Nonlinear Model Fitting Research Articles

Flow characteristics and performance optimization of temperature and pressure reducing valve with droplets evaporation

The influence of water spray arrangement on temperature reduction performance is significant in temperature and pressure reducing valves (TPRV). A verified vapor-liquid two-phase flow in TPRV was established by Euler-Lagrange flow and droplets evaporation model. The heat and mass transfer processes in droplet evaporation are presented. The influence of nozzles number, spray position, injection direction on internal flow characteristics and temperature reduction performance of TPRV was analyzed. An optimized spray water arrangement was determined via nonlinear fitting model, the great temperature reduction performance was obtained. The results show that the mass flow rate of TPRV inlet and outlet has a non-linear variation with the valve opening. The significant pressure gradient occurs in the valve core and chamber. The upward oblique jet at throat makes the temperature near the spray area low and the temperature at throat downstream high. The range of high Mach numbers increases with the increasing of valve opening. The temperature variation of parallel injection and reverse injection is different with the increasing of the nozzles number. The vortex intensity and range have a stronger influence on temperature reduction performance ability. The temperature reduction performance of optimized TPRV is improved by 0.6 %- 3.98 %. The optimization of water spray arrangement on temperature reduction performance is applicable to various loads.

Biocalorimetry-aided monitoring of fungal pretreatment of lignocellulosic agricultural residues

The present study aimed to investigate whether and how non-invasive biocalorimetric measurements could serve for process monitoring of fungal pretreatment during solid-state fermentation (SSF) of lignocellulosic agricultural residues such as wheat straw. Seven filamentous fungi representing different lignocellulose decay types were employed. Water-soluble sugars being immediately available after fungal pretreatment and those becoming water-extractable after enzymatic digestion of pretreated wheat straw with hydrolysing (hemi)cellulases were considered to constitute the total bioaccessible sugar fraction. The latter was used to indicate the success of pretreatments and linked to corresponding species-specific metabolic heat yield coefficients (YQ/X) derived from metabolic heat flux measurements during fungal wheat straw colonisation. An YQ/X range of about 120 to 140 kJ/g was seemingly optimal for pretreatment upon consideration of all investigated fungi and application of a non-linear Gaussian fitting model. Upon exclusion from analysis of the brown-rot basidiomycete Gloeophyllum trabeum, which differs from all other here investigated fungi in employing extracellular Fenton chemistry for lignocellulose decomposition, a linear relationship where amounts of total bioaccessible sugars were suggested to increase with increasing YQ/X values was obtained. It remains to be elucidated whether an YQ/X range being optimal for fungal pretreatment could firmly be established, or if the sugar accessibility for post-treatment generally increases with increasing YQ/X values as long as “conventional” enzymatic, i.e. (hemi)cellulase-based, lignocellulose decomposition mechanisms are operative. In any case, metabolic heat measurement–derived parameters such as YQ/X values may become very valuable tools supporting the assessment of the suitability of different fungal species for pretreatment of lignocellulosic substrates.Key points• Biocalorimetry was used to monitor wheat straw pretreatment with seven filamentous fungi.• Metabolic heat yield coefficients (YQ/X) seem to indicate pretreatment success.• YQ/X values may support the selection of suitable fungal strains for pretreatment.

A portable microfluidic electrochemical sensor with nonlinear fit strategy for wide-range uric acid detection

