From db36e119127a88930892e9dd5ad7a61aad91baed Mon Sep 17 00:00:00 2001 From: "miriam.baglioni" Date: Mon, 30 Nov 2020 20:14:23 +0100 Subject: [PATCH] classes test classes and resources for production of the actionset to include bipFinder score in results --- .../bipfinder/BipDeserialize.java | 23 + .../dhp/actionmanager/bipfinder/BipScore.java | 25 + .../dhp/actionmanager/bipfinder/KeyValue.java | 25 + .../bipfinder/PreparedResult.java | 24 + .../dhp/actionmanager/bipfinder/Score.java | 26 + .../bipfinder/SparkAtomicActionScoreJob.java | 203 +++ .../bipfinder/input_parameters.json | 32 + .../SparkAtomicActionScoreJobTest.java | 160 +++ .../actionmanager/bipfinder/bip_scores.json | 1101 +++++++++++++++++ .../actionmanager/bipfinder/publication.json | 18 + 10 files changed, 1637 insertions(+) create mode 100644 dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/BipDeserialize.java create mode 100644 dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/BipScore.java create mode 100644 dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/KeyValue.java create mode 100644 dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/PreparedResult.java create mode 100644 dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/Score.java create mode 100644 dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/SparkAtomicActionScoreJob.java create mode 100644 dhp-workflows/dhp-aggregation/src/main/resources/eu/dnetlib/dhp/actionmanager/bipfinder/input_parameters.json create mode 100644 dhp-workflows/dhp-aggregation/src/test/java/eu/dnetlib/dhp/actionmanager/bipfinder/SparkAtomicActionScoreJobTest.java create mode 100644 dhp-workflows/dhp-aggregation/src/test/resources/eu/dnetlib/dhp/actionmanager/bipfinder/bip_scores.json create mode 100644 dhp-workflows/dhp-aggregation/src/test/resources/eu/dnetlib/dhp/actionmanager/bipfinder/publication.json diff --git a/dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/BipDeserialize.java b/dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/BipDeserialize.java new file mode 100644 index 000000000..e68b912b2 --- /dev/null +++ b/dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/BipDeserialize.java @@ -0,0 +1,23 @@ +package eu.dnetlib.dhp.actionmanager.bipfinder; + +import java.io.Serializable; +import java.util.ArrayList; +import java.util.HashMap; +import java.util.List; + + +public class BipDeserialize extends HashMap> implements Serializable { + + public BipDeserialize(){ + super(); + } + + public List get(String key) { + + if (super.get(key) == null) { + return new ArrayList<>(); + } + return super.get(key); + } + +} diff --git a/dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/BipScore.java b/dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/BipScore.java new file mode 100644 index 000000000..b1e381955 --- /dev/null +++ b/dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/BipScore.java @@ -0,0 +1,25 @@ +package eu.dnetlib.dhp.actionmanager.bipfinder; + +import java.io.Serializable; +import java.util.List; + +public class BipScore implements Serializable { + private String id; + private List scoreList; + + public String getId() { + return id; + } + + public void setId(String id) { + this.id = id; + } + + public List getScoreList() { + return scoreList; + } + + public void setScoreList(List scoreList) { + this.scoreList = scoreList; + } +} diff --git a/dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/KeyValue.java b/dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/KeyValue.java new file mode 100644 index 000000000..1fadae6fe --- /dev/null +++ b/dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/KeyValue.java @@ -0,0 +1,25 @@ +package eu.dnetlib.dhp.actionmanager.bipfinder; + +import