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Proton pump inhibitors and increased reporting odds of renal neoplasms: FAERS-based adverse event data mining and analysis – DocWire News

December 18, 2021 | Author: admin

This article was originally published here

Expert Opin Drug Saf. 2021 Dec 17. doi: 10.1080/14740338.2022.2020245. Online ahead of print.

ABSTRACT

BACKGROUND: Long-term use of proton pump inhibitors (PPIs) is associated with some safety issues. In this study, data mining was carried out to discover the potential association between renal neoplasms and PPIs.

RESEARCH DESIGN AND METHODS: Neoplasms signals of PPIs were detected in the food and drug administration adverse event reporting system from 2014 to 2020 by examining the reporting odds ratio. Adjusted odds ratios were analyzed by logistic regression.

RESULTS: Signals were detected with respect to renal hemangioma, acquired or unspecified cystic kidney disease and papillary and unspecified renal cell carcinoma, of which intervals between adverse effects onset and medication were 7.00 (3.33, 15.67) years, 5.00 (1.70, 10.25) years and 7.00 (4.72, 12.25) years respectively. The lansoprazole had the strongest signal. Adjusted odds ratios for PPIs associated renal cell carcinoma of cases with or without acquired cystic kidney disease or chronic kidney disease were 1.67 [95% confidence interval (CI) 1.46-1.91] and 1.62 (95% CI 1.41-1.87).

CONCLUSIONS: Exposure of PPIs was related to the raised risk of renal neoplasms. Careful consideration should be given to the possibility of an increased risk when PPIs is administered.

PMID:34915780 | DOI:10.1080/14740338.2022.2020245

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More than Math: Toward a Better Strategy for Advanced Analytics – War on the Rocks

December 18, 2021 | Author: admin

If, like the Armys XVIII Airborne Corps, you plan to use AI when dropping 1,000-pound bombs, you should be sure you are using it correctly. This is why the Department of Defense has been working to incorporate critical thinking, problem-solving, and ethics into AI and machine learning. Following the lead of the commercial sector, the 2020 Department of Defense Artificial Intelligence Education Strategy emphasizes the identification, understanding, and mitigation of bias in AI, as well as the limitations of advanced analytics in the operational environment and the ethical implications of human-machine teaming.

With these goals in mind, our data literacy program at the Joint Special Operations Commands Intelligence Brigade has identified four critical elements for the effective and responsible use of AI. In our experience, effective AI education and talent management include developing individuals steeped in critical thinking and problem-solving skills, building data science teams rather than pursuing individual talent, using a process that emphasizes letting the problem guide the solution, and understanding the importance of complementary skillsets and roles as they contribute to a rich data science ecosystem.

Elements of Responsible AI

Advanced analytics requires more than coding skills, math or fluency in specific software. Our approach to data literacy leverages social science research and commercial best practices to achieve a more wholistic approach to AI in the following ways:

Understanding Algorithms

First, humans will always be more important than either hardware or software. The professional data science community has rebranded critical thinking, problem-solving, and ethics traditionally described as soft skills as the new power skills necessary to translate data science aspiration into real, measurable competitive advantage. As such, our curriculum is designed to fill the analytic utility belt with vital critical-thinking tools. One of the tools we emphasize is identifying biased algorithms. Bias can be compelling, nuanced, and even seductive, especially in very complex or black box algorithms when it seems to confirm conventional wisdom. But biased algorithms are wrong, and often backfire as well. Analytically competitive organizations identify and mitigate bias not only because it is the right thing to do, but also because it gives them a competitive advantage.

While examples of algorithmic bias occur with concerning regularity, facial recognition and matching algorithms have received special focus given their extremely poor performance in identifying women and people of color. Researchers recognized that the problem with these models was that they were built using training sets predominately filled with light-skinned males. Thus, when used to classify a person outside that demographic, their accuracy drops significantly. To ensure that these capabilities are used responsibly and effectively, it is necessary to educate data scientists, methodologists, and end users alike in how to understand model performance and assess the implication of errors.

Transparent, interpretable AI is all the more important in the operational environment. It might be possible to convince an eager sales associate that your black box model will help them reach their annual sales quota. But it is not acceptable to tell a teammate about to put his or her life into the digital hands of your highly predictive but unintelligible model, Trust me, model lift over previous iterations was amazing and overall performance of the algorithm looked great, but I have absolutely no idea how it works. You do not have to educate analysts in the details of backpropagation algorithms so a user can better understand neural networks, but they should at least be generally aware of what is going on under the hood. Taking a cue from the financial services and other regulated industries, the real-world translation of interpretable AI means that if I cannot understand the model, I cannot explain it. If I cannot explain it to the intended end-user, then they are not going to trust and/or use it.

Playing as a Team

Second, in a competitive organization, data science is approached as a full-contact team sport in which no one rides the bench. Increasing data literacy across an organization builds overall analytic maturity and provides broader lift to enterprise-wide efforts. Conversely, organizations that corral their exquisitely qualified data science unicorns in an isolated service center miss opportunities for meaningful end user engagement and participation. Moreover, like the mythical unicorn, individual performers fully skilled in all aspects of data science may not even exist. Commercial experience demonstrates the benefits of embracing this and training a workforce that possesses a varied yet complementary set of data science skills. This approach expands organizational breadth, depth, and sustainable capacity. Furthermore, it also mitigates single points of failure associated with a limited supply of exceptionally talented individuals who are notoriously difficult to recruit, hire, and retain.

The U.S. Air Force is addressing this challenge directly through its innovative TRON program. TRON effectively leverages commercially available training programs in software development and data science. This training is reinforced and enriched through structured internships that enable students to apply their newly acquired skills to real-world problems in a semi-supervised environment. Our data literacy program takes a similar approach to training the military and civilian members of the Joint Special Operations Command Intelligence Brigade to ensure that every member of the team has the foundation-level data acumen necessary to not only support but actively participate in enterprise-wide data science efforts. Like TRON, our program also makes additional education available to students demonstrating aptitude and interest as a means to deepen internal data science capacity.