In this work, we have developed a portable electrochemical sensing device controlled by a smartphone through Bluetooth, which was applied for regular uric acid (UA) monitoring in prevention and healthcare management. On the core lies a nonlinear fitting model (I = k·(N − exp(−K·cbulk + b))) which is proposed for the first time and given from the differential equation based on the theoretical analysis. We believe this model can reflect the intrinsic relationship between the obtained current and the bulk analyte concentration, therefore leading to an expanding UA detection range (10-fold) compared to the widely used linear standard curve model and reducing the requirements for the modification of the electrode materials. Besides, the integration of the microfluidic chip promoted the removal of UA oxidation products at the electrode surface, yielding excellent sensing stability with a relative standard deviation (RSD) lower than 1 % during 10 consecutive runs. Consequently, we acquired the relationship between Square Wave Voltammetry (SWV) current and UA concentration (I = 15.27*(2.05 − exp(−0.00108*(cbulk − 663.5))), R2 = 0.9999) in the range of 5 to 1000 μM with a limit of detection (LoD) of 2.4 μM using a completely unmodified screen-printed carbon electrode (SPCE). Proof of concept experiments using 25× diluted human urine spiked with UA yielded a recovery rate of 87.5–101.4 % and a satisfactory selectivity result, which is within the value expected for clinical use, indicating the potential of the developed instrument and nonlinear fitting model for urinary UA detection.

Predicting Fine Dead Fuel Load of Forest Floors Based on Image Euler Numbers

The fine dead fuel load on forest floors is the most critical classification feature in fuel description systems, affecting several parameters in the manifestation of wildland fires. An accurate determination of this fine dead fuel load contributes substantially to effective wildland fire prevention, monitoring, and suppression. This study investigated the viability of incorporating image Euler numbers into predictive models of fine dead fuel load via the taking photos method. Pinus massoniana needles and Quercus fabri broad leaves—typical fuel types in karst areas—served as the research subjects. Accurate field data were collected in the Tianhe Mountain forests, China, while artificial fine dead fuelbeds of differing loads were constructed in the laboratory. Images of the artificial fuelbeds were captured and uniformly digitized according to various conversion thresholds. Thereafter, the Euler numbers were extracted, their relationship with fuel load was analyzed, and this relationship was applied to generate three load-prediction models based on stepwise regression, nonlinear fitting, and random forest algorithms. The Euler number had a significant relationship with both P. massoniana and Q. fabri fuel loads. At low conversion thresholds, the Euler number was negatively correlated with fuel load, whereas a positive correlation was recorded when this threshold exceeded a certain value. The random forest model showed the best prediction performance, with mean relative errors of 9.35% and 14.54% for P. massoniana and Q. fabri, respectively. The nonlinear fitting model displayed the next best performance, while the stepwise regression model exhibited the largest error, which was significantly different from that of the random forest model. This study is the first to propose the use of image features to predict the fine fuel load on a surface. The results are more objective, accurate, and time-saving than current fuel load estimates, benefiting fuel load research and the scientific management of wildland fires.

Effect of plant-based natural fibers on the mechanical properties and volume change of cement paste

PurposeThe purpose of this paper is to check the feasibility of using biomaterial such as of Phragmites-Australis (PA) in cement paste to achieve sustainable building materials.Design/methodology/approachIn this study, cement pastes were prepared by adding locally produced PA fibers in four different volumes: 0%, 0.5%, 1% and 2% for a duration of 180 days. Bottles and prisms were subjected to chemical shrinkage (CS), drying shrinkage (DS), autogenous shrinkage (AS) and expansion tests. Besides, prism specimens were tested for flexural strength and compressive strength. Furthermore, a mathematical model was proposed to determine the variation length change as function of time.FindingsThe experimental findings showed that the mechanical properties of cement paste were significantly improved by the addition of 1% PA fiber compared to other PA mixes. The effect of increasing the % of PA fibers reduces the CS, AS, DS and expansion of cement paste. For example, the addition of 2% PA fibers reduces the CS, expansion, AS and DS at 180 days by 36%, 20%, 13% and 10%, respectively compared to the control mix. The proposed nonlinear model fit to the experimental data is appropriate with R2 values above 0.92. There seems to be a strong positive linear correlation between CS and AS/DS with R2 above 0.95. However, there exists a negative linear correlation between CS and expansion.Research limitations/implicationsThe PA used in this study was obtained from one specific location. This can exhibit a limitation as soil type may affect PA properties. Also, one method was used to treat the PA fibers.Practical implicationsThe utilization of PA fibers in paste may well reduce the formation of cracks and limit its propagation, thus using a biomaterial such as PA in cementitious systems can be an environmentally friendly option as it will make good use of the waste generated and enhance local employment, thereby contributing toward sustainable development.Originality/valueTo the authors best knowledge, there is hardly any research on the effect of PA on the volume stability of cement paste. Therefore, the research outputs are considered to be original.

