Category Archives: Data Mining
Sociometric data can disrupt the traditional value chain and … – Namibian
The mining industry is rapidly evolving as mines embrace the fourth Industrial Revolution (4IR) with increasingly sophisticated equipment and automation.
The connected mine has become a reality, driving increased production, improving safety, optimising equipment utilisation and more.
However, alongside this technological revolution, we are also seeing both leadership and employee competence struggling to keep up.
People, leadership, and human behaviour are not being integrated with 4IR technologies, which can cause numerous challenges.
Sociometric sensors with artificial intelligence (AI) and machine learning (ML) capabilities can help to address this challenge, disrupt the traditional value chain, and constructively improve culture, behaviour and leadership within mines.
Built on technology, underpinned by people
From heavy machinery and working at scale, the mining industry has now progressed to continuous and autonomous production, with an increased emphasis on electrification, renewables, and the reduction of emissions.
Safety remains a priority, and with 4IR technologies that offer increased automation and autonomy, people can be moved away from hazardous areas and farther from danger.
However, despite technological advances, mines still rely heavily on the human component.
As a result, suboptimal human behaviour and leadership have a significant negative impact on production, equipment utilisation, safety, and the environment. People and leadership are obstacles on the road to leveraging technology and achieving increased production output, enhanced equipment utilisation, and better safety.
Mining organisations need to look at human behaviour and leadership solutions that will complement technology, improve people and leadership performance, and improve metrics that matter across the board.
Measure itto manage itSociometric sensors measure things such as eye contact, voice pitch, body orientation and proximity to others, all of which can be analysed and used to add value.
This is done by collecting data that can be used to support leadership in improving production, equipment utilisation and safety. Through machine learning, this data can be further used to predict what behaviour contributes to specific outcomes.
For example, certain sociometric data could suggest that an accident is imminent.
This could function as a prompt to shut down equipment or warn employees.
Sociometric data can also be used to objectively assess different leadership and learning styles and measure performance against key performance indicators, with data as evidence to help drive behaviour change.
This information can also be combined with other data from wearables, such as heart rate, breathing rate, pulse oxygen levels, and more, to deliver enhanced value.
Disruption drivesinnovationA positive impact on human efficiency and leadership can be enormously beneficial, but human behaviour is typically difficult to objectively measure and monitor. Sociometric sensor technology can effectively address this challenge and help mines bridge the gap between machinery advances and human performance, giving mining organisations the ability to measure and monitor metrics around social and emotional intelligence, as well as traditional metrics of leadership and engagement.
Sensor output from machines and the environment, together with sociometric sensors, can be integrated to create a complete picture of what is happening at any point in time in a mine, with live feedback and ML to radically transform every element of the connected mine.
By mapping leadership and personal interactions against real data, mining organisations will be able to understand what behaviour produces certain results and adjust behaviour and leadership styles in real-time.
By integrating disruptive technologies such as mining automation technology, renewable energy technology, and sociometric sensor technology into mine and process design in an innovative way, it is possible to achieve radical performance improvement breakthroughs.
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Sociometric data can disrupt the traditional value chain and ... - Namibian
The Future of AI and Analytics in Trial Design – Pharmaceutical Executive
The future of AI and analytics in clinical trial protocol design holds great promise, especially when applied to real-world data. This webinar discusses how these advancements have the potential to enhance the efficiency, cost-effectiveness, and success rates of clinical trials, ultimately benefiting both patients and researchers.
Register Free: https://www.pharmexec.com/pe/artificial-intelligence
Event Overview:
Artificial Intelligence and advanced analytics have the potential to revolutionize clinical trial protocol design. Real-world patient data and AI can be leveraged together to facilitate data-driven decision making throughout the trial design continuum, including:
These advancements have the potential to enhance the efficiency, cost-effectiveness, and success rates of clinical trials, ultimately benefiting patients, health systems and researchers.
Three key take-aways
In this webinar, data science experts will discuss how real-world data can be used to:
Speakers:
Lucas GlassVice President, Analytics Center of ExcellenceIQVIA
Lucas Glassis the Vice President of the IQVIA Analytics Center of Excellence (ACOE). The ACOE is a team of over 200 data scientists, engineers, and product managers that research, develop, and operationalize machine learning and data science solutions within the R&D space. Lucas has launched over a dozen machine learning offerings within R&D such as site recommender systems, trial matching solutions, enrollment rate algorithms, drug target interactions, drug repurposing, molecular optimization. Lucas machine learning research which is dedicated to R&D has been accepted at AAAI, WWW, NIPS, ICML, JAMIA, KDD, and many others.
