Sensors
Table of Contents:
- AccelerometerSensor
- BarcodeScanner
- Barometer
- Clock
- GyroscopeSensor
- Hygrometer
- LightSensor
- LocationSensor
- NearField
- OrientationSensor
- Pedometer
- ProximitySensor
- Thermometer
AccelerometerSensor
Properties
- Available
- Available property getter method (read-only property).
- Enabled
- If true, the sensor will generate events. Otherwise, no events are generated even if the device is accelerated or shaken.
- LegacyMode
- Prior to the release that added this property the AccelerometerSensor component passed through sensor values directly as received from the Android system. However these values do not compensate for tablets that default to Landscape mode, requiring the DFS - AppMaker programmer to compensate. However compensating would result in incorrect results in Portrait mode devices such as phones. We now detect Landscape mode tablets and perform the compensation. However if your project is already compensating for the change, you will now get incorrect results. Although our preferred solution is for you to update your project, you can also just set this property to “true” and our compensation code will be deactivated. Note: We recommend that you update your project as we may remove this property in a future release.
- MinimumInterval
- The minimum interval, in milliseconds, between phone shakes
- Sensitivity
- A number that encodes how sensitive the accelerometer is. The choices are: 1 = weak, 2 = moderate, 3 = strong.
- XAccel
- Returns the acceleration in the X-dimension in SI units (m/s^2). The sensor must be enabled to return meaningful values.
- YAccel
- Returns the acceleration in the Y-dimension in SI units (m/s^2). The sensor must be enabled to return meaningful values.
- ZAccel
- Returns the acceleration in the Z-dimension in SI units (m/s^2). The sensor must be enabled to return meaningful values.
Events
- AccelerationChanged(xAccel,yAccel,zAccel)
- Indicates the acceleration changed in the X, Y, and/or Z dimensions.
- Shaking()
- Indicates the device started being shaken or continues to be shaken.
BarcodeScanner
Properties
- Result
- Text result of the previous scan.
- UseExternalScanner
- If true App Inventor will look for and use an external scanning program such as “Bar Code Scanner.”
Events
- AfterScan(result)
- Indicates that the scanner has read a (text) result and provides the result
Methods
- DoScan()
- Begins a barcode scan, using the camera. When the scan is complete, the AfterScan event will be raised.
Barometer
Properties
- AirPressure
- The air pressure in hPa (millibar), if the sensor is available and enabled.
- Available
- Specifies whether or not the device has the hardware to support the Barometer component.
- Enabled
- If enabled, then device will listen for changes.
- RefreshTime
- The requested minimum time in milliseconds between changes in readings being reported. Android is not guaranteed to honor the request. Setting this property has no effect on pre-Gingerbread devices.
Events
- AirPressureChanged(pressure)
- Called when a change is detected in the air pressure (provided in hPa).
Clock
Properties
- TimerAlwaysFires
- Will fire even when application is not showing on the screen if true
- TimerEnabled
- Fires timer if true
- TimerInterval
- Interval between timer events in ms
Events
- Timer()
- Timer has gone off.
Methods
- AddDays(instant,quantity)
- An instant in time some days after the argument
- AddDuration(instant,quantity)
- An instant in time some duration after the argument
- AddHours(instant,quantity)
- An instant in time some hours after the argument
- AddMinutes(instant,quantity)
- An instant in time some minutes after the argument
- AddMonths(instant,quantity)
- An instant in time some months after the argument
- AddSeconds(instant,quantity)
- An instant in time some seconds after the argument
- AddWeeks(instant,quantity)
- An instant in time some weeks after the argument
- AddYears(instant,quantity)
- An instant in time some years after the argument
- DayOfMonth(instant)
- The day of the month
- Duration(start,end)
- Milliseconds elapsed between instants
- DurationToDays(duration)
- convert duration to days
- DurationToHours(duration)
- convert duration to hours
- DurationToMinutes(duration)
- convert duration to minutes
- DurationToSeconds(duration)
- convert duration to seconds
- DurationToWeeks(duration)
- convert duration to weeks
- FormatDate(instant,pattern)
- Text representing the date of an instant in the specified pattern
- FormatDateTime(instant,pattern)
- Text representing the date and time of an instant in the specified pattern
- FormatTime(instant)
- Text representing the time of an instant
- GetMillis(instant)
- The instant in time measured as milliseconds since 1970.
- Hour(instant)
- The hour of the day
- MakeDate(year,month,day)
- Allows the user to set the clock to be a date value. Valid values for the month field are 1-12 and 1-31 for the day field.
- MakeInstant(from)
- An instant in time specified by MM/dd/YYYY hh:mm:ss or MM/dd/YYYY or hh:mm
- MakeInstantFromMillis(millis)
- An instant in time specified by the milliseconds since 1970.
- MakeInstantFromParts(year,month,day,hour,minute,second)
- Allows the user to set the date and time to be displayed when the clock opens. Valid values for the month field are 1-12 and 1-31 for the day field.
- MakeTime(hour,minute,second)
- Allows the user to set the time of the clock - Valid format is hh:mm:ss
- Minute(instant)
- The minute of the hour
- Month(instant)
- The month of the year represented as a number from 1 to 12)
- MonthName(instant)
- The name of the month
- Now()
- The current instant in time read from phone’s clock
- Second(instant)
- The second of the minute
- SystemTime()
- The phone’s internal time
- Weekday(instant)
- The day of the week represented as a number from 1 (Sunday) to 7 (Saturday)
- WeekdayName(instant)
- The name of the day of the week
- Year(instant)
- The year
GyroscopeSensor
Properties
- Available
- Indicates whether a gyroscope sensor is available.
- Enabled
- Enabled property getter method.
- XAngularVelocity
- The angular velocity around the X axis, in degrees per second.
- YAngularVelocity
- The angular velocity around the Y axis, in degrees per second.
- ZAngularVelocity
- The angular velocity around the Z axis, in degrees per second.
Events
- GyroscopeChanged(xAngularVelocity,yAngularVelocity,zAngularVelocity,timestamp)
- Indicates that the gyroscope sensor data has changed. The timestamp parameter is the time in nanoseconds at which the event occurred.
Hygrometer
Properties
- Available
- Specifies whether or not the device has the hardware to support the Hygrometer component.
- Enabled
- If enabled, then device will listen for changes.
- Humidity
- The relative ambient humidity as a percentage, if the sensor is available and enabled.
- RefreshTime
- The requested minimum time in milliseconds between changes in readings being reported. Android is not guaranteed to honor the request. Setting this property has no effect on pre-Gingerbread devices.
Events
- HumidityChanged(humidity)
- Called when a change is detected in the ambient air humidity (expressed as a percentage).
LightSensor
Properties
- Available
- Specifies whether or not the device has the hardware to support the LightSensor component.
- AverageLux
- The average of the 10 most recent light levels measured, in lux.
- Enabled
- If enabled, then device will listen for changes.
- Lux
- The most recent light level, in lux, if the sensor is available and enabled.
- RefreshTime
- The requested minimum time in milliseconds between changes in readings being reported. Android is not guaranteed to honor the request. Setting this property has no effect on pre-Gingerbread devices.
Events
- LightChanged(lux)
- Called when a change is detected in the light level.
LocationSensor
Properties
- Accuracy
- The most recent measure of accuracy, in meters. If no value is available, 0 will be returned.
- Altitude
- The most recently available altitude value, in meters. If no value is available, 0 will be returned.
- AvailableProviders
- CurrentAddress
- Provides a textual representation of the current address or “No address available”.
- DistanceInterval
- Determines the minimum distance interval, in meters, that the sensor will try to use for sending out location updates. For example, if this is set to 5, then the sensor will fire a LocationChanged event only after 5 meters have been traversed. However, the sensor does not guarantee that an update will be received at exactly the distance interval. It may take more than 5 meters to fire an event, for instance.
- Enabled
- Indicates whether the user has specified that the sensor should listen for location changes and raise the corresponding events.
- HasAccuracy
- Indicates whether information about location accuracy is available.
- HasAltitude
- Indicates whether altitude information is available.
- HasLongitudeLatitude
- Indicates whether longitude and latitude information is available. (It is always the case that either both or neither are.)
- Latitude
- The most recently available latitude value. If no value is available, 0 will be returned.
- Longitude
- The most recent available longitude value. If no value is available, 0 will be returned.
- ProviderLocked
- Indicates whether the sensor should allow the developer to manually change the provider (GPS, GSM, Wifi, etc.) from which location updates are received.
- ProviderName
- Indicates the source of the location information. If there is no provider, the string “NO PROVIDER” is returned. This is useful primarily for debugging.
- TimeInterval
- Determines the minimum time interval, in milliseconds, that the sensor will try to use for sending out location updates. However, location updates will only be received when the location of the phone actually changes, and use of the specified time interval is not guaranteed. For example, if 1000 is used as the time interval, location updates will never be fired sooner than 1000ms, but they may be fired anytime after.
Events
- LocationChanged(latitude,longitude,altitude,speed)
- Indicates that a new location has been detected.
- StatusChanged(provider,status)
- Indicates that the status of the location provider service has changed, such as when a provider is lost or a new provider starts being used.
Methods
- LatitudeFromAddress(locationName)
- Derives latitude of given address
- LongitudeFromAddress(locationName)
- Derives longitude of given address
NearField
Properties
- LastMessage
- Returns the content of the most recently received tag.
- ReadMode
- Returns true if in read mode, false if in write mode.
- TextToWrite
- Returns the content of the text that’s going to be written to the tag.
- WriteType
Events
- TagRead(message)
- Indicates that a new tag has been detected. Currently this is only a plain text tag, as specified in the manifest. See Compiler.java.
- TagWritten()
- Event for TagWritten
OrientationSensor
Properties
- Angle
-
Angle property getter method (read-only property). Specifically, this provides the angle in which the orientation sensor is tilted, treating
- Available
- Available property getter method (read-only property).
- Azimuth
- Azimuth property getter method (read-only property).
To return meaningful values the sensor must be enabled.
- Enabled
- Enabled property getter method.
- Magnitude
- Magnitude property getter method (read-only property). Specifically, this returns a number between 0 and 1, indicating how much the device is tilted. For the angle of tilt, use
- Pitch
- Pitch property getter method (read-only property).
To return meaningful values the sensor must be enabled.
- Roll
- Roll property getter method (read-only property).
To return meaningful values the sensor must be enabled.
Events
- OrientationChanged(azimuth,pitch,roll)
- Default OrientationChanged event handler.
This event is signalled when the device's orientation has changed. It reports the new values of azimuth, pich, and roll, and it also sets the Azimuth, Pitch, and roll properties.
Azimuth is the compass heading in degrees, pitch indicates how the device is tilted from top to bottom, and roll indicates how much the device is tilted from side to side.
Pedometer
Properties
- CalibrateStrideLength
- Distance
- The approximate distance traveled in meters.
- ElapsedTime
- Time elapsed in milliseconds since the pedometer was started.
- Moving
- SimpleSteps
- The number of simple steps taken since the pedometer has started.
- StopDetectionTimeout
- The duration in milliseconds of idleness (no steps detected) after which to go into a “stopped” state
- StrideLength
- Set the average stride length in meters.
- UseGPS
- WalkSteps
- the number of walk steps taken since the pedometer has started.
Events
- CalibrationFailed()
- Event for CalibrationFailed
- GPSAvailable()
- Event for GPSAvailable
- GPSLost()
- Event for GPSLost
- SimpleStep(simpleSteps,distance)
- This event is run when a raw step is detected
- StartedMoving()
- Event for StartedMoving
- StoppedMoving()
- Event for StoppedMoving
- WalkStep(walkSteps,distance)
- This event is run when a walking step is detected. A walking step is a step that appears to be involved in forward motion.
Methods
- Pause()
- Pause counting of steps and distance.
- Reset()
- Resets the step counter, distance measure and time running.
- Resume()
- Resumes counting, synonym of Start.
- Save()
- Saves the pedometer state to the phone. Permits permits accumulation of steps and distance between invocations of an App that uses the pedometer. Different Apps will have their own saved state.
- Start()
- Start counting steps
- Stop()
- Stop counting steps
ProximitySensor
Properties
- Available
- Reports whether or not the device has a proximity sensor
- Distance
- Returns the distance from the object to the device
- Enabled
- If true, the sensor will generate events. Otherwise, no events are generated .
- KeepRunningWhenOnPause
- Returns value of keepRunningWhenOnPause
- MaximumRange
- Reports the Maximum Range of the device’s ProximitySensor
Events
- ProximityChanged(distance)
- Triggered when distance (in cm) of the object to the device changes.
Thermometer
Properties
- Available
- Specifies whether or not the device has the hardware to support the Thermometer component.
- Enabled
- If enabled, then device will listen for changes.
- RefreshTime
- The requested minimum time in milliseconds between changes in readings being reported. Android is not guaranteed to honor the request. Setting this property has no effect on pre-Gingerbread devices.
- Temperature
- The temperature in degrees Celsius, if the sensor is available and enabled
Events
- TemperatureChanged(temperature)
- Called when a change is detected in the temperature (in degrees Celsius).


