This study presents two mixed-integer programmings formulations for the unit commitment (UC) problem. First, the authors proposed a variable upper bound-based UC formulation, which is simultaneously ...tight and compact. Moreover, the tighter and relatively compact multi-period formulation is also presented. Both formulations (‘Multi_New’ and ‘Mult’) are tighter than the previous 2-bin (Base) and the tighter characteristic largely reduces the computational time of the formulations. Compared to the ‘Base’ formulation, the proposed formulations reduced by at least 6.6%, even 42.1% in the average time of calculation. The proposed models were tested on 73 instances over a scheduling period of 24 and 48 h. Compared to the ‘Base’ formulation, the initial Gap of ‘New’ formulation is improved by at least 8.4%. Moreover, compared to ‘Multi’ formulation, the compactness of ‘Multi_New’ formulation is improved by at least 33%. In addition, the numeric experiments show dramatic improvements in computational time for their proposed models. They provide evidence that the proposed models have better performance than the previous models.
Mesenchymal stromal cells (MSCs) are fundamentally responsive to environmental cues, including atmospheric gases, temperature, and aggregation. Thus, dose preparation and bedside handling of MSC ...products can influence clinical trial outcomes.Here we recapitulated common clinical conditions and tested the extent of the MSC functional impairment between dose preparation and completion of cell transfer. Reported clinical trial parameters were used to experimentally model the most common MSC clinical formulations, followed by live-cell quantification of adherence, cell spreading, and viability.
Passage 3-5 human umbilical cord MSCs from fresh and cryo-recovered cultures were aliquoted, pelleted, and resuspended at 55,000 cells/ml in complete media, Hank's Balanced Salt Solution (HBSS), HBSS containing 5% human serum albumin (HSA), Plasmalyte-A (PA), or PA containing 5% HSA and incubated at room temperature, 4°C, or 37°C. After 2, 4, 8, or 24 hours, MSCs were aliquoted 1:1 in complete media to assess fitnessCryo-recovered MSCs showed reduced adherence and viability, and impaired spreading particularly in unsupplemented PA and HBSS. These effects increased with time in suspension, particularly at 37°C. However, the addition of HSA mitigated these effects. Preconditioning MSCs with cytokines including combined IFNγ, TNF-α robustly improved viability and functional performance in suboptimal formulation and storage conditions, although it had minimal impact when MSCs were prepared and handled under optimal conditions. These data offer insight into selection of optimal dose preparation and handling procedures to maximize fitness of MSC clinical products and highlight the importance of standardizing MSC products formulation and bedside handling for reproducible and efficacious product performance.
Mixed formulations of the B̄ type, when applied to B-spline representations, bear significant computational cost and lead to a full stiffness matrix. In this work we present an efficient mixed ...formulation for avoiding membrane locking of curved non-polar plane rods in the context of isogeometric analysis. An efficient spline reconstruction of the assumed axial strains obtained by means of a local projection at the element level is performed using the method proposed by Thomas et al. (2015) for the reconstruction of the geometry. In this way a much smaller bandwidth of the stiffness matrix is obtained with respect to the non local B̄-formulation, with significant reduction of the computational cost. The blended mixed formulation splits in two steps: in the first step the local B̄ operators are defined at the element level via projection of the strain measure (both L2-projection and discrete collocation approaches are considered). Successively, by means of the spline reconstruction algorithm, the global projection of the strain measures at the patch level is defined. Numerical experiments show that the proposed method, in addition to completely remove membrane locking, yields the same accuracy and rate of convergence as the non local B̄-method.
•A blended B-bar formulation for plane curved Kirchhoff rods modelled via B-splines is presented.•Assumed strains are evaluated locally in each element, and then blended to reconstruct a global B-spline interpolation.•The formulation removes membrane locking and has the optimal rate of convergence.•The formulation has the same accuracy as the full B-bar method with much smaller computational effort.•The formulation yields a stiffness matrix with a limited half bandwidth, as opposite to the global B-bar method.
