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Mega Process takes us on a tour of Zion Industrial Co., which apparently is the last shovel factory in South Korea. The laborious process starts with freshly-cut oak trees, milling the timber into boards, sanding until round, trimming, then splitting, steaming, and bending the wood to hold the shape for its handle.
The Awesomer
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U.S.A. –-(AmmoLand.com)-— Former Japanese Prime Minister Shinzo Abe was assassinated with a homemade gun on July 8th, 2022, in the Japanese city of Nara.
From video of the event, it appears the assassin fired his weapon at Abe from a distance of 20 to 30 feet. The two distinct shots were fired about a second apart and produced copious amounts of white smoke.
Early reports were of wounds to the neck and chest. It is unknown what sort of projectiles were used in the homemade weapon.
This correspondent made several similar homemade, electrically actuated, multishot, muzzleloading black powder firearms 40 years ago. Electrical firing mechanisms are much easier to make than percussion systems used in modern firearms. A simple switch is a trigger for each barrel. Batteries supply the power to ignite each charge. If you want multiple shots, multiple barrels are used. A nine-volt battery worked well in my 40-year-old experiments, as did a four-pack of AA batteries.
A simple galvanized water pipe is plenty strong to withstand black powder pressures for a few shots. The firearm used in the assassination appears to be close to a 12 gauge bore size. Larger bores and projectiles create sufficient energies at the lower velocities on such firearms.
The propellants for such guns are easily made at home. Many high school students successfully made black powder in my youth. Fireworks can be disassembled as another source of propellant. It has been reported that people in prisons and the former Soviet Union used ground-up match heads as a propellant. Making propellant is much more dangerous than making the guns.
This correspondent disagrees with professor Daniel Foote of Tokyo.
“This actually shows the extent that Japan gun laws are working,” said Daniel Foote, a professor at the University of Tokyo specializing in law and society. “Very few people have the ability to create such a weapon.”
It is unlikely the professor has ever done the experimental work to make such weapons. Forty years ago, this correspondent was able to make a four-shot repeater from 12 dollars of materials obtained from a hardware store and Radio Shack, using about 12 hours’ worth of labor. In order to be effective with such weapons, testing and practice is necessary. It appears the former Naval officer did the preparation required.
Police found several possible explosives during a raid on Yamagami’s home, and the suspect confessed that he had “manufactured multiple pistols and explosives so far,” NHK said.
Other local reports said that he told cops he initially planned to blow up Abe, but decided to instead build a gun because he feared a bomb was less reliable.
Some sources are saying the assassination occurred at 10 feet. In the video referenced above, the distance appears to be 20 to 30 feet. There did not appear to be any sights on the homemade firearm. Today, some sort of laser pointer would be easy to install and bore sight. But NO mention of a laser has been made to this correspondent’s knowledge.
The successful assassination of former Japanese PM Abe, with a homemade firearm in a country with some of the most extreme firearms restrictions on the planet, shows how difficult it is to regulate what is essentially a 15th-century technology.
About Dean Weingarten:
Dean Weingarten has been a peace officer, a military officer, was on the University of Wisconsin Pistol Team for four years, and was first certified to teach firearms safety in 1973. He taught the Arizona concealed carry course for fifteen years until the goal of Constitutional Carry was attained. He has degrees in meteorology and mining engineering, and retired from the Department of Defense after a 30 year career in Army Research, Development, Testing, and Evaluation.
AmmoLand Shooting Sports News
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The Transcend JetDrive Lite is as close as you can get to upgrading the internal storage on your modern MacBook Pro, but hard drive-like speeds limit its uses.
Designed for the 2021 16-inch MacBook Pro and 14-inch MacBook Pro, the Transcend JetDrive Lite 330 is a small chip of solid-state storage that slips into the SD card reader to expand capabilities. You’ll hardly even notice it is there while you can add up to 1TB of additional storage.
When Apple first added an SD card reader to the Mac, a slew of third-paltry adapters flooded the market. We vividly remember when an early company called Nifty MiniDrive launched that had an anodized aluminum edge to blend into your Mac seamlessly.
Early Nifty MiniDrive in red anodized aluminum
There was enough space in these Mac back then that the Nifty MiniDrive would act as an adapter for microSD cards. They even came in multiple colors.
