Methodology For Quantification Research Articles

A Survey on Trusted Distributed Artificial Intelligence

Emerging Artificial Intelligence (AI) systems are revolutionizing computing and data processing approaches with their strong impact on society. Data is processed with automated labelling pipelines rather than providing it as input to the system. The innovative nature increases the overall performance of monitoring/detection/reaction mechanisms for efficient system resource management. However, due to hardware-driven design limitations, networking and trust mechanisms are not flexible and adaptive enough to be able to interact and control the resources dynamically. Novel adaptive software-driven design approaches can enable us to build growing intelligent mechanisms with software-defined networking (SDN) features by virtualizing network functionalities with maximized features. These challenges and critical feature sets have been identified and introduced into this survey with their scientific background for AI systems and growing intelligent mechanisms. Furthermore, obstacles and research challenges between 1950-2021 are explored and discussed with a focus on recent years. The challenges are categorized according to three defined architectural perspectives (central, decentral/autonomous, distributed/hybrid) for emerging trusted distributed AI mechanisms. Therefore, resiliency and robustness can be assured in a dynamic context with an end-to-end Trusted Execution Environment (TEE) for growing intelligent mechanisms and systems. Furthermore, as presented in the paper, the trust measurement, quantification, and justification methodologies on top of Trusted Distributed AI (TDAI) can be applied in emerging distributed systems and their underlying diverse application domains, which will be explored and experimented in our future related works.

Assessment of the Murine Tumor Microenvironment by Multiplex Immunofluorescence.

We describe the staining methods used for simultaneous detection of tumor microenvironment components as well as the automated quantification methodologies. This method uses mouse formalin-fixed paraffin-embedded tissues and multiplex immunofluorescence (Multiplex IF) followed by multispectral imaging. Currently, this methodology has shown to have a valuable role in murine immunoprofiling, and can be useful when evaluating the changes incurred on the tumor microenvironment upon various immunopreventive strategies.

Study of motion amplitudes in large joints of upper and lower limbs and spine joints when using industrial exoskeletons

One of the world trends of Industry 4.0 is the introduction of exoskeletal technologies into production processes. Manufacturers of industrial exoskeletons designed to reduce the negative impact of the work process on the human locomotive apparatus provide for the redistribution of the external load on the human body by means of various technical solutions. However, there is currently no uniform methodology for assessing the operational safety of exoskeletons. A significant biomechanical factor contributing to the safety of industrial exoskeletons as promising means of personal protection of the locomotor apparatus, is possibility to work without significant restrictions on the volume of movements. In this connection, the development of a methodology for objective quantification of the influence of structural and mass dimensions of industrial exoskeletons on the biomechanics of human movements is a topical task. This article presents the results of the measurements of the amplitude of movements in the large joints and joints of the spine, when performing the tests for flexion-extension, adduction-abduction and lateroflexion. The testing of volunteers was carried out on the clinical base of Research Institute of Labor Medicine. The tests were performed both without the use of industrial exoskeletons (Group I) and with their application (Group II). The influence on the biomechanical parameters of volunteer movements by the structural characteristics of the industrial exoskeleton “Exochair” and two different models of the industrial exoskeleton «Exoatlant» was investigated. Parameters of movements of segments of the human body were measured with the help of inertial measuring sensors of the software and hardware complex «Biomechanika Trust-M». Quantitative indicators have been identified which show the limitations of movements caused by the operation of different models of industrial exoskeletons. Thus, the use of the exoskeleton “Exochair” in deep seating limits the flexion in the hip joint by 67.1%, in the knee joint by 56.1%, in the lumbar thoracic spine by 61.9%, and practically does not affect the flexion in the shoulder joint. The first model of the exoskeleton “Exoatlant” limits the amplitude of movements in the hip joint by 41.2% at flexion and by 74.5% at extension. At the same time, the second model limits the abduction of the shoulder joint by 20.5%, and in the lumbar thoracic spine limits forward flexion by 17.3%, rearward extension by 50.6%, bending to the right by 46.4%, and bending to the left by 40.3%.

Characterization of the Canine Retinal Vasculature With Optical Coherence Tomography Angiography: Comparisons With Histology and Fluorescein Angiography.

