Prioritizing nanotechnology in Iran has led to this country’s steady placement among the five pioneers of the nanotechnology field in recent years, and approximately 20 percent of all articles provided by Iranian researchers in 2020 are relative to this area of technology.
Iran has been introduced as the 4th leading country in the world in the field of nanotechnology, publishing 11,546 scientific articles in 2020.
The country held a 6 percent share of the world’s total nanotechnology articles, according to StatNano’s monthly evaluation accomplished in WoS databases.
There are 227 companies in Iran registered in the WoS databases, manufacturing 419 products, mainly in the fields of construction, textile, medicine, home appliances, automotive, and food.
According to the data, 31 Iranian universities and research centers published more than 50 nano-articles in the last year.
In line with China’s trend in the past few years, this country is placed in the first stage with 78,000 nano-articles (more than 40 percent of all nano-articles in 2020), and the U.S. is at the next stage with 24,425 papers. These countries have published nearly half of the whole world’s nano-articles.
In the following, India with 9 percent, Iran with 6 percent, and South Korea and Germany with 5 percent are the other head publishers, respectively.
Almost 9 percent of the whole scientific publications of 2020, indexed in the Web of Science database, have been relevant to nanotechnology.
There have been 191,304 nano-articles indexed in WoS that had to have a 9 percent growth compared to last year. The mentioned articles are 8.8 percent of the whole produced papers in 2020.
Iran ranked 43rd among the 100 most vibrant clusters of science and technology (S&T) worldwide for the third consecutive year, according to the Global Innovation Index (GII) 2020 report.
The country experienced a three-level improvement compared to 2019.
Iran’s share of the world’s top scientific articles is 3 percent, Gholam Hossein Rahimi She’erbaf, the deputy science minister, has announced.
The country’s share in the whole publications worldwide is 2 percent, he noted, highlighting, for the first three consecutive years, Iran has been ranked first in terms of quantity and quality of articles among Islamic countries.
Sourena Sattari, vice president for science and technology has said that Iran is playing the leading role in the region in the fields of fintech, ICT, stem cell, aerospace, and is unrivaled in artificial intelligence.
From our partner Tehran Times
Development of Metaverse in China: Strategies, Potential, and Challenges Part II
China’s metaverse market is anticipated to reach a value of US$12.6 billion in 2023, with a projected compound annual growth rate of 31.93% from 2023 to 2030 (data from China Briefing). The concept of the metaverse, previously confined to the realms of science fiction, has now become a crucial part of China’s economic vision. Traditionally reliant on the manufacturing and export sectors, China perceives the metaverse as a medium for economic diversification, reducing dependence on traditional sectors and creating new opportunities for economic growth.
In the pursuit of economic diversification through the metaverse, China is also focusing on job creation and skills development. The metaverse industry in China is expected to generate numerous new jobs, involving various roles such as software developers, content creators, hardware manufacturers, innovators, entrepreneurs, and virtual architects. This demonstrates the potential of the metaverse in transforming the employment landscape and providing access to new opportunities in various fields.
With its ambitious vision, China is striving to develop a metaverse focusing on the B2B and industrial sectors, aiming to become a global leader in industrial metaverse applications. However, to realize this vision and export metaverse technology as a global standard, China must overcome various challenges, including issues related to data protection and privacy, which will be key to China’s success in leading the industrial metaverse revolution.
Technology and Innovation
In the continually evolving technological era, China needs to innovate and develop metaverse technology that can compete globally. Sustained technological excellence is key to ensuring the relevance and superiority of China’s industrial metaverse applications in global competition.
China needs to focus its research and development (R&D) on key metaverse technologies such as virtual reality (VR), augmented reality (AR), artificial intelligence (AI), and blockchain. Investment in this R&D will enable China to produce proprietary technology and strengthen its domestic metaverse ecosystem. Additionally, collaboration with universities, research institutions, and other technology companies will accelerate the innovation and development of metaverse technology.
