Mining Block Research Articles

Creation of an Anomaly Detection System for Blockchain-based Cloud Deployments that Consider Privacy and Delay.

Cloud-based deployments are increasingly vulnerable to many kinds of assaults; hence powerful anomaly detection systems are needed to prevent security breaches. While effective to some degree, current methods like RSSI, GTM, and APG have drawbacks in terms of scalability, precision, and accuracy. In order to improve the forecast accuracy of cloud assaults and optimize blockchain-based cloud installations, this study suggests a novel anomaly detection system that combines multimodal feature analysis, deep learning models, and QoS-aware sidechains. The suggested strategy considerably surpasses traditional approaches in terms of precision, accuracy, recall, and AUC performance by optimizing feature variance across various sample types and utilizing cutting-edge deep learning algorithms. Additionally, the framework shows increased efficiency in terms of throughput, energy usage, and block mining latency, makingcombining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), the system analyses system logs to find anomalous patterns of activity linked to various threats. The architecture guarantees the transparency and integrity of system records through the use of Blockchain technology, while Deep Learning models offer accurate and quick anomaly detection. The advantages of both Deep Learning and Blockchain technology support the choice to merge them. Blockchain technology ensures the accuracy of anomaly detection and prevents tampering with system records by providing a distributed, immutable ledger. On the other hand, deep learning models produce high precision, accuracy, recall, and AUC measures because of their remarkable pattern recognition skills and ability to adjust to shifting attack strategies. Experimental data analyses show the effectiveness of the suggested framework.

MSCMNet: Multi-scale Semantic Correlation Mining for Visible-Infrared Person Re-Identification

The main challenge in the Visible-Infrared Person Re-Identification (VI-ReID) task lies in extracting discriminative features from different modalities for matching purposes. While existing studies primarily focus on reducing modal discrepancies, the modality information fails to be thoroughly exploited. To solve this problem, the Multi-scale Semantic Correlation Mining network (MSCMNet) is proposed to comprehensively exploit semantic features at multiple scales. The network fuses shallow-level features into the deep network through dimensionality reduction and mapping, and the fused features are utilized to minimize modality information loss in feature extraction. Firstly, considering the effective utilization of modality information, the Multi-scale Information Correlation Mining Block (MIMB) is designed to fuse features at different scales and explore the semantic correlation of fusion features. Secondly, in order to enrich the semantic information that MIMB can utilize, the Quadruple-stream Feature Extractor (QFE) with non-shared parameters is specifically designed to extract information from different dimensions of the dataset. Finally, the Quadruple Center Triplet Loss (QCT) is further proposed to address the information discrepancy in the comprehensive features. Extensive experiments on the SYSU-MM01, RegDB, and LLCM datasets demonstrate that the proposed MSCMNet achieves the greatest accuracy. We release the source code on https://github.com/Hua-XC/MSCMNet.

Development of Best Practice Guideline for the management of hot holes in surface coal mines

Surface coal operations in which mining activities are conducted above old underground workings experience hot holes after drilling. In a coal mine, hot holes are defined as shot holes, which after being drilled have an in-hole ambient temperature of 40°C or above or show a temperature increase of 3°C or more during monitoring. Hot holes and other cavities, such as cracks, pose health and safety risks to workers, such as exposure to hot air and high concentrations of noxious gases released from these holes. In addition, workers may be exposed to premature detonation of explosives caused by in-hole temperature increases and chemical reactions. To this end, Coaltech Research Association commissioned a project to develop a Best Practice Guideline for the management of hot holes. This paper is a compilation of the activities conducted in the development of the Best Practice Guideline between 2021 and 2022. The activities included a review of standard operating procedures and hot-hole temperature-measuring and -monitoring devices, and the assessment of current hot-hole procedures at selected mines. The results indicated that management of hot holes requires a focus on pre-emptive risk assessment of mining blocks, identification of hot holes using the correct temperature-measuring devices, and continuous monitoring of hot holes from the time of drilling until just before blasting. Hot-hole management accessories, such as polyvinyl chloride sleeves, were found to be effective in insulating the hot-hole emulsion from the rock mass temperature, thus preventing the potential for premature detonation.

