Difference Between CD Duplication and CD Replication

Lybrodisc has specialized in the production of music playing equipment for many years and has a wealth of experience.
When you first hear the words duplicate and replicate, can you think of any differences between the two? For most people, one word seems to be synonymous with the other, but this is not the case at all when you talk about CD duplication and CD replication.
In simple terms, CD duplication is the process that most computer owners use for their data or music files. With CD duplication, the information is burned onto a disk. What you need to have for this, is a software and a CD burner that will allow you to automatically burn the information onto a CD, and if you want to have several copies of disks containing the same data, the information needs to be burned again. That is practically how the process of CD duplication works.
CD replication, on the other hand, can be referred to as ‘professional CD burning’. Instead of burning the data onto each individual CD, a process is followed whereby the CD is molded to be an exact copy of the original ‘master copy’. This is the process used to produce the CDs sold on the market ‘“ because, just imagine how tedious it would be if the songs on the thousands of CDs released needed to be burned individually.
So, what are the other key differences between CD duplication and CD replication? CD duplication is more appropriate for personal use. It is actually inexpensive, and convenient for individuals who have computers at home. CD replication is more appropriate for commercial use, and the professional process of inputting the data onto the disk is a more reliable one. CD replication also offers a quicker, more convenient and high-quality way of replicating the data or songs from the master copy to individual disks.

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In the CD and DVD duplication process blank recordable compact discs (CD-R) are used. A burner or duplication is used to copy your data onto the blank CD-R. A CD-R or DVD-R with a printed label looks virtually identical to a replicated disc but with one difference – blank replicated DVD contains an additional element. They possess a laser-sensitive dye that allows the DVD to be “burned” with the video or data from your computer or DVD recorder.
The CD and DVD duplication processes are perfect for quick turn around and small run capability or for instances when the disc needs to be writable. We use professional quality CD-Rs and can produce tens of thousands of burned and printed CDs in a matter of days. After the accomplishment of the CD work, CD stickers are pasted on CD to give a final look and then finally packed in plastic sleeve for saving it from scratches. The Whole procedure is economical and within budget and time saving than duplication.

The first recorded sound was Thomas Edison’s voice, captured on a phonograph in 1877 reciting part of the nursery rhyme song “Mary Had a Little Lamb.”
10 years later, Emile Berliner created the first device that recorded and played back sound using a flat disc, the forerunner of the modern record.
Over the course of the next six decades, records and record players were improved and standardized, with the 33 and 45 RPM records supplanting most other formats in the post-WWII years.
By the 1970s, record player technology had evolved to the point where it has changed little in the intervening half century. In that time, cassette tapes came and went. CDs came and are going. And MP3 players were replaced by phones, as were cameras, pocket planners, and our social lives, more or less.
This year, 2020, marks the first year in more than a generation since record sales — that is to say physical vinyl records — have surpassed CD sales. The reasons for this are twofold: CD sales have dropped dramatically in recent years, while sales of vinyl records are actually up this year. And while you might think it’s nostalgic Boomers or Gen Xers behind the renaissance of records, in fact, surveys show its millennial consumers driving the rising trend in vinyl sales.
The way most people listen to music has changed. “You hear music when you’re in the coffee shop, in the car, in the gym, just walking down the street sometimes, we hear it everywhere,” says Scott Hagen, CEO of Victrola. “In every store, we go into we hear it, and we’re consuming more music than ever before, but not in the same way. The ability to stop and sit and listen to an album from beginning to end, that’s something that always has been and always will be relevant.”
At some point a band, songwriter or home recordist may wish to have cassette duplication made of their songs. These days record companies and publishers prefer cassette, but broadcast radio stations still prefer ¼” reel-to-reel tapes or disc (if your songs are to be played on the air), as the quality is that much better. There are three methods of cassette duplicating available, which are Loop-Bin, High Speed and Real Time.