Removal of Brilliant Green Dye from Aqueous Solutions Using Multi-walled Carbon Nanotubes (MWCNTs): Linear and Nonlinear Isotherm Models and Error Analysis

Current research explains the comparison of linear and nonlinear regression methods for finding the optimal isotherm study using experimental data for the adsorption of BG on multi-walled carbon nanotubes MWCNTs. BG dye maximum adsorption onto MWCNTs occurred at pH 2 and 35°C, with the apparent equilibrium reached after 15 min. In this study, five error functions were used: ERRS, Hybrid, Chi-square (χ2), ARE, and EABS. The values of error functions suggest that the Langmuir Linear type 3 is a suitable isotherm to describe the adsorption of BG on MWCNTs. The results showed that the Langmuir isotherm is a fit good isotherm to describe the adsorption process. The coefficient of non-determination (K2) showed Hybrid, and ERRS were the preferable error functions used to predict the fit of linear and nonlinear isotherm models. Compared with other studies, MWCNTs can be used as a low-cost adsorbent with low contact time for the removal of BG dye from an aqueous solution.

Bivariate and Multivariate Data Cloning through Non Linear Regression Models

Nonlinear regression analysis holds significant popularity in mathematical, engineering, and social science domains. Disciplines like financial matters, biology, and natural chemistry have broadly utilized nonlinear regression models (NLRMs). Cloned datasets have their own importance in such areas which provide the same fit of bivariate and multivariate nonlinear regression models for the actual datasets. This article presents a sequence of cloned datasets that give exactly the same fit of bivariate and multivariate nonlinear regression models.

Electro-oxidative removal of five antibiotics of different classes and their mixture using Ti/Sb-SnO2/PbO2 anode: Kinetics, degradation pathway, and toxicity evaluation

In this study, electro-oxidation of representative antibiotics of five different classes viz. sulfonamides (sulfamethoxazole), penicillin (ampicillin), tetracycline (tetracycline), aminoglycoside (streptomycin), macrolide (erythromycin), and the mixture of representative antibiotics by Ti/Sb-SnO2/PbO2 anode were studied. Ti/Sb-SnO2/PbO2 anode was synthesized and characterized by SEM, EDAX, XRD, CV, and LSV analysis. The physical characterization revealed the compact nature of the PbO2 crystals, composition, and crystal arrangement in β-planes. Further, CV and LSV confirmed the electrode stability under the operating condition with high oxygen evolution potential. The in situ generated reactive oxygen species, such as hydroxyl radicals, hydrogen peroxide, and sulfate radicals, were quantified. Sulfamethoxazole was used as a model antibiotic to study the effect of current density (10–40 mA cm−2), initial concentration (100–1000 μg L−1), and water matrix (25 mM sodium sulfate and secondary effluent). The kinetic constants were determined by adopting a non-linear regression model and curve fitting. The optimum current density was 30 mA cm−2 with k1 = 0.4162 min−1. Under the optimum conditions, degradation of antibiotics was >99 % irrespective of the antibiotic class. The intermediates were identified by LC-MS/MS analysis, and a complete electro-oxidation pathway was proposed for five antibiotics of different classes. The toxicity of the representative antibiotics and mixture was evaluated by V. fischeri bioluminescence inhibition. Overall, the toxicity was found to decrease, and extending the treatment time ensures effective eco-toxicity reduction by allowing safe discharge of the treated water. The observed results confirm that the electro-oxidation with Ti/Sb-SnO2/PbO2 anode is an effective and a low-cost alternative for BDD.