Integrated Optimization of Production Scheduling and Haulage Route Planning in Open-Pit Mines

In mining, deposits are divided into blocks, forming the basis for open-pit mine planning, covering production and haulage route planning. Current studies often stage optimization and lack the consideration of road capacity, leading to suboptimal solutions. A novel approach integrates production scheduling and haulage route planning through a bilevel optimization model. The upper-level model integrates ore mining constraints to establish a mixed-integer production scheduling model, minimizing haulage costs. Spatiotemporal correlation constraints for block mining are determined using a two-stage algorithm. The lower-level model incorporates road capacity, forming a haulage route optimization model based on multicommodity network flow. A solution algorithm with a distance penalty strategy facilitates feedback between the upper and lower levels, achieving optimal solutions. Tested on a real open-pit coal mine with over 5 million blocks, this approach reduces haulage costs by 10.06% compared to stage optimization. Additionally, this approach allows for adjusting haulage demand in both temporal and spatial dimensions, effectively preventing road congestion. This study advances rational mining processes and enhances the efficiency of open-pit mining haulage systems.

A lightweight practical consensus mechanism for supply chain blockchain

We present a consensus mechanism in this paper that is designed specifically for supply chain blockchains, with a core focus on establishing trust among participating stakeholders through a novel reputation-based approach. The prevailing consensus mechanisms, initially crafted for cryptocurrency applications, prove unsuitable for the unique dynamics of supply chain systems. Unlike the broad inclusivity of cryptocurrency networks, our proposed mechanism insists on stakeholder participation rooted in process-specific quality criteria. The delineation of roles for supply chain participants within the consensus process becomes paramount. While reputation serves as a well-established quality parameter in various domains, its nuanced impact on non-cryptocurrency consensus mechanisms remains uncharted territory. Moreover, recognizing the primary role of efficient block verification in blockchain-enabled supply chains, our work introduces a comprehensive reputation model. This model strategically selects a leader node to orchestrate the entire block mining process within the consensus. Additionally, we innovate with a Schnorr Multisignature-based block verification mechanism seamlessly integrated into our proposed consensus model. Rigorous experiments are conducted to evaluate the performance and feasibility of our pioneering consensus mechanism, contributing valuable insights to the evolving landscape of blockchain technology in supply chain applications.

Joint stochastic optimisation of open-pit mine production scheduling with ramp design

ABSTRACT Stochastic optimisation of open-pit mine production scheduling maximises the NPV of a mine’s extraction sequence while satisfying production constraints and minimising deviations from targets. Past approaches consider mining block access based on slope constraints, thus providing no guarantee that the infrastructure required for equipment access is feasible. To address this issue, a joint stochastic optimisation of life-of-mine production scheduling and ramp design is proposed, ensuring feasible equipment access to the related mining blocks. An application at a gold mine demonstrates the value of considering the complex relationship that exists between ramp design, equipment access and production scheduling during optimisation.

Substantiating the rational parameters for a complicated non-transport system when mining low-thickness fireclay deposits

The paper examines a complicated non-transport system for mining a gently sloping fireclay deposit using ESH-10/70 dragline excavators. The research purpose is to substantiate the technological scheme of stripping operations and determine their parameters to reduce the strip-mining costs. Theoretical research is performed using the following methods: methods of scientific analysis of theoretical research, as well as practices of project and production organizations; mining-geometric calculations; variant method for comparing and selecting a mining system. As a result, the dependence of the excavator block mining velocity on the entry way width has been determined, which makes it possible to study the relationship between mining and stripping equipment in time. The change in the re-excavation coefficient depending on the width of the dragline excavator entry way has been studied and its rational value has been determined. The practical value of the research results is in the substantiation of an effective system for mining a gently sloping fireclay deposit.