The Transcend JetDrive Lite 330
It is the Nifty MiniDrive and other early entrants that have set the stage for the Transcend JetDrive Lite 330. It isn’t Transcend’s first drive and they do have some that fit MacBooks as far back as 2010.
All of these drives slip into the SD card slots on your Mac but are sized as to only stick out a hair. Because of this, there is very little overlap in models. Each MacBook with an SD slot has its own corresponding JetDrive.
Back of the 1TB Transcend JetDrive Lite 330
The JetDrive Lite is all plastic with a little lip that protrudes out from the edge of your Mac. It is small enough that it doesn’t get in the way and is just big enough to grasp if you need to remove it.
We were able to remove the JetDrive with just our fingers, but if you have no fingernails you may struggle a bit. Some others we’ve seen have sat flush but required a tool to remove. This way, you can pop it out anytime, anywhere — no tool necessary.
Front of the JetDrive Lite
Transcend has made the JetDrive Lite available in four capacities for the 2021 MacBook Pros. We checked out the 1TB version but 512GB, 256GB, and 128GB capacities are also available.
Transcend is promising speeds of 95 MB/s and 75 MB/s for reading and writing, respectively. In our copious testing, the JetDrive Lite is on par for these numbers.
Blackmagic Disk Speed Test yielded 90.2 MB/s and 75.2 MB/s, in line with expectations. This puts the JetDrive Lite on the same level as UHS-I SD cards
Testing the JetDrive speeds
As the recent MacBook Pros are capable of UHS-II speeds — up to 312 MB/s — we wanted to know why the company had opted for the slower speeds.
They told AppleInsider that the biggest benefit of the JetDrive Lite is its compact size and ability to be perpetually in your machine and ready for use, not for high-speed transfers.
While this is undoubtedly true, jumping to UHS-II speeds seems not only possible — given the size of UHS-II MicroSD cards — but significantly beneficial to the end-user. Despite the convenience, this was our biggest pain point while using the JetDrive Lite.
Moving to UHS-II speeds would surely up the cost of the drive. This may have been a compromise Transcend was unwilling to make.
Apple’s current portable Mac lineup does not support user-upgradable storage. Meaning the storage you opt for on purchase is what you’ll be working with years later.
Side view of the JetDrive Lite
The JetDrive is no substitute for the blazing fast internal SSD but it is by far the easiest way to add up to 1TB of additional storage to your Mac. With its speed limitations, there are practical — and non-practical uses for such storage.
By far one of the most common ways to use the JetDrive is to employ it as your Time Machine backup. This works if you have less data on your internal than the JetDrive. If you have the 256GB or 512GB SSD and don’t use all of it, the 1TB JetDrive will be sufficient for this.
We only had about 300GB of data on our internal drive so we wanted to test using the JetDrive as a backup solution. We enabled this and allowed Time Machine to chug away overnight for the initial backup. When we woke our machine the following morning, it had finished.
Using the JetDrive as a backup device
Each subsequent backup took mere moments — depending on how much the data on our Mac changed. Don’t forget though, that if something happens to your Mac, your backup may be lost too. An off-site backup or cloud backup is always ideal.
We preferred to use our JetDrive though just for extra storage. We started saving images and documents not stored in iCloud. Accessing files is relatively speedy depending on their size.
It’s also proved useful for storing movies. When we head out on trips, we download some movies and TV shows to our Mac to access while on the go. These aren’t crucial files which means if we have to remove the card, we aren’t missing much.
It took roughly 45 seconds for us to transfer a 3.75 GB movie to the JetDrive, which isn’t instantaneous, but fast enough to be useful.
While it works well enough for everyday files, we wouldn’t rely on it for video editing. The speed required to edit a 4K video and the vast size of the library makes the JetDrive unusable for this application.
Cost-wise, the JetDrive Lite 330 is neither good nor bad. Apple charges $200 to upgrade from 512GB to 1TB of storage. You can spend less than that on the 1TB JetDrive Lite, though it is certainly slower.
1TB Transcend JetDrive Lite 330
If you opt for the 2TB MacBook Pro, Apple charges a whopping $600 from the base configuration and $400 over the 1TB option. Some may opt for slower speeds to save hundreds of dollars.
