Search Results (470)

As digitalization reshapes industrial ecosystems, small and medium-sized enterprises (SMEs) in resource-based economies face growing pressure to adapt. This study examines the digital maturity of supplier firms within Chile’s Antofagasta mining cluster, a region that plays a central role in national productivity. A structured survey was conducted with 83 companies, using a ten-dimensional model to assess key areas such as data management, processes, personnel, and technology use. Results show that the average maturity level is 2.5 on a five-point scale, placing most firms at an early stage of digital transformation. While data-related capabilities scored relatively high, critical gaps persist in automation, robotics, and cybersecurity. Company size was moderately correlated with digital maturity, but no consistent relationship was observed with revenue growth. Although most firms acknowledge the relevance of digital technologies, few have formal plans or strategies in place. These findings reveal a structural lag that limits the potential of SMEs to engage fully with Industry 4.0, underscoring the need for tailored support policies and collaborative development initiatives in the mining sector. Full article

This paper proposes algorithms for optimal placement of base stations (BSs) and central stations (CSs) in mining quarries to ensure reliable radio communication for automated machinery. The BS placement is modeled as a minimum dominating set problem, solved using integer linear programming with cutting-plane methods. The CS placement is formulated as a nonlinear programming problem, addressed via a minimum circle covering algorithm. Applied in a 200 km² quarry, the approach achieves full coverage with nine BSs and one CS, minimizing costs and ensuring robust performance. Comparative analyses show superior optimality, scalability, and adaptability, offering a scalable framework for industrial communication infrastructure. Full article

Artificial intelligence (AI) is now the computational core of smart building automation, acting across the entire cyber–physical stack. This review surveys peer-reviewed work on the integration of AI with indoor environmental quality (IEQ) and energy performance, distinguishing itself by presenting a holistic synthesis of the complete technological evolution from IoT sensors to generative AI. We uniquely frame this progression within a human-centric architecture that integrates digital twins of both the building (DT-B) and its occupants (DT-H), providing a forward-looking perspective on occupant comfort and energy management. We find that deep reinforcement learning (DRL) agents, often developed within physics-calibrated digital twins, reduce annual HVAC demand by 10–35% while maintaining an operative temperature within ±0.5 °C and CO₂ below 800 ppm. These comfort and IAQ targets are consistent with ASHRAE Standard 55 (thermal environmental conditions) and ASHRAE Standard 62.1 (ventilation for acceptable indoor air quality); keeping the operative temperature within ±0.5 °C of the setpoint and indoor CO₂ near or below ~800 ppm reflects commonly adopted control tolerances and per-person outdoor air supply objectives. Regarding energy impacts, simulation studies commonly report higher double-digit reductions, whereas real building deployments typically achieve single- to low-double-digit savings; we therefore report simulation and field results separately. Supervised learners, including gradient boosting and various neural networks, achieve 87–97% accuracy for short-term load, comfort, and fault forecasting. Furthermore, unsupervised models successfully mine large-scale telemetry for anomalies and occupancy patterns, enabling adaptive ventilation that can cut sick building complaints by 40%. Despite these gains, deployment is hindered by fragmented datasets, interoperability issues between legacy BAS and modern IoT devices, and the computer energy and privacy–security costs of large models. The key research priorities include (1) open, high-fidelity IEQ benchmarks; (2) energy-aware, on-device learning architectures; (3) privacy-preserving federated frameworks; (4) hybrid, physics-informed models to win operator trust. Addressing these challenges is pivotal for scaling AI from isolated pilots to trustworthy, human-centric building ecosystems. Full article