Lucas started his career in pharmaceutical data science 15 years ago at Center (Galt) working on pharmacovigilance data mining algorithms. Since then, he has worked at the US Department of Justice in healthcare fraud, several small startups, and TTC, llc which was acquired by IMS In 2012.
Lucas holds a BA in Physics from Boston University, a MS in biostatistics from Drexel University, and is a PhD Candidate at Temple University where he is researching deep learning embedding techniques on large scale healthcare data.
Pablo Aran TerolSenior Product Manager, IQVIA Analytics Center of ExcellenceIQVIA
Pablo Aran Terolbegan his career in life sciences nearly a decade ago. After obtaining his PhD in Biophysics from the University of Cambridge, he spent several years working as a postdoctoral researcher studying the biophysical processes responsible for Alzheimers Disease.
Pablo began his career in clinical research as a consultant at IQVIA, working in clinical development programs, pricing, market access and asset evaluation. After several years as a consultant, he worked in offering development and strategic planning before becoming a Senior Product Manager.
Currently, within the IQVIA Analytics Center of Excellence, Pablo leads the strategy and development of technology designed to analyze clinical trial protocol designs to mitigate design risks and ultimately bring therapies to patients faster.
Register Free: https://www.pharmexec.com/pe/artificial-intelligence
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The Future of AI and Analytics in Trial Design - Pharmaceutical Executive
Connected Mining Market worth $22.7 billion by 2028 – Exclusive Report by MarketsandMarkets – Yahoo Finance
CHICAGO, Aug. 2, 2023 /PRNewswire/ -- IoT, AI, and 5G technological breakthroughs will have a major impact on the direction of the market for connected mining. As a result, mining operations will become more automated, operate remotely, and put an emphasis on sustainability and safety. Collaboration ecosystems, edge computing, and cybersecurity will all be crucial in advancing industry innovation and productivity.
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The global Connected Mining Market is expected worth USD 13.3 billion in 2023 and USD 22.7 billion by 2028, growing at CAGR of 11.3% during the forecast period, according to a new report by MarketsandMarkets. The need for real-time data analytics and decision-making in mining operations is fueling the adoption of connected mining technologies.
Browse in-depth TOC on "Connected Mining Market"
306 - Tables 48 - Figures264 - Pages
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Scope of the Report
Report Metrics
Details
Market size available for years
2023-2028
Base year considered
2022
Forecast period
2023-2028
Forecast units
Value (USD) Million/Billion
Segments Covered
By Component, Solutions, Services, Mining Type, deployment mode, Application, Regions
Region covered
North America, Europe, Asia Pacific, Middle East and Africa, and Latin America
Companies covered
ABB (Switzerland), IBM (US), SAP (Germany), Cisco (US), Schneider Electric (France), Komatsu (Japan), Hexagon (Sweden), Caterpillar (US), Rockwell Automation (US), Trimble (US), Siemens (Germany), Howden (Scotland), Accenture (Ireland), PTC (US), Hitachi (Japan), Eurotech Communication (Israel), Wipro (India), MST Global (US), GE Digital (US), Symboticware (Canada), Getac (Taiwan), IntelliSense.io (UK), Zyfra (Finland), Axora (UK), GroundHog (US), SmartMining SpA (Chile), and Applied Vehicle Analysis (Africa).
The Solution segment to record the higher market share during the forecast period
During the forecast period, the Solution segment is anticipated to achieve a higher market share in the Connected Mining Market. The demand for integrated and customized solutions is expected to grow significantly as the mining industry increasingly focuses on digital transformation and operational efficiency. The Solution segment is poised to dominate the market by providing mining companies with the tools and capabilities to address their specific challenges and achieve sustainable growth. With continuous technological advancements and increasing capabilities of connected mining solutions, the Solution segment is well-positioned to lead the market during the forecast period.
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By Mining type, the Surface segment is expected to hold a larger market share during the forecast period
By mining type, the Surface segment is expected to hold a larger market share during the forecast period in the Connected Mining Market. Surface mining refers to the extraction of minerals and resources from the Earth's surface, such as open-pit mining and strip mining. This type of mining is widely used for coal, iron ore, copper, and other minerals closer to the surface. Connected mining solutions offer significant benefits in surface mining operations, enabling real-time equipment monitoring, efficient fleet management, and optimized resource utilization. The ability to track and manage surface mining operations in real-time enhances safety measures, reduces operational costs, and improves overall productivity fueling market growth.