HTML exists to define structure of the contents on the web page, CSS defines its style, while javascript defines the function of various elements on the webpage, how they dynamically intereact with each other and how they respond to user's actions.
Control flow referes to simple actions conducted in sequential manner. An everyday example of a control flow would be an act of waking up:
Accessing data from arrays and objects is very similar in the sense that whenever you want to access data from either an array or an object you have to specify exactly what data you want to access. The difference between the two is that when you specify what data from an array you want to access you specify the numeric order of the elements. When you specify what data you want to access from an object you specify not the numeric order but a qualitative property of the data. For example
Funtion in javascript is a piece of code that accomplishes a single task. They are useful for their reusability - once you have a function that accomplishes a particular task you can always use it in your other code.


Spatial orientation
Revealing geographic differences
Revealing geographic changes
Guide user to a clear editorial point
Simple, but not dumb
Use text to provide context
Don't mislead with geography
Goole My Maps
Goole Fusion Tables
Carto
Mapbox


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Reports the Java 5
for
statement syntax. Such
for
statements are not supported under Java 1.4 and older.
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for ( count = 0; count < 10; count++ ) System.out.print( count + " " );
Outputs:
0 1 2 3 4 5 6 7 8 9
Although the previous example is a counting loop,
the for
statement can be used to implement any of the
three types of loops.
The three parts, initialize, test , and change
can be as complicated as you want.
Here is a for
loop with several statements in the loop body:
int count, sum; sum = 0; for ( count = 0; count <= 5; count++ ) { sum = sum + count ; System.out.print( count + " " ); } System.out.println( "/nsum is: " + sum );
Since the loop body consists of several statements,
they are enclosed in braces {
and }
to make a block.


Server Side Template Injection (SSTI)
Client Side Template Injection (CSTI)
Description
If backend server utilizes any templating engine then it can be vulnerable to template injection.
Injection may occur directly in browser if web app uses a client-side templating engine.
Server-side templating engines
Jinja2
Rails ERB
Liquid Engine
Client-side templating engines
AngularJS
Detection
Check specific template syntax and test simple injections such {{1+1}}
Test for RCE or JavaScript execution.


JsInterfaceProcess(processingEnv: ProcessingEnvironment)
Process JsInterface annotations. The goal is make an extra file for each one to process an execute the javascript event in a single method.


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J.
A.
Shafer,
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/** * @typedef {string} TiddlerReference * * A reference (title) that identifies a tiddler. */ /** * @typedef {$tw.Tiddler|TiddlerReference} Tiddler * * Either a reference (title) that identifies the tiddler or an object * representation. */ /** * @typedef {string|number} Id * * An id used to identify a node or tiddler. Ids may only contain characters * that are valid when used as tiddlywiki field values. */ /** * @typedef {Object} Edge * * @property {Id} id - The id of the edge. * @property {Id} from - An id that refers to a {@link Node} and denotes the * from part of an edge. * @property {Id} to - An id that refers to a {@link Node} and denotes the * to part of an edge. * @property {string} label - The label of the edge that will be displayed * in the rendered graph. * @property {string} [view] - An optional viewname to which the edge is * bound to. * * An edge connects nodes in a vis.Network. */ /** * @typedef {Object} Node * * @property {Id} id - The id of the node. * @property {string} label - The label of the node that will be displayed * in the rendered graph. * * This object is used by the vis.Network. */ /** * @typedef {Object} Hashmap * * Sometimes it makes semantically more sense to call an object a hashmap. * Then it becomes clear that we are talking about a simple key-value store. */ /** * @typedef {Array.<*>|Hashmap.<Id, *>|vis.DataSet} Collection * * A collection contains a group of elements, usually nodes * (see {@link NodeCollection}) or edges (see {@link EdgeCollection}). * * A collection object corresponds to a {@link CollectionTypeString}. */ /** * @typedef {Array.<Edge>|Hashmap.<Id, Edge>|vis.DataSet} EdgeCollection * * A collection of edges. */ /** * @typedef {Array.<Node>|Hashmap.<Id, Node>|vis.DataSet} NodeCollection * * A collection of nodes. */ /** * @typedef {Array.<Tiddler>|Hashmap.<Id, Tiddler>} TiddlerCollection * * A collection of tiddlers. */ /** * @typedef {string|function} TiddlyWikiFilter * * A tiddlywiki filter expression or a compiled filter. */ /** * @typedef {Tiddler|string|ViewAbstraction} View * * A view is identified either by a tiddler reference (title), * a Tiddler instance or a label. Moreover, a ViewAbstraction * may be used as view representation. */ /** * @typedef {array|hashmap|dataset} CollectionTypeString * * Most of tiddlymap's select statements allow the user to choose one * of the specified output types, depending on what is most suitable. * * The output types relate to the different types of * {@link EdgeCollection} and {@link NodeCollection}. */ /** * @typedef {Object} Position * * @property {number} x - The x coordinate. * @property {number} y - The y coordinate. * * An object containing x, y properties. */


Axmill's Project Site
Welcome
Welcome to my website! This site has descriptions on my projects, as well as posts on programming.
Posts
My other website