java.io.Serializable; + +public class KeyValue implements Serializable { + + private String key; + private String value; + + public String getKey() { + return key; + } + + public void setKey(String key) { + this.key = key; + } + + public String getValue() { + return value; + } + + public void setValue(String value) { + this.value = value; + } +} diff --git a/dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/PreparedResult.java b/dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/PreparedResult.java new file mode 100644 index 000000000..bb7648f0c --- /dev/null +++ b/dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/PreparedResult.java @@ -0,0 +1,24 @@ +package eu.dnetlib.dhp.actionmanager.bipfinder; + +import java.io.Serializable; + +public class PreparedResult implements Serializable { + private String id; //openaire id + private String value; //doi + + public String getId() { + return id; + } + + public void setId(String id) { + this.id = id; + } + + public String getValue() { + return value; + } + + public void setValue(String value) { + this.value = value; + } +} diff --git a/dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/Score.java b/dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/Score.java new file mode 100644 index 000000000..b14d971cd --- /dev/null +++ b/dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/Score.java @@ -0,0 +1,26 @@ +package eu.dnetlib.dhp.actionmanager.bipfinder; + +import java.io.Serializable; +import java.util.List; + +public class Score implements Serializable { + + private String id; + private List unit; + + public String getId() { + return id; + } + + public void setId(String id) { + this.id = id; + } + + public List getUnit() { + return unit; + } + + public void setUnit(List unit) { + this.unit = unit; + } +} diff --git a/dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/SparkAtomicActionScoreJob.java b/dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/SparkAtomicActionScoreJob.java new file mode 100644 index 000000000..658555e05 --- /dev/null +++ b/dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/SparkAtomicActionScoreJob.java @@ -0,0 +1,203 @@ +package eu.dnetlib.dhp.actionmanager.bipfinder; + +import com.fasterxml.jackson.databind.ObjectMapper; +import eu.dnetlib.dhp.application.ArgumentApplicationParser; +import eu.dnetlib.dhp.common.HdfsSupport; +import eu.dnetlib.dhp.schema.action.AtomicAction; +import eu.dnetlib.dhp.schema.oaf.*; +import eu.dnetlib.dhp.schema.oaf.KeyValue; +import org.apache.commons.io.IOUtils; +import org.apache.hadoop.hdfs.qjournal.protocol.QJournalProtocolProtos; +import org.apache.hadoop.io.Text; +import org.apache.hadoop.mapred.SequenceFileOutputFormat; +import org.apache.spark.SparkConf; +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.api.java.function.FlatMapFunction; +import org.apache.spark.api.java.function.MapFunction; +import org.apache.spark.api.java.function.MapGroupsFunction; +import org.apache.spark.sql.Dataset; +import org.apache.spark.sql.Encoders; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.SparkSession; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; +import scala.Tuple2; + +import java.io.Serializable; +import java.util.List; +import java.util.Optional; +import java.util.stream.Collectors; + +import static eu.dnetlib.dhp.common.SparkSessionSupport.runWithSparkSession; + +public class SparkAtomicActionScoreJob implements Serializable { + + private static String DOI = "doi"; + private static final Logger log = LoggerFactory.getLogger(SparkAtomicActionScoreJob.class); + private static final ObjectMapper OBJECT_MAPPER = new ObjectMapper(); + + public static void main(String[] args) throws Exception { + + String