Search Results (144)

The electronic judicial evidence preservation systems face various challenges including regulatory control, data exchange, poor credibility, etc. To address these issues, a blockchain-based judicial evidence preservation framework, JudicBlock, is proposed in the present study. It combines the scalability of the Interplanetary File System with the transparency and security of public blockchain. By decentralizing data management and using cryptographic integrity, the system ensures reliable chronological tracking of investigative changes. Unlike traditional approaches, JudicBlock incorporates smart contracts and advanced consensus mechanisms to enforce strict access controls with secure collaboration among the stakeholders. The simulation results show that JudicBlock provides better results over traditional ELR (electronic law records) storage schemes in terms of mining cost, query fetching time, block processing IPFS (Interplanetary file systems) throughput, etc. At a USD 6 mining cost, it appends an average of 23,601 transactions. For 25 blocks, the average query fetching time is 0.852 ms with the cache support of 32 KB. The proposed scheme achieves an average ELR uploading latency improvement of 6.79% over traditional schemes. The results indicate the efficacy of the proposed scheme over the conventional schemes. Full article

This study presents a domain-adapted retrieval-augmented generation (RAG) pipeline that integrates geological knowledge with large language models (LLMs) to support intelligent question answering in the metallogenic domain. Focusing on the Qin–Hang metallogenic belt in South China, we construct a bilingual question-answering (QA) corpus derived from 615 authoritative geological publications, covering topics such as regional tectonics, ore-forming processes, structural evolution, and mineral resources. Using the ChatGLM3-6B language model and LangChain framework, we embed the corpus into a semantic vector database via Sentence-BERT and FAISS, enabling dynamic retrieval and grounded response generation. The RAG-enhanced model significantly outperforms baseline LLMs—including ChatGPT-4, Bing, and Gemini—in a comparative evaluation using BLEU, precision, recall, and F1 metrics, achieving an F1 score of 0.8689. The approach demonstrates high domain adaptability and reproducibility. All datasets and codes are openly released to facilitate application in other metallogenic belts. This work illustrates the potential of LLM-based knowledge engineering to support digital geoscientific research and smart mining. Full article

Background and Objective: The intricate connection between daily behaviours and health necessitates robust monitoring, particularly with the advent of Internet of Things (IoT) systems. This study introduces an innovative approach that exploits the synergy of information from various IoT sources to assess the alignment of behavioural routines with health guidelines. The goal is to improve the readability of behaviour models and provide actionable insights for healthcare professionals. Method: We integrate data from ambient sensors, smartphones, and wearable devices to acquire daily behavioural routines by employing process mining (PM) techniques to generate interpretable behaviour models. These routines are grouped according to compliance with health guidelines, and a clustering method is used to identify similarities in behaviours and key characteristics within each cluster. Results: Applied to an elderly care case study, our approach categorised days into three physical activity levels (Insufficient, Sufficient, Desirable) based on daily step thresholds. The integration of multi-source data revealed behavioural variations not detectable through single-source monitoring. We demonstrated that the proposed visualisations in calendar and timeline views aid health experts in understanding patient behaviours, enabling longitudinal monitoring and clearer interpretation of behavioural trends and precise interventions. Notably, the approach facilitates early detection of behaviour changes during contextual events (e.g., COVID-19 lockdown and Ramadan), which are available in our dataset. Conclusions: By enhancing interpretability and linking behaviour to health guidelines, this work signifies a promising path for behavioural analysis and discovering variations to empower smart healthcare, offering insights into patient health, personalised interventions, and healthier routines through continuous monitoring with IoT-driven data analysis. Full article

Underground mining operations face increasing challenges due to their complex and hazardous environments. One key difficulty is ensuring real-time safety monitoring and disaster prevention. Traditional monitoring systems often suffer from delayed data acquisition and rely heavily on cloud-based processing. These factors limit their responsiveness during emergencies. To address these limitations, this study presents an underground Internet of Things (IoT) monitoring system based on edge computing. The system architecture is composed of three layers: a perception layer for real-time sensing, an edge gateway layer for local data processing and decision-making, and a cloud service layer for storage and analytics. By shifting computation closer to the data source, the system significantly reduces latency and enhances response efficiency. The system is tailored to actual mine-site conditions. It integrates pressure monitoring for artificial expandable pillars and roof subsidence detection in stopes. It has been successfully deployed in a field environment, and the data collected during commissioning demonstrate the system’s feasibility and reliability. Results indicate that the proposed system meets real-world demands for underground safety monitoring. It enables timely warnings and improves the overall automation level. This approach offers a practical and scalable solution for enhancing mine safety and provides a valuable reference for future smart mining systems. Full article

