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This paper aims to propose a novel system design and control algorithm of lower limb exoskeleton, which provides walking assistance and load sharing for the wearer.

In this paper, the valve-controlled asymmetrical hydraulic cylinder is selected for driving the hip and knee joint of exoskeleton. Pressure shoe is developed that purpose on detecting changes in plantar force, and a fuzzy recognition algorithm using plantar pressure is proposed. Dynamic model of the exoskeleton is established, and the sliding mode control is developed to implement the position tracking of exoskeleton. A series of prototype experiments including benchtop test, full assistance, partial assistance and loaded walking experiments are set up to verify the tracking performance and power-assisted effect of the proposed exoskeleton.

The control performance of PID control and sliding mode control are compared. The experimental data shows the tracking trajectories and tracking errors of sliding mode control and demonstrate its good robustness to nonlinearities. sEMG of the gastrocnemius muscle tends to be significantly weakened during assisted walking.

In this paper, a structure that the knee joint and hip joint driven by the valve-controlled asymmetrical cylinder is used to provide walking assistance for the wearer. The sliding mode control is proposed to deal with the nonlinearities during joint rotation and fluids. It shows great robustness and frequency adaptability through experiments under different motion frequencies and assistance modes. The design and control method of exoskeleton is a good attempt, which takes positive impacts on the productivity or quality of the life of wearers.

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.

The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs include data on vehicles with and without intended driving behavior changes, they do not explicitly demonstrate a type of data on vehicles that intend to change their driving behavior but do not execute the behaviors because of safety, efficiency, or other factors. This missing data is essential for autonomous driving decisions. This study aims to extract the driving data with implicit intentions to support the development of decision-making models.

According to Bayesian inference, drivers who have the same intended changes likely share similar influencing factors and states. Building on this principle, this study proposes an approach to extract data on vehicles that intended to execute specific behaviors but failed to do so. This is achieved by computing driving similarities between the candidate vehicles and benchmark vehicles with incorporation of the standard similarity metrics, which takes into account information on the surrounding vehicles' location topology and individual vehicle motion states. By doing so, the method enables a more comprehensive analysis of driving behavior and intention.

The proposed method is verified on the Next Generation SIMulation dataset (NGSim), which confirms its ability to reveal similarities between vehicles executing similar behaviors during the decision-making process in nature. The approach is also validated using simulated data, achieving an accuracy of 96.3 per cent in recognizing vehicles with specific driving behavior intentions that are not executed.

This study provides an innovative approach to extract driving data with implicit intentions and offers strong support to develop data-driven decision-making models for autonomous driving. With the support of this approach, the development of autonomous vehicles can capture more real driving experience from human drivers moving towards a safer and more efficient future.

Scholars have not agreed with each other on how people would behave after experiencing a catastrophic event. They could save more as a precautionary action for future difficulties or save less with a carpe diem attitude. This study aims to attempt to shed light on this debate with empirical observations on how the Covid-19 pandemic has affected household saving decisions.

The two waves of the survey data allowed us to investigate both instantaneous and ongoing effects of Covid-19 on household saving decisions. The instantaneous effect refers to the immediate impact of the crisis, while the ongoing effect refers to the lasting impact of the pandemic when economic recovery had started. The variation in the number of confirmed cases across cities during the two waves provides the source of power for identification. The authors extend their analyses of the impact of Covid-19 on the household saving decision by using ordinary least squares models. Due to the ordered nature of survey responses, the authors also rerun all baseline models using the ordered probit regression method.

This paper studied the impact of the Covid-19 pandemic on household saving decisions in China. This study found that households in the most affected cities would save more during the Covid-19 but tend to save less when the disaster started fading away. Combining findings in Kun et al. (2013) and Filipski et al. (2015), people do become more pessimistic during and after the Covid-19, possibly driving their observed precautionary and cape diem behaviors during the two points of time. Heterogeneity analysis shows that specific households would dramatically change their saving behavior. These observations might be useful for policymakers who concern the economic recovery after this pandemic disaster.

