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The purpose of this paper is to develop and compare conventional and neural network-based controllers for gas turbines.

Design of two different controllers is considered. These controllers consist of a NARMA-L2 which is an artificial neural network-based nonlinear autoregressive moving average (NARMA) controller with feedback linearization, and a conventional proportional-integrator-derivative (PID) controller for a low-power aero gas turbine. They are briefly described and their parameters are adjusted and tuned in Simulink-MATLAB environment according to the requirement of the gas turbine system and the control objectives. For this purpose, Simulink and neural network-based modelling is used. Performances of the controllers are explored and compared on the base of design criteria and performance indices.

It is shown that NARMA-L2, as a neural network-based controller, has a superior performance to PID controller.

This study aims at using artificial intelligence in gas turbine control systems.

This paper provides a novel methodology for control of gas turbines.

In typical model-based calibration, linearization errors are derived inevitably, and non-negligible negative impact will be induced on the identification results if the rotational kinematic errors are not small enough or the lengths of links are too long, which is common in the industrial cases. Thus, an accurate two-step kinematic calibration method minimizing the linearization errors is presented for a six-DoF serial robot to improve the calibration accuracy.

The negative impact of linearization on identification accuracy is minimized by removing the responsible linearized kinematic errors from the complete kinematic error model. Accordingly, the identification results of the dimension-reduced new model are accurate but not complete, so the complete kinematic error model, which achieves high identification accuracy of the rest of the error parameters, is combined with this new model to create a two-step calibration procedure capable of highly accurate identification of all the kinematic errors.

The proportions of linearization errors in measured pose errors are quantified and found to be non-negligible with the increase of rotational kinematic errors. Thus, negative impacts of linearization errors are analyzed quantitatively in different cases, providing the basis for allowed kinematic errors in the new model. Much more accurate results were obtained by using the new two-step calibration method, according to a comparison with the typical methods.

This new method achieves high accuracy with no compromise on completeness, is easy to operate and is consistent with the typical method because the second step with the new model is conveniently combined without changing the sensors or measurement instrument setup.

An accurate prediction of the deformation of retaining structures is critical for ensuring the stability and safety of braced deep excavations, while the high nonlinear and complex nature of the deformation makes the prediction challenging. This paper proposes an explainable boosted combining global and local feature multivariate regression (EB-GLFMR) model with high accuracy, robustness and interpretability to predict the deformation of retaining structures during braced deep excavations.

During the model development, the time series of deformation data is decomposed using a locally weighted scatterplot smoothing technique into trend and residual terms. The trend terms are analyzed through multiple adaptive spline regressions. The residual terms are reconstructed in phase space to extract both global and local features, which are then fed into a gradient-boosting model for prediction.

The proposed model outperforms other established approaches in terms of accuracy and robustness, as demonstrated through analyzing two cases of braced deep excavations.

The model is designed for the prediction of the deformation of deep excavations with stepped, chaotic and fluctuating features. Further research needs to be conducted to expand the model applicability to other time series deformation data.

The model provides an efficient, robust and transparent approach to predict deformation during braced deep excavations. It serves as an effective decision support tool for engineers to ensure the stability and safety of deep excavations.

The model captures the global and local features of time series deformation of retaining structures and provides explicit expressions and feature importance for deformation trends and residuals, making it an efficient and transparent approach for deformation prediction.

This study aims to establish a centralized decision-making game model and manufacturer-led Stackelberg game model based on factors of risk aversion of supply chain members and product greenness. The research aims to study whether the introduction of the “cost + risk sharing” contract affects coordination of this type of green supply by calculating the optimal decision of each mode.

This research designs a supply chain model under centralized and decentralized decision-making. This model uses the Stackelberg game to calculate the optimal decision under decentralized decision-making to evaluate the effect of a green supply chain and then analyze the “cost + risk sharing” contract and the degree of coordination of the supply chain. A sensitivity analysis is conducted on the centralized mode for the impact of variables on the supply chain.

This research finds a double marginalization effect in decentralized decision-making, and the risk aversion coefficient plays a decisive role in the utility of supply chain members. The specific range of risk- and cost-sharing factors allows supply chain members to achieve Pareto improvements and provides decision-making based on the corresponding management strategies according to each other’s risk preference degree.

The influence of each variable on the green supply chain in the centralized mode is studied by MATLAB numerical simulation. It provides reference for green supply chain members to formulate corresponding management strategies according to each other's risk preference degree.

This research innovatively considers manufacturers and retailers to explore the market demand for product greenness. It introduces a novel “cost + risk sharing” contract to coordinate the green supply chain.

Adaptive slicing is a key step in 3D printing as it is closely related to the building time and the surface quality. This study aims to develop an adaptive layering algorithm that can coordinate the optimization of printing quality and efficiency to meet different printing needs.

A multiobjective optimization model is established for printing quality, printing time and layer height based on the variation of surface features, profile slope and curvature of the model. The optimal solution is found by an improved method combining Newton's method and gradient method and adapts to different printing requirements by adjusting the parameter thresholds.

