投稿時間:2021-07-30 09:33:16 RSSフィード2021-07-30 09:00 分まとめ(53件)

カテゴリー等	サイト名等	記事タイトル・トレンドワード等	リンクURL	頻出ワード・要約等/検索ボリューム	登録日
TECH	Engadget Japanese	ソニー、ネックスピーカーSRS-NB10の発売を延期　一部規格を満たさず	https://japanese.engadget.com/sony-233117624.html	bluetooth	2021-07-29 23:31:17
IT	ITmedia 総合記事一覧	[ITmedia News] Robinhood、38ドルでNASDAQ上場	https://www.itmedia.co.jp/news/articles/2107/30/news070.html	itmedianewsrobinhood	2021-07-30 08:18:00
IT	ITmedia 総合記事一覧	[ITmedia エグゼクティブ] 物流・流通DX、事業化の動き　パナや三菱商事系がAIでサプライチェーン効率化	https://mag.executive.itmedia.co.jp/executive/articles/2107/30/news071.html	itmedia	2021-07-30 08:11:00
TECH	Techable（テッカブル）	ケンブリッジ大、スマホのタッチスクリーンをセンサーに！	https://techable.jp/archives/158779	公衆衛生	2021-07-29 23:00:50
AWS	AWS	ML Max TV S01E01 - What is remote development and how do you do it? | Amazon Web Services	https://www.youtube.com/watch?v=KiewuNCpcEs	ML Max TV SE What is remote development and how do you do it Amazon Web ServicesA step by step guide to using AWS Systems Manager to securely connect to a remote development environment hosted on Amazon EC This is designed for data scientists who are looking to use a local IDE on their laptop but want to securely and conveniently access compute resources and data on AWS Check out the ML Max Github Subscribe More AWS videos More AWS events videos ABOUT AWSAmazon Web Services AWS is the world s most comprehensive and broadly adopted cloud platform offering over fully featured services from data centers globally Millions of customers ーincluding the fastest growing startups largest enterprises and leading government agencies ーare using AWS to lower costs become more agile and innovate faster AWS AmazonWebServices CloudComputing MachineLearning AWSDemos	2021-07-29 23:37:12
python	Pythonタグが付けられた新着投稿 - Qiita	Pythonで辞書を使ってエクセルに書き込む	https://qiita.com/Hayate-Leo/items/1740b3c5f655b2760dfc		2021-07-30 08:30:01
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	認証する際の「トークンURLの長さ」について	https://teratail.com/questions/351926?rss=all	認証する際の「トークンURLの長さ」について以前にコチラで質問した、ウェブサービスの続きになります。	2021-07-30 08:02:13
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	Discord.jsで投稿者を取得	https://teratail.com/questions/351925?rss=all	Discordjsで投稿者を取得前提・実現したいDiscordjsで音楽機能を作っているんですがその流れている曲の投稿者を取得する方法が分かりません。	2021-07-30 08:01:50
技術ブログ	Developers.IO	AndroidでもAccessibility Insightsを使ってアクセシビリティ評価をする	https://dev.classmethod.jp/articles/android-accessibilityinsights/	accessibilityinsights	2021-07-29 23:35:13
技術ブログ	Hatena::Engineering	既存のコードから設計を学び、調査力を向上させて、知見を共有しよう	https://developer.hatenastaff.com/entry/2021/07/30/090000_1	開発	2021-07-30 09:00:00
技術ブログ	Hatena::Engineering	既存の機能から設計を学び、調査力を向上させて、知見を共有しよう	https://developer.hatenastaff.com/entry/2021/07/30/090000	既存の機能から設計を学び、調査力を向上させて、知見を共有しようはてなブックマークチームのiditchynyです。	2021-07-30 09:00:00
海外TECH	Ars Technica	Russian module suddenly fires thrusters after docking with space station	https://arstechnica.com/?p=1784020	contingency	2021-07-29 23:37:32
海外TECH	Ars Technica	This 3D-printed soft robotic hand beat the first level of Super Mario Bros.	https://arstechnica.com/?p=1782874	input	2021-07-29 23:08:19
海外TECH	DEV Community	Diseases Prediction Based On Medications Using Indexing In MongoDB	https://dev.to/mohamedhossam/diseases-prediction-based-on-medications-using-indexing-in-mongodb-22e7	Diseases Prediction Based On Medications Using Indexing In MongoDBIn this article we will discuss a feature in DOCTOR Y to predict the current patients medical conditions based on their regular medications using a dataset containing medicines and their corresponding medical conditions And this is done using searching techniques provided from the MongoDB If you don t know what is DOCTOR Y check this post link will be available soon ObjectiveOur goal is to generate a chart that shows the patients conditions in a form of percentages which are calculated based on all the