Slope stability monitoring in open-pit mining remains a critical challenge for geological hazard prevention, where conventional qualitative methods often fail to address dynamic risks. This study proposes an integrated framework combining empirical modeling (slope classification, hazard assessment, and safety ratings) with multi-source real-time monitoring (synthetic aperture radar, machine vision, and Global Navigation Satellite System) to achieve quantitative stability analysis. The method establishes an initial stability baseline through mechanical modeling (Bishop/Morgenstern–Price methods, safety factors: 1.35–1.75 across five mine zones) and dynamically refines it via 3D terrain displacement tracking (0.02 m to 0.16 m average cumulative displacement, 1 h sampling). Key innovations include the following: (1) a convex hull-displacement dual-criterion algorithm for automated sensitive zone identification, reducing computational costs by ~40%; (2) Ku-band synthetic aperture radar subsurface imaging coupled with a Global Navigation Satellite System and vision for centimeter-scale 3D modeling; and (3) a closed-loop feedback mechanism between empirical and real-time data. Field validation at a 140 m high phosphate mine slope demonstrated robust performance under extreme conditions. The framework advances slope risk management by enabling proactive, data-driven decision-making while maintaining compliance with safety standards. 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

Timely, objective pain recognition in dairy cattle is essential for welfare assurance, productivity, and ethical husbandry yet remains elusive because evolutionary pressure renders bovine distress signals brief and inconspicuous. Without verbal self-reporting, cows suppress overt cues, so automated vision is indispensable for on-farm triage. Although earlier systems tracked whole-body posture or static grimace scales, frame-level detection of facial micro-expressions has not been explored fully in livestock. We translate micro-expression analytics from automotive driver monitoring to the barn, linking modern computer vision with veterinary ethology. Our two-stage pipeline first detects faces and 30 landmarks using a custom You Only Look Once (YOLO) version 8-Pose network, achieving a 96.9% mean average precision ($mAP$) at an Intersection over the Union (IoU) threshold of 0.50 for detection and 83.8% Object Keypoint Similarity (OKS) for keypoint placement. Cropped eye, ear, and muzzle patches are encoded using a pretrained MobileNetV2, generating 3840-dimensional descriptors that capture millisecond muscle twitches. Sequences of five consecutive frames are fed into a 128-unit Long Short-Term Memory (LSTM) classifier that outputs pain probabilities. On a held-out validation set of 1700 frames, the system records 99.65% accuracy and an F1-score of 0.997, with only three false positives and three false negatives. Tested on 14 unseen barn videos, it attains 64.3% clip-level accuracy (i.e., overall accuracy for the whole video clip) and 83% precision for the pain class, using a hybrid aggregation rule that combines a 30% mean probability threshold with micro-burst counting to temper false alarms. As an early exploration from our proof-of-concept study on a subset of our custom dairy farm datasets, these results show that micro-expression mining can deliver scalable, non-invasive pain surveillance across variations in illumination, camera angle, background, and individual morphology. Future work will explore attention-based temporal pooling, curriculum learning for variable window lengths, domain-adaptive fine-tuning, and multimodal fusion with accelerometry on the complete datasets to elevate the performance toward clinical deployment. Full article

Ore blending plays a critical role in ensuring feed consistency and optimizing downstream processes in the mining industry. Despite its importance, effective blending remains challenging due to ore variability and operational constraints. This review focuses exclusively on modern, data-driven blending methodologies, with particular emphasis on the application of data science and machine learning (ML) in predicting key process variables and supporting real-time decision-making. It discusses core challenges such as data quality, feature engineering, and model generalization, alongside enabling technologies including sensor integration, automation platforms, and real-time data acquisition systems. By consolidating the recent literature and highlighting emerging trends, this work outlines future directions for advancing intelligent blending systems and underscores the importance of standardized, high-quality data in the development of robust digital solutions for mineral processing. Full article

The emergence of the Internet of Things (IoT) has brought about a significant technological shift, coupled with the rise of intelligent computing. IoT integrates various digital and analogue devices with the Internet, enabling advanced communication between devices and humans.The pervasive adoption of IoT has transformed urban infrastructures into interconnected smart cities. Here, we propose a framework that mathematically models and automates power consumption management for IoT devices in smart city environments ranging from residential buildings to healthcare settings. The proposed framework utilises set theoretic association-rule mining and combines unsupervised preprocessing with frequent-item set mining and iterative numerical optimisation to reduce non-critical energy consumption. Readings are first converted into binary transaction matrices; then a modified Apriori algorithm is applied to extract high-confidence usage patterns and association rules. Dimensionality reduction techniques compress these transaction profiles, while the Gauss–Seidel method computes control set points that balance energy efficiency. The resulting rule set is deployed through a web portal that provides real-time device status, remote actuation, and automated billing. These associative rules generate predictive control functions, optimise the response of the framework, and prepare the framework for future events. A web portal is introduced that enables remote control of IoT devices and facilitates power usage monitoring, as well as automated billing. Full article