Understanding how the Covid-19 pandemic would affect household consumption vs saving decisions is important for the economic recovery after this disaster comes to an end. The analyses presented in this research could be useful for policymakers who concern appropriate policies aiming to boost consumption and economic activities after Covid.

To solve the shortcomings of existed search and rescue drones, search and rescue the trapped people trapped in earthquake ruins, underwater and avalanches quickly and accurately, this paper aims to propose a four-axis eight-rotor rescue unmanned aerial vehicle (UAV) which can carry a radar life detector. As the design of propeller is the key to the design of UAV, this paper mainly designs the propeller of the UAV at the present stage.

Based on the actual working conditions of UAVs, this paper preliminarily estimated the load of UAVs and the diameters of propellers and designed the main parameters of propellers according to the leaf element theory and momentum theory. Based on the low Reynolds number airfoil, this paper selected the airfoil with high lift drag ratio from the commonly used low Reynolds number airfoils. The chord length and twist angle of propeller blades were calculated according to the Wilson method and the maximum wind energy utilization coefficient and were optimized by the Asymptotic exponential function. The aerodynamic characteristics of the designed single propeller and coaxial propeller under different installation pitch angles and different installation distances were analyzed.

The results showed that the design of coaxial twin propellers can increase the load capacity by about 1.5 times without increasing the propeller diameter. When the installation distance between the two propellers was 8 cm and the tilt angle was 15° counterclockwise, the aerodynamic characteristics of the coaxial propeller were optimal.

The novelty of this work came from the conceptual design of the new rescue UAV and its numerical optimization using the Wilson method combined with the maximum wind energy utilization factor and the exponential function. The aerodynamic characteristics of the common shaft propeller were analyzed under different mounting angles and different mounting distances.

This case is about “Red”, a cross-border e-commerce platform developed from a community which was built to share overseas shopping experience. With sharp insights into the consumption behavior of urban white-collar women and riding on its community e-commerce advantage, “Red”, a cross-border e-commerce startup, pulled in three rounds of financing within just 16 months regardless of increasingly competitive market. On the other hand, well-established platforms such as T-mall International and Joybuy also stepped in, and their involvement will also speed up the industry integration and usher in a reshuffling period. Confronted with the “price war” started by those e-commerce giants, in what ways can “Red” adjust its shopping experience and after-sales services to enhance the brand value and sharpen its edge?

This study examines the effect of supply chain (SC) learning (i.e. supplier and customer learnings) on green innovation (i.e. green product and process innovations) and investigates the moderating role of green transformational leadership in the SC learning-green innovation linkage in the construction industry.

Data are gathered from construction firms in Vietnam by a questionnaire survey. Hypotheses of the study framework are tested by hierarchical regression analysis.

Both supplier and customer learnings have positive effects on green innovation (both green process and product innovations). Furthermore, green transformational leadership moderates the linkage between supplier learning and green innovation but does not moderate the linkage between customer learning and green innovation.

Construction firms need to constantly develop capabilities of SC learning for promoting their green innovation.

The present study is one of the first attempts in construction that investigates the importance of SC learning to achieving green innovation as well as the role of green transformational leadership for strengthening the effect of green learning on green innovation.

This study aims to investigate and compare the characteristics of three topologies of moving-magnet linear oscillating actuator (LOA) based on their mover position. Positive aspects and consequences of every topology are demonstrated. Three topologies of axially magnetized moving-magnet LOA; outer mover, inner mover (IM) and dual stator (DS) are designed and examined. Due to its characteristically high thrust density and more mechanical strength, axially magnetized tubular permanent magnets (PMs) are used in these topologies.

