Category Archives: Data Mining
The Role of Robotic Vision in Shaping Global Internet Technologies – Fagen wasanni
Exploring the Impact of Robotic Vision on the Evolution of Global Internet Technologies
The role of robotic vision in shaping global internet technologies is a fascinating and rapidly evolving field. As we delve into the impact of this technology, it becomes clear that robotic vision is not just a futuristic concept, but a reality that is transforming the digital landscape.
Robotic vision, a technology that enables machines to see and interpret the world around them, is revolutionizing the way we interact with the internet. It is a key component in the development of autonomous systems, such as self-driving cars and drones, which rely on the ability to perceive and understand their environment to operate safely and efficiently.
The integration of robotic vision into these systems is made possible through advancements in artificial intelligence (AI) and machine learning. These technologies allow machines to process and analyze visual data, enabling them to make decisions based on what they see. This is a significant leap forward in the evolution of the internet, as it opens up new possibilities for automation and efficiency.
One of the most significant impacts of robotic vision on global internet technologies is in the realm of data collection and analysis. With the ability to see and interpret the world, robots can gather vast amounts of visual data. This data can then be analyzed using AI and machine learning algorithms to extract valuable insights. This process, known as data mining, is becoming increasingly important in a range of industries, from healthcare to retail.
For instance, in the healthcare sector, robotic vision is being used to analyze medical images, such as X-rays and MRIs, to detect diseases at an early stage. In retail, it is being used to track customer behavior and preferences, enabling businesses to offer personalized shopping experiences.
Moreover, the advent of robotic vision is also driving the development of new internet technologies. One such technology is the Internet of Things (IoT), a network of interconnected devices that communicate and exchange data with each other. Robotic vision plays a crucial role in the IoT, as it allows devices to perceive their environment and interact with it in a meaningful way.
For example, a smart home system equipped with robotic vision can monitor its surroundings and adjust its settings based on what it sees. If it detects that its getting dark outside, it can automatically turn on the lights. If it sees that no one is home, it can lower the thermostat to save energy.
In conclusion, the role of robotic vision in shaping global internet technologies is profound. It is driving the development of autonomous systems, revolutionizing data collection and analysis, and paving the way for new technologies like the IoT. As we continue to explore the potential of this technology, we can expect to see even more exciting advancements in the digital landscape. The future of the internet, it seems, is not just about connecting people and information, but also about giving machines the ability to see and understand the world around them.
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The Role of Robotic Vision in Shaping Global Internet Technologies - Fagen wasanni
A New Approach to Space Weather Forecasting: The Power of … – Fagen wasanni
A new approach to space weather forecasting is being developed, utilizing predictive analytics to improve accuracy and provide timely predictions of potentially harmful space weather events. Space weather refers to the dynamic conditions in Earths outer space environment, influenced by the Suns activity, such as solar flares and coronal mass ejections (CMEs), which can impact satellite communications, navigation systems, and power grids.
Previously, space weather forecasting relied on observations of the Sun, solar wind measurements, and Earths magnetic field. However, these methods had limitations in accuracy and lead time, making it difficult to provide reliable forecasts.
Predictive analytics, a branch of advanced analytics, utilizes data mining, machine learning, and artificial intelligence techniques to predict future events. In space weather forecasting, this involves analyzing historical and real-time data from satellites and observatories.
The power of predictive analytics lies in its ability to quickly and efficiently analyze vast amounts of data. With numerous satellites and observatories monitoring the Sun and Earths space environment, processing this data is crucial. Advanced algorithms and machine learning techniques assist forecasters in identifying relevant information for accurate predictions.
Predictive analytics also improves over time through machine learning. As more data is collected and analyzed, the algorithms can be refined, enhancing accuracy and reliability. This iterative process enables predictive analytics to become more effective in forecasting space weather events.
The use of predictive analytics in space weather forecasting offers significant benefits. Improved accuracy enables stakeholders, such as satellite operators and power grid managers, to take proactive measures in protecting their systems from space weather effects. This minimizes disruptions to communications, navigation, and power systems, and reduces the risk of satellite damage.
In conclusion, predictive analytics represents a new frontier in space weather forecasting, providing enhanced accuracy and timely predictions. By employing advanced data mining techniques and sophisticated algorithms, predictive analytics transforms our understanding of the cosmos and its impact on daily life. The potential benefits are clear, paving the way for increased space weather preparedness and resilience.