To increase the safety and efficiency of underground mining processes, it is important to advance automation. An important part of that is to achieve autonomous material loading using load–haul–dump (LHD) machines. To be able to autonomously load material from a muck pile, it is crucial to first detect and characterize it in terms of spatial configuration and geometry. Currently, the technologies available on the market that do not require an operator at the stope are only applicable in specific mine layouts or use 2D camera images of the surroundings that can be observed from a control room for teleoperation. However, due to missing depth information, estimating distances is difficult. This work presents a novel approach to muck pile detection developed as part of the EU-funded Next Generation Carbon Neutral Pilots for Smart Intelligent Mining Systems (NEXGEN SIMS) project. It uses a stereo camera mounted on an LHD to gather three-dimensional data of the surroundings. By applying a topological algorithm, a muck pile can be located and its overall shape determined. This system can detect and segment muck piles while driving towards them at full speed. The detected position and shape of the muck pile can then be used to determine an optimal attack point for the machine. This sensor solution was then integrated into a complete system for autonomous loading with an LHD. In two different underground mines, it was tested and demonstrated that the machines were able to reliably load material without human intervention. Full article

Manufacturing industries are undergoing a significant transformation toward Smart Manufacturing (SM) to meet the ever-evolving demands for customized products. A major obstacle in this transition is the integration of Computer-Aided Process Planning (CAPP) with Scheduling. This integration poses challenges because of conflicting objectives that must be balanced, resulting in the Integrated Process Planning and Scheduling problem. In response to these challenges, this research introduces a novel hybridized machine learning optimization approach designed to assign and sequence setups in Dynamic Flexible Job Shop environments via dispatching rule mining, accounting for real-time disruptions such as machine breakdowns. This approach connects CAPP and scheduling by considering setups as dispatching units, ultimately reducing makespan and improving manufacturing flexibility. The problem is modeled as a Dynamic Flexible Job Shop problem. It is tackled through a comprehensive methodology that combines mathematical programming, heuristic techniques, and the creation of a robust dataset capturing priority relationships among setups. Empirical results demonstrate that the proposed model achieves a 42.6% reduction in makespan, improves schedule robustness by 35%, and reduces schedule variability by 27% compared to classical dispatching rules. Additionally, the model achieves an average prediction accuracy of 92% on unseen instances, generating rescheduling decisions within seconds, which confirms its suitability for real-time Smart Manufacturing applications. Full article

Background: Gold mining is a critical part of the industry of Central Asia, contributing significantly to regional economic growth. However, gold production management faces numerous challenges, including adopting innovative technologies such as AI, using improved logistical equipment, resolving supply chain inefficiencies and disruptions, and incorporating modernized waste management and advancements in gold bar processing technologies. This study explores how advanced technologies and improved logistical processes can enhance efficiency and sustainability. Method: This paper examines gold production processes in Kyrgyzstan, a gold-producing country in Central Asia. The case study approach combines qualitative interviews with industry stakeholders and a system dynamics (SD) simulation model to compare current operations with a technology-based scenario. Results: The simulation model shows improved outcomes when innovative technologies are applied to ore processing, waste refinement, and gold bar production. The results also indicate an approximate twenty-five percent reduction in transport time, a thirty percent decrease in equipment downtime, a thirty percent reduction in emissions, and a fifteen percent increase in gold extraction when using artificial intelligence, smart logistics, and regional smelting. Conclusions: The study concludes with recommendations to modernize equipment, localize processing, and invest in digital logistics to support sustainable mining and improve operational performance in Kyrgyzstan’s gold sector. Full article

Tribological processes in extreme environments pose serious material challenges, requiring coatings that resist both wear and corrosion. This review summarizes recent advances in protective coatings engineered for extreme environments such as high temperatures, chemically aggressive media, and high-pressure and abrasive domains, as well as cryogenic and space applications. A comprehensive overview of promising coating materials is provided, including ceramic-based coatings, metallic and alloy coatings, and polymer and composite systems, as well as nanostructured and multilayered architectures. These materials are deployed using advanced coating technologies such as thermal spraying (plasma spray, high-velocity oxygen fuel (HVOF), and cold spray), chemical and physical vapor deposition (CVD and PVD), electrochemical methods (electrodeposition), additive manufacturing, and in situ coating approaches. Key degradation mechanisms such as adhesive and abrasive wear, oxidation, hot corrosion, stress corrosion cracking, and tribocorrosion are examined with coating performance. The review also explores application-specific needs in aerospace, marine, energy, biomedical, and mining sectors operating in aggressive physiological environments. Emerging trends in the field are highlighted, including self-healing and smart coatings, environmentally friendly coating technologies, functionally graded and nanostructured coatings, and the integration of machine learning in coating design and optimization. Finally, the review addresses broader considerations such as scalability, cost-effectiveness, long-term durability, maintenance requirements, and environmental regulations. This comprehensive analysis aims to synthesize current knowledge while identifying future directions for innovation in protective coatings for extreme environments. Full article