Asia Pacific to hold the larger market size during the forecast period
During the forecast period, the Asia Pacific region is expected to hold a larger market size in the Connected Mining Market. The Asia Pacific region is rich in mineral resources and has witnessed significant growth in mining activities in recent years. The increasing demand for metals, minerals, and resources from various industries, coupled with rapid urbanization and infrastructure development in the region, drives the need for efficient mining operations. As a result, mining companies in the Asia Pacific are increasingly adopting connected mining solutions to improve operational efficiency, safety, and sustainability.
Top Key Companies in Connected Mining Market:
The major players in the Connected Mining Market are ABB (Switzerland), IBM (US), SAP (Germany), Cisco (US), Schneider Electric (France), Komatsu (Japan), Hexagon (Sweden), Caterpillar (US), Rockwell Automation (US), Trimble (US), Siemens (Germany), Howden (Scotland), Accenture (Ireland), PTC (US), Hitachi (Japan), Eurotech Communication (Israel), Wipro (India), MST Global (US), GE Digital (US), Symboticware (Canada), Getac (Taiwan), IntelliSense.io (UK), Zyfra (Finland), Axora (UK), GroundHog (US), SmartMining SpA (Chile), and Applied Vehicle Analysis (Africa).
Recent Developments
June 2022 - Metso Outotec and Dynamox worked together to implement the condition monitoring platform of Dynamox in mining and aggregating operations. The solution is being provided by Metso Outotec to the installed equipment base of the business. Dynamox is a new member of the Metso Outotec partner ecosystem and aids in creating and offering customers simple-to-use digital solutions. The corporation is leveraging analytics and AI to further advance its mining operations.
November 2021 - Hexagon AB, a market pioneer in virtual reality solutions, introduced the HxGN MineEnterprise Platform in November 2021. This new product is intended to increase real-time data management and analytics for mining operations.
August 2021 - A cooperation between Caterpillar and BHP, a global mining, oil, and metals corporation, was established. In order to reduce greenhouse gas (GHG) emissions at BHP's mining locations around the world, the alliance planned to build and execute zero-emissions mining vehicles. In order to enable future mine sites and emission-free machinery, the alliance would help shape the processes, infrastructure, and technology needed.
August 2021 - To improve its position in the Connected Mining Market, Komatsu Ltd., a top manufacturer of mining equipment, said in August 2021 that it had acquired Immersive Technologies, a supplier of training solutions for the mining sector.
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Connected Mining Market Advantages:
Real-time monitoring of machinery, the environment, and employees is made possible by connected mining technology like IoT sensors and wearables. As a result, there are fewer chances of accidents occurring and possible hazards are given early warnings.
Connected Mining improves workflows, equipment use, and maintenance schedules by integrating data from diverse mining processes. As a result, operational effectiveness is improved, downtime is decreased, and productivity is raised overall.
Connected Mining provides predictive maintenance of mining equipment using data analytics and machine learning. Preventing unforeseen malfunctions, lowering repair costs, and extending equipment lifespan all result from proactive maintenance needs assessment.
With connected mining, mining operations may be remotely monitored and managed from centralised control points. This makes it possible to make decisions and changes in real-time to increase efficiency, especially in difficult-to-reach places.
Connected Mining supports resource management optimisation by utilising data-driven insights to reduce resource utilisation such as water and electricity use. This lowers operating expenses and lessens the impact of mining operations on the environment.
Connected Mining promotes more environmentally friendly mining techniques by streamlining operations and resource use. This entails cutting back on carbon emissions, water use, and the mining industry's total ecological imprint.
Real-time data analytics and reporting are offered by connected mining platforms, which enable stakeholders to learn useful information about various elements of mining operations. Better strategic planning and decision-making are made possible by this data-driven methodology.
Better communication and cooperation amongst various stakeholders, including miners, engineers, and management, are encouraged by connected mining. With better coordination as a result of this improved connectedness, operations become more effective.
Regulations pertaining to safety, environmental protection, and working conditions can be met with the help of connected mining solutions. This guarantees that mining firms operate in accordance with pertinent laws and norms.
Report Objectives
To define, describe, and forecast the Connected Mining Market based on segments based on offering, solutions, services, mining type, deployment mode, and application, with regions covered.
To forecast the size of the market segments with respect to five regions: North America, Europe, Asia Pacific (APAC), Middle East and Africa (MEA), and Latin America.