This study aims to compare decision tree and Analysis of Variance (ANOVA) techniques as feature selection methods, combined with Wavelet Packet Decomposition (WPD) for feature extraction, to enhance the diagnosis of faults in belt conveyor idlers. Belt conveyors are widely used in mining for efficient transport, but idlers composed of rollers are frequently subject to failure, making continuous monitoring essential to ensure reliability. Automated diagnostic solutions using vibration signals and machine learning rely on signal processing for feature extraction, often requiring dimensionality reduction or feature selection to improve classification accuracy. Due to the limitations of traditional techniques such as Principal Component Analysis (PCA) in handling temporal variations, Decision Tree and ANOVA emerge as effective alternatives for feature selection. This framework applied to each feature selection method, and Support Vector Machine (SVM) was used as a classification technique. The diagnostic performance of each method, including the case without feature selection, was evaluated. The results showed a higher diagnostic accuracy performance for the approaches that applied the features from the decision tree and from ANOVA. The improvement in the diagnosis of roller failures with feature selection was corroborated with the hit rates of failure mode, severity level, and location of a defective roller above 93.5%. Full article

The recording of minerals extracted from a deposit is crucial for effective planning, exploitation management, and compliance with legal requirements. It also enables improved workplace safety and the minimization of negative environmental impact. Automation in mining optimizes exploitation, transportation, and data management processes, resulting in better forecasting, more accurate resource calculations, and reduced operational costs. The usage of geographic information system tools facilitates data modeling and analysis, enhancing monitoring and mining exploitation management. This paper presents the classical approach to determining the volume of extracted minerals and proposes GIS-based tools for the automation of the volume calculation process. The automation of the process is presented both from a theoretical perspective, providing requirements and parameters for individual calculation procedures, and from a practical perspective, using the example of a typical open pit mine, where the procedure is implemented starting from field measurements, carrying out calculations, and ending with visualization and interpretation. The study highlights the benefits of automating the calculation procedure for the volume of extracted minerals, including task execution acceleration, increased efficiency, reduced calculation time, and minimized human error. This ultimately leads to more precise and consistent results. Full article

Educational assessment relies on well-constructed test items to measure student learning accurately, yet traditional item development is time-consuming and demands specialized psychometric expertise. Automatic item generation (AIG) offers template-based scalability, and recent large language model (LLM) advances promise to democratize item creation. However, fully automated approaches risk introducing factual errors, bias, and uneven difficulty. To address these challenges, we propose and evaluate a hybrid human-in-the-loop (HITL) framework for AIG that combines psychometric rigor with the linguistic flexibility of LLMs. In a Spring 2025 case study at Franklin University Switzerland, the instructor collaborated with ChatGPT (o4-mini-high) to generate parallel exam variants for two undergraduate business courses: Quantitative Reasoning and Data Mining. The instructor began by defining “radical” and “incidental” parameters to guide the model. Through iterative cycles of prompt, review, and refinement, the instructor validated content accuracy, calibrated difficulty, and mitigated bias. All interactions (including prompt templates, AI outputs, and human edits) were systematically documented, creating a transparent audit trail. Our findings demonstrate that a HITL approach to AIG can produce diverse, psychometrically equivalent exam forms with reduced development time, while preserving item validity and fairness, and potentially reducing cheating. This offers a replicable pathway for harnessing LLMs in educational measurement without sacrificing quality, equity, or accountability. Full article