Building a foundation of core data science knowledge across an organization creates an environment for data science talent to grow organically by teaching the workforce to speak a common data science language. Data literacy education, as opposed to merely training in specific capabilities, creates a workforce able to seamlessly, rapidly, and meaningfully integrate novel data sources, methods, and technology, including those currently over the horizon. While we can train for the known, we should educate for the unknown. As described in the Department of Defense AI strategy, foundational concepts that are standardized yet flexible set the conditions necessary for successful innovation, collaboration, implementation, and responsible use of advanced analytics.

Have a Problem-Focused Process

Third, analytically mature organizations know that technical proficiency in specific software tools, or buttonology, is necessary but not sufficient. Reliance exclusively on training in specific coding languages or technology platforms threatens to create inflexible, brittle capacity that is unable to grow or evolve in response to changing conditions and may snap when stressed. If the only thing you know how to use is a hammer, everything will look like a nail. A better alternative, as outlined in the 2020 Artificial Intelligence Education Strategy, involves developing workflows and processes that let the problem guide the solution rather than a specific analytic technology or tool.

The Cross-Industry Standard Process for Data Mining is the most widely accepted methodology for solving problems within the data science community and serves as the framework for our basic course. Similar to the scientific method, which describes a standard research process and workflow, the Cross-Industry Standard Process for Data Mining operationalizes analytic process best practices. Also like the scientific method, it is not limited to specific sources, methods, or technology. Rather, it provides an effective analytic process model that can be used to answer any question that can be solved by data. We elected to use the Cross-Industry Standard Process for Data Mining as a foundational workflow given its ability to support seamless and effective incorporation of novel capabilities as they become available.

Moreover, the use of a standard process model or checklist can also include nudges that prompt analysts to pause, think critically, and check for bias. To return to the previous example, the facial recognition algorithms developed on white males worked until they did not. Even technologically savvy end users continued to use these models until it became apparent that they did not perform well against individuals outside the narrow range of training data. Analytic process models or checklists like Cross-Industry Standard Process for Data Mining that include explicit nudges to evaluate model performance reinforce the fact that all models have errors, particularly as potential real-world application extends from training cases. Developing a solid foundation in critical thinking and reproducible methods in combination with a stable, reproducible analytic workflow will not catch everything. However, by building in checks for bias and errors, this approach will at least cue critical thinking in support of error identification, mitigation, and consequence management. In our experience, using a common data science process model and language builds capacity that can transcend service branch, individual role, and even intelligence discipline setting conditions for mission success in the joint operating environment.

Create an Ecosystem

Finally, every member of our teams should be educated as an informed consumer. While putting fingers to keyboard to write code is not for everyone, the Department of Defenses AI strategy includes nontechnical and less technical roles, or AI Workforce Archetypes, who are increasingly required to procure, manage, field, and adopt progressively sophisticated analytic capabilities. In addition to these archetypes, our program accounts for the increasing significance of the analytic translator role. Like a data science utility player, the translator trades depth of knowledge for breadth and associated domain expertise, which allows them to competently fill multiple roles across the organization and provide continuity with data science efforts. This enables them to serve as important bridge-builders who can identify the mathematical word problem embedded in a thorny business challenge and translate it into actionable data science requirements. They can also ensure the results meet the end user needs and are operationally relevant and actionable.

Again, data science is a team sport. Not only does everyone have a role, even the supporting positions need to understand the playbook. Everyone on our team needs the data literacy necessary to ask the right questions in order to effectively, responsibly, and ethically use advanced analytic capabilities. General understanding of model creation, validation, and associated assumptions can inform responsible use to include checks for bias, as well as identification and mitigation of errors. The Armys XVIII Airborne Corps has taken this concept to the next level with their innovation challenge, the Dragons Lair. Like the popular television program Shark Tank, the Dragons Lair provides a forum where individuals can identify real-world problems and pitch solutions. Teaching the workforce data literacy promotes this type of innovation by developing the ability to identify and describe the word problem, generate actionable requirements, and provide meaningful feedback to proposed solutions.

Conclusion

Ultimately, data science in its foundation is math, not magic. But math can still be difficult to use effectively, responsibly, and ethically. The 2020 Department of Defense AI Education Strategy can serve as a starting point for doing so. In particular, the inclusion of and emphasis on non-math skills provides access to the nontechnical/less technical members of the team and enables the military to realize the promise of advanced analytics. This involves setting the conditions for novel insight and understanding in support of meaningful solutions to some of the hardest problems our nation faces.

L.t. Col. James Mike Blue is a career Army intelligence officer with multiple deployments in Iraq and Afghanistan. He earned his bachelors in history from George Mason University, his masters in intelligence studies from American Military University, and a masters in strategic intelligence from the National Intelligence University.

Lt. Col. Anthony Smith is an operations research systems analyst in the Army. He deployed multiple times to Iraq and Afghanistan as a commander with conventional and Ranger units and was also a data analyst at Army Futures Command. He earned an masters in operations research from the Naval Postgraduate School and a bachelors in management from the U.S. Military Academy.

Colleen McCue, Ph.D., supports the special missions community. Sheis a principal data scientist with CACI International and a CACI Fellow. She earned her Ph.D. in psychology from Dartmouth College and completed a five-year postdoctoral fellowship at the Medical College of Virginia, Virginia Commonwealth University.

Image: Naval Information Warfare Center Pacific

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Outlook on the Big Data and Business Analytics Global Market to 2027 – by Component, Analytics Tool, Deployment Type, Application, Industry Vertical…

December 18, 2021 | Author: admin

DUBLIN, Dec. 17, 2021 /PRNewswire/ -- The "Global Big Data and Business Analytics Market By Component, By Analytics Tool, By Deployment Type, By Application, By Industry Vertical, By Regional Outlook, Industry Analysis Report and Forecast, 2021 - 2027" report has been added to ResearchAndMarkets.com's offering.