To provide detailed information on the major factors (drivers, opportunities, threats, and challenges) influencing the growth of the Connected Mining Market.
To analyze each submarket with respect to individual growth trends, prospects, and contributions to the global Connected Mining Market.
To analyze opportunities in the market for stakeholders by identifying high-growth segments of the global Connected Mining Market.
To profile the key market players, such as top and emerging vendors; provide a comparative analysis based on their business overviews, product offerings, and business strategies; and illustrate the market's competitive landscape.
To track and analyze competitive developments in the market, such as new product launches, product enhancements, partnerships, acquisitions, and agreements and collaborations.
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Iris Energy’s bitcoin mining revenues surge in July – Proactive Investors USA
About William Farrington
William kickstarted his career as a researcher and reporter for a global legal publication, covering everything from public law to M&A. Before moving to Proactive Investors, he worked as a reporter for a major fintech company with a focus on cryptocurrency and blockchain technology.Harking from Queensland, Australia, William obtained first-class honours in journalism and media from Birkbeck University before going on to complete an MA in creative and critical writing. Other jobs have... Read more
Proactive financial news and online broadcast teams provide fast, accessible, informative and actionable business and finance news content to a global investment audience. All our content is produced independently by our experienced and qualified teams of news journalists.
Proactive news team spans the worlds key finance and investing hubs with bureaus and studios in London, New York, Toronto, Vancouver, Sydney and Perth.
We are experts in medium and small-cap markets, we also keep our community up to date with blue-chip companies, commodities and broader investment stories. This is content that excites and engages motivated private investors.
The team delivers news and unique insights across the market including but not confined to: biotech and pharma, mining and natural resources, battery metals, oil and gas, crypto and emerging digital and EV technologies.
Proactive has always been a forward looking and enthusiastic technology adopter.
Our human content creators are equipped with many decades of valuable expertise and experience. The team also has access to and use technologies to assist and enhance workflows.
Proactive will on occasion use automation and software tools, including generative AI. Nevertheless, all content published by Proactive is edited and authored by humans, in line with best practice in regard to content production and search engine optimisation.
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Iris Energy's bitcoin mining revenues surge in July - Proactive Investors USA
Prescriptive and Predictive Analytics: A Comprehensive Market … – Fagen wasanni
The Prescriptive and Predictive Analytics research report offers in-depth market research and provides strategic solutions for businesses based on their specific needs. This report helps businesses gain clarity on current market trends and expected future developments. By leveraging global industry expertise, the report assists clients in making strategic decisions and achieving their growth objectives.
The Market Insights Reports (MIR) team focuses on key areas crucial for customer success in the market. The research employs a data triangulation method, including data mining, analysis of data factors, and primary validation, to provide accurate and reliable information to assist industries in making informed judgments and planning effective advertising and sales promotion strategies.
The Prescriptive and Predictive Analytics market is segmented based on product, customer, and distribution channels. This segmented approach enables businesses to analyze growth opportunities and make strategic decisions regarding core market applications.
The report includes various market segments such as Collection Analytics, Marketing Analytics, Supply-Chain Analytics, Behavioral Analytics, and Talent Analytics. Additionally, it covers applications in Finance & Credit, Banking & Investment, Retail, Healthcare & Pharmaceutical, Insurance, and others.
Key Benefits of the Prescriptive and Predictive Analytics Market Report:
Thorough and dynamic research methodology Complete picture of the competitive scenario in the market Information about the latest technology and product developments Analysis of the impact of industry improvements on future growth Essential historical data and analysis included in the report Easily understandable insights with visual representations
The report provides insights on market expansion, product creation and innovation, competitive analysis, market development, and market diversification. It also answers key questions about market growth, driving factors, major vendors, market scope, trends, and challenges.
In conclusion, the Prescriptive and Predictive Analytics Market report offers comprehensive market intelligence and valuable insights for businesses in various industries. Customized report information is also available to meet specific business needs. MarketInsightsReports is a trusted source for syndicated market research across multiple industry verticals.
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Prescriptive and Predictive Analytics: A Comprehensive Market ... - Fagen wasanni
Towards more sustainable operations – the Finnish Floorball … – International Floorball Federation
CarbonLinks real-time calculation visualises the emissions of league activities and encourages the reduction of match travel.
The Finnish Floorball Federation and CarbonLink, which specializes in real-time measurement of carbon footprints, have started a collaboration aimed at reducing the environmental impact of the federations activities on a fast schedule.
