The Ethereum Improvement Proposal 3675 (EIP-3675) marks a significant shift, transitioning from a Proof of Work (PoW) to a Proof of Stake (PoS) consensus mechanism. This transition resulted in a staggering 99.95% decrease in energy consumption. However, the transition prompts two critical questions: (1). How does EIP-3675 affect miners' dynamics? and (2). How do users determine priority fees, considering that paying too little may cause delays or non-inclusion, yet paying too much wastes money with little to no benefits? To address the first question, we present a comprehensive empirical study examining EIP-3675's effect on miner dynamics (i.e., miner participation, distribution, and the degree of randomness in miner selection). Our findings reveal that the transition has encouraged broader participation of miners in block append operation, resulting in a larger pool of unique miners ($\approx50\times$ PoW), and the change in miner distribution with the increased number of unique small category miners ($\approx60\times$ PoW). However, there is an unintended consequence: a reduction in the miner selection randomness, which signifies the negative impact of the transition to PoS-Ethereum on network decentralization. Regarding the second question, we employed regression-based machine learning models; the Gradient Boosting Regressor performed best in predicting priority fees, while the K-Neighbours Regressor was worst.
This paper is an attempt to find the energy required for the comminution of fault zone rocks and also to determine the energy required to grind ore from infinite size to the desired particle size in non-traditional approach, for various value additions. The results in the present investigations also confirm about the brittleness test and friability tests, whose values depend on the drop weight and its height for different types of fault zone rock. Also the results of its brittleness tests determine the grindability of fault zone rocks. All the outcome results are then secured with the help of decentralized and immutable record-keeping system using Blockchain technology. The Blockchain network in the present investigations not only allows any users to enhance the performance but also it will secure the experimental outcomes in immutable distributed ledgers through smart contracts to increase transparency between users in a trusted manner.
Timothy Bechtel, Lorenzo Capineri, Gennadiy Pochanin, F. Crawford · 6 authors
The modern world is characterized by the pervasive presence of electronic sensors, microprocessors, robotics, and wireless connectivity. Powerful computers can perform virtual simulations that would previously have required physical models or experiments. Conversely, these same machines can control robotic devices that take virtual models and produce physical objects (e.g. 3-D printing). In manufacturing, this represents a new industrial revolution, dubbed âIndustry 4.0.â In brief, the recognized industrial revolutions are: Industry 1.0. The First Industrial Revolution (in the late 18th and early 19th centuries) involved the transition from an agrarian economy to industrial production (e.g. weaving looms and other mechanical devices driven by water wheels and steam), and advances in metallurgy. Industry 2.0. The Second Industrial Revolution (spanning the late 19th and early 20th centuries) brought the widespread use of electric power, mass production on assembly lines, and division of labor (e.g., the Chicago and Cincinnati meat packing plants, the Ford Motor Company) â all providing greatly increased productivity. Industry 3.0. The Third Industrial Revolution involved the integration of electronics and information systems into production, providing intensive automation and application of mechanical/robotic manipulation in production processes. In the closing decades of the 20th century, the proliferation of electronic devices (such as transistors and later integrated circuits) allowed more complete automation of individual machines, supplementing or replacing human operators. This period also spanned the full development of software systems for the control of electronic equipment. Industry 4.0. In the 21st century, Industry 4.0 exploits the âInternet of Thingsâ or IoT (Ashton, 2009) with wired or wireless communications connecting cyber-physical systems (CPSs), which share and analyze information and use it to guide actions. Industry 4.0 is based on six principles (Hermann et al., 2016). Interoperability: the ability of machines, devices, sensors, and people to connect and interact to achieve a common goal. Virtualization: CPSs monitor physical processes and continuously compare a model of the actual world (based on sensor data) with an editable model of the desired world. CPSs even monitor each other and provide alarms when they sense a failure. Decentralization: The increasing demand for customized products and services makes it increasingly difficult to control systems centrally. Embedded computers enable CPSs to make decisions on their own. Nevertheless, it is still necessary to keep track of the whole system at all times. In the context of Industry 4.0 âSmart Factories,â decentralization might mean radio-frequency identification (RFID) tags on components âtellâ production machines which working steps are