0635 Associations Between Ambient Light Exposure and Development of Depressive Symptoms in Women at Risk for Postpartum Depression

Abstract Introduction There is increasing recognition that ambient light exposure plays a critical role in daytime functioning, including mood regulation. In this analysis, we examined associations between 24-hour light exposure patterns and mood in women at risk of developing postpartum depression. Methods Perinatal women ages 18-40 with a history of major depressive disorder or bipolar disorder who did not meet criteria for depression at study enrollment during 3rd trimester wore wrist actigraphs that recorded ambient light levels for one week at 3rd trimester (~33 weeks’ gestation) and again during postpartum weeks 2, 6, and 16. Depressive symptoms were assessed with the Hamilton Depression Rating Scale (HAM-D-17) at the end of each recording week. To summarize the light data, we calculated the arithmetic mean of the log-light levels in twenty-four 60-minute bins across each day and then aggregated each hour across each recording week. We used nonlinear model fitting (cosinor) to calculate area under the curve (AUC) and compared 24-hour and morning (8:00 am to 12:00 pm) light exposure patterns between women with elevated depression symptoms at week 16 (HAM-D-17 > 7) and women with low depression symptoms at study entry and week 16 (HAM-D-17 <=7) using Mann-Whitney U-tests. Results Compared with women who did not become depressed (n=15), those who ultimately developed depression (n=8) had significantly lower morning light exposure at 3rd trimester and at postpartum week 6, but not at postpartum week 16. Effect sizes were moderate at 3rd trimester (eta-squared=0.30) and small at Week 6 (eta squared=0.24). There were no significant differences in the overall 24-hour AUC of light exposure between participants who developed significant depressive symptoms at postpartum week 16 and non-depressed women at any time point [3rd trimester: p=0.35; postpartum week 6: p=0.92; postpartum week 16: p=0.35]. Conclusion In this sample of perinatal women at risk for postpartum depression, new mothers who developed clinically-significant depressive symptoms at postpartum week 16 had lower levels of morning light exposure during 3rd trimester and at 6 weeks postpartum. Lower peripartum morning light exposure may predict the development of postpartum depression. Support (if any) MH086689 to KMS

A combined kinetic analysis for thermal characteristics and reaction mechanism based on non-isothermal experiments: The case of poly(vinyl alcohol) pyrolysis

This work aims to implement one efficient combined kinetic approach for determining thermal kinetic triplets and elucidating reaction mechanism for poly(vinyl alcohol). Thermogravimetric pyrolysis profiles were acquired in inert nitrogen atmosphere with 5, 10, 15, and 20 K/min heating rates, and its thermal decomposition behavior was characterized in details. With respect to two main stages identified in whole pyrolysis process, their activation energies varying with conversion extents were computed by applying several model free methods, including FWO, KAS, FR, and advanced isoconversion approaches. Subsequently, the linear CR and non-linear masterplots model fitting methods were to severely figure out the appropriate reaction mechanism functions. Of particular interest is observed that F3/2 and F2 mechanism models were separately deemed to be appropriate for two stages, but they failed in giving good overlapping details with experimental data. Furthermore, a combined kinetic analysis was carefully conducted to obtain kinetics triplets, which includes model independent analysis and thoroughly considers kinetic compensation effect. Thereby, accurate reaction models were separately optimized and reconstructed for two distinct pyrolysis steps by the imported accommodation functions. This work finally provides a precise methodology to gain insight into thermal behavior and decomposition mechanism of poly(vinyl alcohol) , and improve the prediction accuracy of kinetics.