The Global Big Data and Business Analytics Market size is expected to reach $448 billion by 2027, rising at a market growth of 13% CAGR during the forecast period. Big Data analytics is a way through which enterprises can evaluate a huge amount of data for extracting useful information that is expected to improve their decision-making capability. Additionally, big data and business analytics solutions is expected to enable companies to discover various market trends, hidden patterns, customer preferences, and numerous hidden facts from the data.

Moreover, companies are highly adopting big data analytics to increase their profit, enhance analytics skills, and support risk management capability. In addition, big data analytics assists companies in better understanding the data and providing important information to the concerned people.

In the current scenario, companies are working in a highly vibrant business landscape, and hence witness dynamic changes in the demands of customers. Several companies not only intend to do in-depth analysis regarding the present information about products, customers, services, and business processes, but also willing to generate more insights from the historical data of their earlier performances and better understand previous trends and patterns. Therefore, there is an increase in the adoption of business analytics market software & solutions among numerous industries for evaluating these trends and creating more business opportunities along with framing different strategies based on new insights.

Moreover, demand for analytics solutions and software is directly proportional to the growing trend of big data among enterprises. Business analytics is becoming a crucial part of various business processes as it helps companies to sustain in the competitive market by better understanding the previous trends and consumer buying patterns. By using big data and business analytics, companies can generate new insights and also improve their decision-making process. Moreover, companies can also get the knowledge related to their past, and present patterns and thus, frame various marketing strategies based on these datasets. Through big data and business analytics, organizations can boost their efficiency and productivity.

COVID-19 Impact Analysis

Due to the outbreak of the COVID-19 pandemic, the big data and business analytics market has recorded a sudden downfall in the initial phase of the pandemic. The imposition of several restrictions around the world like travel ban, complete or partial lockdown has forced the population to stay locked inside their houses.

There are numerous companies around the world that have adopted work-from-home culture for their employees, hence boosting the demand for cloud-based big data analytics to organize crucial data of the companies. This is expected to further accelerate the demand for big data and business analytics solutions in the forecast period. There has been an increase in the deployment of advanced analytics solutions in businesses to ensure business continuity and process optimization. Due to this, the growth of the big data and business analytics market is expected to surge in the coming years.

Market Growth Factors:Deployment of improved technologies

The big data and business analytics market is driven by the deployment of automation technologies and the Internet of Things (IoT) by various companies across different business verticals. IoT deals with physical objects or things that are implanted with electronics, network connectivity, software, and sensors, enabling these gadgets to collect and exchange data. Big data and business analytics with the incorporation of IoT help industries in predictive or preventive failure analysis, thus substantially leading to the growth of the market.

Growing requirement for generating more business insights

The cutthroat competition among the companies is motivating them to implement big data and business analytics solutions to support their expansion. In addition, a rise in requirement to generate more insights for business planning is estimated to open new growth prospects for the market, as big data analytics software helps companies to evaluate the factors, which are impacting the results and offers the power of decision optimization.

Market Restraining Factor:Hight cost of big data and business analytics solutions

The cost of big data analytics differs according to the features and applications required for the business. Additionally, these tools are difficult to use and sometimes need training, which is expected to increase the overall cost of operations that affect the deployment rate of these solutions. Depending upon the amount of generated data volume by the companies, the cost of big data and business analytics solutions may fluctuate.

Component Outlook

Based on Component, the market is segmented into Services, Software and Hardware. The services segment dominated the big data and business analytics market with the highest market share in 2020 and is estimated to continue this trend over the forecast period. Factor like high adoption of big data and business analytics services among various end-users that offer efficient functioning is responsible for the growth of this segment.

Analytics Tool Outlook

Based on Analytics Tool, the market is segmented into Dashboard & Data Visualization, Self-Service Tools, Data Mining & Warehousing, Reporting, and Others. The reporting tools segment is anticipated to exhibit the highest growth rate over the forecast period due to the rising complexity and amount of financial data, restricted capabilities of prevailing spreadsheet solutions and increase in the cost of compliance are among the key aspects propelling the growth of this segment.

Deployment Type Outlook

Based on Deployment Type, the market is segmented into On-premise and Cloud. The cloud segment is expected to showcase the fastest growth rate during the forecast period. This growth is attributed to the increasing adoption of cloud-based solutions and services across numerous industries.

Application Outlook

Based on Application, the market is segmented into Customer Analytics, Marketing Analytics, Risk & Credit Analytics, Workforce Analytics, Supply Chain Analytics, and Others. The Customer Analytics market dominated the Global Big Data and Business Analytics Market by Application 2020. The Marketing Analytics market is showcasing a CAGR of 14.6% during (2021 - 2027). The Risk & Credit Analytics market is expected to exhibit a CAGR of 14.8% during the forecast period.

Industry Vertical Outlook

Based on Industry Vertical, the market is segmented into BFSI, IT & Telecom, Healthcare, Retail & eCommerce, Government & Defense, Transportation, Manufacturing, and Others. Advanced analytics techniques are used in the financial and banking sector to improve processes followed by banks, manage risks, and reduce scams. To protect customers from churn, the telecom sector is using analytics techniques, which will minimize the churn by specific marketing programs for particular customers. Further. these analytics are also used in the healthcare industry to detect & manage scams and enhance clinical performance. In addition, analytics is very useful in retail companies to understand the buyer's behavior and accordingly specialize their planning and establish a better market position.

Regional Outlook

Based on Regions, the market is segmented into North America, Europe, Asia Pacific, and Latin America, Middle East & Africa. The Asia-Pacific is estimated to display a considerable growth rate over the forecast period because of the factors like the growing number of people utilizing tablets & smartphones across various nations like China, and India.

The major strategies followed by the market participants are Product Launches. Based on the Analysis presented in the Cardinal matrix; Microsoft Corporation and Amazon Web Services, Inc. are the forerunners in the Big Data and Business Analytics Market. Companies such as Oracle Corporation, IBM Corporation, SAP SE are some of the key innovators in the market.

The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Amazon Web Services, Inc., Fair Isaac Corporation, Hewlett Packard Enterprise, IBM Corporation, Microsoft Corporation, Oracle Corporation, SAP SE, SAS Institute Inc., Tibco Software Inc., and Teradata Corporation.

