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Upgraded water and better sanitation are essential for human health, but it is still a challenge to get admittance to these facilities and the concerns of public health becomes most victims. The purpose of this paper is to analyze the socioeconomic and demographic forecaster linked with admittance to safer water and upgraded sanitation facilities in Rwanda. The study uses the cross-sectional data from the 2014 to 2015 Rwanda Demographic Health Survey and uses linear generalized models for the analysis.

The logit and probit regressions were used to analyze whether or not any forecaster variables influenced the predicted variable.

The findings showed that the households with the highest education background were 11.55 times more probable to have admittance to upgraded water sources compared to those who had none level of education. Likewise, the respondents with secondary and higher education were, respectively, 9.55 times and 4.09 times more probable to have admittance to upgraded latrine facilities. The authors found the increase of household size as significantly associated with admittance to the upgraded water source and latrine facilities compared to those families with fewer household members. The results also found that wealthier households had a larger odds ratio significance in getting admittance to upgraded water sources and sanitation facilities compared to poorer households. The study results found the greatest gap in access to upgraded water sources and sanitation facilities in rural areas compared to urban areas.

The implications of the study results call for water policy formulation and implementation in Rwanda, as well as generally for other developing countries.

In Rwanda, this is the first study that empirically inspected the relationship between socioeconomic and demographic forecasters on admittance to upgraded water and sanitation facilities.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-07-2019-0452

The purpose of this paper is to generate grinding trajectory of unknown model parts simply and efficiently. In this paper, a method of grinding trajectory generation of hybrid robot based on Cartesian space direct teaching technology is proposed.

This method first realizes the direct teaching of hybrid robot based on 3Dconnexion SpaceMouse (3DMouse) sensor, and the full path points of the robot are recorded in the teaching process. To reduce the jitter and make the speed control more freely when dragging the robot, the sensor data is processed by Kalman filter, and a variable admittance control model is established. And the joint constraint processing is given during teaching. After that, the path points are modified and fitted into double B-splines, and the speed planning is performed to generate the final grinding trajectory.

Experiment verifies the feasibility of using direct teaching technology in Cartesian space to generate grinding trajectory of unknown model parts. By fitting all the teaching points into cubic B-spline, the smoothness of the grinding trajectory is improved.

The whole method is verified by the self-developed TriMule-600 hybrid robot, and it can also be applied to other industrial robots.

The main contribution of this paper is to realize the direct teaching and trajectory generation of the hybrid robot in Cartesian space, which provides an effective new method for the robot to generate grinding trajectory of unknown model parts.

The purpose of this paper is to propose a voltage regulation solution in power systems through adjusting the power flow of the system via thyristor controlled series compensator (TCSC). For this aim, a new power flow model has been proposed based on TCSC.

TCSC’s admittance effect has been included as state variable into the Jacobian matrix to avoid divergence problem. TCSC’s admittance is ignored in the bus admittance matrix to prevent rebuilding requirement of the bus admittance matrix in each power flow iteration. So, faster convergence for power flow calculation has been provided. For this aim, new power equations have been obtained. Also, the proposed approach has not required to handle each terminal of TCSC as an individual bus in the power flow calculation. So, increasing of the Jacobian and bus admittance matrixes caused by the total bus number has been prevented.

The proposed approach has been tested on IEEE 57-bus test system. The obtained results have proved that the proposed approach has provided efficient, reliable and fast convergence.

This study is the first one that uses TCSC for voltage regulation in the literature. On the other hand, the results have shown that the approach of considering the TCSC admittance values as state variables provides robust convergence, according to the approaches that consider TCSC firing angles as state variables.

The purpose of this paper is to enable robots to intelligently adapt their damping characteristics and motions in a reactive fashion toward human inputs and task requirements during physical human–robot interaction.

This paper exploits a combination of the dynamical system and the admittance model to create robot behaviors. The reference trajectories are generated by dynamical systems while the admittance control enables robots to compliantly follow the reference trajectories. To determine how control is divided between the two models, a collaborative arbitration algorithm is presented to change their contributions to the robot motion based on the contact forces. In addition, the authors investigate to model the robot’s impedance characteristics as a function of the task requirements and build a novel artificial damping field (ADF) to represent the virtual damping at arbitrary robot states.

The authors evaluate their methods through experiments on an UR10 robot. The result shows promising performances for the robot to achieve complex tasks in collaboration with human partners.

The proposed method extends the dynamical system approach with an admittance control law to allow a robot motion being adjusted in real time. Besides, the authors propose a novel ADF method to model the robot’s impedance characteristics as a function of the task requirements.

This study aims to realize natural and effort-saving motion behavior and improve effectiveness for different operators in human–robot force cooperation.

The parameter of admittance model is identified by deep deterministic policy gradient (DDPG) to realize human–robot force cooperation for different operators in this paper. The movement coupling problem of hybrid robot is solved by realizing position and pose drags. In DDPG, minimum jerk trajectory is selected as the reward objective function, and the variable prioritized experience replay is applied to balance the exploration and exploitation.

A series of simulations are implemented to validate the superiority and stability of DDPG. Furthermore, three sets of experiments involving mass parameter, damping parameter and DDPG are implemented, the effect of DDPG in real environment is validated and could meet the cooperation demand for different operators.

DDPG is applied in admittance model identification to realize human–robot force cooperation for different operators. And minimum jerk trajectory is introduced into reward objective to meet requirement of human arm free movements. The algorithm proposed in this paper could be further extended in the other operation task.

