From 4eb8276493b8f936ecc45bf5316822fce277a4c8 Mon Sep 17 00:00:00 2001 From: "miriam.baglioni" Date: Tue, 14 Dec 2021 11:12:17 +0100 Subject: [PATCH] - --- .../actionmanager/bipfinder/Constants.java | 49 ++++ .../bipfinder/PreparedResult.java | 28 --- .../bipfinder/SparkAtomicActionScoreJob.java | 64 +++--- .../bipfinder/oozie_app/config-default.xml | 30 +++ .../bipfinder/oozie_app/workflow.xml | 34 +++ .../SparkAtomicActionScoreJobTest.java | 210 ++---------------- .../bipfinder/bip_scores_oid.json | 4 + .../bipfinder/publicationoaid.json | 18 ++ 8 files changed, 185 insertions(+), 252 deletions(-) create mode 100644 dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/Constants.java delete 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/resources/eu/dnetlib/dhp/actionmanager/bipfinder/oozie_app/config-default.xml create mode 100644 dhp-workflows/dhp-aggregation/src/test/resources/eu/dnetlib/dhp/actionmanager/bipfinder/bip_scores_oid.json create mode 100644 dhp-workflows/dhp-aggregation/src/test/resources/eu/dnetlib/dhp/actionmanager/bipfinder/publicationoaid.json diff --git a/dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/Constants.java b/dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/Constants.java new file mode 100644 index 000000000..0e08fd06c --- /dev/null +++ b/dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/Constants.java @@ -0,0 +1,49 @@ + +package eu.dnetlib.dhp.actionmanager.bipfinder; + +import java.util.Optional; + +import org.apache.spark.api.java.function.MapFunction; +import org.apache.spark.sql.Dataset; +import org.apache.spark.sql.Encoders; +import org.apache.spark.sql.SparkSession; + +import com.fasterxml.jackson.databind.ObjectMapper; + +import eu.dnetlib.dhp.application.ArgumentApplicationParser; + +public class Constants { + + public static final String DOI = "doi"; + + public static final String UPDATE_DATA_INFO_TYPE = "update"; + public static final String UPDATE_SUBJECT_FOS_CLASS_ID = "subject:fos"; + public static final String UPDATE_CLASS_NAME = "Inferred by OpenAIRE"; + public static final String UPDATE_MEASURE_BIP_CLASS_ID = "measure:bip"; + + public static final String FOS_CLASS_ID = "FOS"; + public static final String FOS_CLASS_NAME = "Fields of Science and Technology classification"; + + public static final String NULL = "NULL"; + + public static final ObjectMapper OBJECT_MAPPER = new ObjectMapper(); + + private Constants() { + } + + public static Boolean isSparkSessionManaged(ArgumentApplicationParser parser) { + return Optional + .ofNullable(parser.get("isSparkSessionManaged")) + .map(Boolean::valueOf) + .orElse(Boolean.TRUE); + } + + 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/java/eu/dnetlib/dhp/actionmanager/bipfinder/PreparedResult.java b/dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/PreparedResult.java deleted file mode 100644 index 493e94417..000000000 --- a/dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/PreparedResult.java +++ /dev/null @@ -1,28 +0,0 @@ - -package eu.dnetlib.dhp.actionmanager.bipfinder; - -import java.io.Serializable; - -/** - * Subset of the information of the generic results that are needed to create the atomic action - */ -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/SparkAtomicActionScoreJob.java b/dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/actionmanager/bipfinder/SparkAtomicActionScoreJob.java index f178451c1..d4dce6ae4 100644 --- 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 @@ -1,6 +1,7 @@ package eu.dnetlib.dhp.actionmanager.bipfinder; +import static eu.dnetlib.dhp.actionmanager.createunresolvedentities.Constants.