Impaktkivimite sekundaarne muutumine hüdrotermaalsetes ja diageneesi-murenemise protsessides: Ries’i meteoriidikraater, Saksamaa

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Lõuna-Saksamaal paiknev ligikaudu 24-kilomeetrise läbimõõduga Reis’i meteoriidikraater (14,3–14,5 Ma) on üks paremini säilinud impaktstruktuure, olles heaks mudelstruktuuriks impakti järgsete protsesside uurimisel. Riesi kraater on ühtlasi ka üks esimesi impaktstruktuure, kus on kirjeldatud ja oletatud impakt-indutseeritud hüdrotermaalset mineralisatsiooni. Hüdrotermaalset muutust on seostatud ennekõike süeviitse kompleksiga ning on kirjeldatud detailsemalt Newsom et al. (1986), Osinski (2003,2004) ja Osinski et al (2004) töödes. Kraatrit täitvate süeviitide hüdrotermaalseid muutuseid/ilminguid iseloomustab sekundaarne savimineralisatsioon ja teoliidistumine, millega kaasneb varajane K-metasomatism koos albitiseerumise ning kloritiseerumise ilmingutega temperatuuridel ~200–300°C (Osinski 2005). Samas pole pindmiste süeviitide hüdrotermaalne mineralisatsioon nii selgelt märgatav/eristatav ning seda on seostatud impaktklaasi muutumisel tekkima hakanud montmorilloniidi tüüpi savifaasiga (Newsom et al 1986), mille alusel on hinnatud pindmiste süeviitide muutumistemperatuuriks < 130 °C. Antud uurimuse mineraloogilised, geokeemilised ja stabiilsete isotoopide analüüsid näitavad, et hüdrotermaalsete protsesside mõju Riesi meteoriidikraatri pindmiste süeviitide muutumisele praktiliselt puudub või ei ole eristatav ning valdav süeviitide muutumine toimub pindmisel murenemisel, madala pH (5–7) ja ioonkontsentratsiooniga (nt sademete vesi) veelises keskkonnas. Erinevalt pindmistest süeviitidest iseloomustavad kraatrit täitvate süeviitide hüdrotermaalseid muutuseid/ilminguid sekundaarne savimineralisatsioon ja tseoliidistumine ning satabiilsete isotoopide uuringud näitavad, et võrreldes pindmiste süeviitidega on nende muutumine toimunud kõrgematemperatuurilises (~100 °C) fluidis, mille pH varieerus >7–8 ning mis võis olla kõrgenenud ioonkontsentratsiooniga. Selline fluidikoostis tõendab sisemiste süeviitide muutumisel tekkinud savimineraalide hüdrotermaalset päritolu anioonhüdrolüütiliselt neutraliseeritud ja lahustuvate komponentide suhtes rikastunud fluidist.The 24-km diameter Ries crater, in Germany, is one of the best-preserved terrestrial complex impact structures; providing a good opportunity to study the evolution of the post-impact cooling in impact craters. The mineralogical, geochemical and stable isotope studies of the surficial and crater- fill suevites from the Ries crater has provided additional constraints on the mineralogical alteration of the Ries impactites and on the origin and evolution of the (geothermal) fluids that were involved in the formation of alteration mineralogy. Previous studies (Newsom et al., 1986; Osinski, 2005) have proposed that the Ries crater suevites have been altered by post-impact aqueous and hydrothermal fluids. Based on mineralogical grounds Newsom et al. (1986) suggested that the surficial suevite was altered at temperatures < 130 °C, and Osinski suggested that the crater-fill suevite was altered at temperatures from 200 – 300 °C. Our mineralogical, geochemical and stable isotope studies suggests that the alteration in surficial suevites is driven mainly by ambient low-temperature weathering, rather than hydrothermal processes and the alteration occured at lower pH and in slightly acidic environment (percolation of meteoric water), showing that the smectite in surficial suevites precipitated in equilibrium with meteoritic fluids. However, alteration in crater filling sequence occurred in the presence of meteoric water-dominated fluid circulation at higher temperatures (40 to 110 °C) than the surficial suevites, but possibly within a normal thermal gradient. The modeled δ11B composition of crater-fill suevites, on the other hand, indicate that the alteration in the crater-fill suevites took place at elevated pH (>8–9). The elevated pH of the alteration fluids in crater filling suevites is possibly related to the effective removal of the available H+ ions in hydrothermal fluid (evolved meteoric water) through anion hydrolysis of impact glass and primary silicates

