Packaging design for competitiveness. Contextualizing the search and adoption of changes from a sustainable supply chain perspective

[EN] The Sustainable Packaging Logistics (SPL) approach seeks sustainable integration of the combined packaging-product-supply chain system orientated to increase competitiveness. However, characterizing which changes make it possible to guide such design in each company and supply chain is an aspect that has not been covered in the literature from different supply chain perspectives. The main goal of this paper is to identify and justify the main actions for improvement in SPL, combined with a proposal of methodology for contextualizing, selecting and implementing each of these potential actions, applying the Action Research approach. Likewise, this paper illustrates the interest of this methodology with its adoption in four different companies and supply chains. This paper opens up new avenues of applied research in packaging design, generating knowledge that contributes to sustainable and competitive improvement.García-Arca, J.; González-Portela Garrido, AT.; Prado-Prado, JC.; González-Boubeta, I. (2022). Packaging design for competitiveness. Contextualizing the search and adoption of changes from a sustainable supply chain perspective. International Journal of Production Management and Engineering. 10(2):115-130. https://doi.org/10.4995/ijpme.2022.16659OJS11513010

13th FINA WORLD CHAMPIONSHIP FINALS: STROKE KINEMATICS AND RACE TIMES ACCORDING TO PERFORMANCE, GENDER AND EVENT

The aim of this work was to compare the stroke kinematics and race times of the freestyle final races at the 13th FINA World Championships between: (i) the three medalists versus the last three finalists; (ii) males versus female swimmers; (iii) all events in each gender. Data was collected from the champioships official web site. There were no significant differences in the stroke kinematics neither in the race times between medallists and non-medallists. There were significant effects in the stroke kinematics and race times according to race event. There were significant effects in the stroke kinematics and race times according to swimmers gender. It seems there are different tactics and biomechanical strategies according to gender and swimming event

Spiking Neural Network With Distributed Plasticity Reproduces Cerebellar Learning in Eye Blink Conditioning Paradigms

In this study, we defined a realistic cerebellar model through the use of artificial spiking neural networks, testing it in computational simulations that reproduce associative motor tasks in multiple sessions of acquisition and extinction. Methods: By evolutionary algorithms, we tuned the cerebellar microcircuit to find out the near-optimal plasticity mechanism parameters that better reproduced human-like behavior in eye blink classical conditioning, one of the most extensively studied paradigms related to the cerebellum. We used two models: one with only the cortical plasticity and another including two additional plasticity sites at nuclear level. Results: First, both spiking cerebellar models were able to well reproduce the real human behaviors, in terms of both "timing" and "amplitude", expressing rapid acquisition, stable late acquisition, rapid extinction, and faster reacquisition of an associative motor task. Even though the model with only the cortical plasticity site showed good learning capabilities, the model with distributed plasticity produced faster and more stable acquisition of conditioned responses in the reacquisition phase. This behavior is explained by the effect of the nuclear plasticities, which have slow dynamics and can express memory consolidation and saving. Conclusions: We showed how the spiking dynamics of multiple interactive neural mechanisms implicitly drive multiple essential components of complex learning processes. Significance: This study presents a very advanced computational model, developed together by biomedical engineers, computer scientists, and neuroscientists. Since its realistic features, the proposed model can provide confirmations and suggestions about neurophysiological and pathological hypotheses and can be used in challenging clinical application

Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fninf. 2017.00007/full#supplementary-materialModeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity). The studied techniques are classified into two main families depending on how the neural-model dynamic evaluation is computed: the event-driven or the time-driven families. Whilst event-driven techniques pre-compile and store the neural dynamics within look-up tables, time-driven techniques compute the neural dynamics iteratively during the simulation time. We propose two modifications for the event-driven family: a look-up table recombination to better cope with the incremental neural complexity together with a better handling of the synchronous input activity. Regarding the time-driven family, we propose a modification in computing the neural dynamics: the bi-fixed-step integration method. This method automatically adjusts the simulation step size to better cope with the stiffness of the neural model dynamics running in CPU platforms. One version of this method is also implemented for hybrid CPU-GPU platforms. Finally, we analyze how the performance and accuracy of these modifications evolve with increasing levels of neural complexity. We also demonstrate how the proposed modifications which constitute the main contribution of this study systematically outperform the traditional event- and time-driven techniques under increasing levels of neural complexity.This study was supported by the European Union NR (658479-Spike Control), the Spanish National Grant NEUROPACT (TIN2013-47069-P) and by the Spanish National Grant PhD scholarship (AP2012-0906). We gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan GPUs for current EDLUT development

Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue

The cerebellar microcircuit has been the work bench for theoretical and computational modeling since the beginning of neuroscientific research. The regular neural architecture of the cerebellum inspired different solutions to the long-standing issue of how its circuitry could control motor learning and coordination. Originally, the cerebellar network was modeled using a statistical-topological approach that was later extended by considering the geometrical organization of local microcircuits. However, with the advancement in anatomical and physiological investigations, new discoveries have revealed an unexpected richness of connections, neuronal dynamics and plasticity, calling for a change in modeling strategies, so as to include the multitude of elementary aspects of the network into an integrated and easily updatable computational framework. Recently, biophysically accurate realistic models using a bottom-up strategy accounted for both detailed connectivity and neuronal non-linear membrane dynamics. In this perspective review, we will consider the state of the art and discuss how these initial efforts could be further improved. Moreover, we will consider how embodied neurorobotic models including spiking cerebellar networks could help explaining the role and interplay of distributed forms of plasticity. We envisage that realistic modeling, combined with closed-loop simulations, will help to capture the essence of cerebellar computations and could eventually be applied to neurological diseases and neurorobotic control systems

Distributed cerebellar plasticity implements generalized multiple-scale memory components in real-robot sensorimotor tasks

The cerebellum plays a crucial role in motor learning and it acts as a predictive controller. Modeling it and embedding it into sensorimotor tasks allows us to create functional links between plasticity mechanisms, neural circuits and behavioral learning. Moreover, if applied to real-time control of a neurorobot, the cerebellar model has to deal with a real noisy and changing environment, thus showing its robustness and effectiveness in learning. A biologically inspired cerebellar model with distributed plasticity, both at cortical and nuclear sites, has been used. Two cerebellum-mediated paradigms have been designed: an associative Pavlovian task and a vestibulo-ocular reflex, with multiple sessions of acquisition and extinction and with different stimuli and perturbation patterns. The cerebellar controller succeeded to generate conditioned responses and finely tuned eye movement compensation, thus reproducing human-like behaviors. Through a productive plasticity transfer from cortical to nuclear sites, the distributed cerebellar controller showed in both tasks the capability to optimize learning on multiple time-scales, to store motor memory and to effectively adapt to dynamic ranges of stimuli.This work was supported by grants of European Union: REALNET (FP7-ICT270434) and Human Brain Project (HBP-604102)

Long term outdoor operation of a tubular airlift pilot photobioreactor and a high rate algal pond as tertiary treatment of urban wastewater.

530 L high rate alga pond (HRAP) and 380 L airlift tubular photobioreactor (TPBR) were operated and compared in a urban wastewater treatment plant (WWTP), with the main purpose of removing nitrogen and phosphorous from the effluent of the WWTP while generating a valuable biomass. The photosynthetic activity in TPBR was during entire experiment higher than HRAP. The maximum areal productivity reached was 8.26 ± 1.43 and 21.76 ± 0.3 g SS m−2 d−1 for HRAP and TPBR respectively. Total nitrogen (TN) removal averaged 89.68 ± 3.12 and 65.12 ± 2.87% for TPBR and HRAP respectively, while for total phosphorus (TP) TPBR and HRAP averaged 86.71 ± 0.61 and 58.78 ± 1.17% respectively. The lipid content showed no significant differences (p < 0.05) between HRAP and TPBR averaging 20.80 ± 0.22 wt%. The main operating disadvantage of TPBR versus HRAP was the sever biofouling which forced to stop the experiment. Under the same conditions of operation TPBR was more limited at low temperatures than HRAP, and HRAP was more light limited than TPBR

Search for the standard model Higgs boson in the H to ZZ to 2l 2nu channel in pp collisions at sqrt(s) = 7 TeV

A search for the standard model Higgs boson in the H to ZZ to 2l 2nu decay channel, where l = e or mu, in pp collisions at a center-of-mass energy of 7 TeV is presented. The data were collected at the LHC, with the CMS detector, and correspond to an integrated luminosity of 4.6 inverse femtobarns. No significant excess is observed above the background expectation, and upper limits are set on the Higgs boson production cross section. The presence of the standard model Higgs boson with a mass in the 270-440 GeV range is excluded at 95% confidence level.Comment: Submitted to JHE