[论文&发表] Hongxia Gao, Yulong Wang, Shuchuang Dong, Kitazawa Daisuke. (2019). Sustainability assessment based on the Aquaculture Intensity Index (AII) approach: a case study in Oita prefecture, Japan. Conference: The 9th East Asian Workshop for Marine Environment and Energy (EAWOMEN2 2019)At: Osaka, Japan.
[章节] Giles B. Sioen, Yulong WANG, Maya F. Dhondt, Kazuo Hiekata, Motoharu Onuki, Takashi Mino. (2019). An SVN model-based approach to assessing the gap between strategy and implementation: The case of Kashiwa-no-ha Smart City. In book: Characterizing the Gap Between Strategy and ImplementationChapter: 4Publisher: Project Management Institute.
[海报] Yulong WANG. (2019). Study on the water pollution purification capacity of bivalve habitats in Tokyo Bay its influences to sustainable coast renaissance policy.
2018
[海报] Yulong WANG. (2018). Modeling of general bivalves (Ruditapes philippinarum and Mercenaria mercenaria) and its applications in coastal area: A case study in Tokyo Bay.
[发表] Maya F. Dhondt, Yulong Wang, Giles B. Sioen, Aigerim Shopenova, Motoharu Onuki, Takashi Mino. (2018). An SVN model-based practice on socio-technical issues: the case of Kashiwa-no-ha Smart City. SDM Symposium 2018 Characterizing The Gap Between Strategy and Implementation.
[发表] Maya DHONDT, Yulong WANG, Aigerim SHOPENOVA, Erick AVELINO, Ritao LYU, Ye SUN, Dr. Giles B. SIOEN. (2018). A Framing Approach Put into Practice: Reflections from GFE Kashiwa-no-ha. The 6th GPSS-GLI International Symposium “Framing to Action: Bringing Sustainability Thinking into Practice”.
[海报] Yulong WANG. (2018). Interaction between coastal water quality and livelihood in the context of urbanization: A case study in Northern Liaodong Bay. Progress Review of GPSS-GLI. (Poster Award: For a very significant contridution in the field of sustainability scicence.)
[海报] Eric DOMPREH, Kevin FLORENTIN, Robert ALFOSI, Vinamra MATHUR, Yulong WANG. (2018). Pathways through sustainability: An Analysis of Exclusivity, Culture and Awareness in Smart City. The 6th GPSS-GLI International Symposium “Framing to Action: Bringing Sustainability Thinking into Practice”.
[论文] Yulong WANG, Zichang SHANGGUAN, Wenjian DONG, Long QI. (2017). Wave Controlling Performance Analysis of Submerged Inclined Plate Breakwater. The Ocean Engineering, 35(6), 55-61. (Peer reviewed)
[论文] Dong, W. J., Shangguan, Z. C., Wang, Y. L., & Liu, D. N. (2017). Experimental Study on Wave Dissipation Coefficient for Semi-Submersible Permeable Tetrahedron. In Applied Mechanics and Materials (Vol. 864, pp. 357-362). Trans Tech Publications.
2016
[论文] Yulong WANG, Zichang SHANGGUAN, Wenjian DONG, Qing QI. (2016). Structural Reliability of Fishing Chains at a Single Anchor in Fishing Port under Typhoon Calamity. Journal of Dalian Ocean University, 31(6), 692-695.(Peer reviewed)
[论文] Yulong WANG, Zichang SHANGGUAN, Wenjian DONG, Bin YU. (2016). Comparison Study onSeawall Structure based on Multilevel Fuzzy Comprehensive Evaluation. Journal of Dalian University, 37(3), 30-34.(Peer reviewed)
2015
[论文] Yu T., Shangguan Z., Wang Y., Shen L. Liu Y., Zhang J.. (2015)."Study on the wave dissipation properties of the no bottom semi-circular wave dissipation block." 4th ICSMIM: 1020-1025.
Welcome to my page, I am Yulong WANG，a PhD student who is currently living in Tokyo.
My research interest focuses on coastal sustainability issues, specifically, focuses on the coastal environment and ecosystem.
Although the total pollutant load control system (TPLCS) of Tokyo Bay had been implemented since 1979, leading to overall improvement of water quality, the hypoxia and anoxia have not been mitigated, and the fish catch still declined continuously. The current research question I am tackling is related to the improvement of the TPLCS, stakeholders' acceptance investigation and environmental feasibility evaluation.
Ph.D. Candidate, Teaching Assistant
The Graduate Program in Sustainability Science Global Leadership Initiative
The University of Tokyo
Master of Engineering (M.Eng.)
Marine System Engineering
Tokyo University of Marine Science and Technology
Assistant Engineer
Fishing Port Planning and Design Department
Dalian Fisheries Planning and Design Institute
Award
Poster Award: For a very significant contribution in the field of sustainability science
University of Tokyo, 2018
Work
2019
[Paper&Presentation] Hongxia Gao, Yulong Wang, Shuchuang Dong, Kitazawa Daisuke. (2019). Sustainability assessment based on the Aquaculture Intensity Index (AII) approach: a case study in Oita prefecture, Japan. Conference: The 9th East Asian Workshop for Marine Environment and Energy (EAWOMEN2 2019)At: Osaka, Japan.
[Chapter] Giles B. Sioen, Yulong Wang, Maya F. Dhondt, Kazuo Hiekata, Motoharu Onuki, Takashi Mino. (2019). An SVN model-based approach to assessing the gap between strategy and implementation: The case of Kashiwa-no-ha Smart City. In book: Characterizing the Gap Between Strategy and ImplementationChapter: 4Publisher: Project Management Institute.
[Poster] Yulong WANG. (2019). Study on the water pollution purification capacity of bivalve habitats in Tokyo Bay its influences to sustainable coast renaissance policy.
2018
[Poster] Yulong WANG. (2018). Modeling of general bivalves (Ruditapes philippinarum and Mercenaria mercenaria) and its applications in coastal area: A case study in Tokyo Bay.
[Presentation] Maya F. Dhondt, Yulong Wang, Giles B. Sioen, Aigerim Shopenova, Motoharu Onuki, Takashi Mino. (2018). An SVN model-based practice on socio-technical issues: the case of Kashiwa-no-ha Smart City. SDM Symposium 2018 Characterizing The Gap Between Strategy and Implementation.
[Presentation] Maya DHONDT, Yulong WANG, Aigerim SHOPENOVA, Erick AVELINO, Ritao LYU, Ye SUN, Dr. Giles B. SIOEN. (2018). A Framing Approach Put into Practice: Reflections from GFE Kashiwa-no-ha. The 6th GPSS-GLI International Symposium “Framing to Action: Bringing Sustainability Thinking into Practice”.
[Poster] Yulong WANG. (2018). Interaction between coastal water quality and livelihood in the context of urbanization: A case study in Northern Liaodong Bay. Progress Review of GPSS-GLI. (Poster Award: For a very significant contridution in the field of sustainability scicence.)
[Poster] Eric DOMPREH, Kevin FLORENTIN, Robert ALFOSI, Vinamra MATHUR, Yulong WANG. (2018). Pathways through sustainability: An Analysis of Exclusivity, Culture and Awareness in Smart City. The 6th GPSS-GLI International Symposium “Framing to Action: Bringing Sustainability Thinking into Practice”.
[Paper] Yulong WANG, Zichang SHANGGUAN, Wenjian DONG, Long QI. (2017). Wave Controlling Performance Analysis of Submerged Inclined Plate Breakwater. The Ocean Engineering, 35(6), 55-61. (Peer reviewed)
[Paper] Dong, W. J., Shangguan, Z. C., Wang, Y. L., & Liu, D. N. (2017). Experimental Study on Wave Dissipation Coefficient for Semi-Submersible Permeable Tetrahedron. In Applied Mechanics and Materials (Vol. 864, pp. 357-362). Trans Tech Publications.
2016
[Paper] Yulong WANG, Zichang SHANGGUAN, Wenjian DONG, Qing QI. (2016). Structural Reliability of Fishing Chains at a Single Anchor in Fishing Port under Typhoon Calamity. Journal of Dalian Ocean University, 31(6), 692-695.(Peer reviewed)
[Paper] Yulong WANG, Zichang SHANGGUAN, Wenjian DONG, Bin YU. (2016). Comparison Study onSeawall Structure based on Multilevel Fuzzy Comprehensive Evaluation. Journal of Dalian University, 37(3), 30-34.(Peer reviewed)
2015
[Paper] Yu T., Shangguan Z., Wang Y., Shen L. Liu Y., Zhang J.. (2015)."Study on the wave dissipation properties of the no bottom semi-circular wave dissipation block." 4th ICSMIM: 1020-1025.
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Using MongoSwift in Mulithreaded Applications
Threadsafe Types
As of MongoSwift 0.2.0, the following types are safe to use across threads:
We make no guarantees about the safety of using any other type across threads.
Best Practices
Each MongoClient
is backed by a pool of server connections. Any time a client or one of its child objects (a MongoDatabase
or MongoCollection
) makes a request to the database (a find
, insertOne
, etc.) a connection will be retrieved from the pool, used to execute the operation, and then returned to the pool when it is finished.
Each MongoClient
uses its own background thread to monitor the MongoDB topology you are connected to.
In order to share the connection pool across threads and minimize the number of background monitoring threads, we recommend sharing MongoClient
s across threads.
Usage With Server-side Swift Frameworks
See the Examples/
directory in the driver GitHub repository for examples of how to integrate the driver in multithreaded frameworks.


