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IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] 出生時育休中だが、人手不足なので一時的に戻ってきてほしい　可能なのか？	https://www.itmedia.co.jp/business/articles/2211/16/news023.html	itmedia	2022-11-16 05:30:00
AWS	AWS Compute Blog	Introducing the price-capacity-optimized allocation strategy for EC2 Spot Instances	https://aws.amazon.com/blogs/compute/introducing-price-capacity-optimized-allocation-strategy-for-ec2-spot-instances/	Introducing the price capacity optimized allocation strategy for EC Spot InstancesThis blog post is written by Jagdeep Phoolkumar Senior Specialist Solution Architect Flexible Compute and Peter Manastyrny Senior Product Manager Tech EC Core Amazon EC Spot Instances are unused Amazon Elastic Compute Cloud Amazon EC capacity in the AWS Cloud available at up to a discount compared to On Demand prices One of the best …	2022-11-15 20:41:49
AWS	AWS Compute Blog	Running AI-ML Object Detection Model to Process Confidential Data using Nitro Enclaves	https://aws.amazon.com/blogs/compute/running-ai-ml-object-detection-model-to-process-confidential-data-using-nitro-enclaves/	Running AI ML Object Detection Model to Process Confidential Data using Nitro EnclavesThis blog post was written by Antoine Awad Solutions Architect Kevin Taylor Senior Solutions Architect and Joel Desaulniers Senior Solutions Architect Machine Learning ML models are used for inferencing of highly sensitive data in many industries such as government healthcare financial and pharmaceutical These industries require tools and services that protect their data in transit …	2022-11-15 20:19:56
海外TECH	MakeUseOf	How to Fix the "These Files Have Properties That Can’t Be Read” Photos Error in Windows 10 & 11	https://www.makeuseof.com/windows-files-properties-cant-be-read/	photos	2022-11-15 20:16:15
海外TECH	MakeUseOf	What Is Twitter Blue and How Much Does It Cost?	https://www.makeuseof.com/what-is-twitter-blue/	twitter	2022-11-15 20:05:15
海外TECH	DEV Community	C++ unit testing with Catch2 🧪👨‍🔬	https://dev.to/batunpc/c-unit-testing-with-catch2-20af	C unit testing with Catch ‍Writing unit tests for my static site generator palpatine taught me an entirely new testing framework called Catch For my Hacktoberfest pull request this year I had done a unit testing contribution for a repository and I am currently working on building a microservice for my cloud computing class which requires us to use Jest for JavaScript unit testing So I can say that I have some experience with unit testing and continuing to develop my skills in it Building good unit testing skills takes time as it requires us to think about our code more deeply What should the input look like What is the expected output and what are some of the edge cases In my normal day to day personal projects I did not pay much attention to testing But I know that it is important for real world development   Trust the code As the name suggests unit testing is about testing small units of code or components of an application or system to ensure that it is behaving the intended way An application has building blocks that work in harmony with one another By testing out these building blocks or units and checking that it is behaving the right way we can trust the code  What if some codes are hard to test Well that means that it probably needs to be broken down This also means that unit testing encourages modularity  If a unit of code uses another unit as a dependency we also need to ensure that the dependent unit is being used the right way   Why choose Catch  There are many established testing frameworks for C such as Google Test Boost Test Cute CppUnit and many more However what sets Catch apart from the others is its ease of use and simpler learning curve Not just that Catch also has great documentation and tutorials that I could read and follow  Also over the summer I completed a Udemy course on CMake and there I found a template for testing with Catch That template was really useful for me in setting up the configurations to get started  As a beginner in writing tests I would say Catch was not as simple for me to learn as more experienced programmers suggest I had to spend a few days reading through the documentation going over YouTube videos and looking through many many examples online before I could write my first test But once I started writing it felt easier to go on  As stated in Catch s documentation  Catch s main advantage is that using it is both simple and natural Test names do not have to be valid identifiers assertions look like normal C boolean expressions and sections provide