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org.deidentifier.arx.gui.model

Class Model

java.lang.Object

org.deidentifier.arx.gui.model.Model

All Implemented Interfaces:

java.io.Serializable

public class Model
extends java.lang.Object
implements java.io.Serializable

This class implements a large portion of the model used by the GUI.

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type
Class and Description

static class
Model.Perspective

The currently selected perspective

Constructor Summary

Constructors

Constructor and Description

Model(java.lang.String name,
     java.lang.String description,
     java.util.Locale locale)

Creates a new instance.

Method Summary

Methods

Modifier and Type
Method and Description

void
addAuditTrailEntry(ModelAuditTrailEntry entry)

Adds an entry to the audit trail

ARXAnonymizer
createAnonymizer()

Creates an anonymizer for the current config.

void
createClonedConfig()

Replaces the output config with a clone of the input config.

void
createConfig()

Creates an ARXConfiguration.

ARXConfiguration
createSubsetConfig()

Creates an ARXConfiguration for the subset.

ARXAnonymizer
getAnonymizer()

Returns the current anonymizer.

java.lang.String[]
getAttributePair()

Returns the last two selected attributes.

java.util.List<ModelAuditTrailEntry>
getAuditTrail()

Returns the audit trail

java.util.Map<java.lang.String,ModelBLikenessCriterion>
getBLikenessModel()

Returns the b-Likeness privacy model.

ModelClassification
getClassificationModel()

Returns the classification model

ModelClipboard
getClipboard()

Returns the clipboard.

CSVSyntax
getCSVSyntax()

Gets the csv config model.

java.util.Map<java.lang.String,ModelDDisclosurePrivacyCriterion>
getDDisclosurePrivacyModel()

Returns the d-disclosure privacy model.

java.lang.String
getDescription()

Returns the project description.

ModelDifferentialPrivacyCriterion
getDifferentialPrivacyModel()

Returns the (e,d)-DP model.

ModelDPresenceCriterion
getDPresenceModel()

Returns the d-presence model.

int[]
getGroups()

Returns a list of indices of all equivalence classes.

int
getHistorySize()

Returns the according parameter.

int
getInitialNodesInViewer()

Returns an upper bound on the number of nodes that will initially
 be displayed in the lattice viewer.

long
getInputBytes()

Returns the size in bytes of the input file.

ModelConfiguration
getInputConfig()

Returns the input configuration.

DataDefinition
getInputDefinition()

Returns the input definition.

ARXPopulationModel
getInputPopulationModel()

Returns the input population model

ModelKAnonymityCriterion
getKAnonymityModel()

Returns the k-anonymity model.

ModelKMapCriterion
getKMapModel()

Returns the k-map model.

java.util.Map<java.lang.String,ModelLDiversityCriterion>
getLDiversityModel()

Returns the l-diversity model.

java.util.Locale
getLocale()

Returns the project locale.

ModelLocalRecoding
getLocalRecodingModel()

Returns the model for local recoding

int
getMaximalSizeForComplexOperations()

When a dataset has more records than this threshold,
 visualization of statistics will be disabled.

int
getMaxNodesInViewer()

Returns the maximal size of a sub-lattice that will be displayed
 by the viewer.

MetricConfiguration
getMetricConfiguration()

Returns the configuration of the metric.

MetricDescription
getMetricDescription()

Returns a description of the metric.

java.lang.String
getName()

Returns the name of this project.

ModelNodeFilter
getNodeFilter()

Returns the current filter.

java.lang.String
getOptimalNodeAsString()

Returns a string representation of the current optimum.

DataHandle
getOutput()

ModelConfiguration
getOutputConfig()

Returns the output config.

DataDefinition
getOutputDefinition()

Returns the output definition.

ARXLattice.ARXNode
getOutputNode()

Returns the currently applied transformation.

java.lang.String
getOutputNodeAsString()

Returns a string representation of the currently applied transformation.

ARXPopulationModel
getOutputPopulationModel()

Returns the output population model, if any.

java.lang.String
getPath()

Returns the path of the project.

Model.Perspective
getPerspective()

java.lang.String
getQuery()

Returns the current query.

ARXResult
getResult()

Returns the current result.

java.util.Set<ModelRiskBasedCriterion>
getRiskBasedModel()

Returns the risk-based model.

ModelRisk
getRiskModel()

Returns the risk model

java.lang.String
getSelectedAttribute()

Returns the currently selected attribute.

java.util.Set<java.lang.String>
getSelectedClasses()

Returns the selected features

java.util.Set<java.lang.String>
getSelectedFeatures()

Returns the selected features

ARXLattice.ARXNode
getSelectedNode()

Returns the selected transformation.

java.util.Set<java.lang.String>
getSelectedQuasiIdentifiers()

Returns a set of quasi identifiers selected for risk analysis

char
getSeparator()

Returns the separator.

double
getSnapshotSizeDataset()

Returns the according parameter.

double
getSnapshotSizeSnapshot()

Returns the according parameter.

ModelProfitabilityCriterion
getStackelbergModel()

Returns the configuration object for the stackelberg privacy model

java.lang.String
getSubsetOrigin()

Returns the origin of the subset.

java.util.Map<java.lang.String,ModelTClosenessCriterion>
getTClosenessModel()

Returns the t-closeness model.

long
getTime()

Returns the execution time of the last anonymization process.

java.lang.Boolean
getUseFunctionalHierarchies()

Returns whether functional hierarchies should be used

java.lang.Boolean
getUseListwiseDeletion()

Returns whether list-wise deletion is used for summary statistics

ModelViewConfig
getViewConfig()

Returns the view configuration.

boolean
isDebugEnabled()

Returns whether debugging is enabled.

boolean
isModified()

Returns whether this project is modified.

boolean
isQuasiIdentifierSelected()

Returns whether a quasi-identifier is selected.

boolean
isSensitiveAttributeSelected()

Returns whether a sensitive attribute is selected.

boolean
isVisualizationEnabled()

Returns whether visualization is enabled.

void
reset()

Resets the model.

void
resetAttributePair()

Returns the last two selected attributes.

void
resetCriteria()

Resets the configuration of the privacy criteria.

void
setAnonymizer(ARXAnonymizer anonymizer)

Sets the anonymizer.

void
setDebugEnabled(boolean value)

Enables debugging.

void
setDescription(java.lang.String description)

Sets the project description.

void
setGroups(int[] groups)

Sets the indices of equivalence classes.

void
setHistorySize(int historySize)

Sets the according parameter.

void
setInitialNodesInViewer(int val)

Sets the according parameter.

void
setInputBytes(long inputBytes)

Sets the size of the input in bytes.

void
setInputConfig(ModelConfiguration config)

Sets the input config.

void
setLocale(java.util.Locale locale)

Sets the project locale.

void
setMaximalSizeForComplexOperations(int numberOfRows)

Sets the according parameter.

void
setMaxNodesInViewer(int maxNodesInViewer)

Sets the according parameter.

void
setMetricDescription(MetricDescription description)

Sets the description of the metric.

void
setModified()

Marks this project as modified.

void
setName(java.lang.String name)

Sets the project name.

void
setNodeFilter(ModelNodeFilter filter)

Sets a filter.

void
setOutput(DataHandle output,
         ARXLattice.ARXNode node)

Sets the current output.

void
setOutputConfig(ModelConfiguration config)

Sets the output config.

void
setPath(java.lang.String path)

Sets the project path.

void
setPerspective(Model.Perspective perspective)

void
setQuery(java.lang.String query)

Sets the query.

void
setResult(ARXResult result)

Sets the result.

void
setSaved()

Marks this project as saved.

void
setSelectedAttribute(java.lang.String attribute)

Sets the selected attribute.

void
setSelectedClasses(java.util.Set<java.lang.String> set)

Sets a set of selected attributes

void
setSelectedFeatures(java.util.Set<java.lang.String> set)

Sets a set of selected attributes

void
setSelectedNode(ARXLattice.ARXNode node)

Sets the selected node.

void
setSelectedQuasiIdentifiers(java.util.Set<java.lang.String> set)

Sets a set of quasi identifiers selected for risk analysis

void
setSnapshotSizeDataset(double snapshotSize)

void
setSnapshotSizeSnapshot(double snapshotSize)

Sets the according parameter.

void
setSubsetManual()

Sets how the subset was defined.

void
setSubsetOrigin(java.lang.String origin)

Sets how the subset was defined.

void
setTime(long time)

Sets the execution time of the last anonymization process.

void
setUnmodified()

Marks this model as unmodified.

void
setUseFunctionalHierarchies(boolean useFunctionalHierarchies)

Sets whether funtional hierarchies should be used during anonymization to esimtate utility

void
setUseListwiseDeletion(boolean useListwiseDeletion)

Sets whether list-wise deletion should be used for summary statistics

void
setViewConfig(ModelViewConfig viewConfig)

Sets the view configuration.

void
setVisualizationEnabled(boolean value)

Sets visualization as enabled/disabled.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

Model

public Model(java.lang.String name,
     java.lang.String description,
     java.util.Locale locale)

Creates a new instance.

Parameters:

name -

description -

locale -

Method Detail

addAuditTrailEntry

public void addAuditTrailEntry(ModelAuditTrailEntry entry)

Adds an entry to the audit trail

Parameters:

entry -

createAnonymizer

public ARXAnonymizer createAnonymizer()

Creates an anonymizer for the current config.

Returns:

createClonedConfig

public void createClonedConfig()

Replaces the output config with a clone of the input config.

createConfig

public void createConfig()

Creates an ARXConfiguration.

createSubsetConfig

public ARXConfiguration createSubsetConfig()

Creates an ARXConfiguration for the subset.

Returns:

getAnonymizer

public ARXAnonymizer getAnonymizer()

Returns the current anonymizer.

Returns:

getAttributePair

public java.lang.String[] getAttributePair()

Returns the last two selected attributes.

Returns:

getAuditTrail

public java.util.List<ModelAuditTrailEntry> getAuditTrail()

Returns the audit trail

Returns:

getClassificationModel

public ModelClassification getClassificationModel()

Returns the classification model

Returns:

getClipboard

public ModelClipboard getClipboard()

Returns the clipboard.

Returns:

getCSVSyntax

public CSVSyntax getCSVSyntax()

Gets the csv config model.

Returns:

getDDisclosurePrivacyModel

public java.util.Map<java.lang.String,ModelDDisclosurePrivacyCriterion> getDDisclosurePrivacyModel()

Returns the d-disclosure privacy model.