Key Topics Covered:

Chapter 1. Market Scope & Methodology

Chapter 2. Market Overview

2.1 Introduction

2.1.1 Overview

2.1.1.1 Market Composition and Scenario

2.2 Key Factors Impacting the Market

2.2.1 Market Drivers

2.2.2 Market Restraints

Chapter 3. Competition Analysis - Global

3.1 Cardinal Matrix

3.2 Recent Industry Wide Strategic Developments

3.2.1 Partnerships, Collaborations and Agreements

3.2.2 Product Launches and Product Expansions

3.2.3 Acquisition and Mergers

3.3 Top Winning Strategies

3.3.1 Key Leading Strategies: Percentage Distribution (2017-2021)

3.3.2 Key Strategic Move: (Product Launches and Product Expansions : 2017, Jun - 2021, Jun) Leading Players

Chapter 4. Global Big Data and Business Analytics Market by Component

4.1 Global Big Data and Business Analytics Services Market by Region

4.2 Global Big Data and Business Analytics Software Market by Region

4.3 Global Big Data and Business Analytics Hardware Market by Region

Chapter 5. Global Big Data and Business Analytics Market by Industry Vertical

5.1 Global BFSI Big Data and Business Analytics Market by Region

5.2 Global IT & Telecom Big Data and Business Analytics Market by Region

5.3 Global Healthcare Big Data and Business Analytics Market by Region

5.4 Global Retail & eCommerce Big Data and Business Analytics Market by Region

5.5 Global Government & Defense Big Data and Business Analytics Market by Region

5.6 Global Transportation Big Data and Business Analytics Market by Region

5.7 Global Manufacturing Big Data and Business Analytics Market by Region

5.8 Global Other Industry Vertical Big Data and Business Analytics Market by Region

Chapter 6. Global Big Data and Business Analytics Market by Analytics Tool

6.1 Global Big Data and Business Analytics Dashboard & Data Visualization Market by Region

6.2 Global Big Data and Business Analytics Self-Service Tools Market by Region

6.3 Global Big Data and Business Analytics Data Mining & Warehousing Market by Region

6.4 Global Big Data and Business Analytics Reporting Market by Region

6.5 Global Big Data and Business Analytics Others Market by Region

Chapter 7. Global Big Data and Business Analytics Market by Deployment Type

7.1 Global On-premise Big Data and Business Analytics Market by Region

7.2 Global Cloud Big Data and Business Analytics Market by Region

Chapter 8. Global Big Data and Business Analytics Market by Application

8.1 Global Customer Analytics Big Data and Business Analytics Market by Region

8.2 Global Marketing Analytics Big Data and Business Analytics Market by Region

8.3 Global Risk & Credit Analytics Big Data and Business Analytics Market by Region

8.4 Global Workforce Analytics Big Data and Business Analytics Market by Region

8.5 Global Supply Chain Analytics Big Data and Business Analytics Market by Region

8.6 Global Others Big Data and Business Analytics Market by Region

Chapter 9. Global Big Data and Business Analytics Market by Region

Chapter 10. Company Profiles

For more information about this report visit https://www.researchandmarkets.com/r/bhkq0b

Media Contact:

Research and Markets

Laura Wood, Senior Manager

press@researchandmarkets.com

For E.S.T Office Hours Call +1-917-300-0470

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The Rising Demand for Digital Skills in the Food Industry – Food Industry Executive

December 18, 2021 | Author: admin

As the food industry undergoes its digital transformation, technology is becoming an integral part of the work. The result is a growing demand for employees with skills that werent previously necessary in the industry. According to a new report from Deloitte and FMI-The Food Industry Association (FMI), almost all food suppliers (95%) named digital skills as the most likely to increase in value within the next three years.

As part of their Future of Work series, Deloitte and FMI drew from the responses of more than 150 food companies to explore the expansion of digital skills in the industry. They found that, excluding the first year of the pandemic, job postings requiring digital skills have gone up significantly among food suppliers (including manufacturers and processors) and retailers.

Deloitte identified a AAA skill set representing analytics, automation, and artificial intelligence skills and used it to pinpoint the specific skills requested in food industry job postings. Here are the top five in-demand skills within four advanced skill clusters:

While retailers have undergone a more dramatic shift in hiring for digital skills, food suppliers have been steadily increasing their digital skills base over the past few years. And the demand for these skills isnt only in the IT department its on the frontline as well. Suppliers required digital skills for 90% of their job postings for scheduler/operations coordinators and service supervisors.

Time will tell whether there will be a sharper uptick in hiring for these skills among suppliers. Challenges such as the need for greater supply chain transparency and meeting sustainability goals could further drive up demand. These skills will also be required for companies that want to move into more digitally driven sales channels or face competitors with stronger consumer data.

Of course, the food industry isnt the only industry increasingly seeking out tech talent, nor is it an easy task right now to find enough employees to fill available roles of any type. So, instead of scurrying to bring on new hires with digital skills, it might be worth pausing to plan for how the work is changing while keeping employees and their experiences at the center of it all.

Deloitte recommends starting with an in-depth assessment of current capabilities and major digital skills gaps. Companies may find that the current workforce can fill in some of those gaps if provided with training opportunities. After figuring out which skills will require new hires, its a good idea to revamp job postings to include the identified skill needs, as well as digital-forward messaging.

Lastly, transparency with employees about plans for tackling skills gaps and how their roles may change going forward is key. The current workforce might even have their own ideas for improving hiring efforts and making the positions more attractive to job seekers.

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The Rising Demand for Digital Skills in the Food Industry - Food Industry Executive

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You would cream your pants at this huge GPU crypto mining factory – TweakTown

December 18, 2021 | Author: admin

I don't know how but Jaxson Davidson somehow has the keys to all of my crypto mining farms, and is showing them off on his personal Twitter account. Davidson has just started up his 4th -- yes 4th -- crypto mine and man is it beautiful.

