Influence of different position modal parameters on milling chatter stability of orthopedic surgery robots.

This research is dedicated to exploring the dynamics of milling chatter stability in orthopedic surgery robots, focusing on the impact of position modal parameters on chatter stability. Initially, we develop a dynamic milling force model for the robotic milling process that integrates both modal coupling and regenerative effects. We then employ the zero-order frequency domain method to derive a chatter stability domain model, visually represented through stability lobe diagrams (SLDs). Through conducting hammer test experiments, we ascertain the robot's modal parameters at varying positions, enabling the precise generation of SLDs. This study also includes experimental validation of the chatter SLD analysis method, laying the groundwork for further examination of chatter stability across different positional modal parameters. Finally, our analysis of the variations in modal parameters on the stability of robot milling chatter yields a theoretical framework for optimizing cutting parameters and developing control strategies within the context of orthopedic surgery robots.

Birth weight and asthma in young adults of a Brazilian birth cohort.

This article aims to evaluate the association between birth weight and asthma in adulthood, estimated by employing structural equation modeling. Cohort study with 1,958 participants aged 23-25 years from Ribeirão Preto, São Paulo, Brazil. Standardized questionnaires were applied and pulmonary function evaluated, including bronchial reactivity with methacholine. A theoretical model was proposed to explore the effects of birth weight and asthma in adulthood. Asthma, socioeconomic status at birth (Birth SES), and current socioeconomic status (Adult SES) were obtained by constructs. Maternal age, sex, skin color, body mass index (BMI), smoking, parental asthma history, history of respiratory infection before five years old, history of hospitalization for lung disease before two years old, and atopy were the studied variables. 14.1% of participants were diagnosed with asthma. Birth weight was associated with asthma (Standardized Coefficient - SCtotal=-0.110; p=0.030), and an indirect effect was also observed (SCindirect=-0.220; p=0.037), mediated by hospitalization before two years and respiratory infection before five years. Lower birth weight showed an increased risk of asthma in adulthood and the SES Birth and Adult SES variables underlie this association.

Use of modelling tools to assess climate change impacts on smallholder oil seed yields in South Africa.

Oil seed crops are the second most important field crops after cereals in the agricultural economy globally. The use and demand for oilseed crops such as groundnut, soybean and sunflower have grown significantly, but climate change is expected to alter the agroecological conditions required for oilseed crop production. This study aims to present an approach that utilizes decision-making tools to assess the potential climate change impacts on groundnut, soybean and sunflower yields and the greenhouse gas emissions from the management of the crops. The Decision Support Tool for Agrotechnology Transfer (DSSAT v4.7), a dynamic crop model and the Cool Farm Tool, a GHG calculator, was used to simulate yields and estimate GHG emissions from these crops, respectively. Four representative concentration pathways (RCPs 2.6, 4.5, 6.0, and 8.5), three nitrogen (0, 75, and 150 kg/ha) and phosphorous (0, 30 and 60 P kg/ha) fertilizer rates at three sites in Limpopo, South Africa (Ofcolaco, Syferkuil and Punda Maria) were used in field trials for calibrating the models. The highest yield was achieved by sunflower across all crops, years and sites. Soybean yield is projected to decrease across all sites and scenarios by 2030 and 2050, except at Ofcolaco, where yield increases of at least 15.6% is projected under the RCP 4.5 scenario. Positive climate change impacts are predicted for groundnut at Ofcolaco and Syferkuil by 2030 and 2050, while negative impacts with losses of up to 50% are projected under RCP8.5 by 2050 at Punda Maria. Sunflower yield is projected to decrease across all sites and scenarios by 2030 and 2050. A comparison of the climate change impacts across sites shows that groundnut yield is projected to increase under climate change while notable yield losses are projected for sunflower and soybean. GHG emissions from the management of each crop showed that sunflower and groundnut production had the highest and lowest emissions across all sites respectively. With positive climate change impacts, a reduction of GHG emissions per ton per hectare was projected for groundnuts at Ofcolaco and Syferkuil and for sunflower in Ofcolaco in the future. However, the carbon footprint from groundnut is expected to increase by 40 to 107% in Punda Maria for the period up to 2030 and between 70-250% for 2050, with sunflower following a similar trend. We conclude that climate change will potentially reduce yield for oilseed crops while management will increase emissions. Therefore, in designing adaptation measures, there is a need to consider emission effects to gain a holistic understanding of how both climate change impacts on crops and mitigation efforts could be targeted.