The goal is part of the federations sustainability programme approved a year ago, where the environment is one of the seven priority areas. The sustainability program is planned until 2026.
In the federation, we have made environmental matters central. We want to leave a vibrant earth for the next generations as well, says Jari Kinnunen, Director of Public Relations of the Finnish Floorball Federation.
Measuring the carbon footprint of private individuals and companies has been done for a long time, but it is still quite new in the activities of organisations. According to Kinnunen, the selection of CarbonLink as a partner was influenced by several factors.
The collection of data takes place automatically from concrete numbers of the federations financial administration, such as invoices. This enables almost real-time and continuous monitoring, on the basis of which decisions can be made. This is also a cost-effective way to get essential information to support decision-making, Kinnunen lists.
He emphasizes the importance of concrete decisions. The federation is well equipped to make decisions, as long as data is obtained and it can be compared.
We dont want to be guilty of greenwashing, and with this system we aim to ensure that the plans do not remain just words.
Although the continuous monitoring of the carbon footprint is an excellent basis for decision-making, it does not in itself provide answers about the means of reducing emissions.
Data mining and understanding must be practiced. There will certainly be masses of information. In a certain sense, data is a good servant, but a bad master, Kinnunen reflects.
Still, practical actions have already been planned. It is easy to identify the competition activities of the federation as burdening the environment in a country of long distances.
The expediency of national series is critically considered. Some of the series will become more regional. The referee appointment also aims to reduce travel by favoring locality , Kinnunen lists.
Shorter trips save not only nature, but also the costs and time of playing floorball, which is often valuable for juniors and their parents.
Floorball has a long tradition of a tournament format, which Kinnunen considers a good model instead of teams traveling separately to individual matches.
Until now, the environmental impact of running league operations has only been roughly estimated, but CarbonLinks system will soon provide data on this issue as well.
Of course, this is still new for us. It is difficult to know what is a lot and what is a little, says Kinnunen.
Comparison is made easier by the fact that the (financial) system has historical data from the previous two years. According to Kinnunen, international competitions (World Floorball Championships) appear as a spike in the statistics, but this is to some extent understandable.
In a certain way, competition and gamification would also suit sports. However, Kinnunen does not consider it appropriate to beat other organisations or sports federations, or to compare in general.
Here we strive to develop our own work.
The emphasis on environmental values can also be seen in the fact that, for example, when negotiating contracts for equipment suppliers of national teams, the sustainability programmes of partners are taken into account in the competition.
In Kinnusens opinion, the federation should set an example in the change of attitude towards the floorball clubs. It is important that the results of the monitoring and the decisions that follow are communicated openly.
In the coming weeks the Finnish Floorball Federation will publish more info about CarbonLink system.
(translated from an news article on salibandy.fi published 12.6.2023 / text by Erno Rautarinta)
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Towards more sustainable operations - the Finnish Floorball ... - International Floorball Federation
Preventing Bias In Machine Learning – Texas A&M Today – Texas A&M University Today
Based on data, machine learning can quickly and efficiently analyze large amounts of information to provide suggestions and help make decisions. For example, phones and computers expose us to machine learning technologies such as voice recognition, personalized shopping suggestions, targeted advertisements and email filtering.
Dr. Na Zou
Texas A&M Engineering
Machine learning impacts extensive applications across diverse sectors of the economy, including health care, public services, education and employment opportunities. However, it also brings challenges related to bias in the data it uses, potentially leading to discrimination against specific individuals or groups.
To combat this problem, Dr. Na Zou, an assistant professor in the Department of Engineering Technology and Industrial Distribution at Texas A&M University, aims to develop a data-centric fairness framework. To support her research, Zou received the National Science Foundations Faculty Early Career Development Program (CAREER) Award.
She will focus on developing a framework from different aspects of common data mining practices that can eliminate or reduce bias, promote data quality and improve modeling processes for machine learning.
Machine learning models are becoming pervasive in real-world applications and have been increasingly deployed in high-stakes decision-making processes, such as loan management, job applications and criminal justice, Zou said. Fair machine learning has the potential to reduce or eliminate bias from the decision-making process, avoid making unwarranted implicit associations or amplifying societal stereotypes about people.
According to Zou, fairness in machine learning refers to the methods or algorithms used to solve the phenomenon that machine learning algorithms naturally inherit or even amplify the bias in the data.
For example, in health care, fair machine learning can help reduce health disparities and improve health outcomes, Zou said. By avoiding biased decision making, medical diagnoses, treatment plans and resource allocations can be more equitable and effective for diverse patient populations.