medications they have taken during a specific period For exampleA patient that is prescribed with medicines ActemraDuexisIndocinThe results should be something like this DatasetThe dataset consists of records each record contains the drug name its corresponding condition and the weight of this condition It is derived from the Medication guide offered by the FDA This dataset is uploaded on DOCTOR Y s database so it can be used by the application server A sample from the datasetAbilify SchizophreniaAbilify Bipolar I DisorderAbilify Major Depressive Disorder MDD Abilify IrritabilityAbilify Tourette s DisorderAbilify Maintena KitSchizophreniaAbilify Maintena KitBipolar I Disorder Applied Search MethodIn order to traverse the collection we can use the default search method in the MongoDB which is the collection scan of complexity O n However we opted to use indexing single field indexing to be exact which is a searching method that uses B tree data structure thus having a complexity of O log n which offers better performance than a collection scan MechanismEach record has a drug name condition and weight the following steps are taken to get percentage of occurrence of each condition A search is conducted for each medicine taken by the user in the drug name field and the matching records are retrieved For example a patient is prescribed with Actemra Duexis Indocin The following records were retrieved Drug nameConditionWeightActemraRheumatoid Arthritis RA ActemraGiant Cell Arteritis GCA ActemraPolyarticular Juvenile Idiopathic Arthritis PJIA ActemraSystemic Juvenile Idiopathic Arthritis SJIA ActemraCytokine Release Syndrome CRS Duexisupper gastrointestinal ulcersDuexisOsteoarthritisDuexisRheumatoid Arthritis RA IndocinRheumatoid Arthritis RA IndocinAnkylosing spondylitis AS IndocinOsteoarthritisIndocinAcute painful shoulderIndocinAcute gouty arthritisAn iteration is done through the retrieved records while putting each new condition into a hash table with the key being the condition and the value being the weight If a condition already exists in the hash table we add its weight to the existing weight in the hash table Now we have a table of patient conditions with their corresponding weights Moving on with the previous example we get the following hash table Key Condition Value Weight Rheumatoid Arthritis RA Osteoarthritisupper gastrointestinal ulcersGiant Cell Arteritis GCA Polyarticular Juvenile Idiopathic Arthritis PJIA Systemic Juvenile Idiopathic Arthritis SJIA Cytokine Release Syndrome CRS Ankylosing spondylitis AS Acute painful shoulderAcute gouty arthritisTotalWe divided each weight by the total sum of weights and multiplied it by to get the percentage of occurrence of each condition From the previous example we get the following results Key Condition Value Weight Rheumatoid Arthritis RA Osteoarthritis upper gastrointestinal ulcers Giant Cell Arteritis GCA Polyarticular Juvenile Idiopathic Arthritis PJIA Systemic Juvenile Idiopathic Arthritis SJIA Cytokine Release Syndrome CRS Ankylosing spondylitis AS Acute painful shoulder Acute gouty arthritis Integration With DOCTOR YThe final diseases and their percentages are sent to the system server which sends them to the client side to be represented on a chart as shown in the figure below	2021-07-29 23:28:46