The increasing frequency of structural failures on construction sites emphasizes the critical role of rigorous supervision in ensuring the quality of both construction processes and materials. Current regulatory frameworks mandate the production of detailed supervision reports to provide comprehensive evaluations of construction quality, material compliance, and site records. This study proposes a novel approach to harnessing unstructured reports for automated quality assessment. Employing text mining techniques, a sentiment lexicon specifically tailored for quality performance evaluation was developed. A corpus-based manual classification was conducted on 291 relevant words and 432 sentences extracted from the supervision reports, assigning sentiment labels of negative, neutral, and positive. This sentiment lexicon was then utilized as fundamental information for the Quality Performance Evaluation Model (QPEM). To validate the efficacy of the QPEM, it was applied to supervision reports from 30 construction sites adhering to legal standards. Furthermore, a Pearson correlation analysis was performed with the actual outcomes based on the legal requirements, including quality test failure rate, material inspection failure rate, and inspection management performance. By leveraging the wealth of unstructured data continuously generated throughout a project’s lifecycle, this model can enhance the timeliness of inspection and management processes, ultimately contributing to improved construction performance. Full article

Electric rope shovels play a critical role in open-pit mining, where their automation and operational efficiency directly affect productivity. This paper presents a LiDAR-based relative positioning method to determine the spatial relationship between the ERS and mining trucks. The method utilizes dynamic DBSCAN for noise removal and RANSAC for truck edge detection, enabling robust and accurate localization. Leveraging this positioning data, a time-optimal trajectory planning strategy is proposed specifically for autonomous swing motion during the unloading process. The planner incorporates velocity and acceleration constraints to ensure smooth and efficient movement, while obstacle avoidance mechanisms are introduced to enhance safety in constrained excavation environments. To execute the planned trajectory with high precision, a neural network-based sliding-mode controller is designed. An adaptive RBF network is integrated to improve adaptability to model uncertainties and external disturbances. Experimental results on a scaled-down prototype validate the effectiveness of the proposed positioning, planning, and control strategies in enabling accurate and autonomous swing operation for efficient unloading. Full article

To address challenges in automated charging operations of bulk explosive trucks in open-pit mines—specifically difficulties in borehole identification, positioning inaccuracies, and low operational efficiency—this study proposes an intelligent collaborative charging system integrating three modular components: (1) an explosive transport vehicle (with onboard terminal, explosive compartment, and mobility system enabling optimal routing and quantitative dispensing), (2) a charging robot (equipped with borehole detection, loading mechanisms, and mobility system for optimized search path planning and precision positioning), and (3) interconnection systems (coupling devices and interfaces facilitating auxiliary explosive transfer). This approach resolves three critical limitations of conventional systems: (i) mechanical arm-based borehole detection difficulties, (ii) blast hole positioning inaccuracies, and (iii) complex transport routing. The experimental results demonstrate that the intelligent cooperative charging method for open-pit mines achieves an 18% improvement in operational efficiency through intelligent collaboration among its modular components, while simultaneously realizing automated and intelligent charging operations. This advancement has significant implications for promoting intelligent development in open-pit mining operations. Full article

Background: While several test-automation maturity models (e.g., CMMI, TMMi, TAIM) exist, none explicitly integrate ISO 9001-based quality management systems (QMS), leaving a gap for organizations that must align E2E test automation with formal quality assurance. Objective: This study proposes a test-automation maturity model (TAMM) that bridges E2E automation capability with ISO 9001/ISO 9004 self-assessment principles, and evaluates its reliability and practical impact in industry. Methods: TAMM comprises eight maturity dimensions, 39 requirements, and 429 checklist items. Three independent assessors applied the checklist to three software teams; inter-rater reliability was ensured via consensus review (Cohen’s κ = 0.75). Short-term remediation actions based on the checklist were implemented over six months and re-assessed. Synergy with the organization’s ISO 9001 QMS was analyzed using ISO 9004 self-check scores. Results: Within 6 months of remediation, mean TAMM score rose from 2.75 → 2.85. Inter-rater reliability is filled with Cohen’s κ = 0.75. Conclusions: The proposed TAMM delivers measurable, short-term maturity gains and complements ISO 9001-based QMS without introducing conflicting processes. Practitioners can use the checklist to identify actionable gaps, prioritize remediation, and quantify progress, while researchers may extend TAMM to other domains or automate scoring via repository mining. Full article