Q-Learning Model for Blockchain Security in Internet of Medical Things Networks

Secure Internet of Medical Things (IoMT) installations must take a long time to complete, which leaves them vulnerable to low energy performance, low throughput, and low packet delivery rates. This limits performance in real-time deployments, which has an effect on healthcare efficiency. Many optimization approaches have been proposed by researchers to deal with these issues, but most of them increase the complexity of mining and thus reduce its scalability. In order to improve the temporal QoS performance of IoMT deployments, this paper proposes the creation of a novel bio-inspired model. In order to evaluate the temporal mining performance (of miners) in terms of mining throughput, block mining ratio, latency, and energy, the proposed model makes use of the Mayfly Optimization (MO) Method. A fitness function based on these indices is then used to decide whether or not the underlying blockchains should be combined or split up. During a split operation, a blockchain is divided into two equal parts, and during a merge operation, the blockchain is saved for later retrieval. The proposed model improves block mining performance by 3.2%, compared to the average QoS optimization model under similar conditions, increases mining speed by 3.5%, decreases energy consumption by 4.9%, increases throughput by 2.8%, and improves performance by 3.2%.

Decentralized asynchronous adaptive federated learning algorithm for securely prediction of distributed power data

Introduction: Improving the precision and real-time speed of electricity data prediction while safeguarding data privacy and security holds immense significance for all power system participants’ decision-making. To surmount the issues of exorbitant computational expenses and privacy breaches of traditional centralized prediction methods, this paper proposes a decentralized asynchronous adaptive federated learning algorithm for securely prediction of distributed power data, which makes predictions from distributed data more flexible and secure.Methods: First, each regional node trains its own deep neural network model locally. After that, the node model parameters are uploaded to the decentralized federated learning chain for ensuring local data protection. Asynchronous aggregated update of the global prediction model is then achieved via block mining and shared maintenance. The algorithm has been enhanced based on the traditional federated learning algorithm, which introduces an asynchronous mechanism while adaptively adjusting the regional node model weights and local update step size to overcomes the inefficiency of traditional methods.Results and Discussion: The experimental analysis of actual electricity price data is conducted to compare and analyze with the centralized prediction model, study the impact of model adoption and parameter settings on the results, and compare with the prediction performance of other federated learning algorithms. The experimental results show that the method proposed in this paper is highly accurate, efficient, and safe.

Blockchain-enabled renewable energy certificate trading: A secure and privacy-preserving approach

In the 21st century, transitioning to renewable energy sources is imperative, with fossil fuel reserves depleting rapidly and recognizing critical environmental issues such as climate change, air pollution, water pollution, and habitat destruction. Embracing renewable energy is not only an environmental necessity but also a strategic move with multiple benefits. By shifting to renewable energy sources and supporting their production through the acquisition of renewable energy certificates, we foster innovation and drive economic growth in the renewable energy sector. This, in turn, reduces greenhouse gas emissions, aligning with global efforts to mitigate climate change. Additionally, renewable energy certificates ensure compliance with regulations that mandate the use of renewable energy, enhancing legal adherence while promoting transparency and trust in energy sourcing. To monitor the uptake of renewable energy, governments have implemented Renewable Energy Certificates (RECs) as a tracking mechanism for the production and consumption of renewable energy. They certify the generation of a specific amount of electricity from renewable sources, allowing for accurate tracking of renewable energy contributions to the overall energy mix. However, there are two main challenges to the existing REC schema: (1) The RECs have not been globally adopted due to inconsistent design; (2) The consumer privacy has not been well incorporated in the design of blockchain. In this study, we investigate the trading of RECs between suppliers and consumers using the directed acyclic graph (DAG) blockchain system and introduce a trading schema to help protect consumer information. The DAG system reduces the intense calculation of typical blockchains for scalability and lowers mining fees by eliminating the mining of blocks. The proposed approach allows renewable energy suppliers and consumers to trade RECs globally and to take advantage of secure trading transactions. Our results demonstrate lower transaction time by 41% and energy consumption by 65% compared to proof-of-stake.