Study on the Optimal Reaction Parameters of C4 Olefin Prepared by Ethanol Catalyzed Reaction Based on XGBoost Regression Prediction Algorithm

The production of C4 olefin by ethanol coupling catalytic reaction has a broad development prospect in the field of renewable energy. In this paper, the influencing factors of C4 olefin preparation process were studied, the influence of temperature and catalyst type on the reaction was analyzed, and the multivariate nonlinear regression fitting model was established under the assumption of ignoring the small probability of experimental error. Then, based on the grey correlation analysis model, the correlation degree between each variable and ethanol conversion rate and C4 olefin selection rate was studied.Then, using the catalyst parameter variables considered above, the relationship equations between four catalyst parameter variables, temperature and C4 olefin yield were calculated, and the overall constructor model of C4 olefin yield under the joint action of five variables was established by using the constructor method. Finally, using MPai data science platform software based on XGBoost regression prediction algorithm, the established constructor was cross-validated by 50% fold, and the optimal solution of reaction parameters was finally obtained.Finally, five experiments were selected to improve the experimental control group data and verify the accuracy of the theoretical value of the optimal solution. By changing the catalyst parameters and temperature, a relative control group was added to the experimental group, and the conclusion of the theoretical parameter optimal solution under unrestricted and temperature restrictions was experimentally verified.

Linear and nonlinear modeling of kinetics and isotherm of malachite green dye adsorption to trimellitic-modified pineapple peel

Adsorption is known for toxic dye removal in water, which can utilize a plant-based material to make a low cost and sustainable adsorbent. This study addresses the issue of preparing a cellulose-based adsorbent that is catered to cationic dye property and address inaccurate data interpretation and parameter estimation errors in adsorption analysis. The objective of this research is to overcome these challenges by modifying the surface of cellulose with carboxyl and utilizing original nonlinear equations with errors evaluation within the applied models to interpret the adsorption. In this paper, the pineapple peel cellulose will be reacted with 1,2,4–benzene tricarboxylic acid for malachite green adsorption. Characterization results showed a peak of C = O at 1712 cm−1, confirming the success of the modification. Through analyzing lowest sum of normalized error in linear and nonlinear model fitting, the best fit was obtained from the nonlinear Sips isotherm, with qm = 51.3 mg g−1, KS = 0.016 L/g, and 1/n = 2.0, as well as PSO kinetics k2 = 0.0142 g mg−1 min−1, qe = 24.8 mg g−1 which suggest that the modification of cellulose promotes collaborative adsorption, a mixing mechanism for MG dye adsorption.

An Alloy Platform of Dual‐Fingerprints for High‐Performance Stroke Screening

AbstractA multi‐modal serum profiling platform holds promise for precision diagnosis of diseases. Still, advanced tools are in demand to deliver the multi‐modal serum profiling. Herein, a bimodal spectrometric protocol is designed for stoke serum profiling using an alloy platform, by integrating label‐free surface‐enhanced Raman spectroscopy (SERS) and laser desorption/ionization mass spectrometry (LDI‐MS). The PdAu@Au concave cube with a wide localized surface plasmonic resonance (LSPR) range simultaneously enhances the signals from SERS and LDI‐MS, enabling high‐throughput co‐detection of vibrational and metabolic fingerprints of 0.1 µL serum in 2 min with simple pretreatment. Further, a dual‐fingerprints screening model of stroke is constructed, by adaptive machine learning with a programming nonlinear fitting model. The area under the curve are 0.949 (0.917–0.977, 95% confidence interval (CI)) and 0.911 (0.812–0.984, 95% CI), in the discovery and validation cohorts, respectively. Finally, five metabolites are identified that correlated to SERS signals and mapped the relevant pathways. This study features high performance in terms of throughput, speed, sample volume, and accuracy, providing new insight into the construction of multiplexed characterization platforms for precision diagnostic.

Nonlinear model fitting analysis of feather growth and development curves in the embryonic stages of Jilin white geese (Anser cygnoides).