Additionally, users of machine learning systems can enhance their experiences across various applications by mitigating bias. For instance, fair algorithms can incorporate individual preferences in recommendation systems or personalized services without perpetuating stereotypes or excluding certain groups.
To develop unbiased machine learning technologies, Zou will investigate data-centric algorithms capable of systemically modifying datasets to improve model performance. She will also look at theories that facilitate fairness through improving data quality, while incorporating insights from previous research in implicit fairness modeling.
The challenge of developing a fairness framework lies in problems within the original data used in machine learning technologies. In some instances, the data may lack quality, leading to missing values, incorrect labels and anomalies. In addition, when the trained algorithms are deployed in real-world systems, they usually face problems of deteriorated performance due to data distribution shifts, such as a covariate or concept shift. Although the data can be incomplete, it is used to make impactful decisions throughout various fields.
For example, the trained models on images from sketches and paintings may not achieve satisfactory performance when used in natural images or photos, Zou said. Thus, the data quality and distribution shift issues make detecting and mitigating models discriminative behavior much more difficult.
If successful, Zou believes the outcome of this project will lead to advances in facilitating fairness in computing. The project will produce effective and efficient algorithms to explore fair data characteristics from different perspectives and enhance generalizability and trust in the machine learning field. This research is expected to impact the broad utilization of machine learning algorithms in essential applications, enabling non-discrimination decision-making processes and prompting a more transparent platform for future information systems.
Receiving this award will help me achieve my short-term and long-term goals, Zou said. My short-term goal is to develop fair machine learning algorithms through mitigating fairness issues from computational challenges and broadening the impact through disseminating research outcomes and a comprehensive educational toolkit. The long-term goal is to extend the efforts to all aspects of society to deploy fairness-aware information systems and enhance society-level fair decision-making through intensive collaborations with industries.
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Preventing Bias In Machine Learning - Texas A&M Today - Texas A&M University Today
Assessing the adoption of blockchain in financial services – FinTech Magazine
Blockchain has been somewhat of a quiet revolutionary. Essentially a distributed database containing an ever-growing list of ordered records blocks its introduction as the enabler of crypto asset trading heralded the beginning of decentralised finance.
But, while blockchain and cryptocurrencies may appear synonymous with one another, the truth is blockchains reach is far more extensive and can be used to monitor supply chains, create digital IDs and enable data sharing.
So, while blockchains association with crypto is well-established, the question remains: is it being used to its full potential in the wider financial services sector?
Understanding blockchain
To gauge blockchains potential in finance more broadly, its important to first understand how it works.
A board member at the Casper Association, Ralf Kubli likens blockchain to a series of remote computers working together to create a trustless independently verifiable, decentralised ledger.
He continues: Every computer on the network, or node, can see every transaction that has ever occurred, and all of this information is encoded cryptographically, decentralised so that any participant can simply run the maths themselves, and confirm that all values are correct.
While for cryptocurrency, certain nodes on the network miners are tasked with ordering batches of transactions into blocks (rewarded for their efforts with newly created crypto assets), Kubli notes that blockchain doesnt have to be used this way. It can be a ledger for virtually any type of data.
Blockchains reach across the financial industry
Kubli continues: While financial institutions such as BlackRock, Siemens and even HSBC are touting tokenisation as a paradigm shift for the economy, with vastly improved systems for delivery versus payment (DvP) settlements, current tokenisation platforms are not digitising the underlying liabilities or cash flows tied to the assets themselves.
Most tokens have a simple embedded PDF that defines its terms and conditions, meaning human intervention is still required to calculate cash flows, which, according to Kubli, can introduce errors and discrepancies, and also prevents this digital capital from being truly automated.
As a result, Lab49s Principal Consultant Yuvraj Sidhu admits the mass adoption of blockchain in traditional financial services is still far off, especially in the institutional landscape, [where] regulatory uncertainty and questions regarding unproven tools and infrastructure have created barriers.
Yet, despite these barriers to adoption, Sidhu feels a need for secure, accurate and instantaneous settlement in the financial services industry is driving many banking institutions to explore ways of leveraging the benefits of blockchain.
He adds that blockchain adoption can proliferate across traditional services once four key barriers are overcome: privacy preservation, regulatory clarity, standardisation, and scalability.
For Peter Greiff, Data and Analytics Team Leader EMEA at DataStax, the issue of blockchains slow cross-market extension is simpler; its a matter of trust, or more specifically, too much trust. Using blockchain is ideal when there is less trust between the parties involved.
