Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Cause, conseguenze e strategie di mitigazione Proponiamo il primo di una serie di articoli in cui affronteremo l’attuale problema dei mutamenti climatici. Presentiamo il documento redatto, votato e pubblicato dall’Ipcc - Comitato intergovernativo sui cambiamenti climatici - che illustra la sintesi delle ricerche svolte su questo tema rilevante.

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Climate Change 2007: The Physical Science Basis.

Electrospinning is a versatile and viable technique for generating ultrathin fibers. Remarkable progress has been made with regard to the development of electrospinning methods and engineering of electrospun nanofibers to suit or enable various applications. We aim to provide a comprehensive overview of electrospinning, including the principle, methods, materials, and applications. We begin with a brief introduction to the early history of electrospinning, followed by discussion of its principle and typical apparatus. We then discuss its renaissance over the past two decades as a powerful technology for the production of nanofibers with diversified compositions, structures, and properties. Afterward, we discuss the applications of electrospun nanofibers, including their use as "smart" mats, filtration membranes, catalytic supports, energy harvesting/conversion/storage components, and photonic and electronic devices, as well as biomedical scaffolds. We highlight the most relevant and recent advances related to the applications of electrospun nanofibers by focusing on the most representative examples. We also offer perspectives on the challenges, opportunities, and new directions for future development. At the end, we discuss approaches to the scale-up production of electrospun nanofibers and briefly discuss various types of commercial products based on electrospun nanofibers that have found widespread use in our everyday life.

Electrospinning and Electrospun Nanofibers: Methods, Materials, and Applications

The science of the total environment

In this provocative book, Barwise and Perry tackle the slippery subject of \"meaning, \" a subject that has long vexed linguists, language philosophers, and logicians.

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Situations and Attitudes.

. Authorship conflicts are a common occurrence in academic publishing, and they can have serious implications for the careers and well-being of the involved researchers as well as the collective success of research organizations. In addition to not inviting relevant contributors to co-author a
paper, the order of authors as well as honorary, gift, and ghost authors are all widely recognized problems related to authorship. Unfair authorship practices disproportionately affect those lower in the power hierarchies – early career researchers, women, researchers from the Global
South, and other minoritized groups. Here we propose an approach to preparing author lists based on clear, transparent, and timely communication. This approach aims to minimize the potential for late-stage authorship conflicts during manuscript preparation by facilitating timely and transparent decisions on potential co-authors and their responsibilities. Furthermore, our approach can help avoid imbalances between
contributions and credits in published papers by recording planned and executed responsibilities. We present authorship guidelines which also include a novel authorship form along with the documentation of the formulation process for a multidisciplinary and interdisciplinary center with
more than 250 researchers. Other research groups, departments, and centers can use or build on this template to design their own authorship
guidelines as a practical way to promote fair authorship practices.

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Clear, transparent, and timely communication for fair authorship decisions : a practical guide

/pdf/from-ionising-radiation-to-air-ion-formation-in-the-lower-39bch5sw7d.pdf

From ionising radiation to air ion formation in the lower atmosphere

Abstract In the Arctic, new particle formation (NPF) and subsequent growth processes are the keys to produce Aitken-mode particles, which under certain conditions can act as cloud condensation nuclei (CCNs). The activation of Aitken-mode particles increases the CCN budget of Arctic low-level clouds and, accordingly, affects Arctic climate forcing. However, the growth mechanism of Aitken-mode particles from NPF into CCN range in the summertime Arctic boundary layer remains a subject of current research. In this combined Arctic cruise field and modeling study, we investigated Aitken-mode particle growth to sizes above 80 nm. A mechanism is suggested that explains how Aitken-mode particles can become CCN without requiring high water vapor supersaturation. Model simulations suggest the formation of semivolatile compounds, such as methanesulfonic acid (MSA) in fog droplets. When the fog droplets evaporate, these compounds repartition from CCNs into the gas phase and into the condensed phase of nonactivated Aitken-mode particles. For MSA, a mass increase factor of 18 is modeled. The postfog redistribution mechanism of semivolatile acidic and basic compounds could explain the observed growth of >20 nm h−1 for 60-nm particles to sizes above 100 nm. Overall, this study implies that the increasing frequency of NPF and fog-related particle processing can affect Arctic cloud properties in the summertime boundary layer.

