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The Paleoproterozoic represents a pivotal but poorly constrained interval in the tidal evolution of the Earth–Moon system. Quantifying Earth–Moon orbital parameters over this interval is fundamental for predicting the long-term dynamical evolution of the Earth–Moon system, yet direct geological evidence remains scarce. In this study, we conducted cyclostratigraphic analyses of the middle and upper members of the Dagushi Formation on the southern margin of the North China Craton, using high-resolution magnetic susceptibility (MS) and phosphorus (P) data as paleoclimate proxies. By employing two independent astrochronologic approaches—the main obliquity estimation method (

k + s_{3}

) and Bayesian inversion (TimeOptMCMC)—we reconstructed key parameters of the Earth–Moon system, including the precession constant k, Earth–Moon distance, and length of day (LOD). The

k + s_{3}

approach yields

k = 98.12 \pm 1.07

arcsec/yr, from which the Earth–Moon distance is derived as 329,732 (+888/−877) km, with a LOD of

17.69 \pm 0.08

h. In contrast, the TimeOptMCMC method produces

k = 95.20 \pm 1.68

arcsec/yr, implying an Earth–Moon distance of 331,292 (+1446/−1418) km, with a LOD of

17.91 \pm 0.13

h. The MS and P indicators exhibit remarkable symmetry and phase synchronicity between their curves in both depth and time domains, serving as a robust indicator of stable sedimentation and primary depositional signals. These results provide direct geological constraints on Earth–Moon system parameters at ∼1787 Ma, contributing to a refined understanding of its tidal evolution during the Paleoproterozoic. Full article

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26 pages, 9242 KB

Open AccessArticle

A Component-Decoupled and Physics-Constrained Hybrid Modeling Framework for Turbojet Engine Performance Prediction

by Huaiping Gu, Linyuan Jia, Hui Duan, Jiajia Wei and Zhen Liu

Aerospace 2026, 13(5), 425; https://doi.org/10.3390/aerospace13050425 - 1 May 2026

Abstract

Accurate turbojet engine performance prediction is crucial for condition monitoring, health management, and safe operation. Conventional component-level models require iterative solutions of strongly nonlinear matching equations and are sensitive to un-modeled effects, limiting accuracy and computational efficiency. Purely data-driven models are efficient but lack explicit physical constraints, resulting in poor interpretability and generalization outside the training domain. To address these issues, this paper proposes a component-decoupled, physically constrained hybrid modeling framework for turbojet engine steady-state performance prediction using on-board measurements. The engine is decomposed into component-level neural sub-models, with physics-guided feature engineering and mutual-information-based feature selection applied to optimize inputs. Component predictions are coupled via aerothermodynamic constraints to reconstruct unmeasured parameters and thrust. Validation on steady-state test data from a 120 kgf class micro turbojet engine shows the model achieves 1.157% maximum relative deviation (MRD) and 0.226% average relative deviation (ARD) for thrust, with MRDs of key gas path parameters within 0.3%. Compared with purely data-driven models, it offers higher accuracy, better generalization, and physically consistent unmeasured parameter estimates, providing a practical approach for engine performance prediction and health management. Full article

27 pages, 2447 KB

Open AccessArticle

A Sequential Cooperative Inversion Framework of DC Resistivity and Frequency-Domain Electromagnetic Data to Enhance Subsurface Imaging in Geoscience and Engineering

by Ramin Varfinezhad, Saeed Parnow, Francois Daniel Fourie and Fabio Tosti

Remote Sens. 2026, 18(9), 1404; https://doi.org/10.3390/rs18091404 - 1 May 2026

Abstract

The characterisation of subsurface electrical resistivity is a fundamental requirement for geoscientific and engineering applications, including groundwater exploration and structural assessments. This study examines the sequential cooperative inversion of direct current resistivity and frequency-domain electromagnetic data and compares the results to the inverse models obtained from separate (individual) inversions of the datasets. The proposed cooperative framework is applied to both synthetic datasets generated through forward modelling and field data acquired at the Morgenzon Farm site, South Africa, to delineate a dolerite dyke of hydrogeological significance. Individual inversions identified distinct features but exhibit limitations: direct current resistivity highlights a two-layered medium with minor anomalies, while frequency-domain electromagnetic data identify a resistive anomaly. In contrast, the sequential cooperative inversion approach, which uses the output of one dataset to constrain the other, provides improved subsurface imaging results, reduces ambiguity, and enables the integration of complementary information from both methods. The results indicate that resistivity models constrained by inverse frequency-domain electromagnetic data provide improved representation of subsurface geometry and amplitude compared to individual approaches. These findings support the use of a non-destructive testing approach for improved subsurface imaging, facilitating better-informed decision-making in infrastructure projects and resource management Full article

