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This paper aims to propose a dynamic parameter identification method based on sensitivity analysis for the 5-degree of freedom (DOF) hybrid robots, to solve the problems of too many identification parameters, complex model, difficult convergence of optimization algorithms and easy-to-fall into a locally optimal solution, and improve the efficiency and accuracy of dynamic parameter identification.

First, the dynamic parameter identification model of the 5-DOF hybrid robot was established based on the principle of virtual work. Then, the sensitivity of the parameters to be identified is analyzed by Sobol’s sensitivity method and verified by simulation. Finally, an identification strategy based on sensitivity analysis was designed, experiments were carried out on the real robot and the results were verified.

Compared with the traditional full-parameter identification method, the dynamic parameter identification method based on sensitivity analysis proposed in this paper converges faster when optimized using the genetic algorithm, and the identified dynamic model has higher prediction accuracy for joint drive forces and torques than the full-parameter identification models.

This work analyzes the sensitivity of the parameters to be identified in the dynamic parameter identification model for the first time. Then a parameter identification method is proposed based on the results of the sensitivity analysis, which can effectively reduce the parameters to be identified, simplify the identification model, accelerate the convergence of the optimization algorithm and improve the prediction accuracy of the identified model for the joint driving forces and torques.

As one of the tomographic imaging techniques, electrical capacitance tomography (ECT) is widely used in many industrial applications. While most ECT sensors have electrodes placed around a cylindrical chamber, the planar ECT sensor has been investigated for depth and defect detection. However, the planar ECT sensor has limited height and depth sensing capability due to its single-sided assessment with the use of only a single-plane design. The purpose of this paper is to investigate a dual-plane miniature planar 3D ECT sensor design using the 3 × 3 matrix electrode array.

The sensitivity map of dual-plane miniature planar 3D ECT sensor was analysed using 3D visualisation, the singular value decomposition and the axial resolution analysis. Then, the sensor was fabricated for performance analysis based on 3D imaging experiments.

The sensitivity map analysis showed that the dual-plane miniature planar 3D ECT sensor has enhanced the height sensing capability, and it is less ill-posed in 3D image reconstruction. The dual-plane miniature planar 3D ECT sensor showed a 28% improvement in reconstructed 3D image quality as compared to the single-plane sensor set-up.

The 3 × 3 matrix electrode array has been proposed to use only the necessary electrode pair combinations for image reconstruction. Besides, the increase in number of electrodes from the dual-plane sensor setup improved the height reconstruction of the test sample.

From the factors that affect shear strength of reinforced concrete (RC) beams, the study examines the effect of controversial parameters, width-to-depth (b/d) and effective length-to-depth (l_eff/d) ratio on shear strength of RC slender beams.

The researchers utilized a database of 676 experimental test results from ACI-DAfStb database, Conducted regression analysis to examine relationship between b/d and l_eff/d ratios and shear strength, compare and analyze sensitivity to changes in b/d and l_eff/d ratios for the selected 12 shear models for RC beams.

Increasing b/d ratio enhanced shear strength until b/d ˜ 3, but further increases had limited impact and increasing l_eff/d ratio resulted in decreased shear strength. From comparative analysis, the models provided by various design standards were found to be safe, with EC-2 and JSCE models being conservative. From considered research models, Campione and Arslan models were conservative, while Kim and White model were observed to be unsafe. Sensitivity analysis indicated ACI318-19, JSCE, CEB-FIP-90 and Arslan models were sensitive to changes in b/d and l_eff/d ratios. National code models generally captured shear strength characteristics well. Certain models suggested a constant/decreasing b/d effect despite observed shear strength enhancement. Most models indicated improved shear strength with an increasing l_eff/d ratio, contrary to experimental findings while TS500 and Hwang models aligned with experimental results.

The study's limitations include the dependence on the available database, which may not encompass all possible experimental scenarios. Further research should aim to expand the database and investigate additional parameters that may influence shear strength in RC beams.

The findings of this study have practical implications for the design and analysis of RC beams by suggesting that the width-to-depth and length-to-depth ratios should be carefully considered to optimize shear strength. The identified models can assist engineers in selecting appropriate shear strength prediction models based on specific design scenarios.

The study contributes to the advancement of knowledge in the field of reinforced concrete beam design, which has implications for the safety and reliability of structural systems. By understanding the factors influencing shear strength, engineers can design more efficient and robust structures, ensuring the safety of buildings and infrastructure.

This study provides valuable insights into the influence of the width-to-depth and effective length-to-depth ratios on shear strength in reinforced concrete beams. It contributes to the understanding of these factors and their impact on shear strength, addressing the lack of consensus among researchers. The comparative analysis of shear models and the sensitivity analyses add value by identifying the models that align better with experimental observations. The study emphasizes the need for accurate models that account for these factors and highlights the importance of further research to refine and develop improved predictive models.