Optimal capacitor placement for radial distribution systems (RDSs) considers minimising the total cost of new fixed capacitors, switchable capacitors, and losses, while satisfying power balance ...equations, limits on bus voltages and capacitor limits. It is a non-convex mixed-integer non-linear programming (MINLP) challenge. In this study, the authors propose a solution method using a line-wise model (LWM) of power balance equations. First, equations for LWM are presented with their Jacobian for solving the power flow problem using Newton–Raphson method. Then, an optimal non-convex MINLP capacitor placement formulation with LWM power balance equations is presented. Thereafter, it is transformed into a convex mixed-integer conic programming formulation using second-order conic relaxation. Both the non-convex and convex optimal capacitor placement formulations are used to study 69-bus and 136-bus RDS. The results are compared with a formulation that uses the branch flow model (BFM) for power balance equations. Results show that the non-convex LWM-based formulation is twice as fast when compared with the BFM-based formulation. The convex LWM-based formulation is from 4 to 30 times as fast when compared with the BFM-based formulation, demonstrating the benefits of the use of the LWM-based formulation for enhancing the solution space of the optimisation problem.
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•PBPK modeling bridges drug properties and PK behaviours in drug discovery and development.•The PBPK is often used in oral formulation development, to address critical issues.•The ...PBPK is mainly applied to complex injectable products as an explanatory tool.•The PBPK is also tried for inhaled, transdermal, and ophthalmic formulation.•Both experimental and computational improvements would facilitate the PBPK in drug delivery.
Physiologically based pharmacokinetic (PBPK) modeling is an important in silico tool to bridge drug properties and in vivo PK behaviors during drug development. Over the recent decade, the PBPK method has been largely applied to drug delivery systems (DDS), including oral, inhaled, transdermal, ophthalmic, and complex injectable products. The related therapeutic agents have included small-molecule drugs, therapeutic proteins, nucleic acids, and even cells. Simulation results have provided important insights into PK behaviors of new dosage forms, which strongly support drug regulation. In this review, we comprehensively summarize recent progress in PBPK applications in drug delivery, which shows large opportunities for facilitating drug development. In addition, we discuss the challenges of applying this methodology from a practical viewpoint.
The objective of this study was to describe nutritional strategies utilized on Canadian dairy farms with automated milking systems (AMS), both at the feed bunk and the concentrate offered at the AMS, ...as well as to determine what dietary components and nutrients, as formulated, were associated with milk production and milking behaviors on those farms. Formulated diets, including ingredients and nutrient content, and AMS data were collected from April 1, 2019, until September 30, 2020, on 160 AMS farms (Eastern Canada East = 8, Ontario ON = 76, Quebec QC = 22, and Western Canada West = 54). Both partial mixed ration (PMR) and AMS concentrate samples were collected from May 1 to September 30, 2019, on 169 farms (East = 12, ON = 63, QC = 42, West = 52). AMS milking data were collected for 154 herds. For each farm (n = 160), milk recording data were collected and summarized by farm to calculate average milk yield and components. Multivariable regression models were used to associate herd-level formulated nutrient composition and feeding management practices with milk production and milking behavior. Milk yield (37.0 ± 0.3 kg/d) was positively associated with the PMR ether extract (EE) concentration (PMR % EE; +0.97 kg/d per percentage point (p.p.) increase) and with farms that fed barley silage as their major forage source on farm (n = 16; +2.18 kg/d) compared with haylage (n = 42), while farms that fed corn silage (n = 96; +1.23 kg/d) tended to produce more milk than farms that fed haylage. Greater milk fat content (4.09 ± 0.28%) was associated with greater PMR-to-AMS concentrate ratio (+0.02 p.p. per unit increase) and total diet net energy for lactation (+0.046 p.p. per 0.1 Mcal/kg increase), but lesser % non-fiber carbohydrates (NFC) of the PMR (−0.016 p.p. per p.p. increase of % NFC). Milk protein content (3.38 ± 0.14%) was positively associated with forage % of the PMR (+0.003 p.p. per p.p. increase of % forage) and total diet % starch (+0.009 p.p. per p.p. increase of % starch), but negatively associated with farms feeding corn silage (−0.1 p.p. compared with haylage) as their major forage. Greater milking frequency (2.77 ± 0.40 milkings/d) was observed on farms with free-flow cow traffic systems (+0.62 milkings/d) and positively associated with feed push-up frequency (+0.013 milkings/d per additional feed push-up), while being negatively associated with PMR NFC content and % forage of the total ration (−0.017 milkings/d per p.p. increase of % forage). Lastly, greater milking refusal frequency (1.49 ± 0.82 refusals/d) was observed on farms with free-flow cow traffic systems (+0.84 refusals/d) and farms feeding barley silage (+0.58 refusals/d) than guided flow and farms feeding either corn silage or haylage, respectively. These data give insight into the ingredients, nutrient formulations and type of diets fed on AMS dairy farms across Canada and the association of those factors with milk production and milking behaviors.