Poultry is subject to varying degrees of feather loss and feather pecking during production, which seriously affects the live appearance and carcass appearance of their commercial traits and greatly reduces the production profitability of the farming enterprise. It also has an impact on down production and quality in the case of geese. In this study, mathematical models (Logistic, Gompertz, and Von Bertalanffy) were used to assess feather growth and development during the embryonic period in Jilin white geese (Anser cygnoides) predicting the weight and length of feathers from the back, chest, and belly tracts at different embryonic ages, to determine which growth model more accurately described feather growth patterns. The result first showed that the primary feather follicles of the Jilin white goose developed at E14 and secondary feather follicles at E18; primary feather follicle density increased and then decreased, whereas secondary feather follicle density increased continuously and the primary and secondary feather follicles developed independently. Secondly, the embryonic feather growth followed a slow-fast-slow pattern, with feathers growing slowly from E12 to E18, quickly from E18 to E24, and then decreasing after E24 until just before emergence (E30). In addition, before E14, feathers were concentrated in the back tracts, and no feathers were found on the head, neck, chest, abdomen, or wings. By E22, the whole body of the embryo was covered with feathers, and the back feathers were the earliest and fastest to develop. Compared to the Gompertz and von Bertalanffy models, the logistic model fit (R2 = 0.997) was the highest, while the sum of residual squares (RSS = 25661.67), Akaike's information criterion (AIC = 77.600), Bayesian information criterion (BIC = 78.191), and mean square error (MSE = 2851.296) were the lowest. Therefore, the logistic model was more suitable for describing the changes in whole-body feather growth during the embryonic period in Jilin white geese. In conclusion, using the growth curve model to explain the relationship between feather growth and embryonic age in geese will potentially speed up the process of genetic improvement in Jilin white geese (A. cygnoides) and thus provide scientific support for molecular genetic breeding.

Global-scale modeling of early factors and country-specific trajectories of COVID-19 incidence: a cross-sectional study of the first 6 months of the pandemic

BackgroundStudies examining factors responsible for COVID-19 incidence have been mostly focused at the national or sub-national level. A global-level characterization of contributing factors and temporal trajectories of disease incidence is currently lacking. Here we conducted a global-scale analysis of COVID-19 infections to identify key factors associated with early disease incidence. Additionally, we compared longitudinal trends of COVID-19 incidence at a per-country level, and classified countries based on COVID-19 incidence trajectories and effects of lockdown responses.MethodsThis is an observational cross-sectional study covering COVID-19 incidence over the first 6 months of the pandemic (Jan 1, 2020 to June 30, 2020). A retrospective analysis was performed using publicly available data for total confirmed COVID-19 cases by country, and using recent data on demographic, meteorological, economic and health-related indicators per country. Data was analyzed in a regression modeling framework. Longitudinal trends were assessed via linear and non-linear model fitting. Competing models of disease trajectories were ranked by the Akaike’s Information Criterion (AIC). A novel approach involving hierarchical clustering was developed to classify countries based on the effects of lockdown measures on new COVID-19 caseloads surrounding the lockdown period.ResultsUnivariate analysis identified 11 variables (employments in the agriculture, service and industrial sectors, percent population residing in urban areas, population age, number of visitors, and temperatures in the months of Jan-Apr) as independently associated with COVID-19 infections at a global level (variable p < 1E-05). Multivariable analysis identified a 5-variable model (percent urban population, percent employed in agriculture, population density, percent population aged 15–64 years, and temperature in March) as optimal for explaining global variations in COVID-19 (model adjusted R-squared = 0.68, model p < 2.20E-16). COVID-19 case trajectories for most countries were best captured by a log-logistic model, as determined by AIC estimates. Six predominant country clusters were identified when characterizing the effects of lockdown intervals on variations in COVID-19 new cases per country.ConclusionsGlobally, economic and meteorological factors are important determinants of early COVID-19 incidence. Analysis of longitudinal trends and lockdown effects on COVID-19 highlights important nuances in country-specific responses to infections. These results provide valuable insights into disease incidence at a per-country level, possibly allowing for more informed decision making by individual governments in future disease outbreaks.