Regulation and Standards
The development of effective and consistent regulations and technical standards is a central challenge in realizing the industrial metaverse. To create internationally acceptable regulations, China needs to consider implementing international standards like GDPR and ensuring transparency and accountability in data processing. Adopting and adapting to international norms will build trust and ensure compliance with existing international norms and standards.
Multilateral dialogue and a participatory approach are also crucial in this process. Through dialogue with various countries and international institutions, and involving stakeholders from the industry, academia, and civil society, China can ensure that diverse perspectives and interests are represented in the development of metaverse regulations and standards. This will assist in agreeing on international norms and standards that are acceptable to the global community.
Additionally, strengthening cybersecurity, education and awareness about regulations, and developing technology ethics that respect human rights and privacy are other important steps. By implementing these measures, China can create a balanced and responsible regulatory framework, enabling sustainable development of the industrial metaverse that is acceptable to the international community.
Cybersecurity and Data Privacy
Cybersecurity and data privacy are crucial elements in the development and export of metaverse technology by China. To gain global trust, it is imperative for China to demonstrate unwavering commitment to protecting user data and privacy. Given the concerns that have arisen from cases like TikTok and Huawei, concrete measures and transparency in user data management are vital to address doubts and suspicions from the international community.
Implementing international data security standards such as ISO/IEC 27001 and independent security audits by trusted third parties are vital initial steps. These will not only strengthen the integrity of security systems but also provide concrete evidence to the international community about China’s seriousness in safeguarding data security and user privacy.
Full transparency in data processing will also play a crucial role in building trust and clarifying data management procedures and practices. Additionally, international cooperation in cybersecurity and the development of cybersecurity education and awareness programs are initiatives that will enhance understanding and vigilance against potential security risks.
Cooperation and dialogue with international institutions and other countries will enable the exchange of knowledge and best practices, strengthening mutual understanding and cooperation on good security practices and standards. By implementing these measures, China can address global concerns about data security and privacy and position itself as a responsible leader in the development of metaverse technology.
Commitment to security and data privacy will be a solid foundation in building mutually beneficial relationships with international partners and expanding the influence of China’s metaverse technology on the global stage.
To address geopolitical challenges and strengthen its position in the global metaverse community, international collaboration is a crucial strategy for China. Concrete forms of this collaboration can include active participation in international forums and dialogues on the metaverse, enabling knowledge and experience sharing and understanding the needs and concerns of various stakeholders. Additionally, undertaking joint research and development projects with international entities will accelerate innovation and provide access to complementary resources and expertise.
The formation of international metaverse consortiums and cooperation in cybersecurity and regulations with other countries and international institutions are also important steps. These will help establish international standards and promote interoperability between metaverse platforms, strengthening security and trust among nations within the metaverse ecosystem.
Exchange programs and training with international institutions can also strengthen relationships between professional communities and enhance the understanding and skills of China’s human resources in the metaverse field. By adopting these international collaboration strategies, China can not only expand and enrich its industrial metaverse development but also significantly contribute to shaping and developing a more inclusive and sustainable global metaverse ecosystem. This will enable China to play a key role in the evolution of the metaverse and promote its vision on a global scale.
China aspires to be a leader in the industrial and B2B metaverse, but multidimensional challenges, including privacy and data protection issues, must be addressed to realize this vision and establish China’s metaverse technology as a global standard. Through an integrated and holistic approach to addressing these challenges, China has the potential to lead the industrial metaverse revolution, unlocking opportunities for innovation and economic growth, and enhancing the development of the industrial and B2B sectors globally.
AI and Physics Advances in the Field of Gravitational Waves and the Alternative of Open Source Science
The Laser Interferometer Gravitational-Wave Observatory (LIGO) consists of ten subsystems, one of which is the Data and Computing Systems (DSC). The data obtained by LIGO not only include the findings from the laser interferometer’s gravitational wave detector, but also contain various independent detectors and recorders that monitor the detector’s environment and the state of its equipment, such as temperature, atmospheric pressure and wind; conditions such as heavy rain, hail, surface vibrations, sound, electric fields and magnetic fields, as well as data on the state of the detector itself such as the position of the plane mirror and lens inside the gravitational wave detector.