海外TECH	DEV Community	Disease Prediction Based On Medical Diagnosis	https://dev.to/ahmedsamy/disease-prediction-based-on-medical-diagnosis-547o	Disease Prediction Based On Medical DiagnosisIn this article we will discuss one of DOCTOR Y s Machine Learning Models This model predicts the current patients medical conditions based on the previous diagnoses from the patient s medical history We used a dataset containing the diseases and their diagnosis and classified it using different machine learning classifiers If you don t know what is DOCTOR Y check this post link will be available soon IdeaPhysicians will spend a lot of time reviewing the patient s previous e prescriptions provided on DOCTOR Y to know their past medical conditions and previous diseases That s why DOCTOR Y provides a summarized chart representing the percentages for suffering from a group of diseases based on previous diagnoses The model is provided with a dataset to train and classify these diseases The model takes the diagnoses as input from previous prescriptions and the output will be the predicted disease based on these diagnoses The snippet below shows how the model works python NLP py The patient has high blood pressure gt Hypertension DatasetIn this model most of the data were collected from Disease Symptom Prediction Dataset from Kaggle Our dataset is used for the disease diagnosis model based on previous diagnoses and it is divided into two columns the disease name and diagnoses for that disease We have rows with unique diseases leaving us with approximately entries for each disease The dataset is balanced However we faced a problem regarding building it from scratch This data may lead to misclassification for diseases based on different diagnoses which will affect the model s accuracy The majority of the data is collected by hand from multiple healthcare sites we looked carefully for definitions and diagnoses for the required diseases and ensured that no entries were duplicated PrognosisPrognosisPrognosisPrognosisFungal infectionMigrainehepatitis AHeart attackAllergyCervical spondylosisHepatitis BVaricose veinsGERDParalysis brain hemorrhage Hepatitis CHypothyroidismChronic cholestasisJaundiceHepatitis DHyperthyroidismDrug ReactionMalariaHepatitis EHypoglycemiaPeptic ulcer diseaeChicken poxAlcoholic hepatitisOsteoarthristisAIDSDengueTuberculosisArthritisDiabetesTyphoidCommon Cold vertigo Paroymsal Positional VertigoGastroenteritisPsoriasisPneumoniaAcneBronchial AsthmaImpetigoDimorphic hemmorhoids piles Urinary tract infectionHypertension Implementation Data PreparationWe prepared the data to be cleaner to obtain better results and we implemented the following preprocessors Stop Words Removal is used to remove stop words like “the “them etc Lowercasing is used to convert all words in subject and body to lowercase Punctuation Removal is used to remove all the punctuations like and replace them with spaces Model DefinitionThe Model is trained on the discussed dataset The Model input the diagnosis The Model output the possible diseases the patient may suffer from Model TrainingWe used three classification algorithms to process this data which are SVM Support Vector Machine NLP LSTM Long Short Term Memory for Natural Language Processing Our model will have one input layer one embedding layer one LSTM layer with neurons and one output layer with neurons since we have labels in the output batch size of and epochs Multinomial Naïve Bayes Evaluation amp ResultsThe Dataset in NLP LSTM model was split by and in SVM and Naïve Bayes was The accuracy of each classification technique used for predicting diseases based on diagnoses AlgorithmAccuracySVM NLP LSTM NAÏVE BAYES The accuracy of the NLP model in training nearly reached accuracy in training and accuracy in the validation phase DiscussionThe least performing model was the LSTM model while the best performing model was the SVM and Naiive model NLP ModelUnfortunately papers did not provide guidelines on configuring the network of this model So we had to use trial and error to choose the hyperparameters The results of the LSTM model are worse than both the SVM and Naïve models by achieving accuracy because the LSTM model reads the data sequentially and it has a memory that helps to keep words and use them in the prediction process so it is more reliable than both Integration With DOCTOR YWe used the Diseases Diagnoses Prediction Model s results and combined them with the Diseases Symptoms Prediction Model s results to calculate the percentage of suffering from a group of diseases based on previous diagnoses the associated symptoms The final diseases and their percentages are sent to the system server which sends them to the client side to be represented on a chart as shown in the figure below	2021-07-29 23:28:46
