Educational videos for practitioners attending Baby Friendly Hospital Initiative workshops supporting breastfeeding positioning, attachment and hand expression skills: Effects on knowledge and confidence

UNICEF Baby Friendly Initiative (BFHI) is the global standard for maternity and community services requiring all practitioners to be trained to support mothers in the essential skills of supporting positioning and attachment, and hand expression. These studies aim to rigorously assess knowledge in nurses, midwives, and doctors in these skills, tested before and after watching short videos demonstrating these skills. Practitioners were attending BFHI education, and the video study was additional. In Phase 1 clinicians in England were randomised to one of two videos (practitioner role play or clinical demonstration). The results showed improvements in knowledge and confidence, and a preference for clinical demonstration by mothers and infants. The clinical demonstration video was evaluated in China in Phase 2 where expert trainers viewed the video after completing the BHFI workshop, and in Phase 3 practitioners viewed the video before the BHFI workshop. Phase 2 with expert trainers only showed improvement in knowledge of hand expression but not positioning and attachment. In Phase 3 clinicians showed improved knowledge for both skills. In all Phases there were statistically significant improvements in confidence in practice in both skills. Viewing short videos increased knowledge, particularly about teaching hand expression, and confidence in both skills

Real-time motion planning based vibration control of a macro-micro parallel manipulator system for super antenna

A macro-micro manipulator (M3) system, composed of a rigid parallel manipulator serially mounted on a flexible cable suspended parallel manipulator, is used to precisely position the feed source of a super antenna. In order to reduce the impact of mechanical vibrations of the macro manipulator and achieve accurate positioning and orientating of the micro manipulator, a real-time motion planning based vibration control strategy is presented. This strategy comprises: (1) To determine the optimal position and orientation of the cable driven parallel manipulator, the real-time optimization is conducted according to the principle of uniform tension in the six driving cables; (2) Synchronized points and the “judge and wait” technique ensure the continuity and synchrony of the trajectory tracking of the two parallel manipulators; (3) The preadjustment of the micro parallel manipulator minimizes the drastic dynamical coupling as a result of its high-speed manipulation. Experimental results of the field model validate the high precision of the M3 system for super antenna when tracking a circular arc trajectory

Novel models for fatigue life prediction under wideband random loads based on machine learning

Machine learning as a data-driven solution has been widely applied in the field of fatigue lifetime prediction. In this paper, three models for wideband fatigue life prediction are built based on three machine learning models, i.e. support vector machine (SVM), Gaussian process regression (GPR) and artificial neural network (ANN). The generalization ability of the models is enhanced by employing numerous power spectra samples with different bandwidth parameters and a variety of material properties related to fatigue life. Sufficient Monte Carlo numerical simulations demonstrate that the newly developed machine learning models are superior to the traditional frequency-domain models in terms of life prediction accuracy and the ANN model has the best overall performance among the three developed machine learning models

Real-time motion planning based vibration control of a macro-micro parallel manipulator system for super antenna

A macro-micro manipulator (M3) system, composed of a rigid parallel manipulator serially mounted on a flexible cable suspended parallel manipulator, is used to precisely position the feed source of a super antenna. In order to reduce the impact of mechanical vibrations of the macro manipulator and achieve accurate positioning and orientating of the micro manipulator, a real-time motion planning based vibration control strategy is presented. This strategy comprises: (1) To determine the optimal position and orientation of the cable driven parallel manipulator, the real-time optimization is conducted according to the principle of uniform tension in the six driving cables; (2) Synchronized points and the “judge and wait” technique ensure the continuity and synchrony of the trajectory tracking of the two parallel manipulators; (3) The preadjustment of the micro parallel manipulator minimizes the drastic dynamical coupling as a result of its high-speed manipulation. Experimental results of the field model validate the high precision of the M3 system for super antenna when tracking a circular arc trajectory

A Self-Adjusting Spectral Conjugate Gradient Method for Large-Scale Unconstrained Optimization

This paper presents a hybrid spectral conjugate gradient method for large-scale unconstrained optimization, which possesses a self-adjusting property. Under the standard Wolfe conditions, its global convergence result is established. Preliminary numerical results are reported on a set of large-scale problems in CUTEr to show the convergence and efficiency of the proposed method

Short-Term Power Load Forecasting Method Based on Improved Exponential Smoothing Grey Model

In order to improve the prediction accuracy, this paper proposes a short-term power load forecasting method based on the improved exponential smoothing grey model. It firstly determines the main factor affecting the power load using the grey correlation analysis. It then conducts power load forecasting using the improved multivariable grey model. The improved prediction model firstly carries out the smoothing processing of the original power load data using the first exponential smoothing method. Secondly, the grey prediction model with an optimized background value is established using the smoothed sequence which agrees with the exponential trend. Finally, the inverse exponential smoothing method is employed to restore the predicted value. The first exponential smoothing model uses the 0.618 method to search for the optimal smooth coefficient. The prediction model can take the effects of the influencing factors on the power load into consideration. The simulated results show that the proposed prediction algorithm has a satisfactory prediction effect and meets the requirements of short-term power load forecasting. This research not only further improves the accuracy and reliability of short-term power load forecasting but also extends the application scope of the grey prediction model and shortens the search interval

Evolution of Cooperation Driven by Reputation-Based Migration

How cooperation emerges and is stabilized has been a puzzling problem to biologists and sociologists since Darwin. One of the possible answers to this problem lies in the mobility patterns. These mobility patterns in previous works are either random-like or driven by payoff-related properties such as fitness, aspiration, or expectation. Here we address another force which drives us to move from place to place: reputation. To this end, we propose a reputation-based model to explore the effect of migration on cooperation in the contest of the prisoner's dilemma. In this model, individuals earn their reputation scores through previous cooperative behaviors. An individual tends to migrate to a new place if he has a neighborhood of low reputation. We show that cooperation is promoted for relatively large population density and not very large temptation to defect. A higher mobility sensitivity to reputation is always better for cooperation. A longer reputation memory favors cooperation, provided that the corresponding mobility sensitivity to reputation is strong enough. The microscopic perception of the effect of this mechanism is also given. Our results may shed some light on the role played by migration in the emergence and persistence of cooperation

A Novel 3-DOF Translational Micromanipulation Parallel Manipulator for Vibration Control of Crystal Oscillators

This paper presents a novel 3-DOF translational micromanipulating parallel manipulator with single type of linear driving and six passive joints. It is chiefly characterized by the constant Jacobian matrix between the input and output. It also has straightforward inverse and forward kinematics, which facilitate the implementation of real-time control. Aiming at the vibration control purpose for a crystal oscillator in electronic equipments, the compactness of the manipulator is also considered and accomplished in the dimension of 75 mm ´ 67 mm ´ 25 mm. We finally achieved the 50 Hz 3DOF translational motions within ± 2 mm ´ ± 2 mm ´ ± 2 mm and validated the design with computational simulation

Nonmonotone Adaptive Barzilai-Borwein Gradient Algorithm for Compressed Sensing

We study a nonmonotone adaptive Barzilai-Borwein gradient algorithm for l1-norm minimization problems arising from compressed sensing. At each iteration, the generated search direction enjoys descent property and can be easily derived by minimizing a local approximal quadratic model and simultaneously taking the favorable structure of the l1-norm. Under some suitable conditions, its global convergence result could be established. Numerical results illustrate that the proposed method is promising and competitive with the existing algorithms NBBL1 and TwIST