Search Results (3,260)

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

STEM education is constantly seeking innovative methods to enhance student learning. Virtual Reality technology can represent a critical tool for effectively teaching complex engineering subjects. This study evaluates an original Virtual Reality software application, entitled Submarine Simulator, which is developed specifically to support competencies in hydrodynamics within an Underwater Engineering course at MINES Paris—PSL. Our application uniquely integrates a customized physics engine explicitly designed for realistic underwater simulation, significantly improving user comprehension through accurate real-time representation of hydrodynamic forces. The study involved a homogeneous group of 26 fourth-year engineering students, all specializing in engineering and sharing similar academic backgrounds in robotics, electronics, programming, and computer vision. This uniform cohort, primarily aged 22–28, enrolled in the same 3-month course, was intentionally chosen to minimize variations in skills, prior knowledge, and learning pace. Through a combination of quantitative assessments and Confirmatory Factor Analysis, we find that Virtual Reality affordances significantly predict user flow state (path coefficient: 0.811) which then predicts user engagement and satisfaction (path coefficient: 0.765). These findings show the substantial educational potential of tailored Virtual Reality experiences in STEM, particularly in engineering, and highlight directions for further methodological refinement. Full article

Desalination plays a critical role in addressing global water scarcity, yet brine disposal remains a significant environmental challenge. This study evaluates a minimal liquid discharge (MLD) membrane-based system integrating high-pressure reverse osmosis (HPRO) and membrane distillation (MD) for brine treatment, with a focus on the Eastern Mediterranean. A techno-economic assessment (TEA) was conducted to analyze the system’s feasibility, water recovery performance, energy consumption, and cost-effectiveness. The results indicate that the hybrid HPRO-MD system achieves a high water recovery rate of 78.65%, with 39.65 m³/day recovered from MD and 39 m³/day from HPRO. The specific energy consumption is 23.2 kWh/m³, with MD accounting for 89% of the demand. The system’s cost is USD 0.99/m³, generating daily revenues of USD 228 in Cyprus and USD 157 in Greece. Compared to conventional brine disposal methods, MLD proves more cost-effective, particularly when considering evaporation ponds. While MLD offers a sustainable alternative for brine management, challenges remain regarding energy consumption and the disposal of concentrated waste streams. Future research should focus on renewable energy integration, advanced membrane technologies, and resource recovery through brine mining. The findings highlight the HPRO-MD MLD system as a promising approach for sustainable desalination and circular water resource management. 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

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

Microbially induced carbonate precipitation (MICP), as an eco-friendly biomineralization technology, has opened up an innovative path for the green and low-carbon development of the mining industry. Unlike conventional methods, its in situ solidification minimizes environmental disturbances and reduces carbon emissions during construction. This article reviews the research on MICP technology in various scenarios within the mining industry, summarizes the key factors influencing the application of MICP, and proposes a future research direction to fill the gap of the lack of systematic guidance for the application of MICP in this field. Specifically, it elaborates on the solidification mechanism of MICP and its current application in the solidification and storage of tailings, heavy metal immobilization, waste resource utilization, carbon sequestration, and field-scale deployment, establishing a technical foundation for broader implementation in the mining sector. Key influencing factors that affect the solidification effect of MICP are discussed, along with critical engineering challenges such as the attenuation of microbial activity and the low uniformity of calcium carbonate precipitation under extreme conditions. Proposed solutions include environmentally responsive self-healing technologies (the stimulus-responsive properties of the carriers extend the survival window of microorganisms), a one-phase low-pH injection method (when the pH = 5, the delay time for CaCO₃ to appear is 1.5 h), and the incorporation of auxiliary additives (the auxiliary additives provided more adsorption sites for microorganisms). Future research should focus on in situ real-time monitoring of systems integrated with deep learning, systematic mineralization evaluation standard system, and urea-free mineralization pathways under special conditions. Through interdisciplinary collaboration, MICP offers significant potential for integrated scientific and engineering solutions in mine waste solidification and sustainable resource utilization. Full article

Innovative technologies have been helping to improve comfort and safety at work in high-risk sectors for years. The study analysed the impact, along with an assessment of potential implementations (opportunities and limitations) of innovative technological solutions for improving occupational safety in two selected sectors of the economy: mining and construction. The technologies evaluated included unmanned aerial vehicles and inspection robots, the Internet of Things and sensors, artificial intelligence, virtual and augmented reality, innovative individual and collective protective equipment, and exoskeletons. Due to the extensive nature of the obtained materials, the research description has been divided into two articles (Part I and Part II). This article presents the first three technologies. After the scientific literature from the Scopus database was analysed, some research gaps that need to be filled were identified. In addition to the obvious benefits of increased occupational safety for workers, innovative technological solutions also offer employers several economic advantages that affect the industry’s sustainability. Innovative technologies are playing an increasingly important role in improving safety in mining and construction. However, further integration and overcoming implementation barriers, such as the need for changes in education, are needed to realise their full potential. Full article