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Rapid growth of Aitken-mode particles during Arctic summer by fog chemical processing and its implication

&amp;lt;p&amp;gt;Urban aerosol pollution has a significant effect on solar irradiance and meteorological characteristics. Using the two online integrated meteorology &amp;amp;#8211; atmospheric composition modelling systems&amp;amp;#160; -&amp;amp;#160; COSMO-Ru2-ART (Consortium for Small-scale Modeling &amp;amp;#8211; Aerosols and Reactive Trace gases) and Enviro-HIRLAM (Environment &amp;amp;#8211; High Resolution Limited Area Model) ) taking into account urbanization effects, we studied the effects of aerosol pollution and its impact on radiative and meteorological characteristics of the atmosphere with focus on the Moscow megacity region (Russia). For the models&amp;amp;#8217; runs, the initial and boundary conditions from the ICON-COSMO-Ru7 and ERA-5 &amp;amp;#160;as well as the CAMS redistributed inventory emissions were utilized.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In order to account for the absorbing aerosol properties of the Moscow urban atmosphere black carbon (BC) emissions were applied according to the ECLIPSE emission inventory, which demonstrated a satisfactory agreement in BC/PM10 ratio with experimental data in Moscow.&amp;amp;#160; A series of models&amp;amp;#8217; simulations over an area of 300x300 km&amp;amp;#160; was performed with a 2 km horizontal grid step with the effects of urban areas (building effects/ BEP, anthropogenic heat fluxes/ AHF in Enviro-HIRLAM and TERRA_URB scheme in COSMO-Ru2-ART), and without their consideration. The estimates of urban aerosol content were made for typical conditions in April-May 2019 and during spring of 2020, when lowered anthropogenic emissions were observed in the Moscow region due to strict lockdown conditions of COVID-19 pandemic.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this study, we accounted for the changes in emissions for the lockdown situation according to the recommendations (Le Qu&amp;amp;#233;r&amp;amp;#233; et al., 2020), which were mainly in agreement with the official statements.&amp;amp;#160; The estimates of aerosol urban properties were tested against the difference between the AERONET measurements obtained in the Moscow megacity and in a relatively clean region at Zvenigorod Scientific Station of the Institute of Atmospheric Physics, Russian Academy of Sciences.&amp;amp;#160; The quality of surface aerosol estimation was verified using the MosEcoMonitoring Agency dataset. The variability of concentration of different aerosol species at ground level and changes in aerosol optical depth and its absorbing properties in the total atmospheric column are discussed.&amp;amp;#160; The various aerosol radiative effects - direct, semidirect and indirect - and the influence of aerosol on selected meteorological characteristics (such as temperature, humidity, cloud cover, etc.) are analyzed. The features of spatio-temporal changes in urban aerosol fields and their effects on meteorology in conditions of elevated and lower emissions of pollutants in typical and lockdown conditions are investigated.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This study is partially supported by the Ministry of Education and Science of the Russian Federation (grant number 075-15-2021-574) and the Finnish Flagship &amp;amp;#8220;Atmosphere and Climate Competence Center&amp;amp;#8221; (Academy of Finland grant 337549). &amp;amp;#160;This research was performed according to the Development Programme of the Interdisciplinary Scientific and Educational School of MSU &amp;amp;#8220;Future Planet and Global Environmental Change&amp;amp;#8221;. The CSC - IT Center for Science Computing (Finland), is acknowledged for computational resources.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;References:&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Le Qu&amp;amp;#233;r&amp;amp;#233; C. et all (2020): Temporary reduction in daily global CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; emissions during the COVID-19 forced confinement, Nat. Clim. Change, 10, 647&amp;amp;#8211;653.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt; 

Pauli Paasonen

Papers

Clear, transparent, and timely communication for fair authorship decisions : a practical guide

From ionising radiation to air ion formation in the lower atmosphere

Rapid growth of Aitken-mode particles during Arctic summer by fog chemical processing and its implication

Aerosol pollution in the Moscow megacity environment and its impact on radiative and meteorological properties of the atmosphere