*; import static eu.dnetlib.dhp.common.SparkSessionSupport.runWithSparkSession; import java.io.Serializable; @@ -18,6 +19,7 @@ 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; @@ -27,8 +29,10 @@ 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.common.ModelConstants; import eu.dnetlib.dhp.schema.oaf.*; import eu.dnetlib.dhp.schema.oaf.KeyValue; +import eu.dnetlib.dhp.schema.oaf.utils.OafMapperUtils; import scala.Tuple2; /** @@ -105,32 +109,29 @@ public class SparkAtomicActionScoreJob implements Serializable { results.createOrReplaceTempView("result"); - Dataset preparedResult = spark + Dataset preparedResult = spark .sql( - "select pIde.value value, id " + + "select id " + "from result " + - "lateral view explode (pid) p as pIde " + - "where dataInfo.deletedbyinference = false and pIde.qualifier.classid = '" + DOI + "'") - .as(Encoders.bean(PreparedResult.class)); + + "where dataInfo.deletedbyinference = false "); bipScores .joinWith( - preparedResult, bipScores.col("id").equalTo(preparedResult.col("value")), + preparedResult, bipScores.col("id").equalTo(preparedResult.col("id")), "inner") - .map((MapFunction, BipScore>) value -> { + .map((MapFunction, BipScore>) value -> { BipScore ret = value._1(); - ret.setId(value._2().getId()); + ret.setId(value._1().getId()); return ret; }, Encoders.bean(BipScore.class)) - .groupByKey((MapFunction) BipScore::getId, Encoders.STRING()) - .mapGroups((MapGroupsFunction) (k, it) -> { - Result ret = new Result(); - ret.setDataInfo(getDataInfo()); - BipScore first = it.next(); - ret.setId(first.getId()); - ret.setMeasures(getMeasure(first)); - it.forEachRemaining(value -> ret.getMeasures().addAll(getMeasure(value))); + .map((MapFunction) bs -> { + Result ret = new Result(); + + ret.setId(bs.getId()); + + ret.setMeasures(getMeasure(bs)); return ret; }, Encoders.bean(Result.class)) @@ -159,7 +160,21 @@ public class SparkAtomicActionScoreJob implements Serializable { KeyValue kv = new KeyValue(); kv.setValue(unit.getValue()); kv.setKey(unit.getKey()); - kv.setDataInfo(getDataInfo()); + kv + .setDataInfo( + OafMapperUtils + .dataInfo( + false, + UPDATE_DATA_INFO_TYPE, + true, + false, + OafMapperUtils + .qualifier( + UPDATE_MEASURE_BIP_CLASS_ID, + UPDATE_CLASS_NAME, + ModelConstants.DNET_PROVENANCE_ACTIONS, + ModelConstants.DNET_PROVENANCE_ACTIONS), + "")); return kv; }) .collect(Collectors.toList())); @@ -168,21 +183,6 @@ public class SparkAtomicActionScoreJob implements Serializable { .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()); } diff --git a/dhp-workflows/dhp-aggregation/src/main/resources/eu/dnetlib/dhp/actionmanager/bipfinder/oozie_app/config-default.xml b/dhp-workflows/dhp-aggregation/src/main/resources/eu/dnetlib/dhp/actionmanager/bipfinder/oozie_app/config-default.xml new file mode 100644 index 000000000..d262cb6e0 --- /dev/null +++ b/dhp-workflows/dhp-aggregation/src/main/resources/eu/dnetlib/dhp/actionmanager/bipfinder/oozie_app/config-default.xml @@ -0,0 +1,30 @@ + + + jobTracker + yarnRM + + + nameNode + hdfs://nameservice1 + + + oozie.use.system.libpath + true + + + hiveMetastoreUris + thrift://iis-cdh5-test-m3.ocean.icm.edu.pl:9083 + + + hiveJdbcUrl + jdbc:hive2://iis-cdh5-test-m3.ocean.icm.edu.pl:10000 + + + hiveDbName + openaire + + + oozie.launcher.mapreduce.user.classpath.first + true + + diff --git a/dhp-workflows/dhp-aggregation/src/main/resources/eu/dnetlib/dhp/actionmanager/bipfinder/oozie_app/workflow.xml b/dhp-workflows/dhp-aggregation/src/main/resources/eu/dnetlib/dhp/actionmanager/bipfinder/oozie_app/workflow.xml index 5311a6614..eef429087 100644 --- a/dhp-workflows/dhp-aggregation/src/main/resources/eu/dnetlib/dhp/actionmanager/bipfinder/oozie_app/workflow.xml +++ b/dhp-workflows/dhp-aggregation/src/main/resources/eu/dnetlib/dhp/actionmanager/bipfinder/oozie_app/workflow.xml @@ -13,6 +13,40 @@ outputPath the path where to store the actionset + + sparkDriverMemory + memory for driver process + + + sparkExecutorMemory + memory for individual executor + + + sparkExecutorCores + number of cores used by single executor + + + oozieActionShareLibForSpark2 + oozie action sharelib for spark 2.