Mine roof water inrush represents a prevalent hazard in mining operations, characterized by its concealed onset, abrupt occurrence, and high destructiveness. Since mine water inrush is controlled by multiple factors, rigorous risk assessment in hydrogeologically complex coal mines is critically important for operational safety. This study focuses on the roof water inrush hazard in coal seams of the Banji coal mine, China. The conventional water-conducting fracture zone height estimation formula was calibrated through comparative analysis of empirical models and analogous field measurements. Eight principal controlling factors were systematically selected, with subjective and objective weights assigned using AHP and EWM, respectively. Game theory was subsequently implemented to compute optimal combined weights. Based on this, the vulnerability index model and fuzzy comprehensive evaluation model were constructed to assess the roof water inrush risk in the coal seams. The risk in the study area was classified into five levels: safe zone, relatively safe zone, transition zone, relatively hazardous zone, and hazardous zone. A zoning map of water inrush risk was generated using Geographic Information System (GIS) technology. The results show that the safe zone is located in the western part of the study area, while the hazardous and relatively hazardous zones are situated in the eastern part. Among the two models, the fuzzy comprehensive evaluation model aligns more closely with actual engineering practices and demonstrates better predictive performance. It provides a reliable evaluation and prediction model for addressing roof water hazards in the Banji coal seam. Full article

In traction networks of mining enterprises, ensuring selective and sensitive protection remains an urgent task, especially in conditions of frequent starts of electric transport and possible cases of short circuits, lack of reliable grounding and increased spreading resistance. Standard methods—maximum current protection (MCP) and differential current protection (DCP)—demonstrate limited efficiency at operating currents less than 800 A, which is typical for remote sections of the contact network. The objective of this study is to develop and experimentally verify a method for adjusting the parameters of current and impulse protection, ensuring reliable shutdown of accidents at low values of short-circuit current without the need to replace equipment. The proposed method is based on transient processes modeled using differential equations and the introduction of a dynamic sensitivity coefficient reflecting the dependence of the setting on the circuit time constant. Universal response characteristics were constructed in normalized coordinates for BAT-49 and VAB-43 switches and RDSh-I and RDSh-II relays. Experiments have confirmed that the application of the method allows for reducing the tripping threshold to 600–650 A, increasing the selectivity of protection to 95% and reducing the probability of false tripping by more than two times compared to MCP/DCP. The response time remained within 35–45 ms, which meets the requirements for high-speed systems. The developed method is adapted to different network sections using the relative coordinates of the energy consumer on the supply section of the traction network and does not require complex digital equipment. This makes it especially effective in field conditions, where it is impossible to upgrade the protection using intelligent adaptive systems. Full article

With the increasing complexity of grid project systems, it is difficult for an individual project management perspective to meet the macro management needs of the project, unapplicable to overall project layout management. However, the current grid project portfolio management (PPM) configuration lacks systematic methodological support, and the synergistic relationships between projects in terms of resources, strategy, and other aspects have not been effectively utilized, making it difficult to optimize the effectiveness of management and investment schemes. Therefore, in this paper, we propose a method called CNMI-PGPC, which combines complex networks and multidimensional indicators to explore the correlations among grid projects, deeply mines the key grid project clusters and the optimal allocation strategy, and is devoted to improving the comprehensive efficiency of grid projects. The methodology was validated on data derived from the Grid Multi-Category Reserve Project (including grid infrastructure, production technology improvement, and grid digitization). The results show that the proposed method can effectively provide a scientific basis for configuring and managing grid projects, support the preferential decision-making tasks of projects, and optimize the layouts of grid projects. We shift from single-project optimization to global synergy, quantify the comprehensive benefits of the project team in terms of economics, strategy, and other dimensions, bridge the gap between the previous individual project assessment perspectives, and provide a systematic decision-making basis for grid project portfolio planning. Full article