Purpose: To present a methodology for quantification of the canine retinal vasculature imaged by optical coherence tomography angiography (OCTA) and validate this approach by comparison with fluorescein angiography (FA) and confocal imaging of retinal wholemounts labelled by immunohistochemistry (IHC).Methods: Six normal adult dogs underwent retinal OCTA imaging in both eyes. The images extracted from the different microvascular plexuses at eight retinal locations spanning the central and mid-peripheral fundus were analyzed using the AngioTool software. FA was performed in one eye and was compared to the OCTA images. Six eyes from three dogs were processed by IHC to examine the retinal vasculature.Results: A total of four retinal plexuses were identified by OCTA in the canine retina, and their density and topographical pattern varied with eccentricity. OCTA offered improved resolution over FA with the advantage of allowing imaging of the individual plexuses. Detection by OCTA of small vessels within the deep capillary plexus was possible and approached the level of resolution achieved with ex vivo imaging of the retinal vasculature by confocal microscopy/IHC. The plexuses herein described are analogous to human retinal vasculature.Conclusion: OCTA can be used to image and quantify non-invasively the vascular retinal networks of the canine retina. We provide normative data in eight different retinal locations that can be imaged non-invasively with this technology. This could support analysis of retinal vascular changes associated with disease and following therapeutic intervention.

Experimental investigation of microvibrations induced by reaction wheels on earth observation satellite

The evaluation of microvibrations effect is crucial for the success of remote sensing missions. The moving parts on the satellite such as reaction wheels can create microvibrations disturbances, which cause degradation of image quality. In our paper we present an experimental investigation methodology for on-ground quantification of wheel noise disturbance transmitted through the structure to the payload. This assessment was carried out for the ALSAT-1B satellite integrating a medium resolution payload. The angular motion along imager bore sight was measured using accelerometers and verified against image quality and mission requirement. The on ground experimental methodology suggests that the effect of microvibrations on a camera can be verified directly.

Object detection for automatic cancer cell counting in zebrafish xenografts.

Cell counting is a frequent task in medical research studies. However, it is often performed manually; thus, it is time-consuming and prone to human error. Even so, cell counting automation can be challenging to achieve, especially when dealing with crowded scenes and overlapping cells, assuming different shapes and sizes. In this paper, we introduce a deep learning-based cell detection and quantification methodology to automate the cell counting process in the zebrafish xenograft cancer model, an innovative technique for studying tumor biology and for personalizing medicine. First, we implemented a fine-tuned architecture based on the Faster R-CNN using the Inception ResNet V2 feature extractor. Second, we performed several adjustments to optimize the process, paying attention to constraints such as the presence of overlapped cells, the high number of objects to detect, the heterogeneity of the cells’ size and shape, and the small size of the data set. This method resulted in a median error of approximately 1% of the total number of cell units. These results demonstrate the potential of our novel approach for quantifying cells in poorly labeled images. Compared to traditional Faster R-CNN, our method improved the average precision from 71% to 85% on the studied data set.

Novel methodology for identification and quantification of microplastics in biological samples

Currently, the evidence of the ingestion of microplastics (MPs) by organisms or the accumulation in different environmental compartments has been achieved using several methodological procedures. However, its uses have not been standardized across studies. In this study, we aim to assess and validate a protocol that can be useful for optimizing the identification and quantification procedures of polyethylene microplastics (PE MPs) in biological samples. Initially, considering that numerous studies filter samples previously digested in cellulosic membranes for isolation and analysis of MPs, we evaluated whether washing these membranes with different reagents could contribute to the complete detachment of particles, as well as to their dispersion in the obtained solutions. However, none of the tested reagents (dimethyl sulfoxide, acetone, ethyl alcohol and chloroform), including purified water, was able to completely remove the MPs adhered to the membranes or facilitate their dispersion in the solutions. On the other hand, we observed that the digestion of the membranes by acetonitrile constituted a procedure that prevents the loss of particles due to adherence, in addition to promoting good dispersion of MPs. Subsequently, we evaluated the use of Neubauer chambers for the quantification of MPs, having observed a good recovery rate (>92%) and results with insignificant variation, in PE MPs solutions with different concentrations (0.15; 0.075 and 0.0375 mg/mL). Ultimately, the validation of the proposed procedures took place from the evaluation of the accumulation of PE MPs in Astyanax spp. juveniles, having demonstrated the efficiency and sensitivity of the method proposed for this purpose. Subsequently, our study provides a methodological alternative that can optimize MPs quantifications in biological samples and reduce the generation of biased or unreliable results.

