Scaling Model Research Articles

A32Towards a taxonomy of scale: a sustainable approach

Taking simulation from concept through to scalable delivery is complex, contested and an under-theorized process. The traditional approaches to scaling innovations, proposed by Everett Rogers in the 1960s is the notion of diffusion of innovation; we argue that this is of limited use in our context of working with NHS health professionals supporting their learning post-covid. Our approach to scaling draws the upon the well-tested seminal works on taxonomies by Coburn (2003) [1] and her dimensional framework, Dede et al (2007) [2], with their educational scaling model, plus the systematic review of Greenhalgh et al (2004) [3] in health services. By considering the ways in which our eight Health Education England (HEE) simulation projects have been delivered, this work presents an emerging framework, designed to enable the orchestration of team discourse about theory, the production of simulation artefacts as tools for design discourse and the identification of scalable systemic pain points. We pay particular attention to scaling innovations in practice and organizational change, which are in our view enabling factors in the sustainable adoption of learning technologies by end users in the workplace. Successful scaling is more than just being about the number of users we can reach. It requires underpinning by an understanding about the changes in practice an innovation can bring about, and how valuable these changes are to stakeholders. Challenges remain as to whether such changes can be sustained over time, and the extent to which users and stakeholders are involved in co-creating the innovation. Individuals within the organizations – and their attitudes, beliefs, and habits – play an equally important role in exploring new technologies and practices with an open mind and perceiving these as an added value in their work environment and daily routines. This work illustrates the need to strategically involve the ‘missing middle’ and starts to identify the key role these people play in that space between where scaling factors reside between top-down strategy and bottom-up initiatives. Authors confirm that all relevant ethical standards for research conduct and dissemination have been met. The submitting author confirms that relevant ethical approval was granted, if applicable.

Analysis of the sustainability status of community-based drinking water supply in Kapongan District, East Java

Ensuring the availability and sustainability of clean water is a key focus of the United Nations Sustainable Development Goals (SDGs) and has emerged as a significant concern for both central and local governments in Indonesia. In the Kapongan District of Situbondo Regency, the government's PAMSIMAS Programme is a proactive initiative designed to secure safe access to drinking water for the community. Within this district, two villages, Landangan and Pokaan, have successfully implemented community-based drinking water provision. The ongoing sustainability of these water supply systems in these villages is pivotal for meeting the continuous drinking water needs of the community, necessitating a comprehensive investigation into its sustainability. The primary objective of this study is to assess the sustainability status of community-based drinking water provision within the ongoing PAMSIMAS program in Kapongan District. The dimensions under consideration encompass environmental, social, economic, technological, and institutional aspects. Employing a quantitative approach, the research utilizes the Rap-SPAM analysis method, a modification of Rapfish, with primary data collected through surveys, observations, and interviews. Additionally, secondary data is acquired through an extensive literature review and consultation with various related agencies. The assessment results reveal a sustainability index of 58.92, classifying it as reasonably sustainable. The Multi-Dimensional Scaling (MDS) model produced Stress values ranging from 0.15 to 0.18 and R2 values between 0.93 to 0.94. The variance between the MDS index and Monte Carlo analysis is less than 5%. In conclusion, this study finds that the community-based water supply facilitated by the PAMSIMAS program in Kapongan District demonstrates a reasonably sustainable status. To enhance its sustainability, there is a need to prioritize and improve dimensions with lower sustainability index values in a holistic manner.

New insight into imbibition micromechanisms and scaling model in fossil hydrogen energy development of tight reservoirs based on NMR