Preclinical assessment of a mathematical model for ablation zone prediction in thyroid laser ablation.

To develop and validate a 3D simulation model to calculate laser ablation (LA) zone size and estimate the volume of treated tissue for thyroid applications, a model was developed, taking into account dynamic optical and thermal properties of tissue change. For validation, ten Yorkshire swines were equally divided into two cohorts and underwent thyroid LA at 3 W/1,400 J and 3 W/1,800 J respectively with a 1064-nm multi-source laser (Echolaser X4 with Orblaze™ technology; ElEn SpA, Calenzano, Italy). The dataset was analyzed employing key statistical measures such as mean and standard deviation (SD). Model simulation data were compared with animal gross histology. Experimental data for longitudinal length, width (transverse length), ablation volume and sphericity were 11.0 mm, 10.0 mm, 0.6 mL and 0.91, respectively at 1,400 J and 14.6 mm, 12.4 mm, 1.12 mL and 0.83, respectively at 1,800 J. Gross histology data showed excellent reproducibility of the ablation zone among same laser settings; for both 1,400 J and 1,800 J, the SD of the in vivo parameters was ≤ 0.7 mm, except for width at 1,800 J, for which the SD was 1.1 mm. Simulated data for longitudinal length, width, ablation volume and sphericity were 11.6 mm, 10.0 mm, 0.62 mL and 0.88, respectively at 1,400 J and 14.2 mm, 12.0 mm, 1.06 mL and 0.84, respectively at 1,800 J. Experimental data for ablation volume, sphericity coefficient, and longitudinal and transverse lengths of thermal damaged area showed good agreement with the simulation data. Simulation datasets were successfully incorporated into proprietary planning software (Echolaser Smart Interface, Elesta SpA, Calenzano, Italy) to provide guidance for LA of papillary thyroid microcarcinomas. Our mathematical model showed good predictability of coagulative necrosis when compared with data from in vivo animal experiments.

Model predictive control (MPC) applied to a simplified model, magnetic nanoparticle hyperthermia (MNPH) treatment process.

Magnetic nanoparticle hyperthermia (MNPH) has emerged as a promising cancer treatment that complements conventional ionizing radiation and chemotherapy. MNPH involves injecting iron-oxide nanoparticles into the tumor and exposing it to an alternating magnetic field (AMF). Iron oxide nanoparticles produce heat when exposed to radiofrequency AMF due to hysteresis loss. Minimizing the non-specific heating in human tissues caused by exposure to AMF is crucial. A pulse-width-modulated AMF has been shown to minimize eddy-current heating in superficial tissues. This project developed a control strategy based on a simplified mathematical model in MATLAB SIMULINK®to minimize eddy current heating while maintaining a therapeutic temperature in the tumor. A minimum tumor temperature of 43 [°C] is required for at least 30 [min] for effective hyperthermia, while maintaining the surrounding healthy tissues below 39 [°C]. A model predictive control (MPC) algorithm was used to reach the target temperature within approximately 100 [s]. As a constrained MPC approach, a maximum AMF amplitude of 36 [kA/m] and increment of 5 [kA/m/s] were applied. MPC utilized the AMF amplitude as an input and incorporated the open-loop response of the eddy current heating in its dynamic matrix. A conventional proportional integral (PI) controller was implemented and compared with the MPC performance. The results showed that MPC had a faster response (30 [s]) with minimal overshoot (1.4 [%]) than PI controller (115 [s] and 5.7 [%]) response. In addition, the MPC method performed better than the structured PI controller in its ability to handle constraints and changes in process parameters.

Designing combination therapies for cancer treatment: application of a mathematical framework combining CAR T-cell immunotherapy and targeted radionuclide therapy.