jsonConfiguration = IOUtils + .toString( + SparkAtomicActionScoreJob.class + .getResourceAsStream( + "/eu/dnetlib/dhp/actionmanager/bipfinder/input_parameters.json")); + + final ArgumentApplicationParser parser = new ArgumentApplicationParser(jsonConfiguration); + + parser.parseArgument(args); + + Boolean isSparkSessionManaged = Optional + .ofNullable(parser.get("isSparkSessionManaged")) + .map(Boolean::valueOf) + .orElse(Boolean.TRUE); + + log.info("isSparkSessionManaged: {}", isSparkSessionManaged); + + final String inputPath = parser.get("inputPath"); + log.info("inputPath {}: ", inputPath); + + final String outputPath = parser.get("outputPath"); + log.info("outputPath {}: ", outputPath); + + final String bipScorePath = parser.get("bipScorePath"); + log.info("bipScorePath: {}", bipScorePath); + + final String resultClassName = parser.get("resultTableName"); + log.info("resultTableName: {}", resultClassName); + + Class inputClazz = (Class) Class.forName(resultClassName); + + SparkConf conf = new SparkConf(); + + runWithSparkSession( + conf, + isSparkSessionManaged, + spark -> { + removeOutputDir(spark, outputPath); + prepareResults(spark, inputPath, outputPath, bipScorePath, inputClazz); + }); + } + + private static void prepareResults(SparkSession spark, String inputPath, String outputPath, + String bipScorePath, Class inputClazz) { + + final JavaSparkContext sc = new JavaSparkContext(spark.sparkContext()); + + JavaRDD bipDeserializeJavaRDD = sc.textFile(bipScorePath) + .map(item -> OBJECT_MAPPER.readValue(item, BipDeserialize.class)); + + + Dataset bipScores = spark.createDataset(bipDeserializeJavaRDD.flatMap(entry -> + entry.keySet().stream().map(key -> { + BipScore bs = new BipScore(); + bs.setId(key); + bs.setScoreList(entry.get(key)); + return bs; + }).collect(Collectors.toList()).iterator() + ).rdd(), Encoders.bean(BipScore.class)); + + System.out.println(bipScores.count()); + + Dataset results = readPath(spark, inputPath, inputClazz); + + results.createOrReplaceTempView("result"); + + Dataset preparedResult = spark.sql("select pIde.value value, id " + + "from result " + + "lateral view explode (pid) p as pIde " + + "where dataInfo.deletedbyinference = false and pIde.qualifier.classid = '" + DOI + "'") + .as(Encoders.bean(PreparedResult.class)); + + Dataset tmp = bipScores.joinWith(preparedResult, bipScores.col("id").equalTo(preparedResult.col("value")), + "inner") + .map((MapFunction, BipScore>) value -> { + BipScore ret = value._1(); + ret.setId(value._2().getId()); + return ret; + }, Encoders.bean(BipScore.class)); + + tmp.groupByKey((MapFunction) value -> value.getId(), Encoders.STRING()) + .mapGroups((MapGroupsFunction) (k, it) -> + { + Result ret = inputClazz.newInstance(); + BipScore first = it.next(); + ret.setId(first.getId()); + + ret.setMeasures(getMeasure(first)); + it.forEachRemaining(value -> ret.getMeasures().addAll(getMeasure(value))); + + return (I)ret; + }, Encoders.bean(inputClazz)) + .toJavaRDD() + .map(p -> new AtomicAction(inputClazz, p)) + .mapToPair( + aa -> new Tuple2<>(new Text(aa.getClazz().getCanonicalName()), + new Text(OBJECT_MAPPER.writeValueAsString(aa)))) + .saveAsHadoopFile(outputPath, Text.class, Text.class, SequenceFileOutputFormat.class); + ; + } + + public static Dataset getBipScoreDataset(Dataset bipdeserialized){ + return bipdeserialized.flatMap((FlatMapFunction) bip -> + bip.keySet().stream().map(key -> { + BipScore bs = new BipScore(); + bs.setId(key); + bs.setScoreList(bip.get(key)); + return bs; + }).collect(Collectors.toList()).iterator() + , Encoders.bean(BipScore.class)); + } + + private static List getMeasure(BipScore value) { + return value.getScoreList() + .stream() + .map(score -> { + Measure