At the same time, off-brand MicroSD cards are as cheap as $50 for 1TB. Name-brand microSD cards are priced more competitively with the JetDrive Lite.
Overall, this makes JetDrive a solid choice to expand your storage without breaking the bank. It could even save you money based on your needs. Regardless, it’s absolutely the most seamless aftermarket solution out there.
AppleInsider News
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This six-part documentary series tells the history of Industrial Light & Magic. It features interviews with famous filmmakers and the people behind George Lucas’ esteemed visual effects studio, and explores how they created some of the most memorable movie scenes of all time. Arrives on Disney+ 7.27.22.
The Awesomer
In response to numerous inquiries from local law enforcement departments, the FBI undertook a comprehensive evaluation of the sidearms available in 1987 (most of which are still made today) to determine which pistol was the best. Thirteen of the most talented instructors that the Federal Bureau of Investigation had to offer all met at a […]
The post The FBI Said This is the Best Handgun (And Why They’re Wrong) appeared first on The Firearm Blog.
The Firearm Blog
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Laravel Ciphersweet is a package by Spatie to integrate searchable field-level encryption in Laravel applications. The package’s readme explains the problem Ciphersweet can help solve as follows:
In your project, you might store sensitive personal data in your database. Should an unauthorised person get access to your DB, all sensitive can be read which is obviously not good.
To solve this problem, you can encrypt the personal data. This way, unauthorized persons cannot read it, but your application can still decrypt it when you need to display or work with the data.
This package is a wrapper for Ciphersweet to integrate its features into Laravel models easily. Here’s an example of a model from the readme’s setup instructions that illustrates what a model looks like using Ciphersweet:
1use Spatie\LaravelCipherSweet\Contracts\CipherSweetEncrypted;
2use Spatie\LaravelCipherSweet\Concerns\UsesCipherSweet;
3use ParagonIE\CipherSweet\EncryptedRow;
4use Illuminate\Database\Eloquent\Model;
5
6class User extends Model implements CipherSweetEncrypted
7{
8 use UsesCipherSweet;
9
10 public static function configureCipherSweet(EncryptedRow $encryptedRow): void
11 {
12 $encryptedRow
13 ->addField('email')
14 ->addBlindIndex('email', new BlindIndex('email_index'));
15 }
16}
This allows you the encrypt a user’s email to keep it safe from unauthorized people reading the data, but give you the ability to decrypt the data to display it or work with it.
Once you have configured this package and set up a model, you can search encrypted data in the database using blind indexes:
1$user = User::whereBlind('email', 'email_index', 'rias@spatie.be');
This package also aids in generating encrypting keys and encrypting model attributes to speed up integration with Ciphersweet.
I want to point out that you should not use this package blindly without understanding the ins and outs of the use case you are trying to solve. You can learn more about CipherSweet on this page, which has many linked resources.
CipherSweet also has PHP-specific documentation to help get you up to speed with the underlying PHP package.
I would also recommend reading Rias’ post, Encrypting Laravel Eloquent models with CipherSweet.
To get started with this package, check it out on GitHub at spatie/laravel-ciphersweet.
Laravel News
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Do you have any idea what this crazy thing is?
So that’s a vehicle drainage rack (!) made by Seda, an Austrian company that makes machines used to dismantle automobiles. You’ve probably seen a video of a car being crushed. What you may not have realized is that prior to crushing it, the cars must be drained of all fluids (gas, oil, brake fluid, radiator fluid, etc.). Dismantling technicians cut into hoses, then the vehicle is lifted and tilted by the rack to drain every last drop of toxic goodness.
I wanted to find video of this in action, and got more than that: Here Seda demonstrates, in about 90 seconds, how to dismantle and recycle an entire car using their machines, including the draining rack:
Seeing the wiring alone was staggering. I’d heard that modern cars have nearly one mile of wiring in them, but seeing it all pulled out and placed in that bin was really something.
Core77
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Former ASU football standout and inspirational speaker Jerone Davison is running for Congress on the Republican ticket in Arizona. So far, it looks like Mr. Davison will be pulling ZERO punches in this fight.