Testing PGF and TikZ support in DocOnce¶
| Authors: | Kristian Gregorius Hustad (krihus at ifi.uio.no) |
|---|---|
| Date: | Aug 5, 2016 |
Summary. Quick demo of how to make figures with TikZ in DocOnce.
Ideas¶
TikZ
is a very strong tool for making figures in LaTeX. DocOnce supports
such figures: if a figure file myfig
exists in a version
myfig.tikz
, DocOnce will, in case of latex
or pdflatex
output,
utilize the myfig.tikz
figure directly. The problem is what do to
with other output formats. In html
format one can use a
corresponding SVG version of the figure, and for other formats, one
needs a plain PNG file. DocOnce will automatically create such
versions of the figure and store them along with myfig.tikz
, as is
done when other figure formats need automatic conversion.
A modest beginning¶
The most fundamental shape is the line in Figure This shape is commonly referred to as a straight line. See the
source code
for how this TikZ figure is defined in LaTeX as a file line.tikz
(and included in DocOnce through FIGURE: [line, width=500] caption
). Such
lines can be combined to form other shapes, e.g., a square
as in Figure This square is formed by four straight lines, but that figure was created directly by
the rectangle
TikZ command.
A grid can also be easily made.
A circle, however, cannot be formed by a finite number of straight lines. It requires special code.
Plotting functions¶
TikZ can be used to plot functions. The next figure will be inlined.
See the source code for how you make this figure.
All details are in the source code.


Red Hat is where the most passionate, creative, and high-performing people in the technology industry come together to make a difference.
I spent most of my undergraduate years at Amrita University. It is a university with six campuses. It is somewhere every student will be questioned with -- 'Why do you come here? Who do you want to become?'.


--- title: (BFDataset) upload keywords: documentation theme, jekyll, technical writers, help authoring tools, hat replacements last_updated: July 3, 2016 tags: [getting_started] sidebar: matlab_docs_sidebar permalink: bfdataset_upload.html folder: mydoc ---
upload Upload data from MATLAB using the Blackfynn Agent OBJ = upload(OBJ, 'path') uploads all files from the folder specified in the 'path' to the platform. OBJ = upload(..., 'folder', 'toPath') uploads the files to a specific path in the dataset. Folders will be created if the path does not exist yet. Separate folders using '/', for example: 'data/experiment1/trial1'. OBJ = upload(..., 'include', 'includeStr') will only upload the files that match the 'includeStr' expression. The include string should be formatted as a globbing pattern. For examples, look here: http://tldp.org/LDP/GNU-Linux-Tools-Summary/html/x11655.htm OBJ = upload(..., 'exclude', 'excludeStr') will exclude files that match the 'excludeStr' expression. The exclusion string should be formatted in the same way as the inclusion string. For example: bf = Blackfynn(); ds = bf.datasets(1); ds.upload('~/Desktop/myFolder'); ds.upload('~/Desktop/myFolder', 'folder', 'folder1/folder2'); ds.upload('~/Desktop/myFolder', 'inlcude', '*.dcm'); ds.upload('~/Desktop/myFolder', 'inlcude', '{*.dcm,*.csv}') ds.upload('~/Desktop/myFolder', 'exclude', '*.DS_Store');
| Access | public |
| Sealed | false |
| Static | false |