Returns:

getBLikenessModel

public java.util.Map<java.lang.String,ModelBLikenessCriterion> getBLikenessModel()

Returns the b-Likeness privacy model.

Returns:

getDescription

public java.lang.String getDescription()

Returns the project description.

Returns:

getDifferentialPrivacyModel

public ModelDifferentialPrivacyCriterion getDifferentialPrivacyModel()

Returns the (e,d)-DP model.

Returns:

getDPresenceModel

public ModelDPresenceCriterion getDPresenceModel()

Returns the d-presence model.

Returns:

getGroups

public int[] getGroups()

Returns a list of indices of all equivalence classes.

Returns:

getHistorySize

public int getHistorySize()

Returns the according parameter.

Returns:

getInitialNodesInViewer

public int getInitialNodesInViewer()

Returns an upper bound on the number of nodes that will initially
 be displayed in the lattice viewer.

Returns:

getInputBytes

public long getInputBytes()

Returns the size in bytes of the input file.

Returns:

getInputConfig

public ModelConfiguration getInputConfig()

Returns the input configuration.

Returns:

getInputDefinition

public DataDefinition getInputDefinition()

Returns the input definition.

Returns:

getInputPopulationModel

public ARXPopulationModel getInputPopulationModel()

Returns the input population model

Returns:

getKAnonymityModel

public ModelKAnonymityCriterion getKAnonymityModel()

Returns the k-anonymity model.

Returns:

getKMapModel

public ModelKMapCriterion getKMapModel()

Returns the k-map model.

Returns:

getLDiversityModel

public java.util.Map<java.lang.String,ModelLDiversityCriterion> getLDiversityModel()

Returns the l-diversity model.

Returns:

getLocale

public java.util.Locale getLocale()

Returns the project locale.

Returns:

getLocalRecodingModel

public ModelLocalRecoding getLocalRecodingModel()

Returns the model for local recoding

Returns:

getMaximalSizeForComplexOperations

public int getMaximalSizeForComplexOperations()

When a dataset has more records than this threshold,
 visualization of statistics will be disabled.

Returns:

getMaxNodesInViewer

public int getMaxNodesInViewer()

Returns the maximal size of a sub-lattice that will be displayed
 by the viewer.

Returns:

getMetricConfiguration

public MetricConfiguration getMetricConfiguration()

Returns the configuration of the metric.

Returns:

getMetricDescription

public MetricDescription getMetricDescription()

Returns a description of the metric.

Returns:

getName

public java.lang.String getName()

Returns the name of this project.

Returns:

getNodeFilter

public ModelNodeFilter getNodeFilter()

Returns the current filter.

Returns:

getOptimalNodeAsString

public java.lang.String getOptimalNodeAsString()

Returns a string representation of the current optimum.

Returns:

getOutput

public DataHandle getOutput()

Returns:

the output

getOutputConfig

public ModelConfiguration getOutputConfig()

Returns the output config.

Returns:

getOutputDefinition

public DataDefinition getOutputDefinition()

Returns the output definition.

Returns:

getOutputNode

public ARXLattice.ARXNode getOutputNode()

Returns the currently applied transformation.

Returns:

getOutputNodeAsString

public java.lang.String getOutputNodeAsString()

Returns a string representation of the currently applied transformation.

Returns:

getOutputPopulationModel

public ARXPopulationModel getOutputPopulationModel()

Returns the output population model, if any. Null otherwise.

Returns:

getPath

public java.lang.String getPath()

Returns the path of the project.

Returns:

getPerspective

public Model.Perspective getPerspective()

Returns:

the perspective

getQuery

public java.lang.String getQuery()

Returns the current query.

Returns:

getResult

public ARXResult getResult()

Returns the current result.

Returns:

the result

getRiskBasedModel

public java.util.Set<ModelRiskBasedCriterion> getRiskBasedModel()

Returns the risk-based model.

Returns:

getRiskModel

public ModelRisk getRiskModel()

Returns the risk model

Returns:

the risk model

getSelectedAttribute

public java.lang.String getSelectedAttribute()

Returns the currently selected attribute.

Returns:

getSelectedClasses

public java.util.Set<java.lang.String> getSelectedClasses()

Returns the selected features

Returns:

getSelectedFeatures

public java.util.Set<java.lang.String> getSelectedFeatures()

Returns the selected features

Returns:

getSelectedNode

public ARXLattice.ARXNode getSelectedNode()

Returns the selected transformation.

Returns:

getSelectedQuasiIdentifiers

public java.util.Set<java.lang.String> getSelectedQuasiIdentifiers()

Returns a set of quasi identifiers selected for risk analysis

Returns:

getSeparator

public char getSeparator()

Returns the separator.

Returns:

getSnapshotSizeDataset

public double getSnapshotSizeDataset()

Returns the according parameter.

Returns:

getSnapshotSizeSnapshot

public double getSnapshotSizeSnapshot()

Returns the according parameter.

Returns:

getStackelbergModel

public ModelProfitabilityCriterion getStackelbergModel()

Returns the configuration object for the stackelberg privacy model

Returns:

getSubsetOrigin

public java.lang.String getSubsetOrigin()

Returns the origin of the subset.

Returns:

getTClosenessModel

public java.util.Map<java.lang.String,ModelTClosenessCriterion> getTClosenessModel()

Returns the t-closeness model.

Returns:

getTime

public long getTime()

Returns the execution time of the last anonymization process.

Returns:

getUseFunctionalHierarchies

public java.lang.Boolean getUseFunctionalHierarchies()

Returns whether functional hierarchies should be used

Returns:

getUseListwiseDeletion

public java.lang.Boolean getUseListwiseDeletion()

Returns whether list-wise deletion is used for summary statistics

Returns:

getViewConfig

public ModelViewConfig getViewConfig()

Returns the view configuration.

Returns:

isDebugEnabled

public boolean isDebugEnabled()

Returns whether debugging is enabled.

Returns:

isModified

public boolean isModified()

Returns whether this project is modified.

Returns:

isQuasiIdentifierSelected

public boolean isQuasiIdentifierSelected()

Returns whether a quasi-identifier is selected.

Returns:

isSensitiveAttributeSelected

public boolean isSensitiveAttributeSelected()

Returns whether a sensitive attribute is selected.

Returns:

isVisualizationEnabled

public boolean isVisualizationEnabled()

Returns whether visualization is enabled.

Returns:

reset

public void reset()

Resets the model.

resetAttributePair

public void resetAttributePair()

Returns the last two selected attributes.

resetCriteria

public void resetCriteria()

Resets the configuration of the privacy criteria.

setAnonymizer

public void setAnonymizer(ARXAnonymizer anonymizer)

Sets the anonymizer.

Parameters:

anonymizer -

setDebugEnabled

public void setDebugEnabled(boolean value)

Enables debugging.

Parameters:

value -

setDescription

public void setDescription(java.lang.String description)

Sets the project description.

Parameters:

description -

setGroups

public void setGroups(int[] groups)

Sets the indices of equivalence classes.

Parameters:

groups -

setHistorySize

public void setHistorySize(int historySize)

Sets the according parameter.

Parameters:

historySize -

setInitialNodesInViewer

public void setInitialNodesInViewer(int val)

Sets the according parameter.

Parameters:

val -

setInputBytes

public void setInputBytes(long inputBytes)

Sets the size of the input in bytes.

Parameters:

inputBytes -

setInputConfig

public void setInputConfig(ModelConfiguration config)

Sets the input config.

Parameters:

config -

setLocale

public void setLocale(java.util.Locale locale)

Sets the project locale.

Parameters:

locale - Null for default locale

setMaximalSizeForComplexOperations

public void setMaximalSizeForComplexOperations(int numberOfRows)

Sets the according parameter.

Parameters:

numberOfRows -

setMaxNodesInViewer

public void setMaxNodesInViewer(int maxNodesInViewer)

Sets the according parameter.

Parameters:

maxNodesInViewer -

setMetricDescription

public void setMetricDescription(MetricDescription description)

Sets the description of the metric.

Parameters:

description -

setModified

public void setModified()

Marks this project as modified.

setName

public void setName(java.lang.String name)

Sets the project name.

Parameters:

name -

setNodeFilter

public void setNodeFilter(ModelNodeFilter filter)

Sets a filter.

Parameters:

filter -

setOutput

public void setOutput(DataHandle output,
             ARXLattice.ARXNode node)

Sets the current output.

Parameters:

output -

node -

setOutputConfig

public void setOutputConfig(ModelConfiguration config)

Sets the output config.

Parameters:

config -

setPath

public void setPath(java.lang.String path)

Sets the project path.

Parameters:

path -

setPerspective

public void setPerspective(Model.Perspective perspective)

Parameters:

perspective - the perspective to set

setQuery

public void setQuery(java.lang.String query)

Sets the query.

Parameters:

query -

setResult

public void setResult(ARXResult result)

Sets the result.

Parameters:

result -

setSaved

public void setSaved()

Marks this project as saved.

setSelectedAttribute

public void setSelectedAttribute(java.lang.String attribute)

Sets the selected attribute.

Parameters:

attribute -

setSelectedClasses

public void setSelectedClasses(java.util.Set<java.lang.String> set)

Sets a set of selected attributes

Parameters:

set -

setSelectedFeatures

public void setSelectedFeatures(java.util.Set<java.lang.String> set)

Sets a set of selected attributes

Parameters:

set -

setSelectedNode

public void setSelectedNode(ARXLattice.ARXNode node)

Sets the selected node.

Parameters:

node -

setSelectedQuasiIdentifiers

public void setSelectedQuasiIdentifiers(java.util.Set<java.lang.String> set)

Sets a set of quasi identifiers selected for risk analysis

Parameters:

set -

setSnapshotSizeDataset

public void setSnapshotSizeDataset(double snapshotSize)

Parameters:

snapshotSize -

setSnapshotSizeSnapshot

public void setSnapshotSizeSnapshot(double snapshotSize)

Sets the according parameter.

Parameters:

snapshotSize -

setSubsetManual

public void setSubsetManual()

Sets how the subset was defined.

setSubsetOrigin

public void setSubsetOrigin(java.lang.String origin)

Sets how the subset was defined.

Parameters:

origin -

setTime

public void setTime(long time)

Sets the execution time of the last anonymization process.