Introduction: Cancer combination treatments involving immunotherapies with targeted radiation therapy are at the forefront of treating cancers. However, dosing and scheduling of these therapies pose a challenge. Mathematical models provide a unique way of optimizing these therapies. Methods: Using a preclinical model of multiple myeloma as an example, we demonstrate the capability of a mathematical model to combine these therapies to achieve maximum response, defined as delay in tumor growth. Data from mice studies with targeted radionuclide therapy (TRT) and chimeric antigen receptor (CAR)-T cell monotherapies and combinations with different intervals between them was used to calibrate mathematical model parameters. The dependence of progression-free survival (PFS), overall survival (OS), and the time to minimum tumor burden on dosing and scheduling was evaluated. Different dosing and scheduling schemes were evaluated to maximize the PFS and optimize timings of TRT and CAR-T cell therapies. Results: Therapy intervals that were too close or too far apart are shown to be detrimental to the therapeutic efficacy, as TRT too close to CAR-T cell therapy results in radiation related CAR-T cell killing while the therapies being too far apart result in tumor regrowth, negatively impacting tumor control and survival. We show that splitting a dose of TRT or CAR-T cells when administered in combination is advantageous only if the first therapy delivered can produce a significant benefit as a monotherapy. Discussion: Mathematical models are crucial tools for optimizing the delivery of cancer combination therapy regimens with application along the lines of achieving cure, maximizing survival or minimizing toxicity.

Multivariate analysis and mathematical modeling of the informativeness of patients cases data in chronic pancreatitis associated with concomitant pathology.

OBJECTIVE: Aim: To investigate and analyze homeostatic disorders in patients with a combination of Chronic Pancreatitis(CP) and Arterial Hypertension (AH) and to develop correcting ways of the detected changes. PATIENTS AND METHODS: Materials and Methods: General clinical, laboratory-instrumental examination of 121 patients, who were undergoing inpatient treatment with a diagnosis of Chronic Pancreatitis in combination with Arterial Hypertension of the II stage during 2021-2022. RESULTS: Results: In the majority of cases of patients signs the increasing in IL-1,6 and Cortisol levels were found. A decrease in Ca to the lower limit of the norm was observed (2.18 ± 0.26 mmol/l to the data of control group patients (2.32 ± 0.12 mmol/l, p= 0.01 ), the levels of trace elements Zn and Se were determined within the reference values. The Atherogenic Index was increased 1.8 times and was significantly different from the control group date. During the FE-1 study, a decrease in the level of this indicator was revealed by 151.71±13.91 mg/g of feces, both to the values of reference values and a significant difference to the data of the control group (241.28±29.17 mg/g of feces, p<0 .05). CONCLUSION: Conclusions: Based on the multivariate linear regression analysis of the obtained data, formulas have been developed that can be used to predict the dynamics of the dependent variable (FE-1, IL-1, Selenium level, Glutathione Peroxidase, blood pressure) according to changes in the studied influencing factors.

Prognostic models of drug-induced neutralizing antibody formation in patients with rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis treated with TNF-α blockersockers.

AIM: This study aimed to construct prognostic mathematical models utilizing multifactorial regression analysis to assess the risk of developing drug-induced neutralizing antibodies in patients with rheumatoid arthritis, psoriatic arthritis, and ankylosing spondylitis treated with tumor necrosis factor alpha blockers.

Flavonoid Oxidation Potentials and Antioxidant Activities-Theoretical Models Based on Oxidation Mechanisms and Related Changes in Electronic Structure.

Herein, I will review our efforts to develop a comprehensive and robust model for the estimation of the first oxidation potential, Ep1, and antioxidant activity, AA, of flavonoids that would, besides enabling fast and cheap prediction of Ep1 and AA for a flavonoid of interest, help us explain the relationship between Ep1, AA and electronic structure. The model development went forward with enlarging the set of flavonoids and, that way, we had to learn how to deal with the structural peculiarities of some of the 35 flavonoids from the final calibration set, for which the Ep1 measurements were all made in our laboratory. The developed models were simple quadratic models based either on atomic spin densities or differences in the atomic charges of the species involved in any of the three main oxidation mechanisms. The best model takes into account all three mechanisms of oxidation, single electron transfer-proton transfer (SET-PT), sequential proton loss electron transfer (SPLET) and hydrogen atom transfer (HAT), yielding excellent statistics (R2 = 0.970, S.E. = 0.043).

Fostering a Holistic Understanding of the Full Volatility Spectrum of Organic Compounds from Benzene Series Precursors through Mechanistic Modeling.

