Official media said Russia provided 80,000 tons of diesel and energy equipment to Cuba

Official media said Russia provided 80,000 tons of diesel and energy equipment to Cuba

2024-11-09 03:38:00

HAVANA (AP) — Russia has donated 80,000 tons of diesel fuel and equipment worth $62 million to Cuba to support its energy industry, Cuban state media reported Friday.

The proposal comes as the island is experiencing a severe energy crisis caused by two cyclones, which has been worsened by a chronic lack of fuel on the island and the continued failure of old thermal power plants.

According to the official Latin News Agency, the initiative was proposed to President Miguel Díaz by Russian Vice President Dmitry Chernyshenko, who was participating in the bilateral meeting, and in accordance with President Vladimir Putin implementation of the “instructions”.

In addition, Chernyshenko said that repair kits for damaged equipment will be sent.

Havana and much of the country’s west remained in danger on Friday after Category 3 Hurricane Rafael hit Cuba on Wednesday, moving from south to northwest and knocking out the country’s energy system with strong winds.

Two weeks ago, another blackout occurred, this time caused by a power plant failure. These weather events often cause huge damage to infrastructure due to events such as falling poles and cables and damaged transformers.

The Cuban government stated that it has also received cooperation intentions from countries such as Mexico, China, Colombia, Venezuela, and the United Nations system.

Chernyshenko arrived in Cuba from Venezuela, where he met with the country’s President Nicolas Maduro.

Cuba, Venezuela and Russia are political allies and have agreed to reject U.S. sanctions that, in the case of the island, are aimed at changing its model of government.

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**Interview with ⁤Data Analyst⁤ Jane Smith on Calculating Worked⁢ Hours in Time Tracking ⁢Systems**

**Host:** Good morning, everyone! Today, we have ⁣Jane Smith, a data analyst with extensive⁢ experience ‍in time tracking ‍and employee management systems. Jane, thank you for joining⁤ us!

**Jane:** Thank ‌you ​for ⁣having me!

**Host:** Let’s dive right in. One common challenge in time tracking is calculating how ‍long employees worked during each‌ session.​ Can you break down how this can be done?

**Jane:** Absolutely! ⁣The first step is to ensure that you have a ‍structured ⁢dataset, typically in a table format, ⁤which includes columns​ for employee IDs, clock-in times, and clock-out times.

**Host:** That sounds straightforward. Once you have that data, what’s the next step?

**Jane:** After gathering the data, you’ll need to‌ calculate the duration of each⁣ work session. This ‍is usually done by subtracting ⁣the clock-in time from the clock-out time for each session.

**Host:** Are there any common pitfalls to watch ⁤out for in this‌ process?

**Jane:** Definitely. One common issue is handling the cases where an ‍employee might⁤ forget to clock out​ or where clocking in and out times are not properly paired. It’s crucial to have⁢ a⁣ method⁤ in ‍place to ⁤validate⁤ the‌ data before ⁢calculations.

**Host:** And what about ‌multiple sessions in a single‌ day? ⁢How can you⁤ handle that?

**Jane:** For ⁤multiple ⁢sessions, you’ll want to group ‍the data by employee and date, then calculate the session durations⁤ for each clock-in/clock-out pair. This⁢ way, you can create a clear ‌picture of ​how ​many hours they worked across the day.

**Host:** That’s very ​insightful. What tools or SQL queries would you recommend ⁣for ⁢someone⁢ venturing into‌ this analysis?

**Jane:** ‍SQL is⁢ a great tool ‍for this. A simple⁣ query could use⁣ `TIMESTAMPDIFF` or similar functions to compute the difference between clock-in and clock-out ​times. Grouping functions can also ‍help to summarize the data effectively.

**Host:** Fantastic advice, Jane. ‌Before we wrap up, any last ‌tips for those tackling⁢ similar problems?

**Jane:** Always ensure your data is clean and well-structured.⁤ And‌ don’t hesitate to test your calculations with sample data to ‍ensure accuracy. ‍Practice makes perfect!

**Host:** Great tips! Thank​ you so much for the insights today, Jane.

**Jane:**⁣ Thank you for having me!

**Host:** And thank ​you to our ​listeners for tuning ‍in! Until next time!

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