Quantification of effects of performance recovery procedures for polymer electrolyte membrane fuel cells

To distinguish and mitigate the reversible and irreversible performance degradation of polymer electrolyte membrane fuel cells (PEMFCs), the evaluation and investigation of recovery procedures are of great importance. In this work, a methodology for quantification of recovery of reversible voltage losses is presented taken a durability test containing well-defined operation periods interrupted by two recovery procedures as an example. Each recovery procedure is characterized by polarization curves and electrochemical impedance spectroscopy (EIS). To evaluate the influence of the two used recovery procedures on the fuel cell performance, four factors are considered: (i) absolute recovered voltage, (ii) absolute non-recovered voltage, (iii) relative recovery related to previous operation period, and (iv) relative recovery related to the beginning of the test. The results show that voltage recovery quantified using polarization curves and voltage data logged during load cycling lead to the same result. Hence, to briefly assess the efficiency of a recovery procedure in a (narrow) current density range covered by the used load cycling profile, in-depth electrochemical characterization is not required. Moreover, EIS data were used to determine the main mechanisms leading to recovery. By this means, weaknesses of specific recovery procedures can be identified in order to propose specific improvements. • Methodology for quantification of reversible voltage losses versus current density. • Reversible losses using logged voltage data and polarization curves leads to similar results. • Analysis of effect of electrochemical measurements on the evolution of recovery. • Analyzed parameters demonstrate recovered performance losses as well as non-recovered losses.

Dependability and Security Quantification of an Internet of Medical Things Infrastructure Based on Cloud-Fog-Edge Continuum for Healthcare Monitoring Using Hierarchical Models

Rising aggressive virus pandemics urge to conduct studies on dependability and security of modern computing systems to secure autonomous and continuous operations of healthcare systems. In that regard, we propose to quantify dependability and security measures of an Internet-of-Medical Things (IoMT) infrastructure relied on an integrated physical architecture of cloud/fog/edge (CFE) computing paradigms in this article. We propose a reliability/availability quantification methodology for the IoMT infrastructure using a hierarchical model of three levels: 1) fault tree (FT) of overall IoMT infrastructure consisting of CFE member systems; 2) FT of subsystems within CFE member systems; and 3) continuous-time Markov chain (CTMC) models of components/devices in the subsystems. We incorporate a number of failure modes for the underlying subsystems, including Mandel-bug related failures and non-Mandel bugs related failure, as well as failures due to cyber-security attacks on software subsystems. Five case-studies of configuration alternation and four operational scenarios of the IoMT infrastructure are considered to comprehend the dependability characteristics of the IoMT physical infrastructure. The metrics of interest include reliability over time, steady state availability (SSA), sensitivity of SSA wrt. selected mean time to failure—equivalent (MTTFeq) and mean time to recovery—equivalent (MTTReq), and sensitivity of SSA wrt. frequencies of cyber-security attacks on software subsystems. The analysis results help comprehend operational behaviors and properties of a typical IoMT infrastructure. The findings of this study can improve the design and implementation of real-world IoMT infrastructures consisting of cloud, fog, and edge computing paradigms.

Weak Precipitin Ring from A Fecal Specimen with Markedly Elevated Alpha-1 Antitrypsin Level Measured by Radial Immunodiffusion

Abstract Radial immunodiffusion (RID) is a classic methodology for antigen quantification that relies on the development of a distinct precipitin ring from precipitated antigen-antibody complex. As the precipitin ring grows, the precipitate at the inner edge of the ring constantly dissolves due to excess antigen flooding from the point of application, while new precipitate forms at the leading edge of the ring. RID plates with anti-human alpha-1-antitrypsin are prepared in our lab to measure fecal alpha-1 antitrypsin (A1AT) to aid in the diagnosis and monitoring of Protein Losing Enteropathy (PLE). The procedure has routinely produced precipitin rings with small radii and distinct edges after incubating at room temperature for 48 hours. Unexpectedly, a fecal specimen from a patient produced an extremely weak and large precipitin ring that could have been easily overlooked. Dilution studies confirmed a highly elevated A1AT level of 67 mg/g dry stool. The very weak and large precipitin ring was reproduced with a spiked specimen with similar A1AT concentration and kept expanding for several days until a distinct ring was formed eventually. Our data highlights a rare example of high-dose hook effect in RID and calls for meticulous attention and caution when reading and interpreting gel-based immunoassays with unexpected markedly elevated results to avoid additional confirmatory testing. In these cases, we recommend repeat testing with diluted specimens.