Spontaneous imbibition is an important mechanism for developing fossil hydrogen energy. However, the research of imbibition micromechanisms and scaling methods in tight reservoirs with complex lithofacies remains limited. This study employed Nuclear magnetic resonance (NMR) monitoring to investigate imbibition at multiscale pores in tight sandy conglomerate samples. Then, a modified shut-in time-scale model was developed analyzing the imbibition modes and control factors. The results showed that the imbibition concludes three stages: suction, diffusion, and stable. The diffusion stage is crucial for promoting recovery. Two modes of imbibition in four lithofacies were distinguished: stable type (recovery>40 %) and sensitive type (recovery<20 %). Samples with medium permeability (0.2mD-0.6mD), bimodal pore distribution, and less illite/smectite mixed-layer (relative content<50 %) had high recovery. Nanopores provided foremost oil supply, while macropores facilitated fluid interchange. “Relay imbibition” from micropores to macropores was an important recovery mechanism. Hydrogen signals during clay expansion and microcracks propagation were identifiable by NMR-T2 after accounting for ferromagnets and temperature. The imbibition diffusion radius derived from T2 accumulation was found to modify the normalized time-scale model for complex lithofacies tight reservoirs. Consequently, the reasonable shut-in time for stable and sensitive reservoirs was concluded to be 32 and 11 days, respectively.

Accounting for precipitation asymmetry in a multiplicative random cascade disaggregation model

Abstract. Analytical multiplicative random cascades (MRCs) are widely used for the temporal disaggregation of coarse-resolution precipitation time series. This class of models applies scaling models to represent the dependence of the cascade generator on the temporal scale and the precipitation intensity. Although determinant, the dependence on the external precipitation pattern is usually disregarded in the analytical scaling models. Our work presents a unified MRC modelling framework that allows the cascade generator to depend in a continuous way on the temporal scale, precipitation intensity and a so-called precipitation asymmetry index. Different MRC configurations are compared for 81 locations in Switzerland with contrasted climates. The added value of the dependence of the MRC on the temporal scale appears to be unclear, unlike what was suggested in previous works. Introducing the precipitation asymmetry dependence into the model leads to a drastic improvement in model performance for all statistics related to precipitation temporal persistence (wet–dry transition probabilities, lag-n autocorrelation coefficients, lengths of dry–wet spells). Accounting for precipitation asymmetry seems to solve this important limitation of previous MRCs. The model configuration that only accounts for the dependence on precipitation intensity and asymmetry is highly parsimonious, with only five parameters, and provides adequate performances for all locations, seasons and temporal resolutions. The spatial coherency of the parameter estimates indicates a real potential for regionalisation and for further application to any location in Switzerland.

On floating point precision in computational fluid dynamics using OpenFOAM

Thanks to the computational power of modern cluster machines, numerical simulations can provide, with an unprecedented level of details, new insights into fluid mechanics. However, taking full advantage of this hardware remains challenging since data communication remains a significant bottleneck to reaching peak performances. Reducing floating point precision is a simple and effective way to reduce data movement and improve the computational speed of most applications. Nevertheless, special care needs to be taken to ensure the quality and convergence of computed solutions, especially when dealing with complex fluid simulations. In this work, we analyse the impact of reduced (single and mixed compared to double) precision on computational performance and accuracy for computational fluid dynamics. Using the open source library OpenFOAM, we consider incompressible, compressible, and multiphase fluid solvers for testing on relevant benchmarks for flows in the laminar and turbulent regime and in the presence of shock waves. Computational gain and changes in the scalability of applications in reduced precision are also discussed. In particular, an ad hoc theoretical model for the strong scaling allows us to interpret and understand the observed behaviours, as a function of floating point precision and hardware specifics. Finally, we show how reduced precision can significantly speed up a hybrid CPU–GPU implementation, made available to OpenFOAM end-users recently, that simply relies on a GPU linear algebra solver developed by hardware vendors.

Pseudo-optimum geometries for 1-100 ton spool compressors with low-GWP refrigerants

