The influence of artificial intelligence on the current trends of material science – Economic Times

The recent years have experienced a burgeoning growth in the development of statistical and machine learning within the domains of materials science and polymer chemistry. Interestingly, or rather unnoticeably, the concept of artificial intelligence was prevalent in the material science community for the past couple of decades. For instance, more than 15 years ago, a symposium proceeding conducted by the Materials Research Society had a session titled Combinatorial and Artificial Intelligence Methods in Materials Science. The trend has evolved recently with contemporary topics like high throughput screening, particle simulation accelerator, and using computational data sets to develop ground states.

The first question I asked myself is, why is this field proliferating now? Furthermore, if the area had been into practice 15 years ago, what happened to the techniques since then? Well, this somewhat resembles the rise and fall of the artificial intelligence, which generally has the crest and the trough, commonly termed as the resurgence and AI winters respectively.

The first spark was seen in 1956, when the context of artificial intelligence was created. Back then, the scientist didnt know how to deal with the computational science. Moreover, there was no proper bridge that could link the experimental data with the theoretical data obtained from computational programming. The domain became more reinforced during the 1980s with the advent of powerful algorithms like backpropagation (for neural networks) and kernel methods (for classification). Now, with the integration of deep learning along with the growth in graphics processing units, the computational techniques have opened up a lot of avenues in the field of material sciences.

But, is the current technique enough to bridge the distance between the materials and the scientific community?

I guess, yes. The primary element which determines the robustness of an artificial intelligence processing and operation is the availability of large volumes of arranged data, which the literature terms as libraries. These libraries enable us to use the machine learning fundamentals, but at the same time provide the scope to interpret them physically.

If harmonized and processed precisely, artificial intelligence not only allows us to accelerate our scientific developments but also the way particular research can be conducted. That is why you will find various recent articles that focus on ways to develop quicker routes to perform the same contemporary experiments. In this context, the Materials Genome Initiative, which was launched in 2011, had the sole intention to accelerate the material discovery process and to scale them up. The primary steps they used to establish the above goals were to apply the high throughput algorithm, both the theoretical and experimental modeling, to develop accessible libraries and repositories. Since then, the datasets have become a traditional solution to deal with complex problems in material sciences. The course of evolution eventually developed various datasets that contain thousands of experimental and theoretical data points including the Automatic Flow for Materials Discovery (AFLOWLIB), Joint Automated Repository for Various Integrated Simulations (JARVIS), density functional theory (DFT)), Polymer Genome, Citrination, and Materials Innovation Network.

The question remains- how exactly do these advanced techniques help us to develop a new perspective in material sciences? Well, let me give you an elementary example. Let say; I have developed a robust library with machine learning which hosts data for alloy designing. Once I know what kind of alloy to fabricate, I can set the parameters in the library to find the most optimized set of materials and operation tools which can fetch me the desired results in the least required time. Can we do the same using experimental and pure theoretical techniques? No, since most of the time shall be consumed while conducting trails from the vast set of the data. Moreover, these libraries can be extended to accelerate the synthesis optimization process, along with integrating train models to classify the crystal structures and defects. The most recent application involves the development of various de novo molecules for reinforced molecular designs for identifying materials with specific properties desired for various sensible operations.

As a concluding note, the availability of such databases and amalgamating them with theoretical and machine learning methods offer the potential to alter how materials science is approached substantially.

DISCLAIMER : Views expressed above are the author's own.

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The influence of artificial intelligence on the current trends of material science - Economic Times

BWIRE: Artificial Intelligence could be helpful in dealing with… – Citizen TV

By Victor Bwire

Its during such times like the current corona virus outbreak that we ask our ourselves hard questions including how can innovations especially Artificial Intelligence (AI), help us deal with improve our responses and handling of such challenges.

Elsewhere it has been shown what AI and machine automated applications can do several things previously done by human; from smart cities, to facial recognition not the basic on your phone, but cameras that can detect that you have been to four malls in the city within the past one week and recognize your face when you are exiting at the airport to fully automated sales points and ticketing centers.

AI-powered tracking and warning systems, intensive observation methodologies and testing can be of huge importance to the country in the war on the corona virus.

Availability of government-run big-data platforms such CCTVs and others stores information of all citizens and foreign nationals and integrates all these for use. With such information, its easier to track those whom s/he had met during that time, and bring them under observation and medical tests.

AIe ensures prompt execution of all these steps. Hospitals, ambulance services, mobile test labs all rely on IT sector and technology to deliver prompt and efficient services.

Outside its negative side, would the implementation of the huduma number in time have been useful to the country in dealing with the current corona virus, especially in tracking down possible suspects, those self isolating and travel history of people- through obviously the issue of individual privacy has been raised before.

There have been previous innovations where technological applications and mobile phones have been used to tracking and offering health services to malaria patients and reproductive health services to teens.