The Application of Thermal Decomposition for Determination of Carbonate Acid-Neutralising Capacity for Improved Acid Mine Drainage Prediction

This study developed an industry-applicable, thermal decomposition methodology for quantification of carbonate mineral acid neutralisation capacity (ANCtherm-carb) for waste rock, tailings, and other mined materials. Standard titration-based methods for ANC can be compromised due to contributions from silicate minerals, ion exchange, Fe-rich carbonates, and other transition metal carbonates. C emission (CO2 and CO) was measured using IR in a N2 atmosphere. Cneut (wt%) was calculated using the C emission at 800 or 1000 °C minus the C emission at 400, 450 or 500 °C and the weight of sample prior to decomposition (Equation (2) of this manuscript). This value was then input into Equation (3) of this manuscript to calculate ANCtherm-carb. Good correlation of ANCtherm-carb for single-mineral carbonates with ANCcalc, calculated from bulk assay concentrations for Mg, K, Na, Ca, and Mn, was achieved. Thereafter, 18 waste rock samples were examined, resulting in the correlation of ANCtherm-carb versus non-standard ANCtitrate-carb (titration methodology adapted to focus on carbonate neutralisation only) with R2 = 0.96. This correlation is valid for samples containing both non-neutralising carbonates (siderite) and sources of neutralisation arising from non-carbonates (Mg-clay) within this waste rock system. Typically, mining operations use total C measurements for assessment of carbonate neutralisation potential in the block and mining model. This method provides an effective means to cheaply analyse for carbonate neutralisation potential with assignment of potentially acid-forming and non-acid-forming blocks to waste rock cells, etc.

A Crack Size Quantification Method Using High-Resolution Lamb Waves.

Traditional tone burst excitation cannot attain a high output resolution, due to the time duration. The received signal is much longer than that of excitation during the propagation, which can increase the difficulty of signal processing, and reduce the resolution. Therefore, it is of significant interest to develop a general methodology for crack quantification through the optimal design of the excitation waveform and signal-processing methods. This paper presents a new crack size quantification method based on high-resolution Lamb waves. The linear chirp (L-Chirp) signal and Golay complementary code (GCC) signal are used as Lamb wave excitation signals. After dispersion removal, these excitation waveforms, based on pulse compression, can effectively improve the inspection resolution in plate-like structures. A series of simulations of both healthy plates and plates with different crack sizes are performed by Abaqus CAE, using different excitation waveforms. The first wave package of the S0 mode after pulse compression is chosen to extract the damage features. A multivariate regression model is proposed to correlate the damage features to the crack size. The effectiveness of the proposed crack size quantification method is verified by a comparison with tone burst excitation, and the accuracy of the crack size quantification method is verified by validation experiments.

Development and Validation of a HS-SPME-GC-SIM-MS Multi-Method Targeting Hop-Derived Esters in Beer

Hop-derived esters contribute to beer flavor and changes in the ester spectrum are believed to be an important driver in the flavor–instability of hoppy beers. To date, there exists no published method that enables reliable quantification of hop-derived esters in beer. As the availability of such a method is vital to enlarge the understanding of beer flavor stability, the current article is concerned with the development, validation, and application of a headspace solid phase microextraction gas chromatography selected ion monitoring mass spectrometry (HS-SPME-GC-SIM-MS) based methodology for quantification of 16 hop-derived esters in beer. The validation data shows that choosing suitable HS-SPME-GC-SIM-MS conditions enabled reliable quantification of esters across a range of 1–200 µg/L, with calculated limits of quantification being well below 1 µg/L. Spiking experiments using terpenes and terpenoids evidenced method robustness, most importantly when two commercially available stable isotope labeled internal standards (d6-geranyl acetate and 13C-methyl octanoate) were used. Application to beer samples indicated that beers brewed with different qualities/quantities of hops and differing in freshness could be well differentiated by HS-SPME-GC-SIM-MS analysis of their ester profile. Supplemental data for this article is available online at at https://doi.org/10.1080/03610470.2021.1994814 .

Quantification and optimization of clot retraction in washed human platelets by Sonoclot coagulation analysis