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A New Approach to Space Weather Forecasting: The Power of ... - Fagen wasanni
China In-Vehicle Payment Market Research Report 2023: The Road … – PR Newswire
DUBLIN, July 25, 2023 /PRNewswire/ -- The "China In-Vehicle Payment Market Research Report, 2023" report has been added toResearchAndMarkets.com's offering.
China In-Vehicle Payment Market Research Report, 2023 analyzes and researches the status quo of China's in-vehicle payment market, components of the industry chain, layout of OEMs and payment platforms, consumer survey, and development trends.
The market demand for in-vehicle payment is rising.
In-vehicle payment refers to the function allowing for payment through in-vehicle communication (e.g., SIM card and WiFi) and IVI system. In-vehicle payment enables car owners to pay for services such as parking, refueling, food ordering and shopping without getting off the car, bringing far more convenient and better experience to users.
According to the survey by the publisher, there are a relatively small number of people using in-car payment at this stage, making up only 17.8% of the total samples. Yet users' willingness to use this function is very high. 72.7% of the consumers who have not used in-car payment yet, or 59.8% of the total samples say they are 'willing to try in-car payment'.
At present, users use in-car payment in such scenarios as parking, highway pass, refueling/charging, and purchasing IVI traffic and APP membership.
The in-vehicle payment industry chain is taking shape.
In terms of supply chain, in-vehicle payment involves two major segments: in-vehicle payment device and in-vehicle payment platform.
In-vehicle payment devices are led by communication devices (SIM card, communication module and T-Box), interaction devices (touch/voice/ face/gesture/fingerprint interaction), and authentication devices (security chip); in-vehicle payment platforms are primarily cloud platform, payment platform, IVI system, ecosystem service platform, ecosystem service provider, and OEM.
As companies in each industry chain segment worked to make layout in recent years, the in-vehicle payment market has kept growing, with the following two major features.
In-vehicle payment is available to more scenarios.
Foreign automakers including BMW, Mercedes-Benz, Honda and Hyundai, and Chinese automakers such as Great Wall Motor, Xpeng Motors, Geely, Chery and AITO have launched their in-car payment function. They have widely deployed this function in parking, refueling/charging and food ordering scenarios, and are also applying it on a small scale in car wash/maintenance/repair services, feature subscription, ticket booking and other scenarios.
For example, in October 2022, BMW added the BMW ConnectedDrive Store to its IVI system via OTA updates. It enables in-car payment for subscriptions, and 13 features such as front seat heating, steering wheel heating and Carplay through the IVI system.
Multimodal interaction is being added to in-vehicle payment.
At present, the most common in-car payment is scan to pay and password-free payment. As in-car multimodal interaction technology improves, face recognition, fingerprint recognition and voice recognition are becoming the new in-car payment interaction and authentication methods.
For example, Mercedes-Benz has added fingerprint recognition and authentication to its latest in-car payment system PAY+; Chery EXEED TX/TXL supports face verification payment, a function allowing users to pay for parking fees or shopping through face recognition. The addition of multimodal interaction makes in-vehicle payment more secure and convenient.
The ecosystem is a key factor affecting in-car payment.
In the mobile payment system, millions of iOS and Android developers have developed various applications and built very rich application ecosystems, meeting living, work and entertainment needs of consumers and making smartphones an indispensable terminal in users' life.
In the in-car payment system, financial institutions like China UnionPay and VISA have developed a series of in-car payment systems; Alipay, Banma Zhixing and Huawei among others have built a variety of vehicle ecosystem platforms and launched a range of in-car services covering parking, refueling, travel, shopping and other scenarios.
Compared with mobile payment, the in-vehicle payment ecosystem is still weak at this stage, only meeting the payment needs in specific scenarios. With the development of intelligent cockpit and high-level autonomous driving, drivers will be freed from driving tasks in specific scenarios and pay more attention to other in-car needs. At this time, creating an in-car living space and building a closed-loop ecosystem with payment as the entrance will become a big demand.
