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The purpose of this paper is to estimate aerodynamic parameters accurately from flight data in the presence of unknown noise characteristics.

The introduced adaptive filter scheme is composed of two parallel UKFs. At every time‐step, the master UKF estimates the states and parameters using the noise covariance obtained by the slave UKF, while the slave UKF estimates the noise covariance using the innovations generated by the master UKF. This real time innovation‐based adaptive unscented Kalman filter (UKF) is used to estimate aerodynamic parameters of aircraft in uncertain environment where noise characteristics are drastically changing.

The investigations are initially made on simulated flight data with moderate to high level of process noise and it is shown that all the aerodynamic parameter estimates are accurate. Results are analyzed based on Monte Carlo simulation with 4000 realizations. The efficacy of adaptive UKF in comparison with the other standard Kalman filters on the estimation of accurate flight stability and control derivatives from flight test data in the presence of noise, are also evaluated. It is found that adaptive UKF successfully attains better aerodynamic parameter estimation under the same condition of process noise intensity changes.

The presence of process noise complicates parameter estimation severely. Since the non‐measurable process noise makes the system stochastic, consequently, it requires a suitable state estimator to propagate the states for online estimation of aircraft aerodynamic parameters from flight data.

This is the first paper highlighting the process noise intensity change on real time estimation of flight stability and control parameters using adaptive unscented Kalman filter.

This paper aims to explore question and answer (Q&A) participation and behavioral patterns on academic social networking sites (ASNSs) from the perspective of multiple subjects such as academic, corporate and government institutions.

Focused on the Q&A service of ASNSs, this study chooses ResearchGate (RG) as the target ASNS and collects a large-scale data set from it, involving a sample of users and a Q&A sample about academic, corporate and government institutions. First, it studies the law of Q&A participation and the distribution of the type of user according to the sample of users. Second, it compares question-asking behavior and question-answering behavior stimulated by questions among the three types of institutions based on the Q&A sample. Finally, it discusses the Q&A participation and behavioral patterns of the three types of institutions in academic Q&A exchanges with full consideration of institutional attributes, and provides some suggestions for institutions and ASNSs.

The results show that these three types of institutions generally have a low level of participation in the Q&A service of RG, and the numbers of questions and answers proposed by institutional users conform to the power-law distribution. There are differences in Q&A participation and Q&A behavioral patterns among academic, corporate and government institutions. Government and academic institutions have more users participating in the Q&A service and their users are more willing to ask questions, while corporate institutions have fewer users who participate in the Q&A service and their users are inclined to provide answers. Questions from corporate institutions attract much more attention than those from the other two types of institutions.

This study reveals and compares the Q&A participation and the behavioral patterns of the three types of institutions in academic Q&A, thus deepening the understanding of the attributes of institutions in the academic information exchange context. In practice, the results can help guide different institutions to use the Q&A service of ASNSs more effectively and help ASNSs to better optimize their Q&A service.

This study aims to research the large cross-section tunnel stability evaluation method corrected after considering the thickness-span ratio.

First, taking the Liuyuan Tunnel of Huanggang-Huangmei High-Speed Railway as an example and taking deflection of the third principal stress of the surrounding rock at a vault after tunnel excavation as the criterion, the critical buried depth of the large section tunnel was determined. Then, the strength reduction method was employed to calculate the tunnel safety factor under different rock classes and thickness-span ratios, and mathematical statistics was conducted to identify the relationships of the tunnel safety factor with the thickness-span ratio and the basic quality (BQ) index of the rock for different rock classes. Finally, the influences of thickness-span ratio, groundwater, initial stress of rock and structural attitude factors were considered to obtain the corrected BQ, based on which the stability of a large cross-section tunnel with a depth of more than 100 m during mechanized operation was analyzed. This evaluation method was then applied to Liuyuan Tunnel and Cimushan No. 2 Tunnel of Chongqing Urban Expressway for verification.

This study shows that under different rock classes, the tunnel safety factor is a strict power function of the thickness-span ratio, while a linear function of the BQ to some extent. It is more suitable to use the corrected BQ as a quantitative index to evaluate tunnel stability according to the actual conditions of the site.

The existing industry standards do not consider the influence of buried depth and span in the evaluation of tunnel stability. The stability evaluation method of large section tunnel considering the correction of overburden span ratio proposed in this paper achieves higher accuracy for the stability evaluation of surrounding rock in a full or large-section mechanized excavation of double line high-speed railway tunnels.

