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Sustainable development is inseparable from rational and responsible use of resources and promotion of green entrepreneurship. The contemporary green development agenda encompasses climate, economic, technical, social, cultural, and political dimensions. International efforts to greening the global development are conducted by the major economies, including China as the world’s largest consumer of energy and the biggest emitter of greenhouse gases. China is aware of its environmental problems, as well as of its part of the overall responsibility for the accomplishment of the sustainable development goals. By means of the decarbonization efforts, the latter are integrated both into the national development agenda (the concept of ecological civilization) and China’s international initiatives (the greening narrative within the Belt and Road Initiative). Over the past decade, China has made a breakthrough on the way to promoting green entrepreneurship and greening of its development (better quality of air and water, renewable energy, electric vehicles, and organic farming). On the other hand, emissions remain high, agricultural land loses productivity, and freshwater resources degrade due to climate change. In conventional industries (oil, coal mining, and electric and thermal energy), decarbonization faces an array of impediments. In this chapter, the authors summarize fundamental provisions of China’s approach to building an ecological civilization and measures to reduce emissions and achieve the carbon neutrality status within the nearest decades. The analysis of obstacles to the decarbonization of the economy and possible prospects for the development of green entrepreneurship summarizes China’s practices for possible use in other countries.

To provide high torques needed to move a robot’s links, electric actuators are followed by a transmission system with a high transmission rate. For instance, gear ratios of 100:1 are often used in the joints of a robotic manipulator. This results into an actuator with large mechanical impedance (also known as nonback-drivable actuator). This in turn generates high contact forces when collision of the robotic mechanism occur and can cause humans’ injury. Another disadvantage of electric actuators is that they can exhibit overheating when constant torques have to be provided. Comparing to electric actuators, pneumatic actuators have promising properties for robotic applications, due to their low weight, simple mechanical design, low cost and good power-to-weight ratio. Electropneumatically actuated robots usually have better friction properties. Moreover, because of low mechanical impedance, pneumatic robots can provide moderate interaction forces which is important for robotic surgery and rehabilitation tasks. Pneumatic actuators are also well suited for exoskeleton robots. Actuation in exoskeletons should have a fast and accurate response. While electric motors come against high mechanical impedance and the risk of causing injuries, pneumatic actuators exhibit forces and torques which stay within moderate variation ranges. Besides, unlike direct current electric motors, pneumatic actuators have an improved weight-to-power ratio and avoid overheating problems.

The aim of this paper is to analyze a nonlinear optimal control method for electropneumatically actuated robots. A two-link robotic exoskeleton with electropneumatic actuators is considered as a case study. The associated nonlinear and multivariable state-space model is formulated and its differential flatness properties are proven. The dynamic model of the electropneumatic robot is linearized at each sampling instance with the use of first-order Taylor series expansion and through the computation of the associated Jacobian matrices. Within each sampling period, the time-varying linearization point is defined by the present value of the robot’s state vector and by the last sampled value of the control inputs vector. An H-infinity controller is designed for the linearized model of the robot aiming at solving the related optimal control problem under model uncertainties and external perturbations. An algebraic Riccati equation is solved at each time-step of the control method to obtain the stabilizing feedback gains of the H-infinity controller. Through Lyapunov stability analysis, it is proven that the robot’s control scheme satisfies the H-infinity tracking performance conditions which indicate the robustness properties of the control method. Moreover, global asymptotic stability is proven for the control loop. The method achieves fast convergence of the robot’s state variables to the associated reference trajectories, and despite strong nonlinearities in the robot’s dynamics, it keeps moderate the variations of the control inputs.