Not the Bee
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Just about a year after releasing darktable 3.6, the darktable team has announced darktable 4.0.0. The update celebrates 10 years of darktable offering photographers open source raw image editing. The major release adds many new features to the open source photography workflow app and raw image editor, including color and exposure mapping, filmic v6, guided Laplacian highlight reconstruction, a new perceptually uniform color space, revamped user interface, performance improvements, and much more.
Color and exposure mapping comprise a new feature in the ‘exposure’ and ‘color calibration’ modules that allows you to define and save a specified target color/exposure for the color pickers. You can match any source object against an arbitrary target color. You can use this tool to perform white balance adjustments against non-gray objects of known color, or ensure consistent color across a batch of images.
Filmic v6 includes a new color science. Darktable writes, ‘This change removes the mandatory desaturation close to medium white and black and replaces it with a true gamut mapping against the output (or export) color space. This allows for more saturated colors, notably in blue skies.’ Darktable 4.0.0 now includes a ‘fully-sanitized color pipeline’ from input (color calibration), creative changes (color balance RGB) and through to output (filmic v6).
Within the ‘highlight reconstruction’ module is a new ‘guided Laplacian’ method. This uses a ‘multi-scale wavelet scheme to extract valid details from non-clipped RGB channel(s)’ and ‘propagates the color gradients from neighboring valid regions using edge-aware color diffusion.’ The team writes that this feature promises to limit color bleeding through edges, such as green leaves bleeding color into a reconstructed blue sky. This method is only available for images captured with a Bayer sensor, so Fujifilm X-Trans users are out of luck here.
Darktable 4 introduces the darktable Uniform Color Space 2022 (darktable UCS 22). It’s a perceptually uniform color space built using psychoperceptual experimental data that was gathered for artistic saturation changes. What’s this actually mean? Darktable UCS 22 ‘ uses a brightness-saturation scheme that compensates for the Helmholtz-Kohlraush effect (accounting for the contribution of colorfulness in perceived brightness) and allows an efficient gamut-mapping against pipeline RGB at a constant brightness. It will make the saturation control in color balance RGB better behaved.’ You can learn a bit more about the Helmholtz-Kohlraush effect in the latter half of the video below.
The user interface has been completely revamped to improve the overall look and consistency. Padding, margins, color, contrast, alignment and icons have been reworked throughout the application. Collapsible sections within modules have been redesigned to improve functionality, plus channel mixer RGB, exposure and color calibration modules include new collapsible sections. The vignetting module has been split into two sections. Superfluous sections have been removed in ‘crop’ and ‘white balance’ tools. The default theme is now Elegant Gray, which is the recommended choice of the darktable team.
The app’s performance and OpenCL settings have been optimized, so performance is more tunable by the user and should be improved overall. There are many more other changes, including a color glossary, new contrast parameters, a new ‘collection filters’ module, improved search, improved export options, improved shortcuts when using sliders, a new raw exposure function, and more. Plus, there are many new bug fixes in the latest update. For the full details, visit darktable.
Darktable 4.0.0 is available now for Linux, macOS and Windows. At that link, you can also download the software’s source code. If you would like to give darktable 4.0.0 a try but don’t know where to start, there’s a very detailed user manual available here.
Articles: Digital Photography Review (dpreview.com)
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To analyze a dataset, you first need to understand the data. Sometimes, you might have no forehand knowledge of a dataset, preventing you from getting the most out of it. As a data analyst, you can use Exploratory data analysis (EDA) to gain knowledge of your dataset before in-depth analysis.
Exploratory data analysis (EDA) investigates a dataset to gain meaningful insights. The process of performing EDA involves querying information about the structure and contents of a dataset.
The Gota package is the most popular for data analysis in Go; it’s like the Python Pandas package but for Go. The Gota package contains many methods for analyzing datasets and reading JSON, CSV, and HTML formats.
Run this command on your terminal in the directory where you have initialized a Go module file:
go get -u github.com/go-gota/gota
The command will install Gota in the local directory, ready for you to import the package to use it.