Peptides derived from protein sources in food exhibit a diverse array of biological activities. The screening, preparation, and functional investigation of bioactive peptides have become a focal area of research. This review summarizes the status of peptide activity mining, including the latest research progress in protein sources, peptide functions, and processing conditions. It critically evaluates the limitations of current bioactive peptide screening methods, including the drawbacks of traditional methods and molecular simulations. The potential of using molecular simulation for the virtual screening of potentially bioactive peptides is summarized. This includes virtual enzymatic digestion, molecular docking, simulation of non-thermal processing technologies, and the construction of organelle/cell models. The driving role of artificial intelligence in molecular simulation is also discussed. In addition, the structural information, mechanism, and structural analysis technique of action of the popular target proteins of foodborne bioactive peptides are summarized to provide a better reference for virtual-reality combinations. Full article

This paper analyses the role of electronic waste (E-waste) as a secondary source of critical and precious minerals, addressing the challenges and opportunities in transitioning towards a circular economy (CE) for electronics. The surging global demand for these essential materials, driven by technological advancements and renewable energy infrastructure, necessitates alternative supply strategies due to the depletion of natural reserves and the environmental degradation associated with primary mining. E-waste contains a rich concentration of valuable metals, such as gold, silver, and platinum, making its recovery a promising solution aligned with CE principles, which can mitigate environmental impacts and ensure long-term material availability. This paper examines the environmental, economic, and technological aspects of E-waste recovery, focusing on core processes such as physical and mechanical separation, pyrometallurgical, hydrometallurgical, bio-metallurgical, and electrochemical techniques. It explores innovative strategies to improve material recovery efficiency and sustainability, with consideration of evolving regulatory frameworks, technological advancements, and stakeholder engagement. The analysis highlights that e-waste, particularly printed circuit boards, can contain 40–800 times more gold than mined ore, with 1000–3000 g of gold per tonne compared to 5–10 g per tonne in traditional ores. Recovery costs using advanced E-waste recycling technologies range between $10,000–$20,000 USD per kilogram of gold, significantly lower than the $30,000–$50,000 USD per kilogram in primary mining. Globally, over 50 million tonnes of E-waste are generated annually, yet less than 20% is formally recycled. Efficient recycling methods can recover up to 95% of base and precious metals under optimized conditions. The paper argues that E-waste recycling presents a viable pathway to conserve critical raw materials, reduce environmental degradation, and enhance circular economic resilience. However, it also emphasizes persistent challenges—including high initial investment, technological limitations in developing regions, and regulatory fragmentation—that must be addressed for scalable adoption. Full article

Plant architecture is a crucial agronomic trait significantly impacting soybean (Glycine max) yield. Traditional breeding has made some progress in optimizing soybean architecture, but it is limited in precision and efficiency. The Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR-associated protein (CRISPR/Cas) system, a revolutionary gene-editing technology, provides unprecedented opportunities for plant genetic improvement. This review outlines CRISPR’s development and applications in crop improvement, focusing specifically on progress regulating soybean architecture traits affecting yield, such as node number, internode length, branching, and leaf morphology. It also discusses the technical challenges for CRISPR technology in enhancing soybean architecture, including that the regulatory network of soybean plant architecture is complex and the development of multi-omics platforms helps gene mining. The application of CRISPR enables precise the regulation of gene expression through promoter editing. Meanwhile, it is also faced with technical challenges such as the editing of homologous genes caused by genome polyploidy, the efficiency of editing tools and off-target effects, and low transformation efficiency. New delivery systems such as virus-induced genome editing bring hope for solving some of these problems. The review emphasizes the great potential of CRISPR technology in breeding next-generation soybean varieties with optimized architecture to boost yield potential. 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

Close-distance coal seam mining in Danhou coal mine has caused serious deformation in the underlying soft rock roadways. The mechanism of this type of deformation is explored through theoretical analysis and numerical simulation, and corresponding control measures are proposed. Firstly, the mechanical model of abutment stress transfer along the underlying rock stratum is established, and the analytical solution of abutment stress at any point of the underlying rock stratum is derived. Secondly, the impact of upper working face mining on the underlying soft rock roadway is investigated through numerical simulation. Subsequently, the stress distribution characteristics of the surrounding rock of the rectangular roadway and straight- wall arch roadway are compared and analyzed. Finally, a support scheme for the underlying soft rock roadway is presented and implemented in engineering practice. Field engineering application results demonstrate that, after the combined support of high-strength bolts and grouting, the average deformation on both sides of the roadway is reduced by 63.4%, and the average floor heave is decreased by 93%. This indicates that the technology effectively controls the deformation of the surrounding rock in soft rock roadways during close-distance coal seam mining. Full article