a nice and local way to share set up and tear down code in tests Setting it up I first had to start by downloading the catch hpp file and then include it  include lt catch catch test macros hpp gt And then I had to define this on top   define CATCH CONFIG MAIN This tells Catch to provide a main only do this in one cpp fileThe TEST CASE macro is used for introducing test conditions It is used to group the test cases for a particular unit of code SECTION is defined beautifully in their documentation  Another way to look at sections is that they are a way to define a tree of paths through the test Each section represents a node and the final tree is walked in depth first manner with each path only visiting only one leaf node TEST CASE CLI Parser works perfectly parser std vector lt std string gt vct palpatine SECTION Without any args REQUIRE THROWS AS get options vct std runtime error As mentioned earlier my CMake course provided me a template to work with that I could modify and use for my testing purposes If you check my CMakeLists txt file you will see  if ENABLE TESTING set TEST MAIN unit tests set TEST SOURCES main cpp SET GCC COVERAGE COMPILE FLAGS fprofile arcs ftest coverage SET GCC COVERAGE LINK FLAGS coverage SET CMAKE CXX FLAGS CMAKE CXX FLAGS GCC COVERAGE COMPILE FLAGS SET CMAKE EXE LINKER FLAGS CMAKE EXE LINKER FLAGS GCC COVERAGE LINK FLAGS add executable TEST MAIN TEST SOURCES target link libraries TEST MAIN PUBLIC PALPATINE LIB argparseCatch CatchWithMain add test NAME TEST MAIN COMMAND TEST MAIN endif ENABLE TESTING Test coverage My project also has a test coverage provider that shows the lines that are covered by the test and the percentage of coverage For my test coverage provider I chose to use gcovr It had clear documentation on setting it up and I could do it without facing any trouble From their documentation Gcovr provides a utility for managing the use of the GNU gcov utility and generating summarized code coverage results The command for running the test coverage is gcovr As you can see in the image below I am able to see the lines covered by my tests If you want your coverage to look colorful just pip it with lolcat Final thoughts ︎Writing tests for C was harder compared to the other projects I have worked on As the codecase evolves I need to also make sure that the tests get updated over time	2022-11-15 20:17:35
海外TECH	DEV Community	Data analysis made simple: Python Pandas tutorial	https://dev.to/educative/data-analysis-made-simple-python-pandas-tutorial-4hn5	Data analysis made simple Python Pandas tutorialThis article was published on the Educative Blog by Amanda Fawcett on June and updated by the Educative team on November Data is an important part of our world In fact of the world s data was created in just the last years Many tech giants have started hiring data scientists to analyze data and extract useful insights for business decisions Currently Python is the most important language for data analysis and many of the industry standard tools are written in Python Python Pandas is one of the most essential in demand tools that any aspiring data analysts need to learn Today we ll introduce you to the essentials of Pandas Today we ll go over Introducing Pandas for PythonPandas Data Types and StructuresSeries the most important operationsDataFrame the most important operationsHow to read and import Pandas dataData Wrangling with PandasNext steps Introducing Pandas for PythonThe Pandas library is one of the most important and popular tools for Python data scientists and analysts as it is the backbone of many data projects Pandas is an open source Python package for data cleaning and data manipulation It provides extended flexible data structures to hold different types of labeled and relational data On top of that it is actually quite easy to install and use Pandas is often used in conjunction with other data science Python libraries In fact Pandas is built on the NumPy package so a lot of the structure between them is similar Pandas is also used in SciPy for statistical analysis or with Matplotlib for plotting functions Pandas can be used on its own with a text editor or with Juptyer Notebooks the ideal environment for more complex data modeling Pandas is available for most versions of Python including Python Think of Pandas as the home for your data where you can clean analyze and transform your data all in one place Pandas is essentially a more powerful replacement for Excel Using Pandas you can do things like Easily calculate statistics about data such as finding the average distribution and median of