Detection of Aliphatically Bridged Multi-Core Polycyclic Aromatic Hydrocarbons in Sooting Flames with Atmospheric-Sampling High-Resolution Tandem Mass Spectrometry.

This paper provides experimental evidence for the chemical structures of aliphatically substituted and bridged polycyclic aromatic hydrocarbon (PAH) species in gas-physe combustion environments. The identification of these single- and multicore aromatic species, which have been hypothesized to be important in PAH growth and soot nucleation, was made possible through a combination of sampling gaseous constituents from an atmospheric pressure inverse coflow diffusion flame of ethylene and high-resolution tandem mass spectrometry (MS-MS). In these experiments, the flame-sampled components were ionized using a continuous VUV lamp at 10.0 eV and the ions were subsequently fragmented through collisions with Ar atoms in a collision-induced dissociation (CID) process. The resulting fragment ions, which were separated using a reflectron time-of-flight mass spectrometer, were used to extract structural information about the sampled aromatic compounds. The high-resolution mass spectra revealed the presence of alkylated single-core aromatic compounds and the fragment ions that were observed correspond to the loss of saturated and unsaturated units containing up to a total of 6 carbon atoms. Furthermore, the aromatic structures that form the foundational building blocks of the larger PAHs were identified to be smaller single-ring and pericondensed aromatic species with repetitive structural features. For demonstrative purposes, details are provided for the CID of molecular ions at masses 202 and 434. Insights into the role of the aliphatically substituted and bridged aromatics in the reaction network of PAH growth chemistry were obtained from spatially resolved measurements of the flame. The experimental results are consistent with a growth mechanism in which alkylated aromatics are oxidized to form pericondensed ring structures or react and recombine with other aromatics to form larger, potentially three-dimensional, aliphatically bridged multicore aromatic hydrocarbons

Transient simulations of the carbon and nitrogen dynamics in northern peatlands: from the Last Glacial Maximum to the 21st century

The development of northern high-latitude peatlands played an important role in the carbon (C) balance of the land biosphere since the Last Glacial Maximum (LGM). At present, carbon storage in northern peatlands is substantial and estimated to be 500 ± 100 Pg C (1 Pg C = 1015 g C). Here, we develop and apply a peatland module embedded in a dynamic global vegetation and land surface process model (LPX-Bern 1.0). The peatland module features a dynamic nitrogen cycle, a dynamic C transfer between peatland acrotelm (upper oxic layer) and catotelm (deep anoxic layer), hydrology- and temperature-dependent respiration rates, and peatland specific plant functional types. Nitrogen limitation down-regulates average modern net primary productivity over peatlands by about half. Decadal acrotelm-to-catotelm C fluxes vary between −20 and +50 g C m−2 yr−1 over the Holocene. Key model parameters are calibrated with reconstructed peat accumulation rates from peat-core data. The model reproduces the major features of the peat core data and of the observation-based modern circumpolar soil carbon distribution. Results from a set of simulations for possible evolutions of northern peat development and areal extent show that soil C stocks in modern peatlands increased by 365–550 Pg C since the LGM, of which 175–272 Pg C accumulated between 11 and 5 kyr BP. Furthermore, our simulations suggest a persistent C sequestration rate of 35–50 Pg C per 1000 yr in present-day peatlands under current climate conditions, and that this C sink could either sustain or turn towards a source by 2100 AD depending on climate trajectories as projected for different representative greenhouse gas concentration pathways

Methodological Individualism, the We-mode, and Team Reasoning

Raimo Tuomela is one of the pioneers of social action theory and has done as much as anyone over the last thirty years to advance the study of social action and collective intentionality. Social Ontology: Collective Intentionality and Group Agents (2013) presents the latest version of his theory and applications to a range of important social phenomena. The book covers so much ground, and so many important topics in detailed discussions, that it would impossible in a short space to do it even partial justice. In this brief note, I will concentrate on a single, though important, theme in the book, namely, the claim that we must give up methodological individualism in the social sciences and embrace instead irreducibly group notions. I wish to defend methodological individualism as up to the theoretical tasks of the social sciences while acknowledging what is distinctive about the social world and collective intentional action. Tuomela frames the question of the adequacy of methodological individualism in terms of a contrast between what he calls the I-mode and the we-mode. He argues that we-mode phenomena are not reducible to I-mode phenomena, and concludes that we must reject methodological individualism. I will argue that the irreducibility of the we-mode to the I-mode, given how the contrast is set up, does not entail the rejection of methodological individualism. In addition, I will argue that the three conditions that Tuomela places on genuine we-mode activities, the group reason, collectivity, and collective commitment conditions, if they are understood in a way that does not beg the question, can plausibly be satisfied by a reductive account. Finally, I will argue that the specific considerations advanced in the book do not give us reason to think that a reductive account cannot be adequate to the descriptive and explanatory requirements of a theory of the social worl

Extracellular matrix synthesis in vascular disease: hypertension, and atherosclerosis

Extracellular matrix (ECM) within the vascular network provides both a structural and regulatory role. The ECM is a dynamic composite of multiple proteins that form structures connecting cells within the network. Blood vessels are distended by blood pressure and, therefore, require ECM components with elasticity yet with enough tensile strength to resist rupture. The ECM is involved in conducting mechanical signals to cells. Most importantly, ECM regulates cellular function through chemical signaling by controlling activation and bioavail- ability of the growth factors. Cells respond to ECM by remodeling their microenvironment which becomes dys- regulated in vascular diseases such hypertension, restenosis and atherosclerosis. This review examines the cellu- lar and ECM components of vessels, with specific emphasis on the regulation of collagen type I and implications in vascular disease