Davidson has uploaded a tease of one of his crypto farms, where you can see hundreds and hundreds and hundreds of graphics cards -- all of them specifically NVIDIA's GeForce RTX 3070 with 8GB of GDDR6 memory -- while his new building "will be all" of NVIDIA's new CMP 170HX cards.

The huge RTX 3070-powered crypto mining farm can be found in the Tonaquint Data Center, Inc. which is in Utah. It seems that Davidson has been using mostly NVIDIA GeForce RTX 3070 Founders Edition graphics cards -- you know, gaming-focused graphics cards and FE cards above that -- not AIB custom models, but rather directly from NVIDIA.

It seems as though that Davidson is working directly with NVIDIA on securing GPUs for his various crypto farms, with Davidson answering a tweet wondering where he's getting the CMP cards from. Davidson said: "Took over a month to secure a direct deal with NVIDIA. Once I get my rigs up, I will start selling them".

Davidson is renting a room for his crypto farm, tweeting: "I rented this room at a local data center. It's costing me $150 a month per rig, for all expenses. AC is pumped under the floor and comes out the grates under the rigs, and vents on the ceiling pull the heat out. This lease is pretty expensive, compared to my other buildings"

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You would cream your pants at this huge GPU crypto mining factory - TweakTown

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Mining of the Cryptcurrency – Programming Insider

December 18, 2021 | Author: admin

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With the coming of virtual currency which is also known as cryptocurrency, everyone started thinking about the cryptocurrency and started looking out for the ways in which they can invest in the virtual currency. Now since the beginning, there have been a limited number of cryptocurrencies in the market, and when people are interested to buy the cryptocurrency from where the new cryptos will come into the market.

So, the process through which the cryptocurrency is mined so that the new virtual currency can be introduced in the market is called the mining of the cryptocurrency. This is a very technical process in which the new cryptocurrency is prepared and saved and then mined into the form of ledgers from where the people can buy such cryptocurrency from different websites which provide the facility of buying of the cryptocurrency which have magnetic properties of bitcoin. These websites also sometimes engage in the process of crypto mining and the mining work needs specialized skills and knowledge and for the same purpose, people engage the crypto miners who mine out the crypto data and bring new cryptocurrency in the crypto market.

These miners are paid in the form of cryptocurrency and a lot of people try themselves to engage in the business of crypto mining as it gives a high return to the people for the work, they have done but due to the lack of specialized knowledge in the field, there are only a few miners available in the market, reason which the crypto miners charge a very high price for the work done by them.

However, mining the cryptocurrency is as important as trading in the cryptocurrency because mining is the only process through which the new cryptocurrency is introduced in the market and people can buy them easily. In recent times with the development of cryptocurrency and the growth of the craze among people about the use of virtual currency has helped tremendously in the growth and development of the crypto industry.

Nevertheless, the popularity is still growing day by day and it is one of the most preferred forms of investment by the people because of the ease to buy it or its high value is also considered as one of the points because it is preferred by the people as one of the basic forms of Investment.

Now when people are liking the form of the investment, they are willing to buy more and more virtual currency and for the same they want the more and more virtual currency to be available in the market. These mined data are stored at several storage houses which store the mined cryptocurrency in the form of ledgers and are kept at a secured place. These mined currencies are always kept in the most secure form so that no intrusion of the 3rd person can take place and the data is completely safe and when the cryptocurrency is sold, the name on the ledgers which are stored in the form of blockchain technology is also changed.

The cryptocurrency is accompanied by blockchain technology where the data of the crypto is stored in the form of ledgers which form the block-like structure and when the structures are grouped together, they form a chain-like structure popularly known as BlockChain. These chains are secured as only the owner of the blocks knows what kind of data is stored in the ledgers and no one else makes it a very secure process.

Even making the payment on the website where you are purchasing cryptocurrency is very easy as they accept the payment in a number of ways and that makes it very easy for the users as they can opt for the payment of their own choice. Also, the payments made on these websites are secured and are made in a very transparent and easy way, either by entering the credit or debit card details or making the payment by using the Internet Banking details, or by opting for the UPI method.

It is very much accepted that cryptocurrency has different types and a large number of currencies are available in the market and the process of mining such data differs from the type of currency and its value in the market. Above all mining of the cryptocurrency is very important as it is the only process through which the balance of the cryptocurrency can be maintained in the market.

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Mining of the Cryptcurrency - Programming Insider

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Fraud Detection and Prevention Market worth $53.4 billion by 2026 – Exclusive Report by MarketsandMarkets – Yahoo Finance

December 18, 2021 | Author: admin

CHICAGO, Dec. 16, 2021 /PRNewswire/ -- According to a research report "Fraud Detection and Prevention Market by Solution (Fraud Analytics, Authentication, and GRC), Service (Managed and Professional), Vertical (BFSI, Retail and eCommerce, and Travel and Transportation), Deployment Mode and Region - Global Forecast to 2026", published by MarketsandMarkets, the FDP Market is expected to grow from USD 22.8 billion in 2021 to USD 53.4 billion by 2026 at a CAGR of 18.5% during the forecast period.

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Technological advancements, penetration of digital technologies, and Bring Your Own Device (BYOD) trend in organizations have greatly influenced work practices and led to an unprecedented rise in data volumes. These factors have led to the adoption of automatic software-based applications for analyzing data in real time, which have replaced the traditional data mining applications and tools. This, in turn, increases the need to update legacy manual fraud detection methods. Hence, FDP vendors are producing new varieties of FDP solutions to detect and prevent all types of frauds committed by fraudsters.

Browse in-depth TOC on "Fraud Detection and Prevention Market"

493 Tables

46 Figures

381 Pages

Download PDF Brochure: https://www.marketsandmarkets.com/pdfdownloadNew.asp?id=1312

By component, Solutions segment to hold the largest market size during the forecast period

On the basis of solutions, the FDP Market is segmented into fraud analytics, authentication, and GRC solutions. The demand for FDP solutions is increasing as they can help enterprises detect fraudulent activities and prevent their occurrence. The number of frauds is growing at a significant rate; however, it is the surge in revenue loss which is driving the demand for FDP solutions. These FDP solutions can work simultaneously to provide fraud-proof enterprise environments.