In this paper, a novel solution has been proposed for the nonlinear optimal control problem of robotic exoskeletons with electropneumatic actuators. As a case study, the dynamic model of a two-link lower-limb robotic exoskeleton with electropneumatic actuators has been considered. The dynamic model of this robotic system undergoes first approximate linearization at each iteration of the control algorithm around a temporary operating point. Within each sampling period, this linearization point is defined by the present value of the robot’s state vector and by the last sampled value of the control inputs vector. The linearization process relies on first-order Taylor series expansion and on the computation of the associated Jacobian matrices. The modeling error which is due to the truncation of higher-order terms from the Taylor series is considered to be a perturbation which is asymptotically compensated by the robustness of the control algorithm. To stabilize the dynamics of the electropneumatically actuated robot and to achieve precise tracking of reference setpoints, an H-infinity (optimal) feedback controller is designed. Actually, the proposed H-infinity controller for the model of the two-link electropneumatically actuated exoskeleton achieves the solution of the associated optimal control problem under model uncertainty and external disturbances. This controller implements a min-max differential game taking place between: (i) the control inputs which try to minimize a cost function which comprises a quadratic term of the state vector’s tracking error and (ii) the model uncertainty and perturbation inputs which try to maximize this cost function. To select the stabilizing feedback gains of this H-infinity controller, an algebraic Riccati equation is being repetitively solved at each time-step of the control method. The global stability properties of the H-infinity control scheme are proven through Lyapunov analysis.

Pneumatic actuators are characterized by high nonlinearities which are due to air compressibility, thermodynamics and valves behavior and thus pneumatic robots require elaborated nonlinear control schemes to ensure their fast and precise positioning. Among the control methods which have been applied to pneumatic robots, one can distinguish differential geometric approaches (Lie algebra-based control, differential flatness theory-based control, nonlinear model predictive control [NMPC], sliding-mode control, backstepping control and multiple models-based fuzzy control). Treating nonlinearities and fault tolerance issues in the control problem of robotic manipulators with electropneumatic actuators has been a nontrivial task.

The novelty of the proposed control method is outlined as follows: preceding results on the use of H-infinity control to nonlinear dynamical systems were limited to the case of affine-in-the-input systems with drift-only dynamics. These results considered that the control inputs gain matrix is not dependent on the values of the system’s state vector. Moreover, in these approaches the linearization was performed around points of the desirable trajectory, whereas in the present paper’s control method the linearization points are related with the value of the state vector at each sampling instance as well as with the last sampled value of the control inputs vector. The Riccati equation which has been proposed for computing the feedback gains of the controller is novel, so is the presented global stability proof through Lyapunov analysis. This paper’s scientific contribution is summarized as follows: (i) the presented nonlinear optimal control method has improved or equally satisfactory performance when compared against other nonlinear control schemes that one can consider for the dynamic model of robots with electropneumatic actuators (such as Lie algebra-based control, differential flatness theory-based control, nonlinear model-based predictive control, sliding-mode control and backstepping control), (ii) it achieves fast and accurate tracking of all reference setpoints, (iii) despite strong nonlinearities in the dynamic model of the robot, it keeps moderate the variations of the control inputs and (iv) unlike the aforementioned alternative control approaches, this paper’s method is the only one that achieves solution of the optimal control problem for electropneumatic robots.

The use of electropneumatic actuation in robots exhibits certain advantages. These can be the improved weight-to-power ratio, the lower mechanical impedance and the avoidance of overheating. At the same time, precise positioning and accurate execution of tasks by electropneumatic robots requires the application of elaborated nonlinear control methods. In this paper, a new nonlinear optimal control method has been developed for electropneumatically actuated robots and has been specifically applied to the dynamic model of a two-link robotic exoskeleton. The benefit from using this paper’s results in industrial and biomedical applications is apparent.