In-vehicle Payment Summary and Trends
Of the users who have used in-car payment:
Key Topics Covered:
1 Overview of In-vehicle Payment1.1 Development History of In-vehicle Payment1.2 Application Scenarios of In-vehicle Payment1.3 In-vehicle Payment System Flow1.4 Mainstream In-vehicle Payment Methods1.5 In-vehicle Payment Industry Chain1.6 In-vehicle Payment Chip1.7 In-vehicle Payment Platform1.8 In-vehicle Payment Ecosystem1.9 In-vehicle Payment Business Layout of OEMs1.10 In-vehicle Payment Patents1.10.1 In-vehicle Payment Patent Map1.10.2 In-vehicle Payment Patent Layout of OEMs1.10.3 In-vehicle Payment Patent Layout of Suppliers1.10.4 In-vehicle Payment Patent Layout of Ecosystem Companies
2 In-vehicle Payment Consumers2.1 Overview of In-vehicle Payment Survey2.2 In-vehicle Payment Usage and Willingness to Use2.3 Frequent Usage Scenarios of In-vehicle Payment2.4 Users' Satisfaction for In-vehicle Payment2.5 Expected Scenarios of In-vehicle Payment2.6 Differences between Actual and Expected Scenarios of In-vehicle Payment2.7 Reasons for Using In-vehicle Payment2.8 Concerns about In-vehicle Payment2.9 In-vehicle Payment Interaction Modes and Payment Method Preferences
3 In-vehicle Payment Layout of OEMs3.1 BMW3.2 Mercedes-Benz3.3 Honda3.4 Hyundai3.5 Renault Samsung Motors3.6 Jaguar Land Rover3.7 Ford3.8 Great Wall Motor3.9 Xpeng Motors3.10 Geely3.11 Chery3.12 AITO3.13 SAIC Volkswagen3.14 SAIC ROEWE3.15 Other OEMs3.15.1 Human Horizons' Layout of In-vehicle Payment Application Scenarios3.15.2 GAC's In-vehicle Payment Patent Filings3.15.3 Xiaomi's In-vehicle Payment Patent Filings
4 In-vehicle Payment Platforms4.1 VISA4.2 China UnionPay4.3 Alipay4.4 Huawei4.5 Other In-vehicle Payment Platforms4.5.1 Xevo4.5.2 IPS Group4.5.3 ZF4.5.4 DABCO
For more information about this report visit https://www.researchandmarkets.com/r/6bsktx
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China In-Vehicle Payment Market Research Report 2023: The Road ... - PR Newswire
The power of AI and predictive modeling in investment decision making – Times of India
Alan Turing first spoke about AI in 1950, asking, can machines think?. In todays era, I would define Artificial Intelligence as the science and engineering of making intelligent machines and computer programs that understand the intelligence of humans. AI may be one of the biggest technological leaps in history, and is poised to unlock new business models, transform industries, reshape peoples jobs, and boost economic productivity, thereby ushering in a modern-day industrial revolution. It is essentially a statistical technique that leverages Machine Learning and data mining to predict and forecast future outcomes through analysis of current and historical data.
Using AI in investment decision-making offers a significant advantage. It can quickly analyze vast amounts of data as against the traditional methodology where some key numbers could be overlooked by human analysts. With its capability in identifying patterns and trends, AI algorithms can analyse various data types, such as social media sentiment, news articles, and financial statements, to identify signals and make predictions about a companys future performance.
Assisting investors in making more informed decisions, reducing risks, optimising investment portfolios and order flow, executing large orders, improving efficiency in financial markets, can be enhanced by AI. AI is in fact transforming the lending industry, streamlining data processing for more efficient creditworthiness evaluations of potential borrowers, optimising underwriting processes, and enabling more effective management of lending portfolios.
The power of AI and Predictive Modeling also allows companies to rely less on human intervention and reduce overheads. The phase of neoclassic economy is long over, wherein it was assumed that only humans could make well-defined preferences and on the basis of it, reach well-informed decisions. It is evident in the current scenario that the instinct of humans is to follow behavioral economics, combining concepts of psychology and economics to get an understanding about how humans follow a certain kind of behavior in the real world.
However, though AI analyzes behaviors of people as well as quantities of data, it is not a substitute asset. Together, AI and Humans can make decision making more precise, management teams define accountability, support investments with accurate data, as AI focuses on monitoring markets whilst humans consider potential shocks such us war or pandemics.