Just like Pandas, Gota supports series and dataframes operations. There are two sub-packages in the Gota package: the series, and the dataframe package. You can import either one or both, depending on your needs.
import (
"github.com/go-gota/gota/series"
"github.com/go-gota/gota/dataframe"
)
You can use any CSV file you like, but the following examples show results from a Kaggle dataset, containing laptop price data.
Gota lets you read CSV, JSON, and HTML file formats to create dataframes using the ReadCSV, ReadJSON, and ReadHTML methods. Here’s how you load a CSV file into a dataframe object:
file, err := os.Open("/path/to/csv-file.csv")
if err != nil {
fmt.Println("file open error")
}
dataFrame := dataframe.ReadCSV(file)
fmt.Println(dataFrame)
You can use the Open method of the os package to open a CSV file. The ReadCSV method reads the file object and returns a dataframe object.
When you print this object, the output is in a tabular format. You can further manipulate the dataframe object using the various methods Gota provides.
The object will only print some of the columns if a dataset has more than a set value.
The dimensions of a dataframe are the number of rows and columns it contains. You can fetch these dimensions using the Dims method of the dataframe object.
var rows, columns = dataFrame.Dims()
Replace one of the variables with an underscore to fetch the other dimension only. You can also query the number of rows and columns individually, using the Nrow and Ncol methods.
var rows = dataFrame.Nrow()
var columns = dataFrame.Ncol()
You’ll need to know the composite data types in a dataset’s columns to analyze it. You can fetch these using the Types method of your dataframe object:
var types = dataFrame.Types()
fmt.Println(types)
The Types method returns a slice containing the column’s data types:
You’ll need the column names to select specific columns for operations. You can use the Names method to fetch them.
var columnNames := dataFrame.Names()
fmt.Println(columnNames)The Names method returns a slice of the column names.
You might have a dataset that contains null or non-numeric values. You can check for such values using the HasNaN and IsNaN methods of a series object:
aCol := dataFrame.Col("display_size")
var hasNull = aCol.HasNaN()
var isNotNumber = aCol.IsNaN()
HasNan checks if a column contains null elements. IsNaN returns a slice of booleans representing whether each value in the column is a number.
Descriptive statistical analysis helps you understand the distribution of numerical columns. Using the Describe method, you can generate a descriptive statistical analysis of your dataset:
description := dataFrame.Describe()
fmt.Println(description)
The Describe method returns metrics like the mean, standard deviation, and maximum values of columns in a dataset. It summarizes these in a tabular format.
You can also be specific and focus on columns and metrics by selecting a particular column, then querying for the metric you want. You should first fetch the series representing a specific column, then use its methods like so:
aCol := dataFrame.Col("display_size")
var mean = aCol.Mean()
var median = aCol.Median()
var minimum = aCol.Min()
var standardDeviation = aCol.StdDev()
var maximum = aCol.Max()
var quantiles25 = aCol.Quantile(25.0)
These methods mirror the results from the descriptive statistical analysis that Describe performs.
One of the final tasks you’ll want to perform is to check the values in a column for a general overview. You can use the Records method to view the values of a column.
aCol := dataFrame.Col("brand")
fmt.Println(aCol.Records())
This method returns a slice of strings containing the values in your selected column:
If you choose to go further and use the Gota package for full data analysis, you’ll need to save data in files. You can use the WriteCSV and WriteJSON methods of dataframe to export files. The methods take in a file that you’ll create using the os package’s Create method.
Here’s how you can export a dataframe using the Gota package.
dataFrame := dataframe.ReadCSV(file)
outputFile, err := os.Create("output.csv")
if err != nil {
log.Fatal(err)
}
err = dataFrame.WriteCSV(outputFile)
if err != nil {
log.Fatalln("There was an error writing the dataframe contents to the file")
}
The dataFrame variable is a representation of the dataframe. When you use the Create method of the os package, it creates a new, empty file with the specified name and returns the file. The WriteCSV method takes in the file instance and returns an error or nil if there’s no error.
An understanding of data and datasets is essential for data analysts and machine learning specialists. It is a critical operation in their work cycle, and exploratory data analysis is one of the techniques they use to achieve that.
There’s more to the Gota package. You can use it for various data wrangling functions in the same way that you’d use the Python Pandas library for data analysis. However, Gota doesn’t support quite as much functionality as Pandas.
MUO – Feed