KERAGAAN TANAMAN KELAPA SAWIT MENGHASILKAN PADA LAHAN BEKAS TAMBANG BATUBARA

The research was on ex-coal mining land located in Kintap District, Tanah Laut Regency, South Kalimantan Province, and the oil palm plantation located in Kelumpang Selatan District, Kotabaru Regency, South Kalimantan Province. The increase in palm oil production in Indonesia is followed by increasing in the area of oil palm plantations every year. Arable land is limited, and a ban on opening new land for oil palm plantations affects oil palm companies to utilize existing marginal land for oil palm plantations. The research aims to find out and compare the growth and production of mature oil palm trees planted on ex-coal mining land and original mineral land. The study was conducted from March to April 2023 and used agronomic methods by determining sample blocks and collecting primary and secondary data. That data was obtainable by measuring trees in 2 ex-coal mining blocks and original mineral blocks. Each block consists of 30 sample trees. The data obtained were then analyzed using the t-test at a significance level of 5%. The results showed the stem circumference, number of fronds, and length of fronds on ex-coal mining land were significantly different from the original mineral land, and stem height, number of leaves, petiole width, petiole thickness, and production on ex-coal mining land didn’t show significant differences from the original mineral land.

Оценка состояния вентиляционной сети рудного тела Le-3 на месторождении «Джульетта»

Effective, high-quality and steady ventilation of mine workings is one of the main factors that determine safe working conditions in an underground mine. The Juliet deposit is characterized with an irregular shape and varying dips and strikes. In some cases, it consists of isolated and mutually remote deposits with different mining and geological characteristics. These features of the deposit dictate the use of complex schemes for the development and mining of the deep-lying layers, which results in formation of a very extensive and branched network of underground workings. The specific features in the development of the Juliet deposit include the fact that a large number of mining blocks are in operation at the same time, and extraction of these block can be carried out using different mining systems. Intensive removal of dust and gases can be ensured only when a sufficiently large amount of air is supplied to the mine and the individual working faces. In order to overcome the overall air drag in the mine, it is necessary to use high-performance and high-pressure fans that would consume enormous power.

A Bayesian hierarchical model for the inference between metal grade with reduced variance: Case studies in porphyry Cu deposits

Ore sorting is a preconcentration technology and can dramatically reduce energy and water usage to improve the sustainability and profitability of a mining operation. In porphyry Cu deposits, Cu is the primary target, with ores usually containing secondary ‘pay’ metals such as Au, Mo and gangue elements such as Fe and As. Due to sensing technology limitations, secondary and deleterious materials vary in correlation type and strength with Cu but cannot be detected simultaneously via magnetic resonance (MR) ore sorting. Inferring the relationships between Cu and other elemental abundances is particularly critical for mineral processing.The variations in metal grade relationships occur due to the transition into different geological domains. This raises two questions - how to define these geological domains and how the metal grade relationship is influenced by these geological domains. In this paper, linear relationship is assumed between Cu grade and other metal grades. We applies a Bayesian hierarchical (partial-pooling) model to quantify the linear relationships between Cu, Au, and Fe grades from geochemical bore core data. The hierarchical model was compared with two other models - ‘complete-pooling’ model and ‘no-pooling’ model. Mining blocks were split based on spatial domain to construct hierarchical model. Geochemical bore core data records metal grades measured from laboratory assay with spatial coordinates of sample location. Two case studies from different porphyry Cu deposits were used to evaluate the performance of the hierarchical model. Markov chain Monte Carlo (MCMC) was used to sample the posterior parameters. Our results show that the Bayesian hierarchical model dramatically reduced the posterior predictive variance for metal grades regression compared to the no-pooling model. In addition, the posterior inference in the hierarchical model is insensitive to the choice of prior. The data is well-represented in the posterior which indicates a robust model. The results show that the spatial domain can be successfully utilised for metal grade regression. Uncertainty in estimating the relationship between pay metals and both secondary and gangue elements is quantified and shown to be reduced with partial-pooling. Thus, the proposed Bayesian hierarchical model can offer a reliable and stable way to monitor the relationship between metal grades for ore sorting and other mineral processing options.