A comprehensive understanding of the full volatility spectrum of organic oxidation products from the benzene series precursors is important to quantify the air quality and climate effects of secondary organic aerosol (SOA) and new particle formation (NPF). However, current models fail to capture the full volatility spectrum due to the absence of important reaction pathways. Here, we develop a novel unified model framework, the integrated two-dimensional volatility basis set (I2D-VBS), to simulate the full volatility spectrum of products from benzene series precursors by simultaneously representing first-generational oxidation, multigenerational aging, autoxidation, dimerization, nitrate formation, etc. The model successfully reproduces the volatility and O/C distributions of oxygenated organic molecules (OOMs) as well as the concentrations and the O/C of SOA over wide-ranging experimental conditions. In typical urban environments, autoxidation and multigenerational oxidation are the two main pathways for the formation of OOMs and SOA with similar contributions, but autoxidation contributes more to low-volatility products. NOx can reduce about two-thirds of OOMs and SOA, and most of the extremely low-volatility products compared to clean conditions, by suppressing dimerization and autoxidation. The I2D-VBS facilitates a holistic understanding of full volatility product formation, which helps fill the large gap in the predictions of organic NPF, particle growth, and SOA formation.

Prediction of the marine spreading of low sulfur fuel oil using the long short-term memory model trained with three-phase numerical simulations.

In this study, we focus on the development and validation of a deep learning (long short-term memory, LSTM)-based algorithm to predict the accidental spreading of LSFO (low sulfur fuel oil) on the water surface. The data for the training was obtained by numerical simulations of artificial geometries with different configurations of islands and shorelines and wind speeds (2.0-8.0 m/s). For simulating the spread of oils in O(102) km scales, the volume of fluid and discrete phase models were adopted, and the kinematic variables of particle location, particle velocity, and water velocity were collected as input features for LSTM model. The predicted spreading pattern of LSFO matched well with the simulation (less than 10 % in terms of the mean absolute error for the untrained data). Finally, we applied the model to the Wakashio LSFO spill accident, considering actual geometry and weather information, which confirmed the practical feasibility of the present model.

A review on the evaluation models and impact factors of greenhouse gas emissions from municipal solid waste management processes.

The total amount of global municipal solid waste (MSW) will reach 3.5 billion tons by 2050, thereby bringing tremendous environmental pressure, especially global warming. Large amounts of greenhouse gases (GHGs) have been released during MSW management (MSWM). Accounting for GHG emissions is a prerequisite for providing recommendations on appropriate treatment options to mitigate emissions from MSWM systems. There are many methods involved in estimating emissions. This paper summarizes the computing models commonly used in each process of the integrated MSWM system and emphasizes the influence of parameters and other factors. Compared with other disposal methods, landfilling has the highest emissions, commonly estimated using first-order decay (FOD) methods. Emission reduction can be realized through waste to energy (WtE) and resource recovery measures. IPCC is commonly used for calculating direct emissions, while LCA-based models can calculate emissions including upstream and downstream processes, whose results depend on assumptions and system boundaries. The estimation results of models vary greatly and are difficult to compare with each other. Besides, large gaps exist between the default emission factors (EFs) provided by models and those F measured in specific facilities. These findings provide a systematic view for a bettering understanding of MSW emissions as well as the estimating methods and also reveal the key points that need be developed in the future.

Experimental and theoretical model of microvascular network remodeling and blood flow redistribution following minimally invasive microvessel laser ablation.

Overly dense microvascular networks are treated by selective reduction of vascular elements. Inappropriate manipulation of microvessels could result in loss of host tissue function or a worsening of the clinical problem. Here, experimental, and computational models were developed to induce blood flow changes via selective artery and vein laser ablation and study the compensatory collateral flow redistribution and vessel diameter remodeling. The microvasculature was imaged non-invasively by bright-field and multi-photon laser microscopy, and optical coherence tomography pre-ablation and up to 30 days post-ablation. A theoretical model of network remodeling was developed to compute blood flow and intravascular pressure and identify vessels most susceptible to changes in flow direction. The skin microvascular remodeling patterns were consistent among the five specimens studied. Significant remodeling occurred at various time points, beginning as early as days 1-3 and continuing beyond day 20. The remodeling patterns included collateral development, venous and arterial reopening, and both outward and inward remodeling, with variations in the time frames for each mouse. In a representative specimen, immediately post-ablation, the average artery and vein diameters increased by 14% and 23%, respectively. At day 20 post-ablation, the maximum increases in arterial and venous diameters were 2.5× and 3.3×, respectively. By day 30, the average artery diameter remained 11% increased whereas the vein diameters returned to near pre-ablation values. Some arteries regenerated across the ablation sites via endothelial cell migration, while veins either reconnected or rerouted flow around the ablation site, likely depending on local pressure driving forces. In the intact network, the theoretical model predicts that the vessels that act as collaterals after flow disruption are those most sensitive to distant changes in pressure. The model results correlate with the post-ablation microvascular remodeling patterns.