(This article belongs to the Special Issue Multiplatform Remote Sensing Techniques for Active Tectonics, Seismotectonics, and Volcanic Hazard Assessment (Second Edition))

21 pages, 529 KB

Open AccessArticle

Profit Maximization of Ethanol Distribution on Manifold Surfaces: A Stochastic Nonlinear Programming Approach

by Emre Tokgoz, Iddrisu Awudu and Theodore Trafalis

Logistics 2026, 10(5), 101; https://doi.org/10.3390/logistics10050101 - 1 May 2026

Abstract

Background. Ethanol distribution in the energy supply chain can be maximized by solving a Location Routing Problem (LRP). Manifold LRP (MLRP) expands on the classic domain assumptions of LRP to manifold surfaces, and it can be applied to profit maximization in ethanol supply chains. Methods. In this work, a hybrid MLRP (H-MLRP) is introduced as a new mixed integer nonlinear programming NP-hard problem assuming discrete facility allocation that requires a mix of truck and train transportation for ethanol distribution from the facility to its customers. Ethanol supply chain profit can be maximized by solving a stochastic nonlinear integer programming problem (SNLP) using ethanol raw materials, production quantity, logistics, railcar shipments, and transit times as the decision variables. H-MLRP and SNLP are combined as a two-stage optimization methodology to design a biofuel energy distribution system for making optimal decisions to maximize ethanol profit. Results. A case study demonstrated the effectiveness of the proposed method on the relocation of an ethanol producer that is currently located in North Dakota (ND) to Oklahoma (OK). In this case study, customer demand destinations and suppliers of raw materials are located in different regions of the United States. Conclusions. The results indicate a good use of the new model for decision-making. Full article

17 pages, 531 KB

Open AccessReview

Genetic Modifications of MSCs to Improve Therapeutic Efficacy

by Dai Ihara and Ayano Narumoto

J. Genome Biotechnol. Genet. 2026, 1(1), 6; https://doi.org/10.3390/jgbg1010006 - 1 May 2026

Abstract

Human mesenchymal stem/stromal cells (MSCs) have attracted significant interest in regenerative medicine due to their self-renewal capacity, immunomodulatory functions, multipotency, and relative ease of isolation and expansion. However, several limitations restrict their clinical application, including cellular heterogeneity, challenges in large-scale expansion, and poor in vivo persistence after transplantation. Systemically administered MSCs are rapidly cleared because of limited adhesion, short survival time, and inefficient extravasation, resulting in suboptimal therapeutic efficacy. To overcome these challenges, various strategies have been developed, such as hypoxic preconditioning, biomaterial-based approaches, and genetic modification. Among these, genetic modification represents a particularly powerful and versatile strategy, as it enables targeted enhancement of specific functional properties of MSCs and even the introduction of novel therapeutic capabilities. In this review, we summarize recent advances in genetically engineered MSCs and categorize these approaches into four functional domains: migration, adhesion, secretion, and survival. We further discuss their therapeutic outcomes across diverse disease models in vivo. Collectively, genetic modification substantially enhances the intrinsic therapeutic potential of MSCs and represents a promising direction for the development of next-generation cell-based therapies. Full article

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21 pages, 1914 KB

Open AccessReview

Land Use Transition and Its Impact on Farmers’ Well-Being in Resource-Exhausted Areas: Research Progress and Key Issues