necessary, making central planning and control obsolete. Real-Time Adaptability: CPSs collect, share, and analyze data in real time. Thus, the plant can react to the failure of any system component and re-route information or parts to another machine. Service Orientation: The services provided by âsmartâ systems can be shared by other participants across company, discipline, and international boundaries. All CPSs can offer their functionalities as a stand-alone service, making it possible to assemble the proper combination of CPSs to make a specific product or service that meets any end-user needs. Modularity: Systems can adapt to changing requirements by replacing or expanding individual âPlug & Playâ modules. With standardized software and hardware interfaces, new modules can be identified and requisitioned automatically and can be utilized immediately. Thus, Industry 4.0 is a new way to develop and adapt manufacturing technologies based on automation and instantaneous exchange of data across potentially physically separated CPS components. Currently, Industry 4.0 is the topic of many scientific conferences (e.g., Industry-4.eu, 2019), which are held all over the world, and address both general organizational issues and individual tasks. In fact, every scientific conference is in one way or another a stage in the advancement of Industry 4.0 technology. With the support of NATO/OTAN Science for Peace and Security (SfPS) Program, Project G5014 - âHolographic and Impulse Subsurface Radar for Landmine and IED Detectionâ (http://www.nato-sfpslandmines.eu/) and its successor Project G5731 âMulti-Sensor Cooperative Robots for Shallow-Buried Explosive Threat Detection,â we are developing Demining 4.0; designing cooperating robotic search-detection-discrimination platforms for humanitarian demining. The platforms are intended to exploit new electromagnetic and physical-acoustic methods and technologies for landmine detection in an open design environment. Humanitarian demining is a high-risk and high-cost task that can benefit from the Industry 4.0 approach. This paper illustrates the ways the systems interact. The first platform, âUgo 1st,â incorporates ultrawideband (UWB) ground penetrating radar (GPR) for rapid target detection and XYZ coordinate determination, as well as holographic subsurface radar (HSR) for discrimination of mines from clutter. These are combined with GPS positioning (real time kinematic for sub-cm precision), and light detection and ranging (LiDAR) and optical sensors (PMD Pico Flexx and Teraranger) for remote navigation, obstacle avoidance, tripwire detection, and HSR image correction. These systems and their connectivity are depicted in Figure 1 on the next page. The prototype Ugo 1st operating in an outdoor test bed is shown in Figure 2 on the next page. In the newly begun successor project, the sensors will be spread across four robotic platforms that will sequentially scan a designated area. The UWB robot will first detect targets and send coordinates to the others, who will interrogate them with HSR and a metal detector. The team will be protected by a âshepherdâ robot which provides detection of tripwires and obstacles that could impede the others. Additional Industry 4.0 principles are invoked in the ways data from these systems can be shared, archived, and processed in a decentralized manner accessible to the worldwide community, and in the use of artificial intelligence to standardize the detection and classification of targets according to their level of potential threat. In the successor project, additional platforms âUgo 2ndâ and âUgo 3rdâ will incorporate an imaging metal detector and the HSR, while âShepherdâ looks ahead for boobytrap tripwires, pits, and other obstacles to be avoided. In true Industry 4.0 fashion, these robots will cooperate autonomously. A preliminary test with Ugo 1st was carried out on three buried targets in natural soil at shallow depth for 20 days. The operator (see Figure 2) drove the robot along the lane with a maximum deviation of ±2 cm (as quantified from the real-time 3D video recorded during the traverse). The signals from the impulse GPR were acquired every 3 cm and processed automatically to determine target positioning on the ground relative to the antenna reference system. Figure 3 shows the results of repeated auto-detection of a single PMN-1 plastic-cased landmine (diameter 95 mm, depth 30 mm) by the moving impulse GPR. Ideally, the positions on this graph should be on a straight line and separated by 3 cm. The errors are within the experimental uncertainties and are due to the variable system speed and soil surface influence on the reflected GPR signals. Figure 4 depicts the plan-view HSR image of the buried landmine on a remote computer screen. The Industry 4.0 paradigm also allows replication of our robotic platform (as well as improvement and adaptation of its design), in different parts of the world with delocalized manufacturing of the physical components. Both experimental and operational field data from the system can be shared and accessed in real time at different locations owing to the web-based software architecture. The generation of large data archives by the system will soon be possible with the design and deployment of continuously connected radar systems.