Parameters:

time -

setUnmodified

public void setUnmodified()

Marks this model as unmodified.

setUseFunctionalHierarchies

public void setUseFunctionalHierarchies(boolean useFunctionalHierarchies)

Sets whether funtional hierarchies should be used during anonymization to esimtate utility

Parameters:

useFunctionalHierarchies -

setUseListwiseDeletion

public void setUseListwiseDeletion(boolean useListwiseDeletion)

Sets whether list-wise deletion should be used for summary statistics

Parameters:

useListwiseDeletion -

setViewConfig

public void setViewConfig(ModelViewConfig viewConfig)

Sets the view configuration.

Parameters:

viewConfig -

setVisualizationEnabled

public void setVisualizationEnabled(boolean value)

Sets visualization as enabled/disabled.

Parameters:

value -

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Once a drug candidate has been identified and undergone study in the laboratory and animals, it enters clinical trial, usually composed of 3 phases. Generally, as the drug candidate goes from phases 1 to 3, the number of volunteers exposed to it increases and the duration of study lengthens. Each phase serves a unique purpose, and each builds on the knowledge gleaned from the earlier research.
Not all clinical trials involve an investigational drug. Some involve medical devices (for example, an insulin pump) and some examine clinical management strategies. The recently launched SMART study (Strategies for the Management of Anti-Retroviral Therapy), which pits early anti-HIV treatment against delayed anti-HIV treatment, is an example of such a study. However, this article focuses on the clinical trial of medications for treating HIV infection.
Although often given long titles and identified by imposing acronyms, a clinical trial of a drug candidate is simply a scientific way to answer specific questions about the new therapy. The questions usually differ from one phase of research to the next. For potential study volunteers, it's important to remember that the question a study asks may not be well thought out or the answer unimportant. All clinical studies are not created equal. Also, most clinical trials of anti-HIV drugs are intended only to obtain regulatory approval for marketing the drug. The trials do not necessarily tell patients and clinicians how best to use a drug. For example, abacavir sulfate (Ziagen) received regulatory approval when research showed that it lowered viral load. But many clinicians are still unsure of whether to use abacavir as part of a first treatment regimen or to save it for later, or whether to combine it with a protease inhibitor or a non-nucleoside reverse transcriptase inhibitor.
In a phase 1 study, researchers usually expose only a handful of volunteers -- sometimes not more than 10, and rarely more than 80 -- to the new therapy. The reason for this is simple: a phase 1 study marks the first time humans receive an investigational drug and researchers want to minimize harm if the therapy proves unsafe. A phase 1 study allows investigators to determine the side effects and safe dose of a new treatment. Put as a question, the phase 1 study asks: "Is this drug safe for people to take, and if so, at what dose?" Phase 1 studies almost never ask, "Is this drug effective?" It can be tempting for research participants, especially if they are eagerly awaiting a new treatment, to just assume that any new drug is effective. But the question of effectiveness -- and the answer to it -- comes later. Although essential to the process of clinical research, a phase 1 study carries the greatest risk to study volunteers.
If the results from the phase 1 study indicate that a drug is safe, it advances to phase 2. In phase 2, investigators may expose a couple of hundred people to the experimental therapy, allowing for further characterization of the drug's safety profile. Phase 2 usually marks the first time that researchers examine whether a drug is efficacious. In the context of HIV clinical therapy research, efficacy means the ability of a drug to produce a therapeutic change at the maximum dose tolerated by humans.
Specifically, investigators are usually interested in changes in the CD4 T cell count or viral load or both. Changes in these surrogate markers are usually the study endpoints, and endpoints are the measurements by which a drug's efficacy is judged. Length of survival and a decrease in disease and disability are the endpoints that truly interest most physicians and patients. But measuring those kinds of clinical endpoints may take years. (The SMART study, for example, will measure clinical endpoints and will last from 6 to 8 years.) So to speed the process along, researchers often use surrogate markers instead of clinical indices as study endpoints. A beneficial change in surrogate markers is thought to predict or stand in for a beneficial change in clinical outcome. Conversely, a negative change in surrogate markers -- a decline in CD4 T cell count or a rise in viral load -- is thought to predict a negative change in clinical outcome.
That's usually true, but not always. Sometimes the therapy itself may alter the predictive value of a surrogate marker. For example, Steven Deeks, MD, has shown that HIV-infected patients who remain on a protease inhibitor do better clinically than their viral loads would indicate. Still, surrogate markers are generally accurate and their use dramatically reduces the amount of time needed to study potential new anti-HIV treatments.
If all goes well, an investigational drug will end up in a phase 3 study where several hundred people receive it for 6 months or longer. The purpose of this phase is to determine the efficacy of an agent in a large number of people. Phase 3 studies also allow for the detection of side effects that may not have emerged in the earlier, small studies. All phase 3 studies involve a control group. The control group does not receive the investigational agent but instead gets a placebo or, more likely nowadays, a standard of care regimen. In HIV care, standard of care treatment might include a protease inhibitor and 2 nucleoside analogs. The control group serves as a benchmark against which the investigational therapy is judged.
For phase 3 studies, investigators usually recruit patients who share certain characteristics in common. For instance, investigators may seek patients who have never before taken anti-HIV drugs, or who have a CD4 count above, or a viral load below, a specific cut-off value. The study volunteers are then randomized -- or assigned by chance -- to either the study group or the control group. Theoretically, randomization eliminates scientific bias and ensures that the only difference between the groups is the treatment they receive. In this way, researchers can compare the effects of the new treatment against the standard of care in a study group whose characteristics are similar to those of the control group. The requirements for participation in a study are known as the inclusion/exclusion criteria. These requirements are spelled out in the study protocol, a set of written instructions for the conduct of the study.
Once a study is complete, researchers analyze the results to see if the investigational therapy was inferior, equivalent, or superior to the standard of care. The difference in results, if any, is determined by comparing the statistical significance of changes in the study endpoints between the study group and the control group. For example, if the study endpoint was a change in viral load, investigators may ask, "What was the average change in the study group's viral load compared to the control group's? Is this difference statistically significant?"
Since the volunteers in both groups shared similar characteristics at the start of the study, their baseline viral loads should have been about equal. And since they were assigned by chance to either the study group or control group, any significant changes at the end of the study in their viral loads should be attributable to the treatment they received -- the treatment being the only difference between them. If, at the end of the study, the viral loads of the 2 groups are roughly the same, the researchers may conclude that the new therapy is equivalent to existing treatments: no better, no worse.
If the viral load of the study group is significantly lower, statistically speaking, than that of the control group, the researchers may conclude that the new treatment is superior to existing treatments. There is a potential catch, however. All statistically significant changes are not necessarily clinically significant. In other words, a statistically significant difference may not actually matter for the health of patients. To see why, consider this analogy. Suppose researchers offer 2 groups of 100 patients a drug designed to increase their earning power. Suppose that at the end of the trial, the study group has earned 5 cents and the control group has earned only 1 cent. Is this difference statistically significant? Yes, it is. But is it economically significant? No, not really. In the US economy, whether people have a nickel or a penny matters very little. Either way, they aren't buying much. Similarly, changes between groups in viral load could be statistically significant without being clinically significant.
Also, changes in viral load or CD4 T cell count do not tell the whole story. Other important considerations are a drug's convenience, tolerability, and safety. Say an investigational drug dramatically reduced viral load but had to be taken 5 times a day on an empty stomach, caused severe nausea, and put the patient at increased risk for liver cancer? Is it a good drug?
Often investigators examine the difference between a drug's treatment effect (its ability to reduce viral load or raise T cell count) and its clinical effect (which includes convenience and tolerability) by performing 2 sets of analysis. In one type of analysis known as on treatment, researchers consider only the treatment effect of a drug and count only those patients who actually take the medication. In other words, if a patient stays on treatment, regardless of side effects or inconvenience, how well does the medication affect the surrogate markers? In another type of analysis, known as intent to treat, researchers include the consequences of a drug's side effects and dosing schedule and count all the patients in a study, whether they take the medication or not. The difference in results between these 2 types of analysis can be sharp.
Say 500 hundred patients are given experimental drug A. If 250 of them take the drug faithfully and it lowers their viral loads to undetectable, an on-treatment analysis will show 100 percent efficacy, even though the other half of the patients did not take the drug because it made them sick. On the other hand, an intent-to-treat analysis -- designed to capture the "real world" results of a medication -- will count all the patients, including those who couldn't stomach the drug's side effects. By this method, the experimental drug will show efficacy of 50 percent, having been credited for working only in those patients who could take it.
For patients deciding whether to participate in a clinical trial, it is important to know what phase of study the drug is in and whether those who are assigned to the control arm will receive a placebo or standard of care. Also, will the study be blinded or open label? In other words, will the volunteers know what arm of the study they are in? In a blinded trial, the volunteers are not told whether they are in the study arm or the control arm. In an open-label trial, they know. Blinded trials adjust for bias on the part of study volunteers -- for example, when volunteers report side effects caused by the medication even though they're actually receiving a sugar pill -- but blinded trials also increase the uncertainty of participating in a study.
Another key question is, "What exactly do the researchers hope to learn from this trial?" At the heart of every clinical trial, regardless of the phase of study, there is a scientific question. Informed study volunteers find out what the question is and decide for themselves whether they think it is worth answering. The informed consent document explains the purpose of the study as well as the risks and benefits the study offers to volunteers.
In reviewing the results of a clinical trial, it is important to know what population of patients the study included. The results of a study can only be generalized to patients who share the characteristics of the study volunteers. For example, if a study included only volunteers who were naive to anti-HIV drugs, the results cannot be generalized to patients who are heavily experienced with anti-HIV drugs. Similarly, a drug that produces good results in volunteers who started out with 500 CD4 T cells may not work as well in patients whose T cell counts are less than 100. The characteristics of the patients who participated in the study are determined by reviewing the inclusion/exclusion criteria.
Finally, an essential issue is whether the results of a study are not only statistically significant, but also clinically significant. What endpoints did the researchers use for evaluating a drug's efficacy? Are results reported from an on-treatment analysis or an intent-to-treat analysis? Clinical trials are most helpful when they ask important questions, are well designed, recruit informed volunteers, and report clinically meaningful information.
Phases 1 to 3: Clinical studies are usually divided into 3 phases, with each phase serving to answer a specific question about an investigational therapy.
Study volunteers: In the context of HIV clinical research, study volunteers are men and women with HIV infection who meet a study's inclusion/exclusion criteria and agree to participate in the trial.
Surrogate markers: Laboratory tests, usually CD4 T cell count and viral load, that are used to estimate the health benefit of an investigational drug.
Study endpoints: The measures used to determine whether an investigational drug works. In HIV clinical research, the study endpoints are usually a change in surrogate markers.
Control group: A group of patients in a study who receive a "sugar pill" or conventional treatment rather than the experimental therapy. A control group is necessary to determine whether an investigational drug offers any benefit over existing therapies.
Placebo: The formal name for a "sugar pill." An inactive substance.
Standard of care: The level or type of care generally given to people with a particular condition.
Study group: A group of patients in a study who receive the experimental drug.
Inclusion/exclusion criteria: The list of characteristics that define who may and may not participate in a clinical study.
Protocol: A written description of a study and a guide to its conduct. Protocols are usually lengthy documents, sometimes running hundreds of pages in length.
Baseline: Refers to anything present at the start of a study. For example, if a patient enters a study with a viral load of 50,000 copies, this is the baseline viral load.
Treatment effect: The effect of an investigational drug on study endpoints (e.g., viral load or CD4 T cell count).
Clinical effect: The effect of an investigational drug on factors other than the study endpoints, including, for example, quality of life.
On treatment: A type of analysis in which researchers count only those patients who actually took the investigational drug for the study duration. This type of analysis excludes patients who failed to take the medicine, whether or not they had a good reason for doing so. On-treatment analysis is best for determining the treatment effect of a new therapy.
Intent to treat: A type of analysis in which researchers count every patient who participated in the study of an investigational drug. This type of analysis includes patients who failed to take the medicine, whether or not they had a good reason for doing so. Intent-to-treat analysis is best for determining the clinical effect of a new therapy.
Blinded: A type of study in which the volunteers are not told whether they are receiving the investigational drug or standard of care. If even the doctor does not know which patients are receiving the investigational drug, the study is called "double blinded." Blinded studies help to reduce bias.
Open label: A type of study in which the volunteers are told whether they are receiving the investigational drug or standard of care.
Informed consent: A document that explains, in easy-to-understand language, the purpose of a study and the risks and benefits it offers for volunteers. Every volunteer in a study should receive a copy of the informed consent document.
Generalized: The act of having taken results from a study and applied them to people who did not actually participate in the study. Results from a clinical trial may be generalized only to patients who share the characteristics of the study volunteers (e.g., similar viral loads or treatment history).
Where do potential anti-HIV drugs come from in the first place? Before any drug candidate reaches clinical trial, researchers must first identify and test it in laboratory studies. How do scientists find these candidates, and what's the process for testing them?
HIV has 3 enzymes that perform important work for the virus. (An enzyme is a protein that catalyzes, or facilitates, chemical reactions.) These enzymes are targets for therapy, and to date researchers have identified and successfully tested inhibitors of two of HIV's enzymes, namely reverse transcriptase and protease. But an inhibitor of the third enzyme, integrase, has so far eluded researchers. Several research groups are still looking for an integrase inhibitor, including one at the University of Houston, led by Kurt Krause, M.D., Ph.D.
According to Krause, the problem with finding an inhibitor for HIV integrase is two-fold. First, the structure of the integrase protein complex isn't fully understood, and the "active site" where a drug might attach is shallow. Reverse transcriptase, in contrast, has a shape similar to a hand with the fingers drawn in, and the palm -- the active site -- serves as a deep pocket to which drug can stick.
In the coming years, Krause (who is also interested in finding treatments for other infectious diseases, including tuberculosis) estimates that advances in technology and improvements in efficiency will reduce the cost of drug discovery by several million dollars. This should help to reduce the cost of treatments and make them more widely available, especially in the developing world.
Back to the RITA! Summer 2002 contents page.