Numerical optimization of longitudinal collimator geometry for novel x-ray field.

Objective. A novel x-ray field produced by an ultrathin conical target is described in the literature. However, the optimal design for an associated collimator remains ambiguous. Current optimization methods using Monte Carlo calculations restrict the efficiency and robustness of the design process. A more generic optimization method that reduces parameter constraints while minimizing computational load is necessary. A numerical method for optimizing the longitudinal collimator hole geometry for a cylindrically-symmetrical x-ray tube is demonstrated and compared to Monte Carlo calculations.Approach. The x-ray phase space was modelled as a four-dimensional histogram differential in photon initial position, final position, and photon energy. The collimator was modeled as a stack of thin washers with varying inner radii. Simulated annealing was employed to optimize this set of inner radii according to various objective functions calculated on the photon flux at a specified plane.Main results. The analytical transport model used for optimization was validated against Monte Carlo calculations using Geant4 via its wrapper, TOPAS. Optimized collimators and the resulting photon flux profiles are presented for three focal spot sizes and five positions of the source. Optimizations were performed with multiple objective functions based on various weightings of precision, intensity, and field flatness metrics. Finally, a select set of these optimized collimators, plus a parallel-hole collimator for comparison, were modeled in TOPAS. The evolution of the radiation field profiles are presented for various positions of the source for each collimator.Significance. This novel optimization strategy proved consistent and robust across the range of x-ray tube settings regardless of the optimization starting point. Common collimator geometries were re-derived using this algorithm while simultaneously optimizing geometry-specific parameters. The advantages of this strategy over iterative Monte Carlo-based techniques, including computational efficiency, radiation source-specificity, and solution flexibility, make it a desirable optimization method for complex irradiation geometries.

Bio-thermal response during laser haemorrhoidoplasty: an exclusive analytical and numerical approach for theoretical investigation.

Haemorrhoidal disease is identified by declension of the inflamed and bleeding of vascular tissues of the anal canal. Traditionally, haemorrhoids are associated with chronic constipation and the most common symptoms are irritation in anus region, pain and discomfort, swelling around anus, tender lumps around the anus and rectal bleeding (depending upon the grade of haemorrhoid). Among the several conventional treatment procedures (commonly mentioned as, rubber band litigation, sclerotherapy and electrotherapy), laser haemorrhoidoplasty is an out-patient and less-invasive laparoscopic procedure. From literature survey it has been observed that an exclusive theoretical model depicting the impact of 1064 nm wavelength laser wave on living tissues subjected to haemorrhoid therapy is not available. This research work is a pioneering attempt to develop a theoretical study attributing specifically on laser therapy of haemorrhoid treatment based on Pennes' biological heat transfer model. The corresponding mathematical model has been solved by analytical method to establish thermal response of tissue during the treatment and also the same has been solved a numerical approach based on finite difference method to validate the feasibility of former method due to unavailability of any theoretical model. Impact of variation of blood perfusion term, laser pulse time and optical penetration depth on temperature response of skin tissue is captured. The tissue temperature decreases along with time of laser exposure with increasing the blood perfusion rate as it carries away large amount of heat. With the increase in laser pulse time, tissue temperature declines due to shorter pulse time resulting in higher energy consumed by electrons. The research outcome is successfully validated with less than 1% of error observed between the appointed analytical and numerical scheme.

Gas explosion characteristics and spray control mechanism in underground square.