Over 10 years ago, I developed the algorithm of trading securities. This was nothing more than an AI system analyzing large amounts of data, including historical market trends, news articles, financial reports, and more. We could identify patterns, trends and make investment decisions based on a wider range of data that was not possible for a trader alone. We predicted more accurately the future of market trends and price movements. Our technology enabled us to decide if we should buy, hold or sell a security.
Our traders accessed a better picture of risks supported by our technology providing a full risk assessment of market volatility or even geopolitical risks. We could achieve maximum returns with an efficient portfolio.
For onboarding clients to be financed, we are standardizing credit profiles via AI technology. Our credit team can use tools minimizing time, analyzing more data to decide about investing. In compliance, we assess the humans behind the business and conduct risk management, including security, regulatory compliance, fraud, AML and KYC guidelines.
To survive in the financial industry, you need to champion digital transformation. AI will bring multiple positive effects such as accuracy, precision in predicting future needs; optimization of capital investment decisions; reduction of manual data entry and analysis. We also cannot ignore the risks that this transition process brings, e.g., regulatory, data protection, consolidation between machines and humans. Our daily decision-making processes must guarantee a full alignment between humans and machines.
Views expressed above are the author's own.
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The power of AI and predictive modeling in investment decision making - Times of India
Meta fined $20 million for mining Aussies’ data with its VPN – iTnews
The Federal Court has ordered Facebook Israel and Onavo Protect VPN to pay $10 million each for failing to inform Australian users that the two Meta subsidiaries were mining their personal activity data via a free VPN service.
Onavo shared anonymised and aggregated data with Meta, including users internet and app activity, such as records of every app that they accessed and the time that they spent using those apps.
The two companies admitted in joint court submissions that Onavo Protects listings on app stores did not mention that it collected users data as a business intelligence tool or that data would be directed to support Metas market research activities.
The VPN was promoted on platforms like Google and Apple App Store as a way to protect personal information and to keep you and your data safe.
Available for download in Australia from February 2016 to February 2019, Onavo Protect was installed more than 270,000 times before the service ceased in May 2019.
US-based Onavo and Onavo Mobile, based in Israel, were mobile analytics companies that were acquired by Facebook in October 2013. After the acquisition, Onavo Mobile became Facebook Israel.
"While Onavo Protect was advertised and promoted as protecting users' personal information and keeping their data safe, in fact, Facebook Israel and Onavo used the app to collect an extensive variety of data about users' mobile device usage," Justice Wendy Abraham wrote in her judgement today.
Abraham ordered both companies to pay $10 million each to the Commonwealth of Australia and $400,000 towards the Australian Competition and Consumer Commissions (ACCC) legal costs.
"I am satisfied the agreed penalty of $20 million, in the circumstances, satisfies the significant element of deterrence required in this proceeding,"
The ACCC filed legal proceedings in 2020, alleging both Meta subsidiaries engaged in conduct liable to mislead and be in breach of the Australian Consumer Law.
In the case of the Onavo Protect app, we were concerned that consumers seeking to protect their privacy through a virtual private network were not clearly told that in downloading and using this app they were actually facilitating the use of their data for Metas commercial benefit," ACCC chair Gina Cass-Gottlieb said in a statement.
We believe Australian consumers should be able to make an informed choice about what happens to their data based on clear information that is not misleading."
Meta said in a statement that, there was no allegation by the ACCC that the app did not function properly as an online security toolProtecting the privacy and security of people's data is fundamental to how Meta's business works.
Over the last several years, we have built tools to give people more transparency and control over how their data is used, and we design every new product and feature with privacy in mind."
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Meta fined $20 million for mining Aussies' data with its VPN - iTnews
Sergey Brin’s return to Google could change the future of AI – ReadWrite
Few names are as synonymous with innovation and success as Sergey Brin. As a co-founder of Google, Brin has played a pivotal role in shaping the digital revolution that has transformed the way we live and interact with information. Now, he returns to the forefront once again, lending his expertise to craft Googles cutting-edge Gemini AI system. In this article, we will delve into Brins journey and explore the fascinating world of Gemini AI.
Sergey Brins story begins in Moscow, Russia, where he was born on August 21, 1973. At the age of six, he moved to the United States with his family, seeking a better life and greater opportunities. It was during his time at Stanford University that Brins entrepreneurial spirit truly took flight. In 1996, he met Larry Page, and the duo embarked on a journey that would change the course of technology forever.