Loop-Bin is a high speed form of duplicating where a 1″ or ½” master tape is first made from your ¼” master tape. It is then put on a machine which runs at 32 or 64 times normal speed along with slave units which copy reels of cassette tape. The cassette tape is then fed into empty cassette shells; this method is used for producing anything from 500 to 100,000 copies and is mostly used by independent and major record companies.

High Speed
A master cassette or ¼” reel is run at 8 or 16 times its normal speed along with slave cassette units. These slave units copy both sides simultaneously in stereo or mono and there can be one slave unit copying one cassette, or many slaves copying many cassettes at once. High Speed duplicating can cater for short runs (100+) to runs of thousands.

Real Time
A ¼” reel or cassette master is played at normal speed (which could be 15ips or 7½ips for reels or 1⅞ips if it’s a cassette). A bank of 5 to 50 cassette decks all run together to copy at normal speed. Generally, real time duplication caters mainly for runs of 10 to around 1000.

Noise Reduction
Most cassette duplicators can encode your cassettes with Dolby B and some can encode with Dolby C noise reduction. However, if you use a high speed duplicator and you want Dolby on your copies, then make sure your master cassette is recorded initially with Dolby on it. You should then be able to have the copies reproduced with Dolby. High Speed or Real Time duplicating are most likely to suit the home recordist, band, songwriter or small label.

Your Master Tape
This should be ¼” reel-to-reel running at 15ips or 7½ips stereo half track or quarter track. You can use cassette masters (from the studio), but they are not as good quality as reel-to-reel. Do remember also, that if your songs are not in the right running order, then a Duplicating Suite can re-edit the tape, but there may be an additional charge. If you choose the Loop-Bin or High Speed methods be prepared for a charge for making their copy master which is necessary for each of these processes.

Tape Types
When you telephone or go to see a Duplicator ask what tape he uses; for example Ferric, Chrome or Metal, and also, find out what brand it is. A named brand like Agfa, BASF, TDK or Maxell are all pretty much a safe bet. If he used a name you do not know, then listen to a copy, preferably of your master, and compare the quality with other tape brands. You may decide to use your own bought tapes instead of those supplied by the duplicators, in which case there will be a charge per hour to copy onto your own tape, which can be anywhere from £5.00 to £10.00 per hour plus VAT.

Because the CD replication involves quite a bit of setup it’s usually done for larger runs.
Most manufacturers do it on orders of a thousand or more. We replicate CDs in quantities as low as 300.
However, what do you do if you need less than three hundred discs?

How to avoid duplication of types in MVVM

I’m learning the MVVM pattern and something that comes up often is a duplication of data-types.

Say I have a Person datatype. Intuitively I want it to look like this:

public class Person {     public string FirstName { get; set; }     public string LastName { get; set; }     public uint Age { get; set; } } 

But instead, the code will have a PersonViewModel a PersonModel class and in many cases there’s also a PersonData class which is used for serialization.

A PersonViewModel inherits from a base class for ViewModels and the setters will have RaisePropertyChanged calls.

public class PersonViewModel: ViewModelBase {     private string _firstName;      public string FirstName     {         get         {             return _firstName;         }         set         {             _firstName = value;             OnPropertyChanged("Person");         }     }     ... } 

A PersonModel does not call OnPropertyChanged and does not inherit from ViewModel. Also one of the reasons for this is to not bind the view to the model directly, rather to bind it to a ViewModel.

A PersonData will have the same properties but they’ll be marked with the DataMember attribute and the class will have the DataContract attribute.

I have 2 questions:

1) Is it really necessary to have so many classes for the same data type? They usually have the exact same properties. Is it not better to just have one class per data type?

2) One of the problems is that changing one class requires you to change the other classes as well. I thought about using an interface that looks like this:

public interface IPerson {     public string FirstName { get; set; }     public string LastName { get; set; }     public uint Age { get; set; } } 

and have the 3 classes implement the interface. If indeed all these classes are necessary is it a good solution for the consistency problem?