Clot retraction is a pivotal process for haemostasis, where platelets develop a contractile force in fibrin meshwork and lead to the increased rigidity of clot. The pathophysiological alteration in contractile forces generated by the platelet-fibrin meshwork can lead to haemostatic disorders. Regardless of its utter significance, clot retraction remains a limited understood process owing to lack of quantification methodology. Sonoclot analysis is a point-of-care technique used in clinical laboratories for whole blood analysis that providesin vitroqualitative as well as quantitative assessment of coagulation process from initial fibrin formation to clot retraction. Human washed platelets were isolated by differential centrifugation method and analysed via optical imaging, microscopy and Sonoclot analysis using 1-2×108 /mL of washed platelets, 1U/mL of thrombin, 1mg/mL of fibrinogen and 1mM of calcium chloride. In this study, we demonstrate the novelty of this instrument in the quantitative evaluation of clot retraction in washed platelets and attempted to optimize the reference range of Sonoclot parameters including ACT - 87.3±20.997, CR - 16.23±3.538 and PF - 3.57±0.629, (n=10). Sonoclot analysis provides a simple and quantitative method to better understand in vitro clot retraction and its modulation by retraction components including platelet count, fibrinogen and platelet-fibrin interaction compared with existing conventional methods. Sonoclot may prove to be a valuable tool in thrombus biology research to understand fundamental basis of blood clot retraction.

On the Need to Determine Accurately the Impact of Higher-Order Sensitivities on Model Sensitivity Analysis, Uncertainty Quantification and Best-Estimate Predictions

This work aims at underscoring the need for the accurate quantification of the sensitivities (i.e., functional derivatives) of the results (a.k.a. “responses”) produced by large-scale computational models with respect to the models’ parameters, which are seldom known perfectly in practice. The large impact that can arise from sensitivities of order higher than first has been highlighted by the results of a third-order sensitivity and uncertainty analysis of an OECD/NEA reactor physics benchmark, which will be briefly reviewed in this work to underscore that neglecting the higher-order sensitivities causes substantial errors in predicting the expectation and variance of model responses. The importance of accurately computing the higher-order sensitivities is further highlighted in this work by presenting a text-book analytical example from the field of neutron transport, which impresses the need for the accurate quantification of higher-order response sensitivities by demonstrating that their neglect would lead to substantial errors in predicting the moments (expectation, variance, skewness, kurtosis) of the model response’s distribution in the phase space of model parameters. The incorporation of response sensitivities in methodologies for uncertainty quantification, data adjustment and predictive modeling currently available for nuclear engineering systems is also reviewed. The fundamental conclusion highlighted by this work is that confidence intervals and tolerance limits on results predicted by models that only employ first-order sensitivities are likely to provide a false sense of confidence, unless such models also demonstrate quantitatively that the second- and higher-order sensitivities provide negligibly small contributions to the respective tolerance limits and confidence intervals. The high-order response sensitivities to parameters underlying large-scale models can be computed most accurately and most efficiently by employing the high-order comprehensive adjoint sensitivity analysis methodology, which overcomes the curse of dimensionality that hampers other methods when applied to large-scale models involving many parameters.

Supero-temporal retinal amyloid count predicts hippocampal volume, verbal memory and cognitive-related quality of life

As retinal amyloid deposition correlate with and even precede brain amyloidosis, retinal imaging may become a non-invasive and non-ionizing biomarker for Alzheimer's disease. Retinal autofluorescencence using a specific amyloid-binding fluorophore curcumin and laser ophthalmoscopy is an emerging methodology for retinal amyloid quantification. In this study, we aimed to evaluate the relationship between the retinal amyloid counts (RAC) in the supero-temporal quadrant and brain volumes, and cognitive performance.

Dynamically orthogonal ray equations with adaptive reclustering

The vast size of the ocean coupled with the Nyquist criterion often makes high-frequency ocean acoustics computationally intractable without approximation. A common approach is to employ ray theory to develop an approximate Green’s function for the acoustic wave equation. But due to the dynamic nature of ocean fields and the many sources of uncertainty in the temperature, salinity, density, sea surface, bathymetry, neither the sound speed nor the source and receiver locations are precisely known, and a single deterministic solution of the ray equations is often insufficient. In place of Monte Carlo simulations, we develop and numerically solve dynamically orthogonal ray equations to efficiently approximate solutions to the stochastic acoustic wave equation. The Lagrangian nature of the ray equations introduces a new computational challenge for reduced-order models; a naive approach requires realizations to be reconstructed at each time step. To remedy this, we employ a Taylor expansion of the sound speed around “mean rays” and propose a novel, adaptive algorithm to determine when the modes and coefficients of the reduced-order model must be reclustered and/or augmented to preserve accuracy. This offers a unique methodology for uncertainty quantification in the presence of non-Gaussian statistics which may be utilized in forward modeling as well as in acoustic inverse problems.