Tech giants including Google, Facebook, Huawei through their various applications have been working over night elsewhere to support the dealing of the corona virus outbreak. Hopefully, Kenya, which has the presence of such big techs will eventually benefit from such technological innovation.

Today while reading an article by Eunice Kilinzo online, technology and medical services, my mind went to the many other stories I have read relating to technological innovations including mobile phone applications that have helped in enhancing the delivery of health services to Kenyans. Kilonzo talks about Ada a mobile application that uses artificial intelligence (AI) to track symptoms to get to the probable cause of an ailment.

The app, developed by Ada Health, a Germany-based health tech company, combines a database for 160 different diseases with intelligent reasoning technology.

Its reported that South Korea is fighting the virus by using big-data analysis, AI-powered advance warning systems and intensive observation methodology-the government-run big-data platform stores information of all citizens and resident foreign nationals and integrates all government organisations, hospitals, financial services, mobile operators, and other services into it, which is then integrated and used.

Huawei who are behind the 5G technology and working with Safaricom, who you be assisting Kenya in coming with applications through mobile phones to in mapping out and alerting health providers about the epicenters. Mobile phone operators safaricom and airtel have already reduced and or removed charges on their money transfer services.

I know Huawei since January started on a work from home service, office cleaning, social distance disinfecting office vehicles, employee shuttle buses and checking every employees and guests temperature, ensuring that all staff who had travelled from any country with any cases have been undergoing 14 days self-isolation and requiring all employees submit daily survey to confirm their health and those of their family in case they need support from us.

The bigger assignment I would expect from them is to scale up management of the critical telecommunications infrastructure and IT systems for government as well as telecommunications companies, which is the back borne of the countrys public awareness, information sharing and money transfer services.

Huawei must ensure that all telecoms systems function and can handle the current cashless economy we are dealing with because of the outbreak including working with Safaricom for M-PESA as well as other critical hardware and software.

I belong to a group started by facebook called Coronavirustechhandbook.com, where guys are posting innovations and efforts by tech experts and companies to make a contribution to the handling of the outbreak. For example, http://www.trackmycircle.com site where you log your contacts and you will be notified by email when a peer (or 3rd degree) is found COVID-19 positive so that you can self-isolate. Another interesting innovation is on http://www.worldmeters.info

With such big technology giants like Google, Facebook, IBM and other having AL research hubs across the continent, we expect technology to play a big roe in dealing with the corona virus on the continent.

Could we see AI-powered drones used in the tracking and monitoring and identification of cases and related interventions in combating the outbreak? We want to see players like the media using technology like skype, google recorders and related audio applications to carry out interviews and bring is news without necessarily attending face-to- face interviews and reporting to newsrooms.

Can those in charge of dissemination of information consider doing multimedia messages that can be shared across the country including with community and locally based journalists to help in public education.

Bwire is the Head of Media Development and Strategy at the Media Council of Kenya

Video Of The Day: PSA: How to protect yourself and others from Coronavirus

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BWIRE: Artificial Intelligence could be helpful in dealing with... - Citizen TV

Asia Pacific Artificial Intelligence in Fashion Market to 2027 – Featuring Amazon.com, Catchoom and Facebook Among Others – ResearchAndMarkets.com -…

DUBLIN--(BUSINESS WIRE)--The "Asia Pacific Artificial Intelligence in Fashion Market to 2027 - Regional Analysis and Forecasts by Offerings; Deployment; Application; End-User Industry" report has been added to ResearchAndMarkets.com's offering.

The Asia Pacific artificial intelligence in fashion market accounted for US$ 55.1 Mn in 2018 and is expected to grow at a CAGR of 39.0% over the forecast period 2019-2027, to account for US$ 1015.8 Mn in 2027.

Real-time consumer behavior insights and increased operational efficiency are driving the adoption of artificial intelligence in fashion industry. Moreover, the availability of a large amount of data originating from different data sources is one of the key factors driving the growth of AI technology across the fashion industry.

Artificial Intelligence has already disrupted several industries, including the retail and fashion industry. The fashion industry so far has been one of the primary adopters of the technology. The fashion retailers these days are leveraging several revolutionary technologies, including machine learning, like augmented reality (AR) and artificial intelligence (AI), to make seamless shopping experiences across the channels, from online models to brick and mortar stores. Fashion retailers are progressively moving towards the AI integration within their supply chain, where more focus is being on customer-facing AI initiatives.

The artificial intelligence in fashion market is fragmented in nature due to the presence of several end-user industries, and the competitive dynamics in the market are anticipated to change during the coming years. In addition to this, various initiatives are undertaken by governmental bodies to accelerate the artificial intelligence in fashion market further.

The governments of various countries in this region are trying to attract FDIs in the technology sector with the increasing need for enhanced technology-related services. For instance, China's government relaxed the restrictions on new entries with an objective to encourage overseas and private capital to invest in its economy. This factor is anticipated to drive the demand for artificial intelligence in fashion market in this region.