Simulation models are often employed for guiding compressor prototype development, particularly for novel devices like the spool compressor. The spool compressor is a novel compressor technology that can provide competitive performance for low-pressure refrigerants (like R1234ze(E)) with a compact design. A parametric study is presented to find the optimum geometric aspect ratio for five refrigerants (CO2, R134a, R454B, R468B and R1234ze(E)) and a range of cooling capacities between 1-100 RT. A pseudo-optimum geometry is explored by varying the aspect ratio, defined by the eccentricity ratio (rotor diameter to bore diameter), and the compressor L/D ratio (axial length to bore diameter), varying from 0.75-0.8 and 0.4-3, respectively. A scaling model is developed to vary the compressor geometry and ports. A spool compressor model, developed by Bradshaw and Groll (2013), is extended and by validating the model using data from 30 RT and 40 RT prototype compressors using R134a and R1234ze(E). The model is then used to predict the performance of a spool compressor with the various geometric inputs and refrigerants. The results suggest that the volumetric and isentropic efficiencies can be maximized by using an eccentricity ratio of 0.75 and L/D ratios from 1-1.5 for various displacement volumes. The results showed that the optimum isentropic efficiency increases asymtoptically from 1 to 100 RT for most working fluids and it increases linearly for CO2. Additionally, the compressor performance becomes insensitive to the aspect ratio for large compressor sizes.

Hubungan Persepsi Bimbingan Karir dengan Motivasi Bekerja pada Siswa Kelas XI Sekolah Menengah Kejuruan (SMK) di Sidoarjo

This research is motivated by the phenomenon of vocational school graduates who are unemployed, and many students do not know their future goals. The purpose of this study was to determine the relationship between career guidance perceptions and work motivation in class XI students of vocational high school (SMK) in Sidoarjo. The method used in this research is quantitative with a correlational approach. The data collection technique in this study used a psychological scale with a Likert scaling model, namely the career guidance perception scale and work motivation scale. The population in this study were 64 students of class XI SMK TPI Gedangan Sidoarjo. Determination of the research sample using total sampling, so that the research sample was taken from the total population, as many as 64 students. Data analysis was carried out using Spearman's Rho Product Moment correlation technique using the SPSS 25 for windows program. The results of data analysis showed a correlation coefficient of 0.996 with a significance of 0.000 &lt;0.05, meaning that there was a positive relationship between perceptions of career guidance and work motivation.

Flow-Dependent Relative Permeability Scaling for Steady-State Two-Phase Flow in Porous Media: Laboratory Validation on a Microfluidic Network

Conventionally, the relative permeabilities of two immiscible fluid phases flowing in porous media are considered and expressed as functions of saturation. Yet, this has been put into challenge by theoretical, numerical, and laboratory studies of flow in artificial pore network models and real porous media. These works have revealed a significant dependency of the relative permeabilities on the flow rates, especially when the flow regime is capillary to capillary-viscous dominated, and part of the disconnected nonwetting phase remains mobile. These studies suggest that relative permeability models should include the functional dependence on flow intensities. However, revealing the explicit form of such dependence remains a persistent problem. Just recently, a general form of dependence was inferred based on extensive simulations with the DeProF model for steady-state two-phase flows in pore networks. The simulations revealed a systematic dependence of the relative permeabilities on the local flow rate intensities. This dependence can be described analytically by a universal scaling functional form of the actual independent variables of the process, namely, the capillary number, Ca, and the flow rate ratio, r. The proposed scaling incorporated a kernel function, the intrinsic dynamic capillary pressure (IDCP) function, describing the transition between capillarity- and viscosity-dominated flow phenomena. In a parallel laboratory study, SCAL measurements provided a preliminary proof-of-concept on the applicability of the model. In the laboratory study presented here, we examine the applicability of the scaling model by taking extensive, ex-core measurements of relative permeabilities for steady-state co-injections of two immiscible fluids within an artificial microfluidic pore network, across different flow regimes in Ca and r. From these measurements, we calculate the values of the mobility ratio, and we compare these to the corresponding values of the flow rate ratio. We also extract the IDCP curve, the locus of critical flow conditions, whereby the process is more efficient in terms of energy utilization – accounted by the nonwetting phase flow rate per unit of total power provided to the process, as well as the locus of flow conditions of equal relative permeabilities. We show that the degree of consistency between flow rate ratio and mobility ratio values, the IDCP curve, the locus of critical flow conditions, and the locus of equal relative permeabilities, as well as some associated invariant characteristic values, can be used for assessing the extent of end effects and for characterizing the flow as capillary- or viscous-dominated. The proposed scaling introduces new opportunities for enhancing SCAL protocols and their associated applications. These include the characterization of systems and flow conditions, dynamic rock typing, evaluation of capillary end effects, as well as the advancement of more efficient field-scale simulators. Additionally, it paves the way in designing more energy-efficient EOR interventions.

