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Source channel @OnePlusOTA · Post #607 · 5月27日

OnePlus Nord 2 OxygenOS 12.1 C.04 IND System • Fixed the issue that the lock screen interface displayed abnormally when charging • Fixed the issue that the screen brightness displayed abnormally in certain scenarios • Fixed the occasional issue that the desktop text displayed abnormally in certain scenarios Camera • Optimized the anti-shake effect when shooting videos • Optimized the speed of enabling Camera in certain scenarios Others • Fixed the issue of abnormal crash when enabling Fortnite MD5 Component (my_manifest): c949151afe63f1cfe9fda80d0d541abc Component (my_product): 408223966738c5d0a71f39b211bb1592 Component (my_bigball): 8253f6c910a4bc7cbfe044b3b1f79751 Component (my_stock): f08eb9a61ed03567965cbc76d980e6a3 Component (my_heytap): 28db2abbedc1eafc8947749e91b197fc Component (my_carrier): f0b3b8bd50cc13f4d2a1ebdad9f75f22 Component (system_vendor): e5d935f73c54cc08ae04c9e5abeefe20 Component (my_region): ceb333df4f651e82e5c71a9d76da3273 SHA-1 Full: a3de2e204668cc33c7134bf062bb5f6873a28bce Size Component (my_manifest): 1.22 MB (1278656) Component (my_product): 413.80 MB (433902450) Component (my_bigball): 578.54 MB (606645588) Component (my_stock): 615.30 MB (645192760) Component (my_heytap): 508.90 MB (533621509) Component (my_carrier): 1.04 MB (1088872) Component (system_vendor): 2.49 GB (2675632293) Component (my_region): 3.35 MB (3513520) Full: 4.56 GB (4893267850) Downloads ColorOS Global Server: Component (my_manifest) Component (my_product) Component (my_bigball) Component (my_stock) Component (my_heytap) Component (my_carrier) Component (system_vendor) Component (my_region) Google OTA Server: Full Exported by MlgmXyysd Color OTA Bot@OnePlusOTA #Oxygen#denniz#India#Component#Full#Stable#DN2101

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Borkena

@borkena · Post #5593 · 2025/12/21 16:33

ኢትዮጵያን እንደ ዩጎዝላቪያ ሳይበትኗት እንታደጋት! (ዶ/ር አክሎግ ቢራራ) https://youtu.be/zzOEyMG2V1g?si=5S9RKRlf9BTaurwt#Ethiopia#analysis

Repositorio data science

@repo_science · Post #3999 · 2024/01/22 11:01

#TimeSeries#Analysis#Python ⌚️ Forecasting Models and Time Series for Business in Python Time Series Analysis in Python. Demand Planning & Business Forecasting. Forecast with 6 Models: Prophet, ARIMA & More. ----- Main channel: @repo_science Coupons: @freecoupons_reposcience -----

Daily Channels

@dailychannels · Post #6767 · 2026/03/23 01:00

Channel: Propheta Indicator Signals Members: ~2.5K 💢 Username: @propheta_indicator Description: 😎 WE MILK THE EXCHANGES! 🔥 Reviews & Results - @propheta_reviews 📊 Performance Reports - @propheta_reports 🤖 Get Access - @ProphetaAccountBot Contact us: @propheta_help 🏷 Tags: #crypto_fx_trading #crypto#trading#signals#analysis#news https://lve.to/4rck4ca4c6

Venezuelanalysis

@venanalysis · Post #1850 · 2025/01/11 21:16

The Venezuelanalysis staff gathered to discuss the recent events surrounding Maduro’s third presidential term inauguration and the challenges ahead. The topics included an update on the situation on the ground, María Corina Machado's (fake?) arrest and the US response. Click to watch: https://venezuelanalysis.com/video/venezuelas-maduro-presidential-inauguration-recap-and-lookahead/ #Livestream#Analysis#Venezuela#PresidentialInauguration

Daily Channels

@dailychannels · Post #5943 · 2025/03/26 13:00

Channel: Bitcoin Trading Nicole Members: ~20.77K 💢 Username: @bitcointradingnicole Description: Nicole Bitcoin Trading is a place to be, where experts calls are backedup with sound Technical analysis. t.me/PayoutProof t.me/BitcoinAlgoPumps t.me/CryptoTradingNicole For VIP & Pump Contact: @NicoleCrypto 🏷 Tags: #crypto_fx_trading #bitcoin#trading#crypto#analysis#investing https://telegramchannels.me/channels/bitcointradingnicole