Unknown cybercriminals breached the Lizard Squad's LizardStresser distributed denial of service (DDoS) service, which serves customers as an attack-for-hire tool. There are more than 14,000 registered users, with $11,000 collected via bitcoin to help pay for DDoS attacks against thousands of website URLs and IP addresses.
The Lizard Squad reportedly brought down Microsoft Xbox Live and Sony PlayStation Network (PSN) to promote its service.
Lizard Squad appears to have a number of younger members contributing to its cybercriminal operation, though several reported members have been arrested across the world. However, core members of the group still haven't been identified and arrested, so future cyberattacks are expected to continue against higher-profile targets.


text:
Nicolás de Jesús López Rodríguez (nascut el 31 d'octubre de 1936 a Barranca) és un cardenal dominicà de l'Església Catòlica que actualment serveix com a arquebisbe de Santo Domingo i presideix l'Església Catòlica Dominicana com a President de la seva Conferència Episcopal. És el segon dominicà en haver estat creat cardenal (el primer va ser Octavio Antonio Beras Rojas).

Biografia
Fill de Perfecto Ramón López Salcedo y de Delia Ramona Rodríguez, va néixer a Barranca, a la província de La Vega. Inicià els seus estudis de filosofia i teologia al Seminari Pontifici Sant Tomàs d'Aquino de Santo Domingo; també estudià al Centre Internacional per a la Formació Sociològica del Clergat a Roma, a la Pontifícia Universitat de Sant Tomàs de Roma, on es doctorà en ciències socials, i a la Pontifícia Universitat Gregoriana de Roma.

Orde sacerdotal
Ordenat prevere el 18 de març de 1961 per Francisco Panal Ramírez OFM, bisbe de La Vega. Serví com a vicari cooperador de la catedral de La Vega entre 1961-63. Continuà els seus estudis a Roma entre 1964 i 1966; i allà va obtenir la seva diplomatura de sociologia pastoral al Centre Internacional per a la Formació Sociològica del Clergar (CISIC) i la llicenciatura en ciències socials a la Universitat Pontifícia de Sant Tomàs d'Aquino in Urbe (Angelicum).

En concloure els seus estudis a Roma, tornà a La Vega, on treballà pastoralment entre 1966-68. Continuà els seus estudis a Roma entre 1968 i 1969. A la diòcesi de La Vega, entre 1968 i 1978, va ser assessor per a la pastoral diocesana per a la família i la joventut, assessor eclesiàstic del Moviment Familiar Cristià, del Moviment dels Cursets de Cristiandat, vicari diocesà per a la pastoral i pro-vicari general (1970–1976) i, finalment, vicari general (1976–1978).

A més del seu Castellà nadiu, parla italià, anglès, alemany, portuguès i llatí.

Episcopat
Va ser elegit i nomenat bisbe de San Francisco de Macorís pel Papa Pau VI el 16 de gener de 1978, va ser consagrat el 25 de febrer de 1978 pel cardenal Octavio Antonio Beras Rojas, arquebisbe metropolità de Santo Domingo, assistit per Juan Antonio Flores Santana, bisbe de La Vega, i per Jesús María De Jesús Moya, bisbe auxiliar de Santiago de los Caballeros. Va ser rector de la Universitat de San Francisco de Macorís entre 1979 i 1984. Promogut a la Seu Metropolitana i Primada d'Amèrica el 15 de novembre de 1981. Assistí a la VI Assemblea Ordinària del Sínode de Bisbes, celebrada a la ciutat del  Vaticà del 29 de setembre al 28 d'octubre de 1983 i a la II Assemblea Extraordinària del Sínode de Bisbes del 24 de novembre al 8 de desembre de 1985. Va ser elegit president de la Conferència Episcopal Dominicana al juliol de 1984 i serví com a President del Consell Episcopal Llatinoamericà (CELAM) entre el 25 d'abril de 1991 i el 1994.

Cardenalat
Creat cardenal el 28 de juny de 1991, rebé la birreta vermella i el títol de cardenal prevere de S. Pio X alla Balduina al consistori celebrat a Sant Pere del Vaticà el 28 de juny de 1991. Assistí a la I Assemblea Especial per a Europa del Sínode de Bisbes, a la Ciutat del Vaticà, del 28 de novembre al 14 de desembre de 1991; a la IV Conferència General de l'Episcopal Llatinoamericà, a Santo Domingo, del 12 al 28 d'octubre de 1992, on va ser un dels tres presidents delegats. Llegat pontifici al XLV Congrés Eucarístic Internacional celebrat a Sevilla, del 7 al 13 de juny de 1993.

Té el títol de Primat d'Amèrica perquè l'arxidiòcesi de Santo Domingo va ser la primera seu erigida al Nou Món.

Participà com a cardenal elector als conclaves de 2005 i 2013.

A la Cúria Pontifícia pertany a les Congregacions per al Clergat i  per als Instituts de Vida Consagrada i Societats de Vida Apostòlica, al Consell Pontifici per a les Comunicacions Socials i a la Comissió Pontifícia per a l'Amèrica Llatina (CAL).

Opinions
López Rodríguez organitzà una trobada de bisbes llatinoamericans celebrada a Santo Domingo el 1992, que va ser vista considerada d'opinions molt tradicionals i descrita com a "temporalista i piramidal" pel veterà observador vaticà Giancarlo Zizola. Tenia inclinacions integristes, insistint en que el parlament dominicà aprovés una llei instituint que la festa de l'Anunciació, el 25 de març, fos una festa nacional.

La controvèrsia del nomenament de l'ambaixador estatunidenc gai
El 27 de juny de 2013, mentre que estava sent entrevistat després de la benedicció d'una oficina del govern durant la seva reobertura, un periodista li preguntà sobre el nomenament de Barack Obama de James "Wally" Brewster, Jr., un homosexual declarat, com a ambaixador dels Estats Units a la República Dominicana. López Rodríguez expressà la seva oposició al nomenament d'un ambaixador homosexual, afirmant que això promouria l'aprovació del matrimoni homosexual a la República Dominicana. Moments després un altre periodista li preguntà per l'impàs diplomàtic i comercial entre Haití i la República Dominicana a causa de la grip aviar a la República Dominicana, vetant-se la importació d'ous i d'aviram. El cardenal López Rodríguez va riure i afirmà «Després de parlar sobre "maricons" i lesbianes, ara parlarem de pollastres.»  A la República Dominicana els homosexuals són habitualment referits desprectivament com a "ocells".