Using physics-informed neural networks with small datasets to predict the length of gas turbine nozzle cracks

We created a Physics-Informed Neural Network (PINN) to model the propagation of fatigue cracks. The analyzed object is a high-pressure Nozzle of an industrial gas turbine. The models are based on a Recurrent Neural Network with an embedded Feedforward Neural Network to estimate the stress intensity factor. The thermal stresses are calculated based on engine operational data, leveraging a Finite Element Analysis. However, the time series are available just for 54% of the start-stop cycles, and only 13 crack measures were recorded. Three separate models were trained based on ten, two, and one observation, respectively. The importance of the empirical data was regulated during the training to avoid solutions inconsistent with the underlying physics. The models generalize well and predict accurately also outside the training domain. Additionally, we propose a novel method of scaling models based on PINNs and transferring knowledge between domains. It enables predicting in the target domain, even if damage measures are unavailable. The obtained results confirm the effectiveness of this approach.

Gene expression extraction in cervical cancer by segmentation of microarray images using a novel fuzzy method

ABSTRACT It is necessary to obtain gene expression values to identify gene biomarkers involved in all types of cancers, and microarray data is one of the best data for this purpose. In order to extract gene expression values from microarray images that have different challenges. This article presents a completely automatic and comprehensive method that can deal with the various challenges in these images and obtain gene expression values with high accuracy. A pre-processing approach is proposed for contrast enhancement using a genetic algorithm and for removing noise and artefacts in microarray cells using wavelet transform based on a complex Gaussian scaling model. For each point, the coordinate centre is determined using Self Organising Maps. Then, using a new hybrid model based on the Fuzzy Local Information Gaussian Mixture Model (FLIGMM), the position of each spot is accurately determined. In this model, various features are obtained using local information about pixels, considering the pixel neighbourhood correlation coefficient. Finally, the gene expression values are obtained. The performance of the proposed algorithm was evaluated using real microarray images of cervical cancer from the GMRCL microarray dataset as well as simulated images. The results show that the proposed algorithm achieves 90.91% and 98% accuracy in segmenting microarray spots for noiseless and noisy spots, respectively.

The Relationship Between Self-Efficacy and Subjective Welfare of Adolescent Santri in Islamic Boarding Schools

This study aims to determine the relationship between self-efficacy and subjective well-being in adolescent students at Pondok An-Nur Sidoarjo. This type of research is quantitative correlational. The variables in this study are self efficacy (x) and subjective well being (y). The study was conducted at the An-Nur Islamic Boarding School, Sidoarjo with a population of 190 teenage students. a sample of 123 teenage students using simple random sampling. The data collection technique uses a psychological scale with a Likert scaling model, namely the self-efficacy scale and the subjective well-being scale. The hypothesis in this study is that there is a positive relationship between self-efficacy and subjective well-being in adolescent students at Pondok An-Nur Sidoarjo. Data analysis used product moment correlation technique with the help of SPSS 25 for windows program. The results of data analysis correlation value of 0.883 with a significance of 0.000 &lt;0.05, meaning that there is a positive relationship between self-efficacy and subjective well-being in adolescent students at Pondok An-Nur Sidoarjo. The higher the self efficacy, the higher the subjective well being. On the other hand, the lower the self-efficacy, the lower the subjective well-being of adolescent students.

Predicting Volume of Distribution in Neonates: Performance of Physiologically Based Pharmacokinetic Modelling.