Reasons to Buy

Key Topics Covered:

1. Introduction

2. Key Takeaways

3. Research Methodology

4. Artificial Intelligence in Fashion Market Landscape

4.1 Market Overview

4.2 PEST Analysis - Asia Pacific

4.3 Ecosystem Analysis

4.4 Expert Opinions

5. Artificial Intelligence in Fashion Market - Key Market Dynamics

5.1 Key Market Drivers

5.1.1 Accessibility of massive amount of data from different data sources

5.1.2 Real time consumer behaviour insights and increased operational efficiency are driving the adoption of AI in fashion industry

5.2 Key Market Restraints

5.2.1 Concerns associated with data privacy and security

5.3 Key Market Opportunities

5.3.1 Advent of Natural Language Programming (NLP) to fashion industry

5.4 Future Trend

5.4.1 Prediction of Fashion Trends With AI

5.5 Impact Analysis of Drivers and Restraints

6. Artificial Intelligence in Fashion Market - Asia Pacific Market Analysis

6.1 Overview

6.2 Asia Pacific Artificial Intelligence in Fashion Market Forecast and Analysis

6.3 Market Positioning - Five Key Players

7. Asia Pacific Artificial Intelligence in Fashion Market - By Offerings

7.1 Overview

7.2 Asia Pacific Artificial Intelligence in Fashion Market Breakdown, by Offerings, 2018 & 2027

7.3 Solutions

7.4 Services

8. Asia Pacific Artificial Intelligence in Fashion Market - By Deployment

8.1 Overview

8.2 Asia Pacific Artificial Intelligence in Fashion Market Breakdown, by Deployment, 2018 & 2027

8.3 On-premise

8.4 Cloud

9. Asia Pacific Artificial intelligence in fashion Market - By Application

9.1 Overview

9.2 Asia Pacific Artificial intelligence in fashion Market Breakdown, By Application, 2018 & 2027

9.3 Product Recommendation

9.4 Virtual Assistant

9.5 Product Search and Discovery

9.6 Creative Designing and Trend Forecasting

9.7 Customer Relationship Management (CRM)

9.8 Others

10. Asia Pacific Artificial intelligence in fashion Market Analysis - By End User Industry

10.1 Overview

10.2 Asia Pacific Artificial intelligence in fashion Market Breakdown, By End User Industry, 2018 & 2027

10.3 Apparel

10.4 Accessories

10.5 Cosmetics

10.6 Others

11. Asia Pacific Artificial Intelligence in Fashion Market - Country Analysis

11.1 Overview

11.1.1 APAC Artificial Intelligence in Fashion Market Breakdown, By Key Country

11.1.1.2 China Artificial Intelligence in Fashion Market Revenue and Forecast to 2027 (US$ Mn)

11.1.1.3 India Artificial Intelligence in Fashion Market Revenue and Forecast to 2027 (US$ Mn)

11.1.1.4 Japan Artificial Intelligence in Fashion Market Revenue and Forecast to 2027 (US$ Mn)

11.1.1.5 South Korea Artificial Intelligence in Fashion Market Revenue and Forecast to 2027 (US$ Mn)

11.1.1.6 Rest of APAC Artificial Intelligence in Fashion Market Revenue and Forecast to 2027 (US$ Mn)

12. Artificial Intelligence in Fashion Market - Industry Landscape

12.1 Overview

12.2 Market Initiative

12.3 New Development

13. Company Profiles

13.1 Adobe Inc.

13.2 Alphabet Inc. (Google)

13.3 Amazon.com, Inc.

13.4 Catchoom

13.5 Facebook Inc.

13.6 Huawei Technologies Co., Ltd.

13.7 IBM Corporation

13.8 Microsoft Corporation

13.9 Oracle Corporation

13.10 SAP SE

For more information about this report visit https://www.researchandmarkets.com/r/cw9ef5

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Asia Pacific Artificial Intelligence in Fashion Market to 2027 - Featuring Amazon.com, Catchoom and Facebook Among Others - ResearchAndMarkets.com -...

The next step in digital transformation: is Artificial Intelligence production-ready for green sand foundries? – Foundry-Planet.com

Kasper and Frans, thank you for joining us today. To kick off, can you tell us briefly why using Artificial Intelligence (AI) in a green sand foundry is a good idea?Kasper: DISA has been helping foundries collect, visualise and analyse their data with our Monitizer suite for a few years now. Adding AI capabilities to do more with this data is a logical next step and its a big one. Monitizer | PRESCRIBE which is what our AI product is called harnesses the power of AI to optimise the whole foundry process, significantly reducing scrap while increasing capacity and production predictability.

Frans: Theres a lot of hype around AI so at DataProphet, we like to quote real results to show whats possible. Over the last two years, the average AI-driven defect reduction across all of our manufacturing customers is 40%. With some, its 80% or 100%. Few foundries take full advantage of Industry 4.0 techniques so the potential for them is enormous.