In 1998, Brin and Page founded Google, a search engine that would revolutionize the way we access and navigate the vast expanse of the internet. With its simple yet powerful algorithm, Google quickly rose to prominence, becoming the go-to search engine for millions around the world. Brins expertise in data mining and machine learning played a crucial role in refining and improving the search engines capabilities.
Fast forward to the present day, and Brin is once again at the forefront of technological advancement. His involvement in the development of Googles Gemini AI system showcases his unwavering commitment to pushing boundaries and exploring the untapped potential of artificial intelligence. Gemini AI represents a leap forward in machine learning, enabling computers to understand and interact with human language in a more intuitive and nuanced manner.
Gemini AI harnesses the power of deep learning and neural networks to process vast amounts of data and extract meaningful insights. By analyzing patterns and understanding context, the system can accurately interpret and respond to natural language queries. This has tremendous implications for various industries, ranging from customer service and virtual assistants to healthcare and finance. The ability to communicate with machines in a more human-like manner opens up endless possibilities for efficiency and innovation.
As an expert in machine learning and data analysis, Sergey Brins contributions to the development of Gemini AI have been invaluable. His deep understanding of the intricacies of artificial intelligence has helped shape the system into a powerful tool that can revolutionize the way we interact with technology. Brins visionary leadership and commitment to pushing the boundaries of what is possible have been instrumental in bringing Gemini AI to life.
Googles Gemini AI system is still in its early stages, but the potential it holds is immense. As the technology continues to evolve, we can expect to see it integrated into various Google products and services, transforming the way we engage with technology on a daily basis. From voice assistants and smart home devices to personalized search results, Gemini AI has the potential to enhance our digital experiences in ways we could only have dreamed of before.
As with any powerful technology, there are ethical considerations to be taken into account. Sergey Brin and the team at Google are acutely aware of the need to ensure that AI is developed and deployed responsibly. They are committed to addressing issues such as bias, privacy, and transparency to ensure that Gemini AI is a force for good in our rapidly advancing digital world. Transparency and accountability are key pillars of Googles approach to AI development.
Sergey Brins remarkable journey from a young immigrant to one of the most influential figures in the tech industry is a testament to his unwavering passion for innovation. With his involvement in Googles Gemini AI system, Brin continues to shape the future of technology, pushing the boundaries of what is possible. As Gemini AI evolves and becomes an integral part of our digital lives, we can look forward to a future where human-machine interaction is more intuitive, efficient, and impactful than ever before.
So, let us embrace the advancements brought forth by Sergey Brin and Googles Gemini AI system, and embark on a new era of intelligent technology that enhances our lives in ways we never thought possible.
First reported onWall Street Journal
Featured Image Credit: Unsplash
Brad is the editor overseeing contributed content at ReadWrite.com. He previously worked as an editor at PayPal and Crunchbase. You can reach him at brad at readwrite.com.
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Sergey Brin's return to Google could change the future of AI - ReadWrite
Research Data Engineer job with CHARLES STURT UNIVERSITY … – Times Higher Education
The Role
You will have a strong background in data mining as well as wrangling and integrating large volumes of data from various sources. The position demands a level of quality and rigor, along with an awareness of data analytics and data science functions.
You should possess exceptional data investigation skills, capable of producing high-quality, curated data assets for downstream teams' self-service analytics. You will be a go-to person for all data-related queries and perform the duties of both an Information Analyst and Data Engineer seamlessly.
Your work in the Institute will be focussed in one of the following priority areas:
To be successful you will have:
Application Requirements
Applicants are expected to apply online and address the selection criteria in the position description. If you experience difficulties applying online or for further information on completing the application process please visit our how to apply page or contact us.
About Us
Charles Sturt University is a young and growing university committed to developing far-sighted people who help their communities grow and flourish. We make a significant contribution to the prosperity and vibrancy of our rural and regional communities, with a reach and impact across Australia and internationally. We work together with industry, communities and students to create new thinking, inspire each other and make a positive and progressive contribution to the world.
Artificial Intelligence and Cyber Futures Institute (AICF) is a new research Institute at Charles Sturt University aiming to become a world class research centre of excellence in data science, artificial intelligence, and cyber security, to pursue the agenda of regional discovery, showing how AI developed 'off the beaten track and 'in the wild can better serve not only rural and regional communities, but society as a whole, creating a new comparative advantage for Australia internationally.