海外TECH	DEV Community	Disease Prediction Based On Medical Side Symptoms	https://dev.to/markamoussa/disease-prediction-based-on-medical-side-symptoms-55fk	Disease Prediction Based On Medical Side SymptomsIn this article we will discuss one of DOCTOR Y s Machine Learning Models This model predicts the current patients medical conditions based on the associated symptoms with the previous diagnoses from the patient s medical history We used a dataset containing the diseases and their symptoms in a checker format and classified it using different machine learning classifiers If you don t know what is DOCTOR Y check this post link will be available soon IdeaPhysicians will spend a lot of time reviewing the patient s previous e prescriptions provided on DOCTOR Y to know their past medical conditions and previous diseases That s why DOCTOR Y provides a summarized chart that represents the percentages for suffering from a group of diseases based on the associated symptoms with the previous diagnoses The model is provided with a dataset to train and classify these symptoms The model takes the symptoms as input from previous prescriptions and the output will be the predicted disease based on these symptoms The snippet below shows how the model works python symptoms disease py continous sneezing shivering chills Allergy DatasetIn this model we used Disease Symptom Prediction Dataset This dataset is balanced However feature vectors samples in the data had a redundancy problem We chose the features of the unique vector unique samples and fed it to machine learning algorithms then we reconstructed the data in a Boolean form to facilitate the process of training the model and get better results to obtain a refactored dataset The data is in a checker format where we have columns the last column is the diseases and the others are all the symptoms We have a total of entries and unique disease averaging entries per disease The table below is a sample of the symptoms and you can find the full list here SymptomsSymptomsSymptomsSymptomsSymptomsitchingskin rashnodal skin eruptionscontinuous sneezingshiveringvisual disturbancesreceiving blood transfusionreceiving unsterile injectionscomastomach bleedingirregular sugar levelcoughhigh feversunken eyesbreathlessnessswelling of stomachswelled lymph nodesmalaiseblurred and distorted visionphlegmThe table below shows the diseases in the full dataset PrognosisPrognosisPrognosisPrognosisFungal infectionMigrainehepatitis AHeart attackAllergyCervical spondylosisHepatitis BVaricose veinsGERDParalysis brain hemorrhage Hepatitis CHypothyroidismChronic cholestasisJaundiceHepatitis DHyperthyroidismDrug ReactionMalariaHepatitis EHypoglycemiaPeptic ulcer diseaeChicken poxAlcoholic hepatitisOsteoarthristisAIDSDengueTuberculosisArthritisDiabetesTyphoidCommon Cold vertigo Paroymsal Positional VertigoGastroenteritisPsoriasisPneumoniaAcneBronchial AsthmaImpetigoDimorphic hemmorhoids piles Urinary tract infectionHypertension Implementation Data PreparationFor the decision tree algorithm we used PCA to normalize our data and reduce our features from to and we transformed our training and testing data on the vector produced from the PCA Model DefinitionThe Model is trained on the discussed dataset The Model Input the symptoms The Model Output the possible diseases the patient may suffer from Model TrainingWe used five classification algorithms to process the data Decision Tree Random Forest