The state of Tennessee is part of the United States that houses a special set of school districts known as the Black Belt. Named for the black fertile land, utilized for the agricultural industry for hundreds of years in the south, these school districts have the lowest levels of achievement among the one hundred and thirty six school districts in Tennessee. The purpose of this study is to identify just how extensive these achievement discrepancies are between Black Belt school students and non-Black Belt school students by answering the following research question: are Black Belt school students disproportionately scoring lower on college admittance exams (ACT) than students in non-Black Belt school districts? The data for this study was gathered from the Tennessee Report Card for Education over a period of ten years. Pooled time series cross-sectional regression analysis was the datatesting device employed in the study. The findings suggest that Black Belt students are disproportionately scoring lower on college admittance exams compared to non-Black Belt students. Policymakers need to use caution when generalizing this study because it only represents those Black Belt school districts in Tennessee.

With the increasing requirements for intelligence in the field of aviation manufacturing, manual assembly can hardly adapt to the trend of future production. The purpose of this study is to realize the semi-automatic assembly of the movable airfoil by proposing a human-robot collaborative assembly strategy based on adaptive admittance control.

A logical judgment system for operating intentions is introduced in terms of different situations of the movements; hence, a human cognition-based adaptive admittance control method is developed to curb the damage of inertia; then virtual limit walls are raised on the periphery of the control model to ensure safety; finally, simulated and experimental comparisons with other admittance control methods are conducted to validate the proposed method.

The proposed method can save at least 28.8% of the time in the stopping phase which effectively compensates for inertia during the assembly process and has high robustness concerning data disturbances.

Due to the human-robot collaboration to achieve compliant assembly of movable airfoils can preserve human subjectivity while overcoming the physical limits of humans, which is of great significance to the investigation of intelligent aircraft assembly, the proposed method that reflects the user's naturalness and intuitiveness can not only enhance the stability and the flexibility of the manipulation, but also contribute to applications of industrial robots in the field of human-robot collaboration.

With the increasing demands of industrial applications, it is imperative for robots to accomplish good contact-interaction with dynamic environments. Hence, the purpose of this research is to propose an adaptive fractional-order admittance control scheme to realize a robot–environment contact with high accuracy, small overshoot and fast response.

Fractional calculus is introduced to reconstruct the classical admittance model in this control scheme, which can more accurately describe the complex physical relationship between position and force in the interaction process of the robot–environment. In this control scheme, the pre-PID controller and fuzzy controller are adopted to improve the system force tracking performance in highly dynamic unknown environments, and the fuzzy controller is used to improve the trajectory, transient and steady-state response by adjusting the pre-PID integration gain online. Furthermore, the stability and robustness of this control algorithm are theoretically and experimentally demonstrated.

The excellent force tracking performance of the proposed control algorithm is verified by constructing highly dynamic unstructured environments through simulations and experiments. In simulations and experiments, the proposed control algorithm shows satisfactory force tracking performance with the advantages of fast response speed, little overshoot and strong robustness.

The control scheme is practical and simple in the actual industrial and medical scenarios, which requires accurate force control by the robot.

A new fractional-order admittance controller is proposed and verified by experiments in this research, which achieves excellent force tracking performance in dynamic unknown environments.

The purpose of this paper is to eliminate instability which may occur when a human stiffens his arms in physical human–robot interaction by estimating the human hand stiffness and presenting a modified vibration index.

Human hand stiffness is first estimated in real time as a prior indicator of instability by capturing the arm configuration and modeling the level of muscle co-contraction in the human’s arms. A time-domain vibration index based on the interaction force is then modified to reduce the delay in instability detection. The instability is confirmed when the vibration index exceeds a given threshold. The virtual damping coefficient in admittance controller is adjusted accordingly to ensure stability in physical human–robot interaction.

By estimating the human hand stiffness and modifying the vibration index, the instability which may occur in stiff environment in physical human–robot interaction is detected and eliminated, and the time delay is reduced. The experimental results demonstrate significant improvement in stabilizing the system when the human operator stiffens his arms.

The originality is in estimating the human hand stiffness online as a prior indicator of instability by capturing the arm configuration and modeling the level of muscle co-contraction in the human’s arms. A modification of the vibration index is also an originality to reduce the time delay of instability detection.

The purpose of this paper is to present a method which enables a robot to learn both motion skills and stiffness profiles from humans through kinesthetic human-robot cooperation.

Admittance control is applied to allow robot-compliant behaviors when following the reference trajectories. By extending the dynamical movement primitives (DMP) model, a new concept of DMP and stiffness primitives is introduced to encode a kinesthetic demonstration as a combination of trajectories and stiffness profiles, which are subsequently transferred to the robot. Electromyographic signals are extracted from a human’s upper limbs to obtain target stiffness profiles. By monitoring vibrations of the end-effector velocities, a stability observer is developed. The virtual damping coefficient of admittance controller is adjusted accordingly to eliminate the vibrations.

The performance of the proposed methods is evaluated experimentally. The result shows that the robot can perform tasks in a variable stiffness mode as like the human dose in the teaching phase.

DMP has been widely used as a teaching by demonstration method to represent movements of humans and robots. The proposed method extends the DMP framework to allow a robot to learn not only motion skills but also stiffness profiles. Additionally, the authors proposed a stability observer to eliminate vibrations when the robot is disturbed by environment.