In finance, the number of use cases where all those transactions take place without trust is actually small. Banks and financial institutions rely on compliance regulations and backing by governments, which is why they are trusted.
So, while Greiff can see blockchain being useful for records over time and providing tracking for transactions, its use for secure monetary transactions isnt strictly necessary at established financial institutions.
Bringing blockchain to finance
Given blockchains use in traditional financial institutions could look markedly different from its function as a fraud safeguard when it comes to crypto, financial services firms are understanding the wider potential of blockchain slowly but surely, according to Sidhu.
However, before this potential can be truly actualised, Kubli feels the implementation of open banking standards, ultimately introducing smart financial contracts as a means to remedy the current barriers to adoption, will enable legacy institutions to begin implementing blockchain-based innovations.
He adds: Ensuring standardisation via smart financial contracts will enable things like an entire mortgage process, including the underlying obligations, to be placed on the blockchain and become machine-readable, executable, and automated.
This will bring about the true financial potential that is currently missing, and even bring new stability to the existing finance world.
The possibilities blockchain can bring to traditional financial services dont stop there. Sidhu believes asset tokenisation will become an effective way to automate the security issuance process and democratise access to previously illiquid asset classes.
Private equity is a good example currently, private firms such as start-ups have limited options when raising capital. Tokenisation provides a pathway to open new markets and liquidity to these types of firms, he adds.
Post-trading clearing and settlement, too, is something Sidhu feels blockchain can help innovate, calling the current process highly inefficient and fragmented.
The industry currently spends over US$130bn annually in this area, so, unsurprisingly, organisations such as the Federal Reserve and Bank of England are experimenting with blockchain technologies to automate these processes.
For Greiff, combining blockchain technology with distributed databases and streaming technologies will allow financial institutions to get real-time updates for on-chain transactions, and the ability to apply analytics to that data in real-time as well.
Scaling blockchain: the drawbacks
While the scope for blockchains use at traditional financial institutions is evident, should barriers to adoption be overcome, it will need to scale significantly from its current models of use.
With this comes problems, though. Crypto data mining has faced criticism for the amount of energy it consumes through the traditional Proof of Work (PoW) model employed by the likes of Bitcoin and other crypto networks.
However, the more energy-efficient Proof of Stake (PoS) mining model could be the way forward for financial institutions. Crypto trading platform Ethereum recently switched from the traditional PoW to a PoS model and reported a 99% drop in the amount of energy needed to secure the network.
Notwithstanding the perks of PoS models, a switch in mining models is just one barrier to scalability. As Greiff notes: Blockchain cant meet some of the transaction throughput or analytics requirements that companies have.
For example, Bitcoin has around 19.3m coins and sees 500,000-650,000 global transactions per day. Ethereum reached a maximum of 1.9m transactions on one day but tends to be around the 1.2m transactions per day level globally.
In comparison, banks, retailers and payment systems providers cover billions of transactions per day, which is an order of magnitude higher.
To reach the next level of scale, blockchain platforms must innovate ways to carry out real-time analysis on a far greater number of transactions, to meet anti-money laundering and anti-fraud requirements.
The future of blockchain
So, while blockchains path to mass use in traditional financial services is still a bit murky, the standardisation of blockchain-powered financial assets, such as CBDCs, is something Kubli feels we are destined to see.
He adds: As regulations tighten, any assets that dont embrace standardisation will be at risk of non-compliance. There could be exceptions, of course, but any projects that want to be adopted by major financial players will likely adopt this approach.
Once implemented, tokenised financial assets can bring in improved liquidity and new forms of financing for the entire economy.
Sidhu agrees, saying the timing of when tokenised financial assets hit the market will depend on the institutional sector. Wall Street firms such as JP Morgan Chase, BlackRock, Fidelity and Goldman Sachs are defining their approach to blockchain technology, while 114 countries including the UK and US are exploring CBDCs.
Tokenisation of real-world assets could one day be a multi-trillion-dollar market. However, if blockchain and crypto are to become the norm in the future, strides in regulatory harmonisation, standardisation and technology are required.
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Assessing the adoption of blockchain in financial services - FinTech Magazine
UK and Zambia announce renewables and mining investments – Mining Technology
The British Government has agreed a partnership with Zambia covering clean energy and critical mineral supply worth more than $3.7bn (2.91bn).