Our Expert Execution System (EES), enabled by AI, has helped a foundry in South Africa cut defect rates in grey iron engine block castings by 50% in the first month. For the first time ever, they achieved zero internal defects on all shipped castings over three months and now save over $100k every month.

How does AI help deliver these kinds of results?Kasper: The key word here is automation. Many green sand foundries already collect and analyse process data but its usually limited to single sub-processes like moulding or pouring. The data for each process stays separate and basic manual analysis is done using spreadsheets or simple statistics.With an entire foundry line, optimisation can involve hundreds or even thousands of variables across all the different process stages. Making sense of that complexity manually is just impossible. AI automates this analysis, using the cloud to access vast computing capacity. Thats the only way to handle the complex, large sets of data that will give us new insight that will in turn make a genuine difference to a foundrys performance.

So what does an AI solution like Monitizer | PRESCRIBE actually do?Frans: It starts by analysing historic production and quality data to learn from past mistakes and corrections, to find what works and what doesnt. It considers how the parameters within and across all the different processes are related, how each one influences the other and what the ultimate combined effect on quality is.From that analysis, Monitizer | PRESCRIBE finds the optimal process parameters and tolerances for a particular casting and process. Knowing the best recipe, it can prescribe hence the name the best actions to take to improve quality.

Kasper: A good example is where, even though all your process parameters are within tolerance, you still might see bad quality castings. Often, this is because one metric is slightly high, another is slightly low and so on. Its a specific combination of values that produces the defect, not a single extreme one. Because the AI has learnt how parameters like grain size, moisture content, pouring speed or inoculation rate influence each other, it can pick the right settings for minimum defects.

So thats like a much more effective version of todays offline analysis. How does AI help you apply those learnings during real production?Kasper: Monitizer | PRESCRIBE applies what it has learnt to live data keeping an eye on what your foundry is doing right now, in real time. That gives you dynamic process control, reacting instantly as conditions change, like ambient air temperature or sand moisture content, and telling operators on the line the optimal settings or actions to take in time to prevent defects occurring. It keeps on learning too, constantly optimising the production process towards zero scrap and improving other metrics like productivity and resource use.Frans: Data-driven, real-time optimisation is sophisticated second-order control. By constantly monitoring machine and process data, then telling you which adjustments to make and again monitoring their effect, our AI tool gradually gets every part of your process running in harmony. You achieve a stable operating regime with the best quality and minimum quality variance. A good analogy is with an autonomous car which can automatically keep you in the middle of a motorway lane.By constantly computing the optimum process parameters, our AI keeps your process in the middle of the lane.

Its clear that automation and data analytics have enormous potential but many foundries have yet to adopt the basics here. So is it really possible for any green sand foundry to make use of AI?Kasper: We see digital as a four-step journey where you start with data collection and visualisation, then move at your own speed towards analytics, AI and automatic process control. Of course, we can help customers do all of that very quickly if they want to.Our NoriGate is the only hardware involved for data collection and everything else is a cloud service which we can deploy in any foundry or with existing data collection infrastructure. That makes it very quick and resource-efficient to deploy. You wont need any new IT hardware, data scientists or any extra staff.

We can digitise every step in the green sand process, take data from paper records or pull it from Excel, and give you a single trustworthy, time-stamped database ready for investigation. At each step, you can achieve significant benefits.The point is that, no matter if you are just starting out or are digitally advanced, there are things we can do that help you take the next step very rapidly indeed.

So you dont have to be a rocket scientist to make use of AI?Frans: AIs inner workings can be complicated to understand but together we have developed it into a packaged service that works for foundries. Its not hard to implement it and its not capital-intensive. As Kasper says, everything you need to collect, store and report on the data is already available from DISA and well proven.Some foundries think they are too old school for digital, but AI projects can be realised when theres no strong data environment or even if they havent really previously captured data at all. Our partnership with DISA enables very rapid digital progress in any type of foundry.

Does your partnership between an industrial AI company and a foundry equipment expert make your solution different to the other AI products we see emerging?Kasper: A lot of vendors say they have an AI system, but a pure IT company may never have seen a foundry from the inside before. We bring a combination of deep foundry experience and DataProphets award-winning expertise in manufacturing data science with more than 35 engineers, statisticians and computer scientists dedicated to developing AI solutions. This collaboration makes our service uniquely practical and effective. Its already tried and tested in a green sand foundry environment and were finding that fact is very attractive for customers. For example, we are currently installing the full Monitizer suite including MonitizerPRESCRIBE at a large European foundry group.

From DataProphets point of view, how does DISAs experience in green sand foundries help an AI project succeed?Frans: When you implement an AI solution in manufacturing, its vital to capture domain knowledge completely and correctly. As the leading OEM supplier, DISA know green sand intimately and are very much the experts in the foundry environment. They know what to do and which questions to ask right at the start. That means value from a running system arrives in weeks, not months or years.

DISAs customers also trust them to keep their promises and they understand that MonitizerPRESCRIBE will be delivered and managed through them. If DISA puts its name to it, customers know it will be an effective, high quality product and that will be supported in five years time and in ten or twenty years too.