A comparison of the proposed nonlinear optimal (H-infinity) control method against other linear and nonlinear control schemes for electropneumatically actuated robots shows the following: (1) Unlike global linearization-based control approaches, such as Lie algebra-based control and differential flatness theory-based control, the optimal control approach does not rely on complicated transformations (diffeomorphisms) of the system’s state variables. Besides, the computed control inputs are applied directly on the initial nonlinear model of the electropneumatic robot and not on its linearized equivalent. The inverse transformations which are met in global linearization-based control are avoided and consequently one does not come against the related singularity problems. (2) Unlike model predictive control (MPC) and NMPC, the proposed control method is of proven global stability. It is known that MPC is a linear control approach that if applied to the nonlinear dynamics of the electropneumatic robot, the stability of the control loop will be lost. Besides, in NMPC the convergence of its iterative search for an optimum depends on initialization and parameter values selection and consequently the global stability of this control method cannot be always assured. (3) Unlike sliding-mode control and backstepping control, the proposed optimal control method does not require the state-space description of the system to be found in a specific form. About sliding-mode control, it is known that when the controlled system is not found in the input-output linearized form the definition of the sliding surface can be an intuitive procedure. About backstepping control, it is known that it cannot be directly applied to a dynamical system if the related state-space model is not found in the triangular (backstepping integral) form. (4) Unlike PID control, the proposed nonlinear optimal control method is of proven global stability, the selection of the controller’s parameters does not rely on a heuristic tuning procedure, and the stability of the control loop is assured in the case of changes of operating points. (5) Unlike multiple local models-based control, the nonlinear optimal control method uses only one linearization point and needs the solution of only one Riccati equation so as to compute the stabilizing feedback gains of the controller. Consequently, in terms of computation load the proposed control method for the electropneumatic actuator’s dynamics is much more efficient.

The study aims to provide concrete service remediation and enhancement for LLM developers such as getting user forgiveness and breaking through perceived bottlenecks. It also aims to improve the efficiency of app users' usage decisions.

This paper takes the user reviews of the app stores in 21 countries and 10 languages as the research data, extracts the potential factors by LDA model, exploratively takes the misalignment between user ratings and textual emotions as user forgiveness and perceived bottleneck and uses the Word2vec-SVM model to analyze the sentiment. Finally, attributions are made based on empathy.

The results show that AI-based LLMs are more likely to cause bias in user ratings and textual content than regular APPs. Functional and economic remedies are effective in awakening empathy and forgiveness, while empathic remedies are effective in reducing perceived bottlenecks. Interestingly, empathetic users are “pickier”. Further social network analysis reveals that problem solving timeliness, software flexibility, model updating and special data (voice and image) analysis capabilities are beneficial in breaking perceived bottlenecks. Besides, heterogeneity analysis show that eastern users are more sensitive to the price factor and are more likely to generate forgiveness through economic remedy, and there is a dual interaction between basic attributes and extra boosts in the East and West.

The “gap” between negative (positive) user reviews and ratings, that is consumer forgiveness and perceived bottlenecks, is identified in unstructured text; the study finds that empathy helps to awaken user forgiveness and understanding, while it is limited to bottleneck breakthroughs; the dataset includes a wide range of countries and regions, findings are tested in a cross-language and cross-cultural perspective, which makes the study more robust, and the heterogeneity of users' cultural backgrounds is also analyzed.

This work examines a repairable machining system’s reliability by considering multiple failure scenarios, including individual component failures, hardware and software malfunctions, failures resulting from shared causes and failures caused by human error. When a system is susceptible to several modes of failure, the primary goal is to forecast availability and other reliability metrics as well as to calculate the expected profit of the repairable machining system.

The process of recovering after a system failure involves inspecting the system and fixing any malfunctions that may have occurred. The repair procedures for all kinds of faults are taken to follow a general distribution to represent real-time circumstances. We develop a non-Markovian stochastic model representing different system states that reveal working, failed, degraded, repair and delayed repair states. Laplace transformation and the supplementary variable technique are used to assess the transient states of the system.

Analytical expressions for system performance indices such as availability, reliability and cost-benefit analysis are derived. The transient probabilities when the system experiences in different states such as failed, degraded and delayed states are computed. The results obtained are validated using Mathematica software by performing a numerical illustration on setting default values of unknown parameters. This ensures the accuracy and reliability indices of the analytical predictions.