The volume of distribution at steady state (Vss) in neonates is still often estimated through isometric scaling from adult values, disregarding developmental changes beyond body weight. This study aimed to compare the accuracy of two physiologically based pharmacokinetic (PBPK) Vss prediction methods in neonates (Poulin & Theil with Berezhkovskiy correction (P&T+) and Rodgers & Rowland (R&R)) with isometrical scaling. PBPK models were developed for 24 drugs using in-vitro and in-silico data. Simulations were done in Simcyp (V22) using predefined populations. Clinical data from 86 studies in neonates (including preterms) were used for comparison, and accuracy was assessed using (absolute) average fold errors ((A)AFEs). Isometric scaling resulted in underestimated Vss values in neonates (AFE: 0.61), and both PBPK methods reduced the magnitude of underprediction (AFE: 0.82-0.83). The P&T+ method demonstrated superior overall accuracy compared to isometric scaling (AAFE of 1.68 and 1.77, respectively), while the R&R method exhibited lower overall accuracy (AAFE: 2.03). Drug characteristics (LogP and ionization type) and inclusion of preterm neonates did not significantly impact the magnitude of error associated with isometric scaling or PBPK modeling. These results highlight both the limitations and the applicability of PBPK methods for the prediction of Vss in the absence of clinical data.

Enhancement of Electromagnetic Scattering Computation Acceleration Using LSTM Neural Networks

This paper presents the electromagnetic long short-term memory neural network (EM-LSTMNN) approach to accelerate radar cross-section (RCS) calculations in optimizing low RCS for electrically large targets. The proposed method converts the conventional electromagnetic numerical calculation into an efficient numerical calculation using the LSTM neural network, resulting in a significant improvement in RCS computation speed. To assess the effectiveness of this approach, a downscaled model of a large-sized ship is employed as the target for low RCS optimization. Each modification made to the target’s mesh data during the optimization process is stored in the dataset as a new element. As the ship scaling model undergoes modifications during the optimization process, the new mesh data are recorded, thus adding a new element to the dataset at each time step. This forms a time series representation of the mesh model. By utilizing the dataset collected throughout the optimization process, the proposed EM-LSTMNN model is trained using data-driven approaches, with a training time of approximately 106 s. It is worth noting that this training time is significantly smaller compared to existing methods that employ fully connected neural networks. The performance of the proposed approach is demonstrated by comparing the RCS calculation results obtained through this method with those obtained through traditional electromagnetic simulations.

Novel modeling approach integrating population pharmacokinetics and interspecies scaling to predict human pharmacokinetics of the new anti-tuberculosis agent telacebec (Q203)

Telacebec is a new anti-tuberculosis agent with promising therapeutic activity and a favorable safety profile. This study aimed to characterize the pharmacokinetics of telacebec via interspecies scaling and population pharmacokinetic modeling for the prediction of human pharmacokinetics. Preclinical pharmacokinetic data were obtained from mice, rats, and dogs following intravenous and oral doses of telacebec. A population pharmacokinetic model was developed to describe the pharmacokinetic data from all three species. The disposition parameters were well correlated with the body weight for all species using an allometric equation. Thus, the allometric scaling was incorporated into the population pharmacokinetic model, which could simultaneously describe the plasma concentration vs. time data from all preclinical studies as well as the Phase 1A clinical study. The developed model was used to predict the pharmacokinetics of telacebec after IV injection, including the clearance (CL) of 168.58 [118.86 – 238.73] mL/min and volume of distribution (Vss) of 968.84 [396.87 – 2831.31] L for 80-kg human. The absolute bioavailability of telacebec in humans in the fed state was estimated as 70.34 ± 9.91%. Finally, the population pharmacokinetic model with allometric scaling was utilized to simulate the plasma concentration vs. time profiles of telacebec after multiple oral doses in humans. The model-predicted profiles well agreed with the observed data in Phase 1B clinical trial. The present pharmacokinetic model may help better understand the activity of telacebec, leading to the design of optimal dosing regimens and new formulation development.

