Lesson Learnt and Future of AI Applied to Manufacturing

This chapter touches on several aspects related to the role of Artificial Intelligence (AI) and Machine Learning (ML) in the manufacturing sector, and is split in different sub-chapters, focusing on specific new technology enablers that have the potential of solving or minimizing known issues in the manufacturing and, more in general, in the Industrial Internet of Things (IIoT) domain. After introducing AI/ML as a technology enabler for the IoT in general and for manufacturing in particular, the next four sections detail two key technology enablers (EdgeML and federated learning scenarios, challenges and tools), one most important area of the IoT system that needs to decrease energy consumption and increase reliability (reduce receiver Processing complexity and enhancing reliability through multi-connectivity uplink connections), and finally a glimpse at the future describing a promising new technology (Embodied AI), its link with millimetre waves connectivity and potential business impact

Power-Adaptive Computing in Future Energy Networks

The current electricity grid is undergoing major changes. There is increasing pressure to move away from power generation from fossil fuels, both due to ecological concerns and fear of dependencies on scarce natural resources. Increasing the share of decentralized generation from renewable sources is a widely accepted way to a more sustainable power infrastructure. However, this comes at the price of new challenges: generation from solar or wind power is not controllable and only forecastable with limited accuracy. To compensate for the increasing volatility in power generation, exerting control on the demand side is a promising approach. By providing flexibility on demand side, imbalances between power generation and demand may be mitigated. This work is concerned with developing methods to provide grid support on demand side while limiting the associated costs. This is done in four major steps: first, the target power curve to follow is derived taking both goals of a grid authority and costs of the respective load into account. In the following, the special case of data centers as an instance of significant loads inside a power grid are focused on more closely. Data center services are adapted in a way such as to achieve the previously derived power curve. By means of hardware power demand models, the required adaptation of hardware utilization can be derived. The possibilities of adapting software services are investigated for the special use case of live video encoding. A method to minimize quality of experience loss while reducing power demand is presented. Finally, the possibility of applying probabilistic model checking to a continuous demand-response scenario is demonstrated

Modelling and analysing the power consumption of idle servers

Abstract-To the best of our knowledge, there have been no efforts in devising power consumption prediction models for an idle server, where this latter contributes approximately 66% of the maximum power drain. In this paper, we propose power consumption prediction models for idle servers by taking into account their constituent components such as processor, memory, hard disk, fan and power supply unit. To this end, we identify the relevant energy-related attributes of each component necessary for the idle power consumption predictions. Furthermore, based on the proposed models, we provide an in-depth analysis by considering several types of servers (e.g. rackable, blade, etc) having different hardware characteristics and energy-aware mechanisms

Mapping of Self-organization Properties and Non-functional Requirements in Smart Grids

International audienceFuture electrical power networks will be composed of large collections of autonomous components. Self-organization is an organizational concept that promises robust systems with the ability to adapt themselves to system perturbations and failures and thus may yield highly robust systems with the ability to scale freely to almost any size. In this position paper the authors describe the well-established process of use case based derivation of non-functional requirements in energy systems and propose a mapping strategy for aligning properties of self-organizing systems with the ICT- and automation system requirements. It is the strong belief of the authors that such a mapping will be a key factor in creating acceptance of and establishing self-organization in the domain of electrical energy systems

The translation regulator Zar1l controls timing of meiosis in Xenopus oocytes

Oocyte maturation and early embryo development occur in vertebrates in the near absence of transcription. Thus, sexual reproduction of vertebrates critically depends on the timely translation of mRNAs already stockpiled in the oocyte. Yet how translational activation of specific mRNAs is temporally coordinated is still incompletely understood. Here, we elucidate the function of Zar1l, a yet uncharacterized member of the Zar RNA-binding protein family, in Xenopus oocytes. Employing TRIM-Away, we demonstrate that loss of Zar1l accelerates hormone-induced meiotic resumption of Xenopus oocytes due to premature accumulation of the M-phase-promoting kinase cMos. We show that Zar1l is a constituent of a large ribonucleoparticle containing the translation repressor 4E-T and the central polyadenylation regulator CPEB1, and that it binds directly to the cMos mRNA. Partial, hormone-induced degradation of Zar1l liberates 4E-T from CPEB1, which weakens translational repression of mRNAs encoding cMos and likely additional M-phase-promoting factors. Thus, our study provides fundamental insights into the mechanisms that ensure temporally regulated translation of key cell cycle regulators during oocyte maturation, which is essential for sexual reproductivity.publishe