m = new Measure(); + m.setId(score.getId()); + m.setUnit(score.getUnit().stream() + .map(unit -> { + KeyValue kv = new KeyValue(); + kv.setValue(unit.getValue()); + kv.setKey(unit.getKey()); + kv.setDataInfo(getDataInfo()); + return kv; + }).collect(Collectors.toList())); + return m; + }).collect(Collectors.toList()); + } + + + + private static DataInfo getDataInfo() { + DataInfo di = new DataInfo(); + di.setInferred(false); + di.setInvisible(false); + di.setDeletedbyinference(false); + di.setTrust(""); + Qualifier qualifier = new Qualifier(); + qualifier.setClassid("sysimport:actionset"); + qualifier.setClassname("Harvested"); + qualifier.setSchemename("dnet:provenanceActions"); + qualifier.setSchemeid("dnet:provenanceActions"); + di.setProvenanceaction(qualifier); + return di; + } + + + private static void removeOutputDir(SparkSession spark, String path) { + HdfsSupport.remove(path, spark.sparkContext().hadoopConfiguration()); + } + + public static Dataset readPath( + SparkSession spark, String inputPath, Class clazz) { + return spark + .read() + .textFile(inputPath) + .map((MapFunction) value -> OBJECT_MAPPER.readValue(value, clazz), Encoders.bean(clazz)); + } + + +} diff --git a/dhp-workflows/dhp-aggregation/src/main/resources/eu/dnetlib/dhp/actionmanager/bipfinder/input_parameters.json b/dhp-workflows/dhp-aggregation/src/main/resources/eu/dnetlib/dhp/actionmanager/bipfinder/input_parameters.json new file mode 100644 index 000000000..ae844a0c9 --- /dev/null +++ b/dhp-workflows/dhp-aggregation/src/main/resources/eu/dnetlib/dhp/actionmanager/bipfinder/input_parameters.json @@ -0,0 +1,32 @@ +[ + { + "paramName": "issm", + "paramLongName": "isSparkSessionManaged", + "paramDescription": "when true will stop SparkSession after job execution", + "paramRequired": false +}, +{ +"paramName": "ip", +"paramLongName": "inputPath", +"paramDescription": "the URL from where to get the programme file", +"paramRequired": true +}, +{ +"paramName": "o", +"paramLongName": "outputPath", +"paramDescription": "the path of the new ActionSet", +"paramRequired": true +}, + { + "paramName": "rtn", + "paramLongName": "resultTableName", + "paramDescription": "the path of the new ActionSet", + "paramRequired": true + }, + { + "paramName": "bsp", + "paramLongName": "bipScorePath", + "paramDescription": "the path of the new ActionSet", + "paramRequired": true + } +] \ No newline at end of file diff --git a/dhp-workflows/dhp-aggregation/src/test/java/eu/dnetlib/dhp/actionmanager/bipfinder/SparkAtomicActionScoreJobTest.java b/dhp-workflows/dhp-aggregation/src/test/java/eu/dnetlib/dhp/actionmanager/bipfinder/SparkAtomicActionScoreJobTest.java new file mode 100644 index 000000000..64518234e --- /dev/null +++ b/dhp-workflows/dhp-aggregation/src/test/java/eu/dnetlib/dhp/actionmanager/bipfinder/SparkAtomicActionScoreJobTest.java @@ -0,0 +1,160 @@ +package eu.dnetlib.dhp.actionmanager.bipfinder; + +import com.fasterxml.jackson.databind.ObjectMapper; +import eu.dnetlib.dhp.schema.action.AtomicAction; +import eu.dnetlib.dhp.schema.oaf.*; +import eu.dnetlib.dhp.schema.oaf.KeyValue; +import org.apache.commons.io.FileUtils; +import org.apache.hadoop.io.Text; +import org.apache.hadoop.mapred.SequenceFileOutputFormat; +import org.apache.spark.SparkConf; +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.api.java.function.FilterFunction; +import org.apache.spark.api.java.function.ForeachFunction; +import org.apache.spark.api.java.function.MapFunction; +import org.apache.spark.api.java.function.MapGroupsFunction; +import org.apache.spark.sql.Dataset; +import org.apache.spark.sql.Encoders; +import org.apache.spark.sql.SparkSession; +import org.junit.jupiter.api.AfterAll; +import