columnsUse data visualization tools such as Matplotlib to easily create plot bars histograms and more Clean your data by filtering columns by particular criteria or easily removing values Manipulate your data flexibly using operations like merging joining reshaping and moreRead write and store your clean data as a database txt file or CSV file Popularity of PandasAs we learned Python is the most popular programming language for data analytics and many of the popular machine learning and visualization libraries are written in Python including Pandas NumPy TensorFlow Matplotlib Scikit learn and more In fact Python ranked th in the StackOverflow Developer Survey for the most popular programming languages among professional developers It is beloved for its simplicity easy learning curve and improved library support Pandas is an important part of data analytics It consistently ranks highly among the most popular and loved libraries It also consistently ranks highly for most wanted programming tools a sure sign that Pandas is a sought after tool for developers around the world Learning Pandas is an important step to becoming a data analyst First Step Installing PandasYou can install Pandas using the built in Python tool pip and run the following command pip install pandas Pandas Data Structures and Data TypesA data type is like an internal construct that determines how Python will manipulate use or store your data When doing data analysis it s important to use the correct data types to avoid errors Pandas will often correctly infer data types but sometimes we need to explicitly convert data Let s go over the data types available to us in Pandas also called dtypes object text or mixed numeric or non numeric valuesint integer numbers bool true false valuesfloat floating point numberscategory finite list of text valuesdatetime Date and time valuestimedelta ns differences between two datetimesA data structure is a particular way of organizing our data Pandas has two data structures and all operations are based on those two objects SeriesDataFrameThink of this as a chart for easy storage and organization where Series are the columns and the DataFrame is a table composed of a collection of series Series can be best described as the single column of a D array that can store data of any type DataFrame is like a table that stores data similar to a spreadsheet using multiple columns and rows Each value in a DataFrame object is associated with a row index and a column index Series the most important operationsWe can get started with Pandas by creating a series We create a series by invoking the pd Series method and then passing a list of values We print that series using the print statement Pandas will by default count the index from We then explicitly define those values series pd Series print series Let s look at a more complex example importing pandas in our programimport pandas as pd Defining a series objectsrs pd Series printing series valuesprint The Series values are print srs values printing series indexesprint nThe Index values are print srs index values gt The Series values are The Index values are How does this work Well the srs values function on line returns the values stored in the Series object and the function srs index values on line returns the index values Assign names to our valuesPandas will automatically generate our indexes so we need to define them Each index corresponds to its value in the Series object Let s look at an example where we assign a country name to population growth rates importing pandas in our programimport pandas as pd Defining a series objectsrs pd Series index China India USA Brazil Pakistan Set Series namesrs name Growth Rate Set index namesrs index name Country printing series valuesprint The Indexed Series values are print srs gt The Indexed Series values are CountryChina India USA Brazil Pakistan Name Growth Rate dtype floatHow does this work Two attributes of the Series object are used on line and line The attribute srs name sets the name of our series object The attribute srs index name then sets the name for the indexes Select entries from a SeriesTo select entries from a Series we select elements based on the index name or index number import numpy as npimport pandas as pdsrs pd Series np arange index ind ind ind ind ind ind srs index name Index print The original Series n srs print nSeries element at index ind print srs ind Fetch element at index named indprint nSeries element at index print srs Fetch element at index print nSeries elements at multiple indexes n print srs ind ind Fetch elements at