Comprehensive Analysis of Blockchain Algorithms

INTRODUCTION: Blockchain technology has gained significant attention across various sectors as a distributed ledger solution. To comprehend its applicability and potential, a comprehensive understanding of blockchain's essential elements, functional traits, and architectural design is imperative. Consensus algorithms play a critical role in ensuring the proper operation and security of blockchain networks. Consensus algorithms play a vital role in maintaining the proper operation of a blockchain network, and their selection is crucial for optimal performance and security.
 OBJECTIVES: The objective of this research is to analyse and compare various consensus algorithms based on their performance and efficiency in mining blocks.
 METHODS: To achieve this, an experimental model was developed to measure the number of mined blocks over time for different consensus algorithms.
 RESULTS: The results provide valuable insights into the effectiveness and scalability of these algorithms. The findings of this study contribute to the understanding of consensus algorithm selection and its impact on the overall performance of blockchain systems.
 CONCLUSION: The findings of this study contribute to the understanding of consensus algorithm selection and its impact on the overall performance of blockchain systems. By enhancing our knowledge of consensus algorithms, this research aims to facilitate the development of more secure and efficient blockchain applications.

Less Is More: Understanding Network Bias in Proof-of-Work Blockchains

Blockchains are becoming increasingly important in today’s Internet, enabling large-scale decentralized applications with strong security and transparency properties. In a blockchain system, participants maintain and update the server-side state of an application by appending data as blocks onto an immutable, distributed ledger through a consensus protocol within a peer-to-peer network. There has been a significant increase in profit in mining blocks. For instance, Bitcoin miners currently receive over USD 200,000 per mined block. An essential determinant of these rewards is the time it takes to disseminate newly mined blocks across the network. This paper addresses the challenge of optimizing mining rewards by exploring topology design in a wide-area blockchain network utilizing a Proof-of-Work consensus protocol. We show that under low block times, the geographical location of a miner critically impacts the number of successful blocks mined by the miner. We also show that a miner may improve its success rate by increasing its connectivity to the network. However, contrary to the general wisdom that a faster network is always better for a miner, we show that increasing network connectivity (e.g., by adding more neighbors) is beneficial to a miner only up to a point after which the miner’s rewards degrade. This is because when a miner improves its connectivity, it inadvertently also aids other miners in increasing their connectivity. We also present a network-level collusion attack in which a miner can increase its block success rate by becoming part of a tightly connected cluster. Here too, we observe that the mining gains obtained increase with cluster size only up to a point, and decrease thereafter. Our findings highlight that the network topology is a key variable affecting miner performance in PoW blockchains that must not be overlooked. We demonstrate our observations via detailed simulations modeled using real-world measurement data.

МЕТОДЫ ОПРЕДЕЛЕНИЯ ОСНОВНЫХ ФАКТОРОВ, ВЛИЯЮЩИХ НА ЭФФЕКТИВНОСТЬ ТЕХНОЛОГИИ ДОБЫЧИ БЛОКОВ КАМНЯ МАШИНАМИ УДАРНОГО ДЕЙСТВИЯ

This paper presents the main factors influencing the efficiency of rock destruction by impact loading along the contour during mining of blocks in strong rocks, such as the magnitude of impact loading (energy of a single impact), geometric parameters of the working tool, parameters of the fracture scheme, etc. Approaches applied by research institutions in mining science have been scrutinized. A methodology of experimental work to determine the main parameters of fracture depending on the force, energy, and design parameters of the tool is developed. The graphs of dependence of specific energy consumption on the height of the craggy layer as well as of dependence of specific energy consumption of destruction on the energy of a single blow A and of specific energy consumption of marble destruction by a chisel on the height of the craggy layer and change of work value A on the depth of wedge penetration h into the rock are presented.