The purpose of this study was to prepare carboxylated attapulgite (APT-COOH) and then be used as one of the ligands to prepare metal organic framework (MOF) hybrid materials to reduce the cost of MOF materials and improve the dispersed condition of APT. And then the materials were used to enrich anionic dye Congo red from aqueous solution.

The MOF hybrid materials were designed by means of facile reflux method rather than hydrothermal method, characterized by Thermogravimetric Analysis (TGA), Fourier Transform Infrared (FTIR) Spectrometer and pore structure. The dispersed degree of APT-COOH in the MOF materials was validated according to adsorption efficiency for Congo red.

Due to introduction of APT-COOH, the microenvironment of the MOF materials changed, leading to different adsorption behaviors. Compared to the MOF material without APT-COOH, the adsorption capacities of the hybridized MOF materials with different amounts of APT-COOH introduced increased by 4.58% and 15.55%, respectively, as the initial concentration of Congo red solution of 300 mg/L. Meantime, hybridized MOF materials were suitable to remove Congo red with low concentration, while the MOF material without APT-COOH was appropriate to enrich Congo red with high concentration.

The microstructure of MOF hybrid materials in detail is the further and future investigation.

This study will provide a method to reduce the cost of MOF materials and a theoretical support to treat anionic dyes from aqueous solution.

APT-COOH was prepared and used as one of the ligands to synthesize MOF material to improve the dispersed degree of APT-COOH and reduce the cost of the MOF materials. The adsorption efficiency was greatly enhanced with low concentration of Congo red solution, and the results indicated that hydrogen bonding, electrostatic interaction, and p-p conjugation were involved in the adsorption process. The prepared MOFs materials exhibited excellent adsorption efficiency, which made the present materials highly promising and potentially useful in practical application as adsorbents to enrich anionic dyes such as Congo red from aqueous solution.

Presents development and characterisation of a new metal/polymer composite material for use in fused deposition modelling (FDM) rapid prototyping process with the aim of application to direct rapid tooling. The work represents a major development in reducing the cost and time in rapid tooling.

The material consists of iron particles in a nylon type matrix. The detailed formulation and characterisation of the thermal properties of the various combinations of the new composites are investigated experimentally. Results are compared with other metal/polymer composites used in rapid tooling.

The feedstock filaments of this composite have been produced and used successfully in the unmodified FDM system for direct rapid tooling of injection moulding inserts. Thermal properties are found to be acceptable for rapid tooling applications for injection moulding.

Introduces an entirely new metal based composite material for direct rapid tooling application using FDM RP system with desired thermal properties and characteristics. This will reduce the cost and time of manufacturing tooling inserts and dies for injection moulding.

Digital technologies (DTs) are one of the most important and beneficial ways to enhance the effectiveness of supply chain finance activities. Based on resource orchestration theory, the purpose of this study is to apply the “resources-capability-performance” framework to investigate how DT deployment (in terms of both breadth and depth) impacts supply chain financing performance (SCFP).

Hierarchical regression analysis was applied in the theoretical modeling examination. Through the sample of 380 survey questionnaires from the Chinese manufacturing industry, this study empirically validated the proposed model.

Results of this study present that both the breadth and depth of DT deployment positively impact SCFP, whereas supply chain transparency (SCT) acts as a mediator between them. Moreover, financing alignment positively moderates the relationship between the breadth of DT deployment and SCT.

From an integrated perspective, this study highlights the dual deployment ways of DTs to improve SCFP. Moreover, this research further enriches and extends the application of resource orchestration theory by providing theoretical mechanisms for the mediating role of SCT and elucidating the moderating role of financing alignment.

This study aims to investigate how small and medium-sized enterprises (SMEs’) supply chain specific investment (SCSIs) affects supply chain financing performance (SCFP) in the innovative industrial finance model, and further analyze the internal mechanisms and important contextual factors.

Based on signaling theory, this study constructs a mediating and moderating model to examine the influencing mechanisms of SMEs’ SCSIs on SCFP, including the mediating effect of opportunism and the moderating effect of digital technology deployment (DTD). A multiple regression analysis is conducted to verify the theoretical hypotheses, using questionnaire data collected from 288 SMEs in China.

The empirical findings indicate that both SMEs’ supply chain asset-specific and relationship-specific investments can significantly promote SCFP. Also, SMEs’ SCSIs can improve SCFP by reducing the occurrence of opportunism perceived by supply chain partners. The breadth of DTD positively moderates the relationship between the two types of SCSIs and SCFP, while the depth of DTD has no significant moderating effect on the relationship between SCSIs and SCFP.