by Xiao Liu, Jun Yang and Enyi Zhao

Land 2026, 15(5), 774; https://doi.org/10.3390/land15050774 - 1 May 2026

Abstract

Land use transition and its effects on farmers’ well-being are central to the transformation and sustainable development of resource-exhausted areas (REAs). While extensive research has emerged in recent years, there remains a critical lack of systematic synthesis and clarity regarding key scientific issues in this domain. To bridge this research gap, an R-based bibliometric analysis was conducted on an extensive corpus encompassing 8245 papers on land use transition and 931 papers on farmers’ well-being published between 2001 and 2024, systematically investigating the mechanisms of transition, regional transformation dynamics, and the multi-dimensional determinants of well-being. The findings indicate that: (1) land use transition research has evolved from spatial patterns to management strategies, yet it lacks comprehensive regional and multi-scale characterization; (2) although land use is recognized as central to REA studies, the underlying theoretical frameworks require significant refinement; and (3) research on farmers’ well-being has shifted from broad ecosystem services to multidimensional micro-analyses, though the explicit correlation mechanisms with land use remain unclear. Based on these insights, four pivotal directions are identified for future research in REAs: establishing theoretical and analytical frameworks that link land use transitions to well-being under regional development logic; uncovering the spatiotemporal processes and multi-scale driving mechanisms of these transitions; quantitatively measuring their impacts on multidimensional well-being; and developing regulatory policies that balance regional coordination with well-being enhancement. This review provides a robust scientific foundation for optimizing land resources and promoting sustainable human–environment interactions in REAs. Full article

(This article belongs to the Special Issue Urban–Rural Land Governance and Sustainable Development in New Era)

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26 pages, 1936 KB

Open AccessReview

Germline and Embryonic Mechanisms in the Epigenetic Inheritance of Neurodevelopmental and Cognitive Traits in Mammals

by Mehmet Kizilaslan, Zeynep Kizilaslan and Hasan Khatib

Biomolecules 2026, 16(5), 669; https://doi.org/10.3390/biom16050669 - 1 May 2026

Abstract

Epigenetic mechanisms profoundly regulate gene expression, developmental trajectories, and phenotypic variation, extending biological influence beyond DNA sequence alone. A growing body of evidence suggests that environmental exposures, including pollutants, drugs, stress, and diet, can induce germline and early embryonic epimutations that alter developmental programs with lasting consequences for neurodevelopmental and cognitive outcomes. However, the fields most relevant to these processes have largely developed independently. These include germline epigenetics, early embryonic patterning, neurodevelopment and cognitive regulation, and intergenerational or transgenerational inheritance. Each field has its own conceptual frameworks and mechanistic models. This fragmentation obscures the biological reality that these systems are tightly interconnected: environmentally induced epigenetic perturbations in gametes can reshape the epigenetic landscape of the early embryo, influence lineage allocation during gastrulation, and ultimately modify the molecular architecture of the developing central nervous system. A systems–biology perspective capable of linking germline epimutations and early embryonic epigenetic instability to later neurodevelopmental and cognitive phenotypes and their potential inheritance is therefore required. This review synthesizes current evidence across these traditionally isolated domains and proposes a coherent mechanistic framework linking germ cell epimutations and early embryonic epigenetic instability to the emergence of neurodevelopmental and cognitive phenotypes. By bridging these conceptual gaps, we aim to establish a cohesive foundation for understanding how early epigenetic disruptions generate long-lasting and in some cases heritable effects on brain development and cognitive function. Full article

(This article belongs to the Special Issue Epigenetic Programming of Cellular States)

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36 pages, 9440 KB

Open AccessArticle

Characterising the Sound Field of an Ovoid Bullring: The Real Maestranza de Caballería, Seville

by Sara Girón, Manuel Martín-Castizo and Miguel Galindo

Appl. Sci. 2026, 16(9), 4439; https://doi.org/10.3390/app16094439 - 1 May 2026

Abstract

The Real Maestranza de Caballería in Seville features one of the most prominent Spanish bullrings, characterized by a notable architectural design. Its distinctive ovoid geometry resulted from a protracted construction history (1761–1881), during which the floor plan adapted to pre-existing urban structures. Beyond its architectural significance, the sounds perceived within such venues constitute traces of collective memory and form part of an intangible cultural heritage relevant for understanding the sociocultural context of such spaces. This work provides an acoustic characterisation of the bullring through field measurements. Reverberation time and other monaural and binaural descriptors were determined using 3D impulse responses obtained from strategically placed sources and receivers. This analysis is complemented by examining the sound energy distribution of early reflections in the time–frequency domain to define the acoustic signature of the venue, namely the characteristic pattern of early reflections that unequivocally determines its sound response, and identify the provenance of reflections. In the Maestranza, music and silence are hallmarks of its identity, contributing to a complex auditory environment. The results highlight how its geometry and tiered seating create a differentiated sound field, potentially contributing to the preservation of the site as a cultural landmark. Full article