Money launderers or criminals may forge fake signatures and Identifications (IDs) to commit digital frauds using someone's identity. FDP solutions help in reducing digital frauds, illegal transactions, tax evasion attempts, and other payment corruptions by identifying and detecting fraudulent activities in the system and reporting them to assigned authorities on time.

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In deployment mode, cloud deployment to grow at a higher CAGR during the forecast period

According to Flexera, a computer software company, enterprises are set to spend around 15-16% of their revenue into cloud hosting services by 2020. Another study by Cisco stated that "53% of organizations host at least 50% of their infrastructure in the cloud." The investment in cloud services and shifting of businesses from traditional to cloud are expected to accelerate the adoption of cloud-based solutions and services by enterprises. Owing to the increasing pace of digitalization, security breaches and fraud cases especially identity theft and online fraud has also increased. These increasing trend of eCommerce and online retail and digital payments has increased the adoption rate of online FDP solutions among end-user verticals to combat with fraud and compliance intruders.

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North America to hold the largest market size during the forecast period

North America is expected to be the largest contributor in terms of the market size in the FDP Market. It is one of the most affected regions in the world by money laundering and terrorist financing crime activities; as a result, it has the highest number of FDP providers. Banks, governments, and financial institutes in this region face ever-increasing challenges related to frauds compelling them to implement advanced technological approaches for managing fraud protection. Further, organic and inorganic growth strategies among major FDP vendors are expected to drive the FDP Market growth in North America. For example, in July 2020, NewDay, a financial services company partnered with RSA Security, financial crime prevention and predictive analytics company, to deliver advanced fraud protection for digital payments and address the requirements of the EMV 3-D Secure protocol.

Key Players:

Major vendors in the global FDP Market include BAE Systems (UK), Nice Actimize (US), FICO (US), LexisNexis (US), TransUnion (US), Kount (US), Software AG (Germany), RSA Security (US), Fiserv (US), FIS (US), ACI Worldwide (US), Experian (Ireland), SecuroNix (US), Accertify (US), Feedzai (US), CaseWare (Canada), FRISS (Netherland), MaxMind (US), Gurucul (US), DataVisor (US), PayPal (US), Visa (US), SAS institute (US), SAP SE (Germany), Microsoft Corporation (US), F5, Inc (US), Ingenico (France), AWS (US), PerimeterX (US), OneSpan (US), Signifyd (US), Cleafy (Italy) and Pondera Solutions (US). It also includes an in-depth competitive analysis of the key FDP Market players, along with their company profiles, business overviews, product offerings, recent developments, and market strategies.

Browse Adjacent Markets: Information Security Market Research Reports & Consulting

Related Reports:

Anti-money Laundering Market by Component, Solution (KYC/CDD and Watchlist, Transaction Screening and Monitoring), Deployment Mode, End User (Banking and Financials, Gaming/Gambling Organizations), and Region - Global Forecast to 2025

eGRC Market with COVID-19 by Offering (Software and Services), Software (Usage and Type), Type (Policy Management, Compliance Management, Audit Management, and Risk Management), Business Function, End User, and Region - Global Forecast to 2026

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Know 10 Things Companies Will Look for in Data Science Candidates in 2022 – Analytics Insight

December 18, 2021 | Author: admin

Here is the list of things that data science candidates must take into consideration for getting recruited

Data science is becoming a critical mission to more and more businesses. One of the biggest challenges in this mission is recruiting skilled data professionals. In tech companies, the demand is no longer confined to the high-tech and software realms. The significance of leveraging data science tools and techniques has boosted the demand for data professionals. Over the past couple of years, there has been massive growth in data science-based jobs in sectors like education, marketing, and manufacturing. This phenomenon has driven many data science aspirants to choose this domain as a career. This article lists the things that companies will look for in data science candidates in 2022.

Machine learning is no surprise as the most important skill to have for a data scientist. Data mining and Data analysis are the key activities that every data scientist has to go through. Strong statistical modeling is required to be a better data scientist. Companies are expecting a good knowledge of deep learning since it provides state-of-the-art techniques to solve some interesting real-time problems in fields like NLP and Computer Vision. Employers are expecting the candidates to know big data technologies due to the huge rise in the amount of data recorded every day. In real-time, companies might be working on huge datasets where these skills will come in handy.

Being good at engineering machine learning (ML) algorithms is one thing. Being good at understanding business problems is another thing. But merging those two and figuring out how to solve business problems with ML is a whole other deal. You need to be able to translate real-world problems into machine learning problems that you can solve.

Everybody hates repetitive tasks. Some people hate it so much that they do whatever they can to automate it. It is about everything from buzzwordy things such as autoML and GitHub co-pilot, to automating the setup of the code environment and generally everything-as-code, to even automating daily time registration, etc. Automation and optimization are some of the hallmark mindsets of great developers/data scientists.

Passion and curiosity are qualities that are desirable for anyone working with technology. Data science being the great beast that it is, it is an even more ubiquitous prerequisite in this specific field. In many other technical fields, you can specialize in a set of skills and use these to drive business value for years on end, perhaps with the need to learn a new programming language or tool every X years. Data science, however, is inherently a scientific discipline that is developing daily.

If you are starting to learn Data Science, In the beginning, youll find it hard to choose the right programming language. Though there are many languages, the competition has always been among Python and R itself. The industry is still in favor of Python due to its rich libraries followed by the R language. SQL is a must for every data scientist. Though it doesnt fit to be treated as a programming language I still included it here by taking my chances. After python and R there seems to be good demand for SAS and C++ languages.

Data science is a scientific discipline. However, when you get employed as a data scientist, the job is usually about applying data science tools to create business value. Rarely is it about doing research, coming up with new algorithms, breaking new ground, etc.,

Having a background in bioinformatics, quantum physics, or other scientific fields is advantageous when venturing into data science; it means you are used to reading research papers, have done statistical analyses before, maybe a bit of programming, etc. Having a fancy education, however, is by no means a requirement. It is just a few years of structured learning. But naturally, what you have done and achieved previously is considered when applying for new jobs.

