In the midst of a global COVID-19 pandemic, a top priority for many pharma and biopharma companies is to get a vaccine developed, produced and delivered to the public as quickly as possible. Ushering a vaccine through rigorous testing protocols and regulatory approvals is not an easy (or quick) effort, but incorporating advanced data analytics could help accelerate the process. Data analytics has proven effective in speeding vaccine development both by enabling more efficient Design of Experiments (DOE) and by creating rapid-scale production rollout processes.
Looking for ways to improve the efficiency of its power plant operations while reducing costs and environmental emissions, the Department of Power and Water at Michigan State University (MSU) began a study using multivariate data analytics that led to some surprising findings. The results have implications that could help other operators of large-scale power facilities reduce their carbon footprint and improve power plant operations.
Finding the right balance between efficient power output from boilers and other energy producing equipment while also reducing environmental emissions is an important objective for power plant operators. Governments and environmental agencies around the world establish emission standards as part of air pollution regulations, but finding the right way to meet the standards can vary greatly depending on location, equipment and other operating factors.
While many other industries have implemented multivariate data analysis software for process optimization and control, it is still not very common in the pulp and paper industry. However, multivariate data analysis has a very promising potential for both cost reductions and quality improvements in pulp and paper mills. No capital investments are needed, the implementation can be done remotely, and the software typically requires no permits.
There is a strong demand for devices such as mobile phones, tablets and large screen TVs all over the globe. The business is competitive, which puts pressure on prices. At the same time, production costs are fairly high due to complex production processes. Consequently, a high yield becomes paramount for good profit margins. Multivariate data analysis (MVDA) is being employed by an increasing number of manufacturing companies to increase yield, and the electronics industry is no exception. This article provides examples of where and how real-time data analytics can be used in the electronics industry.
The potential for Artificial Intelligence (AI) is enormous and the applications seemingly unlimited. One subset of AI, deep learning, offers the promise of efficiently solving a large range of challenges involving unstructured data by harnessing neural networks to save time and money, and even perform seemingly impossible tasks.
Deep learning has revolutionized the fields of artificial intelligence, computer vision, speech recognition, and more in recent years. Deep learning can draw information from unstructured data such as images or text in a way that was unthinkable a decade ago. In industries like Pharma and Biopharma, deep learning can help all the way from understanding how cells work using live cell imaging to monitoring manufacturing using audio.
SIMCA 16 offers improved ribbons, tours, wizards, data merging, multiblock analysis and more.
SIMCA is a multivariate data analytics tool that helps users make sense of complex data by transforming numbers and statistics into visual information for easy interpretation and understanding. Across many industries ranging from pharmaceuticals and chemicals to food and beverage manufacturers to academia, SIMCA helps production managers and researchers a like make better decisions in order to take action quickly and with confidence.
Recently the FDA issued a new draft guidance for continuous manufacturing of small molecule drugs. With these draft guidelines the FDA wants to engage more pharmaceutical manufacturers to shift from traditional batch/start-stop processing to continuous manufacturing. The main advantages associated with these processes are more room for modularity, automation and flexibility due to a smaller footprint, but also more consistent quality of the drug product. Of course the main incentive for the FDA to promote this way of processing is that it believes that this will have a positive impact on drug prices and prevent drug shortages.
Breast cancer is the most commonly diagnosed cancer amongst women worldwide and a leading cause of cancer related deaths among females. It’s the second most common type of cancer overall. According to the International Agency for Research on Cancer Research, there were more than 2 million new cases in 2018.
Speed and changing market conditions are significant challenges for businesses across many industries and markets. The need for improved efficiency, the ability to adapt to changing market conditions and digitalization are often key drivers of change. By focusing on data, a company can make significant improvements in processes or systems, and gain insights into the driving forces behind business operations. Data analytics is the key to business optimization.
Many elderly people are afraid of falling – and for good reasons. Falls can have serious consequences for the individual but also the fear of falling could have serious effects on health and independence. A new research project at Luleå University of Technology in Sweden has taken a closer look at fall-related concerns among elderly people, using multivariate data analysis, MVDA, with the ultimate goal of finding diagnostic and training methods that could help reduce falls. Results from the first studies have given some interesting answers.
Mining information in unstructured text can be a real challenge. Patent documents, for example, provide a rich source of technological and scientific knowledge that can reveal technological trends as well as information on the legal landscape of the market. This makes analysis of the vast and ever-growing number of patents an important part of corporate business strategies.