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Neural networks
Contents
13. Neural networks¶
Artificial neural networks are computational systems that can learn to perform tasks by considering examples, generally without being programmed with any task-specific rules. It is supposed to mimic a biological system, wherein neurons interact by sending signals in the form of mathematical functions between layers. All layers can contain an arbitrary number of neurons, and each connection is represented by a weight variable.
The field of artificial neural networks has a long history of development, and is closely connected with the advancement of computer science and computers in general. A model of artificial neurons was first developed by McCulloch and Pitts in 1943 to study signal processing in the brain and has later been refined by others. The general idea is to mimic neural networks in the human brain, which is composed of billions of neurons that communicate with each other by sending electrical signals. Each neuron accumulates its incoming signals, which must exceed an activation threshold to yield an output. If the threshold is not overcome, the neuron remains inactive, i.e. has zero output.
This behaviour has inspired a simple mathematical model for an artificial neuron.
Here, the output \(y\) of the neuron is the value of its activation function, which have as input a weighted sum of signals \(x_i, \dots ,x_n\) received by \(n\) other neurons.
Conceptually, it is helpful to divide neural networks into four categories:
general purpose neural networks for supervised learning,
neural networks designed specifically for image processing, the most prominent example of this class being Convolutional Neural Networks (CNNs),
neural networks for sequential data such as Recurrent Neural Networks (RNNs), and
neural networks for unsupervised learning such as Deep Boltzmann Machines.
In natural science, DNNs and CNNs have already found numerous applications. In statistical physics, they have been applied to detect phase transitions in 2D Ising and Potts models, lattice gauge theories, and different phases of polymers, or solving the Navier-Stokes equation in weather forecasting. Deep learning has also found interesting applications in quantum physics. Various quantum phase transitions can be detected and studied using DNNs and CNNs, topological phases, and even non-equilibrium many-body localization. Representing quantum states as DNNs quantum state tomography are among some of the impressive achievements to reveal the potential of DNNs to facilitate the study of quantum systems.
In quantum information theory, it has been shown that one can perform gate decompositions with the help of neural.
The applications are not limited to the natural sciences. There is a plethora of applications in essentially all disciplines, from the humanities to life science and medicine.
An artificial neural network (ANN), is a computational model that consists of layers of connected neurons, or nodes or units. We will refer to these interchangeably as units or nodes, and sometimes as neurons.
It is supposed to mimic a biological nervous system by letting each neuron interact with other neurons by sending signals in the form of mathematical functions between layers. A wide variety of different ANNs have been developed, but most of them consist of an input layer, an output layer and eventual layers in-between, called hidden layers. All layers can contain an arbitrary number of nodes, and each connection between two nodes is associated with a weight variable.
Neural networks (also called neural nets) are neural-inspired nonlinear models for supervised learning. As we will see, neural nets can be viewed as natural, more powerful extensions of supervised learning methods such as linear and logistic regression and soft-max methods we discussed earlier.
13.1. Feed-forward neural networks¶
The feed-forward neural network (FFNN) was the first and simplest type of ANNs that were devised. In this network, the information moves in only one direction: forward through the layers.
Nodes are represented by circles, while the arrows display the connections between the nodes, including the direction of information flow. Additionally, each arrow corresponds to a weight variable (figure to come). We observe that each node in a layer is connected to all nodes in the subsequent layer, making this a so-called fully-connected FFNN.
13.2. Convolutional Neural Network¶
A different variant of FFNNs are convolutional neural networks (CNNs), which have a connectivity pattern inspired by the animal visual cortex. Individual neurons in the visual cortex only respond to stimuli from small sub-regions of the visual field, called a receptive field. This makes the neurons well-suited to exploit the strong spatially local correlation present in natural images. The response of each neuron can be approximated mathematically as a convolution operation. (figure to come)
Convolutional neural networks emulate the behaviour of neurons in the visual cortex by enforcing a local connectivity pattern between nodes of adjacent layers: Each node in a convolutional layer is connected only to a subset of the nodes in the previous layer, in contrast to the fully-connected FFNN. Often, CNNs consist of several convolutional layers that learn local features of the input, with a fully-connected layer at the end, which gathers all the local data and produces the outputs. They have wide applications in image and video recognition.
13.3. Recurrent neural networks¶
So far we have only mentioned ANNs where information flows in one direction: forward. Recurrent neural networks on the other hand, have connections between nodes that form directed cycles. This creates a form of internal memory which are able to capture information on what has been calculated before; the output is dependent on the previous computations. Recurrent NNs make use of sequential information by performing the same task for every element in a sequence, where each element depends on previous elements. An example of such information is sentences, making recurrent NNs especially well-suited for handwriting and speech recognition.
13.4. Other types of networks¶
There are many other kinds of ANNs that have been developed. One type that is specifically designed for interpolation in multidimensional space is the radial basis function (RBF) network. RBFs are typically made up of three layers: an input layer, a hidden layer with non-linear radial symmetric activation functions and a linear output layer (‘’linear’’ here means that each node in the output layer has a linear activation function). The layers are normally fully-connected and there are no cycles, thus RBFs can be viewed as a type of fully-connected FFNN. They are however usually treated as a separate type of NN due the unusual activation functions.
13.5. Multilayer perceptrons¶
One uses often so-called fully-connected feed-forward neural networks with three or more layers (an input layer, one or more hidden layers and an output layer) consisting of neurons that have non-linear activation functions.
Such networks are often called multilayer perceptrons (MLPs).
According to the Universal approximation theorem, a feed-forward neural network with just a single hidden layer containing a finite number of neurons can approximate a continuous multidimensional function to arbitrary accuracy, assuming the activation function for the hidden layer is a non-constant, bounded and monotonically-increasing continuous function.
Note that the requirements on the activation function only applies to the hidden layer, the output nodes are always assumed to be linear, so as to not restrict the range of output values.
The output \(y\) is produced via the activation function \(f\)
This function receives \(x_i\) as inputs. Here the activation \(z=(\sum_{i=1}^n w_ix_i+b_i)\). In an FFNN of such neurons, the inputs \(x_i\) are the outputs of the neurons in the preceding layer. Furthermore, an MLP is fully-connected, which means that each neuron receives a weighted sum of the outputs of all neurons in the previous layer.
First, for each node \(i\) in the first hidden layer, we calculate a weighted sum \(z_i^1\) of the input coordinates \(x_j\),
Here \(b_i\) is the so-called bias which is normally needed in case of zero activation weights or inputs. How to fix the biases and the weights will be discussed below. The value of \(z_i^1\) is the argument to the activation function \(f_i\) of each node \(i\), The variable \(M\) stands for all possible inputs to a given node \(i\) in the first layer. We define the output \(y_i^1\) of all neurons in layer 1 as
where we assume that all nodes in the same layer have identical activation functions, hence the notation \(f\). In general, we could assume in the more general case that different layers have different activation functions. In this case we would identify these functions with a superscript \(l\) for the \(l\)-th layer,
where \(N_l\) is the number of nodes in layer \(l\). When the output of all the nodes in the first hidden layer are computed, the values of the subsequent layer can be calculated and so forth until the output is obtained.
The output of neuron \(i\) in layer 2 is thus,
where we have substituted \(y_k^1\) with the inputs \(x_k\). Finally, the ANN output reads
We can generalize this expression to an MLP with \(l\) hidden layers. The complete functional form is,
which illustrates a basic property of MLPs: The only independent variables are the input values \(x_n\).
This confirms that an MLP, despite its quite convoluted mathematical form, is nothing more than an analytic function, specifically a mapping of real-valued vectors \(\hat{x} \in \mathbb{R}^n \rightarrow \hat{y} \in \mathbb{R}^m\).
Furthermore, the flexibility and universality of an MLP can be illustrated by realizing that the expression is essentially a nested sum of scaled activation functions of the form
where the parameters \(c_i\) are weights and biases. By adjusting these parameters, the activation functions can be shifted up and down or left and right, change slope or be rescaled which is the key to the flexibility of a neural network.
We can introduce a more convenient notation for the activations in an A NN.
Additionally, we can represent the biases and activations as layer-wise column vectors \(\hat{b}_l\) and \(\hat{y}_l\), so that the \(i\)-th element of each vector is the bias \(b_i^l\) and activation \(y_i^l\) of node \(i\) in layer \(l\) respectively.
We have that \(\mathrm{W}_l\) is an \(N_{l-1} \times N_l\) matrix, while \(\hat{b}_l\) and \(\hat{y}_l\) are \(N_l \times 1\) column vectors. With this notation, the sum becomes a matrix-vector multiplication, and we can write the equation for the activations of hidden layer 2 (assuming three nodes for simplicity) as
13.5.1. Matrix-vector notation and activation¶
The activation of node \(i\) in layer 2 is
This is not just a convenient and compact notation, but also a useful and intuitive way to think about MLPs: The output is calculated by a series of matrix-vector multiplications and vector additions that are used as input to the activation functions. For each operation \(\mathrm{W}_l \hat{y}_{l-1}\) we move forward one layer.
13.5.2. Activation functions¶
A property that characterizes a neural network, other than its connectivity, is the choice of activation function(s). As described in, the following restrictions are imposed on an activation function for a FFNN to fulfill the universal approximation theorem
Non-constant
Bounded
Monotonically-increasing
Continuous
The second requirement excludes all linear functions. Furthermore, in a MLP with only linear activation functions, each layer simply performs a linear transformation of its inputs.
Regardless of the number of layers, the output of the NN will be nothing but a linear function of the inputs. Thus we need to introduce some kind of non-linearity to the NN to be able to fit non-linear functions Typical examples are the logistic Sigmoid
and the hyperbolic tangent function
The sigmoid function are more biologically plausible because the output of inactive neurons are zero. Such activation function are called one-sided. However, it has been shown that the hyperbolic tangent performs better than the sigmoid for training MLPs. has become the most popular for deep neural networks
%matplotlib inline
"""The sigmoid function (or the logistic curve) is a
function that takes any real number, z, and outputs a number (0,1).
It is useful in neural networks for assigning weights on a relative scale.
The value z is the weighted sum of parameters involved in the learning algorithm."""
import numpy
import matplotlib.pyplot as plt
import math as mt
z = numpy.arange(-5, 5, .1)
sigma_fn = numpy.vectorize(lambda z: 1/(1+numpy.exp(-z)))
sigma = sigma_fn(z)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(z, sigma)
ax.set_ylim([-0.1, 1.1])
ax.set_xlim([-5,5])
ax.grid(True)
ax.set_xlabel('z')
ax.set_title('sigmoid function')
plt.show()
"""Step Function"""
z = numpy.arange(-5, 5, .02)
step_fn = numpy.vectorize(lambda z: 1.0 if z >= 0.0 else 0.0)
step = step_fn(z)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(z, step)
ax.set_ylim([-0.5, 1.5])
ax.set_xlim([-5,5])
ax.grid(True)
ax.set_xlabel('z')
ax.set_title('step function')
plt.show()
"""Sine Function"""
z = numpy.arange(-2*mt.pi, 2*mt.pi, 0.1)
t = numpy.sin(z)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(z, t)
ax.set_ylim([-1.0, 1.0])
ax.set_xlim([-2*mt.pi,2*mt.pi])
ax.grid(True)
ax.set_xlabel('z')
ax.set_title('sine function')
plt.show()
"""Plots a graph of the squashing function used by a rectified linear
unit"""
z = numpy.arange(-2, 2, .1)
zero = numpy.zeros(len(z))
y = numpy.max([zero, z], axis=0)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(z, y)
ax.set_ylim([-2.0, 2.0])
ax.set_xlim([-2.0, 2.0])
ax.grid(True)
ax.set_xlabel('z')
ax.set_title('Rectified linear unit')
plt.show()
13.6. The multilayer perceptron (MLP)¶
The multilayer perceptron is a very popular, and easy to implement approach, to deep learning. It consists of
A neural network with one or more layers of nodes between the input and the output nodes.
The multilayer network structure, or architecture, or topology, consists of an input layer, one or more hidden layers, and one output layer.
The input nodes pass values to the first hidden layer, its nodes pass the information on to the second and so on till we reach the output layer.
As a convention it is normal to call a network with one layer of input units, one layer of hidden units and one layer of output units as a two-layer network. A network with two layers of hidden units is called a three-layer network etc etc.
For an MLP network there is no direct connection between the output nodes/neurons/units and the input nodes/neurons/units. Hereafter we will call the various entities of a layer for nodes. There are also no connections within a single layer.
The number of input nodes does not need to equal the number of output nodes. This applies also to the hidden layers. Each layer may have its own number of nodes and activation functions.
The hidden layers have their name from the fact that they are not linked to observables and as we will see below when we define the so-called activation \(\hat{z}\), we can think of this as a basis expansion of the original inputs \(\hat{x}\). The difference however between neural networks and say linear regression is that now these basis functions (which will correspond to the weights in the network) are learned from data. This results in an important difference between neural networks and deep learning approaches on one side and methods like logistic regression or linear regression and their modifications on the other side.
13.6.1. From one to many layers, the universal approximation theorem¶
A neural network with only one layer, what we called the simple perceptron, is best suited if we have a standard binary model with clear (linear) boundaries between the outcomes. As such it could equally well be replaced by standard linear regression or logistic regression. Networks with one or more hidden layers approximate systems with more complex boundaries.
As stated earlier, an important theorem in studies of neural networks, restated without proof here, is the universal approximation theorem.
It states that a feed-forward network with a single hidden layer containing a finite number of neurons can approximate continuous functions on compact subsets of real functions. The theorem thus states that simple neural networks can represent a wide variety of interesting functions when given appropriate parameters. It is the multilayer feedforward architecture itself which gives neural networks the potential of being universal approximators.
13.7. Deriving the back propagation code for a multilayer perceptron model¶
As we have seen now in a feed forward network, we can express the final output of our network in terms of basic matrix-vector multiplications. The unknowwn quantities are our weights \(w_{ij}\) and we need to find an algorithm for changing them so that our errors are as small as possible. This leads us to the famous back propagation algorithm.
The questions we want to ask are how do changes in the biases and the weights in our network change the cost function and how can we use the final output to modify the weights?
To derive these equations let us start with a plain regression problem and define our cost function as
where the \(t_i\)s are our \(n\) targets (the values we want to reproduce), while the outputs of the network after having propagated all inputs \(\hat{x}\) are given by \(y_i\). Below we will demonstrate how the basic equations arising from the back propagation algorithm can be modified in order to study classification problems with \(K\) classes.
With our definition of the targets \(\hat{t}\), the outputs of the network \(\hat{y}\) and the inputs \(\hat{x}\) we define now the activation \(z_j^l\) of node/neuron/unit \(j\) of the \(l\)-th layer as a function of the bias, the weights which add up from the previous layer \(l-1\) and the forward passes/outputs \(\hat{a}^{l-1}\) from the previous layer as
where \(b_k^l\) are the biases from layer \(l\). Here \(M_{l-1}\) represents the total number of nodes/neurons/units of layer \(l-1\). The figure here illustrates this equation. We can rewrite this in a more compact form as the matrix-vector products we discussed earlier,
With the activation values \(\hat{z}^l\) we can in turn define the output of layer \(l\) as \(\hat{a}^l = f(\hat{z}^l)\) where \(f\) is our activation function. In the examples here we will use the sigmoid function discussed in our logistic regression lectures. We will also use the same activation function \(f\) for all layers and their nodes. It means we have
13.7.1. Derivatives and the chain rule¶
From the definition of the activation \(z_j^l\) we have
and
With our definition of the activation function we have that (note that this function depends only on \(z_j^l\))
With these definitions we can now compute the derivative of the cost function in terms of the weights.
Let us specialize to the output layer \(l=L\). Our cost function is
The derivative of this function with respect to the weights is
The last partial derivative can easily be computed and reads (by applying the chain rule)
13.7.2. Bringing it together, first back propagation equation¶
We have thus
Defining
and using the Hadamard product of two vectors we can write this as
This is an important expression. The second term on the right handside measures how fast the cost function is changing as a function of the \(j\)th output activation. If, for example, the cost function doesn’t depend much on a particular output node \(j\), then \(\delta_j^L\) will be small, which is what we would expect. The first term on the right, measures how fast the activation function \(f\) is changing at a given activation value \(z_j^L\).
Notice that everything in the above equations is easily computed. In particular, we compute \(z_j^L\) while computing the behaviour of the network, and it is only a small additional overhead to compute \(f'(z^L_j)\). The exact form of the derivative with respect to the output depends on the form of the cost function. However, provided the cost function is known there should be little trouble in calculating
With the definition of \(\delta_j^L\) we have a more compact definition of the derivative of the cost function in terms of the weights, namely
It is also easy to see that our previous equation can be written as
which can also be interpreted as the partial derivative of the cost function with respect to the biases \(b_j^L\), namely
That is, the error \(\delta_j^L\) is exactly equal to the rate of change of the cost function as a function of the bias.
We have now three equations that are essential for the computations of the derivatives of the cost function at the output layer. These equations are needed to start the algorithm and they are
The starting equations.
and
and
An interesting consequence of the above equations is that when the activation \(a_k^{L-1}\) is small, the gradient term, that is the derivative of the cost function with respect to the weights, will also tend to be small. We say then that the weight learns slowly, meaning that it changes slowly when we minimize the weights via say gradient descent. In this case we say the system learns slowly.
Another interesting feature is that is when the activation function, represented by the sigmoid function here, is rather flat when we move towards its end values \(0\) and \(1\) (see the above Python codes). In these cases, the derivatives of the activation function will also be close to zero, meaning again that the gradients will be small and the network learns slowly again.
We need a fourth equation and we are set. We are going to propagate backwards in order to the determine the weights and biases. In order to do so we need to represent the error in the layer before the final one \(L-1\) in terms of the errors in the final output layer.
13.7.3. Final back propagating equation¶
We have that (replacing \(L\) with a general layer \(l\))
We want to express this in terms of the equations for layer \(l+1\). Using the chain rule and summing over all \(k\) entries we have
and recalling that
with \(M_l\) being the number of nodes in layer \(l\), we obtain
This is our final equation.
We are now ready to set up the algorithm for back propagation and learning the weights and biases.
13.7.4. Setting up the Back propagation algorithm¶
The four equations provide us with a way of computing the gradient of the cost function. Let us write this out in the form of an algorithm.
First, we set up the input data \(\hat{x}\) and the activations \(\hat{z}_1\) of the input layer and compute the activation function and the pertinent outputs \(\hat{a}^1\).
Secondly, we perform then the feed forward till we reach the output layer and compute all \(\hat{z}_l\) of the input layer and compute the activation function and the pertinent outputs \(\hat{a}^l\) for \(l=2,3,\dots,L\).
Thereafter we compute the ouput error \(\hat{\delta}^L\) by computing all
Then we compute the back propagate error for each \(l=L-1,L-2,\dots,2\) as
Finally, we update the weights and the biases using gradient descent for each \(l=L-1,L-2,\dots,2\) and update the weights and biases according to the rules
The parameter \(\eta\) is the learning parameter discussed in connection with the gradient descent methods. Here it is convenient to use stochastic gradient descent (see the examples below) with mini-batches with an outer loop that steps through multiple epochs of training.