The last point on my list is actual data science experience. Naturally, it is advantageous if the candidate has been exposed to various disciplines within the field; working with computer vision, natural language processing, forecasting, classic supervised/unsupervised techniques, general deep learning, etc.

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COVID-19, Automation and ESG Expected to Dominate the Credit Scene in 2022 – S&P Global

December 18, 2021 | Author: admin

This article is written and published by S&P Global Market Intelligence, a division independent from S&P Global Ratings.

2021 has proven to be a very interesting year. As of the fourth quarter, the S&P 500 had jumped more than 25% since January, with energy leading all sectors due to increases in crude oil and fuel prices caused by rising demand and limited supply growth.[1] Strong corporate earnings also boosted stock prices and stoked a risk-on sentiment that is expected to continue into 2022. In addition, three important topics were in the spotlight: COVID-19 and variants, automation and climate change.

The impact of COVID-19 on the global economy has been unique, as it has not only affected demand, but also cross-border supply chains, which will continue to weigh on the creditworthiness of some sectors.The emergence of the new omicron variant is a stark reminder that the impact of the pandemic is far from over.

The pace of technological disruption was supercharged by the pandemic, and there is now an even greater push to become digitally resilient to remain competitive. Credit and risk management teams are no exception. From dynamic financial spreading tools to cloud-based storage, a host of capabilities are being actively deployed to help streamline and automate risk assessment processes to improve efficiencies and stay ahead of the curve.

Climate-related risk is increasingly a focus of governments and regulators across the globe. Central banks and regulators are exploring mandatory climate risk disclosures and climate stress testing, while the Network for Greening of the Financial System (NGFS) supports integrating climate risk into financial stability monitoring and supervision. Consequently, financial institutions are focused on disclosure and management of climate change impacts.

This blog touches on these three areas in more detail.

As we head into 2022, we see:[2]

Improving, but still vulnerable credit markets with largely positive credit momentum, reflecting favorable financing conditions and a powerful economic recovery. This could be derailed if persistently high inflation pushes central banks to aggressively tighten monetary policy, triggering significant market volatility and repricing risks. New COVID-19 variants could also undermine confidence and recovery prospects. The weakest areas of credit markets often still highly sensitive to the ongoing impact of the pandemic are most exposed, particularly highly leveraged corporates and some emerging markets.

Fewer downgrades and low default rates with robust economic growth and largely favorable funding conditions, pointing to a steady overall ratings performance. However, persistent supply chain disruptions and high input costs could weigh on growth and ratchet up the pressure on so-far resilient corporate margins. Inflationary pressures are clouding the outlook for emerging markets still grappling with the pandemic. Leverage continues to build up in the riskiest parts of the credit markets, leaving them exposed to shifts in market sentiment.

Risk of aftershocks from inflation and high global debt pose significant risks. Persistent inflation, tied to supply disruptions and soaring energy prices, could trigger wage inflation and push major central banks, the Fed in particular, to hike rates sooner and faster. This could generate market volatility, likely amplified by elevated global debt levels. New variants could weaken the global economic recovery, as could China's policy and economic developments. Beyond COVID-19, credit markets face significant longer-term uncertainties around energy transition, cyber risk and evolving financial systems in an increasingly digital economy.

Credit and risk management professionals face numerous challenges every day, increasing the need to work faster and smarter than ever before. This has driven many firms to look at ways to digitize their credit risk workflows to help improve efficiencies. To look at some of the trends reshaping credit risk practices, we conducted a survey[3] to gather insights from over 200 professionals in countries around the world to see what steps they were taking before the COVID-19 pandemic took hold and how this unprecedented time has accelerated change. We found that:

Digitization efforts started well before COVID-19 given the growing push for credit and risk management teams to improve operating procedures and the efficiency and quality of decision making. 75% of respondents were already working on digitization efforts before the pandemic hit to capitalize on a range of benefits, including enhanced risk control and management, improved efficiencies and better early warning systems.

The pandemic underscored the need for more timely and granular data as credit and risk professionals were challenged by the lack of essential information at the start of the pandemic, especially when it came to assessing small- and medium-sized enterprises. This spurred firms to consider a range of new approaches on the data front, including combining alternative and traditional data, using data mining and machine-learning techniques to extract new and deeper insights and upgrading platforms for faster data delivery.

Existing analytical approaches came under pressure given the wave of non-performing exposures seen during the pandemic. Many credit and risk management professionals focused on enhancing their early warning systems to quickly identify potential problems, updating models to better estimate probabilities of default andmonitoring portfolios in a more granular manner with back-testing exercises and internal ratingsbased models.

Steps to Aid Digitization

As we continue to enhance our product line in response to market needs, we have looked at ways to make it easier for machines to perform additional tasks for improved speed and scalability. In doing so, we understand that:

Machine-based activities wont replace the need for subjective judgement in credit analysis. For example, RatingsXpress: Research on Xpressfeed provides bulk access to credit research from S&P Global Ratings for textual data analysis, enabling users to:

This product complements RatingsDirect, which is the official desktop offering for S&P Global Ratings credit ratings and research. RatingsDirect enablesusers to uncover deep insights within the data with visualization and other analytical tools providing the all-important why behind the numbers.

New product features are needed to support automation. Ease of use, timeliness, and completeness are always important, as are:

Natural disasters as the result of climate change are increasing in both intensity and frequency, resulting in significant financial losses for companies. This is bringing several issues to the forefront.

There are two important types of climate-related risks that need to be evaluated: physical and transitional. Physical risks refer to either acute physical hazards, such as more frequent and extreme weather events (e.g., storms, hurricanes and floods), or the chronic and longer-term effects of climate change, such as changing weather patterns and sea level rise. Transition risks refer to the costs associated with the market, technological, policy, legal and reputational risks associated with moving to a low-carbon economy. For example, market risks due to reduced demand for higher-carbon products or policy and legal risks due to increased operating costs from government actions to increase the price of carbon.