Phenolic compounds in surface water: methodology and occurrence in Doce River, Brazil

Phenolic compounds are widely spread in surface water, mainly in developing countries, where sewage and wastewater treatment are still reduced. Thus, this work quantified these pollutants in the Doce River analyzing the associated risk for the environment and human health. This river is in the state of Minas Gerais in Brazil and was recently impacted by the collapse of a mining dam that compromised the resilience of the entire watershed. For that purpose, a methodology for simultaneous identification and quantification of 17 different phenols was developed. It was possible to verify phenolic compounds' occurrence with concentration ranging from 0.13 to 24.16µg·L-1. 2-Nitrophenol and bisphenol A appeared in all samples analyzed. The analytical method was processed using solid-phase extraction (SPE) (C18 cartridge), gas chromatography with FID, and mass spectrometry to define the analytes' retention time. For case validation, the selectivity, linearity, detection and quantification limits, sensitivity, precision, accuracy, resolution, matrix effect, and peak quality were assessed. Four different solvents were tested in the recovery-grade trials, which were dichloromethane, methanol, acetonitrile, and ethyl acetate. Among them, methanol had a better performance and was used throughout all analyses. The phenolic compounds had a recovery degree higher than 50% after SPE, regardless of the matrices.

Uncertainty Quantification of Regional Cardiac Tissue Properties in Arrhythmogenic Cardiomyopathy Using Adaptive Multiple Importance Sampling.

Introduction: Computational models of the cardiovascular system are widely used to simulate cardiac (dys)function. Personalization of such models for patient-specific simulation of cardiac function remains challenging. Measurement uncertainty affects accuracy of parameter estimations. In this study, we present a methodology for patient-specific estimation and uncertainty quantification of parameters in the closed-loop CircAdapt model of the human heart and circulation using echocardiographic deformation imaging. Based on patient-specific estimated parameters we aim to reveal the mechanical substrate underlying deformation abnormalities in patients with arrhythmogenic cardiomyopathy (AC).Methods: We used adaptive multiple importance sampling to estimate the posterior distribution of regional myocardial tissue properties. This methodology is implemented in the CircAdapt cardiovascular modeling platform and applied to estimate active and passive tissue properties underlying regional deformation patterns, left ventricular volumes, and right ventricular diameter. First, we tested the accuracy of this method and its inter- and intraobserver variability using nine datasets obtained in AC patients. Second, we tested the trueness of the estimation using nine in silico generated virtual patient datasets representative for various stages of AC. Finally, we applied this method to two longitudinal series of echocardiograms of two pathogenic mutation carriers without established myocardial disease at baseline.Results: Tissue characteristics of virtual patients were accurately estimated with a highest density interval containing the true parameter value of 9% (95% CI [0–79]). Variances of estimated posterior distributions in patient data and virtual data were comparable, supporting the reliability of the patient estimations. Estimations were highly reproducible with an overlap in posterior distributions of 89.9% (95% CI [60.1–95.9]). Clinically measured deformation, ejection fraction, and end-diastolic volume were accurately simulated. In presence of worsening of deformation over time, estimated tissue properties also revealed functional deterioration.Conclusion: This method facilitates patient-specific simulation-based estimation of regional ventricular tissue properties from non-invasive imaging data, taking into account both measurement and model uncertainties. Two proof-of-principle case studies suggested that this cardiac digital twin technology enables quantitative monitoring of AC disease progression in early stages of disease.

Integration of fine grinding and H2SO4 leach for the liberation, dissolution and redistribution of long-life radionuclides in copper flotation concentrate

The economically significant deposits – iron oxide-copper-gold (IOCG) usually contains elevated amount of naturally occurring radioactive material (NORM), which hinders the utilization of IOCG deposits and leads to the waste of resources on site due to the presence of these radionuclides. Herein, a new fine grinding – H2SO4 leach method was used to efficiently dissolve and liberate long-life radionuclides (238U, 230Th, 226Ra, 210Pb, 210Po) from uranium-bearing NORM that are finely disseminated in the major economical minerals. The fine grinding treatment reduced the average particle sizes, interrupted the mineral textures and partially dissolved uranium minerals, thereby liberating them onto the newly formed secondary copper phases. The following H2SO4 leach changed the chemical and phase compositions by dissolving non-copper elements where the locked 238U and 230Th were largely removed close to the safe activity (1 Bq/g) and 226Ra, 210Pb, 210Po were mobilized onto the copper mineral surfaces. For the first time, the quantitative correlations between radionuclide species and major mineral phases were built by QXRD and radionuclide analysis. Such quantification methodology of radionuclide deportment can be applied in the processing a broad range of NORM-bearing resources, providing a crucial guidance in radionuclide management for environmental and process engineers.