Honors
  Gran Creu de l'orde d'Isabel la Catòlica (Espanya)

Referències

Arquebisbes
Cardenals creats per Joan Pau II
Dominicans
Gran Creu de l'orde d'Isabel la Catòlica
Alumnes de la Pontifícia Universitat Gregoriana
Alumnes de la Universitat Pontifícia de Sant Tomàs d'Aquino
Membres de la congregació per al Clergat
Religiosos americans

redpajama_set_name: RedPajamaWikipedia


Freedom Healthcare is dedicated to the integrity of our service programs and the quality of our care. We are also committed to a culture of reporting and continuous improvement. We are driven to ensure that all organizational services are following best practices, and are conducted lawfully, ethically, and with compassion. We are committed to preserving and protecting the rights and dignity of our patients at all costs.
Freedom takes pride in providing quality patient care as well as ensuring customer and employee satisfaction. We believe in our employees, patients, and our patient's families having a voice, both for opportunity for improvement and/or for compliment. Our compliance committee regularly meets to stay in line with the company's CEO, the committee is committed to open communication with each and every individual regarding Freedom's services and programs. The "Compliance Needs to Know" link is our way of hearing the voices of our patients, their families, and our employees throughout our company and community.
Please utilize the confidential link below to voice any questions, concerns, suggestions for safety and improvement, or to report any misconduct or unethical practices or behaviors associated with the organization. You may remain anonymous. Thank you in advance for your time and consideration.
The facility accepts all patients who meet the admission criteria for the provided level of care without bias. All patients will be admitted to the least restrictive level of care as treatment should be undertaken only as necessary.


Starting your home-based business is a massive hurdle in itself, so congratulations if you've already jumped over it. You shouldn't rest on your laurels, though, as actually running your business is an even tougher task. Running it successfully is an even harder job still, but it's a job you must take on if you want your venture to provide you with a stable, good living.
To see what needs to be done to run a business successfully, make sure to check out the advice laid out below.
In this day and age, there's no excuse for your business's communication not to be as clear, concise and coherent as it would be in a traditional office space… but only if you do something about it. The way you and your employees communicate with one another is only going to be of the office standard if you resolve to improve your communication efforts. To the point, this means that you have to embrace the latest communicative tech.
A kind of tech that falls into this field and has proven to better home-based business communication is VoIP. By embracing this kind of tech, you will streamline your phoning system and tailor it to your business's needs. What's more, if you use a reputable VoIP provider like www.monstervoip.com, you'll also be able to make use of auto-provisioning. This tech feature will offer you the chance to instantly connect to extensions, rather than having to do it manually.
Your business isn't going to be successful if you're constantly taking two days to do a task that could have been completed in one. Speed up your working process by creating a concentration-promotion environment for yourself.
It's always advisable to have a daily cut off point at which you put your work down and resolve to relax. If productivity strikes you, though, and you have a brilliant idea for your business that simply cannot wait, don't be afraid to work. As long as you don't make a habit out of doing so, working whenever you feel productive, no matter the time or the place, is a sure-fire way to take your business to the next level.


YORK, PA., U.S.A., January 26, 2015: Extrusion and blow molding innovations that substantially reduce downtime, save energy, and enhance automation will be on display at the NPE2015 exhibit of Graham Engineering Corporation (Booth W3142). One highlight will be a demonstration of a new modular extruder design for medical processing that reduces job changeovers to a matter of minutes while preserving clean-room integrity.
American Kuhne Modular Quick-Change™ extrusion system. This system radically reduces time for job changes and removes the contaminating change-over activity from the clean-room environment by "hot-swapping" the barrel, screw, and die head. The modular extruder design allows the removal of the barrel and its replacement by a pre-heated barrel with pre-mounted tooling, all within a few minutes. The process will be a live demonstration at the show.
American Kuhne SMED Quick-Change™ design options. These extruder design options minimize change-over time and maximize production availability. Design options displayed on the energy-efficient American Kuhne E3 extruder at the show will include: rapid screw change via quick-mount pusher; fast material change via rotary hopper design; and instant tooling change via dual-hinge design.
Welex XSL Navigator® control system. An example of the synergy across the brands, XSL Navigator® is adapted from the award-winning XBM Navigator® control system, which Graham Engineering has used on its blow molding systems for nearly a decade. The XSL Navigator now offers intuitive navigation on a Welex line that makes it easy for the operator to set up quickly and work easily with the machine. Built on a 'soft' industrial PC, the system also enables processors to integrate data from a machine or line to an ERP system using client-server architecture. Features of XSL Navigator® will be illustrated on the roll-stand of a Welex sheet line.
Welex Converge CTS™ & Welex Hercules® sheet lines. Company representatives will also be available to take visitors through presentations of Welex Converge CTS™, dryer-less Conical Twin Screw technology for processors running PLA and PET, and Welex Hercules™ industrial sheet lines. Welex Converge CTS™ provides energy savings potential and superior IV retention versus parallel twin screw designs due to its higher pressure and torque capability, resulting in reduced dwell time. In its trial of the Welex Converge CTS™ system, NatureWorks measured virtually no loss of molecular weight through eight passes of its Ingeo® PLA resin.
Graham Revolution MVP™ rotary wheel extrusion blow molder. NPE2015 marks the commercial launch of the next generation of the flagship Graham wheel technology, Graham Revolution MVP™, that features an all-new, Modular Clamp design as well as a Variable Pitch option. These innovations enable flexibility and configurability to allow a range of package sizes to be manufactured on a single platform. And the modular clamp enables offline maintenance, repair, or replacement of a single clamp station to minimize downtime. The modular clamp design will be exhibited on the Graham High Speed wheel and the variable pitch feature will be demonstrated live at the show.
Graham Engineering Accumulator Head one-hour color or material change-over. The innovative design of Graham Engineering's spiral flow diverter head enables it to be lifted for easy cleaning. This enables color or material change-over to take place within one hour, saving material vs. conventional change-over that requires multiple, large shots, as well as valuable production time. Visitors can see the spiral flow diverter head in its retracted position as well as a short video of a live change-over.


See, what did I tell you? From your bedroom to the living room…your kitchen and home office, there are thrifty organization ideas for your entire home- and all you need to get started is head to your local Goodwill!
Lots of great upcycle projects! Looks like I need to take another Goodwill and thrifting trip. Lol. Love them all! Thanks so much for sharing my rustic utensil holder.


text:
#include <fstream>
#include <string>
#include <iostream>
#include <vector>

#include "cmd_flags.h"
#include "octree.h"
#include "filenames.h"
#include "math_functions.h"

using std::vector;
using std::string;
using std::cout;
using std::endl;
using cflags::Require;
const float kPI = 3.14159265f;

DEFINE_string(filenames, kRequired, "", "The input filenames");
DEFINE_string(output_path, kOptional, ".", "The output path");
DEFINE_string(axis, kOptional, "y", "The upright axis of the input model");
DEFINE_int(depth, kOptional, 6, "The maximum depth of the octree");
DEFINE_int(full_depth, kOptional, 2, "The full layer of the octree");
DEFINE_int(rot_num, kOptional, 12, "Number of poses rotated along the upright axis");
DEFINE_float(offset, kOptional, 0.55f, "The offset value for handing thin shapes");
DEFINE_bool(node_dis, kOptional, false, "Output per-node displacement");
DEFINE_bool(node_feature, kOptional, false, "Compute per node feature");
DEFINE_bool(split_label, kOptional, false, "Compute per node splitting label");
DEFINE_bool(adaptive, kOptional, false, "Build adaptive octree");
DEFINE_int(adp_depth, kOptional, 4, "The starting depth of adaptive octree");
DEFINE_float(th_distance, kOptional, 2.0f, "The threshold for simplifying octree");
DEFINE_float(th_normal, kOptional, 0.1f, "The threshold for simplifying octree");
DEFINE_bool(key2xyz, kOptional, false, "Convert the key to xyz when serialization");
DEFINE_bool(extrapolate, kOptional, false, "Exptrpolate the node feature");
DEFINE_bool(save_pts, kOptional, false, "Save the average points as signal");
DEFINE_bool(verbose, kOptional, true, "Output logs");

// OctreeBuilder shows a basic example for building an octree with a point cloud
class OctreeBuilder {
 public:
  bool set_point_cloud(string filename) {
    // load point cloud
    bool succ = point_cloud_.read_points(filename);
    if (!succ) {
      cout << "Can not load " << filename << endl;
      return false;
    }
    string msg;
    succ = point_cloud_.info().check_format(msg);
    if (!succ) {
      cout << filename << endl << msg << endl;
      return false;
    }

    // deal with empty points
    int npt = point_cloud_.info().pt_num();
    if (npt == 0) {
      cout << "This is an empty points!" << endl;
      return false;
    }

    // bounding sphere
    bounding_sphere(radius_, center_, point_cloud_.points(), npt);
    //radius_ = 128.0;
    //center_[0] = center_[1] = center_[2] = 128.0;

    // centralize & displacement
    float trans[3] = { -center_[0], -center_[1], -center_[2] };
    point_cloud_.translate(trans);
    if (FLAGS_offset > 1.0e-10f) {
      float offset = FLAGS_offset * 2.0f * radius_ / float(1 << FLAGS_depth);
      point_cloud_.displace(offset);
      radius_ += offset;
    }

    return true;
  }

  void set_octree_info() {
    octree_info_.initialize(FLAGS_depth, FLAGS_full_depth, FLAGS_node_dis,
        FLAGS_node_feature, FLAGS_split_label, FLAGS_adaptive, FLAGS_adp_depth,
        FLAGS_th_distance, FLAGS_th_normal, FLAGS_key2xyz, FLAGS_extrapolate,
        FLAGS_save_pts, point_cloud_);

    // the point cloud has been centralized,
    // so initializing the bbmin & bbmax in the following way
    float bbmin[] = { -radius_, -radius_, -radius_ };
    float bbmax[] = { radius_, radius_, radius_ };
    octree_info_.set_bbox(bbmin, bbmax);
  }

  void build_octree() {
    octree_.build(octree_info_, point_cloud_);
  }

  void save_octree(const string& output_filename) {
    // Modify the bounding box before saving, because the center of
    // the point cloud is translated to (0, 0, 0) when building the octree
    octree_.mutable_info().set_bbox(radius_, center_);
    octree_.write_octree(output_filename);
  }

 public:
  Points point_cloud_;
  float radius_, center_[3];
  OctreeInfo octree_info_;
  Octree octree_;
};

int main(int argc, char* argv[]) {
  bool succ = cflags::ParseCmd(argc, argv);
  if (!succ) {
    cflags::PrintHelpInfo("\nUsage: Octree.exe");
    return 0;
  }

  // file path
  string file_path = FLAGS_filenames;
  string output_path = FLAGS_output_path;
  if (output_path != ".") mkdir(output_path);
  else output_path = extract_path(file_path);
  output_path += "/";

  vector<string> all_files;
  get_all_filenames(all_files, file_path);

