfort. The results show that (1) highly saturated façade colors significantly increase cognitive load and decrease color evaluation. (2) Brightness has a non-linear relationship with visual perception and subjective evaluation, either too high or too low a brightness value will cause visual fatigue and thus reduce the color evaluation, and when the brightness is maintained at 40–80%, there will be a better color experience. (3) Warm tones such as red, orange, and yellow can strengthen the color perception to enhance the color evaluation of the façade; among cold tones, cyan and purple can also strengthen the visual perception of the color of the façade to enhance the color evaluation. After validation, the proposed new color comfort assessment method is feasible and comprehensive, and we hope it can provide a reference for the planning of old residential buildings and communities in the future. Full article

As a common vegetable variety, asparagus is rich in B vitamins, vitamin A, and trace elements such as folate, selenium, iron, manganese, and zinc. With the increasing market demand, China has become the world’s largest cultivated area for asparagus production and product exportation. However, traditional asparagus grading mostly relies on manual visual judgment and needs a lot of manpower input to carry out the classification operation, which cannot meet the needs of large-scale production. To address the high labor cost and labor-intensive production process resulting from the large amount of manpower input and low accuracy of existing asparagus grading devices, this study proposed an improved asparagus grading system and method based on TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) objective evaluation and SVM (support vector machine) prediction. The key structure of classification device was analyzed first, the key components were designed, and the structural parameters were determined by theoretical calculation. Through analysis of the factors affecting asparagus quality, three key attributes were determined: length, diameter, and bruises, which were used as reference attributes to conduct experimental analysis. Then, the graded control groups were set up, combining the TOPSIS principle with weighting, and a score for each asparagus sample was determined. These scores were compared with those of a graded control group to derive the grade of each asparagus, and these subsets of the dataset were used as the training set and the test set, excluding the error caused by the subjectivity of the manual judgment. Based on a comparison of the accuracies of different machine learning models, the support vector machine (SVM) was determined to be the most accurate, and four SVM methods were used to evaluate the test set: linear SVM, quadratic SVM, cubic SVM, and medium Gaussian SVM. The test results showed that the grading device was feasible for asparagus. The bruises had a large influence on asparagus quality. The training accuracy of the medium Gaussian SVM method was high (96%), whereas its test accuracy was low (86.67%). The training accuracies and test accuracy of the quadratic and cubic SVM methods were 93.34%. The quadratic SVM and cubic SVM were demonstrated to have better generalization ability than the medium Gaussian SVM method for predicting unknown grades of asparagus and meeting the operational requirements of the asparagus grading. Full article