The spray system mechanism during a gas explosion in an underground square pipeline is complex. In this paper, the underground square of Fuxin City is selected as the research object. FLACS numerical simulation software is used to analyze the spatial and temporal distribution characteristics of a gas explosion in an underground square pipeline with an unopened spray system using combustion and combustion rate models. Different spray pressures were compared and analyzed to determine the optimal spray control pressure, and the spray system mechanism was clarified. The results revealed that the gas explosion overpressure is divided into the overpressure gentle, overpressure rising, and overpressure decay stages, corresponding to a trend of rapid growth and slow decline. The influence of spray pressure on the gas explosion exhibits a promotion-inhibition-promotion trend, corresponding to 0-0.2 MPa, 0.2-0.6 MPa, and 0.6-1.6 Mpa, respectively. The peak overpressure and overpressure propagation rates are the lowest at 0.6 MPa, and the explosion suppression effect is the most pronounced. The spray system mechanism varies with the explosion overpressure stages. Generally, the time to peak value, that is, the peak time, the overall duration of the explosion, and the duration of the explosion stage decrease, whereas the peak explosion overpressure decreases.

Mathematical modelling of clonal reduction therapeutic strategies in acute myeloid leukemia.

Over the years, the overall survival of older patients diagnosed with acute myeloid leukemia (AML) has not significantly increased. Although standard cytotoxic therapies that rapidly eliminate dividing myeloblasts are used to induce remission, relapse can occur due to surviving therapy-resistant leukemic stem cells (LSCs). Hence, anti-LSC strategies have become a key target to cure AML. We have recently shown that previously approved cardiac glycosides and glucocorticoids target LSC-enriched CD34+ cells in the primary human AML 8227 model with more efficacy than normal hematopoietic stem cells (HSCs). To translate these in vitro findings into humans, we developed a mathematical model of stem cell dynamics that describes the stochastic evolution of LSCs in AML post-standard-of-care. To this, we integrated population pharmacokinetic-pharmacodynamic (PKPD) models to investigate the clonal reduction potential of several promising candidate drugs in comparison to cytarabine, which is commonly used in high doses for consolidation therapy in AML patients. Our results suggest that cardiac glycosides (proscillaridin A, digoxin and ouabain) and glucocorticoids (budesonide and mometasone) reduce the expansion of LSCs through a decrease in their viability. While our model predicts that effective doses of cardiac glycosides are potentially too toxic to use in patients, simulations show the possibility of mometasone to prevent relapse through the glucocorticoid's ability to drastically reduce LSC population size. This work therefore highlights the prospect of these treatments for anti-LSC strategies and underlines the use of quantitative approaches to preclinical drug translation in AML.

A re-activation model for 169Yb intensity modulated brachytherapy sources accounting for spatiotemporal isotopic composition.

BACKGROUND: Intensity modulated brachytherapy based on partially shielded intracavitary and interstitial applicators is possible with a cost-effective 169Yb production method. 169Yb is a traditionally expensive isotope suitable for this purpose, with an average γ-ray energy of 93 keV. Re-activating a single 169Yb source multiple times in a nuclear reactor between clinical uses was shown to theoretically reduce cost by approximately 75% relative to conventional single-activation sources. With re-activation, substantial spatiotemporal variation in isotopic source composition is expected between activations via 168Yb burnup and 169Yb decay, resulting in time dependent neutron transmission, precursor usage, and reactor time needed per re-activation. PURPOSE: To introduce a generalized model of radioactive source production that accounts for spatiotemporal variation in isotopic source composition to improve the efficiency estimate of the 169Yb production process, with and without re-activation. METHODS AND MATERIALS: A time-dependent thermal neutron transport, isotope transmutation, and decay model was developed. Thermal neutron flux within partitioned sub-volumes of a cylindrical active source was calculated by raytracing through the spatiotemporal dependent isotopic composition throughout the source, accounting for thermal neutron attenuation along each ray. The model was benchmarked, generalized, and applied to a variety of active source dimensions with radii ranging from 0.4 to 1.0 mm, lengths from 2.5 to 10.5 mm, and volumes from 0.31 to 7.85 mm3, at thermal neutron fluxes from 1 × 1014 to 1 × 1015 n cm-2 s-1. The 168Yb-Yb2O3 density was 8.5 g cm-3 with 82% 168Yb-enrichment. As an example, a reference re-activatable 169Yb active source (RRS) constructed of 82%-enriched 168Yb-Yb2O3 precursor was modeled, with 0.6 mm diameter, 10.5 mm length, 3 mm3 volume, 8.5 g cm-3 density, and a thermal neutron activation flux of 4 × 1014 neutrons cm-2 s-1. RESULTS: The average clinical 169Yb activity for a 0.99 versus 0.31 mm3 source dropped from 20.1 to 7.5 Ci for a 4 × 1014 n cm-2 s-1 activation flux and from 20.9 to 8.7 Ci for a 1 × 1015 n cm-2 s-1 activation flux. For thermal neutron fluxes ≥2 × 1014 n cm-2 s-1, total precursor and reactor time per clinic-year were maximized at a source volume of 0.99 mm3 and reached a near minimum at 3 mm3. When the spatiotemporal isotopic composition effect was accounted for, average thermal neutron transmission increased over RRS lifetime from 23.6% to 55.9%. A 28% reduction (42.5 days to 30.6 days) in the reactor time needed per clinic-year for the RRS is predicted relative to a model that does not account for spatiotemporal isotopic composition effects. CONCLUSIONS: Accounting for spatiotemporal isotopic composition effects within the RRS results in a 28% reduction in the reactor time per clinic-year relative to the case in which such changes are not accounted for. Smaller volume sources had a disadvantage in that average clinical 169Yb activity decreased substantially below 20 Ci for source volumes under 1 mm3. Increasing source volume above 3 mm3 adds little value in precursor and reactor time savings and has a geometric disadvantage.

