G20: On Behalf of the Rest?

AbstractMajor developments in the last three decades have set the scene for the rise of novel problems on global scale. The unprecedented level of interdependence and interconnectedness between countries, firms and institutions has paved the way for the emergence of, both, novel practices that increase the quality of life and intriguingly complicated issues of global governance. The relationship between global actors are so intertwined that striving for predictability is barely feasible. In spite of the enhanced capabilities gained through involvement in the economic and financial value creation process, there are perils ahead for better global governance. Major issues pose global actors in terms of credibility, building and ensuring sustainability, erosion of capacity to fulfill promises and increasing fragility of financial markets as well as issues regarding depleting energy resources, environment and security. G-20 emerged as a remedial governance structure in the wake of the 2008 financial turmoil making sure that the prominent dynamic emerging countries are seated around the table. The expansion of G-8 into G-20 including the new global powerhouses has many positive implications. However, ongoing debates regarding this structure oscillate between hope and contestation. This conceptual paper intends to draw a general framework regarding the representative capability of G-20 members and discuss the hybrid quality of this so called steering committee given the era of turbulence that the world is heading towards

SOME CURVATURE PROPERTIES ON PARACONTACT METRIC (k;μ)-MANIFOLDS WITH RESPECT TO THE SCHOUTEN-VAN KAMPEN CONNECTION

The object of the present paper is to characterize paracontact metric (k;μ)-manifolds satisfying certain semisymmetry curvature conditions with respect to the Schouten-van Kampen connection

Formaldehyde Adsorption and Sensing: A Density Functional Theory Study on Pd4 Nanocluster Decorated CNT Structure

The sensing of formaldehyde, one of the volatile organic compounds used in chemical processes, is very important. In this study, the adsorption and sensing of formaldehyde molecule on Pd4 nanocluster decorated carbon nanotube (Pd4-CNT) was investigated by using DFT method. The WB97XD hybrid method was used in DFT calculations. The adsorption energy value was calculated as −8.1 kJ/mol. This low adsorption energy confirms the very short recovery time and the predominance of weak interactions. There was a decrease of approximately 20% in the HOMO-LUMO gap after the interaction. This result shows that the Pd4-CNT can be used as a sensor at room temperature

A versatile and reconfigurable microassembly workstation

In this paper, a versatile and reconfigurable microassembly workstation designed and realized as a research tool for investigation of the problems in microassembly and micromanipulation processes and recent developments on mechanical and control structure of the system with respect to the previous workstation are presented. These developments include: (i) addition of a manipulator system to realize more complicated assembly and manipulation tasks, (ii) addition of extra DOF for the vision system and sample holder stages in order to make the system more versatile (iii) a new optical microscope as the vision system in order to visualize the microworld and determine the position and orientation of micro components to be assembled or manipulated, (iv) a modular control system hardware which allows handling more DOF. In addition several experiments using the workstation are presented in different modes of operation like tele-operated, semiautomated and fully automated by means of visual based schemes

Robust topology optimisation of lattice structures with spatially correlated uncertainties

The uncertainties in material and other properties of structures are usually spatially correlated. We introduce an efficient technique for representing and processing spatially correlated random fields in robust topology optimisation of lattice structures. Robust optimisation considers the statistics of the structural response to obtain a design whose performance is less sensitive to the specific realisation of the random field. We represent Gaussian random fields on lattices by leveraging the established link between random fields and stochastic partial differential equations (SPDEs). It is known that the precision matrix, i.e. the inverse of the covariance matrix, of a random field with Mat\'ern covariance is equal to the finite element stiffness matrix of a possibly fractional PDE with a second-order elliptic operator. We consider the discretisation of the PDE on the lattice to obtain a random field which, by design, considers its geometry and connectivity. The so-obtained random field can be interpreted as a physics-informed prior by the hypothesis that the elliptic SPDE models the physical processes occurring during manufacturing, like heat and mass diffusion. Although the proposed approach is general, we demonstrate its application to lattices modelled as pin-jointed trusses with uncertainties in member Young's moduli. We consider as a cost function the weighted sum of the expectation and standard deviation of the structural compliance. To compute the expectation and standard deviation and their gradients with respect to member cross-sections we use a first-order Taylor series approximation. The cost function and its gradient are computed using only sparse matrix operations. We demonstrate the efficiency of the proposed approach using several lattice examples with isotropic, anisotropic and non-stationary random fields and up to eighty thousand random and optimisation variables