The promotion of autonomous vehicles introduces privacy and security risks, underscoring the pressing need for responsible innovation implementation. To more effectively address the societal risks posed by autonomous vehicles, considering collaborative engagement of key stakeholders is essential. This study aims to provide insights into the governance of potential privacy and security issues in the innovation of autonomous driving technology by analyzing the micro-level decision-making processes of various stakeholders.

For this study, the authors use a nuanced approach, integrating key stakeholder theory, perceived value theory and prospect theory. The study constructs a model based on evolutionary game for the privacy and security governance mechanism of autonomous vehicles, involving enterprises, governments and consumers.

The governance of privacy and security in autonomous driving technology is influenced by key stakeholders’ decision-making behaviors and pivotal factors such as perceived value factors. The study finds that the governmental is influenced to a lesser extent by the decisions of other stakeholders, and factors such as risk preference coefficient, which contribute to perceived value, have a more significant influence than appearance factors like participation costs.

This study lacks an investigation into the risk sensitivity of various stakeholders in different scenarios.

The study delineates the roles and behaviors of key stakeholders and contributes valuable insights toward addressing pertinent risk concerns within the governance of autonomous vehicles. Through the study, the practical application of Responsible Innovation theory has been enriched, addressing the shortcomings in the analysis of micro-level processes within the framework of evolutionary game.

A parabolic trough solar collector is an advanced concentrated solar power technology that significantly captures radiant energy. Solar power will help different sectors reach their energy needs in areas where traditional fuels are in use. This study aims to examine the sensitivity analysis for optimizing the heat transfer and entropy generation in the Jeffrey magnetohydrodynamic hybrid nanofluid flow under the influence of motile gyrotactic microorganisms with solar radiation in the parabolic trough solar collectors. The influences of viscous dissipation and Ohmic heating are also considered in this investigation.

Governing partial differential equations are derived via boundary layer assumptions and nondimensionalized with the help of suitable similarity transformations. The resulting higher-order coupled ordinary differential equations are numerically investigated using the Runga-Kutta fourth-order numerical approach with the shooting technique in the computational MATLAB tool.

The numerical outcomes of influential parameters are presented graphically for velocity, temperature, entropy generation, Bejan number, drag coefficient and Nusselt number. It is observed that escalating the values of melting heat parameter and the Prandl number enhances the Nusselt number, while reverse effect is observed with an enhancement in the magnetic field parameter and bioconvection Lewis number. Increasing the magnetic field and bioconvection diffusion parameter improves the entropy and Bejan number.

Nanotechnology has captured the interest of researchers due to its engrossing performance and wide range of applications in heat transfer and solar energy storage. There are numerous advantages of hybrid nanofluids over traditional heat transfer fluids. In addition, the upswing suspension of the motile gyrotactic microorganisms improves the hybrid nanofluid stability, enhancing the performance of the solar collector. The use of solar energy reduces the industry’s dependency on fossil fuels.

The main stance of this paper is to draw an authentic and rigorous outlook in terms of the financial and operational performance of small lodging establishments (SLEs) and put forth achievable and practical economic solutions that demonstrate the relative effectiveness of the adopted measures. This paper also suggests practical solutions to help minimize SLEs' financial vulnerability to long-term crisis and to boost their resilience with relative measures by applying recovery revival strategies for this particular segment of the lodging industry.

The authors have picked a locally owned resort hotel in Central Florida area and structured a real-life, case study-based inductive approach that is purposeful and offers rich economic outlook and analysis for the entire lodging industry, especially for the resort-hotel type of accommodation facilities. The main reason for why they only focus on one company is that they can fully understand the financial effects of COVID-19 on resort type of hotels and layout countering strategies. To achieve paper objectives, they have implemented cost–benefit (C–B), break-even (B-E) analyses along with a sensitivity testing approach.

The most striking result was that during the state-mandated shutdown period in 2020, overhead and overall operational costs associated with room sales and revenues were very high during this period that shrank the contribution margin ratio for rooms CMRw (room) and eventually yielded high sales volumes to be achieved at the B-E points vs lower sales volumes with almost the same average daily rate (ADR) levels needed for the B-E levels.

Future studies should specifically delve further into a portfolio of SLEs in the region or state or nation wise because the units comprising the SLEs might be too small to muster the changes required to bounce forward for the entire lodging industry in the world.

The resort's revenue re-optimization focus should center on financial re-benchmarking and business re-viability stress under different levels of shock scenarios. According to the different scenarios and calibrations for the ADRs, room nights, net present values (NPVs) of cash flows and profit margins derived from our main analyses, minimizing expenses and preserving cash would be the best key strategy for financial recovery during an ongoing COVID-19 pandemic.

It is obvious that the lodging, hospitality and tourism industry are the hardest-hit industries by the harsh and adverse effects of COVID-19. The effects of pandemic are differently shaped on operations in different industries and subsectors. Therefore, the operational and financial evaluation for the SLEs as the core and a catalyst in the entire lodging industry can shed a light on the strategic financial recovery procedures with broadly applicable real-life and endogenous capabilities and reasoning.