Watershed-scale provenance heterogeneity within Eocene nonmarine basin fill: Southern Greater Green River Basin, western USA

Weathering, erosion, and sediment transport in modern landscapes may be investigated via direct observation of attributes such as elevation, relief, bedrock lithology, climate, drainage organization, watershed extent, and others. Studies of ancient landscape evolution lack this synoptic perspective, however, and instead must rely more heavily on downstream records of fluvial deposits. Provenance analysis based on detrital grain ages has greatly enhanced the utility of such records but has often focused broadly on regional to continental scales. This approach may overlook important details of localized watersheds, which could lead to significant misinterpretation of past sediment dispersal patterns. The present study, therefore, explores the impact of geographic and stratigraphic sampling density on detrital zircon provenance, based on a high-density investigation of U-Pb ages (N = 23, n = 4905) obtained from a narrow chronostratigraphic range (∼2 m.y.) within a relatively small (∼25,000 km2) area of an Eocene nonmarine sedimentary basin. Based on multi-dimensional scaling and DZmix modeling, these strata comprise seven distinct, approximately isochronous detrital zircon (DZ) chronofacies, defined as “. . . a group of sedimentary rocks that contains a specified suite of detrital zircon age populations” (Lawton et al., 2010). Four of these DZ chronofacies reflect long-distance transport from extrabasinal source areas. DZ chronofacies CO-1 and CO-2 are interpreted to derive from a primary sediment source in central Colorado (USA), corroborating previously proposed long-distance sediment transport via the Aspen paleoriver. DZ chronofacies ID-1 and ID-2 are interpreted to have been delivered to the basin from central Idaho by the Idaho paleoriver. In contrast, DZ chronofacies UT-1 and UT-2 are interpreted to reflect local drainage from the Uinta Uplift south of the basin, and DZ chronofacies WY-1 is interpreted to have been sourced from the Rawlins, Granite, and Sierra Madre uplifts to the north and east via the Toya Puki paleoriver. Lateral transitions between different DZ chronofacies in some cases occur over distances as little as 5 km, implying that depositional systems carrying sand from disparate watersheds directly competed to fill available basin accommodation. The results of this study reveal a high degree of complexity of Eocene rivers that converged on the Greater Green River Basin, indicating that their deposits contain a rich record of fine-scale landscape evolution across much of the Laramide foreland and Cordilleran orogen. These results illustrate the need for adequate sample density when assessing basin-scale provenance and offer a cautionary consideration for researchers using sandstone (and incorporated authigenic cement) in other nonmarine basins as the basis for paleoaltimetry or detrital thermochronology studies.

Algebraic synthesis and input-output analysis of 1-DOF multi-loop linkages with a constant transmission ratio between two adjacent parallel, intersecting or skew axes

This paper deals with the algebraic synthesis and input-output analysis of one degree-of-freedom (1-DOF) multi-loop planar, spherical, and spatial linkages for increasing or decreasing angular motion with a desired constant transmission ratio between two adjacent parallel, intersecting, and skew axes respectively. Based on the rotation scaling model and motion polynomials over dual quaternions, an algebraic synthesis method including three procedures is proposed to exactly construct novel multi-loop linkages owning arbitrarily prescribed constant transmission ratios and input/output axes. To illustrate this method, several 1-DOF multi-loop planar, spherical, and spatial linkages for rotation scaling are synthesized by designating various input and output axes as well as transmission ratios. Taking some multi-loop planar, spherical, and spatial linkages as examples, input-output analysis is carried out to verify their transmission characteristics. The results demonstrate that the generated 1-DOF multi-loop linkages can indeed transmit motion with prescribed constant transmission ratios and input/output axes. This work provides a framework for further investigation on mechanisms performing specified tasks for motion transmission.