multiple indexes gt The original Series n Indexind ind ind ind ind ind dtype int Series element at index ind Series element at index Series elements at multiple indexes Indexind ind dtype intHow does that work Well the elements from the Series are selected in ways On line the element is selected based on the index name On line the element is selected based on the index number Keep in mind that index numbers start from On line multiple elements are selected from the Series by selecting multiple index names inside the Drop entries from a SeriesDropping and unwanted index is a common function in Pandas If the drop index name function is called with a given index on a Series object the desired index name is deleted import numpy as npimport pandas as pdsrs pd Series np arange index ind ind ind ind ind ind srs index name Index print The original Series n srs srs srs drop ind drop index named indprint The New Series n srs gt The original Series n Indexind ind ind ind ind ind dtype int The New Series n Indexind ind ind ind ind dtype int Here the output that the ind index is dropped Also an index can only be dropped by specifying the index name and not the number So srs drop srs does not work There are many other functions conditions and logical operators we can apply to our series object to make productive use of indexes Some of those functions are The condition srs srs will return a series object containing indexes with values equal to name str optional gives a name to the Series copy bool default False allows us to copy data we inputThe notnull function will return a series object with indexes assigned to False for NaN or null values and the remaining indexes are assigned True DataFrame the most important operationsThere are several ways to make a DataFrame in Pandas The easiest way to create one from scratch is to create and print a df import pandas as pddf pd DataFrame Column Column a column with a string Column Column True False True False True print df gt Column Column Column Column a True column False with True a False string TrueWe can also create a dict and pass our dictionary data to the DataFrame constructor Say we have some data on vegetable sales and want to organize it by type of vegetable and quantity Our data would look like this data peppers carrots And now we pass it to the constructor using a simple command quantity pd DataFrame data quantityHow did that work Well each item or value in our data will correspond with a column in the DataFrame we created just like a chart The index for this DataFrame is listed as numbers but we can specify them further depending on our needs Say we wanted to know the quantity per month That would be our new index We do that using the following command quantity pd DataFrame data index June July August September quantity Get info about your dataOne of the first commands you run after loading your data is info which provides all the essential information about a dataset import pandas as pddf pd DataFrame Column Column a column with a string Column Column True False True False True df info gt lt class pandas core frame DataFrame gt RangeIndex entries to Data columns total columns Column non null intColumn non null objectColumn non null floatColumn non null booldtypes bool float int object memory usage bytesFrom that you can access more information with other operations like shape which outputs a tuple of rows columns This is super useful for telling us the size of our data especially after we ve cleaned it That way we can know what was removed We can also print a dataset s column names to find typos or formatting inconsistencies We use the columns operator to do so You can then rename your columns easily On top of that the rename method allows us to rename columns similar to a Search and Replace function of a Word doc Searching and selecting in our DataFrameWe also need to know how to manipulate or access the data in our DataFrame such as selecting searching or deleting data values You can do this either by column or by row Let s see how it s done The easiest way to select a column of data is by using brackets We can also use brackets to select multiple columns Say we only wanted to look at June s vegetable quantity quantity loc June Note loc and iloc are used for locating data iloc locates by numerical index loc locates by the index name This is similar to list slicing in Python Pandas DataFrame object also provides methods to select specific columns The following example shows how it can be done import pandas as pddf pd read csv cancer stats csv print df columns print columns of DataFrameprint nThe