Fire and climate: Biomass burning recorded in ice and lake cores

Human activities including fossil fuel burning are currently altering the global climate system at rates faster than ever recorded in geologic time. Biomass burning causes carbon dioxide emissions equal to 50% of those from fossil-fuel combustion and so are highly likely to influence future climate change. However, aerosols continue to be one of the least understood aspects of the modern climate system and even less is known about their past influence. Ice and lake core proxy records provide quantifiable data on past fire regimes across most spatial and temporal scales. Some monosaccharide anhydrides such as levoglucosan, mannosan and galactosan are used as specific molecular markers for biomass burning as they can only be produced by combustion processes at temperatures greater than 300 °C and are present in both ice and lake cores. Other paleofire tracers such as microcharcoal, polycyclic aromatic hydrocarbons, and pollen records augment the fire history derived at single sites or across regions. As both pyrochemical and climate parameters are determined from the same depth and time within the ice or sediment matrix, the multi-proxy nature of ice and lake cores presents an ideal material to investigate the links between fires and climate change

Anomaly detection in quasi-periodic energy consumption data series: a comparison of algorithms

The diffusion of domotics solutions and of smart appliances and meters enables the monitoring of energy consumption at a very fine level and the development of forecasting and diagnostic applications. Anomaly detection (AD) in energy consumption data streams helps identify data points or intervals in which the behavior of an appliance deviates from normality and may prevent energy losses and break downs. Many statistical and learning approaches have been applied to the task, but the need remains of comparing their performances with data sets of different characteristics. This paper focuses on anomaly detection on quasi-periodic energy consumption data series and contrasts 12 statistical and machine learning algorithms tested in 144 different configurations on 3 data sets containing the power consumption signals of fridges. The assessment also evaluates the impact of the length of the series used for training and of the size of the sliding window employed to detect the anomalies. The generalization ability of the top five methods is also evaluated by applying them to an appliance different from that used for training. The results show that classical machine learning methods (Isolation Forest, One-Class SVM and Local Outlier Factor) outperform the best neural methods (GRU/LSTM autoencoder and multistep methods) and generalize better when applied to detect the anomalies of an appliance different from the one used for training

2,4-Dimethoxy­benzaldehyde azine

The title mol­ecule, C18H20N2O4, is located on a crystallographic centre of symmetry. The meth­oxy groups are coplanar with the benzene ring [interplanar angles of 14.4 (2) and 3.1 (3)°], indicating a conjugation effect

Anomaly Detection in Small-Scale Industrial and Household Appliances

Anomaly detection is concerned with identifying rare events/ observations that differ substantially from the majority of the data. It is considered an important task in the energy sector to enable the identification of non-standard device conditions. The use of anomaly detection techniques in small-scale residential and industrial settings can provide useful insights about device health, maintenance requirements, and downtime, which in turn can lead to lower operating costs. There are numerous approaches for detecting anomalies in a range of application scenarios such as prescriptive appliance maintenance. This work reports on anomaly detection using a data set of fridge power consumption that operates on a near zero energy building scenario. We implement a variety of machine and deep learning algorithms and evaluate performances using multiple metrics. In the light of the present state of the art, the contribution of this work is the development of a inference pipeline that incorporates numerous methodologies and algorithms capable of producing high accuracy results for detecting appliance failures

Two-colour generation in a chirped seeded Free-Electron Laser

We present the experimental demonstration of a method for generating two spectrally and temporally separated pulses by an externally seeded, single-pass free-electron laser operating in the extreme-ultraviolet spectral range. Our results, collected on the FERMI@Elettra facility and confirmed by numerical simulations, demonstrate the possibility of controlling both the spectral and temporal features of the generated pulses. A free-electron laser operated in this mode becomes a suitable light source for jitter-free, two-colour pump-probe experiments

(E)-4-Octyloxybenzaldehyde thio­semicarbazone

In the title compound, C16H25N3OS, the thio­semicarbazone group adopts an E configuration with respect to the C=N bond and is almost coplanar with the benzene ring, forming a dihedral angle of 9.3 (1)°. In the crystal packing, the mol­ecules lie along the a axis in an anti­parallel arrangement and are held in place by van der Waals inter­actions. As a consequence, there is relatively low anisotropic thermal motion in the terminal atoms of the n-octyl chain

Using XDAQ in Application Scenarios of the CMS Experiment

XDAQ is a generic data acquisition software environment that emerged from a rich set of of use-cases encountered in the CMS experiment. They cover not the deployment for multiple sub-detectors and the operation of different processing and networking equipment as well as a distributed collaboration of users with different needs. The use of the software in various application scenarios demonstrated the viability of the approach. We discuss two applications, the tracker local DAQ system for front-end commissioning and the muon chamber validation system. The description is completed by a brief overview of XDAQ.Comment: Conference CHEP 2003 (Computing in High Energy and Nuclear Physics, La Jolla, CA

Complex Molecules That Fold Like Proteins Can Emerge Spontaneously

Folding can bestow macromolecules with various properties, as evident from nature\u2019s proteins. Until now complex folded molecules are the product either of evolution or of an elaborate process of design and synthesis. We now show that molecules that fold in a well-defined architecture of substantial complexity can emerge autonomously and selectively from a simple precursor. Specifically, we have identified a self-synthesizing macrocyclic foldamer with a complex and unprecedented secondary and tertiary structure that constructs itself highly selectively from 15 identical peptide-nucleobase subunits, using a dynamic combinatorial chemistry approach. Folding of the structure drives its synthesis in 95% yield from a mixture of interconverting molecules of different ring sizes in a one-step process. Single-crystal X-ray crystallography and NMR reveal a folding pattern based on an intricate network of noncovalent interactions involving residues spaced apart widely in the linear sequence. These results establish dynamic combinatorial chemistry as a powerful approach to developing synthetic molecules with folding motifs of a complexity that goes well beyond that accessible with current design approaches. The fact that such molecules can form autonomously implies that they may have played a role in the origin of life at earlier stages than previously thought possibl

The CMS Event Builder

The data acquisition system of the CMS experiment at the Large Hadron Collider will employ an event builder which will combine data from about 500 data sources into full events at an aggregate throughput of 100 GByte/s. Several architectures and switch technologies have been evaluated for the DAQ Technical Design Report by measurements with test benches and by simulation. This paper describes studies of an EVB test-bench based on 64 PCs acting as data sources and data consumers and employing both Gigabit Ethernet and Myrinet technologies as the interconnect. In the case of Ethernet, protocols based on Layer-2 frames and on TCP/IP are evaluated. Results from ongoing studies, including measurements on throughput and scaling are presented. The architecture of the baseline CMS event builder will be outlined. The event builder is organised into two stages with intelligent buffers in between. The first stage contains 64 switches performing a first level of data concentration by building super-fragments from fragments of 8 data sources. The second stage combines the 64 super-fragments into full events. This architecture allows installation of the second stage of the event builder in steps, with the overall throughput scaling linearly with the number of switches in the second stage. Possible implementations of the components of the event builder are discussed and the expected performance of the full event builder is outlined.Comment: Conference CHEP0