Naïve Bayes K Nearest Neighbor KNN Artificial Neural Networks ANN is illustrated in the figure below which shows that the model has one input layer with neurons since we have symptoms one hidden layer and one output layer with neurons since we have labels as outputs batch size of and epochs Evaluation amp ResultsThe Dataset was spilt by for all the classifiers The accuracy of each classification technique used for predicting diseases based on symptoms AlgorithmAccuracyDecision Tree Random Forest KNN Naïve Bayes ANN DiscussionThe models showed a decent performance and very high accuracy The best results were provided by ANN while Naïve Bayes amp KNN amp Random Forest provided comparable results However while working with real and unseen data the Random Forest showed the best results out of all the classifiers The table below shows the details of each model AlgorithmReviewDecision TreeWorking on the original data resulted in low accuracy Features Reduction to normalize data and reduce dimensions lead to better results Random ForestThis model showed promising results and we observed that when increasing the number of estimators the results improved significantly KNNWe observed that when K was reduced the results improved and on trial and error experimentation we chose to be the value of K more experimentation may lead to a better accuracy Naïve BayesThis model showed good performance on the original data achieving very high accuracy ANNUnfortunately papers did not provide guidelines on configuring the network of this model So we had to use trial and error and determined the following hyperparameters Number of layers Number of neurons unit Epoch Number Number of batches The neural network did not find it challenging to train and after epochs the training accuracy was good and the test accuracy was also good Integration With DOCTOR YWe used the Diseases Symptoms Prediction Model s results and combined them with the Diseases Diagnoses Prediction Model s results to calculate the percentage of suffering from a group of diseases based on previous diagnoses the associated symptoms The final diseases and their percentages are sent to the system server which sends them to the client side to be represented on a chart as shown in the figure below	2021-07-29 23:28:44
海外TECH	DEV Community	Azure DevOps: Limit User Visibility and Collaboration to Specific Projects	https://dev.to/n3wt0n/azure-devops-limit-user-visibility-and-collaboration-to-specific-projects-2mgg	Azure DevOps Limit User Visibility and Collaboration to Specific ProjectsEver had the need to restrict some users to just specific projects in Azure DevOps Today I m gonna show you how to do that IntroToday we talk about a new feature that has been released recently in Azure DevOps and that allows you to limit the user visibility and collaboration to specific projects I m talking about the Limit user visibility and collaboration to specific projects Preview Feature VideoAs usual if you are a visual learner or simply prefer to watch and listen instead of reading here you have the video with the whole explanation and demo which to be fair is much more complete than this post Link to the video If you rather prefer reading well let s just continue The ProblemBy default users added to an organization can view all organization metadata and settings This includes viewing the list of users in the organization list of projects billing details usage data and anything that s accessible through the organization settings This includes viewing the list of users in the organization list of projects billing details usage data and anything that s accessible through the organization settings This is because people pickers provide support for searching all users and groups added to Azure AD not just those users and groups added to your projectAnd until now there was no