org.junit.jupiter.api.Assertions; +import org.junit.jupiter.api.BeforeAll; +import org.junit.jupiter.api.Test; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; +import scala.Tuple2; + +import java.io.IOException; +import java.nio.file.Files; +import java.nio.file.Path; +import java.util.List; +import java.util.stream.Collectors; + +public class SparkAtomicActionScoreJobTest { + + private static final ObjectMapper OBJECT_MAPPER = new ObjectMapper(); + + private static SparkSession spark; + + private static Path workingDir; + private static final Logger log = LoggerFactory + .getLogger(SparkAtomicActionScoreJobTest.class); + + @BeforeAll + public static void beforeAll() throws IOException { + workingDir = Files + .createTempDirectory(SparkAtomicActionScoreJobTest.class.getSimpleName()); + log.info("using work dir {}", workingDir); + + SparkConf conf = new SparkConf(); + conf.setAppName(SparkAtomicActionScoreJobTest.class.getSimpleName()); + + conf.setMaster("local[*]"); + conf.set("spark.driver.host", "localhost"); + conf.set("hive.metastore.local", "true"); + conf.set("spark.ui.enabled", "false"); + conf.set("spark.sql.warehouse.dir", workingDir.toString()); + conf.set("hive.metastore.warehouse.dir", workingDir.resolve("warehouse").toString()); + + spark = SparkSession + .builder() + .appName(SparkAtomicActionScoreJobTest.class.getSimpleName()) + .config(conf) + .getOrCreate(); + } + + @AfterAll + public static void afterAll() throws IOException { + FileUtils.deleteDirectory(workingDir.toFile()); + spark.stop(); + } + + @Test + public void numberDistinctProjectTest() throws Exception { + String bipScoresPath = getClass().getResource("/eu/dnetlib/dhp/actionmanager/bipfinder/bip_scores.json").getPath(); + String inputPath = getClass() + .getResource( + "/eu/dnetlib/dhp/actionmanager/bipfinder/publication.json") + .getPath(); + + SparkAtomicActionScoreJob.main( + new String[] { + "-isSparkSessionManaged", + Boolean.FALSE.toString(), + "-inputPath", + inputPath, + "-bipScorePath", + bipScoresPath, + "-resultTableName", + "eu.dnetlib.dhp.schema.oaf.Publication", + "-outputPath", + workingDir.toString() + "/actionSet" + }); + + + final JavaSparkContext sc = new JavaSparkContext(spark.sparkContext()); + + JavaRDD tmp = sc + .sequenceFile(workingDir.toString() + "/actionSet", Text.class, Text.class) + .map(value -> OBJECT_MAPPER.readValue(value._2().toString(), AtomicAction.class)) + .map(aa -> ((Publication) aa.getPayload())); + + Assertions.assertTrue(tmp.count() == 1); + +// Dataset verificationDataset = spark.createDataset(tmp.rdd(), Encoders.bean(Publication.class)); +// verificationDataset.createOrReplaceTempView("project"); + +// Dataset execverification = spark +// .sql( +// "SELECT id, class classification, h2020topiccode, h2020topicdescription FROM project LATERAL VIEW EXPLODE(h2020classification) c as class "); +// +// Assertions +// .assertEquals( +// "H2020-EU.3.4.7.", +// execverification +// .filter("id = '40|corda__h2020::2c7298913008865ba784e5c1350a0aa5'") +// .select("classification.h2020Programme.code") +// .collectAsList() +// .get(0) +// .getString(0)); + + + + + } + + private static List getMeasure(BipScore value) { + return value.getScoreList() + .stream() + .map(score -> { + Measure m = new Measure(); + m.setId(score.getId()); + m.setUnit(score.getUnit().stream() + .map(unit -> { + eu.dnetlib.dhp.schema.oaf.KeyValue kv = new KeyValue(); + kv.setValue(unit.getValue()); + kv.setKey(unit.getKey()); + kv.setDataInfo(getDataInfo()); + return kv; + }).collect(Collectors.toList())); + return m; + }).collect(Collectors.toList()); + } + + + private static DataInfo getDataInfo() { + DataInfo di = new