Our aim is to develop a centre of excellence in regional NSW. We'll do this by:
Charles Sturt University offers a great work-life balance, professional development opportunities and generous financial benefits.
We are an equal opportunity employer committed to diversity and inclusion. This is demonstrated through our Workplace Gender Equality Agency Employer of Choice Citation and our Athena SWAN Bronze Institutional Award. Charles Sturt is also a participating member in the Australian Workplace Equality Index. Applications are encouraged from Indigenous Australians; people with a disability; women (particularly for senior and non-traditional roles); people who identify as LGBTIQA+; and those from culturally and linguistically diverse backgrounds.
Charles Sturt University offers a great work-life balance, professional development opportunities and generous financial benefits.
This position is open to Australian Citizens and Permanent Residents; or applicants who hold a current valid work visa commensurate with this position.
Further Information
Additional information is available in the position description or by contacting:
Professor Ganna Pogrebna | Executive Director, Artificial Intelligence and Cyber Futures Institute | gpogrebna@csu.edu.au | Ph: +61 472 719 063
Closing Date: 11 pm, 11 August 2023
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Research Data Engineer job with CHARLES STURT UNIVERSITY ... - Times Higher Education
Is G Mining Ventures (CVE:GMIN) Using Too Much Debt? – Simply Wall St
Howard Marks put it nicely when he said that, rather than worrying about share price volatility, 'The possibility of permanent loss is the risk I worry about... and every practical investor I know worries about.' So it might be obvious that you need to consider debt, when you think about how risky any given stock is, because too much debt can sink a company. We note that G Mining Ventures Corp. (CVE:GMIN) does have debt on its balance sheet. But is this debt a concern to shareholders?
Debt and other liabilities become risky for a business when it cannot easily fulfill those obligations, either with free cash flow or by raising capital at an attractive price. Ultimately, if the company can't fulfill its legal obligations to repay debt, shareholders could walk away with nothing. However, a more usual (but still expensive) situation is where a company must dilute shareholders at a cheap share price simply to get debt under control. Of course, plenty of companies use debt to fund growth, without any negative consequences. When we think about a company's use of debt, we first look at cash and debt together.
See our latest analysis for G Mining Ventures
As you can see below, at the end of March 2023, G Mining Ventures had US$12.6m of debt, up from none a year ago. Click the image for more detail. However, its balance sheet shows it holds US$120.9m in cash, so it actually has US$108.3m net cash.
The latest balance sheet data shows that G Mining Ventures had liabilities of US$25.2m due within a year, and liabilities of US$108.8m falling due after that. On the other hand, it had cash of US$120.9m and US$1.17m worth of receivables due within a year. So its liabilities outweigh the sum of its cash and (near-term) receivables by US$11.9m.
Of course, G Mining Ventures has a market capitalization of US$399.0m, so these liabilities are probably manageable. However, we do think it is worth keeping an eye on its balance sheet strength, as it may change over time. Despite its noteworthy liabilities, G Mining Ventures boasts net cash, so it's fair to say it does not have a heavy debt load! The balance sheet is clearly the area to focus on when you are analysing debt. But ultimately the future profitability of the business will decide if G Mining Ventures can strengthen its balance sheet over time. So if you want to see what the professionals think, you might find this free report on analyst profit forecasts to be interesting.
Given its lack of meaningful operating revenue, investors are probably hoping that G Mining Ventures finds some valuable resources, before it runs out of money.
We have no doubt that loss making companies are, in general, riskier than profitable ones. And we do note that G Mining Ventures had an earnings before interest and tax (EBIT) loss, over the last year. And over the same period it saw negative free cash outflow of US$65m and booked a US$3.8m accounting loss. However, it has net cash of US$108.3m, so it has a bit of time before it will need more capital. Overall, we'd say the stock is a bit risky, and we're usually very cautious until we see positive free cash flow. The balance sheet is clearly the area to focus on when you are analysing debt. But ultimately, every company can contain risks that exist outside of the balance sheet. For example G Mining Ventures has 2 warning signs (and 1 which shouldn't be ignored) we think you should know about.
At the end of the day, it's often better to focus on companies that are free from net debt. You can access our special list of such companies (all with a track record of profit growth). It's free.