LOAs are designed and optimized using parametric sweep, in term of design parameters and output parameters like thrust force, stroke and operating resonance frequency of the LOA. All the pros and cons of each topology are investigated and compared. Output parameters of the LOAs are compared using same size of the investigated LOAs. Mover mass, which plays a vital role in resonant operation, is analyzed for IM and DS designs. Investigated LOAs are compared with conventional designs of LOA for compressor in refrigeration system with regards of motor constant, stroke and thrust per PM mass.

This paper analyzes three topologies of moving-magnet LOAs. The basic difference between investigated LOAs is the radius of tubular-shaped mover from its central axis. All the design parameters are compared and concluded that thrust per PM mass of IMLOA is maximum. OMLOA provides maximum motor constant of value 180 N/A. DSLOA provides thrust force with motor constant 120 N/A and required intermediate materials of PMs. All the three designs give the best results in terms of motor constant and thrust per PM mass, compared to conventional designs of LOA.

This paper determines the impact of mover position from its central axis in a tubular-shaped moving-magnet LOA. This work is carried out in correspondence of latest papers of LOA.

This paper aims to investigates a novel design of a modular moving magnet linear oscillating actuator (MMM-LOA) with the capability of coupling modules, based on their application and space requirements.

Proposed design comprised of modules, and modules are separated by using nonmagnetic materials. Movable part of the proposed design of LOA is composed of permanent magnets (PMs) having axial magnetization direction and tubular structure. Stator of the proposed design is composed of one coil individually in a module. Dimensions of the design parameters are optimized through parametric analysis using COMSOL Multi Physics software. This design is analyzed up to three modules and their response in term of electromagnetic (EM) force and stroke are presented. Influence of adding modules is analyzed for both directions of direct current (DC) and alternating input loadings.

Proposed LOA shows linear increase in magnitude of EM force by adding modules. Motor constant of the investigated LOA is 264 N/A and EM force per PM mass is 452.389 N/kg, that shows significant improvement. Moreover, proposed LOA operates in feasible region of stroke for compressor application. Furthermore, this design uses axially magnetized PMs which are low cost and available in compact tubular structure.

Proposed LOA shows the influence of adding modules and its effect in term of EM force is analyzed for DC and alternating current (AC). Moreover, overall performance and structural topology is compared with state-of-the-art designs of LOA. Improvement with regard of motor constant and EM force per PM mass shows originality and scope of this paper.

Internet of things (IoT) is essential in technical, social and economic domains, but there are many challenges that researchers are continuously trying to solve. Traditional resource allocation methods in IoT focused on the optimal resource selection process, but the energy consumption for allocating resources is not considered sufficiently. This paper aims to propose a resource allocation technique aiming at energy and delay reduction in resource allocation. Because of the non-deterministic polynomial-time hard nature of the resource allocation issue and the forest optimization algorithm’s success in complex problems, the authors used this algorithm to allocate resources in IoT.

For the vast majority of IoT applications, energy-efficient communications, sustainable energy supply and reduction of latency have been major goals in resource allocation, making operating systems and applications more efficient. One of the most critical challenges in this field is efficient resource allocation. This paper has provided a new technique to solve the mentioned problem using the forest optimization algorithm. To simulate and analyze the proposed technique, the MATLAB software environment has been used. The results obtained from implementing the proposed method have been compared to the particle swarm optimization (PSO), genetic algorithm (GA) and distance-based algorithm.

Simulation results show that the proper performance of the proposed technique. The proposed method, in terms of “energy” and “delay,” is better than other ones (GA, PSO and distance-based algorithm).

The paper presents a useful method for improving resource allocation methods. The proposed method has higher efficiency compared to the previous methods. The MATLAB-based simulation results have indicated that energy consumption and delay have been improved compared to other algorithms, which causes the high application of this method in practical projects. In the future, the focus will be on resource failure and reducing the service level agreement violation rate concerning the number of resources.

The proposed technique can solve the mentioned problem in the IoT with the best resource utilization, low delay and reduced energy consumption. The proposed forest optimization-based algorithm is a promising technique to help enterprises participate in IoT initiatives and develop their business.