CERN openlab Whitepaper on Future IT Challenges in Scientific Research

This whitepaper describes the major IT challenges in scientific research at CERN and several other European and international research laboratories and projects. Each challenge is exemplified through a set of concrete use cases drawn from the requirements of large-scale scientific programs. The paper is based on contributions from many researchers and IT experts of the participating laboratories and also input from the existing CERN openlab industrial sponsors. The views expressed in this document are those of the individual contributors and do not necessarily reflect the view of their organisations and/or affiliates

Process improvement in BAe Systems and the wider aerospace sector

Purpose: To research the change management processes used to implement ‘world class’ improvements in a major aerospace company, BAE SYSTEMS, and to propose a model for process improvement in the wider aerospace sector. Design/methodology/approach: The research was undertaken as a longitudinal study over a period of five years. A variety of research methodologies were used at various stages of the research including action research and observation. Semi-structured and unstructured interviews were used to gather qualitative data along with documentary evidence of the processes being used. Findings: There are three key findings. Firstly, an understanding of the production stages in the aerospace sector: future project; new product; sustain and return to work. Secondly details of a matrix-based approach and the issues regarding its implementation in a large organisation are discussed. Thirdly, a generic set of principles to aid process improvement in the aerospace sector is proposed. Research limitations/implications: Given that the study is based in one company, there are issues regarding the generalisation of the results. A potential further research project would entail the implementation of the proposed generic principles in another aerospace organisation. Practical implications: For BAE SYSTEMS, this research project aided their understanding of the issues involved in rolling out a process improvement program in a large organisation.Originality/value: Until recently, most of the research into process improvement had either been universalistic or aimed at another type of industry, such as the automotive industry. This research helps to address the specific needs of the aerospace industry

Continuous-flow reactors for the rapid evolution and validation of kinetic motifs

In this paper we apply the concept of a kinetic motif as a simple way to represent all the time-dependent behaviour in a single-step or multi-step reaction system. Small-scale continuous-flow reactors offer the potential to rapidly collect large amounts of data while accessing conventionally challenging experimental conditions. The scope of the approach is demonstrated on reaction case study examples

Intelligent Simulation Modeling of a Flexible Manufacturing System with Automated Guided Vehicles

Although simulation is a very flexible and cost effective problem solving technique, it has been traditionally limited to building models which are merely descriptive of the system under study. Relatively new approaches combine improvement heuristics and artificial intelligence with simulation to provide prescriptive power in simulation modeling. This study demonstrates the synergy obtained by bringing together the "learning automata theory" and simulation analysis. Intelligent objects are embedded in the simulation model of a Flexible Manufacturing System (FMS), in which Automated Guided Vehicles (AGVs) serve as the material handling system between four unique workcenters. The objective of the study is to find satisfactory AGV routing patterns along available paths to minimize the mean time spent by different kinds of parts in the system. System parameters such as different part routing and processing time requirements, arrivals distribution, number of palettes, available paths between workcenters, number and speed of AGVs can be defined by the user. The network of learning automata acts as the decision maker driving the simulation, and the FMS model acts as the training environment for the automata network; providing realistic, yet cost-effective and risk-free feedback. Object oriented design and implementation of the simulation model with a process oriented world view, graphical animation and visually interactive simulation (using GUI objects such as windows, menus, dialog boxes; mouse sensitive dynamic automaton trace charts and dynamic graphical statistical monitoring) are other issues dealt with in the study

The NASA SBIR product catalog

The purpose of this catalog is to assist small business firms in making the community aware of products emerging from their efforts in the Small Business Innovation Research (SBIR) program. It contains descriptions of some products that have advanced into Phase 3 and others that are identified as prospective products. Both lists of products in this catalog are based on information supplied by NASA SBIR contractors in responding to an invitation to be represented in this document. Generally, all products suggested by the small firms were included in order to meet the goals of information exchange for SBIR results. Of the 444 SBIR contractors NASA queried, 137 provided information on 219 products. The catalog presents the product information in the technology areas listed in the table of contents. Within each area, the products are listed in alphabetical order by product name and are given identifying numbers. Also included is an alphabetical listing of the companies that have products described. This listing cross-references the product list and provides information on the business activity of each firm. In addition, there are three indexes: one a list of firms by states, one that lists the products according to NASA Centers that managed the SBIR projects, and one that lists the products by the relevant Technical Topics utilized in NASA's annual program solicitation under which each SBIR project was selected

Machine learning for the sustainable energy transition: a data-driven perspective along the value chain from manufacturing to energy conversion