Modeling study on oil spill transport in the Great Lakes: The unignorable impact of ice cover.

The rise in oil trade and transportation has led to a continuous increase in the risk of oil spills, posing a serious worldwide concern. However, there is a lack of numerical models for predicting oil spill transport in freshwater, especially under icy conditions. To tackle this challenge, we developed a prediction system for oil with ice modeling by coupling the General NOAA Operational Modeling Environment (GNOME) model with the Great Lakes Operational Forecast System (GLOFS) model. Taking Lake Erie as a pilot study, we used observed drifter data to evaluate the performance of the coupled model. Additionally, we developed six hypothetical oil spill cases in Lake Erie, considering both with and without ice conditions during the freezing, stable, and melting seasons spanning from 2018 to 2022, to investigate the impacts of ice cover on oil spill processes. The results showed the effective performance of the coupled model system in capturing the movements of a deployed drifter. Through ensemble simulations, it was observed that the stable season with high-concentration ice had the most significant impact on limiting oil transport compared to the freezing and melting seasons, resulting in an oil-affected open water area of 49 km2 on day 5 with ice cover, while without ice cover it reached 183 km2. The stable season with high-concentration ice showed a notable reduction in the probability of oil presence in the risk map, whereas this reduction effect was less prominent during the freezing and melting seasons. Moreover, negative correlations between initial ice concentration and oil-affected open water area were consistent, especially on day 1 with a linear regression R-squared value of 0.94, potentially enabling rapid prediction. Overall, the coupled model system serves as a useful tool for simulating oil spills in the world's largest freshwater system, particularly under icy conditions, thus enhancing the formulation of effective emergency response strategies.

Reliable sustainable management strategies for flare gas recovery: technical, environmental, modeling, and economic assessment: a comprehensive review.

The gas flaring network is an inseparable constituent commonly present in most of the oil and gas refineries and petrochemical facilities conferring reliable operational parameters. The improper disposal of burn-off gases improperly results in environmental problems and loss of economic resources. In this regard, waste to energy transforming nexus, in accord with the "carbon neutrality" term, has potentially emerged as a reasonable pathway to preserve our planet. In a transdisciplinary manner, the present review article deeply outlines the different up-to-date strategies developed to recover the emitted gases (flaring minimization) into different value-added products. To analyze the recovery potential of flare gases, different technologies, and decision-making factors have been critically reviewed to find the best recovery methods. We recommend more straightforward recovery methods despite lower profits. In this regard, electricity generation seems to be an appropriate option for application in small amounts of flaring. However, several flare gas utilization processes such as syngas manufacturing, reinjection of gas into petroleum reservoirs, and production of natural gas liquid (NGL) are also recommended as options because of their economic significance, technological viability (both onshore and offshore), and environmental benefits. Moreover, the adopted computational multi-scale data assimilation for predictive modeling of flare gas recovery scenarios has been systematically reviewed, summarized, and inspected.