Effects of 8-weeks of daily time restricted feeding and aerobic exercise on fat oxidation – A randomized controlled trial

Introduction & Purpose Substrate metabolism, especially lipid metabolism and thus fat oxidation, is of special interest to reduce the risk of metabolic diseases (diabetes, high cholesterol or triglycerides, etc), and improve athletic performance (Aird et al., 2018). Thus, patients and athletes are recommended to engage in exercise training at a mild to moderate intensity where fat oxidation is high. Fasting is known to increase lipolysis (i.e., fat oxidation) and therefore may represent a simple intervention to increase training induced adaptions in fat oxidation (Venables & Jeukendrup, 2008). However, breaking the fast with a carbohydrate meal prior to aerobic training may limit these benefits because carbohydrate consumption is known to prioritize carbohydrate oxidation during exercise (Achten & Jeukendrup, 2003). Therefore, we tested the hypothesis that 8 weeks of aerobic exercise training at a workload that maximizes fat oxidation would improve the maximal rate of fat oxidation during exercise. Moreover, engaging in a fasting regime would augment the improvements in fat oxidation, but breaking the fast prior to training with a carbohydrate rich snack would attenuate the improvements in fat oxidation. Methods Thirty-six participants (28 females, 8 males) were randomized into three groups. 1) One group fasted for at least 14 hours prior to training, 2) One group fasted, but consumed a carbohydrate rich snack 30 minutes prior to training, 3) One exercise only control group where participants could eat ad libitum. Pre-tests included anthropometric measurements, a bio-impedance-analyses, and cycle ergometry combined with indirect calorimetry to identify maximum rates of fat oxidation (fatmax). All participants exercised on a stationary bike 3x/week for 60 min at a heart rate that corresponded to 90-100% of their individualized fatmax values. Pre-test measurements were repeated after the intervention. Rates of fat and carbohydrate oxidation, energy expenditure and heart rate were analysed as workload matched and absolute data using a series of 3 x 2 mixed ANOVAs. Results 3 x 2 mixed ANOVA showed no group-by-time interactions for any workload-matched data (fatmax: p = .371, η²p = .058; CHO: p = .540, η²p = .037; EE: p = .470, η²p = .045; HR: p = .570, η²p = .033). No significant group-by-time interactions at the absolute maximal fat oxidation rate were observed (fatmax: p = .262, η²p = .078; CHO: p = .966, η²p = .002; EE: p = .111, η²p = .125; HR: p = .618, η²p = .029). Discussion and conclusion In the current study, the addition of a 16-hour fasting window did not provide any additional improvements in fat oxidation rates beyond an ad libitum control group. This is surprising as both low-intensity training (Achten & Jeukendrup, 2004) and TRF (Jong-Yeon et al., 2002) have independently been shown to improve fat oxidation. We conclude that fatmax training is an effective lifestyle intervention to improve fat oxidation in young healthy individuals. Collectively, these data suggest fatmax training independent of the fed state might be a useful lifestyle intervention in healthy individuals looking to maintain or improve their metabolic health and avoid future metabolic disease. References Achten, J., & Jeukendrup, A. E. (2003). The effect of pre-exercise carbohydrate feedings on the intensity that elicits maximal fat oxidation. Journal of Sports Sciences, 21(12), 1017–1025. https://doi.org/10.1080/02640410310001641403 Achten, J., & Jeukendrup, A. E. (2004). Optimizing fat oxidation through exercise and diet. Nutrition, 20(7-8), 716–727. https://doi.org/10.1016/j.nut.2004.04.005 Aird, T. P., Davies, R. W., & Carson, B. P. (2018). Effects of fasted vs fed-state exercise on performance and post-exercise metabolism: A systematic review and meta-analysis. Scandinavian Journal of Medicine and Science in Sports, 28(5), 1476–1493. https://doi.org/10.1111/sms.13054 Jong-Yeon, K., Hickner, R. C., Dohm, G. L., & Houmard, J. A. (2002). Long- and medium-chain fatty acid oxidation is increased in exercise-trained human skeletal muscle. Metabolism, 51(4), 460–464. https://doi.org/10.1053/meta.2002.31326 Venables, M. C., & Jeukendrup, A. E. (2008). Endurance training and obesity: Effect on substrate metabolism and insulin sensitivity. Medicine & Science in Sports & Exercise, 40(3), 495–502. https://doi.org/10.1249/MSS.0b013e31815f256