3) Is the distinction between “Model” and “ViewModel” really necessary in the data-types level? Sure I can understand why it’s a good idea to have one unit of code be responsible for invoking commands, presenting data, etc… and have another unit of code be responsible for the business logic, but is it really necessary when we’re dealing with data types that are being used across the entire codebase?

Minimizing code duplication without using macros or sacrificing speed

Here’s a snippet of code:

An inlined function:

inline void rayStep(const glm::vec3 &ray, float &rayLength, const glm::vec3 &distanceFactor, glm::ivec3 &currentVoxelCoordinates, const glm::ivec3 &raySign, const glm::ivec3 &rayPositive, glm::vec3 &positionInVoxel, const int smallestIndex) {     rayLength += distanceFactor[smallestIndex];     currentVoxelCoordinates[smallestIndex] += raySign[smallestIndex];     positionInVoxel += ray * glm::vec3(distanceFactor[smallestIndex]);     positionInVoxel[smallestIndex] = 1 - rayPositive[smallestIndex]; } 

It’s usage:

glm::ivec3 distanceFactor = (glm::vec3(rayPositive) - positionInVoxel) / ray;      if (distanceFactor.x < distanceFactor.y)     {         if (distanceFactor.x < distanceFactor.z)         {             rayStep(ray, rayLength, distanceFactor, currentVoxelCoordinates, raySign, rayPositive, positionInVoxel, 0);         }         else         {             rayStep(ray, rayLength, distanceFactor, currentVoxelCoordinates, raySign, rayPositive, positionInVoxel, 2);         }     }     else     {         if (distanceFactor.y < distanceFactor.z)         {             rayStep(ray, rayLength, distanceFactor, currentVoxelCoordinates, raySign, rayPositive, positionInVoxel, 1);         }         else         {             rayStep(ray, rayLength, distanceFactor, currentVoxelCoordinates, raySign, rayPositive, positionInVoxel, 2);         }     } 

I really dislike the way the usage of the function looks like. One way I could fix it is to calculate the index of the smallest component and then just use the body of the function directly in the code:

int smallestIndex = (distanceFactor.x < distanceFactor.y) ? (distanceFactor.x < distanceFactor.z ? 0 : 2) : (distanceFactor.y < distanceFactor.z ? 1 : 2); rayLength += distanceFactor[smallestIndex]; currentVoxelCoordinates[smallestIndex] += raySign[smallestIndex]; positionInVoxel += ray * glm::vec3(distanceFactor[smallestIndex]); positionInVoxel[smallestIndex] = 1 - rayPositive[smallestIndex]; 

This looks much cleaner to me.

So why haven’t I done that, if it bothers me so much?

The benefit of the above code is that value of the smallestComponentIndex of the function is know at compile time – the value is given as a constant and the function is inlined. This enables the compiler to do some optimizations which it wouldn’t be able to do if the value were unknown during the compile time – which is what happens in the example with double ternary operator.

The performance hit in my example is not small, the code goes from 30ms to about 45ms of execution time – that’s 50% increase.

This seems negligible, but this is a part of a simple ray tracer – if I want to scale it to do more complex calculations, I need this part to be as fast as possible, since it’s done once per ray intersection. This was run on low resolution with a single ray per pixel, with no light sources taken into account. A simple ray cast, really, hence the runtime of 30ish ms.

Is there any way I can have both the code expression and speed? Some nicer way to express what I want to do, while making sure that value of the smallestComponentIndex is known at compile time?

About duplication IP Settings on Cisco L3 Switch

I set up the IP for Cisco L3 switch, but why is the error only eth 1/2 even though there is an IP duplication as shown below?
I am a beginner. . .
Please teach me!