Several benchmarks are applied to verify this new method. The proposed method has also been compared with the uniform layering method, it reduces the volume error by 46.4% and shortens the printing time by 28.1% and is compared with five existing adaptive layering methods to demonstrate its superior performance.

Compared with other methods with only one layered result, this method is a demand-oriented algorithm that can obtain different results according to different needs and it can reach a trade-off between the building time and the surface quality.

The purpose of this study aims to modify a self-mixing laser mouse as an extremely cost-effective displacement sensor to measure the mechanical oscillation of a commercial shaker and a nano-positioning stage.

This kind of laser mouse, mostly consisting of a pair of vertical cavity surface emitting lasers, two photodiodes and an integrated signal processing unit, is capable of directly giving the x-axis and y-axis components of the measured vibrating displacement. Based on the laser self-mixing interference, the velocity of the object is coded into the Doppler frequency shift of the feedback light, which allows accurate determination of the vibration of the object.

A commercial shaker has been used to provide standard harmonic oscillation to test the displacement sensor. Within a vibrating frequency range of 110 Hz, the experimental results show that the micrometer scale resolution has been achieved at the velocity of up to 2 m/s, which is much improved compared with the image-based optical mouse. Furthermore, the measurements of the two dimensional displacement of a nano-positioning stage are performed as well. The minimum measurable velocity limit for this sensor has been discussed in detail, and the relative measurement error can be greatly reduced by appropriate selection of the modulation frequency of the triangular injection current.

These results demonstrate the feasibility of this device for the industrial vibration sensing applications.

The purpose of this paper is to explore the relationship between extrinsic, intrinsic and social rewards and two components of organizational commitment and finally Chinese workers turnover intention in public and private sector.

A questionnaire was utilized as the method for data collection. Structural equation modeling was utilized to examine survey data obtained from 202 employees in the southern part of China.

The findings exhibit that extrinsic, social and intrinsic rewards were significantly related to affective and normative commitment. Findings suggest that satisfaction with extrinsic benefits, supervisor support, coworker support, autonomy, training and participation in decision making has substantial impact on employee’s affective and normative commitment. However, affective and normative commitment was negatively related to employee turnover intention.

This study covers different public and private-sector organization employees working in China. Therefore other geographical areas could be designated for future research endeavors with a bigger sample size.

With the purpose of boosting employee commitment, managers must provide their employees with greater autonomy, appropriate training and participation in decision making in the organization, as well as enhancing supervisor and coworker support.

This research investigates how Chinese employees with different categories of organizational rewards react to different kinds of organizational commitment and turnover intention in Chinese organizational context.

This study aims to analyse 508 financially distressed firm-year observations for the period 2010–2018 of Pakistan Stock Exchange (PSX) listed firms to examine the magnitude of indirect financial distress costs (IFDC) and to investigate which firm-specific variable is relatively important in explaining these indirect costs. This will not only enrich empirical literature but also helpful in cross-country comparison.

Optimal model selection along with panel data analysis technique is used to select the most optimal model to observe the findings. Financial distress is measure through Altman’s Z-score and firm-specific variables cover leverage, level of intangible assets, investment policy, tangible assets, firm’s size, level of liquid assets and Tobin’s Q of sample firms.

The findings of this study show that the average size of IFDC for the sample observations is 6.70%. In addition to this, finding further suggest that leverage, the level of intangible assets and changes in investment policy have positive while the size of the firm and Tobin’s Q have a significant negative impact on IFDC. Further, this paper argues that the level of tangible assets and liquid assets are statistically unimportant in observing the IFDC for PSX financially distressed firm-year observations.

The findings of this study provide more insight to corporate managers and investors about the association between firm-specific financial characteristics and IFDC concerning Pakistani firms. Furthermore, this study contributes to the existing literature by adding new evidence from developing countries such as Pakistan which are helpful for regulatory bodies and policymakers in the formulation of long-term strategies to manage the financial distress costs.

The study extends the body of existing literature on IFDC regarding Pakistan. The results suggest that policymakers may pay special attention to the quality of a firm’s capital structure strategies while predicting corporate financial distress costs.

The color TV industry in China has become a mature industry. Its development demonstrates and provides reference implications for how developing industries within a country can achieve a technological leap. This paper aims to address this issue.

An exploratory case study approach is taken to find the key factors in the technological catch‐up of China's traditional industry.

In the study it is found that China's color TV industry, as a mature traditional industry, has four‐dimensional key factors affecting the catch‐up of technology in the flat‐panel stage: market; merger and acquisition; international cooperation innovation for patent; and the roles of internal reform. “Market” is the window of opportunity and challenge; “merger and acquisition” is the key factor for making the patent convert from external mode to internal mode by deviant‐track. The “international cooperation innovation for patent” is also a key factor to ensure achievement of technology catch‐up and sustainable technological innovation. The role of internal reform promoter is the key factor in the technological catch‐up process in which taking entrepreneur as the core.

The article describes Changhong's flat‐panel TV technology catch‐up mode, analyzes four‐dimensional key factors affecting the technology catch‐up of China's color TV industry; the Chinese color TV industry as mature industry; and discusses which developments have demonstrated how to achieve a technological leap in developing industries.