effective way to change this behavior As I said until now The SolutionTo restrict users from this information you can enable the Limit user visibility and collaboration to specific projects preview feature for your organization Once enabled the Project Scoped Users group which is an organization level security group will be added to your Azure DevOps organization It can be found by navigating to the Organization Settings gt PermissionsWhen you add Users and groups to this new group they will see a banner stating that the administrator has limited their visibility After that they will have two limitations When accessing the Organization Settings most of the items will be hidden And about the people selection the people picker search will be limited to only the AAD Users that have been added to the project the user is scoped to And this applies also to the tagging of users in Work Items and Comments ConclusionsComment down below and let me know if this new feature solves any issue you had in the past with user management Also checkout this video where I talk about how to properly secure and Azure DevOps Organization Like share and follow me for more content YouTube Buy me a coffeePatreonCoderDave io WebsiteMerchFacebook page‍GitHubTwitterLinkedInPodcast	2021-07-29 23:24:05
海外TECH	DEV Community	5 Helpful Html Global Attributes	https://dev.to/ayabouchiha/5-helpful-html-global-attributes-e8a	Helpful Html Global AttributesHello I m Aya Bouchiha today we ll talk about some useful HTML global attributes Definition of global attributesglobal attributes are Html attributes that can be used on any HTML elements like title and hidden Heplful HTML Global Attributes accesskeyaccesskey this attribute lets you specify a keyboard shortcut to focus an element The way to activate the accesskey depends on the browser and its platformCode example lt div gt lt h gt Lorem lt h gt lt p gt Lorem ipsum dolor sit amet consectetur adipiscing lt p gt lt a accesskey a href product gt more details lt a gt lt div gt dirdir lets you specify the text s direction Code example lt DOCTYPE html gt lt html gt lt head gt lt meta http equiv content type content text html charset utf gt lt head gt lt body gt lt div gt lt p dir rtl gt right to left lt p gt lt p dir ltr gt left to right lt p gt lt p dir auto gt Hello lt p gt lt p dir auto gt السلامعليكم lt p gt lt div gt lt body gt lt html gt Output data data this is one of the most useful attributes it lets you store extra and custom data on your HTML tag In addition You can access this attribute in CSS and also Javascript It should be at least one character and must not contain any uppercase letters lt DOCTYPE html gt lt html gt lt head gt lt style type text css media all gt address data user country Morocco before content attr data user country display block lt style gt lt head gt lt head gt lt body gt lt address class address data full name Aya Bouchiha data email developer aya b gmail com data job full stack web developer data user country Morocco data id data bg red gt Posted by lt a href gt Aya Bouchiha lt a gt lt br gt Email Address lt a href mailto developer aya b gmail com gt developer aya b gmail com lt a gt lt br gt Phone Number lt a href tel gt lt a gt lt br gt lt address gt lt script gt const addressElement document querySelector address const id document querySelector data id lt address class address gt … lt address gt console log id Aya Bouchiha console log addressElement dataset fullName developer aya b gmail com console log addressElement getAttribute data email lt script gt lt body gt lt html gt Output titletitle lets you show more information about an HTML element lt div gt lt h gt Sport lt h gt lt p gt ed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium totam rem aperiam eaque ipsa quae ab illo inventore veritatis et quasi architecto lt p gt lt a href sport