This study has discussed the important and novel issue of how financially distressed SMEs can send effective signals to financial institutions by increasing their SCSIs in supply chain finance mode. By revealing the influencing mechanisms of SMEs’ SCSIs on SCFP, this study contributes to expanding the research on the antecedents of SCFP from the dimension of interorganizational transactions. This study also enriches the perspectives of signaling theory by exploring the interaction between signal sender and signal intermediary.

Damage of engineering structures is a nonlinear evolutionary process that spans across both material and structural levels, from mesoscale to macroscale. This paper aims to provide a comprehensive review of damage analysis methods at both the material and structural levels.

This study provides an overview of multiscale damage analysis of engineering structures, including its definition and significance. Current status of damage analysis at both material and structural levels is investigated, by reviewing damage models and prediction methods from single-scale to multiscale perspectives. The discussion of prediction methods includes both model-based simulation approaches and data-driven techniques, emphasizing their roles and applications. Finally, summarize the main findings and discuss potential future research directions in this field.

In the material level, damage research primarily focuses on the degradation of material properties at the macroscale using continuum damage mechanics (CDM). In contrast, at the mesoscale, damage research involves analyzing material behavior in the meso-structural domain, focusing on defects like microcracks and void growth. In structural-level damage analysis, the macroscale is typically divided into component and structural scales. The component scale examines damage progression in individual structural elements, such as beams and columns, often using detailed finite element or mesoscale models. The structural scale evaluates the global behavior of the entire structure, typically using simplified models like beam or shell elements.

To achieve realistic simulations, it is essential to include as many mesoscale details as possible. However, this results in significant computational demands. To balance accuracy and efficiency, multiscale methods are employed. These methods are categorized into hierarchical approaches, where different scales are processed sequentially, and concurrent approaches, where multiple scales are solved simultaneously to capture complex interactions across scales.

The purpose of this study is to present the state of the art for fiber Bragg grating (FBG) acceleration sensing technologies from two aspects: the principle of the measurement dimension and the principle of the sensing configuration. Some commercial sensors have also been introduced and future work in this field has also been discussed. This paper could provide an important reference for the research community.

This review is to present the state of the art for FBG acceleration sensing technologies from two aspects: the principle of the measurement dimension (one-dimension and multi-dimension) and the principle of the sensing configuration (beam type, radial vibration type, axial vibration type and other composite structures).

The current research on developing FBG acceleration sensors is mainly focused on the sensing method, the construction and design of the elastic structure and the design of a new information detection method. This paper hypothesizes that in the future, the following research trends will be strengthened: common single-mode fiber grating of the low cost and high utilization rate; high sensitivity and strength special fiber grating; multi-core fiber grating for measuring single-parameter multi-dimensional information or multi-parameter information; demodulating equipment of low cost, small volume and high sampling frequency.

The principle of the measurement dimension and principle of the sensing configuration for FBG acceleration sensors have been introduced, which could provide an important reference for the research community.

In a typical problem of allocating a fixed resource across a set of decision-making units (DMUs), the decision maker will decide how much resource to allocate to each DMU. Real-world decision-making contexts frequently pose challenges in precisely assessing each DMU’s performance on distinct parameters and in ascertaining the relative importance of these parameters, since resource allocation often precedes the decision-making process. Conventional decision-making approaches fall short in effectively addressing such complexities. In this paper, the authors propose a stochastic multi-attribute acceptability analysis (SMAA)-based model that can deal with the resource allocation problem with uncertain attribute values and unknown attribute weights.

The SMAA-based model first regards the resource to be allocated as an evaluation attribute and then proposes a fair resource allocation solution that minimizes the gap between the holistic acceptability of each DMU. To address the optimization of the SMAA-based model, two heuristic strategies were devised: a modified particle swarm optimization (PSO) approach and an adaptive algorithm, both designed to yield acceptable outcomes within a constrained time frame.

The resource allocation model based on SMAA can provide an effective analytical framework for the resource allocation problem of different DMUs in uncertain environments. Meanwhile, the PSO algorithm and adjustment algorithm proposed in this paper can help decision-makers quickly find a fair resource allocation plan.

Primarily, this study introduces a novel approach rooted in SMAA for tackling resource allocation dilemmas where attribute values are uncertain and weight information is absent. Secondly, a fairness-centric strategy that minimizes the disparity in overall acceptability among various DMUs is presented. Thirdly, two heuristic algorithms, namely, PSO algorithm and adjustment algorithm, are developed to solve the problem.