Find out whether G Mining Ventures is potentially over or undervalued by checking out our comprehensive analysis, which includes fair value estimates, risks and warnings, dividends, insider transactions and financial health.
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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Is G Mining Ventures (CVE:GMIN) Using Too Much Debt? - Simply Wall St
Research Fellow, Healthcare Data Science job with NATIONAL … – Times Higher Education
Overview
We are looking to recruit Research Fellows to join our project in healthcare data science. These projects are hosted at theInstitute of Data Science, National University of Singapore. We will be working primarily with Professors Mengling Feng. The selected candidate will be working in a truly cross-disciplinary team, where he/she will have the opportunities to work with clinical experts from SingHealth and NUHS. The selected candidate will also be part of our collaborations with international institutes, such as MIT and Harvard.
Interested individual please send your cover letter and resume to emailDr Mengling FENG at ephfm@nus.edu.sg.
The Role
Job Summary:The Research Fellow will be responsible for undertaking in-depth research and innovation in machine learning, data science, and artificial intelligence on trusted collaborative machine learning that lead to publications in top-tier international conferences and journals, as well as real-world implementations.
Responsibilities:
Requirements
Covid-19 Message
At NUS, the health and safety of our staff and students are one of our utmost priorities, and COVID-vaccination supports our commitment to ensure the safety of our community and to make NUS as safe and welcoming as possible. Many of our roles require a significant amount of physical interactions with students/staff/public members. Even for job roles that may be performed remotely, there will be instances where on-campus presence is required.
Taking into consideration the health and well-being of our staff and students and to better protect everyone in the campus, applicants are strongly encouraged to have themselves fully COVID-19 vaccinated to secure successful employment with NUS.
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Research Fellow, Healthcare Data Science job with NATIONAL ... - Times Higher Education
IT Spending in Railways Market Leading Company Analysis and … – Digital Journal
PRESS RELEASE
Published July 29, 2023
Infinity Business Insights has recently released its latest research study titled "IT Spending in Railways Market," encompassing over 110 pages of in-depth analysis. The report focuses on the business strategies adopted by key and emerging players in the industry and provides valuable insights into the current market developments, landscape, technologies, drivers, opportunities, market outlook, and status from 2023 to 2030.
The worldwide IT Spending in Railways market is expected to grow at a booming CAGR of 9.4% during 2023-2030. It also shows the importance of the IT Spending in Railways market main players in the sector, including their business overviews, financial summaries, and SWOT assessments.
Click to get Global IT Spending in Railways Market Research Sample PDF Copy Here @ https://www.infinitybusinessinsights.com/request_sample.php?id=1375782&sk
Major players in the market includeAccenture, ALTEN, Altran Technologies, IBM, SAP, ABB, Alcatel-Lucent, Alstom, Hitachi, Bombardier, Capgemini, CGI, Cisco Systems, DXC Technology, GE Transportation, Huawei Technologies, Indra Sistemas, Infosys, Siemens, TCS, Tech Mahindra, Wipro.
The IT Spending in Railways Market refers to the industry that involves investments in information technology (IT) solutions and services by railway companies and operators. IT spending in railways encompasses various technology areas, such as software, hardware, networking, data analytics, cybersecurity, and communication systems. These investments aim to modernize railway operations, enhance safety and security, improve passenger experience, optimize asset management, and streamline overall railway services. IT solutions in railways may include ticketing systems, passenger information systems, train control and signaling, asset maintenance software, and real-time monitoring tools. The market addresses the increasing demand for digital transformation and efficiency improvements in the railway industry.
The primary objective of the IT Spending in Railways Market studies is to gather highly valuable insights that can aid companies in making crucial decisions. In today's competitive world, every business sector must stay informed about various aspects of the IT Spending in Railways Market. Regularly updated analytics are essential to navigate the dynamic environment and serve as a reliable resource for making significant commercial market choices. The combination of primary and secondary sources ensures the provision of accurate information, backed by interviews with market experts, adding credibility to the examination process.