Klassiõpetajate arusaam õuesõppest, selle rakendamist takistavatest teguritest ning erinevustest traditsioonilise õppega klassiruumis

https://www.ester.ee/record=b5370461*es

Investeeringute atraktiivsus Kagu-Aasia majutusturul

http://tartu.ester.ee/record=b2614148~S1*es

Detection of app collusion potential using logic programming

Mobile devices pose a particular security risk because they hold personal details (accounts, locations, contacts, photos) and have capabilities potentially exploitable for eavesdropping (cameras/microphone, wireless connections). The Android operating system is designed with a number of built-in security features such as application sandboxing and permission-based access control. Unfortunately, these restrictions can be bypassed, without the user noticing, by colluding apps whose combined permissions allow them to carry out attacks that neither app is able to execute by itself. While the possibility of app collusion was first warned in 2011, it has been unclear if collusion is used by malware in the wild due to a lack of suitable detection methods and tools. This paper describes how we found the first collusion in the wild. We also present a strategy for detecting collusions and its implementation in Prolog that allowed us to make this discovery. Our detection strategy is grounded in concise definitions of collusion and the concept of ASR (Access-Send-Receive) signatures. The methodology is supported by statistical evidence. Our approach scales and is applicable to inclusion into professional malware detection systems: we applied it to a set of more than 50,000 apps collected in the wild. Code samples of our tool as well as of the detected malware are available

Siluri Lau sündmuse isotoopgeokeemia ja mineraloogia Bebirva-110 läbilõikes Leedus

Käesolevas töös uuritakse Siluri ajastu Lau sündmust ning sellega kaasnevat Kesk-Ludfordi süsiniku isotoobi ekskursiooni Leedu lõunaosas puuritud Bebirva-110 läbilõikes, mis esindab Balti basseini madalamerelist keskkonda. Uuringus kasutatakse karbonaatide stabiilsete isotoopide (δ13C ja δ18O), mineraloogia (XRD) ja õhikute elementide kaardistamise andmeid (XRF). Kesk-Ludfordi isotoopekskursioon võib olla põhjustatud globaalse kliima jahenemisest, polaaralade jäätumisest ning glatsioeustaatilisest veetaseme langusest. Sündmust vaadeldakse fatsiaalsel profiilil sügavamerelisest keskkonnast madalaveeliseni, võrreldes Bebirva-110 läbilõiget läänepoolsemate Bebirva-111, Vidukle-61 ja Milaičiai-103 läbilõigetega. Töö tulemused viitasid, et raske süsiniku isotoobi 13C anomaalia oodatud võimendumist rannikupoolses keskkonnas Lau sündmuse puhul ei esine, vaid vastupidi, süsinikueksursiooni isotoopsignaal on nõrgem selles läbilõikes. See võib olla põhjustatud kihtide väljakiildumisest madalaveelises keskkonnas mille põhjuseks oli glatsioeustaatiline veetaseme langus

Android Malware Detection Using Parallel Machine Learning Classifiers

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Mobile malware has continued to grow at an alarming rate despite on-going mitigation efforts. This has been much more prevalent on Android due to being an open platform that is rapidly overtaking other competing platforms in the mobile smart devices market. Recently, a new generation of Android malware families has emerged with advanced evasion capabilities which make them much more difficult to detect using conventional methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers

Android malware detection: An eigenspace analysis approach

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The battle to mitigate Android malware has become more critical with the emergence of new strains incorporating increasingly sophisticated evasion techniques, in turn necessitating more advanced detection capabilities. Hence, in this paper we propose and evaluate a machine learning based approach based on eigenspace analysis for Android malware detection using features derived from static analysis characterization of Android applications. Empirical evaluation with a dataset of real malware and benign samples show that detection rate of over 96% with a very low false positive rate is achievable using the proposed method

High Accuracy Android Malware Detection Using Ensemble Learning

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.With over 50 billion downloads and more than 1.3 million apps in Google's official market, Android has continued to gain popularity among smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature-based methods become less potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. Thus, this study proposes an approach that utilises ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3–99% detection accuracy with very low false positive rates