DataInfo(); + di.setInferred(false); + di.setInvisible(false); + di.setDeletedbyinference(false); + di.setTrust(""); + Qualifier qualifier = new Qualifier(); + qualifier.setClassid("sysimport:actionset"); + qualifier.setClassname("Harvested"); + qualifier.setSchemename("dnet:provenanceActions"); + qualifier.setSchemeid("dnet:provenanceActions"); + di.setProvenanceaction(qualifier); + return di; + } +} diff --git a/dhp-workflows/dhp-aggregation/src/test/resources/eu/dnetlib/dhp/actionmanager/bipfinder/bip_scores.json b/dhp-workflows/dhp-aggregation/src/test/resources/eu/dnetlib/dhp/actionmanager/bipfinder/bip_scores.json new file mode 100644 index 000000000..a4f3fa8b8 --- /dev/null +++ b/dhp-workflows/dhp-aggregation/src/test/resources/eu/dnetlib/dhp/actionmanager/bipfinder/bip_scores.json @@ -0,0 +1,1101 @@ +{"10.1001/10-v4n2-hsf10003": [{"id": "influence", "unit": [{"value": "1.62759106106e-08", "key": "score"}]}, {"id": "popularity", "unit": [{"value": "0.22519296", "key": "score"}]}]} +{"10.1001/2012.jama.10456": [{"id": "influence", "unit": [{"value": "1.66083991022e-08", "key": "score"}]}, {"id": "popularity", "unit": [{"value": "0.8690688", "key": "score"}]}]} +{"10.1001/2013.jamadermatol.729": [{"id": "influence", "unit": [{"value": "2.0769133069e-08", "key": "score"}]}, {"id": "popularity", "unit": [{"value": "0.0279936", "key": "score"}]}]} +{"10.1001/archderm.107.6.853": [{"id": "influence", "unit": [{"value": "1.95361113607e-08", "key": "score"}]}, {"id": "popularity", "unit": [{"value": "0.600657255944", "key": "score"}]}]} +{"10.1001/archderm.1926.02370130020002": [{"id": "influence", "unit": [{"value": "1.48611319422e-08", "key": "score"}]}, {"id": "popularity", "unit": [{"value": "8.20903253662e-16", "key": "score"}]}]} +{"10.1001/archpediatrics.2012.157": [{"id": "influence", "unit": [{"value": "1.42089114725e-08", "key": "score"}]}, {"id": "popularity", "unit": [{"value": "0.1575936", "key": "score"}]}]} +{"10.1002/bjs.1800830139": [{"id": "influence", "unit": [{"value": "3.73834652128e-08", "key": "score"}]}, {"id": "popularity", "unit": [{"value": "1.73174997252", "key": "score"}]}]} 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Ce qui nous frappe en premier lieu dans le roman de Melchior Mbonimpa, Le Totem des Baranda, c'est l'efficacité de sa fonction didactique. Le livre est porteur d'un message, d'une vision sur l'histoire de l'Afrique des Grands Lacs qu'on rencontre rarement dans l'actualité ou l'histoire immédiate que nous livrent les médias.

Cette thèse se penchera sur un aspect de ce roman qui a particulièrement attiré notre attention: la représentation de la résistance à travers l'affirmation identitaire des personnages féminins. Dans notre analyse de ce thème, nous accordons une grande importance au contexte socioculturel et à l'imaginaire collectif qui nourrissent l'auteur et dans lesquels s'inscrivent ses personnages. L'étude soulignera la fonction sociale de l'écrivain et relèvera la contribution de la fiction à la culture africaine. Nous mettrons en évidence les positions idéologiques de l'auteur et le message de sensibilisation qu'il adresse à diverses catégories de lecteurs: ceux qui se trouvent en Afrique, les Africains de la diaspora, et les lecteurs non-Africains qui s'intéressent à son œuvre. Cette orientation idéologique affecte évidemment la manière dont s'exprime la résistance des pnncIpaux personnages féminins du roman. Elle détermine la place que cette fiction accorde à la femme, et qui correspond sans doute à la place que, selon les souhaits de l'auteur, la femme devrait occuper dans le contexte culturel africain. Notre étude insiste aussi sur le fait que dans ce roman, la modernité se nourrit de la culture traditionnelle et ne renie pas les valeurs du passé.