There is emerging consensus among financial regulators regarding the need to assess the risks and opportunities posed by the move to a greener economy. Many regulators have already introduced, or are in the process of introducing, climate-related stress testing exercises for financial institutions, which is important to test the resilience to climate shocks. Regulators are starting to be mindful about the burden posed on financial institutions, especially for modelling risks and opportunities. For example, the Bank of England's guidelines call for a deep-dive analysis for the biggest exposures/large-revenue companies in a portfolio and an aggregate view for the remainder.

There is a critical need for clear definitions and common standards across the globe on climate data, reporting and scenario analysis. Clarity and consistency will contribute to a better understanding of the risks and opportunities inherent in the transition.

This is a nascent field and new approaches are needed. One such solution is Climate Credit Analytics, developed by S&P Global Market Intelligence and Oliver Wyman.[4]This powerful capability translates climate scenarios into drivers of financial performance tailored to specific industries, such as production volumes, fuel costs and capital expenditures. These drivers are then used to forecast complete company financial statements under various climate scenarios and assess potential changes in counterparty credit scores and probabilities of default.[5]

It will be important to watch these three areas as 2022 progresses to understand the short- and long-term effects on the global economy and credit markets.

Stay on top of the latest credit risk news and thought leadership with Credit Risk Perspectives from S&P Global Market Intelligence.

[1] Insight Weekly: US stock performance; banks' M&A risk; COVID-19 vaccine makers' earnings, November 30, 2021, http://www.spglobal.com/marketintelligence/en/news-insights/blog/insight-weekly-us-stock-performance-banks-ma-risk-covid-19-vaccine-makers-earnings.

[2] Comments from COVID-19 Impact: Key Takeaways from Our Articles, December 1, 2021, https://www.spglobal.com/ratings/en/research/articles/200204-coronavirus-impact-key-takeaways-from-our-articles-11337257.

[3] See Digitization in Credit Risk Management report at http://www.spglobal.com/marketintelligence/en/news-insights/blog/the-future-of-risk-management-digitization-in-credit-risk-management.

[4] Oliver Wyman is a global management consulting firm and is not an affiliate of S&P Global, or any of its divisions.

[5] S&P Global Ratings does not contribute to or participate in the creation of credit scores generated by S&P Global Market Intelligence. Lowercase nomenclature is used to differentiate S&P Global Market Intelligence credit model scores from the credit ratings issued by S&P Global Ratings.

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Are Institutions Heavily Invested In K92 Mining Inc.’s (TSE:KNT) Shares? – Simply Wall St

December 18, 2021 | Author: admin

Every investor in K92 Mining Inc. (TSE:KNT) should be aware of the most powerful shareholder groups. Institutions often own shares in more established companies, while it's not unusual to see insiders own a fair bit of smaller companies. We also tend to see lower insider ownership in companies that were previously publicly owned.

With a market capitalization of CA$1.7b, K92 Mining is a decent size, so it is probably on the radar of institutional investors. In the chart below, we can see that institutions are noticeable on the share registry. We can zoom in on the different ownership groups, to learn more about K92 Mining.

See our latest analysis for K92 Mining

Many institutions measure their performance against an index that approximates the local market. So they usually pay more attention to companies that are included in major indices.

We can see that K92 Mining does have institutional investors; and they hold a good portion of the company's stock. This implies the analysts working for those institutions have looked at the stock and they like it. But just like anyone else, they could be wrong. It is not uncommon to see a big share price drop if two large institutional investors try to sell out of a stock at the same time. So it is worth checking the past earnings trajectory of K92 Mining, (below). Of course, keep in mind that there are other factors to consider, too.

Hedge funds don't have many shares in K92 Mining. Van Eck Associates Corporation is currently the largest shareholder, with 11% of shares outstanding. Meanwhile, the second and third largest shareholders, hold 5.4% and 4.9%, of the shares outstanding, respectively. Furthermore, CEO John Lewins is the owner of 1.3% of the company's shares.

Our studies suggest that the top 25 shareholders collectively control less than half of the company's shares, meaning that the company's shares are widely disseminated and there is no dominant shareholder.

While studying institutional ownership for a company can add value to your research, it is also a good practice to research analyst recommendations to get a deeper understand of a stock's expected performance. There are a reasonable number of analysts covering the stock, so it might be useful to find out their aggregate view on the future.

The definition of company insiders can be subjective and does vary between jurisdictions. Our data reflects individual insiders, capturing board members at the very least. Company management run the business, but the CEO will answer to the board, even if he or she is a member of it.

I generally consider insider ownership to be a good thing. However, on some occasions it makes it more difficult for other shareholders to hold the board accountable for decisions.

Shareholders would probably be interested to learn that insiders own shares in K92 Mining Inc.. This is a big company, so it is good to see this level of alignment. Insiders own CA$78m worth of shares (at current prices). If you would like to explore the question of insider alignment, you can click here to see if insiders have been buying or selling.

The general public, who are usually individual investors, hold a 47% stake in K92 Mining. While this group can't necessarily call the shots, it can certainly have a real influence on how the company is run.

It's always worth thinking about the different groups who own shares in a company. But to understand K92 Mining better, we need to consider many other factors. Take risks for example - K92 Mining has 2 warning signs we think you should be aware of.

If you are like me, you may want to think about whether this company will grow or shrink. Luckily, you can check this free report showing analyst forecasts for its future.

NB: Figures in this article are calculated using data from the last twelve months, which refer to the 12-month period ending on the last date of the month the financial statement is dated. This may not be consistent with full year annual report figures.

Have feedback on this article? Concerned about the content? Get in touch with us directly. Alternatively, email editorial-team (at) simplywallst.com.

This article by Simply Wall St is general in nature. We provide commentary based on historical data and analyst forecasts only using an unbiased methodology and our articles are not intended to be financial advice. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your financial situation. We aim to bring you long-term focused analysis driven by fundamental data. Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material. Simply Wall St has no position in any stocks mentioned.

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