Smart Earth Technologies: a pressing need for abating pollution for a better tomorrow

Artificial intelligence investments reduce risks to critical mineral supply

This paper employs insights from earth science on the financial risk of project developments to present an economic theory of critical minerals. Our theory posits that back-ended critical mineral projects that have unaddressed technical and non-technical barriers, such as those involving lithium and cobalt, exhibit an additional risk for investors which we term the "back-ended risk premium". We show that the back-ended risk premium increases the cost of capital and, therefore, has the potential to reduce investment in the sector. We posit that the back-ended risk premium may also reduce the gains in productivity expected from artificial intelligence (AI) technologies in the mining sector. Progress in AI may, however, lessen the back-ended risk premium itself by shortening the duration of mining projects and the required rate of investment by reducing the associated risk. We conclude that the best way to reduce the costs associated with energy transition is for governments to invest heavily in AI mining technologies and research.

Analyzing failures in adoption of smart technologies for medical waste management systems: a type-2 neutrosophic-based approach

Medical waste management (MWM) systems are considered among the most important urban systems nowadays. Cities in different countries prefer to transform their infrastructure based on sustainability guidelines and practices. Meanwhile, smart technologies such as Internet of Things (IoT) and blockchain are being recently used in different urban systems of cities that aim to transform into smart cities. MWM systems are one of the main targets of integrating such smart technologies to maximize economic and social profits and minimize environmental issues. However, the transformation of traditional MWM systems into smart MWM systems and the adoption of such technologies can be a very resource-consuming task. One of the possible tasks in this process can be the identification of factors that cause failure in the adoption of smart technologies. Therefore, this study proposes a multi-criteria evaluation model based on type-2 neutrosophic numbers (T2NNs) to identify factors contributing to failure in the adoption of IoT and blockchain in smart MWM systems in Istanbul, Turkey. Results of the case study indicate that training for different stakeholders, market acceptance, transparency, and professional personnel are the main factors that lead to failure in the adoption of smart technologies. Training for different stakeholders, market acceptance, transparency, and professional personnel factors obtained distance values of 0.494, 0.381, 0.375, and 0.278, respectively, against the best factor which is security and privacy. In order to validate the results of the proposed approach, a sensitivity analysis test is performed. Results of this study can be useful for governmental and private MWM and green companies that are planning to adopt IoT and blockchain within their waste management (WM) system.

The Influence of the Development of the Internet of Things Industry on the Optimization of the High- and New-Tech Industry Structure under the Wireless Mobile Network

At present, the pressure on China's economic development is increasing day by day due to the profound changes in the internal and external environment. The global economic pattern is undergoing significant changes in terms of the external environment. Adjusting and optimizing the industrial structure will aid in achieving the goal of facilitating transformation through steady growth in the short term. Meanwhile, it will also accelerate sustainable economic development. In this study, the relevant theories of industrial structure optimization are described based on the impact of wireless mobile networks and the Internet of things (IoT) industry. Based on the gray correlation degree, the high- and new-tech industries under the development of the IoT industry are analyzed and the impact of optimization of the high- and new-tech industry structure is investigated. The results show that the development of the IoT industry has driven the development of the high- and new-tech industry. The gray correlation between the development of the IoT industry and the high- and new-tech industry obtained is 0.64, indicating a strong correlation. The average output share of the electronic computer and office equipment manufacturing industries is 47.09%. The average output ratio of the industrial structure optimization of the electronics and communication manufacturing industry is 42.55%. Moreover, the proportion of the output of medical manufacturing and medical equipment and instrument manufacturing industrial structure optimization is small, 15.63% and 10.54%, respectively. The results have significant value in the research on the impact of the development of the IoT industry on the high- and new-tech industry under the wireless mobile network and the effect of its industrial structure optimization.

Smart IoT and Machine Learning-based Framework for Water Quality Assessment and Device Component Monitoring

Water is the most important natural element present on earth for humans, yet the availability of pure water is becoming scarce and decreasing. An increase in population and rise in temperatures are two major factors contributing to the water crisis worldwide. Desalinated, brackish water from the sea, lake, estuary, or underground aquifers is treated to maximize freshwater availability for human consumption. However, mismanagement of water storage, distribution, or quality leads to serious threats to human health and ecosystems. Sensors, embedded and smart devices in water plants require proactive monitoring for optimal performance. Traditional quality and device management require huge investments in time, manual efforts, labour, and resources. This research presents an IoT-based real-time framework to perform water quality management, monitor, and alert for taking actions based on contamination and toxic parameter levels, device and application performance as the first part of the proposed work. Machine learning models analyze water quality trends and device monitoring and management architecture. The results display that the proposed method manages water monitoring and accessing water parameters efficiently than other works.

Smart Silk Origami as Eco-sensors for Environmental Pollution

Origami folding is an easy, cost-effective, and scalable fabrication method for changing a flat material into a complex 3D functional shape. Here, we created semicrystalline silk films doped with iron oxide particles by mold casting and annealing. The flat silk films could be loaded with natural dyes and folded into 3D geometries using origami principles following plasticization. They performed locomotion under a magnetic field, were reusable, and displayed colorimetric stability. The critical parameters for the design of the semi-autonomous silk film, including ease of folding, shape preservation, and locomotion in the presence of a magnetic field, were characterized, and pH detection was achieved by eye and by digital image colorimetry with a response time below 1 min. We demonstrate a practical application—a battery-free origami silk boat—as a colorimetric sensor for waterborne pollutants, which was reusable at least five times. This work introduces silk eco-sensors and merges responsive actuation and origami techniques.

Determinants of ecological footprint in OCED countries: do environmental-related technologies reduce environmental degradation?

The world is in a clash between the perspectives of economic expansion and sustainable environment. The high pace of technological progress opens space for fostering economic growth but at the same time, it creates a big dilemma for humans in protecting the environmental quality. The environmentally specific technologies are expected to help human beings to achieve dual objectives of economic prosperity and environmental sustainability. Despite its importance, attention to the role of environmental-related technologies in reducing environmental degradation is limited. This paper, therefore, intends to discover the impact of environmental-related technologies on the ecological footprint for 20 OECD from 1990 to 2015. The results endorse a long-run relationship between ecological footprint and green technologies, renewable energy, international trade, energy intensity, and real income. Environmental-related technologies and renewable energy consumption are found to be impetuous to sustainable development. The study provides relevant implications for policymakers to support the development and adoption of green technologies.