  #pragma omp parallel for
  for (int i = 0; i < all_files.size(); i++) {
    OctreeBuilder builder;
    bool succ = builder.set_point_cloud(all_files[i]);

    string filename = extract_filename(all_files[i]);
    if (!succ) {
      if (FLAGS_verbose) cout << "Warning: " + filename + " is invalid!\n";
      continue;
    }
    builder.set_octree_info();

    // data augmentation
    float angle = 2.0f * kPI / float(FLAGS_rot_num);
    float axis[] = { 0.0f, 0.0f, 0.0f };
    if (FLAGS_axis == "x") axis[0] = 1.0f;
    else if (FLAGS_axis == "y") axis[1] = 1.0f;
    else axis[2] = 1.0f;

    if (FLAGS_verbose) cout << "Processing: " + filename + "\n";
    for (int v = 0; v < FLAGS_rot_num; ++v) {
      // output filename
      char file_suffix[64];
      sprintf(file_suffix, "_%d_%d_%03d.octree", FLAGS_depth, FLAGS_full_depth, v);

      // build
      builder.build_octree();

      // save octree
      builder.save_octree(output_path + filename + file_suffix);

      // rotate point for the next iteration
      builder.point_cloud_.rotate(angle, axis);

      // message
      //cout << "Processing: " << filename.substr(filename.rfind('\\') + 1) << endl;
    }
  }

  if (FLAGS_verbose) cout << "Done: " << FLAGS_filenames << endl;
  return 0;
}

redpajama_set_name: RedPajamaGithub


text:
Q: Extract specific text within content I am working on email content which is dumped as dataframe value in row , and this has to be separated as different based on starting from:  untill till next from:
Sample data:
"______________________________________________
From:   Kumar M,
Sent:   Tuesday, 21 October 2014 7:30 AM
To: Deo, Ravinesh; G S, Venkatesh;
Cc: Monteleone, Elif; Kabyanga, Isaac
Subject:    FW: Please Approve the Qlik  Access.

Hi Ravi,

We will work on the providing David access to Ql and an email will be sent out once the access is set up.

Regards,
Santhosh

______________________________________________
From:   Deo, Ravinesh
Sent:   Tuesday, 21 October 2014 7:20 AM
To: Kabyanga, Isaac; Kumar M, Santhosh
Cc: Monteleone, Elif
Subject:    FW: Please Approve the Qlikview Access.

Hi Isaac/Santhosh,

Appreciate if you can grant access to David Dennis for GPA – Timor.

David is CEO Timor Leste.

Thanks
Ravi

_____________________________________________
From: Dennis, David (Timor)
Sent: Tuesday, 21 October 2014 11:34 AM
To: Deo, Ravinesh
Subject: FW: Please Approve the Q GPA Access.

Here you go - appreciate your help Rgds

______________________________________________
From:   Dennis, David (Timor)
Sent:   Thursday, 9 October 2014 11:33 AM
To: Buchanan, Geoffrey (Solomon Islands)
Subject:    Please Approve the Qlikview Access.

Hello,

Can you please review the attached form and click ' Manager Approval' to approve.

Thanks"

i have refered here and i have used this below code
ex <- gsub("^[from:](.*?)[from:]$", "",impordata$Problem.Description[i] )

but this gives all the mails in particular row !
Desired Output:
1
From:   Kumar M,
    Sent:   Tuesday, 21 October 2014 7:30 AM
    To: Deo, Ravinesh; G S, Venkatesh;
    Cc: Monteleone, Elif; Kabyanga, Isaac
    Subject:    FW: Please Approve the Qlik  Access.

    Hi Ravi,

    We will work on the providing David access to Ql and an email will be sent out once the access is set up.

    Regards,
    Santhosh

[2]
From:   Deo, Ravinesh
    Sent:   Tuesday, 21 October 2014 7:20 AM
    To: Kabyanga, Isaac; Kumar M, Santhosh
    Cc: Monteleone, Elif
    Subject:    FW: Please Approve the Qlikview Access.

    Hi Isaac/Santhosh,

    Appreciate if you can grant access to David Dennis for GPA – Timor.

    David is CEO Timor Leste.

    Thanks
    Ravi

[3]
 From: Dennis, David (Timor)
    Sent: Tuesday, 21 October 2014 11:34 AM
    To: Deo, Ravinesh
    Subject: FW: Please Approve the Q GPA Access.

    Here you go - appreciate your help Rgds

[4]
From:   Dennis, David (Timor)
    Sent:   Thursday, 9 October 2014 11:33 AM
    To: Buchanan, Geoffrey (Solomon Islands)
    Subject:    Please Approve the Qlikview Access.

    Hello,

    Can you please review the attached form and click ' Manager Approval' to approve.

    Thanks"

and used regmatches
#Converted a row as vector to apply regmatches
vec <- as.vector(impordata$Problem.Description[1])

matc <-regmatches(vec, gregexpr("(^[from:]).*?($[from:])", vec, perl = TRUE))

No use of this too,
Can somebody correct it ! or provide some help

A: You could use strsplit fucntion.
> strsplit(gsub("(?s)^_+\\s+", "", x, perl=T) , "_+\\s*(?=From:)", perl=T)[[1]]
[1] "From:   Kumar M,  \nSent:   Tuesday, 21 October 2014 7:30 AM\nTo: Deo, Ravinesh; G S, Venkatesh;\nCc: Monteleone, Elif; Kabyanga, Isaac\nSubject:    FW: Please Approve the Qlik  Access.\n\n\nHi Ravi,\n\nWe will work on the providing David access to Ql and an email will be sent out once the access is set up.   \n\nRegards,\nSanthosh\n\n"
[2] "From:   Deo, Ravinesh  \nSent:   Tuesday, 21 October 2014 7:20 AM\nTo: Kabyanga, Isaac; Kumar M, Santhosh\nCc: Monteleone, Elif\nSubject:    FW: Please Approve the Qlikview Access.\n\nHi Isaac/Santhosh,\n\nAppreciate if you can grant access to David Dennis for GPA – Timor.\n\nDavid is CEO Timor Leste.\n\nThanks\nRavi\n\n"
[3] "From: Dennis, David (Timor) \nSent: Tuesday, 21 October 2014 11:34 AM\nTo: Deo, Ravinesh\nSubject: FW: Please Approve the Q GPA Access.\n\nHere you go - appreciate your help Rgds\n\n"
[4] "From:   Dennis, David (Timor)  \nSent:   Thursday, 9 October 2014 11:33 AM\nTo: Buchanan, Geoffrey (Solomon Islands)\nSubject:    Please Approve the Qlikview Access.\n\nHello,\n\nCan you please review the attached form and click ' Manager Approval' to approve.\n\nThanks"

A: Take a look at strsplit:
splits <- strsplit(paste0(vec, collapse = "\n"), "_{45}")[[1]][-1]
cat(splits)
cat(splits[1])
# _
# From:   Kumar M,
# Sent:   Tuesday, 21 October 2014 7:30 AM
# To: Deo, Ravinesh; G S, Venkatesh;
# Cc: Monteleone, Elif; Kabyanga, Isaac
# Subject:    FW: Please Approve the Qlik  Access.
#
#
# Hi Ravi,
#
# We will work on the providing David access to Ql and an email will be sent out once the access is set up.
#
# Regards,
# Santhosh

redpajama_set_name: RedPajamaStackExchange


24 hours security with CCTV.
A credit card number is required to confirm / guarantee the booking.
Smoking is not permitted within the residences premises. Penalty of RM350.00 will be imposed if caught.
Full payment for room is required upon check in and miscellaneous deposit will be collected.
Once Checked In & Paid NO Shorten Stay & Refund Allowed.
No parties or large gatherings are permitted in the apartments. A surcharge of RM 500.00 will apply if such activities were found held.
Housekeeping Service Will Be Provided 1 Week 3 Times or on Alternate Days Basis – NO Service On Sundays.
In compliance with the policy of the Malaysian Government, a Tourism Tax of RM10 flat rate per unit per night will be imposed to all foreign guests.This ruling shall be effective 1st September 2017 and will be reflective separately in the room bill and for room rate. The tax is not inclusive of the service charge and GST.Malaysian are exempted from paying the levy.


Immigration Minister, Sylvi Listhaug of Fremskrittsparti (Frp) reacted strongly to the controversial Imam, Said Rageah being invited to speak to a Muslim community in Stavanger.
Eighteen years ago, a talk by the Imam was canceled in Sweden after controversial statements made by him came to light, wrote Rogaland's newspaper.The Islamic Training Center in Stavanger, has invited the Islamic pastor from Canada to speak to the Muslim community in Hillevåg.
Rageah said, among other things, that those who blasphemed should be sentenced to death, and that women should only leave the home if necessary.
'If this Imam is a supporter of the death penalty for blasphemy, then it is completely reprehensible, and incompatible with Norwegian and Western values,' wrote Listhaug in an email to the newspaper.
The government also wrote that they are working on a bill that enables a list of hate preachers to be denied entry to Norway because they pose a threat to public order. Listhaug pointed out that Denmark already has such a list.
The Islamic Training Center is the largest Muslim community in Rogaland, and is a member of the Muslim Council in Rogaland. NRK news has visited the mosque in Stavanger, where Rageah was to hold lectures,but the mosque has not yet allowed the minister to interview them.
'When we tolerate Hege Storhaug, and other critics of Islam, we must tolerate Muslims inviting those who hold violent opinions, as long as they don't encourage legal violations,' said Council spokesperson, Summer Ejaz.
Be the first to comment on "Listhaug critical of Imam invitation, and will deny hate speech religious leader entry to Norway"