* + + + spark2ExtraListeners + com.cloudera.spark.lineage.NavigatorAppListener + spark 2.* extra listeners classname + + + spark2SqlQueryExecutionListeners + com.cloudera.spark.lineage.NavigatorQueryListener + spark 2.* sql query execution listeners classname + + + spark2YarnHistoryServerAddress + spark 2.* yarn history server address + + + spark2EventLogDir + spark 2.* event log dir location + 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 index f2158748b..f040ee836 100644 --- 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 @@ -28,6 +28,7 @@ import com.fasterxml.jackson.databind.ObjectMapper; import eu.dnetlib.dhp.schema.action.AtomicAction; import eu.dnetlib.dhp.schema.oaf.Publication; +import eu.dnetlib.dhp.schema.oaf.Result; public class SparkAtomicActionScoreJobTest { @@ -69,13 +70,13 @@ public class SparkAtomicActionScoreJobTest { } @Test - void matchOne() throws Exception { + void testMatch() throws Exception { String bipScoresPath = getClass() - .getResource("/eu/dnetlib/dhp/actionmanager/bipfinder/bip_scores.json") + .getResource("/eu/dnetlib/dhp/actionmanager/bipfinder/bip_scores_oid.json") .getPath(); String inputPath = getClass() .getResource( - "/eu/dnetlib/dhp/actionmanager/bipfinder/publication.json") + "/eu/dnetlib/dhp/actionmanager/bipfinder/publicationoaid.json") .getPath(); SparkAtomicActionScoreJob @@ -95,223 +96,48 @@ public class SparkAtomicActionScoreJobTest { final JavaSparkContext sc = new JavaSparkContext(spark.sparkContext()); - JavaRDD tmp = sc + 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())); + .map(aa -> ((Result) aa.getPayload())); - assertEquals(1, tmp.count()); + assertEquals(4, tmp.count()); - Dataset verificationDataset = spark.createDataset(tmp.rdd(), Encoders.bean(Publication.class)); - verificationDataset.createOrReplaceTempView("publication"); + Dataset verificationDataset = spark.createDataset(tmp.rdd(), Encoders.bean(Result.class)); + verificationDataset.createOrReplaceTempView("result"); Dataset execVerification = spark .sql( - "Select p.id oaid, mes.id, mUnit.value from publication p " + + "Select p.id oaid, mes.id, mUnit.value from result p " + "lateral view explode(measures) m as mes " + "lateral view explode(mes.unit) u as mUnit "); - Assertions.assertEquals(2, execVerification.count()); - + Assertions.assertEquals(12, execVerification.count()); Assertions .assertEquals( - "50|355e65625b88::ffa5bad14f4adc0c9a15c00efbbccddb", - execVerification.select("oaid").collectAsList().get(0).getString(0)); - - Assertions - .assertEquals( - "1.47565045883e-08", - execVerification.filter("id = 'influence'").select("value").collectAsList().get(0).getString(0)); - - Assertions - .assertEquals( - "0.227515392", - execVerification.filter("id = 'popularity'").select("value").collectAsList().get(0).getString(0)); - - } - - @Test - void matchOneWithTwo() 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_2.