title more details about sport gt more details lt a gt lt div gt hiddenhidden indicates that that the element is not yet or is no longer relevant browsers do not display the element with a hidden attribute lt section gt lt h gt Title lt h gt lt p gt Hi lt mark hidden gt I m Aya Bouchiha lt mark gt lt br gt This is a simple paragraph lt p gt lt a href gt more details lt a gt lt section gt Output Summaryaccesskey specify a keyboard shortcut to focus an element dir specify the text s direction data store extra and custom data on your html tag title shows more information about an HTML element hidden hide an element To Contact Me email developer aya b gmail comtelegram Aya BouchihaHave a great day	2021-07-29 23:02:35
Apple	AppleInsider - Frontpage News	Beatles producer says Spatial Audio album doesn't sound right, plans new mix	https://appleinsider.com/articles/21/07/29/beatles-producer-says-spatial-audio-album-doesnt-sound-right-plans-new-mix?utm_medium=rss	Beatles producer says Spatial Audio album doesn x t sound right plans new mixLegendary Beatles producer Giles Martin in an interview this week discussed the advent of Dolby Atmos the technology on which Apple s Spatial Audio format is built revealing that he intends to create a new mix of Sgt Pepper s Lonely Hearts Club Band because the current version doesn t sound quite right Speaking with Rolling Stone Martin explained that Sgt Pepper s Lonely Hearts Club Band was among the first albums ーperhaps the first ーto receive a Dolby Atmos mix While the result sounds good it doesn t sound right in part because the mix was meant to be a theatrical presentation Sgt Pepper s how it s being presented right now I m actually going to change it It doesn t sound quite right to me It s out in Apple Music right now But I m gonna replace it It s good But it s not right Martin said Sgt Pepper s was I think the first album ever mixed in Dolby Atmos And we did that as a theatrical presentation I liked the idea of the Beatles being the first to do something It s cool that they can still be the first to do something So Sgt Pepper s is a theatrical mix that s then being converted into a smaller medium Therefore it s not quite right Read more	2021-07-29 23:52:44
海外科学	NYT > Science	Biden Seeks to Revive Vaccine Effort With New Rules and Incentives	https://www.nytimes.com/2021/07/29/us/politics/biden-vaccine-mandates.html	coronavirus	2021-07-29 23:22:17
海外科学	NYT > Science	As Virus Cases Rise, Another Contagion Spreads Among the Vaccinated: Anger	https://www.nytimes.com/2021/07/27/health/coronavirus-vaccination-hesitancy-delta.html	As Virus Cases Rise Another Contagion Spreads Among the Vaccinated AngerFrustrated by the prospect of a new surge many Americans are blaming the unvaccinated A tougher stance may backfire some experts warn	2021-07-29 23:47:26
金融	金融総合：経済レポート一覧	FX Daily(7月28日)～FOMC後、ドル円は109円台後半まで小緩む	http://www3.keizaireport.com/report.php/RID/463425/?rss	fxdaily	2021-07-30 00:00:00
金融	金融総合：経済レポート一覧	タントラムなきテーパリングへ（ＦＯＭＣ）～関心は利上げまでの道筋へ：Market Flash	http://www3.keizaireport.com/report.php/RID/463431/?rss	marketflash	2021-07-30 00:00:00
金融	金融総合：経済レポート一覧	米国　ＦＲＢの目標に向けて前進でテーパリングが近づく （21年7月27、28日ＦＯＭＣ）～９月以降の労働市場の改善ペース加速でテーパリング決定か：Fed Watching	http://www3.keizaireport.com/report.php/RID/463432/?rss	fedwatching	2021-07-30 00:00:00
金融	金融総合：経済レポート一覧	FOMC　テーパリング議論の開始を宣言～景気回復の進展を認めるも、ハト派化もタカ派化もあり得る：米国	http://www3.keizaireport.com/report.php/RID/463433/?rss	大和総研	2021-07-30 00:00:00
金融	金融総合：経済レポート一覧	「国際金融都市・東京」構想の改訂案をどうみるか：リサーチ・アイ No.2021-025	http://www3.keizaireport.com/report.php/RID/463435/?rss	日本総合研究所	2021-07-30 00:00:00
金融	金融総合：経済レポート一覧	FRBのパウエル議長の記者会見～Coming meetings：井上哲也のReview on Central Banking	http://www3.keizaireport.com/report.php/RID/463436/?rss	comingmeetings	2021-07-30 00:00:00