Is this AI solution just for DISA customers?Kasper: The entire Monitizer suite, including NoriGate and MonitizerPRESCRIBE, is machine-agnostic, so its not limited to DISA machines or even to the green sand process. Monitizer is a Norican-wide solution, so every foundry can benefit from it, whether its pouring iron or die-casting aluminium.

Frans, with your experience, how do you think foundries compare to other manufacturers in their application of digital tools?Frans: Some other manufacturing environments are now quite sophisticated in their use of software and data, which is not often the case for foundries. With IoT infrastructure and Expert Execution Systems like MonitizerPRESCRIBE, there is a real opportunity for foundries to leapfrog the older IoT systems and access the very latest technology without having to make an enormous investment.

Are there any common misconceptions about AI you hear from your foundry customers?Frans: They can be worried that their data might be used in another customers AI which never happens. MonitizerPRESCRIBE can ingest and interpret all a customers foundry data and that certainly doesnt include data from other customers.

Monitizer | PRESCRIBE is designed with full tenant sandboxing: every clients datastore, database, and model is uniquely encrypted, and every component is isolated from every other component in the system. It is not possible to mix data or models between clients and the data is safeguarded with every possible measure.

Kasper: Some people think AI needs another in-house IT system thats big, complex and very expensive. But Monitizer | PRESCRIBE is an online service, it simply gives you a tool to optimise quality and productivity. Also, when we talk to foundry staff, some fear an AI system will come in and take over their job. But this isnt about taking jobs. The information AI gives will help them make better decisions and improve their own performance. It will make them look good.

Are there any other AI-related advantages for foundry owners and their workforces?Kasper: Theres a generational change going on in our industry. Engineers with 30 or 40 years experience are retiring and our customers are worried that their knowledge of how to keep their own unique processes running correctly will be lost. But their knowledge is encoded within historical process data. Monitizer | PRESCRIBE can access that and put it to work. With more automation, the foundry also becomes a cleaner, more attractive place to work. You can spend most of the time in an office-like control room, which will be more appealing to todays potential recruits.

Frans: By learning from human intelligence, expressed in millions of decisions made over the years, the AI becomes the central knowledgebase for the foundry. Then it can support less experienced engineers and operators in their decision making. A lot of value for manufacturing customers lies in selecting and extracting those good decisions so theyre never lost.

If AI helps foundries move from offline analysis to continuous guidance, what comes next?Frans: The end goal is a foundry that runs its own processes automatically similar to what the autonomous vehicle industry is aiming to achieve with cars. Staff will gradually move from continuously analysing processes and adjusting machines to focus on tasks theyre better suited for like innovation, creation and ideation. The plant of the future will re-configure itself for the optimal delivery of new customer orders, adjusting its configuration, production schedule, energy consumption and staff roles to give maximum efficiency.

Kasper: The system will adjust settings automatically, for example, when sand properties change, and you need more additives, or if the humidity changes and the sand dries out faster so you need to add more moisture. All these variations are corrected manually today and, even with Monitizer PRESCRIBEs real-time advice, usually still will be, but the system will handle it all automatically in future.

How close is this fully autonomous future?Frans: Were not there yet, but it will definitely happen for some foundries in the next few years. Most foundries are starting to collect data and analyse it, so they are being assisted by data today. Our system goes from there to guiding them with specific real-time recommendations. The self-driving foundry is the next stop on the journey.

Kasper: Were already helping customers fully automate parts of their DISA line, like moulding and pouring, or sand mixing and moulding, though complete automation of the whole line is a little way ahead at the moment. But I think it will arrive a lot sooner than completely autonomous cars.

Many thanks to both Kasper and Frans for a fascinating explanation of how they are working together to bring AI to foundries.

DISAs AI solution Monitizer | PRESCRIBE is currently live with selected pilot customers and will be available in the coming months. More information can be found here. [https://www.disagroup.com/en-gb/foundry-products/digital-solutions/monitizer/monitizer-prescribe]

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The next step in digital transformation: is Artificial Intelligence production-ready for green sand foundries? - Foundry-Planet.com

This Artificial Intelligence Stock Raised Its Dividend on "Black Thursday" – Nasdaq

As many now know, last Thursday was an historic day in the stock market. On March 13, 2020, the S&P 500 plunged 9.5% in a single day, the worst daily drop since "Black Monday" in 1987. The plunge came the day after President Trump delivered an underwhelming speech that included a European travel ban. However, stocks rallied on Friday after news of more government stimulus, emergency measures to boost testing, and the purchasing of oil for the country's strategic reserve. Negotiations for a comprehensive support package for the economy are also ongoing.

However, one tech company was tuning out the noise. Semiconductor equipment maker Applied Materials (NASDAQ: AMAT) decided to announce an increase in its dividend on the exact same day the market went into freefall. Is that a sign of confidence, or foolishness?

Image source: Getty Images.