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())); - - assertEquals(1, tmp.count()); - - Dataset verificationDataset = spark.createDataset(tmp.rdd(), Encoders.bean(Publication.class)); - verificationDataset.createOrReplaceTempView("publication"); - - Dataset execVerification = spark - .sql( - "Select p.id oaid, mes.id, mUnit.value from publication p " + - "lateral view explode(measures) m as mes " + - "lateral view explode(mes.unit) u as mUnit "); - - Assertions.assertEquals(4, execVerification.count()); - - Assertions - .assertEquals( - "50|355e65625b88::ffa5bad14f4adc0c9a15c00efbbccddb", - execVerification.select("oaid").collectAsList().get(0).getString(0)); - - Assertions - .assertEquals( - 2, - execVerification.filter("id = 'influence'").count()); - - Assertions - .assertEquals( - 2, - execVerification.filter("id = 'popularity'").count()); - - List tmp_ds = execVerification.filter("id = 'influence'").select("value").collectAsList(); - String tmp_influence = tmp_ds.get(0).getString(0); - assertTrue( - "1.47565045883e-08".equals(tmp_influence) || - "1.98956540239e-08".equals(tmp_influence)); - - tmp_influence = tmp_ds.get(1).getString(0); - assertTrue( - "1.47565045883e-08".equals(tmp_influence) || - "1.98956540239e-08".equals(tmp_influence)); - - assertNotEquals(tmp_ds.get(1).getString(0), tmp_ds.get(0).getString(0)); - - } - - @Test - void matchTwo() 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_3.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())); - - assertEquals(2, tmp.count()); - - Dataset verificationDataset = spark.createDataset(tmp.rdd(), Encoders.bean(Publication.class)); - verificationDataset.createOrReplaceTempView("publication"); - - Dataset execVerification = spark - .sql( - "Select p.id oaid, mes.id, mUnit.value from publication p " + - "lateral view explode(measures) m as mes " + - "lateral view explode(mes.unit) u as mUnit "); - - Assertions.assertEquals(4, execVerification.count()); - - Assertions - .assertEquals( - 2, - execVerification.filter("oaid = '50|355e65625b88::ffa5bad14f4adc0c9a15c00efbbccddb'").count()); - - Assertions - .assertEquals( - 2, - execVerification.filter("oaid = '50|acm_________::faed5b7a1bd8f51118d13ed29cfaee09'").count()); - - Assertions - .assertEquals( - 2, - execVerification.filter("id = 'influence'").count()); - - Assertions - .assertEquals( - 2, - execVerification.filter("id = 'popularity'").count()); - - Assertions - .assertEquals( - "1.47565045883e-08", - execVerification + "6.63451994567e-09", execVerification .filter( - "oaid = '50|355e65625b88::ffa5bad14f4adc0c9a15c00efbbccddb' " + + "oaid='50|arXiv_dedup_::4a2d5fd8d71daec016c176ec71d957b1' " + "and id = 'influence'") .select("value") .collectAsList() .get(0) .getString(0)); - Assertions .assertEquals( - "1.98956540239e-08", - execVerification + "0.348694533145", execVerification .filter( - "oaid = '50|acm_________::faed5b7a1bd8f51118d13ed29cfaee09' " + - "and id = 'influence'") + "oaid='50|arXiv_dedup_::4a2d5fd8d71daec016c176ec71d957b1' " + + "and id = 'popularity_alt'") .select("value") .collectAsList() .get(0) .getString(0)); - Assertions .assertEquals( - "0.282046161584", - execVerification + "2.16094680115e-09", execVerification .filter( - "oaid = '50|acm_________::faed5b7a1bd8f51118d13ed29cfaee09' " + - "and id = 'popularity'") - .select("value") - .collectAsList() - .get(0) - .getString(0)); - - Assertions - .assertEquals( - "0.227515392", - execVerification - .filter( - "oaid = '50|355e65625b88::ffa5bad14f4adc0c9a15c00efbbccddb' " + + "oaid='50|arXiv_dedup_::4a2d5fd8d71daec016c176ec71d957b1' " + "and id = 'popularity'") .select("value") .collectAsList() diff --git a/dhp-workflows/dhp-aggregation/src/test/resources/eu/dnetlib/dhp/actionmanager/bipfinder/bip_scores_oid.json b/dhp-workflows/dhp-aggregation/src/test/resources/eu/dnetlib/dhp/actionmanager/bipfinder/bip_scores_oid.json new file mode 100644 index 000000000..5dcf486d0 --- /dev/null +++ b/dhp-workflows/dhp-aggregation/src/test/resources/eu/dnetlib/dhp/actionmanager/bipfinder/bip_scores_oid.json @@ -0,0 +1,4 @@ +{"50|arXiv_dedup_::4a2d5fd8d71daec016c176ec71d957b1": [{"id": "influence", "unit": [{"value": "6.63451994567e-09", "key": "score"}]}, {"id": "popularity_alt", "unit": [{"value": "0.348694533145", "key": "score"}]}, {"id": "popularity", "unit": [{"value": "2.16094680115e-09", "key": "score"}]}]} +{"50|dedup_wf_001::05b1f8ce98702f69d07aa5f0429de1e3": [{"id": "influence", "unit": [{"value": "6.25057357279e-09", "key": "score"}]}, {"id": "popularity_alt", "unit": [{"value": "7.0208", "key": "score"}]}, {"id": "popularity", "unit": [{"value": "2.40234462244e-08", "key": "score"}]}]} +{"50|dedup_wf_001::08823c8f5c3ca2eae523817036cdda67": [{"id": "influence", "unit": [{"value": "5.54921449123e-09", "key": "score"}]}, {"id": "popularity_alt", "unit": [{"value": "0.0", "key": "score"}]}, {"id": "popularity", "unit": [{"value": "3.53012887452e-10", "key": "score"}]}]} +{"50|dedup_wf_001::0e72b399325d6efcbe3271891a1dfe4c": [{"id": "influence", "unit": [{"value": "1.63466096315e-08", "key": "score"}]}, {"id": "popularity_alt", "unit": [{"value": "20.9870879741", "key": "score"}]}, {"id": "popularity", "unit": [{"value": "5.49501495323e-08", "key": "score"}]}]} \ No newline at end of file diff --git a/dhp-workflows/dhp-aggregation/src/test/resources/eu/dnetlib/dhp/actionmanager/bipfinder/publicationoaid.json b/dhp-workflows/dhp-aggregation/src/test/resources/eu/dnetlib/dhp/actionmanager/bipfinder/publicationoaid.json new file mode 100644 index 000000000..6a4506816 --- /dev/null +++ b/dhp-workflows/dhp-aggregation/src/test/resources/eu/dnetlib/dhp/actionmanager/bipfinder/publicationoaid.json @@ -0,0 +1,18 @@ +{"author":[{"fullname":"Niskala, Eino","name":"Eino","pid":[],"rank":1,"surname":"Niskala"}],"bestaccessright":{"classid":"RESTRICTED","classname":"Restricted","schemeid":"dnet:access_modes","schemename":"dnet:access_modes"},"collectedfrom":[{"key":"10|openaire____::4692342f0992d91f9e705c26959f09e0","value":"VTT Research Information 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central problem for the function and manufacture of materials where performance relies on nanoscale structure is to control the compatibility and interactions of the building blocks. In natural materials, such as nacre, there are examples of multifunctional macromolecules that have combined binding affinities for different materials within the same molecule, thereby bridging these materials and acting as a molecular glue. Here, we describe the use of a designed multifunctional protein that is used for self-assembly of nanofibrillar cellulose. Recent advances in the production of cellulose nanofibrils have given inspiration for new uses of cellulosic materials. Cellulose nanofibrils have mechanical and structural features that open new possibilities for performance in composites and other nanoscale materials. Functionalisation was realised through a bi-functional fusion protein having both an ability to bind to cellulose and a second functionality of surface activity. The cellulose-binding function was obtained using cellulose-binding domains from cellulolytic enzymes and the surface activity through the use of a surface active protein called hydrophobin. Using the bi-functional protein, cellulose nanofibrils could be assembled into tightly packed thin films at the air/water interface and at the oil/water interface. It was shown that the combination of protein and cellulose nanofibrils resulted in a synergistic improvement in the formation and stability of oil-in-water emulsions resulting in emulsions that were stable for several months. 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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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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. 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