Cogni-Sec: A secure cognitive enabled distributed reinforcement learning model for medical cyber–physical system

The advent of the Internet of Things (IoT) has resulted in significant technical development in the healthcare sector, enabling the establishment of Medical Cyber–Physical Systems (MCPS). The increased number of MCPS generates a massive amount of privacy-sensitive data, hence it is important to enhance the security of devices and data transmission in MCPS. Earlier several research studies were undertaken in order to enhance security in healthcare, but none of them could adapt to changing behaviors of data attacks. Here the role of blockchain and Reinforcement Learning (RL) comes into play since it can adjust itself to the nature of changing attacks, thus preventing any kind of attacks. This work proposes a solution, named Cogni-Sec, which employs a decentralized cognitive blockchain and Reinforcement Learning architecture and addresses the security issue. Blockchain is incorporated in the approach for data storage and transmission to increase the degree of security in the MCPS modules. Hyperledger Fabric is applied as the blockchain base which shows transaction query results with nearly 10% increased throughput, 69% less memory consumption, and 15% lower CPU usage when compared to Ethereum. Further security risk at the block mining level within a blockchain network is reduced by introducing distributed Reinforcement Learning architecture in replacement for the miner nodes, which imitates the cognitive behavior of miners in a distributed environment. Different multi-agent learning systems have been evaluated for building the mining agent. Among these, the a3c agent in distributed learning setup yields the optimum cumulative reward with a median value of 54.5 and minimizes the maximum number of data threats.

Innovation of Cross-Border E-Commerce Logistics Model Based on Decentralized Block Mining Model

Innovation of Cross-Border E-Commerce Logistics Model Based on Decentralized Block Mining Model

Data Modifications in Blockchain Architecture for Big-Data Processing.

Due to the immutability of blockchain, the integration with big-data systems creates limitations on redundancy, scalability, cost, and latency. Additionally, large amounts of invaluable data result in the waste of energy and storage resources. As a result, the demand for data deletion possibilities in blockchain has risen over the last decade. Although several prior studies have introduced methods to address data modification features in blockchain, most of the proposed systems need shorter deletion delays and security requirements. This study proposes a novel blockchain architecture called Unlichain that provides data-modification features within public blockchain architecture. To achieve this goal, Unlichain employed a new indexing technique that defines the deletion time for predefined lifetime data. The indexing technique also enables the deletion possibility for unknown lifetime data. Unlichain employs a new metadata verification consensus among full and meta nodes to avoid delays and extra storage usage. Moreover, Unlichain motivates network nodes to include more transactions in a new block, which motivates nodes to scan for expired data during block mining. The evaluations proved that Unlichain architecture successfully enables instant data deletion while the existing solutions suffer from block dependency issues. Additionally, storage usage is reduced by up to 10%.

Secure and Robust Demand Response Using Stackelberg Game Model and Energy Blockchain.

Demand response (DR) has been studied widely in the smart grid literature, however, there is still a significant gap in approaches that address security, privacy, and robustness of settlement processes simultaneously. The need for security and robustness emerges as a vital property, as Internet of Things (IoT) devices become part of the smart grid; in the form of smart meters, home energy management systems (HEMSs), intelligent transformers, and so on. In this paper, we use energy blockchain to secure energy transactions among customers and the utility. In addition, we formulate a mixed-strategy stochastic game model to address uncertainties in DR contributions of agents and achieve optimal demand response decisions. This model utilizes the processing hardware of customers for block mining, stores customer DR agreements as distributed ledgers, and offers a smart contract and consensus algorithm for energy transaction validation. We use a real dataset of residential demand profiles and photovoltaic (PV) generation to validate the performance of the proposed scheme. The results show the impact of electric vehicle (EV) discharging and customer demand reduction on increasing the probability of successful block mining and improving customer profits. Moreover, the results demonstrate the security and robustness of our consensus algorithm for detecting malicious activities.