A comparison of dexmedetomidine, moxonidine and alpha-methyldopa effects on acute, lethal cocaine toxicity

Background: The treatment of cocaine toxicity is an important subject for emergency physicians. We investigated the effects of dexmedetomidine, moxonidine and alpha-methyldopa on acute cocaine toxicity in mice. Objectives: The aim of this study was to evaluate the effects of dexmedetomidine, moxonidine and alpha-methyldopa in a mouse model of acute cocaine toxicity. Materials and Methods: We performed an experiment consisting of four groups (n = 25 each). The first group received normal saline solution, the second group received 40 μg/kg of dexmedetomidine, the third group received 0.1 mg/kg of moxonidine and the fourth group received 200 mg/kg of alpha-methyldopa, all of which were intraperitoneally administered 10 minutes before cocaine hydrochloride (105 mg/kg). All animals were observed for seizures (popcorn jumping, tonic-clonic activity, or a loss of the righting reflex) and lethality over the 30 minutes following cocaine treatment. Results: The ratio of animals with convulsions was lower in all treated groups when compared to the control (P 0.05). In addition, the time to lethality was also longer in the same group (P < 0.001). Conclusions: The present study provides the first experimental evidence in support of dexmedetomidine treatment for cocaine-induced seizures. Premedication with dexmedetomidine reduces seizure activity in a mouse model of acute cocaine toxicity. In addition, while dexmedetomidine may be effective, moxonidine and alpha-methyldopa did not effectively prevent cocaine-induced lethality. © 2015, Iranian Red Crescent Medical Journal

A Reference-less Quality Metric for Automatic Speech Recognition via Contrastive-Learning of a Multi-Language Model with Self-Supervision

The common standard for quality evaluation of automatic speech recognition (ASR) systems is reference-based metrics such as the Word Error Rate (WER), computed using manual ground-truth transcriptions that are time-consuming and expensive to obtain. This work proposes a multi-language referenceless quality metric, which allows comparing the performance of different ASR models on a speech dataset without ground truth transcriptions. To estimate the quality of ASR hypotheses, a pre-trained language model (LM) is fine-tuned with contrastive learning in a self-supervised learning manner. In experiments conducted on several unseen test datasets consisting of outputs from top commercial ASR engines in various languages, the proposed referenceless metric obtains a much higher correlation with WER scores and their ranks than the perplexity metric from the state-of-art multi-lingual LM in all experiments, and also reduces WER by more than $7\%$ when used for ensembling hypotheses. The fine-tuned model and experiments are made available for the reproducibility: https://github.com/aixplain/NoRefERComment: arXiv admin note: substantial text overlap with arXiv:2306.1257

NoRefER: a Referenceless Quality Metric for Automatic Speech Recognition via Semi-Supervised Language Model Fine-Tuning with Contrastive Learning

This paper introduces NoRefER, a novel referenceless quality metric for automatic speech recognition (ASR) systems. Traditional reference-based metrics for evaluating ASR systems require costly ground-truth transcripts. NoRefER overcomes this limitation by fine-tuning a multilingual language model for pair-wise ranking ASR hypotheses using contrastive learning with Siamese network architecture. The self-supervised NoRefER exploits the known quality relationships between hypotheses from multiple compression levels of an ASR for learning to rank intra-sample hypotheses by quality, which is essential for model comparisons. The semi-supervised version also uses a referenced dataset to improve its inter-sample quality ranking, which is crucial for selecting potentially erroneous samples. The results indicate that NoRefER correlates highly with reference-based metrics and their intra-sample ranks, indicating a high potential for referenceless ASR evaluation or a/b testing