First Column print df Sex head Fetch the sex colum from DataFrameprint nThe type of this column is str type df Sex n print nThe Second Column print df Under head Fetch the Under colum from DataFrameprint nThe type of this column is str type df Under n print nThe Last Column print df head Fetch the colum from DataFrameprint nThe type of this column is str type df n gt Index u Sex u Under u u u u u u u u u dtype object The First Column Males Females Males Females MalesName Sex dtype objectThe type of this column is lt class pandas core series Series gt The Second Column Name Under dtype intThe type of this column is lt class pandas core series Series gt The Last Column Name dtype intThe type of this column is lt class pandas core series Series gt On line the df columns function displays the names of all columns present We access a column by its name On lines and df column name is used to get the st nd and last columns Visit the original article on the Educative Blog to get some hands on coding practice with DataFrames and the cancer stats csv dataset Create a new DataFrame from pre existing columnsWe can also grab multiple columns and create a new DataFrame object from it import pandas as pddf pd read csv test csv print df columns print nThe original DataFrame print df head print nThe new DataFrame with selected columns is n new df pd DataFrame df columns Sex Under print new df head gt Index u Sex u Under u u u u u u u u u dtype object The original DataFrame Sex Under Males Females Males Females Males The new DataFrame with selected columns is Sex Under Males Females Males Females Males Reindex data in a DataFrameWe can also reindex the data either by the indexes themselves or the columns Reindexing with reindex allows us to make changes without messing up the initial setting of the objects Note The rules for reindexing are the same for Series and DataFrame objects importing pandas in our programimport pandas as pd Defining a series objectsrs pd Series index China India USA Brazil Pakistan Set Series namesrs name Growth Rate Set index namesrs index name Country srs srs reindex China India Malaysia USA Brazil Pakistan England print The series with new indexes is n srs srs srs reindex China India Malaysia USA Brazil Pakistan England fill value print nThe series with new indexes is n srs gt The series with new indexes is n CountryChina India Malaysia NaNUSA Brazil Pakistan England NaNName Growth Rate dtype float nThe series with new indexes is n CountryChina India Malaysia USA Brazil Pakistan England Name Growth Rate dtype float How did that work Well on line the indexes are changed The new index name is added between Row and Row One line the columns keyword should be specifically used to reindex the columns of DataFrame The rules are the same as for the indexes NaN values were assigned to the whole column by default How to read or import Pandas dataIt is quite easy to read or import data from other files using the Pandas library In fact we can use various sources such as CSV JSON or Excel to load our data and access it Let s take a look at each Reading and importing data from CSV filesWe can import data from a CSV file which is common practice for Pandas users We simply create or open our CSV file copy the data paste it into our Notepad and save it in the same directory that houses your Python scripts You then use a bit of code to read the data using the read csv function build into Pandas import pandas as pddata pd read csv vegetables csv print data read csv will generate the index column as a default so we need to change this for the first column is the index column We can do this by passing the parameter index col to tell Pandas which column to index data pd read csv data csv index col Once we ve used Pandas to sort and clean data we can then save it back as the original file with simple commands You only have to input the filename and extension How simple df to csv new vegetables csv Reading and importing data from JSONSay you have a JSON file A JSON file is basically like a stored Python dict so Pandas can easily access and read it using the read json function Let s look at an example df pd read json purchases json Just like with CSV files once we ve used Pandas to sort and clean data we can then save it back as the original file with simple commands You only have to input the filename and extension df to json new purchases json Reading and importing data from Excel fileSay you have an Excel file You can similarly use the read excel function to access and read that data import pandas as pddata pd read excel workers xlsx print data