SW(config)# SW(config)# interface mgmt 0 SW(config-if)# ip address SW(config-if)# no shutdown SW(config-if)# exit SW(config)# SW(config)# SW(config)# interface vlan 10 SW(config-if)# ip address SW(config-if)# exit SW(config)# SW(config)# SW(config)# int ethernet 1/1 SW(config-if)# no switchport SW(config-if)# ip address SW(config-if)# no shut SW(config-if)# SW(config-if)# SW(config-if)# int ethernet 1/2 SW(config-if)#  SW(config-if)# no switchport SW(config-if)# ip address % overlaps with address configured on Ethernet1/1 SW(config-if)# 

How to avoid code duplication caused by javascript dict access?

I have two functions that group a list of dicts according to the value of a certain key. Here’s how my array looks:

const sections = [   {     file_id: '1',     heading_level: 4,     readme_file_name: 'Quick.Quick.md',     section_codes: [       1,       3,     ],     section_id: '1',     title: 'Nimble',   },   {     file_id: '1',     heading_level: 2,     readme_file_name: 'Quick.Quick.md',     section_codes: [       3,       4,     ],     section_id: '2',     title: 'Swift Version',   },   // ... ]; 

Some times I need to group the sections by it’s heading_level key value, and other times I need to group by the first value on section_codes array.

I’ve created a single function for each grouping, but the code is exactly the same, except for the line where I access the desired key. So I’ve tried removing duplication, and here’s where I’ve gotten so far:

// Helper func function getValueForKey(key, section) {   const originalValue = section[key];    if (Array.isArray(originalValue)) {     return originalValue[0];   }    return originalValue; }  export function groupSectionsByKey(key, sections) {   const groupedSections = {};    sections.forEach((section) => {     const groupKey = getValueForKey(key, section);     const codeArray = groupedSections[groupKey];      if (codeArray) {       codeArray.push(section);     } else {       groupedSections[groupKey] = [section];     }   });    return groupedSections; } 

This getValueForKey function seems a bit off for me, but I’m not sure if there is a better way to do this. Does anyone have some feedback?


JSON flattening with object duplication on array property for CSV generation

I am looking for a way to transform JSON data into a flat “csv-like” data object. In some way, I am looking to “sqlirize” a mongodb collection. I have already check some json flat libraries in NPM but non of them quite solve my problem. I have solved it in my own way but wanted to know if there is a more efficient way.

I have a collection that presents the data through an API in the following way:

[{     "data": {         "name": "John",         "age": 23,         "friends": [{             "name": "Arya",             "age": 18,             "gender": "female"         }, {             "name": "Sansa",             "age": 20,             "gender": "female"         }, {             "name": "Bran",             "age": 17,             "gender": "male"         }]     } }, {     "data": {         "name": "Daenerys",         "age": 24,         "friends": [{             "name": "Grey Worm",             "age": 20,             "gender": "male"         }, {             "name": "Missandei",             "age": 17,             "gender": "female"         }]     } }] 

This is the function that I have created to reflat a safe-flattened json (e.i.: everything is flattened except arrays)

const { cloneDeep } = require('lodash') const flatten = require('flat')  const reflatten = (items) => {   const reflatted = []    items.forEach(item => {     let array = false      for (const key of Object.keys(item)) {       if (Array.isArray(item[key])) {         array = true          const children = Array(item[key].length).fill().map(() => cloneDeep(item))          for (let i = 0; i < children.length; i++) {           const keys = Object.keys(children[i][key][i])            keys.forEach(k => {             children[i][`$  {key}.$  {k}`] = children[i][key][i][k]           })           delete children[i][key]           reflatted.push(children[i])         }         break       }     }     if (!array) {       reflatted.push(item)     }   })    return reflatted.length === items.length     ? reflatted     : reflatten(reflatted) }  const rows = []  for (const item of items) {   const flat = [flatten(item)]    rows.push(...reflatten(flat)] }  console.log(rows) 

The expected (and current) output is the following:

[{     "data.name": "John",     "data.age": 23,     "data.friends.name": "Arya",     "data.friends.age": 18,     "data.friends.gender": "female" }, {     "data.name": "John",     "data.age": 23,     "data.friends.name": "Sansa",     "data.friends.age": 20,     "data.friends.gender": "female" }, {     "data.name": "John",     "data.age": 23,     "data.friends.name": "Bran",     "data.friends.age": 17,     "data.friends.gender": "male" }, {     "data.name": "Daenerys",     "data.age": 24,     "data.friends.name": "Grey Worm",     "data.friends.age": 20,     "data.friends.gender": "male" }, {     "data.name": "Daenerys",     "data.age": 24,     "data.friends.name": "Missandei",     "data.friends.age": 17,     "data.friends.gender": "female" }] 

Although I achieved the expected output, I keep wondering if there are other libraries there or if there is a more efficient way of doing it.

efficient duplication of Time Machine backups to remote location

I have been using both Arq and Time Machine for backup for years, but a year or so ago I got unlimited Google Drive space and decided to try making a remote backup (with Arq) of my Time Machine drive.

This may seem ridiculous, but it helps ameliorate a major problem with Arq: you can’t move your remote backup from one service to another. For example if you signed up for Amazon’s drive when it was $ 5 a year for unlimited space, and used it for your Arq backups, and then decided not to pay $ 120 a year for sufficient space for your backups when Amazon said “oh, just kidding!” and changed their prices, you’re unable to move your backup history to another location. (Since history is the whole purpose of a backup system, this is the one reason I no longer recommend Arq to anyone. I used to enthusiastically recommend it to friends and family.) Backing up your Time Machine disk allows you to have a remote backup that you can move from one service to another without losing any history. (The only obvious downside is that you must first restore the whole Time Machine backup if your Time Machine disk has died and you want to change cloud provider at the same time.)

There is a non-obvious downside, however: Arq really sucks for this use case, for reasons I don’t understand. It gets through about 3400 GB (out of around 3600 GB) around the first day, then runs for days and days making virtually no progress, and then might at some point start to make some progress. I don’t know if I have ever seen it complete the backup. Many times I need to shut down my laptop before it completes.

I’d like to have an efficient way to backup my Time Machine drive to a remote service. It can’t depend on (e.g.) running rsync or Resilio Sync on the remote service.

In case it’s relevant for any reason, I have fiber, so the bandwidth is limited by the devices on both ends and any throttling the service does, rather than by the quantity of data pushed.)

One thing that Arq got right is the end-to-end encryption, so any replacement should include end-to-end encryption. I’m not about to trust anyone with everything.

TCP handshake packets duplication

Using Wireshark I’m observing that all handshake packets starting from first SYN packet are duplicated on two different ports.

SYN packets sent from port 56078 and 56079:

11  22:57:11.726692 X.X.X.X 66  X.X.X.X TCP 56078 → 80 [SYN] Seq=0 Win=64240 Len=0 MSS=1460 WS=256 SACK_PERM=1 12  22:57:11.727364 X.X.X.X 66  X.X.X.X TCP 56079 → 80 [SYN] Seq=0 Win=64240 Len=0 MSS=1460 WS=256 SACK_PERM=1 

SYN, ACK received to 56078 and 56079:

14  22:57:11.851809 X.X.X.X 66  X.X.X.X TCP 80 → 56079 [SYN, ACK] Seq=0 Ack=1 Win=29200 Len=0 MSS=1440 SACK_PERM=1 WS=128 17  22:57:11.852657 X.X.X.X 66  X.X.X.X TCP 80 → 56078 [SYN, ACK] Seq=0 Ack=1 Win=29200 Len=0 MSS=1440 SACK_PERM=1 WS=128 

Same thing with the last ACK packets.

Why does this happen?

The code duplication reducing in the linked list implementation

I am writing the linked list data structure, using this question’s requirements (the methods set).

I have written two working methods, which do the similar thing and differs by only few lines. So, I tried to refactor them and although I move the duplicate part to the separate function, the code became more verbose. Also, it added two function calls overhead, that is not good for data structure implementations.