Under the clean energy and green investment partnership, the UK will aim to generate $3.1bn of private sector investment for the Zambian mining and renewable energy industries. A further $600m of funding will come in the form of UK Government-backed investments.
Approximately 70% of Zambias export earnings come from mining. In 2022, Zambia was the worlds eighth-largest copper producer, and second in Africa. In the same year, it was also the worlds 13th-largest cobalt producer.
Cobalt and copper are both vital components of lithium-ion batteries vital in the energy transition. Securing a consistent supply of both would aid Britains stated goals of reaching net-zero carbon emissions by 2050, and attracting investment into the country for transition technology producers such as battery makers.
The Zambian renewable energy sector will also receive investment. The country hosts a large share of the Zambezi River basin and hydroelectric power generates 94% of the countrys power mix, totalling 2,257MW. As a result, Zambia produces renewable electricity at a price of around 8 per kilowatt-hour (kWh), approximately half of the average cost in Africa, making the country a cheap yet bountiful site for renewable investments.
Solar energy investment is also an area of promise in Zambia, with the country averaging around 5.5kWh/m of solar irradiation per day, showcasing the high potential for large-scale solar generation. Zambia experiences around 3,000 hours of sunshine annually.
In 2022, the UK Government stated its aim to diversify its critical mineral strategy by collaborating with international partners and enhancing overseas mineral markets. In 2019, Britain agreed the UK-Zambia Green Growth Compact for collaboration with the country, on which the latest investments are based.
Following a visit to Zambia where the deal was agreed, British Foreign Secretary James Cleverly stated: The UK-Zambia Green Growth Compact and our landmark agreement on critical minerals will support investment between UK and Zambian business, creating jobs in both countries, and improving environmental and social standards.
Zambia has previously been referred to as a safe haven for foreign direct investment into the mining and energy sectors.
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UK and Zambia announce renewables and mining investments - Mining Technology
Komatsu’s Operation Guidance Monitor for smaller mines and quarries – International Mining
Posted by Paul Moore on 3rd August 2023
Away from very large open pit mines Komatsu offers digital solutions for smaller mines and quarries. Operator Guidance Monitor (OGM) it says is a revolutionary tool designed to optimise fleets of rigid dump trucks, and to reduce unit production costs. Its easy-to-use, clear program allows users to set Key Performance Indicator (KPI) targets and adjust operating parameters, to let Komatsu trucks operate in the most efficient way possible.
The OEM states: OGM is a tool thanks to which operators control and optimise their work in real time. It helps raise and refine their skills, resulting in higher fuel savings and productivity, and increased safety on the jobsite. Marek Skrzydel, Quarry Manager at Heidelberg Materials says: This system allows us to analyse data in real time and we can analyse production and discuss the achieved goals by operator on an ongoing basis.
Customers can quickly set parameters such as payload, planned duration and fuel consumption for each work cycle, idle time, and production (tonnes per hour), target by loading and dumping location. The OGM screen also shows real-time warnings for dangerous operating events, such as excessive speed and sudden braking.
Thanks to a unique login ID, operators performance can be analysed individually. Can-bus data from the machines operation are sent automatically, the user has access to them via a webbased dashboard. The data can be filtered by date, machine, shift, operator and loading and dumping locations.
Operator guidance monitor is one of the new digital Komatsu solutions to support our customers with their daily quarry operation, says Wouter Boon, Telematics Specialist at Komatsu Europe. Operator guidance monitor gives a possibility to set visualised KPI for the operators and follow up and adjust if needed in real time.
In the back office, target KPIs and event thresholds such as speed limits are set up for each haul road from any remote location. The status of each target can be followed by date, time, shift, or operator. The automatic data acquisition from the machine allows remote reporting of production and event data via the dashboard.
Each operator has his own OGM account. Before starting work, he or she logs directly into the tools easy-to-operate 8 in monitor installed inside the cabin. After logging in, the system automatically detects and displays the haul road specific KPIs on the monitor, according to which the operator adjusts the pace and dynamics of the haulers work.Thanks to real-time feedback of operation data, each operator can follow up on individual KPI, as optimal values when loading, transporting, and unloading material.
With parameter values updated in real time, the operator learns how to operate the machine at its best. No input from the operator is required and self-learning is encouraged through this visualisation of operation performance. This all results in fuel savings and more efficient production, achieved in the safest way possible. With real time operator alarms for unsafe operation, such as over speeding or sudden braking, job site safety is improved and can be monitored continuously.
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Komatsu's Operation Guidance Monitor for smaller mines and quarries - International Mining