text:
Nissan, Sumitomo Partner to Back Japan Localities in Green Push
River Davis
9:17 AM IST, 22 Dec 2021 1:39 PM IST, 22 Dec 2021 9:17 AM IST, 22 Dec 2021 1:39 PM IST, 22 Dec 2021
(Bloomberg) -- Nissan Motor Co. is teaming up with trading giant Sumitomo Corp. to work with local governments in Japan seeking to become carbon-free, offering consulting and planning for energy and mobility services.
The two, along with auto-leasing firm Sumitomo Mitsui Auto Service Co., plan to contribute expertise to municipalities lacking the knowledge and personnel needed to plan and roll out renewable energy, they said in a joint statement Wednesday.
They'll work with local governments to introduce electric vehicles, electricity derived from renewable sources and car-sharing systems for residents and local businesses.
The announcement comes as Japan seeks to deliver on its recent climate pledge to achieve net-zero carbon emissions by 2050. The push to go green is spreading on a local level as well with around 500 villages, towns, wards and cities including Tokyo having committed to achieve carbon-neutrality over the next three decades.
"We will powerfully support these local governments' decarbonization efforts," Nissan Chief Executive Officer Makoto Uchida said at a briefing Wednesday.
Separately, Nissan — which is itself aiming to make the whole of its operations carbon neutral by 2050 — will begin selling electricity "generated from virtually 100% renewable energy" to its employees in Japan starting next fiscal year, the company announced Wednesday.
Based on the results of field tests, Nissan will also work to identify a partner to offer "attractive power pricing plans" to owners of Nissan EVs such as its Leaf and upcoming Ariya model.
Stay Updated With Business News On BloombergQuint

redpajama_set_name: RedPajamaCommonCrawl


HMD Global may not be a shining example of how to cooperate with the Android developer community, but they at least seem to be committed to software updates. Nokia devices have been receiving a lot of Android Pie updates lately. The Nokia 6.1 recently received Android Pie and now the Nokia 7.1 is joining the ranks.
The Nokia 7.1 was announced in October as part of the Android One program. It's available in the U.S. among other places. The device has a Snapdragon 636 SoC, 3/4GB of RAM, 32/64GB of storage, 5.8-inch display, dual rear cameras, and more. It's a nice Android One device, but it launched with Android 8.1 Oreo. Thankfully, Android Pie is already available.
Chief Product Officer at HMD Global, Juho Sarvikas, announced on Twitter that Android Pie is rolling out for the Nokia 7.1. The update brings the usual array of Pie goodies, such as Adaptive Battery, App Actions, Slices, Adaptive Brightness, and so much more. Since this is an Android One device, Nokia keeps the software pretty clean. This is also how Nokia devices are able to receive Pie before many others.
Juho notes that this is a phased rollout, so it will not be available on all devices at the start. You may have to wait a while for the OTA. However, if you don't want to wait, you can download the OTA manually with the link below. Big thanks to XDA Recognized Developer yshalsager for the link!


text:
Plays and Playscripts
Faust By Johann Wolfgang von Goethe
Johann Wolfgang von Goethe (1749-1832) adopted the story of the wandering conjuror who accepts Mephistopheles's offer of a pact, selling his soul for the devil's greater knowledge.
Faust Summary
Faust: Pt.1 by Johann Wolfgang von Goethe
The legend of Faust grew up in the sixteenth century, a time of transition between medieval and modern culture in Germany. Johann Wolfgang von Goethe (1749-1832) adopted the story of the wandering conjuror who accepts Mephistopheles's offer of a pact, selling his soul for the devil's greater knowledge; over a period of 60 years he produced one of the greatest dramatic and poetic masterpieces of European literature. David Luke's recent translation, specially commissioned for The World's Classics series, has all the virtues of previous classic translations of Faust, and none of their shortcomings. Cast in rhymed verse, following the original, it preserves the essence of Goethe's meaning without sacrifice to archaism or over-modern idiom. It is as near an 'equivalent' rendering of the German as has been achieved.
Faust Reviews
"Luke demonstrates a rare genius in his translation: he maintains essential meaning, retains meter, and recreates rhyme.... As far as his introduction is concerned, it is a marvel of lucid exposition."--The German Quarterly
Customer Reviews - Faust


text:
Trends in Glass Fiber and Composite Industries
Sponsored by MichelmanApr 12 2018
Can you start by introducing yourself, telling us about the background of Michelman?
I work as the Group Marketing Director of the Industrial Manufacturing (IM) group at Michelman, a global manufacturer and developer of environmentally-friendly and water-based advanced materials for industry. These include fiber or fabric sizing and several fiber surface treatments.
In addition to that, we provide services and solutions for the coatings, printing and packaging markets. Michelman has evolved and became diverse since it has developed the first water-based coatings for the paper converting industry. Due to this, the company can meet the needs of its valued customers and the various industries.
In 2011, Michelman formally launched their Fibers & Composites Global Business and now develop fiber sizing, binders and lubricants used by fiberglass and other reinforcing fiber manufacturers. Michelman are the only producer of fiber sizing to have production facilities throughout the world and continue to make investments in our future.
My background includes over thirty years of engineering plastics, chemical intermediate manufacturing, fiberglass manufacturing, polymer composite component manufacturing, engineered wood release coatings and polymer science. I have worked for organizations such as LNP Engineering Plastics Inc., General Electric Plastics, Ticona Polymers Inc., Celanese, Johns Manville, and Arizona Chemical. I have commercialized numerous new products in the fields of plastics and composites while being a co-author of a patent in the field of polymer science.
In addition to my role of advancing and promoting our water-based technology, Michelman has provided me additional opportunities to play an active role in various industry groups (IACMI, SPE Composite Division, etc.) I also actively work with university-industry collaborations to advance the composite industry forward with the development and commercialization of new products.
Could you tell us about the technology you specialize in and its impact?
Established in 1949, Michelman is a company popular for its abilities in the area of emulsification and polymer dispersion technologies. Its success when it comes to polypropylene sizing applications has paved the way for it to acquire the Hydrosize® Technologies, Inc. Since then, the company has continuously innovated its process and materials capabilities.
Michelman produces a diverse line of fiber sizing and surface treatments that are utilized by fiber companies and composite producers to manufacture more durable, lightweight and sturdy composite parts. Fiber sizing has been widely used to protect the fiber during the production process and also, to contribute to the high-quality performance of these parts.
The company's technology provides an avenue for the clients to design the reinforcement fiber's surface chemistry to the matrix resin's chemistry. Hence, the interfacial adhesion between the polymers and fibers are optimized.
Michelman became the interfacial adhesion experts due to the company's expertise and the popular Hydrosize® brand. The products are diverse and are enhanced for a wide range of thermoset, thermoplastic and high temperature resin matrices. Also, the materials are utilized in glass fiber, basalt fiber, natural fiber, and carbon fiber production.
The fiber sizing improves the properties of composite fibers, including impact, increased heat and fatigue resistance suitable for a wide variety of end-use applications in various industries like aerospace, automotive, defense and electronics industries.
For instance, the following are graphs for the high temperature thermal stability of 3 Michelman products:
Hydrosize® HP-1632 - An APE free specialty coating and high temperature fiber sizing that gives outstanding abrasion resistance and adhesion properties.
Hydrosize® U5-01 - A specialty coating and fiber sizing that provides improved higher strength, thermal stability, and elongation to break. This product is also APE free.
Hydrosize® PA845H – Manufactured to be utilized as a carbon fiber sizing for high temperature applications, this product enhances adhesion to many polyamide matrices.
Who are Michelman's main customers?
Michelman is a company that has established relationships with other companies throughout the composite industry value chain. The firm's main focus is geared toward connecting with technical textile producers and fiber manufacturers.
Michelman usually connect and engage with larger and mid-sized glass and carbon fiber companies. Also, we call on alternative fiber producers, including those who produce aramid, basalt, natural and synthetic fibers. We have relationships and work with textile producers who are experts in finishing veils, scrums, pre-pregs/organo-sheets and reinforcement fabrics. Subsequently, Michelman has relationships with compounders and other downstream manufacturers to aid in their interfacial adhesion issues. Sometimes, the company works directly with these companies but also act as a bridge for fiber and fabric manufacturers.
Where is Michelman currently seeing growth in its markets and what trends are influencing that growth?
The company has been a part of the traditional glass fiber market over the past years and has gained an outstanding reputation for creating solutions for its valued clients. The products include fiber sizing for both continuous and chopped strand fiber that act as the thermoset and thermoplastic composite markets.
These markets tend to grow because of the many company and industry drivers like automotive lightweighting and energy efficiency. Recently, carbon fiber and other alternative fibers have started to gain popularity in the composite industry. The need for automotive lightweighting is now being tackled across the various areas of the structural components industry, including functional components.
For example, the producers of under the hood components, which need high heat performance and resistance to a diverse array of fluids and chemicals, are considering using composite alternatives, instead of traditional metals.
Under high heat, the polyamide based composites should perform effectively. Also, they should provide for water/ethylene glycol exposure for long periods. Michelman has focused mainly in developing solutions for this particular space. The most recent portfolio of the company, Hydrosize® Link, meets these obstacles and challenges.
Hydrosize® Link is a series of polyurethane film formers that are developed to provide improved reactivity with the polyamide matrix resin. These high-quality and best-performing sizing products are added during the process of fiber manufacturing. They are also triggered during compounding to heighten the interfacial adhesion between the polymer matrix and fiber. This enhanced adhesion gives outstanding resistance to the glycol fluids utilized in coolant systems. Also, it allows the car makers to manufacture cost-effective components that are high-performing and conform to the specifications needed.
Below is the graph that shows how Hydrosize® Link gives an excellent resistance to glycol fluids used in coolant systems.
In addition, continuous fiber thermoplastic processes and formats, like that of thermoplastic prepreg or organo-sheets, are starting to become well-known in the composites industry. These applications benefit from higher fiber aspect ratios, hence, allowing for a better and stronger structural performance.
Also, thermoplastic composites are more striking because of their fast part forming cycle times, recyclability, absorption of low-moisture, and high impact strength. Some industries, including the aerospace, defense and transportation industries, are expected to push the thermoplastic composites further due to innovations and improvements in new process technologies.
Lightweighting in car manufacturing can help companies attain enhanced mileage targets. Also, thermoplastic composites play a pivotal role because they offer a promising cost and performance balance, and they have fast cycle times. Moreover, these materials and processes continue to improve and evolve, hence, OEMs can now consider them for more structural uses.
I understand that Michelman collaborated in a project to manufacture a composite overwrapped pressure vessel (COPV) made with recycled carbon fiber, could you tell us a bit more about this?
Michelman has collaborated with Vartega Inc., a manufacturer of recycled carbon fiber. The company also connected with Steelhead Composites, a producer of light-weight and durable cylinders for fuel storage. These collaborations aim to develop custom sizing that enables the first ever composite overwrapped pressure vessel (COPV) to be created from continuous recycled carbon fiber. Michelman's sizing provided the opportunity for the recycled carbon fiber to attain higher and better performance than virgin carbon fiber when creating the COPV.
Before this breakthrough, recycled carbon fiber has been used as chopped fiber or filler or only as secondary products. However, with this successful collaboration, it has provided a milestone for the industry. The effective fabrication of this COPV shows that reclaimed carbon fiber can be successfully recycled to manufacture high quality OEM components and products.
Michelman has a wide range of fiber sizing solutions designed to use with recycled and reclaimed carbon fiber, like the Hydrosize® U2-04, Hydrosize® HP3-02 and Hydrosize® PA845H. These designs are compatible with previously sized recycled carbon fiber, usually epoxy, as well as with most common thermoplastic resins like polyamide (PA or nylon), high temperature thermoplastics like PEEK and PPS, and polycarbonate (PC).
Is there demand for more sustainable materials?
In fact, there is no growing interest in the usage of more sustainable materials, including biopolymers like polylactide acid (PLA) and biofibers like Kenaf, Hemo and Bamboo. Michelman has been an active member of various companies and government agencies to develop custom surface treatments or sizing that will provide better incorporation of these biofibers into polymer composites. Natural fibers are more challenging because of moisture sensitivity. The company is connecting with its customers and partners to address this problem.
What new materials and technologies are currently entering the advanced materials sector and what do you foresee on the horizon?
In the advanced material and composites sector, there are continuous innovations and developments taking place. The union of process and materials innovation paves the way for new product ideas geared toward higher performance and enhanced environmental impact. For instance, many material and process technologies like unidirectional tape, comingled fabrics, fast-cure thermosets, and liquid thermoplastics, among others, are driving the industry further into more success.
The material and process combination and innovation allow the composite industry to target more or higher challenging structural and functional applications, which have usually been the domains of metals. Definitely, the use of an optimized sizing and surface treatment will help in creating higher performing composites.
What are the major challenges facing the sector?
One of the primary trials for the composite industry is the capability to design and validate the new materials and processes. One solution to this predicament is to develop the software utilized to model and predict the behavior of these anisotropic materials.
The Department of Energy (DOLE) is catalyzing production innovation by supporting the Institute for Advanced Composites Manufacturing (IACMI). Purdue University is the leader in the institute's modeling and simulation area. At the moment, it is researching and developing a plan for the integration of several software applications into just one organized software composite modeling system.
The capacity to mimic and model composite material performance aids in the acceleration of adoption and decreases the cost to validate through elimination of the need to prototype part design. Also, the new drivers of new levels of partnership through the industry are brought about by understanding how these processes and materials work together and acknowledging the positive economic impact of fiber and fabric sizing within manufacturing.
Some government-supported programs like the IACMI and Composites Germany – AVK Federation of Reinforced Plastics, are initiating forums where sizing companies, material firms, processors, and original equipment manufacturers (OEMs) can work hand in hand in open discussions.
Michelman's collaboration with Steelhead and Vartega to create the composite overwrapped pressure vessel using continuous recycled carbon fiber works as a paradigm of IACMI-enabled industry supply chain collaboration.
What's the next step for Michelman?
Nowadays, there is a higher demand for composite materials because these are more durable, light-weight and economical than conventional materials. Composite materials conform with Michelman's purpose of 'Innovating a Sustainable Future with Expert Materials' and also, align well with the global trends of energy efficiency and sustainability.
Michelman has embarked on a solution to connect and network throughout the value chain that will open the doors to anticipate market trends. In addition to that, in order to develop solutions for future needs. The strategy includes creating an important commitment to raise the capabilities through the attainment of many sophisticated and modern equipment. These will pave the way to more effectively develop and partially validate product concepts. Michelman hopes to improve the level of innovation and adoption of modern concepts in the composites industry.
How do you think that the research community, industry, manufacturers, and government best work together to enable new developments?
Some agencies like Composites Germany – AVK Federation of Reinforced Plastics and the IACMI, open the doors for various groups, industries, government agencies and the academe to unite and work together with the common goal of advancing the use of composites to make products stronger, more energy efficient and light-weight. These public-private partnerships aid in the acceleration of the development and adoption of composites through the creation of opportunities for open forums and collaborations. These have led to faster development and implementation of modern manufacturing technologies.
Polymer composites allow the OEM's outstanding flexibility in their design freedom. Aside from that, it provides great versatility in their ability to make assembly operations simple and integrate functionality. As a result, there is an acceleration in the acquisition of new concepts. Moreover, some research and development programs aid in the development of a strong value chain and support a growing advanced composites industry.
In your opinion, what are the other hot topics in reinforced plastics, and indeed other materials, right now?
The other topics of interest in reinforced plastic include designing for end-of-life and recyclability, possibility of incorporating nanomaterials, and multi-million solutions like combinations of many metals and reinforced plastics. The composites continue to play a crucial role in advanced materials innovation and research in disruptive technologies. One illustration of this effort are nanomaterials.
Furthermore, sustainable materials and a decrease of the environmental impact will continue to drive innovation in reinforced plastics. Lightweighting for car manufacturers and aerospace applications will continue to be an industry driver. Lastly, metal replacement is increasing across several industries such as construction as the demand for composites has increased, and has penetrated various market spaces.
This information has been sourced, reviewed and adapted from materials provided by Michelman.
For more information on this source, please visit Michelman.
Michelman. (2020, May 06). Trends in Glass Fiber and Composite Industries. AZoM. Retrieved on January 21, 2021 from https://www.azom.com/article.aspx?ArticleID=15662.
Michelman. "Trends in Glass Fiber and Composite Industries". AZoM. 21 January 2021. <https://www.azom.com/article.aspx?ArticleID=15662>.
Michelman. "Trends in Glass Fiber and Composite Industries". AZoM. https://www.azom.com/article.aspx?ArticleID=15662. (accessed January 21, 2021).
Michelman. 2020. Trends in Glass Fiber and Composite Industries. AZoM, viewed 21 January 2021, https://www.azom.com/article.aspx?ArticleID=15662.
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Beautiful ladies, dressed to impress. The Carlo Rino Fashion Show at Aman Central leaves you wanting more. Check out the Carlo Rino 2018 A/W Collection now @ https://shop.carlorino.net or head to a Carlo Rino Boutique today.