According to the special report Global Warming of 1.5 °C of the IPCC, climate action is not only necessary but more than ever urgent. The world is witnessing rising sea levels, heat waves, events of flooding, droughts, and desertification resulting in the loss of lives and damage to livelihoods, especially in countries of the Global South. To mitigate climate change and commit to the Paris agreement, it is of the uttermost importance to reduce greenhouse gas emissions coming from the most emitting sector, namely the energy sector. To this end, large-scale penetration of renewable energy systems into the energy market is crucial for the energy transition toward a sustainable future by replacing fossil fuels and improving access to energy with socio-economic benefits. With the advent of Industry 4.0, Internet of Things technologies have been increasingly applied to the energy sector introducing the concept of smart grid or, more in general, Internet of Energy. These paradigms are steering the energy sector towards more efficient, reliable, flexible, resilient, safe, and sustainable solutions with huge environmental and social potential benefits. To realize these concepts, new information technologies are required, and among the most promising possibilities are Artificial Intelligence and Machine Learning which in many countries have already revolutionized the energy industry. This thesis presents different Machine Learning algorithms and methods for the implementation of new strategies to make renewable energy systems more efficient and reliable. It presents various learning algorithms, highlighting their advantages and limits, and evaluating their application for different tasks in the energy context. In addition, different techniques are presented for the preprocessing and cleaning of time series, nowadays collected by sensor networks mounted on every renewable energy system. With the possibility to install large numbers of sensors that collect vast amounts of time series, it is vital to detect and remove irrelevant, redundant, or noisy features, and alleviate the curse of dimensionality, thus improving the interpretability of predictive models, speeding up their learning process, and enhancing their generalization properties. Therefore, this thesis discussed the importance of dimensionality reduction in sensor networks mounted on renewable energy systems and, to this end, presents two novel unsupervised algorithms. The first approach maps time series in the network domain through visibility graphs and uses a community detection algorithm to identify clusters of similar time series and select representative parameters. This method can group both homogeneous and heterogeneous physical parameters, even when related to different functional areas of a system. The second approach proposes the Combined Predictive Power Score, a method for feature selection with a multivariate formulation that explores multiple sub-sets of expanding variables and identifies the combination of features with the highest predictive power over specified target variables. This method proposes a selection algorithm for the optimal combination of variables that converges to the smallest set of predictors with the highest predictive power. Once the combination of variables is identified, the most relevant parameters in a sensor network can be selected to perform dimensionality reduction. Data-driven methods open the possibility to support strategic decision-making, resulting in a reduction of Operation & Maintenance costs, machine faults, repair stops, and spare parts inventory size. Therefore, this thesis presents two approaches in the context of predictive maintenance to improve the lifetime and efficiency of the equipment, based on anomaly detection algorithms. The first approach proposes an anomaly detection model based on Principal Component Analysis that is robust to false alarms, can isolate anomalous conditions, and can anticipate equipment failures. The second approach has at its core a neural architecture, namely a Graph Convolutional Autoencoder, which models the sensor network as a dynamical functional graph by simultaneously considering the information content of individual sensor measurements (graph node features) and the nonlinear correlations existing between all pairs of sensors (graph edges). The proposed neural architecture can capture hidden anomalies even when the turbine continues to deliver the power requested by the grid and can anticipate equipment failures. Since the model is unsupervised and completely data-driven, this approach can be applied to any wind turbine equipped with a SCADA system. When it comes to renewable energies, the unschedulable uncertainty due to their intermittent nature represents an obstacle to the reliability and stability of energy grids, especially when dealing with large-scale integration. Nevertheless, these challenges can be alleviated if the natural sources or the power output of renewable energy systems can be forecasted accurately, allowing power system operators to plan optimal power management strategies to balance the dispatch between intermittent power generations and the load demand. To this end, this thesis proposes a multi-modal spatio-temporal neural network for multi-horizon wind power forecasting. In particular, the model combines high-resolution Numerical Weather Prediction forecast maps with turbine-level SCADA data and explores how meteorological variables on different spatial scales together with the turbines' internal operating conditions impact wind power forecasts. The world is undergoing a third energy transition with the main goal to tackle global climate change through decarbonization of the energy supply and consumption patterns. This is not only possible thanks to global cooperation and agreements between parties, power generation systems advancements, and Internet of Things and Artificial Intelligence technologies but also necessary to prevent the severe and irreversible consequences of climate change that are threatening life on the planet as we know it. This thesis is intended as a reference for researchers that want to contribute to the sustainable energy transition and are approaching the field of Artificial Intelligence in the context of renewable energy systems

The Surprising Reach of FDA Regulation of Cannabis Even after Rescheduling

As more states legalize cannabis, the push to “deschedule” it from the Controlled Substances Act is gaining momentum. At the same time, the Food and Drug Administration (FDA) recently approved the first conventional drug containing a cannabinoid derived from cannabis—cannabidiol (CBD) for two rare seizure disorders. This would all seem to bode well for proponents of full federal legalization of medical cannabis. But some traditional providers are wary of drug companies pulling medical cannabis into the regular small molecule drug development system. The FDA’s focus on precise analytical characterization and on individual active and inactive ingredients may be fundamentally inconsistent with the “entourage effects” theory of medical cannabis. Traditional providers may believe that descheduling cannabis would free them to promote and distribute their products free of federal intervention, both locally and nationally. Other producers appear to assume that descheduling would facilitate a robust market in cannabis-based edibles and dietary supplements. In fact, neither of these things is true. If cannabis were descheduled, the FDA’s complex and comprehensive regulatory framework governing foods, drugs, and dietary supplements would preclude much of this anticipated commerce. For example, any medical claims about cannabis would require the seller to complete the rigorous new drug approval process, the cost of which will be prohibitive for most current traditional providers. Likely also unexpected to some, there is no pathway forward for conventional foods containing cannabis constituents, with the (probably exclusive) exception of certain hemp seed ingredients, if those foods cross state lines. And it will certainly come as a shock to many that federal law already prohibits the sale of dietary supplements containing CBD—including those already on the market as well as those made from “hemp,” which has recently been descheduled under the 2018 Farm Bill. This Article describes in detail the surprising reach of the FDA and then outlines three modest, but legal, pathways forward for cannabis-based products in a world where cannabis has been descheduled