Ainsi, le parcours narratif montre que dans leur résistance, les héroïnes de la généalogie vivent à leur époque, mais chacune porte plus loin une mission transmise par les précédentes.

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The superconducting transition in the magnetic resistance of members of the family of bismuth based high temperature superconductors was investigated. Measurements were performed in magnetic fields up to 1.7 T. Small current densities ranging from 0.03 A/cm² to 3.0 A/cm² were applied. The resistivity of Bi₂Sr₂CaCu₂Ox single crystals was analyzed in terms of the thermally activated flux flow expression, ρ =ρ₀U/T exp(-U/T) where T is the temperature. It was found that the activation energy was given by Uα(Hsinθ)^(⁻α) where α≈1/3 and that the prefactor had the form, ρ₀Hsinθ, where H was the applied field and θ the angle of the field with respect to the CuO₂ planes. Results demonstrated that dissipation could be accounted for by the motion of two-dimensional vortices whose density is given by the field, Hsinθ, projected on the CuO₂ planes. Measurements of the resistivity and current dependent resistivity were performed with two Sn-doped and two Sb-doped polycrystalline Bi(1.7)Pb(0.3)Sr₂Ca₂Cu₃O(y) samples. Features in the temperature derivative of the resistivity curves were associated with the presence of a superconducting transition between superconducting grains, coupled by weak links with a distribution of critical currents and critical temperatures, and the superconducting transition within grains. The transition between grains was more strongly suppressed in temperature with the application of a magnetic field in samples with weaker coupling between grains. The presence of a transition in a magnetic field due to weak links between grains was verified at 77 K by the observation of a current dependent resistivity in a magnetic field. Measurements of a Bi₂Sr₂CaCu₂Ox diffusion grown thick film ring were done. The transverse voltage, the voltage at the centre of a 120 μm thick branch with respect to the centre of a 76 μm thick branch, was measured. A higher critical temperature from the presence of more texturing in the 76 μm branch as determined by separate resistivity, x-ray and scanning electron microscopy measurements was consistent with the measurement of a crossover from a negative to positive transverse voltage as the temperature of the sample went through its superconducting transition.

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This study examines Leonard Cohen's novel Beautiful Losers through the lenses of allegorical and authorial theories to appreciate how the novel uses allegorical techniques to code into symbolic terms an exploration of the polysemous nature of the word ''translation.'' The first chapter studies the stylistic and conceptual dimensions of allegory as a literary genre - as critics like Northrop Frye, Angus Fletchet, and Maureen Quilligan help to define it - while arguing that Cohen's novel is consciously allegorical, challenging readers to interpret what it \"means,\" or may mean. The second chapter performs an intensive re-reading of Beautiful Losers, examining how the novel uses complex systems of verbal play (particularly puns) to coordinate a reunification of various dichotomies historical \"reality\"/imaginative myth, secularity/spirituality, enslavement/sanctification, among others - employed throughout the text. The thesis concludes that the novel is perpetually playing with various types of translation (spiritual, linguistic, physical, and so forth), affirming the need for emotionally-charged, devotional forms of expression (like song and prayer) over more clinical attempts to reorder or recreate the world and its inhabitants. Ultimately, this discussion argues that an understanding of the allegorical dimensions of Beautiful Losers may illuminate how Cohen's other works (particularly his songs) may be studied as attempts to associate word with voice, to emphasize the process of expression (translation) rather than just the finished product.