An adaptive prefix-assignment technique for symmetry reduction

This paper presents a technique for symmetry reduction that adaptively assigns a prefix of variables in a system of constraints so that the generated prefix-assignments are pairwise nonisomorphic under the action of the symmetry group of the system. The technique is based on McKay's canonical extension framework [J.~Algorithms 26 (1998), no.~2, 306--324]. Among key features of the technique are (i) adaptability---the prefix sequence can be user-prescribed and truncated for compatibility with the group of symmetries; (ii) parallelizability---prefix-assignments can be processed in parallel independently of each other; (iii) versatility---the method is applicable whenever the group of symmetries can be concisely represented as the automorphism group of a vertex-colored graph; and (iv) implementability---the method can be implemented relying on a canonical labeling map for vertex-colored graphs as the only nontrivial subroutine. To demonstrate the practical applicability of our technique, we have prepared an experimental open-source implementation of the technique and carry out a set of experiments that demonstrate ability to reduce symmetry on hard instances. Furthermore, we demonstrate that the implementation effectively parallelizes to compute clusters with multiple nodes via a message-passing interface.Comment: Updated manuscript submitted for revie

Reconstructing pedigrees: some identifiability questions for a recombination-mutation model

Pedigrees are directed acyclic graphs that represent ancestral relationships between individuals in a population. Based on a schematic recombination process, we describe two simple Markov models for sequences evolving on pedigrees - Model R (recombinations without mutations) and Model RM (recombinations with mutations). For these models, we ask an identifiability question: is it possible to construct a pedigree from the joint probability distribution of extant sequences? We present partial identifiability results for general pedigrees: we show that when the crossover probabilities are sufficiently small, certain spanning subgraph sequences can be counted from the joint distribution of extant sequences. We demonstrate how pedigrees that earlier seemed difficult to distinguish are distinguished by counting their spanning subgraph sequences.Comment: 40 pages, 9 figure

Large Networks of Diameter Two Based on Cayley Graphs

In this contribution we present a construction of large networks of diameter two and of order $\frac{1}{2}d^2$ for every degree $d\geq 8$, based on Cayley graphs with surprisingly simple underlying groups. For several small degrees we construct Cayley graphs of diameter two and of order greater than $\frac23$ of Moore bound and we show that Cayley graphs of degrees $d\in\{16,17,18,23,24,31,\dots,35\}$ constructed in this paper are the largest currently known vertex-transitive graphs of diameter two.Comment: 9 pages, Published in Cybernetics and Mathematics Applications in Intelligent System

Incremental learning of skills in a task-parameterized Gaussian Mixture Model

The final publication is available at link.springer.comProgramming by demonstration techniques facilitate the programming of robots. Some of them allow the generalization of tasks through parameters, although they require new training when trajectories different from the ones used to estimate the model need to be added. One of the ways to re-train a robot is by incremental learning, which supplies additional information of the task and does not require teaching the whole task again. The present study proposes three techniques to add trajectories to a previously estimated task-parameterized Gaussian mixture model. The first technique estimates a new model by accumulating the new trajectory and the set of trajectories generated using the previous model. The second technique permits adding to the parameters of the existent model those obtained for the new trajectories. The third one updates the model parameters by running a modified version of the Expectation-Maximization algorithm, with the information of the new trajectories. The techniques were evaluated in a simulated task and a real one, and they showed better performance than that of the existent model.Peer ReviewedPostprint (author's final draft

Land Subsidence monitoring 2016 - 2018 analysis using GNSS CORS UDIP and DinSAR in Semarang

Land Subsidence is phenomena likey common and occurred due to natural cause, loading, and geological setting. In the coastal area land subsidence became worse, cause influence by sea-level rise, The impact land subsidence can lead to wider expansion (flooding area called rob), damage or cracking construction/building and large of maintenance cost. Semarang is the capital city in Central Jawa have experienced in land subsidence in several decades. The north of Semarang was reported a higher rate of land subsidence compared with the south. It was believed that the land subsidence areas were affected by young alluvium, ground extraction and a load of the building. To anticipate, land subsidence should be monitored and detected in an early stage. The most effective way of monitoring land subsidence using GPS, DInSAR to evaluate the characteristic of land subsidence. The GPS observation was conducted in 2016 – 2018 using CORS UDIP as a base station and Sentinel Data was conducted to analyzed the subsidence rate in Semarang. The result showed land subsidence rate in several areas was distributed both spatially and temporally

Feature engineering workflow for activity recognition from synchronized inertial measurement units

The ubiquitous availability of wearable sensors is responsible for driving the Internet-of-Things but is also making an impact on sport sciences and precision medicine. While human activity recognition from smartphone data or other types of inertial measurement units (IMU) has evolved to one of the most prominent daily life examples of machine learning, the underlying process of time-series feature engineering still seems to be time-consuming. This lengthy process inhibits the development of IMU-based machine learning applications in sport science and precision medicine. This contribution discusses a feature engineering workflow, which automates the extraction of time-series feature on based on the FRESH algorithm (FeatuRe Extraction based on Scalable Hypothesis tests) to identify statistically significant features from synchronized IMU sensors (IMeasureU Ltd, NZ). The feature engineering workflow has five main steps: time-series engineering, automated time-series feature extraction, optimized feature extraction, fitting of a specialized classifier, and deployment of optimized machine learning pipeline. The workflow is discussed for the case of a user-specific running-walking classification, and the generalization to a multi-user multi-activity classification is demonstrated.Comment: Multi-Sensor for Action and Gesture Recognition (MAGR), ACPR 2019 Workshop, Auckland, New Zealan