FOLFIRINOX Pharmacodynamic Interactions in 2D and 3D Pancreatic Cancer Cell Cultures

The multi-drug combination regime, FOLFIRINOX, is a standard of care chemotherapeutic therapy for pancreatic cancer patients. However, systematic evaluation of potential pharmacodynamic interactions among multi-drug therapy has not been reported previously. Here, pharmacodynamic interactions of the FOLFIRINOX agents (5-fluorouracil (5-FU), oxaliplatin (Oxa) and SN-38, the active metabolite of irinotecan) were assessed across a panel of primary and established pancreatic cancer cells. Inhibition of cell proliferation was quantified for each drug, alone and in combination, to obtain quantitative, drug-specific interaction parameters and assess the nature of drug interactions. The experimental data were analysed assuming Bliss independent interactions, and nonlinear regression model fitting was conducted in SAS. Estimates of the drug interaction term, psi (ψ), revealed that the Oxa/SN-38 combination appeared synergistic in PANC-1 (ψ = 0.6, 95% CI = 0.4, 0.9) and modestly synergistic, close to additive, in MIAPaCa-2 (ψ = 0.8, 95% CI = 0.6, 1.0) in 2D assays. The triple combination was strongly synergistic in MIAPaCa-2 (ψ = 0.2, 95% CI = 0.1, 0.3) and modestly synergistic/borderline additive in PANC-1 2D (ψ = 0.8, 95% CI = 0.6, 1.0). The triple combination showed antagonistic interactions in the primary PIN-127 and 3D PANC-1 model (ψ > 1). Quantitative pharmacodynamic interactions have not been described for the FOLFIRINOX regimen; this analysis suggests a complex interplay among the three chemotherapeutic agents. Extension of this pharmacodynamic analysis approach to clinical/translational studies of the FOLFIRINOX combination could reveal additional pharmacodynamic interactions and guide further refinement of this regimen to achieve optimal clinical responses.Graphical abstract

ConditionSenseNet: A Deep Interpolatory ConvNet for Bearing Intelligent Diagnosis Under Variational Working Conditions

Deep learning, with its ability of feature mining and logical judgement, has been widely studied in industrial intelligent diagnosis, including bearing fault diagnosis. However, an explicable and representable expression of deep learning architecture for the variational working conditions has been rarely discussed while it is known that vibration features from bearings are seriously influenced by variational working conditions. In this article, a deep interpolation ConvNet (DICN) architecture with three special layers, consisting of multiple sub-ConvNet units, weight unit, and fusion unit, is presented with the basic deep ConvNet architecture. Different from the traditional network, the first sub-ConvNet extracts the fault features under different working conditions, while the corresponding condition weight unit is learned from a working condition identification task. With the principle of interpolation theory, fusion unit is employed to achieve a sound fault feature representation under unknown working condition, which is named as ConditionSenseNet (CSN). This CSN architecture provides a way to dynamically express the crucial features hidden in the samples with the influence of working conditions suppressed, especially the variational working factors will be interpolated in this nonlinear fitting model. Additionally, three experimental studies are tested to verify the effectiveness of the proposed DICN method for bearing intelligent diagnosis under variational working conditions. The results and comparisons with other seven deep learning models show the proposed method shows outstanding robustness and higher accuracy where the accuracy of DICN is higher than the one of convolution neural network by more than 9% even if the working condition is variational.