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travail de thèse est consacré à l'extension de l'Inversion Dynamique non-linéaire (NDI-Nonlinear Dynamic Inversion) pour un ensemble plus grand de systèmes non-linéaires, tout en garantissant des conditions de stabilité suffisantes. La NDI a été étudiée dans le cas de diverses applications, y compris en aéronautique et en aérospatiale. Elle permet de calculer des lois de contrôle capables de linéariser et de découpler un modèle non-linéaire à tout point de fonctionnement de son enveloppe d'état. Cependant cette méthode est intrinsèquement non-robuste aux erreurs de modélisation et aux saturations en entrée. En outre, dans un contexte non-linéaire, l'obtention d'une garantie quantifiable du domaine de stabilité atteint reste à l'heure actuelle complexe. Contrairement aux approches classiques de la NDI, notre méthodologie peut être considérée comme un cadre de compensation non-linéaire généralisé qui permet d'intégrer les incertitudes et les saturations en entrée dans le processus de conception. En utilisant des stratégies de contrôle antiwindup, la loi de pilotage peut être calculée grâce à un simple processus en deux phases. Dans ce cadre de travail généralisé des transformations linéaires fractionnaires (LFT - Linear Fractional Transformations) de la boucle fermée non-linéaire peuvent être facilement déduites pour l'analyse de la stabilité robuste en utilisant des outils standards pour de systèmes linéaires. La méthode proposée est testée pour le pilotage d'un véhicule de rentrée atmosphérique de type aile delta lors de ses phases hypersonique, transsonique et subsonique. Pour cette thèse, un simulateur du vol incluant divers facteurs externes ainsi que des erreurs de modélisation a été développé dans Simulink.\n\nThis thesis work is devoted to extending Nonlinear Dynamic Inversion (NDI) for a large scale of\nnonlinear systems while guaranteeing sufficient stability conditions. NDI has been studied in a wide range of applications, including aeronautics and aerospace. It allows to compute nonlinear control laws able to decouple and linearize a model at any operating point of its state envelope. However, this method is inherently non-robust to modelling errors and input saturations. Moreover, obtaining a quantifiable guarantee of the attained stability domain in a nonlinear control context is not a very straightforward task. Unlike standard NDI approaches, our methodology can be viewed as a generalized nonlinear compensation framework which allows to incorporate uncertainties and input saturations in the design process. Paralleling anti-windup strategies, the controller can be computed through a single multichannel optimization problem or through a simple two-step process. Within this framework, linear fractional transformations of the nonlinear closed-loop can be easily derived for robust stability analysis using standard tools for linear systems. The proposed method is tested for the flight control of a delta wing type reentry vehicle at hypersonic, transonic and subsonic phases of the atmospheric reentry. For this thesis work, a Flight Mechanics simulator including diverse external factors and modelling errors was developed in Simulink."}],"externalReference":[],"extraInfo":[],"format":[],"fulltext":[],"id":"50|artemis___fr::630e47d8b572e3df0e91327d6d8f036d","instance":[{"accessright":{"classid":"OPEN","classname":"Open Access","schemeid":"dnet:access_modes","schemename":"dnet:access_modes"},"collectedfrom":{"key":"10|driver______::66c20c26ac26136628f5207819ae1abc","value":"Archives des thèses et mémoires de l’ISAE (ArTeMIS)"},"dateofacceptance":{"dataInfo":{"deletedbyinference":false,"inferenceprovenance":"","inferred":false,"invisible":false,"provenanceaction":{"classid":"sysimport:crosswalk:repository","classname":"Harvested","schemeid":"dnet:provenanceActions","schemename":"dnet:provenanceActions"},"trust":"0.9"},"value":"2012-09-21"},"distributionlocation":"","hostedby":{"key":"10|driver______::66c20c26ac26136628f5207819ae1abc","value":"Archives des thèses et mémoires de l’ISAE 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