By methodically examining the system in its several states, we will be able to spot possible problems and offer efficient fixes for recovery. The system administrators would check to see if a minor or major repair is needed, or if a replacement is occasionally taken into consideration to prevent recurring repairs.

The purpose of this study is to solve the problem that existing researches ignore the long-term and staged nature of digital transformation, failing to conduct specific discussions for different stages. It responded the call by constructing a three-stage evolutionary model to analyze the impact of digital transformation at different stages on the sustainable performance of manufacturing enterprises. The moderating effect of core technology capabilities is also explored, guided by the theory of assimilation innovation.

Based on the panel data of Chinese listed manufacturing companies from 2012 to 2020, this study empirically investigate the impact of digital transformation (digital process, digital operation and digital ecology) on sustainability performance (economic performance and environmental performance).

The findings indicate that digital operations and digital ecology significantly improve economic performance and environmental performance. Furthermore, the core technological capacity of the enterprise serves to modify the positive correlation between digital transformation at each stage and sustainable performance to some extent. In other words, when an enterprise is equipped with the requisite technological capacity, the digital transformation at each stage accelerates both economic performance and environmental performance, which in turn is conducive to an improvement in the enterprise’s sustainable development performance.

The findings contribute to the theoretical framework of digital transformation and sustainable development in all stages of enterprises. Furthermore, they provide guidance for achieving sustainable development through the implementation of digital transformation and the enhancement of core technological capacity.

Environmental sustainability orientation has emerged to drive firms into eco-friendly production. Yet, the consequence of this new strategic thinking on firms’ green innovations, especially small- and medium-scale enterprises (SMEs), remains unresolved. Recognizing that the connection between environmental sustainability orientation and green innovation may not always be direct, the study theorizes that dynamic capability and entrepreneurial orientation may form part of the boundary conditions that strengthen its effect on small enterprises’ green innovation. The study adjoins the dynamic capability theory with the entrepreneurial orientation theory to test this relationship among small businesses within a developing economy. Results from the partial least squares–structural equation modeling (PLS-SEM) suggest that environmental sustainability orientation will result in green innovation when the SME’s dynamic capability can develop a creative reconfiguration of knowledge and new distinctive resources to support this new strategic direction. Similarly, findings from the study suggest that environmental sustainability orientation will translate into better green innovation outcomes when the SME entrepreneurial orientation has a solid attraction to protect the ecosystem and does not perceive green innovation as a risky enterprise.

As artificial intelligence and machine learning become increasingly integrated into daily life, both individuals and institutions are growing dependent on these technologies. However, it's crucial to acknowledge that such advancements can introduce potential flaws or vulnerabilities. A case in point is the investigation conducted by the non-profit organization ProPublica into the COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) risk assessment tool – a tool widely used by US courts to assess the likelihood of a defendant reoffending. To address the issue of underlying biases, including racial biases, which can lead to inaccurate predictions and significant social harm, we are delving into the current literature on algorithmic bias in decision systems. We are also exploring the evolving considerations of fairness and accountability in machine learning. Specifically, within the realm of predictive policing algorithms employed in the criminal justice system, our focus is on recent studies aimed at mitigating biases in algorithmic decision-making. This involves reassessing recidivism rates and implementing adversarial debiasing in conjunction with fairness metrics.

The emergence of smart cities, metropolises that integrate physical infrastructure, digital technology, and data analytics, and that focus on urban sustainability, have profoundly changed urban development. In the modern digital era, robust infrastructure has become an indispensable catalyst for urban advancement. Kuwait is dedicated to the integration of diverse renewable energy technologies in the development of smart cities that enhance energy security, promote innovation, and contribute to global climate change mitigation efforts. Focusing on smart cities within Gulf Cooperation Council (GCC) countries, a review is presented of how successfully they have effectively combined technology, infrastructure, and sustainability to serve as models for new global and regional developments. Insights into what makes a city smart are provided in different settings.