金融	金融総合：経済レポート一覧	FRBのテーパリングと米国経済・金融情勢の不確実性：木内登英のGlobal Economy ＆ Policy Insight	http://www3.keizaireport.com/report.php/RID/463437/?rss	lobaleconomypolicyinsight	2021-07-30 00:00:00
金融	金融総合：経済レポート一覧	個人投資家向けESG投資の意識調査【サマリー】	http://www3.keizaireport.com/report.php/RID/463439/?rss	個人投資家	2021-07-30 00:00:00
金融	金融総合：経済レポート一覧	第209回 信用保証利用企業動向調査（2021年4-6月期実績、7-9月期見通し）～信用保証利用企業の資金繰りは、改善している。景況は、持ち直しの動きがみられるものの、厳しい状況が続いている	http://www3.keizaireport.com/report.php/RID/463443/?rss	持ち直し	2021-07-30 00:00:00
金融	金融総合：経済レポート一覧	中国本土証券市場への海外資金流入と強まる逆風：アジア・マンスリー 2021年8月号	http://www3.keizaireport.com/report.php/RID/463467/?rss	中国本土	2021-07-30 00:00:00
金融	金融総合：経済レポート一覧	金融緩和策を維持も、テーパリング議論の継続を表明したFOMC	http://www3.keizaireport.com/report.php/RID/463472/?rss	野村アセットマネジメント	2021-07-30 00:00:00
金融	金融総合：経済レポート一覧	FOMC、量的金融緩和縮小開始に向けて進展～声明文に「経済が目標に向けて前進」と明記：マーケットレポート	http://www3.keizaireport.com/report.php/RID/463474/?rss	三井住友トラスト	2021-07-30 00:00:00
金融	金融総合：経済レポート一覧	FOMC（7月27・28日）の注目点～年明け以降のテーパリング開始に向け議論が進展：マーケットレポート	http://www3.keizaireport.com/report.php/RID/463475/?rss	開始	2021-07-30 00:00:00
金融	金融総合：経済レポート一覧	7月FOMC 金融政策の現状維持を決定～テーパリング開始時期は今後複数の会合で見極めか	http://www3.keizaireport.com/report.php/RID/463477/?rss	現状維持	2021-07-30 00:00:00
金融	金融総合：経済レポート一覧	KAMIYAMA Seconds!：コロナ禍後の回復が遅れるリスクは？	http://www3.keizaireport.com/report.php/RID/463478/?rss	kamiyamaseconds	2021-07-30 00:00:00
金融	金融総合：経済レポート一覧	FRBはFOMCでテーパリングの議論を開始～金融緩和縮小は慎重に行われ株価は堅調推移へ	http://www3.keizaireport.com/report.php/RID/463479/?rss	三井住友	2021-07-30 00:00:00
金融	金融総合：経済レポート一覧	2021年7月FOMCレビュー～テーパリング開始の地ならしが進む：市川レポート	http://www3.keizaireport.com/report.php/RID/463480/?rss	三井住友	2021-07-30 00:00:00
金融	金融総合：経済レポート一覧	ゆうちょ資産研レポート2021年7月号～ポストコロナの消費展望 / コロナ感染拡大から1年。「巣ごもり」消費の考察と景気を占う「プチ贅沢」マインドについて /今年度末までの長期・超長期金利の見通し...	http://www3.keizaireport.com/report.php/RID/463482/?rss	感染拡大	2021-07-30 00:00:00
金融	金融総合：経済レポート一覧	米ＦＯＭＣ（21年7月）～予想通り、金融政策を維持。声明でテーパリング開始条件の達成に向けた進展を示唆：経済・金融フラッシュ	http://www3.keizaireport.com/report.php/RID/463484/?rss	予想通り	2021-07-30 00:00:00
金融	金融総合：経済レポート一覧	第12回「私たちのお金が生み出すサイクルを「未来のチカラ」に」～豊かな暮らしにつながる～「未来のチカラ」	http://www3.keizaireport.com/report.php/RID/463503/?rss	三井住友トラスト	2021-07-30 00:00:00
金融	金融総合：経済レポート一覧	【注目検索キーワード】持続可能な観光	http://search.keizaireport.com/search.php/-/keyword=持続可能な観光/?rss	検索キーワード	2021-07-30 00:00:00
金融	金融総合：経済レポート一覧	【お薦め書籍】アフターコロナのニュービジネス大全　新しい生活様式×世界15カ国の先進事例	https://www.amazon.co.jp/exec/obidos/ASIN/4799327437/keizaireport-22/	生活様式	2021-07-30 00:00:00
金融	日本銀行：RSS	金融政策決定会合議事録等（2011年1月～6月開催分）	http://www.boj.or.jp/mopo/mpmsche_minu/record_2011/gjrk.htm	金融政策決定会合	2021-07-30 08:50:00
ニュース	BBC News - Home	Pregnant women urged to get jab as majority unvaccinated	https://www.bbc.co.uk/news/health-58014779	england	2021-07-29 23:41:36
ニュース	BBC News - Home	Migrant women and babies held in shocking conditions, MPs find	https://www.bbc.co.uk/news/uk-58019981	covid	2021-07-29 23:38:52
ニュース	BBC News - Home	Dairy giant Arla says driver crisis hitting milk supply	https://www.bbc.co.uk/news/business-58012884	lorry	2021-07-29 23:01:31
ニュース	BBC News - Home	Empty shop numbers rise as Covid continues to bite	https://www.bbc.co.uk/news/business-58007313	bitethe	2021-07-29 23:03:34
ニュース	BBC News - Home	The Papers: 'No jab, no job' threat and 'red alert' for travel	https://www.bbc.co.uk/news/blogs-the-papers-58020875	rules	2021-07-29 23:37:31
ビジネス	ダイヤモンド・オンライン - 新着記事	ボーイング宇宙船、打ち上げ延期　ISSでトラブル - WSJ発	https://diamond.jp/articles/-/278233	延期	2021-07-30 08:24:00
ビジネス	ダイヤモンド・オンライン - 新着記事	機内で嫌がらせ常態化、マスク着用規定など火種に - WSJ発	https://diamond.jp/articles/-/278234	嫌がらせ	2021-07-30 08:04:00
LifeHuck	ライフハッカー［日本版］	優柔不断だからこそより良い選択ができることもある	https://www.lifehacker.jp/2021/07/237244how-being-ambivalent-can-help-you-make-better-decisions.html	優柔不断	2021-07-30 08:30:00
ビジネス	東洋経済オンライン	｢ずれた結論を出す人｣と出さない人の決定的な差 正しく｢問題｣を認識･解決するための3つの方法 | 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/-/442680?utm_source=rss&utm_medium=http&utm_campaign=link_back	東洋経済オンライン	2021-07-30 08:30:00