The idea is pretty simple, post your various gaming IDs for games and services i.e. Steam.
And if you want to add some information just update your post instead of making a new one.
Last edited by Techpriest on Tue Mar 05, 2013 2:31 am, edited 1 time in total.
Also More to come later, Maybe.
Steam and Origin are both Steakumz!
I apologize for my grammar, I'm English and I'm on the internet but if you can understand who cares, right?
Last edited by kolemsai on Tue May 07, 2013 9:49 am, edited 1 time in total.
Typically if you see Knux or Theranos in a game, it's probably me.
You can call me Knux or Theranos.
"The Emperor, eternal may He ever be. By his side I will never leave. For this day I serve Him well. Tomorrow I die with His battle yell "
Edit: Had to update the steam account. I forgot I changed my profile name.
Last edited by Gorione on Wed Oct 02, 2013 1:15 pm, edited 1 time in total.
Add me on either. I re-upped my Battle.net account. Weeeee!
Last edited by CaleeaLu on Fri Dec 06, 2013 10:31 pm, edited 2 times in total.
I can't find you on Steam. There are 3 vibles and none of them are Swedish.
I can't find you either, Shawn.


text:
Zac Taylor gives injury updates on Higgins, Boyd, Hendrickson and Hurst
Mostly positive, all things considered.
By Jason Marcum@marcum89 Dec 12, 2022, 3:32pm EST
Share All sharing options for: Zac Taylor gives injury updates on Higgins, Boyd, Hendrickson and Hurst
The Cincinnati Bengals may have won Sunday, but it was costly.
Throughout the 23-10 win over the Cleveland Browns, Cincinnati watched Tyler Boyd (dislocated finger), Joseph Ossai (shoulder) and Trey Hendrickson (broken wrist) all get banged up, while Tee Higgins was essentially a late scratch after injuring his hamstring in pregame warmups.
Thankfully, one of these injuries are believed to be season-ending, and it's possible they're all out for just a week or two, at least based on what Zac Taylor said during his Monday press conference.
Bengals snap counts vs. Browns: Emergency situation at wide receiver
5 things we learned from the Bengals' cathartic win over the Browns
First off, Taylor said no one is going on injured reserve, suggesting that all of these guys will be back within the next four weeks.
Hendrickson's wrist issue isn't serious, and Taylor isn't ready to rule out the star pass-rusher for the team's Week 15 tilt vs. the Tampa Bay Buccaneers.
As for Boyd and Higgins, they're now day-to-day, while there's no real update on Ossai.
Making matters worse is Cincinnati is star tight end Hayden Hurst is already out with a calf injury, and he remains week-to-week.
Injury updates per Zac Taylor:
Trey Hendrickson: wrist issue, "will see where it's at as we go through the week." Not ready to rule out vs TB
Tyler Boyd: dislocated finger, day-to-day
Tee Higgins: hamstring, day-to-day
Joseph Ossai: shoulder, see how the week goes
— Marisa Contipelli (@BengalsMarisa) December 12, 2022
On Hayden Hurst: "We'll continue to take that week to week... See where that's at this week."
— Charlie Goldsmith (@CharlieG__) December 12, 2022
While there's a real chance most, if not all of these guys are sidelined for Week 15, it doesn't sound like any of them will be out more than a few weeks. The problem is Cincinnati's last four games are against playoff-contending teams in the Bucs, New England Patriots, Buffalo Bills, and Baltimore Ravens.
The key for Cincinnati will be getting to Week 18 with a chance to clinch the AFC North with a win over Baltimore and having all of these guys back. If that happens, you have to like the Bengals' odds of winning the North for a second-straight year, which would be the first time ever the franchise has won consecutive division titles.
Cincinnati Bengals vs. Tampa Bay Bucs: Everything to know for Week 15 at Raymond James Stadium
Bengals OL Film Grades Week 15
Bengals open as road favorites against Buccaneers


Tesco Mobile is currently blocking the portable hotspot feature in the new iOS 4.3 for the iPhone 4.
The update, which was revealed at the same time as the iPad 2, lets users send their data allowance from their phone via Wi-Fi signal.
However, some TechRadar readers complained the portable hotspot feature wasn't there on their Tesco Mobile-powered iPhone 4, despite updating to the new iOS.
"Tesco Mobile doesn't currently support the functionality for personal Wi-Fi spots but plans to be able to introduce this soon for customers," said the spokesperson.
The source didn't confirm whether the new feature will cost anything extra to users though, something most other networks are doing to the ire of its customer base.