Advertisers Harness LED Modular Display Technologies for Innovative Campaigns

LED modular displays have expanded the avenue for proponents of the advertisement industry. A variety of LED modular displays find use in outdoor events as they enable brands cater to different size and configurations within a set advertising budget. Advancement being made in digital display technologies is key trend boosting the LED modular displays market. Some of the commonly emerging applications of LED modular displays are railway stations, airports, bus stops, retail stores, auditoriums, and shopping malls. Over the years, developing economies have seen the growing number of live events and concerts in multiplexes. This has fueled the prospective demand for LED modular displays for gaining seamless images and high-resolution video content streaming features.

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Manufacturers Keen on Meeting Customization Needs

Manufacturers have been relentlessly making design innovation to meet the consumer demand. The focus areas are the freedom in installation, control mechanism, and media technologies. End users in the LED modular display market have gained access to new content configurations. The popularity of LED video walls is growing among media and broadcasting industries and sporting event managers. A variety of interactive technologies have expanded the choice for these end users, augmenting the revenue potential in the LED modular display market.

Top players are keen on meeting customization requirements of end users in the LED modular display market. A growing number of players are attempting innovations on the lines of offering scalability in real-world environments. Companies are interlocked with fierce competition to gain a stronghold in the market. Several players have expanded their research and development funding to accelerate the development of innovative technologies for LED modular display market. They are targeting high-traffic application areas to meet wide cross-section of demand. Some of the well-entrenched players in the LED modular display market are Daktronics, Barco N.V., and Shenzhen Dicolor Optoelectronics Co. Ltd.

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Machine Learning speeds up discovery of new materials for industrial processes

A research initiative by researchers at Northwestern University and University of Toronto employs machine learning to create best building blocks for the assembly of framework materials for use in a targeted application.

The findings of the initiative is published in Nature Machine Intelligence. It demonstrates that the using artificial intelligence approaches can help to propose novel materials for diverse applications. The use of machine learning to separate carbon dioxide from industrial combustion process is an example. In fact, AI approaches are promising to accelerate the design process of materials.

Meanwhile, in a bid to improve segregation of chemicals in industrial processes, a team of researchers at Northwestern University and University of Toronto in collaboration with experts at the University of Ottawa and Harvard University set out to find the best reticular frameworks.

The frameworks can be viewed as tailored molecular sponges. The frameworks are formed via self-assembly of molecular building blocks put in different arrangements. Furthermore, the frameworks represent a new family of crystalline porous materials that have proven to be promising to address several technology challenges.

Demonstrated use of automated platform aided build-up of Design Frameworks

“Earlier, to build the frameworks, it involved building an automated discovery platform. The platform generates the design of various molecular frameworks, thus significantly reducing the time required to find optimal materials for use in this process,” said the lead author of the study.

In fact, in the demonstrated use of the platform, frameworks that are discovered are strongly competitive against some of best-performing materials used for the separation of CO2 till date.

Nonetheless, the unpredictable amount of time and massive trial-and-error efforts needed to find new materials are some perennial challenges for addressing CO2 separation.

AI finds use for drug repurposing consequent upon study

In a breakthrough development, researchers have developed a machine-learning method to determine how to improve the outcomes of existing medications for diseases these are not prescribed. The method involves crunching massive amounts of data.

In fact, the objective of the work is to expedite drug repurposing – a concept not new in the pharma sector. For example, Botox injections that were first used to treat crossed eyes now find use to treat migraine, and are a top cosmetic strategy to lessen the appearance of wrinkles.

However, to discover new uses of existing medication is a mix of few things. It is time-consuming, involves expensive randomized clinical trials, and serendipity. The success of such efforts ensure a drug deemed effective for one medical condition will be useful for some other condition as well.

Meanwhile, researchers at the Ohio State University developed a framework that combines massive datasets pertaining to patient care with high-powered computation. This leads to arrive at repurposed drug molecules and estimated effects of existing medications on predefined outcomes.

Whilst the focus of the study is to propose repurpose of drugs to prevent stroke and heart failure in patients with coronary artery disease- the flexible nature of the framework enables its application for most diseases.

“Besides this, the work showcases how artificial intelligence can be used to examine how a drug works on a patient. In addition, the framework expedites hypothesis generation and speeds up clinical trials,” said a scientific associate at the Ohio State University.

Nonetheless, drug decisions will remain with clinicians.

Notably, drug repurposing is an attractive vocation. This is because it could lower the risk related with safety testing of new medications.

How has Coronavirus Pandemic Impacted Dry Vacuum Pump Motor Market?

The dry vacuum pump motor market is estimated to observe promising growth across the assessment period of 2019-2029 on the back of the benefits etched to them. Eco-friendly, good pumping capacity of high water vapor solvents, less power consumption, reduced cooling water usage, and others are some of the vital benefits influencing the growth prospects of the dry vacuum pump motor market.

The utilization of dry vacuum pump motors in varied industries such as chemical, food and beverages, electronics and semiconductor, printing, packaging, and others may bring immense growth opportunities for the dry vacuum pump motor market.

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High Costs Associated with Machinery to Dampen Growth Prospects of Dry Vacuum Pump Motor Market

The dry vacuum pump motor market growth landscape looks promising but some factors may prove to be growth dampeners. The prominent growth restraints for the dry vacuum pump motor market are the high costs of the machinery and the heaviness. However, research and development activities are in progress for improving on these restraints.

COVID-19 Impact

The novel coronavirus outbreak has moderately impacted the dry vacuum pump motor market. The first half of the year 2020 was the most disastrous phase in terms of the growth of the dry vacuum pump motor market. As manufacturing units were closed, it resulted in the decline of production and demand. Thus, this aspect had a negative impact on the growth of the dry vacuum pump motor market.

Nevertheless, the easing in the lockdown restrictions to bring the economy on track has provided a golden opportunity for the dry vacuum pump motor market to regain its lost growth.

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Some well-established players in the dry vacuum pump motor market are Wintek, Gast Manufacturing, Inc., Agilent Technologies, Ebara Corporation, Vooner FloGard Corporation, Welch, ULVAC, Gardner Denver, Inc., Dekker Vacuum Technologies, Inc., and Samson Pump A/S.