Once we call the read excel function we pass the name of the Excel file as our argument so read excel will open the file s data We can the print to display the data If we want to go one step further we can add the loc method from earlier allowing us to read specific rows and columns of our file import pandas as pddata pd read excel workers xlsx print data loc Name Salary Data Wrangling with PandasOnce we have our data we can use the data wrangling processes to manipulate and prepare data for analysis The most common data wrangling processes are merging concatenation and grouping Let s get down to the basics of each of those Merging with PandasMerging is used when we want to collect data that shares a key variable but they are located in different DataFrames To merge DataFrames we use the merge function Say we have df and df import pandas as pdd subject id student name Mark Khalid Deborah Trevon Raven df pd DataFrame d columns subject id student name print df import pandas as pddata subject id student name Eric Imani Cece Darius Andre df pd DataFrame data columns subject id student name print df So how do we merge them It s simple with the merge function pd merge df df on subject id Grouping with PandasGrouping is how we categorize our data If a value occurs in multiple rows of a single column the data related to that value in other columns can be grouped together Just like with merging it s simpler than it sounds We use the groupby function Take a look at this example import pandas libraryimport pandas as pdraw Name Darell Darell Lilith Lilith Tran Tran Tran Tran John Darell Darell Darell Position Year Marks df pd DataFrame raw group df groupby Year print group get group gt Marks Name Position Year Tran John ConcatenationConcatenation is a long word that means to add a set of data to another We use the concat function to do so To clarify the difference between merge and concatenation merge combines data on shared columns while concat combines DataFrames across columns or rows print pd concat df df Some other common data wrangling processes that you should know are Mapping data and finding duplicatesFinding outliers in dataData Aggregation Reshaping dataReplace amp rename Wrapping up and next stepsNow that you have a good sense of Python Pandas and the countless benefits it offers it s important to know what to learn next Once you get the basics down like how to import read and wrangle your data it s time to tackle the next stage of data analysis for Python StatisticsNumPyAdvanced data wrangling Visualizations for data Data scrapingReal world projectsEducative s course Predictive Data Analysis for Python covers all these concepts and more with hands on practice and industry standard examples You can master Pandas through quizzes interactive examples and real behavior analysis By the end you ll be a confident data analyst Keep reading about Python and data analysis on EducativeStop Using Excel for Data Analytics Upgrade to PythonLevel up your Python skills with these challengesPandas Cheat Sheet Top commands and operations Start a discussionWhat is your favorite use of data analysis in the real world Was this article helpful Let us know in the comments below	2022-11-15 20:04:16
Apple	AppleInsider - Frontpage News	Hands on with Emergency SOS via Satellite on iPhone 14	https://appleinsider.com/articles/22/11/15/hands-on-with-emergency-sos-via-satellite-on-iphone-14?utm_medium=rss	Hands on with Emergency SOS via Satellite on iPhone Apple has officially released the ability to get in touch with emergency services by texting via satellite on iPhone Here s how the service works and how you can test it yourself Using Emergency SOS via SatelliteThe new feature available on iPhone models was released without a beta phase Instead it showed up on users devices after being enabled server side Read more	2022-11-15 20:54:46
Apple	AppleInsider - Frontpage News	Always-on display more customizable in iOS 16.2 beta 3	https://appleinsider.com/articles/22/11/15/always-on-display-more-customizable-in-ios-162-beta-3?utm_medium=rss	Always on display more customizable in iOS beta Users will have the option to toggle off the wallpaper or notifications for the iPhone Pro always on display in iOS New always on toggles in iOS beta When Apple introduced the iPhone Pro with an always on display users were quick to point out that the feature was a little overwhelming for some The screen showed a dimmed version of the wallpaper widgets Live Activities notifications and the clock Read more	2022-11-15 20:26:41