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Global IT Spending in Railways Market: Segmental Analysis
IT Spending in Railways Market by Type:
ServicesSoftwareHardware
IT Spending in Railways Market by Application:
Facilities ManagementAsset ManagementPassenger ManagementOthers
The IT Spending in Railways market report includes the following countries in different regions:
The regions mentioned above encompass a comprehensive coverage of key markets, offering a global perspective on the market landscape. Heres an overview of the regions and the countries they include:
IT Spending in Railways Market Challenges and Risks::
The IT Spending in Railways Market faces challenges and risks. Firstly, modernizing legacy systems and integrating new technologies demand significant investments and technical expertise. Secondly, ensuring cybersecurity and protecting critical railway infrastructure from cyber threats is essential. Thirdly, complex and large-scale IT projects may face implementation delays and cost overruns. Additionally, budget constraints and changing funding priorities can impact IT spending in railways. Furthermore, interoperability issues among different IT systems may hinder seamless data exchange. Lastly, balancing the need for enhanced passenger experience and operational efficiency while meeting regulatory compliance can be challenging in the IT spending in railways market.
The IT Spending in Railways Market report provides clients with verified factual data, endorsed by industry experts and business leaders. It presents a detailed chapter-wise explanation of every aspect of the Protein Purification Chromatography Column market, delving into drivers, trends, opportunities, leading segments, and current trending segments with specific examples. The report also includes profiles of prominent players, along with an analysis of their business expansion strategies.
Research Methodology:
This study employs a robust research methodology that involves data collection through data collection modules with a large sample size. The collected data is then analyzed using statistical & coherent models to derive meaningful insights. Key components of the market report include market share analysis and key trend analysis. To ensure accuracy and reliability, the research team at Infinity Business Insights utilizes a data triangulation approach that involves data mining, analysis of data variables' impact on the market, and validation through primary sources such as industry experts. Various data models are utilized, including the Vendor Positioning Grid, Market Time Line Analysis, Market Overview and Guide, Company Positioning Grid, Company Market Share Analysis, Standards of Measurement, and Asia-Pacific vs. Regional & Vendor Share Analysis. For further inquiries, you can request an analyst call.
Benefits of the IT Spending in Railways Market Report: The IT Spending in Railways Market report is the result of a meticulous and dynamic research methodology, ensuring its credibility and accuracy. It offers an extensive analysis of the latest trends and emerging opportunities within the IT Spending in Railways market. The report contains a wealth of data on the most recent technological advancements and product developments in the IT Spending in Railways industry. The study encompasses a broad range of insights regarding the impact of these innovations on the future growth of the IT Spending in Railways industry. Readers can leverage the comprehensive data and trends provided in the report to make informed decisions and drive business growth. The report presents its insights in a user-friendly manner, featuring graphical representations such as bar graphs, statistics, and pie charts, facilitating easy comprehension. Key components, such as market drivers, challenges, restraints, and opportunities, are thoroughly elucidated to offer a complete understanding of the IT Spending in Railways market. The report also provides a comprehensive overview of the economic scenario of the market, including its benefits and limitations.
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Extracts from Table of Content:
Chapter 1. Executive SummaryChapter 2. Industry Outlook3.1. IT Spending in Railways Global Market segmentation3.2. IT Spending in Railways Global Market size and growth prospects, 2023 20303.3. IT Spending in Railways Global Market Value Chain Analysis3.3.1. Vendor landscape3.4. Regulatory Framework3.5. Market Dynamics3.5.1. Market Driver Analysis3.5.2. Market Restraint Analysis3.6. Porters Analysis3.6.1. Threat of New Entrants3.6.2. Bargaining Power of Buyers3.6.3. Bargaining Power of Buyers3.6.4. Threat of Substitutes3.6.5. Internal Rivalry3.7. PESTEL AnalysisChapter 4. IT Spending in Railways Global Market Type OutlookChapter 5. IT Spending in Railways Global Market Application OutlookChapter 6. IT Spending in Railways Global Market Geography Outlook6.1. IT Spending in Railways Industry Share, by Geography, 2023 & 20306.2. North America6.2.1. IT Spending in Railways Market 2023 -2030 estimates and forecast, by Type6.2.2. IT Spending in Railways Market 2023 -2030, estimates and forecast, by application6.2.3. The U.S.6.2.4. Canada6.3. Europe6.3.3. Germany6.3.4. The UK6.3.5. FranceChapter 7. Competitive LandscapeChapter 8. AppendixContinue...
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IT Spending in Railways Market Leading Company Analysis and ... - Digital Journal