(This article belongs to the Special Issue Next-Generation Architectural Acoustics and Vibration: Sustainability, Equity, and Innovation)

15 pages, 244 KB

Open AccessArticle

Analysis of Prognostic Factors Affecting Quality of Life After Ischemic Stroke

by Edyta Laska, Elżbieta Musz and Marcin Skrok

J. Clin. Med. 2026, 15(9), 3471; https://doi.org/10.3390/jcm15093471 - 1 May 2026

Abstract

Background: Ischemic stroke remains a major cause of disability and reduced quality of life (QoL). This study aimed to identify factors associated with QoL after ischemic stroke, with particular emphasis on independence, illness acceptance, social support, comorbidity status, and the timeliness of diagnosis and treatment. Methods: This single-center cross-sectional study included 100 consecutively recruited patients after ischemic stroke hospitalized in the Department of Neurology with the Stroke Unit at the S. Żeromski Specialist Hospital in Krakow. Data were collected using an author-designed questionnaire and standardized instruments: the World Health Organization Quality of Life-BREF (WHOQOL-BREF), the Multidimensional Scale of Perceived Social Support (MSPSS), the Lawton Instrumental Activities of Daily Living Scale (IADL), and the Acceptance of Illness Scale (AIS). Statistical analysis included Spearman’s rank correlation coefficient and the Mann–Whitney U, Friedman, and Kolmogorov–Smirnov tests. Results: Significant positive correlations were found between all WHOQOL-BREF domains and IADL, AIS, and MSPSS scores. The strongest correlations were observed between IADL and the physical and psychological QoL domains. A strong positive correlation was also found between IADL and AIS (rho = 0.88; p < 0.001). Better QoL and greater independence were observed in patients with fewer comorbidities. Patients who received timely diagnosis and treatment achieved better outcomes in terms of QoL, IADL, and AIS. Perceived social support was comparable across MSPSS subscales (p = 0.56) but positively correlated with all QoL domains (rho = 0.55–0.64; p < 0.001). Conclusions: Better QoL after ischemic stroke was associated with greater independence, higher illness acceptance, stronger perceived social support, and timely diagnosis and treatment, suggesting that post-stroke QoL is related to both functional and psychosocial factors. Full article

(This article belongs to the Special Issue Clinical Perspectives in Stroke Rehabilitation)

23 pages, 2404 KB

Open AccessArticle

LLM-Powered Multi-Agent Collaborative Framework for Generative Design of Stretchable Energy Harvesters

by Enpu Lei, Ping Lu and Kama Huang

Energies 2026, 19(9), 2198; https://doi.org/10.3390/en19092198 - 1 May 2026

Abstract

The design of stretchable energy harvesting systems entails complex multiphysics coupling between electromagnetic and mechanical domains, typically requiring engineers to proficiently use disparate simulation tools and optimization algorithms. This steep learning curve, combined with the absence of integrated workflows, poses a substantial obstacle to efficient design. To overcome these challenges, we present StretchCopilot, a multi-agent collaborative framework driven by Large Language Models (LLMs) for the generative design of stretchable radio frequency (RF) energy harvesters operating in the 2.45 GHz band. In contrast to conventional approaches dependent on manual iteration or isolated algorithmic methods, our framework utilizes a graph-based state machine architecture (LangGraph) to coordinate specialized agents. It interprets high-level user instructions, such as “design a robust energy harvester capable of withstanding 15% strain”, and autonomously manages domain-specific solvers, including inverse design networks and rectifier circuit synthesis tools, through a unified interface. Experimental evaluations indicate that the framework effectively streamlines the design workflow, allowing users to produce desired rectenna (rectifying antenna) systems via natural language interactions. Case studies confirm that, once the underlying surrogate models are fully trained, the proposed approach compresses the marginal design time from several hours to within minutes, while ensuring consistent energy harvesting performance under mechanical deformation. Full article

(This article belongs to the Special Issue Emerging Trends in Energy Harvesting Materials and Technologies for Sustainable Energy)