5G private mobile networks are becoming a platform for ‘wire-free’ networking for professional applications in smart industry sectors, such as automated warehousing, logistics, autonomous vehicle deployments in campus environments, mining, material processing, and more. It is expected that most of these Machine-to-Machine (M2M) and Industrial Internet of Things (IIoT) communication paths will be realized wirelessly, as the advantages of providing flexibility are obvious compared to hard-wired network installations. Unfortunately, the deployment of private 5G networks in smart industries has faced delays due to a combination of high costs, technical challenges, and uncertain returns on investment, which is reflected in troublesome access to fully operational private networks. To obtain insight into occupational exposure to radiofrequency electromagnetic fields (RF EMF) emitted by 5G private mobile networks, an analysis of RF EMF due to different types of 5G equipment was carried out on a real case scenario in the production and logistic (warehouse) industrial sector. A private standalone (SA) 5G network operating at 3.7 GHz in a real industrial environment was numerically modeled and compared with in situ RF EMF measurements. The results show that RF EMF exposure of the workers was far below the existing exposure limits due to the relatively low power (1 W) of indoor 5G base stations in private networks, and thus similar exposure scenarios could also be expected in other deployed 5G networks. In the analyzed RF EMF exposure scenarios, the radio transmitter—so-called ‘radio head’—installation heights were relatively low, and thus the obtained results represent the worst-case scenarios of the workers’ exposure that are to be expected due to private 5G networks in smart industries. Full article

The global port and maritime industry is experiencing a new paradigm shift known as the artificial intelligence transformation (AX). Thus, domestic container-terminal companies should focus beyond mere automation to a paradigm shift in AI that encompasses operational strategy, organizational structure, system, and human resource management. This study proposes a resilience-based AX strategy and implementation system that allows domestic container-terminal companies to proactively respond to the upcoming changes in the global supply chain, thus securing sustainable competitiveness. In particular, we aim to design an AI-based governance model to establish a trust-based logistics supply chain (trust value chain). As a research method, the core risk factors of AX processes were scientifically identified via text-mining and fault-tree analysis, and a step-by-step execution strategy was established by applying a backcasting technique based on scenario planning. Additionally, by integrating social control theory with new governance theory, we designed a flexible, adaptable, and resilience-oriented AI governance system. The results of this study suggest that the AI paradigm shift should be promoted by enhancing the risk resilience, trust, and recovery of organizations. By suggesting AX strategies and policy as well as institutional improvement directions that embed resilience to secure the sustainable competitiveness of AI-based smart ports in Korea, this study serves as a basis for establishing strategies for the domestic container-terminal industry and for constructing a global leading model. Full article

The last-mile delivery crisis, exacerbated by the surge in e-commerce demands, continues to face persistent challenges. Logistics companies often overlook the possibility that recipients may not be at the designated delivery location during courier distribution, leading to interruptions in the delivery process and spatiotemporal mismatches between couriers and users. Parcel lockers (PLCs), as a contactless self-pickup solution, mitigate these mismatches but suffer from low utilization rates and user dissatisfaction caused by detour-heavy pickup paths. Existing PLC strategies prioritize operational costs over behavioral preferences, limiting their real-world applicability. To address this gap, we propose a user-centric path planning model that integrates spatiotemporal trajectory mining with kernel density estimation (KDE) to optimize PLC selection and conducted a small-scale experimental study. Our framework integrated user behavior and package characteristics elements: (1) Behavioral filtering: This extracted walking trajectories (speed of 4–5 km/h) from 1856 GPS tracks of four campus users, capturing daily mobility patterns. (2) Hotspot clustering: This identified 82% accuracy-aligned activity hotspots (50 m radius; ≥1 h stay) via spatiotemporal aggregation. (3) KDE-driven decision-making: This dynamically weighed parcel attributes (weight–volume–urgency ratio) and route regularity to minimize detour distances. Key results demonstrate the model’s effectiveness: a 68% reduction in detour distance for User A was achieved, with similar improvements across all test subjects. This study enhances last-mile logistics by integrating user behavior analytics with operational optimization, providing a scalable tool for smart cities. The KDE-based framework has proven effective in campus environments. Its future potential for expansion to various urban settings, ranging from campuses to metropolitan hubs, supports carbon-neutral goals by reducing unnecessary travel, demonstrating its potential for application. Full article

