Estimation of temporal and spatial variations in groundwater recharge in unconfined sand aquifers using Scots pine inventories

Acknowledgements. This study was made possible through funding from the EU 7th Framework programme GENESIS (contract number 226536), AQVI project (no. 128377) in Academy of Finland AKVA research programme, the Renlund Foundation, VALUE doctoral school and Maa- ja vesitekniikan tuki ry. We would like to express our gratitude to Geological survey of Finland, Finnish Forest Administration (Metsähallitus) and Finnish Forest Centre (Metsäkeskus), Finnish meteorological institute, Finnish environmental administration and National land survey of Finland for providing data sets and expert knowledge that made this study possible in its current extent. To reproduce the research in the paper, data from above-mentioned agencies can be made available for purchase on request from the corresponding agency, other data can be provided by the corresponding author upon request. We thank Per-Erik Jansson for his assistance with the CoupModel and Jarkko Okkonen (GTK), anonymous reviewer, and Angelo Basile for their critical comments that significantly improved the manuscript.Peer reviewedPublisher PD

“That’s not evolving, that’s devolving!”:incidental vocabulary learning through watching TikTok videos

Abstract. The term “digital natives” was coined in 2001 (Prensky) to illustrate a common narrative that modern teenagers are acclimatised to digital environments. In recent years, the debate has focused on social media. One of the claims made in favour of allowing teenagers to use platforms such as TikTok is that it broadens their English vocabulary. TikTok is a social media platform that has recently gained popularity especially amongst youth. As an application, TikTok provides versatile content in various different overlapping forms. This thesis examines whether Finnish ninth grade pupils are able to learn new L2 English vocabulary from watching TikTok videos with no intentional vocabulary teaching. Hence, the focus of this study is on incidental L2 vocabulary learning through social media platform TikTok. The aim of this study is to understand how the context in which unfamiliar vocabulary appears impacts the learning, and how consuming media content such as TikTok videos might broaden pupils’ vocabulary. In order to achieve this goal, the study defined three research questions concerning 1) the size of the participants’ English vocabulary, 2) what impact does watching TikTok videos have on vocabulary knowledge and 3) how the contexts in which the vocabulary appear in affect the learning of the individual words. The data for this study consist of material from a pre-test, intervention, and a post-test. The pre-test consists of an adapted version of the Vocabulary Size Test (VST) by Paul Nation that incorporates the test vocabulary from the chosen TikTok videos. The test consists of 140 test items in a form on multiple-choice questions and determines the receptive vocabulary sizes of the participants. The intervention consists of showing the participants a selection of six different TikTok videos in a variety of syntactic and semantic contexts. After the intervention, the retention of the chosen test items was investigated by using an adapted version of the Vocabulary Knowledge Test (VKT) by Paribakth and Wesche (1997). The analyses of the data from the VST consist of calculating the vocabulary sizes for each participant as well as analysing the distribution of those vocabulary sizes. The VKT analysis includes calculating the VKT scores of each participant and well as calculating and comparing the VKT results of the individual test items. After, the contexts of the test items were analysed to reveal the impact context has on retention of the test items. The vocabulary sizes of the participants ranged from 3,300 word families to 9,900 word families, indicating that most of the participants’ vocabulary belonged to what Schmitt and Schmitt (2014) refer to as the “mid-frequency” range. That is, the participants had sufficiently