Apple	AppleInsider - Frontpage News	How to use Emergency SOS via Satellite on iPhone 14	https://appleinsider.com/inside/iphone-14/tips/how-to-use-emergency-sos-via-satellite-on-iphone-14?utm_medium=rss	How to use Emergency SOS via Satellite on iPhone If you re off the grid can t see wireless and need help and have an iPhone you can use Emergency SOS via Satellite Here s how to use it Emergency SOS via SatelliteThe iPhone has been a fantastic tool for people in trouble in a variety of ways including its most basic of allowing users to call for emergency services However while the United States has a lot of its land covered by some form of cellular signal sometimes it s just not available Read more	2022-11-15 20:47:38
海外TECH	Engadget	Lucid teases its three-row Gravity electric SUV	https://www.engadget.com/lucid-gravity-ev-suv-reservation-date-200050593.html?src=rss	Lucid teases its three row Gravity electric SUVLucid had more to show today than its less expensive Air models The auto startup has finally teased its second EV the long in the making Gravity SUV The vehicle will share the swooping visual language of the Air while offering up to three rows of seating that can accommodate seven people It should also have a next gen version of Lucid s touchscreen heavy cockpit and purportedly offer more range than any other EV outside of the Air The company cautions that the Gravity design and specifications aren t final What Lucid is showing now is largely what you d expect however ーit s a big people hauler with perks like a panoramic roof As with Tesla s Model X the three row configuration doesn t leave much room in the back for either passengers or cargo The rear space is flexible according to Lucid so you might not be stuck if you need to carry a large load Lucid MotorsYou will be waiting a while to buy the SUV Reservations for the Gravity open in early and it won t reach the US or Canada until Customers outside those countries will have to be more patient and the firm hasn t disclosed pricing It s safe to presume the EV will be expensive though when even the entry Air starts at This is more for customers who would otherwise consider a Model X Mercedes EQS SUV or Polestar The Gravity could be Lucid s most important EV to date whatever it costs The Air may be fast but its sedan form factor limits its appeal in an increasingly SUV centric market The new model could help Lucid reach a wider audience even if the clientele will still be limited to wealthy buyers willing to take a chance on a young brand	2022-11-15 20:00:50
海外ニュース	Japan Times latest articles	NATO on alert after reports of stray Russian missiles hitting Polish village	https://www.japantimes.co.jp/news/2022/11/16/world/russia-ukraine-poland-missiles/	NATO on alert after reports of stray Russian missiles hitting Polish villageThe defense alliance investigates reports that two people were killed in an explosion in eastern Poland with Russia denying claims that its missiles had hit	2022-11-16 05:38:50
ニュース	BBC News - Home	Ryan Reynolds speaks Welsh during awards acceptance	https://www.bbc.co.uk/news/uk-wales-63643228?at_medium=RSS&at_campaign=KARANGA	wales	2022-11-15 20:28:46
ビジネス	ダイヤモンド・オンライン - 新着記事	慶應三田会に社会人が入れる！「穴場21ルート」全公開、英語なし・面接のみも - 最強学閥「慶應三田会」 人脈・金・序列	https://diamond.jp/articles/-/312313	慶應義塾	2022-11-16 05:25:00
ビジネス	ダイヤモンド・オンライン - 新着記事	【スクープ】アマゾンがペット保険参入へ、あいおいニッセイ同和損保と共同開発で最終調整 - Diamond Premium News	https://diamond.jp/articles/-/312992	diamondpremiumnews	2022-11-16 05:22:00
ビジネス	ダイヤモンド・オンライン - 新着記事	ANA・JAL、3期ぶりの最終黒字を見込むも「惨状」続く旅客数の実態 - コロナで明暗！【月次版】業界天気図	https://diamond.jp/articles/-/312202		2022-11-16 05:20:00
ビジネス	ダイヤモンド・オンライン - 新着記事	シニア向け分譲マンション「騰落率」ランキング！トップは5年で価格が2倍超に - 老人ホーム・サ高住・シニア分譲マンション 50代も必見！シニアの住まい選び	https://diamond.jp/articles/-/312746	シニア向け分譲マンション「騰落率」ランキングトップは年で価格が倍超に老人ホーム・サ高住・シニア分譲マンション代も必見シニアの住まい選び自立した自由気ままなセカンドライフを望むアクティブシニアの間で今、じわりと人気が高まっているのが、入居可能年齢に歳や歳などの条件を設けた「シニア向け分譲マンション」だ。	2022-11-16 05:15:00
ビジネス	ダイヤモンド・オンライン - 新着記事	ANA「40歳年収1000万円」復活！“年収3割減地獄”から脱しJALに再逆転【ANA・JAL給料対決】 - ANA・JAL 黒字回復後の修羅	https://diamond.jp/articles/-/312721	全日本空輸	2022-11-16 05:10:00
ビジネス	ダイヤモンド・オンライン - 新着記事	ECB「小幅利上げ」に修正？ユーロ圏の景気後退リスクに配慮 - 経済分析の哲人が斬る！市場トピックの深層	https://diamond.jp/articles/-/312961	政策金利	2022-11-16 05:05:00
ビジネス	東洋経済オンライン	ライフ創業･清水信次が壮絶人生96年で得た悟り 動乱を生き抜いて流通界に多大な影響を残した | 百貨店・量販店・総合スーパー | 東洋経済オンライン	https://toyokeizai.net/articles/-/633011?utm_source=rss&utm_medium=http&utm_campaign=link_back	東洋経済オンライン	2022-11-16 05:40:00
ビジネス	東洋経済オンライン	｢ほぼ仕事しない"幽霊社員"も｣リモートの大問題 ｢2年､出社なし…｣リモートは即やめるべき？ | リーダーシップ・教養・資格・スキル | 東洋経済オンライン	https://toyokeizai.net/articles/-/633119?utm_source=rss&utm_medium=http&utm_campaign=link_back	上場企業	2022-11-16 05:20:00