Sustainability Assessment & Energy Efficiency Oriented Simulation

Sustainability assessment is considered as one of the crucial strategies to realize sustainable design and manufacturing process. In this talk, I will present a modular design methodology for achieving sustainable design as well as fulfilling functional requirements with a novelty 6R concept (reuse, recycle, reduce, recover, redesign, and remanufacture). For the sustainable assessment of machining process, a new approach was proposed on the consideration of environmental, economic, and social criteria for selecting the optimal machining strategy from sustainable manufacturing viewpoint. Then, an energy consumption modeling was developed to characterize the relationship between machining process variables and energy consumption for material removal processes based on thermal equilibrium and empirical modelling. Face milling test was conducted on CNC machining center to illustrate validity of the proposed method to define sustainability performance of machining process, and optimize the energy-saving of a machining worksho

Inhibitory neurones of the spinal substantia gelatinosa mediate interaction of signals from primary afferents: Inhibitory SG neurones mediate primary afferent interaction

The spinal substantia gelatinosa (SG; lamina II) is a major synaptic zone for unmyelinated (C) primary afferents. Whereas a substantial proportion of intrinsic SG neurones are GABAergic inhibitory, their relationship to afferent activity is unknown. In spinal cord slices from a transgenic mouse in which certain GABAergic lamina II neurones are labelled with green fluorescent protein (GFP), we compared primary afferent input with local efferent connections made by inhibitory SG neurones. Simultaneous whole-cell recordings from characterized neurones establish that inhibitory SG neurones receive monosynaptic input from a subset of unmyelinated primary afferents and connect to other lamina II cells that have input from a different set of afferents, permitting interactions between distinctive afferent messages. Certain lamina II inhibitory cells were found to connect to one another by reciprocal links. Inhibitory lamina II connections appear arranged to modulate activity from different sets of peripheral unmyelinated fibres through neural circuitry that includes disinhibition

Media coverage of stand your ground laws deters crime in some cities, but not in others

So-called ‘stand your ground laws’ – which give people the right to use deadly force to defend themselves – have now been in place for a decade. In new research which uses a Texas shooting incident as a case study, Ling Ren, Yan Zhang, and Jihong “Solomon” Zhao examine whether or not the publicity over shooting incidents where the law is invoked helps to deter crime – specifically residential and business burglaries. They find that such media coverage of high-profile incidents does have a deterrent effect in some nearby cities, but not in others

Vision Sensor based Action Recognition for Improving Efficiency and Quality under the Environment of Industry 4.0

In the environment of industry 4.0, human beings are still an important influencing factor of efficiency and quality which are the core of product life cycle management. Hence, monitoring and analyzing humans\u27 actions are essential. This paper proposes a vision sensor based method to evaluate the accuracy of operators\u27 actions. Each action of operators is recognized in real time by a Convolutional Neural Network (CNN) based classification model in which hierarchical clustering is introduced to minimize the effects of action uncertainty. Warnings are triggered when incorrect actions occur in real time and applications of action analysis of workers on a reducer assembling line show the effectiveness of the proposed method. The research is expected to provide a guidance for operators to correct their actions to reduce the cost of quality defects and improve the efficiency of workforce

Spinal cord injury-induced attenuation of GABAergic inhibition in spinal dorsal horn circuits is associated with down-regulation of the chloride transporter KCC2 in rat: Silent spinal circuits speak up after SCI

Most spinal cord injury (SCI) patients suffer from chronic pain. Effective therapy for this pain is lacking, and the underlying mechanisms are poorly understood. The spinal superficial dorsal horn (SDH) contains neuronal circuits capable of modulating primary afferent information involved in pain processing. KCC2 is an isoform of the K+–Cl− cotransporter that contributes to the regulation of transmembrane anion gradient which plays a key role in shaping GABAA receptor-mediated signalling in the CNS. We tested the hypothesis that SCI causes down-regulation of KCC2 distal to the injury and contributes to the neuronal hyperresponsiveness and pain-related behaviours. SCI was a hemisection at T13 level of adult Sprague–Dawley rats. Spinal sagittal slices with attached dorsal roots (DR) were prepared from L4 to L6 level. The reversal potentials of GABA responses (EGABA) and DR-evoked IPSPs and EPSPs of L4-6 SDH neurones in sham-operated and SCI rats were compared using gramicidin-perforated patch-clamp recordings. Here we report that thoracic SCI-induced down-regulation of KCC2 in the lumbar SDH parallels the development of allodynia. The subsequent changes of EGABA in SDH neurones attenuate the GABAA receptor-mediated inhibitory synaptic transmission. These changes cause certain normally subthreshold primary A and C fibre inputs to evoke action potential output in SDH neurones. We conclude that SCI induces KCC2 down-regulation and subsequent changes of EGABA in the SDH below the injury site. The resulting disinhibition unmasks normally ineffective SDH neuronal circuits and may contribute to the below-level central pain-related behaviours after incomplete SCI

Multi-Objective Considered Process Parameter Optimization of Welding Robots Based on Small Sample Size Dataset

The welding process is characterized by its high energy density, making it imperative to optimize the energy consumption of welding robots without compromising the quality and efficiency of the welding process for their sustainable development. The above evaluation objectives in a particular welding situation are mostly influenced by the welding process parameters. Although numerical analysis and simulation methods have demonstrated their viability in optimizing process parameters, there are still limitations in terms of modeling accuracy and efficiency. This paper presented a framework for optimizing process parameters of welding robots in industry settings, where data augmentation was applied to expand sample size, auto machine learning theory was incorporated to quantify reflections from process parameters to evaluation objectives, and the enhanced non-dominated sorting algorithm was employed to identify an optimal solution by balancing these objectives. Additionally, an experiment using Q235 as welding plates was designed and conducted on a welding platform, and the findings indicated that the prediction accuracy on different objectives obtained by the enlarged dataset through ensembled models all exceeded 95%. It is proven that the proposed methods enabled the efficient and optimal determination of parameter instructions for welding scenarios and exhibited superior performance compared with other optimization methods in terms of model correctness, modeling efficiency, and method applicability