advanced vocabulary to manage basic situations but were not able to handle advanced academic or specialist vocabulary. The results of the study revealed that watching TikTok videos can lead to recognition of the previously unfamiliar words. However, only the retention of one test item was statistically significant, which implies that incidental vocabulary learning alone is not a sufficient way of re-taining new L2 English vocabulary, but that deliberate vocabulary teaching is required as well. The contexts of the test items were found to affect the vocabulary learning, and the most influential contextual aspects found in this study were repetition and humour.Sanaston oheisoppiminen TikTok videoiden katsomisen kautta. Tiivistelmä. Termi ”diginatiivit” keksittiin vuonna 2001 (Prensky) kuvaamaan kuvatakseen yleistä käsitystä siitä, kuinka nykypäivän nuoret ovat sopeutuneet digitaalisiin ympäristöihin. Viime vuosina keskustelu on keskittynyt erityisesti sosiaaliseen mediaan. Yhden nuorten sosiaalisen median käyttöä puoltavan väitteen mukaan esimerkiksi TikTokin kaltaiset alustat laajentaisivat nuorten englannin kielen sanavarastoa. TikTok on sosiaalisen median alusta, joka on kasvattanut viime aikoina nopeasti suosiotaan erityisesti nuorten keskuudessa tarjoten monipuolista sisältöä tekstien eri muodoissa. Tässä tutkielmassa tarkastellaan sitä, että oppivatko suomalaiset yhdeksäsluokkalaiset uutta englannin kielen sanastoa TikTokin kautta ilman tarkoituksellista englannin kielen opetusta. Tutkielmassa täten keskitytään englannin sanaston oheisoppimiseen TikTokin kautta. Tutkielman tavoitteena on ymmärtää kuinka tuntemattomien sanojen konteksti vaikuttaa oppimiseen ja kuinka TikTok-videoiden katsominen saattaa laajentaa osallistujien englannin sanavarastoa. Tämän tavoitteen saavuttamiseksi määritettiin kolme tutkimuskysymystä koskien 1) osallistujien englannin kielen sanavaraston kokoa, 2) sitä, miten TikTok-videoiden katselu vaikuttaa sanaston tietämykseen ja 3) sitä, miten sanojen konteksti vaikuttaa yksittäisten sanojen oppimiseen. Tutkielman aineisto koostuu esitestistä, interventiosta, sekä jälkitestistä. Esitestinä toimi muokattu versio Paul Nationin sanavarastoa mittaavasta testistä (engl. Vocabulary Size Test (VST)), johon on sisällytetty valittujen TikTok-videoiden sanastoa. Testiin sisältyi 140 monivalintakysymystä, jotka määrittivät osallistujien reseptiivisen sanavaraston koon. Interventio koostui kuuden eri TikTok-videon katsomisesta. Videot sisälsivät materiaalia monessa eri syntaktisessa ja semanttisessa kontekstissa. Intervention jälkeen koesanojen oppimista tutkittiin Paribakhtin ja Weschen (1997) sanaston osaamista mittaavalla testillä (engl. Vocabulary Knowledge Test (VKT)). VST:n analyysi koostui jokaisen osallistujan sanavaraston koon laskemisesta sekä tulosten jakauman analysoimisesta. VKT:n analyysi puolestaan koostui kokeeseen osallistujien pisteiden laskemisesta sekä yksittäisten koesanojen pisteytyksen laskemisesta ja analysoinnista. Viimeisessä vaiheessa koesanojen konteksteja analysoitiin, jotta saataisiin selville, miten konteksti vaikuttaa tuntemattomien sanojen oppimiseen. Osallistujien sanavarastojen koot vaihtelivat 3,300 sanaperheestä 9,900 sanaperheeseen, mikä osoittaa, että suurin osa osallistujien sanavarastosta on toistumistiheydeltään keskitasoa (Schmitt ja Schmitt, 2014). Tämä tarkoittaa sitä, että osallistujien sanavarasto oli riittävän edistyksellistä jokapäiväisen kielen ymmärtämiseksi, mutta ei tarpeeksi laajaa akateemisen sanaston tai asiantuntijasanaston ymmärtämiseksi. TikTok-videoiden katsominen voi tutkimustulosten perusteella johtaa aikaisemmin tuntemattomien sanojen tunnistamiseen. Kuitenkin vain yksi koesanojen tuloksista oli tilastollisesti merkittävä. Oheisoppiminen ei siis yksin ole riittävä tapa uuden englannin kielen sanaston oppimiselle, vaan tarkoituksellinen opettaminen on myös tarpeellista. Tutkimuksen tulosten mukaan tuntemattomien sanojen konteksti vaikuttaa sanaston oppimiseen. Merkittävimmiksi kontekstuaalisiksi vaikuttajiksi osoittautuivat sanojen toistojen määrä sekä huumori