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Chem-E-Car Electrical Team
Electrical Design
Completed:
November, 2018
1st Place at 2018 Pacific Northwest Regional Chem-E-Car Competition
1st Place Poster at 2018 National AICHE Competition
Refracting Rays
Physics and Mathematics
Completed:
May, 2013
Bronze Medal at 2013 National Science Fair


What to Do About Poverty
2 min read
50 years ago this month Lyndon Johnson gave his War on Poverty speech, which was the beginning of most of the U.S government's anti-poverty programs. While these programs have made a clear dent in poverty, they have not come close to eliminating poverty. We need to do more. In this post I will detail how America can solve the poverty issue.
- The U.S government should commit to alleviating unemployment and maintaining full employment. This should include
- The creation of a Works Progress Administration style jobs program that would be able to offer every unemployed person who is willing and able to work a job at a living wage. This program would employ the workers on infrastructure programs, providing public services, and projects done in collaboration with private non-profit groups. This program should continue even after the effects of the recession are over.
- The government should provide grants to unemployed workers and low income individuals who intend to start a business. This program would evaluate proposals based on their cost, the number of people employed by the businesses, and the benefit to the public that would be created by the proposals.
- The U.S government should aggressively use monetary policy to encourage private investment. This should include a continuation of the zero interest rate policy -- possibly indefinitely.
- The government should aggressively use fiscal policy to keep the economy at full employment. This should include increased deficits when the economy is sluggish and reduced deficits/ surpluses when there is a high risk of inflation.
- The current set of anti-poverty programshttps:/
/ theojones.name/ index.php/ 2013/ 09/ 17/ best-way-eliminate-poverty-united-states/ "> should be replaced with a basic income program that would provide a universal cash grant to all U.S adult citizens that is delivered unconditionally and without means testing . - https:/
/ theojones.name/ index.php/ 2014/ 01/ 05/ done-u-s-health-care-system/ ">A universal health care system should be adopted.
#lyndonjohnson #old #old #uncategorized #worksprogressadministration


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Amazing presentatino here: https://conference.hitb.org/hitbsecconf2017ams/materials/D2T2%20-%20Antonios%20Atlasis%20-%20An%20Attack-in-Depth%20Analysis%20of%20Multicast%20DNS%20and%20DNS%20Service%20Discovery.pdf
mDNS and DNS-SD are two protocols designed and used for Zero Configuration Networking (ZeroConf).
–Zero Conf = automatic IP configuration + host name resolution + target service discovery.
Used by many devices (Apple TV, Chromecast, home speakers, NAS devices, etc.).
mDNS [RFC 6762] provides the ability to perform DNS-like operations on the local link.
–Using UDP port 5353 (source and destination).
DNS-SD [RFC 6763] allows clients to discoverinstances of a desired service in a domain using standard DNS queries.
–DNS-SD can be used with both unicast DNS and mDNS.
Can use a tool Pholus to help recon


OUR TOWN
November 28-December 1, 1977
in LLT's New Arabian Theatre Playhouse
Director - Ruth Tomlinson
THE CAST
Stage Manager - Ernest Graves
Assistant Stage Manager - Wess Hughes
Dr. Gibbs - Bill Mullins
Joe Crowell - Bill Peale
Howie Newsome - Will Hughes
Mrs. Gibbs - Barbara Helen Mullins
Mrs. Webb - Monica Holcomb
George Gibbs - Lewis Holloway
Rebecca Gibbs - Janet Swartzfager
Wally Webb - Edward Schneider
Emily Webb - Nita Faye Ferris
Professor Willard - Bob Hynson
Mr. Webb - Frank Martin
Audience Members - Lynn Howard, Connie Farrell
Simon Stimson - Miles Bilek
Mrs. Soams - Evelyn Skelton
Constable Warren - Ted Morgan
Si Crowell - Mike Waldrup
Baseball Players - Edwin McAllister, David Parrish
Sam Craig - Stuart McBride
Joe Studdard - John Sumrall
Farmer McCarthy - Deacon Neatherly
Mr. Carter - Granville Walters
The Townspeople
Cathy Anderson, Suzanne Anderson, Edna Bilek, Cathy Bustin,
Dale Corley, Rosemary Farrell, Sue Lightsey, Greta McBride,
Mary Mobberly, Anne Sanders, Margie Valentine, Virginia Wilson