Impact and wetting of polysilazane droplets on a metal surface

Spray coating with liquid polymers is a crucial step in producing polymer-derived ceramic (PDC) coatings before curing the precursor. However, the interactions between polymer droplets and target surfaces during the spray coating process have not been adequately characterized in existing literature. This experimental study aims to comprehensively examine the previously unknown droplet impact dynamics for a specific ceramic precursor, Polysilazane, on Inconel surfaces. The diameter of Polysilazane droplets was kept constant, while their impact velocity was varied to achieve different impact conditions. The same parameter range was also explored with water, water-glycerol, and ethanol droplets for comparison purposes. High-speed videos were captured to observe the instantaneous impact process, while low-speed videos were recorded at specific instances to document the gradual wetting process at later stages. The investigation revealed unique spreading dynamics of Polysilazane droplets, including smooth contact angle variation during the transition from impact to lateral spreading, absence of delayed wetting, no formation of wavy structures on the droplet interface during spreading, and no retraction process within the studied conditions (Weber number up to 1200). Conversely, Polysilazane displayed similarities in the normalized spreading curves with other liquids. Additionally, the maximum spread factor of Polysilazane could be well predicted using existing universal scaling models designed for different liquid types. Lastly, Polysilazane exhibited a distinctive post-impact wetting process that persisted for an extended duration. The wetting dynamics of Polysilazane could be effectively described by an empirical exponential law originally developed for partial wetting systems.

The Scaling Analysis and Numerical Simulation of Single-phase Natural Circulation

In this work, scaling analysis and numerical simulation of the single-phase natural circulation loop with water are presented. First, grid-dependency testing and model validation are performed. Then, according to the assumption of the boundary conditions, the effect of the heat input and structural parameters on the heat transfer characteristic is studied, including the boundary condition and temperature of the steam fluid. The results show that the scaling model can accurately express the flow heat transfer characteristics of the original model, and can reach a steady state under different working conditions. The formation of stable natural cycles is the result of coupling the boundary conditions of the heating and cooling segments under the fixed structure reference. The mass flow rate on the steady state decrease with the increase of the external fluid temperature in the cooling section, and as the heat transfer coefficient of the external convection heat transfer coefficient increases.

Technoeconomic analysis of supercritical fluid extraction process for recycling rare earth elements from neodymium iron boron magnets and fluorescent lamp phosphors

This study offers a comprehensive and rigorous analysis of the technoeconomics of supercritical fluid extraction (SCFE) technology as applied to the recovery of rare earth elements (REEs) from end-of-life neodymium iron boron magnets and fluorescent lamp phosphors. Drawing on an array of data sources including laboratory results, literature data, scaling models, scenario analysis, and sensitivity analysis, this study conducts a sound economic analysis of the SCFE process at an industrial scale. The study renders a detailed estimation of the costs and revenues associated with the process and identifies the primary factors that impact its profitability. The findings demonstrate that the SCFE of REEs from these feedstocks can be economically viable under certain circumstances, with the efficiency of extracting terbium, dysprosium, and neodymium and the price of their respective oxides emerging as key drivers of profitability. By providing valuable insights into the feasibility of the SCFE process for REEs recovery, this study informs future research and development activities in the field.

Individual tree volume estimation with terrestrial laser scanning: Evaluating reconstructive and allometric approaches

Accurate estimates of above-ground tree biomass within forest inventories are essential for calibration and validation of biomass mapping products based on Earth observation data. Terrestrial laser scanning (TLS) enables detailed and non-destructive volume estimation of individual trees, which can be converted to biomass with wood basic density. Existing TLS-based approaches range from simple geometrical features to virtual 3D reconstruction of entire trees. Validating such approaches with weight measurements is a key step before the integration of TLS or other close-range technologies into operational applications such as forest inventories. In this study, we firstly evaluate individual tree volume estimation approaches based on 3D reconstruction through quantitative structure models (QSM) against destructive reference data of 60 trees and compare them to operational allometric scaling models (ASM). Secondly, we determine the explanatory power of TLS-derived geometric parameters regarding total wood, stem, coarse wood and fine branch volume. We observe similar accuracy in merchantable (¿7 cm) wood compartments for ASMs (NRMSE = 25 %) and QSMs (NRMSE = 29 %), with QSMs showing better results for broadleaves than conifers and generally overestimating fine branch volume. Feature selection shows that a combination of stem diameters and volume of convex hulls around tree crowns has the most potential to model the entire tree volume including branches, especially for conifers. In cases where the quality of available point clouds is insufficient for QSMs, 3D information can thus still be utilised by deriving geometric parameters. The integration of crown dimension parameters into new allometric models could substantially improve the estimation of branch wood volume.