The mineral extraction industry is vital for nations’ economic growth, as it provides raw materials for various industries. Implementing sustainable mining practices in this sector can contribute to its long-term growth and stability. However, Pakistan lacks a well-defined sustainability assessment framework for mining, leaving a critical gap in research and practice. Existing internationally developed frameworks are not directly applicable, as they were designed for contexts where the mining industry predominantly uses mechanized operations. In contrast, Pakistan’s extraction process relies heavily on manual methods, making it necessary to develop a context-specific framework. A fuzzy analytical hierarchy process (AHP) was employed to prioritize these indicators and sub-indicators for the sustainability assessment of Pakistan’s mineral industry. The findings of this study highlight that the environmental dimension ranks as the highest priority, followed by social and economic dimensions. Among the environmental indicators, pollution and smart technologies each received a weight of 0.40, which was also the case for the social indicator of discrimination and nepotism, as well as the economic indicators of GDP growth and wealth creation. Furthermore, the results suggest that the extensive use of smart technologies for pollution control is a key factor in fostering environmental sustainability. Full article

The Manufacturing Message Specification (MMS) protocol is frequently used to automate processes in IEC 61850-based substations and smart-grid systems. However, it may be susceptible to a variety of cyber-attacks. A frequently used protection strategy is to deploy intrusion detection systems to monitor network traffic for anomalies. Conventional approaches to detecting anomalies require a large number of labeled samples and are therefore incompatible with high-dimensional time series data. This work proposes an anomaly detection method for high-dimensional sequences based on a bidirectional LSTM autoencoder. Additionally, a text-mining strategy based on a TF-IDF vectorizer and truncated SVD is presented for data preparation and feature extraction. The proposed data representation approach outperformed word embeddings (Doc2Vec) by better preserving critical domain-specific keywords in MMS traffic while reducing the complexity of model training. Unlike embeddings, which attempt to capture semantic relationships that may not exist in structured network protocols, TF-IDF focuses on token frequency and importance, making it more suitable for anomaly detection in MMS communications. To address the limitations of existing approaches that rely on labeled samples, the proposed model learns the properties and patterns of a large number of normal samples in an unsupervised manner. The results demonstrate that the proposed approach can learn potential features from high-dimensional time series data while maintaining a high True Positive Rate. Full article

In this paper, we consider the problem of accurate, transparent, and interpretable detection, as well as the localization of false data injection attacks (FDIAs) in smart grids. In order to address that problem, we employ our knowledge discovery machine learning/data mining (ML/DM) approach—implemented as a collection of fuzzy rule-based classifiers (FR-BCs)—characterized by a genetically optimized accuracy–interpretability trade-off. Our approach uses our generalization (showing better performance) of the well-known SPEA2 method to carry out the genetic learning and multiobjective optimization process. The main contribution of this work is designing—using our approach—a collection of fast, accurate, and interpretable FR-BCs for FDIA detection and localization from the recently published FDIA data that describe various aspects of FDIAs in smart grids. Our approach generates FDIAs’ detection and localization systems characterized by very high accuracy (97.8% and 99.5% for the IEEE 14-bus and 118-bus systems, respectively) and very high interpretability (on average, 4.6 and 3.8 simple fuzzy rules for earlier-mentioned systems, respectively, i.e., a few easy to comprehend fuzzy rules). The contribution of this paper also includes a comparative analysis of our approach and 12 alternative methods applied to the same FDIAs’ data. This analysis shows that our approach totally outperforms the alternative approaches in terms of transparency and interpretability of FDIA detection and localization decisions while remaining competitive or superior in terms of the accuracy of generated decisions. Full article

Smart education is an important direction of future educational development, aiming to improve the intelligence level of the existing digital education system and achieve the deep integration of information technology and mainstream education business. Most of the existing cognitive diagnostic models are trained and tested based on the answers to known questions, and the dependence on the trained questions leads to poor prediction results of the model for unknown questions. To solve that problem, this paper divides the questions of the dataset into the usual quizzes (known questions) and the final exam questions (unknown questions), which are used for training and testing, respectively. The cognitive diagnosis model based on a convolutional neural network (CNNCD) is proposed. Firstly, the attention mechanism is used to dig out the intra-layer relationships among students, questions and knowledge points, which alleviates the problem of insufficient information mining among students, questions and knowledge points. Secondly, two multi-layer (general and special) one-dimensional convolutional neural networks are combined to model the cognitive diagnosis of the process from students’ usual grades to students’ mastery of knowledge points. Finally, two multi-layer (general and special) one-dimensional convolutional neural networks are used to predict students’ final exam scores. Experiments on three public datasets and self-made BOIT datasets show that the proposed method is superior to other comparison models on these three evaluation indexes, indicating the feasibility and effectiveness of the proposed model. Full article