Applied Materials announced that it would raise its quarterly dividend by a penny, from $0.21 to $0.22, a 4.8% boost. Applied's dividend yield is now 1.86%, but that's with a very modest 27.5% payout ratio. The higher dividend will be paid out on June 11, to shareholders of record as of May 21. CEO Gary Dickerson said: "We are increasing the dividend based on our strong cash flow performance and ongoing commitment to return capital to shareholders. ... We believe the AI-Big Data era will create exciting long-term growth opportunities for Applied Materials."

Semiconductors and semiconductor equipment companies have historically been known to be cyclical parts of the tech industry. However, it appears Applied Materials believes the overarching trends for faster and smarter semiconductors should help the company power through a near-term economic disruption. As chip-makers make smaller and more advanced chips, Applied's machines are a necessary expenditure.

But can the long-term trends buffer the company in a times of a potential global recession?

It should be known that the semiconductor industry was already in a downturn last year in 2019, and was beginning to come out of it in early 2020. For Applied, last quarter's results exceeded the high end of its previous guidance, with revenue up 11% and earnings per share up 21%.On Feb. 12, management also guided for solid sequential growth in Q2 even while lowering its prior numbers by $300 million because of coronavirus as of that date.

On a Feb. 12 conference call with analysts, Dickerson reiterated that optimism:

We believe we can deliver strong double-digit growth in our semiconductor business this year as our unique solutions accelerate our customers' success in the AI-Big Data era... our current assessment is that the overall impact for fiscal 2020 will be minimal. However, with travel and logistics restrictions, we do expect changes in the timing of revenues during the year. We are actively managing the situation in collaboration with our customers and suppliers.

While many businesses across the world have seen severe interruptions, it's unclear if the chip industry will be affected as much as others, despite its reputation for cyclicality. While consumer-related electronics may take a temporary hit to demand, a more stay-at-home economy means the need for faster connections, which could actually increase demand for servers and base stations.

Memory chip research website DrameXchange released a report on March 13, outlining its current projections for the DRAM and NAND flash industries as of March 1, along with an updated "bear case" scenario should the coronavirus crisis escalate into a global recession, which was updated on March 12.

Category

Current 2020 Projections

Bear Case 2020 Projections

Notebook computer shipments

(2.6%)

(9%)

Server shipments

5.1%

3.1%

Smartphone shipments

(3.5%)

(7.5%)

DRAM price growth

30%

20%

NAND flash price growth

15%

(5%)

Data source: DrameXchange.

Notice that the enterprise-facing server industry looks poised to withstand a potential severe downturn much better than consumer-facing notebook or smartphone industry. In addition, DRAM prices are poised to increase in 2020 even in a recession, as prices had already crashed last year and the industry cut back on capacity. NAND flash had an earlier downturn than DRAM, and was already beginning to come out of it, so it has more potential with a decline in pricing.

In addition, the largest global foundry Taiwan Semiconductor (NYSE: TSM), just said on March 11 that its capacity for leading-edge 5nm chip production was already "fully booked," and that volume production would begin in April. That indicates continued strong demand for leading-edge logic chips.

So while there may be some more softness in certain parts of the chip industry, there are still relatively strong segments as well. Therefore, Applied may not face revenue declines in 2020, but rather a mere absence of previously forecast growth. Yet even if that happens, growth will likely be deferred to 2021, not totally lost, as eventually the demand for chips will increase.

After its decline, Applied Materials stock trades at just 17 times trailing earnings, and just 14.7 times projected 2020 earnings, though 2020 projections may come down. Still, that's a reasonable price to pay for Applied, especially in a zero-interest rate environment. The company has just as much cash as debt, and its recent dividend raise on the market's darkest day in recent history shows long-term confidence. Risk-tolerant investors with a long enough time horizon thus may want to give Applied -- and the entire chip sector -- a look after the dust settles.

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Billy Duberstein owns shares of Applied Materials and Taiwan Semiconductor Manufacturing. His clients may own shares of the companies mentioned. The Motley Fool owns shares of and recommends Taiwan Semiconductor Manufacturing. The Motley Fool recommends Applied Materials. The Motley Fool has a disclosure policy.

The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.

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This Artificial Intelligence Stock Raised Its Dividend on "Black Thursday" - Nasdaq

During time of crisis, Rafi’s reaches out to the community – Olean Times Herald

OLEAN When Amber Rafi-Sultan learned she would have to close down her restaurant Monday, she began making plans to help others in the community who might be strained from the coronavirus pandemic that has put many people out of work.

Rafi-Sultan, who owns Rafis Platter on Wayne Street with her husband, Aamir Sultan, decided to offer curbside meals to the community from 11:30 a.m. to noon in the upcoming days to help others. The meals will be handed out each day while supplies last.

I am cooking food to help the community, Rafi Sultan explained on Tuesday. Im going to keep doing it until Ive depleted everything from the restaurants supplies.