In terms of data acquisition, e.g. the LIGO in Hanford (Washington State), and the H1 and H2 Data AcQuisition (DAQ) interferometers recorded a total of 12,733 channels, of which 1,279 were fast channels.
The upgraded LIGO is designed to record data acquisitions greater than 300,000 channels, including about three thousand fast channels. This is a typical problem in big data analysis and processing, which requires powerful computing resources and advanced algorithms to effectively process such a huge amount of data.
Adaptive filtering technology is used in the search for gravitational wave signals. This is a technology based on waveform analysis, which requires the creation of a reasonable physical model of the source of gravitational waves, and the generation of thousands of processing steps based on the use of these models to match signals in gravitational wave data to find relevant events.
Moreover, unlike telescopes, gravitational lenses distort objects into blurred rings and arcs and it is therefore quite difficult to understand them. Applying AI neural networks to the analysis of gravitational wave images will be ten million times faster than the original method. The AI neural network can discover new shots and determine the properties, mass distribution and magnification levels of background galaxies. Neural networks will help us identify interesting objects and analyse them quickly, which will provide more time to explore and ask questions about the universe.
As things stand, there are at least several aspects worthy of attention when applying Artificial Intelligence to the analysis and processing of gravitational wave big data.
In “supervised learning” the matched filter method must know the waveform of the signal. This time, the analysis of the gravitational wave deformation data must match the waveforms in the huge library of shapes available. This is obviously a process with an enormous computational workload. How to improve efficiency and reduce the computational load? The consumption of resources is undoubtedly worthy of in-depth study.
In the “unsupervised learning” of gravitational wave detection, instead, the waveforms of a large number of events are unknown. For supernovae and rotating neutron stars, the current accumulation of astronomical observations cannot provide a theoretical estimate of the intensity of the gravitational waves they release, which requires the use of “unsupervised learning”, i.e. algorithms to discover unknown patterns in gravitational wave data.
In the “integrated learning” strategy, there are other types of gravitational waves, such as continuous and primordial ones, which are different from the gravitational waves detected as in the double black hole merger, mentioned in the previous article. They have not been detected yet, as in the case of continuous gravitational waves from rotating neutron stars: besides requiring a higher detector sensitivity, extremely high requirements are also placed on the data analysis capabilities.
Astronomical research is too far removed from ordinary people and, in the United States and in the rest of the world, people often complain that the costs are too high. Although research into gravitational wave data analysis technology has no direct commercial value, the migration of technology to the necessary algorithm can be considered in the future and technology is being developed for the proper analysis and processing of big data. The latter, however, can be applied to other commercial or purely academic fields to produce value through research.
Most gravitational wave data analysis uses one-dimensional signal processing technology, which can be transferred to spectral data analysis, sensing data analysis and general data analysis.
Indeed, as is well-known, AI technology has been used for a long time in space and non-space exploration, including computer vision, speech recognition, natural language processing, machine learning, etc. Detectors, however, also help us obtain images, information and data from the universe and then transmit them. As human beings improve their ability to understand the universe, Artificial Intelligence will play an increasingly important role in the future, and the space exploration science and the refinement of AI technology will eventually benefit human society as a whole.
Thinking of Newton, we would say: “If you look at the stars, you will see an apple. If you see an apple, you will study its origin. If you study its origin, you will have a law. If you have a law, you will want a conclusion”. It often happens, however, that a conclusion can end up in a contradiction, and if we get into a contradiction we will have to look at the stars again.
Space exploration, AI technology and human society are probably in this dialectical relationship of renewal – just think of the theory of relativity and quantum physics – that continues to stimulate the imagination and the power of development of the human mind and civilisations.