Six target industries were identified in the 2015 Atlantic County Economic Development Strategy and Action Plan as having the most opportunities to attract and grow businesses and promote economic diversification.
Atlantic County embraces twenty-first century technologies to help keep pace with an ever-changing economy. Egg Harbor Township is home to the Federal Aviation Administration’s William J. Hughes Technical Center campus, adjacent to the Atlantic City International Airport.
Situated on 58 acres on the campus of the Technical Center is the Stockton Aviation Research & Technology Park. The facility is expected to generate research and advance aviation sciences with the development of 400,000 square feet of office space to attract commercial, government and academic organizations. Development at the site is subject to FAA approval.
Life Sciences includes two strong components for Atlantic County: (1) understanding and improving the health of residents, and (2) sustaining the ecosystem within the region. Healthcare is a major industry in Atlantic County. AtlantiCare is southeast New Jersey’s largest health care provider, delivering cutting-edge technologies, and top-notch service.
The AtlantiCare Medical Center was recognized with a 2009 Baldrige Award, one of just five organizations in the entire nation to be honored for innovation and performance excellence. Shore Medical Center was one of three hospitals nationwide to receive a Gold Seal of Approval for healthcare quality by the Joint Commission for Lumbar and Cervical Spine Surgery. It is nearing the completion of a $125 million expansion that will include a new surgical pavilion, medical office building, entrance and lobby. It recently opened a $1.8 million Pediatric Care Center, the only one of its kind in New Jersey which features a separate pediatric emergency treatment area combined with long-term, inpatient treatment rooms.
Atlantic County attracts millions of annual visitors and accounted for nearly 18 percent of the nearly $40 billion in statewide tourism-related revenues in 2014. Atlantic County’s tourism sector remains a leading industry and source of employment with eight Atlantic City casinos, world-class restaurants and entertainment, beautiful boardwalks and beaches, scenic parks and recreation amenities, preserved wildlife areas, vibrant arts, history and culture, and growing agritourism and eco-tourism interest.
Due largely to the presence of Atlantic City's casinos, leisure, and hospitality accounts for the largest number of jobs in the county with a total of 46,156 jobs or 41.7% of the total employment in 2011, according to the NJ Department of Labor, based on 12 operating casino hotels within the Tourism District of Atlantic City. Total gross revenues from 2012 were reported at $3.052 billion. Click Here for more information on casinos in Atlantic County.
Experience world class shopping at various retail centers throughout Atlantic County. Located in the center of Atlantic City is Tanger Outlets, The Walk, comprised of approximately 100 outlet stores and the newest addition Bass Pro Shops. Hamilton Mall and Wrangleboro Consumer Square in Mays Landing are home to many big name retailers including Target, Macy's, and Kohl's, as well as many smaller stores and eateries. For more information on shopping in Atlantic County click here.
Specialty manufacturing provides value added products for the existing agricultural industry as well as the life sciences and aerospace and aviation target industries. Agriculture is a prominent industry in Atlantic County with a wide spectrum of focus ranging from vegetable hydroponics to shellfish aquaculture. Specialty manufacturing in this sector relates to farm-to-table production. Medical manufacturing provides a synergistic component in the life sciences industry supply chain. Hospitals require medical apparatuses, test kits, and surgical supplies. Future growth in medical research may be increasing appealing to pharmaceutical manufactures.
Current entrepreneurial companies provide one of the highest average annual wages in the region second only to aeronautics and aviation. More than 6,000 people in Atlantic County currently earn their livings based on the innovation of hardworking, successful small business concerns.
New Jersey is known as the Garden State, and agriculture remains a prominent industry in Atlantic County. The Town of Hammonton is proudly proclaimed as the “Blueberry Capital of the World.” The growth of farm-to-table options complements our area’s world-class dining and entertainment venues. Locally grown grains and outstanding water quality provide farm-to-tap options and benefit our award-winning wineries, breweries, and distilleries. Food processing and warehousing opportunities with access to major metropolitan markets also contribute to this industry.


THE WITCHES A film review by Mark R. Leeper Copyright 1990 Mark R. Leeper
Capsule review: Jim Henson's last film is a charming modern fairy tale of a boy foiling a plot by the witches of England. It captures some of the fun horror of traditional fairy tales. Unfortunately, it loses some of its inspiration about mid-film. The story would be ideally suited to animation and doing it in live action is an impressive if not altogether necessary feat. Rating: low +2.
The perfect medium for showing imaginative images visually is animation. With animation, if you can visualize it, you can put it on a screen. 1973's FANTASTIC PLANET, while lacking in story values, may well be the most visually imaginative science fiction film ever made. Unfortunately for animated fantasy, STAR WARS came out four years later and showed that imaginative live action was coming of age and animated fantasy became a sideshow. Only the Japanese seem to recognize the possibilities of animation as a medium for fantasy, and even in Japan fantastic animation is falling short of the real potential of the medium. In the United States and Europe audiences want live action even if it is at the expense of imagination. Films such as BATMAN and DICK TRACY are trying to impress the world with how well they can overcome problems that would not even arise with animation. DICK TRACY was able to make some expensive boxoffice stars really look much like the comic strip characters. BATMAN was less successful visually in making Jack Nicholson look like the Joker. Both of these films could have had better stories and looked absolutely perfect had they been animated, but they would have died at the boxoffice. Audiences really want live action and are quite willing to sacrifice story values and imagination for the thrill of seeing things done in live action.
THE WITCHES is a fine new live-action fantasy film that almost matches in imagination and charm what Walt Disney was able to do with animation back in the 1940s.
Fairy tales are not all sweetness and fun, and they are not just for children. Most fairy tales are horror stories told on a level that all ages can appreciate them. And that is just what THE WITCHES is. From the very beginning this film lays down some blood-curdling folklore about witches. Some of it is really the stuff paranoia is made of. Witches can live right next door, they smell children from great distances, they have no toes. Take note, those of you who feel children must be protected from the sort of scary stories that children have been raised on from time immemorial. The stories are told to young Luke (played by Jasen Fisher) by his Norwegian grandmother (played by Swedish actor/director Mai Zettering) and it is a good thing she told him. He shortly has to fight a convocation of the witches of England in their plot to turn all English children into mice. And shortly is how he has to fight them, since he is one of the first two children turned into mice.
Jim Henson used his Muppet technology to portray the mouse Luke when he does something non-mouselike. When a trained mouse can be used, it is. The problem there is that the Muppet mouse has a cute face that the real mouse apparently found very difficult to mimic. The combining of realistic mouse movements with a humanlike personality for the mouse would be, of course, much simpler for Disney to do in animation than it was for Henson to do in live action, and the result would have been much more successful. The makeup for the witches is similarly nicely executed. Anjelica Huston's Grand High Witch makeup fails to convince totally that this is really the face of a living being, but it is well-detailed.
THE WITCHES is, and is likely to remain, Nicholas Roeg's only children's film. (He claims he made it for his own newly-born child.) Allan Scott's script, based on the novel by Roald Dahl, raises some gooseflesh early on, but loses much of its power to chill once Luke is a cute mouse and the story concentrates more on his mission than on the horror of the witches. Scott also manages to throw in some double entendres clearly not intended for the younger audience. In smaller roles there is Rowan Atkinson (television's Black Adder) as a hotel manager clearly of the Basil Fawlty style. Bill Peterson (Dickie Bird in Bill Forsyte's COMFORT AND JOY) plays the father of a mouse who once was a gluttonous child.
It would be unfair to down-rate this film because it is not doing anything to advance animated film. The film as it stands *is* charming and works (at least generally) in live action. It is a nice fantasy and deserves some credit for not aiming specifically at a youth or teenage market. It is just a decent fantasy film that is there for whatever audience it finds. I rate it a low +2 on the -4 to +4 scale.
Mark R. Leeper att!mtgzx!leeper<EMAIL_ADDRESS>.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


| Package | Description |
|---|---|
| org.opensaml.xml |
Base classes for working with XML as Java objects and configuring the library.
|
| org.opensaml.xml.encryption.impl |
Implementations of the interfaces for XMLObjects that represent XML encryption types.
|
| org.opensaml.xml.schema.impl |
Implementations of the interfaces for XMLObjects that represent XML schema types.
|
| org.opensaml.xml.security |
Interfaces and classes used in representing cryptographic credentials, evaluating the trustworthiness of security of tokens, etc.
|
| org.opensaml.xml.security.credential |
Interfaces and classes related to credentials and ways to represent
them.
|
| org.opensaml.xml.signature.impl |
Implementations of the interfaces for XMLObjects that represent XML signature types.
|
| org.opensaml.xml.util |
Utility classes for working with XML, XMLObjects, and various data types.
|
| Class and Description |
|---|
| AttributeMap
A map of attribute names and attribute values that invalidates the DOM of the attribute owning XMLObject when the
attributes change.
|
| IDIndex
Class which provides storage for the ID-to-XMLObject index mapping on an owning
XMLObject . |
| IndexedXMLObjectChildrenList
A list which indexes XMLObjects by their schema type and element QName for quick retrival based on those items.
|
| Class and Description |
|---|
| AttributeMap
A map of attribute names and attribute values that invalidates the DOM of the attribute owning XMLObject when the
attributes change.
|
| IndexedXMLObjectChildrenList
A list which indexes XMLObjects by their schema type and element QName for quick retrival based on those items.
|
| XMLObjectChildrenList
Resizable list for the children of XMLObjects.
|
| Class and Description |
|---|
| AttributeMap
A map of attribute names and attribute values that invalidates the DOM of the attribute owning XMLObject when the
attributes change.
|
| IndexedXMLObjectChildrenList
A list which indexes XMLObjects by their schema type and element QName for quick retrival based on those items.
|
| Class and Description |
|---|
| ClassIndexedSet
Set implementation which provides indexed access to set members via their class,
and which allows only one instance of a given class to be present in the set.
|
| Class and Description |
|---|
| ClassIndexedSet
Set implementation which provides indexed access to set members via their class,
and which allows only one instance of a given class to be present in the set.
|
| Class and Description |
|---|
| IndexedXMLObjectChildrenList
A list which indexes XMLObjects by their schema type and element QName for quick retrival based on those items.
|
| IndexingObjectStore
This class is used to store instances of objects that may be created independently but are, in fact, the same object.
|
| Pair
Container for a pair of objects.
|
| Class and Description |
|---|
| AbstractSingletonFactory
An abstract Template design pattern implementation of
SingletonFactory . |
| AttributeMap
A map of attribute names and attribute values that invalidates the DOM of the attribute owning XMLObject when the
attributes change.
|
| ClassIndexedSet
Set implementation which provides indexed access to set members via their class,
and which allows only one instance of a given class to be present in the set.
|
| IDIndex
Class which provides storage for the ID-to-XMLObject index mapping on an owning
XMLObject . |
| IndexedXMLObjectChildrenList
A list which indexes XMLObjects by their schema type and element QName for quick retrival based on those items.
|
| IndexingObjectStore.StoredObjectWrapper
Wrapper class that keeps track of the reference count for a stored object.
|
| SingletonFactory
An interface for factory classes which implement a singleton pattern for producing an
output class based on an input class.
|
| XMLObjectChildrenList
Resizable list for the children of XMLObjects.
|
Copyright © 1999-2014. All Rights Reserved.