Rafi-Sultan noted she could freeze her food supplies at the restaurant, but decided she wanted to help everyone in need at this time.

Right now, kindness is the most important thing, she continued. Because everyone is in a state of panic we cannot panic, weve got to help each other in any which way we can.

Rafi-Sultan said she began cooking early Tuesday to have dozens of meals ready to hand out to the public at the side of the restaurant. Additional meals were also delivered to St. Elizabeth Motherhouse in Allegany, the Warming House soup kitchen and the Genesis House homeless shelter.

Rafi-Sultan said her whole team of employees have volunteered to help hand out the meals to the community. One of her employees, Bradley Manning, said he was very busy handing out meals of pasta primavera and salad in the restaurant parking lot.

A lot of people who came through today did not have their families with them, Manning said. Theres been times when weve all needed help, but didnt ask when we needed it.

A mother who stopped by the restaurant to pick up meals for her six children said she appreciated the gesture of kindness from the restaurant.

Its a very nice thing that theyre doing, the mother said while sitting in her vehicle. Not everybody does this.

Rafi-Sultan noted the Wayne Street restaurant recently reopened after it was closed for a short while to allow time for the family to open its new restaurant in Ellicottville. Both facilities are now closed.

But its OK, God has a plan, she said with resolve.

(Contact reporter Kate Day Sager at kates_th@yahoo.com. Follow her on Twitter, @OTHKate)

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During time of crisis, Rafi's reaches out to the community - Olean Times Herald

Microsoft and GitHub Strengthen Their Hold on Open Source – WIRED

Microsoft will soon control more of the open source software development ecosystem.

GitHub, which Microsoft bought in 2018, said Monday that it will acquire NPM, which offers a crucial service for JavaScript developers. Terms of the deal were not disclosed.

GitHub is the most popular place to host open source software on the web and is home to around 100 million code repositories. NPM, short for "node package manager," hosts packages written for the popular JavaScript programming platform Node, and provides tools for managing those packages. According to a blog post from NPM cofounder Isaac Schlueter, the company hosts 1.3 million packages, which are downloaded 75 billion times per month. The company's website says customers include Slack, Netflix, Visa, and Nike.

The companies are important because developers today tend not to write applications entirely from scratch. Instead they typically stitch programs together from open source packages of codelike those hosted on NPMthat handle common features, like communication with databases or verifying passwords.

Everything you ever wanted to know about Linux, GNU, and how big companies are making money off of free, collaboration-based software.

If you wanted to create an open source Node package, you might upload the code to GitHub in order to work with other programmers on it. But you'd probably also upload it to NPM, from which developers would install and manage it. GitHub also launched its own package management service last year called GitHub Package Registry.

NPM raised $8 million in venture capital in 2015. Like GitHub, NPM charges users who want to host code on its service privately instead of making it publicly available. Companies might want to do this so that they can manage open source and proprietary software through the same tools.

Despite the important role it plays in software development, NPM struggled. The Register reported last year that the company had laid off around 20 or 25 percent of its employees, including an employee only a month away from vesting his stock options and three people who were attempting to form a union at the company. NPM was the subject of five complaints last year with the National Labor Relations Board, alleging "coercive statements" and retaliation. All the complaints were closed following informal settlements, according to the NLRB website. NPM declined to comment, and GitHub did not immediately respond to a request for comment.

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How open source might prove helpful during the coronavirus pandemic – TechRepublic

Commentary: As bad as things may get due to COVID-19, open source just might make life a little better, at least regarding an economic downturn and possible recession.

Image: Getty Images/iStockphoto

Over the last few weeks, there's been plenty of bad news. The way things are looking with the coronavirus pandemic, we're in for even more bad news over the coming weeks and, likely, months. In a time when people's health is at risk, money doesn't matter much. Even so, economists are starting to utter the "R" word, as consumers and businesses delay spending amidst novel coronavirus uncertainty, which will, in turn, create even more hardship.

One bright spot is that more organizations will turn to open source as they seek to do more with less, which was the case from 2007 to 2008 during theGreat Recession, as well as during the dot-com bust of 2000 to 2001. Open source adoption has been accelerating for a long time, but as open source vendors and communities experienced during the last recession, it tends to do even better as things get bad.

According to McKinsey & Co. analysis, there are at least three possible outcomes to the coronavirus pandemic. In the best case scenario, the world responds in somewhat similar fashion to China, and we see GDP growth for 2020 fall from roughly 2.5% to 2.0%. In this scenario, the US economy recovers by the end of the first quarter. Happy(ish) days.

Of course, few companies operate like China with strong state control, which means we're more likely to see something like McKinsey's second scenario: A global slowdown. This is the world in which we're currently living, where governments and private entities are combining to have workers stay home, public gatherings are banned, etc. Even with such measures, global GDP gets chopped in half, falling to between 1% and 1.5%, according to McKinsey. Days are not so happy, but by the middle of Q2 life becomes somewhat normal again.