Using isotopes to constrain water flux and age estimates in snow-influenced catchments using the STARR (Spatially distributed Tracer-Aided Rainfall-Runoff) model

Acknowledgements. This work was funded by the NERC/JPI SIWA project (NE/M019896/1) and the European Research Council ERC (project GA 335910 VeWa). Numerical simulations were performed using the Maxwell High Performance Computing Cluster of the University of Aberdeen IT Service, provided by Dell Inc. and supported by Alces Software. The isotope work in Krycklan is funded by the KAW Branch-Point project together with SKB and SITES. We would like to thank Marjolein van Hui- jgevoort for her help with the STARR code, and Masaki Hayashi and two anonymous reviewers for their insightful suggestions that significantly improved the paper. The Supplement related to this article is available online at https://doi.org/10.5194/hess-21-5089-2017-supplement.Peer reviewedPublisher PD

Succinct Dictionary Matching With No Slowdown

The problem of dictionary matching is a classical problem in string matching: given a set S of d strings of total length n characters over an (not necessarily constant) alphabet of size sigma, build a data structure so that we can match in a any text T all occurrences of strings belonging to S. The classical solution for this problem is the Aho-Corasick automaton which finds all occ occurrences in a text T in time O(|T| + occ) using a data structure that occupies O(m log m) bits of space where m <= n + 1 is the number of states in the automaton. In this paper we show that the Aho-Corasick automaton can be represented in just m(log sigma + O(1)) + O(d log(n/d)) bits of space while still maintaining the ability to answer to queries in O(|T| + occ) time. To the best of our knowledge, the currently fastest succinct data structure for the dictionary matching problem uses space O(n log sigma) while answering queries in O(|T|log log n + occ) time. In this paper we also show how the space occupancy can be reduced to m(H0 + O(1)) + O(d log(n/d)) where H0 is the empirical entropy of the characters appearing in the trie representation of the set S, provided that sigma < m^epsilon for any constant 0 < epsilon < 1. The query time remains unchanged.Comment: Corrected typos and other minor error

DETERMINANTS OF RESIDENTIAL PER CAPITA WATER DEMAND OF MAKURDI METROPOLIS

 This report presents the findings of the study on the determinants of residential per capita water demand of Makurdi metropolis in Benue State, Nigeria. Data for the study was obtained by the use of questionnaires, oral interviews and observations. The data was analyzed using SPSS. Twenty variables were considered in the multiple regression analysis for developing a consumption model. Seven variables were found to influence residential per capita water consumption significantly. Level of education, gender, kitchen type, number of cars, and well as a source were positively significant while, household size and number of children below 6 years influence the per capita water demand negatively. The multiple regression analysis showed R2 of 0.434 implying that the model explains 43% of the variation in residential per capital water demand of Makurdi. The F test (F= 14.236, p= 0.01) showed that the variables in the model combine together to predict the residential per capita water demand of Makurdi metropolis. The consideration of the various factors identified as influencing the residential per capita water consumption in Makurdi metropolis is recommended. http://dx.doi.org/10.4314/njt.v35i2.2

Environmental tracers and indicators bringing together groundwater, surface water and groundwater-dependent ecosystems: importance of scale in choosing relevant tools

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Linear Parsing Expression Grammars

PEGs were formalized by Ford in 2004, and have several pragmatic operators (such as ordered choice and unlimited lookahead) for better expressing modern programming language syntax. Since these operators are not explicitly defined in the classic formal language theory, it is significant and still challenging to argue PEGs' expressiveness in the context of formal language theory.Since PEGs are relatively new, there are several unsolved problems.One of the problems is revealing a subclass of PEGs that is equivalent to DFAs. This allows application of some techniques from the theory of regular grammar to PEGs. In this paper, we define Linear PEGs (LPEGs), a subclass of PEGs that is equivalent to DFAs. Surprisingly, LPEGs are formalized by only excluding some patterns of recursive nonterminal in PEGs, and include the full set of ordered choice, unlimited lookahead, and greedy repetition, which are characteristic of PEGs. Although the conversion judgement of parsing expressions into DFAs is undecidable in general, the formalism of LPEGs allows for a syntactical judgement of parsing expressions.Comment: Parsing expression grammars, Boolean finite automata, Packrat parsin

Reasoning about embedded dependencies using inclusion dependencies

The implication problem for the class of embedded dependencies is undecidable. However, this does not imply lackness of a proof procedure as exemplified by the chase algorithm. In this paper we present a complete axiomatization of embedded dependencies that is based on the chase and uses inclusion dependencies and implicit existential quantification in the intermediate steps of deductions

Modeling the Isotopic Evolution of Snowpack and Snowmelt: Testing a Spatially Distributed Parsimonious Approach

Use of stable water isotopes has become increasingly popular in quantifying water flow paths and travel times in hydrological systems using tracer-aided modeling. In snow-influenced catchments, snowmelt produces a traceable isotopic signal, which differs from original snowfall isotopic composition because of isotopic fractionation in the snowpack. These fractionation processes in snow are relatively well understood, but representing their spatiotemporal variability in tracer-aided studies remains a challenge. We present a novel, parsimonious modeling method to account for the snowpack isotope fractionation and estimate isotope ratios in snowmelt water in a fully spatially distributed manner. Our model introduces two calibration parameters that alone account for the isotopic fractionation caused by sublimation from interception and ground snow storage, and snowmelt fractionation progressively enriching the snowmelt runoff. The isotope routines are linked to a generic process-based snow interception-accumulation-melt model facilitating simulation of spatially distributed snowmelt runoff. We use a synthetic modeling experiment to demonstrate the functionality of the model algorithms in different landscape locations and under different canopy characteristics. We also provide a proof-of-concept model test and successfully reproduce isotopic ratios in snowmelt runoff sampled with snowmelt lysimeters in two long-term experimental catchment with contrasting winter conditions. To our knowledge, the method is the first such tool to allow estimation of the spatially distributed nature of isotopic fractionation in snowpacks and the resulting isotope ratios in snowmelt runoff. The method can thus provide a useful tool for tracer-aided modeling to better understand the integrated nature of flow, mixing, and transport processes in snow-influenced catchments