Simple Methods to Predict Particle Size for Growth-Only Systems Undergoing One or More Temperature Cycles

Three simple methods are presented for predicting particle size distribution (PSD) percentiles when subjecting a pharmaceutical crystallization wet-milled slurry to a single temperature cycle for two of Biogen’s drug substances. The methods are (I) a mathematical manipulation of the wet-milled PSD based on particle death during dissolution; (II) a linear empirical model fit of product PSD percentiles as a function of the wet-milled PSD percentiles and the amount of material dissolved; and (III) a nonlinear model fit to the change in PSD as a function of the amount of material dissolved and the particle surface area/volume ratio. The three methods were verified on production scale batches. Method II’s approach was also used for particle size control during manufacture by implementing a temperature solved from the model using the batch’s wet-milled particle size percentiles and a final target size. Method III was found to be the most accurate at prediction because it models particle surface area to volume ratio influencing crystal dissolution and growth rates. Method II was found to be not as accurate as method III; however, it was easily implementable during process control at the production scale. Method I provided the quickest estimate of final particle size using only the initial PSD and a solubility expression. Method III was recommended for best particle size prediction.

Embedded Quantitative MRI T1ρ Mapping Using Non-Linear Primal-Dual Proximal Splitting.

Quantitative MRI (qMRI) methods allow reducing the subjectivity of clinical MRI by providing numerical values on which diagnostic assessment or predictions of tissue properties can be based. However, qMRI measurements typically take more time than anatomical imaging due to requiring multiple measurements with varying contrasts for, e.g., relaxation time mapping. To reduce the scanning time, undersampled data may be combined with compressed sensing (CS) reconstruction techniques. Typical CS reconstructions first reconstruct a complex-valued set of images corresponding to the varying contrasts, followed by a non-linear signal model fit to obtain the parameter maps. We propose a direct, embedded reconstruction method for T mapping. The proposed method capitalizes on a known signal model to directly reconstruct the desired parameter map using a non-linear optimization model. The proposed reconstruction method also allows directly regularizing the parameter map of interest and greatly reduces the number of unknowns in the reconstruction, which are key factors in the performance of the reconstruction method. We test the proposed model using simulated radially sampled data from a 2D phantom and 2D cartesian ex vivo measurements of a mouse kidney specimen. We compare the embedded reconstruction model to two CS reconstruction models and in the cartesian test case also the direct inverse fast Fourier transform. The T RMSE of the embedded reconstructions was reduced by 37–76% compared to the CS reconstructions when using undersampled simulated data with the reduction growing with larger acceleration factors. The proposed, embedded model outperformed the reference methods on the experimental test case as well, especially providing robustness with higher acceleration factors.

Temporal variability of 137Cs concentrations in coastal sediments off Fukushima

Large amounts of radiocesium were released into marine environments following the Fukushima Daiichi Nuclear Power Plant accident in March 2011. Released radiocesium influenced not only marine environment but also marine biota in Fukushima. Since marine biota as fisheries products is important for Japanese market, it is important to assess the distribution of radiocesium in coastal environment off Fukushima for safety concerns of radioactive contamination. Radiocesium concentrations in sediments are important for understanding fishing ground conditions and for proving the safety of fisheries products in Fukushima. In this study, monthly monitoring data collected from May 2011 to March 2020 were analyzed to describe the temporal variability of 137Cs concentrations in coastal sediments off Fukushima (total of 3647 samples from eight lines at depths of 7–125 m off Fukushima, and three sites in Matsukawa-ura Lagoon). The 137Cs concentration in sediment showed a decreasing trend, but our nonlinear model fitting suggested that this rate of decrease had slowed down. Additionally, 137Cs concentrations were up to 4.08 times greater in shallow sampling sites (7, 10, 20 m depth) following heavy rainfall events (before five months vs. after five months), such as typhoons. These observations were consistent with increasing input from particulate 137Cs fluxes from rivers and increasing dissolved 137Cs concentrations in seawater. Finally, our numerical modeling suggested that riverine 137Cs input could maintain 137Cs concentrations in coastal sediment. These results indicate that riverine 137Cs input following heavy rainfall events is the main factor for maintaining 137Cs concentrations in coastal sediments near the Fukushima Daiichi Nuclear Power Plant.