SEE:Coronavirus: Critical IT policies and tools every business needs (TechRepublic Premium)

In the final scenario--pandemic and recession--coronavirus turns out to not be seasonal, pushing problems well in Q3, which means economic recovery doesn't really kick in until Q4. Global GDP falls to between 1.5 and 0.5%. Nothing remotely happy in this scenario.

All of these scenarios portend financial hardship for people around the world, compounding the physical hardships we will already endure. To minimize the negative impacts on individuals throughout this time, companies will need to figure out how to operate more efficiently. As in the 2007/2008 recession, open source will become ever more appealing.

Minus the serious health concerns, the economic fallout could be similar to what we're about to experience globally. Talk to those who lived through that recession, however, and a different narrative emerges for those working at open source companies. Without wanting to smugly minimize the economic hardships that others experienced, it's worth digging into open source as a way to benefit all.

According to Nick White (then at SpringSource), "The impact on open source companies lasted 90 days and then we were back hiring and in fact acquiring other companies." This tallies with my own experience, working at the time for Alfresco. We went through the recession profitable, with revenue growing strongly each year. As I wrote in 2008 for CNET (Open-source innovation in a recession), "Open source breeds communities, which in turn add value to the software, making this innovation more of a group effort (and, hence, potentially a less costly effort)."

SEE: Coronavirus and its impact on the enterprise (TechRepublic Premium)

Today, buying into open source requires even less risk than it did back then, when companies were still testing the waters. Today for things like data infrastructure, open source is already recognized as the safe, innovative choice. Adding to this, there are a number of open source companies that have been thriving as they increasingly deliver open source software as a service, and should do even better as companies try to make the most of tightened budgets:

There are other companies, from MariaDB to DataStax to Percona and beyond, that have experienced exceptional results. In talking with sources at a range of these companies, the slowdown seems to be giving them a bump, as suggested above.

SEE:10 ways to prevent developer burnout (free PDF)(TechRepublic)

I cite these examples because it's publicly available data, but of course much of the benefits that organizations will derive from open source in this difficult time will come from unpaid adoption of open source software. They'll use Apache Flink for event-driven applications; Envoy as an edge proxy; and more. They'll be more willing to trade time to save money (rather than trading money to save time with commercially supported open source). It will end up being a good thing for those companies and the people who work for them.

Not that it will make things easy. These next few months are going to be hard, and especially for those who don't know anything about open source software like Flink, Envoy, Elasticsearch, etc. But in some way, if their jobs are saved because a smart engineer figured out how to do more with less using open source, they're going to benefit, even if they don't know who to thank.

SEE: How open-source software is tackling COVID-19 coronavirus (ZDNet)

Disclosure: I work for AWS, but nothing herein relates to my work there.

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How open source might prove helpful during the coronavirus pandemic - TechRepublic

Open Source Software Market 2020 Will Generate New Growth Opportunities in The Upcoming Year to Expand its Size in Overseas Market by Intel, Epson,…

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Shareware, Bundled Software, BSD(Berkeley Source Distribution): Shareware, Bundled Software, BSD(Berkeley Source Distribution)

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Acquire Market Research is a market research-based company empowering companies with data-driven insights. We provide Market Research Reports with accurate and well-informed data, Real-Time with Real Application. A good research methodology proves to be powerful and simplified information that applied right from day-to-day lives to complex decisions helps us navigate through with vision, purpose and well-armed strategies.

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Open Source Software Market 2020 Will Generate New Growth Opportunities in The Upcoming Year to Expand its Size in Overseas Market by Intel, Epson,...

Global Open Source Software Market 2020 Competition Landscape And A Corresponding Detailed Analysis Of The Major Vendor/Manufacturers – Daily Science

The research report creates a full-fledged draft of overview of the global Open Source Software market considering base year as 2018 and forecast period as 2019 to 2025. The Open Source Software market report delivers an in-depth study of market size, country-level market size, region, segmentation market growth, market share, sales analysis, value chain optimization, market players, the competitive landscape, recent developments, strategic market growth analysis, trade regulations, opportunities analysis, technological innovations, and area marketplace expanding. The Open Source Software market landscape and leading manufacturers offers competitive landscape and market development status including the overview of every individual market players.

This study covers following key players:

IntelEpsonIBMTranscendOracleAcquiaOpenTextAlfrescoAstaroRethinkDBCanonicalClearCenterCleversafeCompiereContinuent

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The report delivers the detailed data of big companies with information about their revenue margins, sales data, upcoming innovations and development, business models, strategies, investments, and business estimations. The report also offers a major microscopic view at the market and identifies the footprints of the manufacturers with the help of understanding the global revenue of vendors along with price and sales.

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Market segment by Type, the product can be split into:

SharewareBundled SoftwareBSD(Berkeley Source Distribution)

Market segment by Application, split into:

BMForumphpBBPHPWind

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Some Major TOC Points:1 Report Overview2 Global Growth Trends3 Market Share by Key Players4 Breakdown Data by Type and ApplicationContinued

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