Coke: building on happiness to support “Positive Solutions”

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and EconomicsThis project is about how significant the positive solutions mean to Coca Cola Co. and emphasizes that if all three factors, GloCal Vision, Positive Psychology and the Stakeholder Theory are taken into consideration simultaneously then tailored positive solutions can be the result. Based on these indicators and compared with Dove’s success, we have found that Coke’s “Open Happiness” campaign is not qualified as a positive solution for obeying neither positive psychology nor the stakeholder theory. Later, after screening four solutions, one positive solution is kept and admitted to help Coke be more successful

Application of UAV in Road Safety in Intelligent Areas

With the continuous development of remote sensing(RS) technology, thesurface information can be collected conveniently and quickly by usingthe popular unmanned aerial vehicle(UAV). The application of UAVlow altitude RS technology in road safety in intelligent area has certainpractical significance. It can provide safety warning for most drivers, andprovide auxiliary decision-making for the road supervision department.Through the collection, processing, calculation and analysis of the roadimage, the UAV can find out the road obstacles with potential safety hazards, identify the road pit, calculate the radius and depth of the road pitthrough the digital mapping system, predict the accident risk according todifferent speed and provide scientific basis for the road safety monitoring.At the same time, UAV can provide repair scheme for damaged roads,estimate the quantity of materials needed for repair, and achieve the targetof resource saving and efficiency improvement. The experimental resultsshow that the UAV can not only provide scientific prediction informationfor driving safety, but also provide relatively accurate material consumption for road repair

Neural control for constrained human-robot interaction with human motion intention estimation and impedance learning

In this paper, an impedance control strategy is proposed for a rigid robot collaborating with human by considering impedance learning and human motion intention estimation. The least square method is used in human impedance identification, and the robot can adjust its impedance parameters according to human impedance model for guaranteeing compliant collaboration. Neural networks (NNs) are employed in human motion intention estimation, so that the robot follows the human actively and human partner costs less control effort. On the other hand, the full-state constraints are considered for operational safety in human-robot interactive processes. Neural control is presented in the control strategy to deal with the dynamic uncertainties and improve the system robustness. Simulation results are carried out to show the effectiveness of the proposed control design

Human-robot co-carrying using visual and force sensing

In this paper, we propose a hybrid framework using visual and force sensing for human-robot co-carrying tasks. Visual sensing is utilized to obtain human motion and an observer is designed for estimating control input of human, which generates robot's desired motion towards human's intended motion. An adaptive impedance-based control strategy is proposed for trajectory tracking with neural networks (NNs) used to compensate for uncertainties in robot's dynamics. Motion synchronization is achieved and this approach yields a stable and efficient interaction behavior between human and robot, decreases human control effort and avoids interference to human during the interaction. The proposed framework is validated by a co-carrying task in simulations and experiments

Resonance Assignments and Secondary Structure Predictions of the As(III) Metallochaperone ArsD in Solution

ArsD is a metallochaperone that delivers As(III) to the ArsA ATPase, the catalytic subunit of the ArsAB pump encoded by the arsRDABC operon of Escherichia coli plasmid R773. Conserved ArsD cysteine residues (Cys12, Cys13 and Cys18) construct the As(III) binding site of the protein, however a global structural understanding of this arsenic binding remains unclear. We have obtained NMR assignments for ArsD as a starting point for probing structural changes on the protein that occur in response to metalloid binding and upon formation of a complex with ArsA. The predicted solution structure of ArsD is in agreement with recently published crystallographic structural results

Prognostic nutritional index: A potential biomarker for predicting the prognosis of decompensated liver cirrhosis

BackgroundPrognostic nutritional index (PNI) is an independent predictor of the prognosis of various diseases. However, the prognosis value of PNI in patients with decompensated liver cirrhosis (DLC) remains unknown. The study aimed to investigate the prognostic significance of PNI in patients with DLC.MethodsA total of 214 eligible patients were enrolled in the study’s development cohort between January 2018 and March 2021. The clinical primary study endpoints were mortality at 3 and 6 months. Receiver operating characteristic (ROC) curve analysis was used to assess the PNI’s prediction accuracy, and Youden’s index was utilized to determine the PNI’s optimal cut-off value. Moreover, based on the optimal cut-off value, patients were categorized into high and low PNI groups. Multivariate logistic regression analysis was used to determine independent risk factors for mortality, while the relationship between PNI and the risk of death was identified and demonstrated using restricted cubic splines (RCS). A validation cohort of 139 patients was to verify the predictive power of the PNI.ResultsIn the development cohort, the mortality rate at 3 and 6 months were 10.3% (22) and 14.0% (30), respectively. The PNI had comparable predictive power with the MELD score at all follow-up endpoints. Decreased PNI was an independent predictor of adverse prognosis at all follow-up endpoints. The RCS revealed a linear correlation between PNI and the risk of death. We confirmed that lower PNI was an independent predictor of poor prognosis in the validation cohort.ConclusionThe findings showed that lower PNI is an independent factor of poor outcomes and might be utilized as a potentially promising prognostic predictor in patients with DLC

Bayesian estimation of human impedance and motion intention for human-robot collaboration

This article proposes a Bayesian method to acquire the estimation of human impedance and motion intention in a human-robot collaborative task. Combining with the prior knowledge of human stiffness, estimated stiffness obeying Gaussian distribution is obtained by Bayesian estimation, and human motion intention can be also estimated. An adaptive impedance control strategy is employed to track a target impedance model and neural networks are used to compensate for uncertainties in robotic dynamics. Comparative simulation results are carried out to verify the effectiveness of estimation method and emphasize the advantages of the proposed control strategy. The experiment, performed on Baxter robot platform, illustrates a good system performance