This study aims to explore the relationship between risk governance characteristics (chief risk officer [CRO], chief financial officer [CFO] and senior directors [SENIOR]) and regulatory adjustments (RAs) in Organization for Economic Cooperation and Development public commercial banks.

Using principal component analysis (PCA) and regression models, the research analyzes a representative data set of these banks.

A significant negative correlation between risk governance characteristics and RAs is found. Sensitivity analysis on the regulatory Tier 1 capital ratio and the total capital ratio indicates mixed outcomes, suggesting a complex relationship that warrants further exploration.

The study’s limited sample size calls for further research to confirm findings and explore risk governance’s impact on banks’ capital structures.

Enhanced risk governance could reduce RAs, influencing banking policy.

The study advocates for improved banking regulatory practices, potentially increasing sector stability and public trust.

This study contributes to understanding risk governance’s role in regulatory compliance, offering insights for policymaking in banking.

External stakeholder risks (ESRs) caused by unfavorable behaviors hinder the success of project portfolios (PPs). However, due to complex project dependency and numerous risk causality in PPs, assessing ESRs is difficult. This research aims to solve this problem by developing an ESR-PP two-layer fuzzy Bayesian network (FBN) model.

A two-layer FBN model for evaluating ESRs with risk causality and project dependency is proposed. The directed acyclic graph (DAG) of an ESR-PP network is first constructed, and the conditional probability tables (CPTs) of the two-layer network are further presented. Next, based on the fuzzy Bayesian network, key variables and the impact of ESRs are assessed and analyzed by using GeNIe2.3. Finally, a numerical example is used to demonstrate and verify the application of the proposed model.

The proposed model is a useable and effective approach for ESR assessment while considering risk causality and project dependency in PPs. The impact of ESRs on PP can be calculated to determine whether to control risk, and the most critical and heavily contributing risks and project(s) in the developed model are identified based on this.

This study extends prior research on PP risk in terms of stakeholders. ESRs that have received limited attention in the past are explored from an interaction perspective in the PP domain. A new two-layer FBN model considering risk causality and project dependency is proposed, which can synthesize different dependencies between projects.

The geometric parameters of the compressor blade have a noteworthy influence on compressor stability, which should be meticulously designed. However, machining inaccuracies cause the blade geometric parameters to deviate from the ideal design, and the geometric deviation exhibits high randomness. Therefore, the purpose of this study is to quantify the uncertainty and analyze the sensitivity of the impact of blade geometric deviation on compressor stability.

In this work, the influence of blade geometric deviation is analyzed based on a subsonic compressor rotor stage, and three-dimensional numerical simulations are used to compute samples with different geometric features. A method of combining Halton sequence and non-intrusive polynomial chaos is adopted to carry out uncertainty quantitative analysis. Sobol’ index and Spearman correlation coefficient are used to analysis the sensitivity and correlation between compressor stability and blade geometric deviation, respectively.

The results show that the compressor stability is most sensitive to the tip clearance deviation, whereas deviations in the leading edge radius, trailing edge radius and chord length have minimal impact on the compressor stability. And, the effects of various blade geometric deviations on the compressor stability are basically independent and linearly superimposed.

This work provided a new approach for uncertainty quantification in compressor stability analysis. The conclusions obtained in this work provide some reference value for the manufacturing and maintenance of rotor blades.

Water pipes degrade over time for a variety of pipe-related, soil-related, operational, and environmental factors. Hence, municipalities are necessitated to implement effective maintenance and rehabilitation strategies for water pipes based on reliable deterioration models and cost-effective inspection programs. In the light of foregoing, the paramount objective of this research study is to develop condition assessment and deterioration prediction models for saltwater pipes in Hong Kong.

As a perquisite to the development of condition assessment models, spherical fuzzy analytic hierarchy process (SFAHP) is harnessed to analyze the relative importance weights of deterioration factors. Afterward, the relative importance weights of deterioration factors coupled with their effective values are leveraged using the measurement of alternatives and ranking according to the compromise solution (MARCOS) algorithm to analyze the performance condition of water pipes. A condition rating system is then designed counting on the generalized entropy-based probabilistic fuzzy C means (GEPFCM) algorithm. A set of fourth order multiple regression functions are constructed to capture the degradation trends in condition of pipelines overtime covering their disparate characteristics.

Analytical results demonstrated that the top five influential deterioration factors comprise age, material, traffic, soil corrosivity and material. In addition, it was derived that developed deterioration models accomplished correlation coefficient, mean absolute error and root mean squared error of 0.8, 1.33 and 1.39, respectively.

It can be argued that generated deterioration models can assist municipalities in formulating accurate and cost-effective maintenance, repair and rehabilitation programs.