The question:

  1. Do you see another way to duplication elimination?
  2. I understand, that in this particular case, it will be better to leave everything as it is, without refactoring. But from the point of best production practices and “pythonic” way, which variant will be better?

Before refactoring

def insert_before_key(self, key, value):         new_node = Node(value)         prev, curr = self._find_key(key)          if curr:             if prev:                 new_node.next = curr            # differs                 prev.next = new_node            # differs             else:                 new_node.next = self.head       # differs                 self.head = new_node            # differs      def insert_after_key(self, key, value):         new_node = Node(value)         prev, curr = self._find_key(key)          if curr:             if prev:                 new_node.next = curr.next       # differs                 curr.next = new_node            # differs             else:                 new_node.next = self.head.next  # differs                 self.head.next = new_node       # differs 

After refactoring

# Common part was moved to this function def insert(self, key, value, with_prev, non_prev):     new_node = Node(value)     prev, curr = self._find_key(key)      if curr:         if prev:             with_prev(prev, curr, new_node)         else:             non_prev(prev, curr, new_node)  # put two little chunks of code to the nested functions  # and pass them to the "insert" function, that implement the logic def insert_before_key(self, key, value):     def with_prev(prev, curr, new_node):         new_node.next = curr         prev.next = new_node      def non_prev(prev, curr, new_node):         new_node.next = self.head         self.head = new_node      self.insert(key, value, with_prev, non_prev)  def insert_after_key(self, key, value):     def with_prev(prev, curr, new_node):         new_node.next = curr.next         curr.next = new_node      def non_prev(prev, curr, new_node):         new_node.next = self.head.next         self.head.next = new_node      self.insert(key, value, with_prev, non_prev) 

Android transparent activity duplication issue

Android transparent activity background duplication issue

I’ve made transparent activity using style:

<resources>  <!-- Base application theme. --> <style name="AppTheme" parent="Theme.AppCompat.Light.NoActionBar">     <item name="windowNoTitle">true</item>     <item name="windowActionBar">false</item>     <item name="android:windowIsTranslucent">true</item>     <item name="android:windowBackground">@android:color/transparent</item>     <item name="android:windowContentOverlay">@null</item>     <item name="android:windowNoTitle">true</item>     <item name="android:windowIsFloating">false</item>     <item name="android:backgroundDimEnabled">false</item>     <item name="android:windowAnimationStyle">@android:style/Animation</item>      <!-- Customize your theme here. -->     <item name="colorPrimary">@color/colorPrimary</item>     <item name="colorPrimaryDark">@color/colorPrimaryDark</item>     <item name="colorAccent">@color/colorAccent</item> </style> 


<?xml version="1.0" encoding="utf-8"?> 

<LinearLayout         android:layout_gravity="bottom"         android:layout_margin="8dp"         android:background="@drawable/notification_layout_rect"         android:id="@+id/clWin"         android:orientation="vertical"         android:layout_width="match_parent"         android:layout_height="250dp">  </LinearLayout> 

And code:

class MainActivity : AppCompatActivity() {  override fun onCreate(savedInstanceState: Bundle?) {     super.onCreate(savedInstanceState)     setContentView(R.layout.activity_main)     makeStatusBarTransparent() }  private fun makeStatusBarTransparent() {     if (Build.VERSION.SDK_INT >= 21) {         window.clearFlags(WindowManager.LayoutParams.FLAG_TRANSLUCENT_STATUS)         window.statusBarColor = resources.getColor(android.R.color.transparent)     } } 


Issue: But when I am clicking on Overview navigation button several times I got background duplication effect. Is there is a way to fix this issues ?

enter image description here

Issue is reproducable at least on android 5.1.1 and 7.0

Source code: https://www.dropbox.com/s/6xghi6a3wten42a/TransparentActivity.zip?dl=0