Vision transformers (ViT) detectors excel in processing natural images. However, when processing remote sensing images (RSIs), ViT methods generally exhibit inferior accuracy compared to approaches based on convolutional neural networks (CNNs). Recently, researchers have proposed various structural optimization strategies to enhance the performance of ViT detectors, but the progress has been insignificant. We contend that the frequent scarcity of RSI samples is the primary cause of this problem, and model modifications alone cannot solve it.

To address this, we introduce a faster RCNN-based approach, termed QAGA-Net, which significantly enhances the performance of ViT detectors in RSI recognition. Initially, we propose a novel quantitative augmentation learning (QAL) strategy to address the sparse data distribution in RSIs. This strategy is integrated as the QAL module, a plug-and-play component active exclusively during the model’s training phase. Subsequently, we enhanced the feature pyramid network (FPN) by introducing two efficient modules: a global attention (GA) module to model long-range feature dependencies and enhance multi-scale information fusion, and an efficient pooling (EP) module to optimize the model’s capability to understand both high and low frequency information. Importantly, QAGA-Net has a compact model size and achieves a balance between computational efficiency and accuracy.

We verified the performance of QAGA-Net by using two different efficient ViT models as the detector’s backbone. Extensive experiments on the NWPU-10 and DIOR20 datasets demonstrate that QAGA-Net achieves superior accuracy compared to 23 other ViT or CNN methods in the literature. Specifically, QAGA-Net shows an increase in mAP by 2.1% or 2.6% on the challenging DIOR20 dataset when compared to the top-ranked CNN or ViT detectors, respectively.

This paper highlights the impact of sparse data distribution on ViT detection performance. To address this, we introduce a fundamentally data-driven approach: the QAL module. Additionally, we introduced two efficient modules to enhance the performance of FPN. More importantly, our strategy has the potential to collaborate with other ViT detectors, as the proposed method does not require any structural modifications to the ViT backbone.

With burgeoning interest in the low-altitude economy, applications of long-endurance unmanned aerial vehicles (LE-UAVs) have increased in remote logistics distribution. Given LE-UAVs’ advantages of wide coverage, strong versatility and low cost, in addition to logistics distribution, they are widely used in military reconnaissance, communication relay, disaster monitoring and other activities. With limited autonomous intelligence, LE-UAVs require regular periodic and non-periodic control from ground control resources (GCRs) during flights and mission execution. However, the lack of GCRs significantly restricts the applications of LE-UAVs in parallel.

We consider the constraints of GCRs, investigating an integrated optimization problem of multi-LE-UAV mission planning and GCR allocation (Multi-U&G IOP). The problem integrates GCR allocation into traditional multi-UAV cooperative mission planning. The coupling decision of mission planning and GCR allocation enlarges the decision space and adds complexities to the problem’s structure. Through characterizing the problem, this study establishes a mixed integer linear programming (MILP) model for the integrated optimization problem. To solve the problem, we develop a three-stage iterative optimization algorithm combining a hybrid genetic algorithm with local search-variable neighborhood decent, heuristic conflict elimination and post-optimization of GCR allocation.

Numerical experimental results show that our developed algorithm can solve the problem efficiently and exceeds the solution performance of the solver CPLEX. For small-scale instances, our algorithm can obtain optimal solutions in less time than CPLEX. For large-scale instances, our algorithm produces better results in one hour than CPLEX does. Implementing our approach allows efficient coordination of multiple UAVs, enabling faster mission completion with a minimal number of GCRs.

Drawing on the interplay between LE-UAVs and GCRs and considering the practical applications of LE-UAVs, we propose the Multi-U&G IOP problem. We formulate this problem as a MILP model aiming to minimize the maximum task completion time (makespan). Furthermore, we present a relaxation model for this problem. To efficiently address the MILP model, we develop a three-stage iterative optimization algorithm. Subsequently, we verify the efficacy of our algorithm through extensive experimentation across various scenarios.