22 pages, 14961 KB

Open AccessArticle

From Single-Look to Multi-Temporal SAR Despeckling: A Latent-Space Guided Transfer Learning Approach

by Baojing Pan, Ze Yu, Xianxun Yao, Zhiqiang Tian and Wei Ren

Remote Sens. 2026, 18(9), 1402; https://doi.org/10.3390/rs18091402 - 1 May 2026

Abstract

Synthetic Aperture Radar (SAR) images are affected by speckle noise, which limits their application in fine object interpretation and quantitative analysis. Recent deep learning-based single-image SAR despeckling methods have made significant progress in spatial structure modeling but struggle to exploit temporal redundancy in multi-temporal data. Existing multi-temporal despeckling methods usually rely on complex spatiotemporal network structures, which are prone to overfitting or excessive smoothing of details when training samples are limited. To address these challenges, this paper proposes a latent-space-guided multi-temporal SAR despeckling method from the perspective of transfer learning and representation alignment, achieving effective knowledge transfer from single-image SAR despeckling to multi-temporal despeckling tasks. The method treats the single-image SAR despeckling task as a knowledge source domain, using stable latent space representations learned from the pre-trained single-image despeckling model as prior constraints. A latent space regularization mechanism is introduced during the training of the multi-temporal despeckling model, thereby establishing an explicit representation bridge between the 2D spatial model and the 3D spatiotemporal model. With this strategy, the multi-temporal model inherits the structural perception capability of the single-image model under limited training samples, improving speckle suppression while effectively maintaining image detail and structural consistency. Additionally, a pure convolutional network architecture is employed to support variable-length multi-temporal sequence input, enhancing the method’s adaptability under different temporal sampling conditions. Full article

(This article belongs to the Special Issue Advancing Synthetic Aperture Radar: Imaging, Processing, and Applications in Remote Sensing)

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44 pages, 2137 KB

Open AccessArticle

P3CRID: A Threat Model Methodology for Smart Homes

by Shruti Kulkarni, Alexios Mylonas and Stilianos Vidalis

Algorithms 2026, 19(5), 347; https://doi.org/10.3390/a19050347 - 1 May 2026

Abstract

Threat modelling is a methodology employed for identifying and analysing threats and applicable mitigations for web applications, mobile applications, infrastructure, and environments including smart home environments. Threat modelling starts with a tabletop exercise to identify threats. It provides extremely important insights into what can go wrong if certain events or a series of events take place. The identification of these events is critical to ensuring the right mitigation strategies are applied. Threat modelling also helps to identify security controls that may be assumed to provide required security, but, in reality, may not be addressing the existing and applicable threat(s). Existing literature, in the public domain and in academia, discusses threat materialisation for smart homes; however, entry points for a threat to materialise and exploit these vulnerabilities are not explored and a dedicated threat model for smart home environments is currently unavailable. Whilst threats can be mitigated by smart home device manufacturers, there are also mitigations that need to be applied by smart home owners who are both technology-aware and technology-unaware. In this paper, we propose a structured, domain-specific threat modelling methodology for smart home environments. The methodology models threats from a smart home owner’s perspective, identifies entry points and the mitigations that need to be implemented by a smart home owner. It also acknowledges that the attack surface expands and contracts and is not constant; which is addressed by applying zero-trust principles. Full article

(This article belongs to the Special Issue Artificial Intelligence in Modern Cybersecurity: Changes, Applications and Challenges)

30 pages, 10099 KB

Open AccessArticle

A State-of-the-Art Engineering Synthesis of Port Pavement Infrastructure Systems

by Christina N. Tsaimou and Vasiliki K. Tsoukala

Infrastructures 2026, 11(5), 157; https://doi.org/10.3390/infrastructures11050157 - 1 May 2026

Abstract

Ports are complex infrastructure systems operating under adverse marine environments, diverse loading regimes, and significant economic pressures. Among their critical assets are pavement infrastructures that serve multiple functional domains, including container handling and storage areas, internal circulation corridors, passenger–vehicle interfaces, and auxiliary parking zones. However, existing port pavement research remains predominantly concentrated on heavy-duty container applications, while other functional categories are comparatively underexplored. This study develops a structured engineering synthesis of port pavement infrastructure assets by integrating bibliometric mapping, conducted using Scopus-indexed publications, with a functional–structural analysis of worldwide practices. Following the identification of research trends, additional insights from engineering-oriented studies and technical guidance documents were incorporated to strengthen the practical relevance of the investigation. These findings indicate that functional classification should precede structural design decisions, enabling the systematic identification of loading conditions, serviceability requirements, and transition demands across port environments. Heavy-duty operational zones require high-stiffness systems capable of resisting concentrated and repetitive loads, while circulation areas are particularly sensitive to low-speed traffic effects. In contrast, passenger and mixed-use zones necessitate hybrid design strategies that balance structural adequacy with serviceability and long-term durability under marine exposure, whereas auxiliary areas are primarily governed by cost-efficiency and maintenance considerations. The overall research provides a rational basis for investment prioritization, material selection, lifecycle planning, and performance-based pavement management within multifunctional port environments. Full article