Easy Painting
Painting
A variety of color matching recommendation, let you DIY own paintings.
Materials
App's vast resources include animals, plants, literature, datura, flowers, Facebook, a variety of classifications, to conceive your own color scheme.


The Google Drive CMS is a Google Sheet with a custom menu option to publish the content of that spreadsheet to your site's API. Using this workflow you could send the contents of your Google Sheet
{% for instruction in instructions %}
{% include "includes/index/_instruction.html" %}
{% endfor %}


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{% extends "application/layout.html" %} {% block page_title %}{{ serviceName }} - Start{% endblock %} {% block c_content %}
Back
This means if anyone has paid for the work you've done, whether directly or into your business.
This means records of costs, sales and expenses.
You must send a copy of your most recent accounts to the Carer's Allowance Unit.
Most businesses work out their annual profits to April as it matches the tax year, but some don't - this is known as a trading year.


|
MeterLogger
|
#include <esp8266.h>
#include "driver/uart.h"
#include "mqtt.h"
#include "mqtt_rpc.h"
#include "crypto/crypto.h"
#include "crypto/aes.h"
#include "crypto/sha256.h"
#include "crypto/hmac-sha256.h"
#include "wifi.h"
#include "config.h"
#include "debug.h"
#include "httpd.h"
#include "httpd_user_init.h"
#include "user_config.h"
#include "unix_time.h"
#include "cron/cron.h"
#include "led.h"
#include "ac/ac_out.h"
#include "utils.h"
#include "user_main.h"
#include "captdns.h"
#include "tinyprintf.h"
#include "driver/ext_spi_flash.h"
#include "watchdog.h"
#include "version.h"
#include "kmp_request.h"
#include "ets_sys.h"
#include "user_interface.h"
Go to the source code of this file.
Variables | |
| bool | fast_boot |
| MQTT_Client | mqtt_client |
| static os_timer_t | sample_timer_first |
| static os_timer_t | sample_timer |
| static os_timer_t | config_mode_timer |
| static os_timer_t | sample_mode_timer |
| static os_timer_t | mqtt_connected_first_mqtt_rpc_timer |
| static os_timer_t | mqtt_connected_defer_timer |
| static os_timer_t | kmp_request_send_timer |
| uint8_t | mqtt_connected_first_mqtt_rpc_state |
| uint8_t | mesh_ssid [AP_SSID_LENGTH+1] |
|
static |
Definition at line 229 of file user_main.c.
|
static |
Definition at line 371 of file user_main.c.
| ICACHE_FLASH_ATTR void meter_is_ready | ( | void | ) |
Definition at line 406 of file user_main.c.
| ICACHE_FLASH_ATTR void meter_sent_data | ( | void | ) |
Definition at line 467 of file user_main.c.
| ICACHE_FLASH_ATTR void mqtt_connected_cb | ( | uint32_t * | args | ) |
Definition at line 602 of file user_main.c.
|
static |
Definition at line 93 of file user_main.c.
| ICACHE_FLASH_ATTR void mqtt_data_cb | ( | uint32_t * | args, |
| const char * | topic, | ||
| uint32_t | topic_len, | ||
| const char * | data, | ||
| uint32_t | data_len | ||
| ) |
Definition at line 667 of file user_main.c.
| ICACHE_FLASH_ATTR void mqtt_disconnected_cb | ( | uint32_t * | args | ) |
Definition at line 644 of file user_main.c.
| ICACHE_FLASH_ATTR void mqtt_flash_error | ( | uint16_t | calculated_crc, |
| uint16_t | saved_crc | ||
| ) |
Definition at line 1214 of file user_main.c.
Referenced by minute_timer_func(), mqtt_rpc_open_until(), mqtt_rpc_open_until_delta(), mqtt_rpc_set_pwd(), mqtt_rpc_set_ssid(), mqtt_rpc_set_ssid_pwd(), mqtt_rpc_start_ap(), and mqtt_rpc_stop_ap().
| ICACHE_FLASH_ATTR void mqtt_ping_response_cb | ( | uint32_t * | args | ) |
Definition at line 656 of file user_main.c.
| ICACHE_FLASH_ATTR void mqtt_published_cb | ( | uint32_t * | args | ) |
Definition at line 652 of file user_main.c.
| ICACHE_FLASH_ATTR void mqtt_send_wifi_scan_results_cb | ( | const struct bss_info * | info | ) |
Definition at line 866 of file user_main.c.
Referenced by mqtt_rpc_scan().
| ICACHE_FLASH_ATTR void mqtt_timeout_cb | ( | uint32_t * | args | ) |
Definition at line 660 of file user_main.c.
|
static |
Definition at line 143 of file user_main.c.
|
static |
Definition at line 358 of file user_main.c.
|
static |
Definition at line 250 of file user_main.c.
| ICACHE_FLASH_ATTR void system_init_done | ( | void | ) |
Definition at line 1163 of file user_main.c.
| ICACHE_FLASH_ATTR void user_gpio_init | ( | ) |
Definition at line 927 of file user_main.c.
| void ICACHE_FLASH_ATTR user_init | ( | void | ) |
Definition at line 1031 of file user_main.c.
| ICACHE_FLASH_ATTR void wifi_changed_cb | ( | uint8_t | status | ) |
Definition at line 587 of file user_main.c.
|
static |
Definition at line 57 of file user_main.c.
| bool fast_boot |
Definition at line 35 of file user_main.c.
|
static |
Definition at line 66 of file user_main.c.
| uint8_t mesh_ssid[AP_SSID_LENGTH+1] |
Definition at line 75 of file user_main.c.
Referenced by minute_timer_func().
| MQTT_Client mqtt_client |
Definition at line 54 of file user_main.c.
|
static |
Definition at line 60 of file user_main.c.
| uint8_t mqtt_connected_first_mqtt_rpc_state |
Definition at line 68 of file user_main.c.
|
static |
Definition at line 59 of file user_main.c.
|
static |
Definition at line 58 of file user_main.c.
|
static |
Definition at line 56 of file user_main.c.
|
static |
Definition at line 55 of file user_main.c.


| Lub 25 | 08 September 19 | 25 | miles | |
| Name | Position | Road bike | Actual Time | Avg. mph |
| Matt Plews | 1 | 01:02:30 | 24.00 | |
| Jonathan Durnin | 2 | 01:04:31 | 23.25 | |
| Nik Kershaw | 3 | 01:05:35 | 22.87 | |
| Alistair Watson | 4 | 01:08:16 | 21.97 | |
| Ian Waterfield | 5 | 01:08:47 | 21.81 | |
| Stuart Dawkins | 6 | 1 | 01:10:42 | 21.22 |
| Matthew Vallins | 7 | 2 | 01:13:45 | 20.34 |
| Simon Ward | 8 | 01:16:27 | 19.62 | |
| Stewart Holland | 9 | 3 | 01:23:23 | 17.99 |
| Julian Middleton | DNF |


Case study
How working in the community with training providers and employers helps young people make progress towards work.
| Location | North east |
| ESF funding | Skills Funding Agency |
| ESF target group | Young people |
Step Up is a programme in South Tyneside for young people who aren’t in education, employment or training (NEET) or who are at risk of becoming NEET. The project helps them with employment and training.
Step Up is operated by an organisation called Springboard. It works with training organisations and other partners, supporting agencies and employers in the area.
By April 2013, of 325 young people who had taken part in Step Up:
Transitional and mentoring officers work in the community to recruit young people onto the programme. They’re the first point of contact for participants, assessing their needs, identifying their career objectives and referring them to further training and related activities. They continue their support and mentoring role throughout the training.
The Step Up programme has benefited from effective employer engagement. Local employers have agreed to recruit and train young people, with 25 employers providing placements for Step Up participants by April 2013.
Read more about the European Social Fund 2007 to 2013
Published: 13 May 2014


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