Daily Channels

@dailychannels · Post #6000 · 2025/04/11 01:00

Channel: Crypto Trading Signals ✅ Members: ~8.19K 💢 Username: @binancefuturetrading Description: Who are we? We are a group of professional traders who focus mainly on crypto publicity projects and crypto Trading. 🏷 Tags: #crypto_fx_trading #cryptocurrency#bitcoin#trading#analysis#investments https://telegramchannels.me/channels/binancefuturetrading

djangoproject

@djangoproject · Post #336 · 2017/05/09 05:24

https://dzone.com/articles/pyflakes-passive-checker There are several code #analysis tools for Python. The most well known is pylint. Then there’s pychecker and now we’re moving on to #pyflakes. The pyflakes project is a part of something known as the Divmod Project. Pyflakes doesn’t actually execute the code it checks, unlike #pychecker. Of course, #pylint also doesn’t execute the code. Regardless, we’ll take a quick look at it and see how pyflakes works and if it’s better than the competition.

Crypto M - Crypto News

@CryptoM · Post #64526 · 2026/04/09 06:14

🚀 Polymarket Traders' Earnings: Only 0.015% Achieve $5,000 Monthly Crypto analyst Andrey Sergeenkov's recent analysis reveals that a mere 0.015% of Polymarket traders managed to earn at least $5,000 monthly for four consecutive months. According to NS3.AI, the study examined trading data spanning from April 2024 to April 1, 2026, highlighting the challenges faced by traders in achieving consistent profitability on the platform. #Polymarket#Crypto#Trading#Earnings#Profitability#Analysis#NS3AI

Data Analytics

@sqlspecialist · Post #1644 · 2025/05/23 18:46

✨The STAR method is a powerful technique used to answer behavioral interview questions effectively. It helps structure responses by focusing on Situation, Task, Action, and Result. For analytics professionals, using the STAR method ensures that you demonstrate your problem-solving abilities, technical skills, and business acumen in a clear and concise way. Here’s how the STAR method works, tailored for an analytics interview: 📍 1. Situation Describe the context or challenge you faced. For analysts, this might be related to data challenges, business processes, or system inefficiencies. Be specific about the setting, whether it was a project, a recurring task, or a special initiative. Example: “At my previous role as a data analyst at XYZ Company, we were experiencing a high churn rate among our subscription customers. This was a critical issue because it directly impacted revenue.”* 📍 2. Task Explain the responsibilities you had or the goals you needed to achieve in that situation. In analytics, this usually revolves around diagnosing the problem, designing experiments, or conducting data analysis. Example: “I was tasked with identifying the factors contributing to customer churn and providing actionable insights to the marketing team to help them improve retention.”* 📍 3. Action Detail the specific actions you took to address the problem. Be sure to mention any tools, software, or methodologies you used (e.g., SQL, Python, data #visualization tools, #statistical#models). This is your opportunity to showcase your technical expertise and approach to problem-solving. Example: “I collected and analyzed customer data using #SQL to extract key trends. I then used #Python for data cleaning and statistical analysis, focusing on engagement metrics, product usage patterns, and customer feedback. I also collaborated with the marketing and product teams to understand business priorities.”* 📍 4. Result Highlight the outcome of your actions, especially any measurable impact. Quantify your results if possible, as this demonstrates your effectiveness as an analyst. Show how your analysis directly influenced business decisions or outcomes. Example: “As a result of my analysis, we discovered that customers were disengaging due to a lack of certain product features. My insights led to a targeted marketing campaign and product improvements, reducing churn by 15% over the next quarter.”* Example STAR Answer for an Analytics Interview Question: Question: *"Tell me about a time you used data to solve a business problem."* Answer (STAR format): 🔻*S*: “At my previous company, our sales team was struggling with inconsistent performance, and management wasn’t sure which factors were driving the variance.” 🔻*T*: “I was assigned the task of conducting a detailed analysis to identify key drivers of sales performance and propose data-driven recommendations.” 🔻*A*: “I began by collecting sales data over the past year and segmented it by region, product line, and sales representative. I then used Python for #statistical#analysis and developed a regression model to determine the key factors influencing sales outcomes. I also visualized the data using #Tableau to present the findings to non-technical stakeholders.” 🔻*R*: “The analysis revealed that product mix and regional seasonality were significant contributors to the variability. Based on my findings, the company adjusted their sales strategy, leading to a 20% increase in sales efficiency in the next quarter.” Hope this helps you 😊

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