23 pages, 4146 KB

Open AccessArticle

Wireless High Rotational Speed Assessment by Exploiting an RF Sensor Tag System and Equivalent-Time Reconstruction

by Armin Gharibi, Filippo Costa and Simone Genovesi

Sensors 2026, 26(9), 2834; https://doi.org/10.3390/s26092834 - 1 May 2026

Abstract

Rotational speed monitoring is essential in many industrial and electromechanical systems. This paper presents a rotational speed measurement method based on a wireless impedance sensing system leveraging the radio-frequency coupling between a passive resonant tag and a coplanar waveguide (CPW) probe. The sensing mechanism exploits periodic variations in the real part of the probe impedance caused by the relative alignment between the rotating tag and the stationary probe. While the impedance signal is inherently periodic, the usable speed range of sampling-based measurement systems is fundamentally constrained by their acquisition rate. To overcome this limitation without requiring higher-rate instrumentation, an equivalent-time sampling (ETS) reconstruction approach is proposed. Sparse and nonuniform impedance samples collected over multiple revolutions are mapped into an equivalent phase domain and combined to reconstruct the waveform associated with a single rotation period. The method is reader-agnostic in principle, as it only requires time-stamped monitoring of a periodic RF observable at a selected frequency; however, experimental validation in this work is performed using a vector network analyzer (VNA). Experimental results obtained on a rotating platform with speeds ranging from 150 RPM to 4000 RPM demonstrate that the proposed method reduces the mean relative estimation error to below 5% across the full range, compared to errors exceeding 70% for conventional peak-based estimation above 1000 RPM. These results highlight the effectiveness of the ETS approach in extending the operational range of RF impedance-based rotational sensing under severe undersampling conditions. The proposed framework is generalizable to other periodic RF sensing configurations where signal periodicity can be exploited across multiple acquisition cycles. Full article

(This article belongs to the Special Issue RF and IoT Sensors: Design, Optimization and Applications)

15 pages, 1264 KB

Open AccessArticle

Linking Induced Polarisation Signatures to Flotation Response

by Unzile Yenial-Arslan and Elizaveta Forbes

Minerals 2026, 16(5), 480; https://doi.org/10.3390/min16050480 - 1 May 2026

Abstract

The induced polarisation (IP) technique is a geophysical method used to measure chargeability and resistivity, providing crucial insights into subsurface geological structures. Traditionally, IP measurements have been instrumental in exploring disseminated sulphide deposits, leveraging the strong polarisation response of metallic particles. It provides valuable insights about rock mineralisation, matrix composition, and formation polarizability by analysing electrical parameters. However, their potential to predict metallurgical performance remains largely unexplored. This study evaluates whether IP parameters—chargeability and resistivity—can serve as geometallurgical indicators for copper sulphide ores. The evaluation integrates IP measurements with mineralogical and flotation data. Artificial pyrite–sand mixtures and five real ore samples from Mount Isa were analysed using the sample core IP tester and mineral liberation analysis, followed by collectorless flotation tests. Statistical analysis demonstrated a strong correlation between resistivity and chalcopyrite recovery (R² = 0.90, p = 0.99), as well as a moderate correlation between chargeability and chalcopyrite selectivity (R² = 0.72, p = 0.93). These findings demonstrate that IP captures key textural and electrochemical features governing flotation behaviour, including pyrite abundance, mineral liberation, and galvanic interactions. The results highlight IP as a promising rapid-assessment tool for identifying ore variability and forecasting flotation response, with potential integration into geometallurgical models and mine-to-mill optimisation. Further validation across broader ore domains is recommended to refine the predictive capability of IP-based indicators. Full article

(This article belongs to the Section Mineral Processing and Extractive Metallurgy)

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