Nevertheless, we must be careful not to stray into the grotesque, and I think we must deal with the topic without coming to the conclusion that one day Artificial Intelligence will confine humans into a zoo for protected species, as – hopefully – all this will always be created and managed by humans, who are still the sole solvers of the problems overriding standards and are the real constituents of the world in which we live.
With the right tools, AI methods can be applied to scientists’ workflows. As mentioned above, the aim is not to replace human intelligence, but to significantly improve it and reduce the workload. This research verified part of Einstein’s theory of relativity and the connection between time and space. To date it is also the starting point for research into gravitational wave astronomy. Even the uninitiated will be able to begin to understand the universe in depth and at a faster speed, including dark energy, gravity and neutron stars.
The contribution of this research is that, by combining the power of Artificial Intelligence and supercomputers, huge data experiments can be solved instantaneously, and all of this can be reproduced, rather than just limited to testing whether Artificial Intelligence can only be used to face and solve other important challenges.
Among other things, a solution to step up progress is the open source (open code) model, which is used by other research groups without any preclusion. In a nutshell, open source is the source code made freely available for possible changes and redistribution. Products include permission to use the source code, design documents or product content freely available to the public.
Open source is a decentralised software development model that encourages open collaboration. One of the fundamental principles of open source software development is peer production. The use of the term originated with software, but has expanded beyond the field of software to cover other content and forms of open collaboration. The open source movement in software began as a response to the limitations of proprietary code. (end)
The AI Wars Between Nations: A New Arms Race or a Catalyst for Collaboration?
In the 20th century, the nuclear arms race defined the global power struggle. Today, the race to develop the most advanced artificial intelligence (AI) capabilities is driving the narrative of international competition. Nations are investing billions into R&D, striving to have the superior AI technology. This ‘AI war’ evokes a range of emotions: from fear of robotic armies to concerns over subtle, deep-fake propagandas. But is this war only about outdoing each other, or can it spur collaborative breakthroughs?
The Nature of the AI Wars
Unlike traditional arms races, the AI race isn’t just about the creation of lethal machinery. It encompasses economic, technological, ethical, and military dimensions. From improving healthcare diagnostics to forecasting economic shifts and enhancing national security, the potential applications of AI are boundless.
However, as nations rush to outpace each other, concerns arise. There’s the risk of under-regulated AI technologies causing unintentional harm, be it through biased algorithms or intrusive surveillance. On the military front, the emergence of autonomous lethal weapons can redefine the rules of warfare, making conflicts more impersonal and potentially more devastating.
Economic and Strategic Implications
Nations that lead in AI will undeniably hold significant geopolitical leverage. AI has the potential to reshape economies, making certain industries obsolete while giving rise to new ones. A dominant position in AI can mean economic prosperity, more jobs, and greater influence in international decisions.
Moreover, the strategic advantages are evident. AI can enhance cybersecurity, predict and mitigate threats, and offer a strategic edge in negotiations and diplomacy by providing real-time insights.
Collaboration Over Competition?
Despite the competitive undertones, the AI race holds enormous potential for collaborative growth. Just as the Space Race of the Cold War era eventually led to joint space missions and international space stations, the AI race can pave the way for shared research, ethical standards, and mutual growth.
Collaborative efforts can help address some of AI’s biggest challenges, including:
1. Ethical Guidelines: By working together, nations can create universal standards and ethics for AI development, ensuring technologies respect human rights and democratic values.
2. Shared Research: Pooling resources can accelerate breakthroughs in areas like healthcare, climate change, and energy, benefiting humanity at large.
3. Global Security: Jointly developed frameworks can regulate the deployment of AI in warfare, preventing uncontrolled escalation.
The AI wars between nations, while driven by a desire for supremacy, don’t have to end in zero-sum outcomes. The very nature of AI – its reliance on vast amounts of data, the benefits of diverse perspectives, and its global applications – makes collaboration not only advantageous but also essential.
As the world treads this new frontier, the hope is that nations recognize the transformative power of AI not just as a tool of dominion but as a means to forge a more interconnected, prosperous, and harmonious future.
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