Zain KSA wins Best 5G Infrastructure Deployment award

Zain KSA has been awarded the Best 5G Infrastructure Deployment title during Telecom Review Leaders’ Summit that took place in Dubai. (Supplied)
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Updated 12 December 2019

Zain KSA wins Best 5G Infrastructure Deployment award

RIYADH: Zain KSA has been awarded the Best 5G Infrastructure Deployment title during Telecom Review Leaders’ Summit that took place in Dubai on Tuesday, December 10, 2019. Over 600 attendee’s ICT companies from more than 23 countries worldwide participated in the 13th edition of the event which focused on the main trends in the ICT sector, namely: 5G network, cyber security, data traffic and digitalization.

Commenting on the new accolade, Chief Technology Officer Eng. AbdulRahman bin Hamad Al-Mufadda at Zain KSA, “the Best Infrastructure Deployment award reaffirms Zain’s role in being a digital lifestyle provider. He emphasized Zain KSA’s role in assisting the development and transformation the Kingdom is witnessing, mainly in terms of enabling and unlocking opportunities for people by creating a sophisticated infrastructure in line with the Kingdom’s digital transformation."

Al-Mufadda added that Zain KSA is continuously enhancing its services and enabling its customers with a new lifestyle to benefit from ultramodern technology by rapidly upgrading its offerings and adapting to the latest advancements in the sector.

Zain KSA has recently announced the completion of its first phase of its 5G network with a wide coverage encompassing 27 cities across the Kingdom through 2,600 towers. At the time of roll-out, the company’s 5G network was the 3rd largest globally and the first in the Middle East, Africa, and Europe.

The 5G network boasts a speed 10 times faster than its predecessor, supporting impressive uploading and downloading rates that will make for an exceptional user experience. The higher capacity of the 5G network, as compared to that of the 4G, will enable more devices to connect to one telecom tower simultaneously without interfering with the user experience. And the low latency where subscribers can enjoy fast response during their internet usage.

As the largest ICT industry event, the Telecom Review Leaders’ Summit offers ICT players and experts from all over the globe the opportunity to connect and share the latest innovations in the industry. Additionally, the conference features in-depth panel discussions and a demo area where participating companies can showcase their cutting-edge solutions. The Telecom Review Awards recognize industry leaders for their efficiency and their efforts in promoting the telecom and ICT sector globally.

About Zain KSA:
It is a leading digital service provider in the Kingdom founded in 2008. It offers many services including telecom services, 5G network, digital payment services, cloud computing, IoT solutions, fiber internet services, drones, and many others.

For more information, please contact:

Salman AlHarbi - Corporate Communication Senior Manager - Cell: +966592443093- [email protected]


J-Clinic study identifies powerful new drug

Updated 26 February 2020

J-Clinic study identifies powerful new drug

A powerful new antibiotic compound has been identified by researchers at MIT using a machine-learning algorithm. The drug killed many of the world’s most problematic disease-causing bacteria in laboratory tests, including some strains that are resistant to all known antibiotics. It also cleared infections in two different mouse models.

The computer model, which can screen more than a 100 million chemical compounds in a matter of days, is designed to pick out potential antibiotics that kill bacteria using different mechanisms than those of existing drugs. 

Regina Barzilay and James Collins, who are faculty co-leads for MIT’s Abdul Latif Jameel Clinic for Machine Learning in Health (J-Clinic), are the senior authors of the study, which appears in Cell. The first author of the paper is Jonathan Stokes, a post-doc at MIT and the Broad Institute of MIT and Harvard.

J-Clinic is a key part of the MIT Quest for Intelligence and focuses on developing machine-learning technologies to revolutionize the prevention, detection, and treatment of disease.

In their new study, the researchers also identified several other promising antibiotic candidates, which they plan to test further. They believe the model could also be used to design new drugs, based on what it has learned about chemical structures that enable drugs to kill bacteria.

“The machine-learning model can explore, in silico, large chemical spaces that can be prohibitively expensive for traditional experimental approaches,” said Barzilay, the Delta Electronics professor of electrical engineering and computer science in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL).

Over the past few decades, very few new antibiotics have been developed, and most of those newly approved antibiotics are slightly different variants of existing drugs. Current methods for screening new antibiotics are often prohibitively costly, require a significant time investment, and are usually limited to a narrow spectrum of chemical diversity.

“We’re facing a growing crisis around antibiotic resistance, and this situation is being generated by both an increasing number of pathogens becoming resistant to existing antibiotics, and an anemic pipeline in the biotech and pharmaceutical industries for new antibiotics,” Collins said.

“The world is in desperate need of new antibiotics to combat dangerous diseases, so it is hugely encouraging that the team at J-Clinic at MIT, has helped make a breakthrough in finding a genuinely new one using machine learning,” said Fady Jameel, Community Jameel president, international. “For decades, Community Jameel has been committed to supporting research that can help improve people’s lives. Combatting the risk from antibiotic-resistant infections, like tuberculosis, could have a profound impact on us all.”

The research was funded and made possible by a number of supporters including the Abdul Latif Jameel Clinic for Machine Learning in Health.