Data on the pharmacokinetics of tacrolimus during pregnancy are limited. Therefore, the aim of this retrospective study was to characterize the whole‐blood pharmacokinetics of tacrolimus throughout ...pregnancy. In this single‐center retrospective cohort study, whole‐blood tacrolimus trough concentrations corrected for the dose (concentration‐to‐dose C/D ratios) were compared before, monthly during, and after pregnancy in kidney, liver, and lung transplant recipients who became pregnant and gave birth between 2000 and 2022. Descriptive statistics and linear mixed models were used to characterize changes in tacrolimus C/D ratios before, during, and after pregnancy. The total study population included 46 pregnancies (31 pregnant women). Nineteen, 21, and 6 pregnancies were following kidney, liver, and lung transplantation, respectively. Immediate‐release or extended‐release formulations were used in 54.5% and 45.5% of the women, respectively. Tacrolimus C/D ratios significantly (P < .001) decreased (−48%) compared to the prepregnancy state at 7 months of pregnancy. These ratios recovered within 3 months postpartum (P = .002). C/D ratios tended to be lower during treatment with an extended‐release formulation than with an immediate‐release formulation (P = .071). Transplantation type did not significantly affect C/D ratios during pregnancy (P = .873). In conclusion, we found that tacrolimus whole‐blood pharmacokinetics change throughout pregnancy, with the lowest C/D ratios (48% decrease) in the 7th month of pregnancy. In general, the decrease in C/D ratios seems to stabilize from month 4 onward compared to prepregnancy.
Poorly water-soluble drugs continue to be a problematic, yet important class of pharmaceutical compounds for treatment of a wide range of diseases. Their prevalence in discovery is still high, and ...their development is usually limited by our lack of a complete understanding of how the complex chemical, physiological and biochemical processes that occur between administration and absorption individually and together impact on bioavailability. This review defines the challenge presented by these drugs, outlines contemporary strategies to solve this challenge, and consequent in silico and in vitro evaluation of the delivery technologies for poorly water-soluble drugs. The next steps and unmet needs are proposed to present a roadmap for future studies for the field to consider enabling progress in delivery of poorly water-soluble compounds.
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•NSFnets involve the VP and VV formulations of the Navier-Stokes equations.•NSFnets can directly simulate and sustain turbulence at Reτ∼1,000.•A study on the weights in the loss function to enhance ...accuracy is performed.•Transfer learning of NSFnets can reduce computational cost and enhance accuracy.•NSFnets can solve the ill-posed or inverse problems better than CFD solvers.
In the last 50 years there has been a tremendous progress in solving numerically the Navier-Stokes equations using finite differences, finite elements, spectral, and even meshless methods. Yet, in many real cases, we still cannot incorporate seamlessly (multi-fidelity) data into existing algorithms, and for industrial-complexity applications the mesh generation is time consuming and still an art. Moreover, solving ill-posed problems (e.g., lacking boundary conditions) or inverse problems is often prohibitively expensive and requires different formulations and new computer codes. Here, we employ physics-informed neural networks (PINNs), encoding the governing equations directly into the deep neural network via automatic differentiation, to overcome some of the aforementioned limitations for simulating incompressible laminar and turbulent flows. We develop the Navier-Stokes flow nets (NSFnets) by considering two different mathematical formulations of the Navier-Stokes equations: the velocity-pressure (VP) formulation and the vorticity-velocity (VV) formulation. Since this is a new approach, we first select some standard benchmark problems to assess the accuracy, convergence rate, computational cost and flexibility of NSFnets; analytical solutions and direct numerical simulation (DNS) databases provide proper initial and boundary conditions for the NSFnet simulations. The spatial and temporal coordinates are the inputs of the NSFnets, while the instantaneous velocity and pressure fields are the outputs for the VP-NSFnet, and the instantaneous velocity and vorticity fields are the outputs for the VV-NSFnet. This is unsupervised learning and, hence, no labeled data are required beyond boundary and initial conditions and the fluid properties. The residuals of the VP or VV governing equations, together with the initial and boundary conditions, are embedded into the loss function of the NSFnets. No data is provided for the pressure to the VP-NSFnet, which is a hidden state and is obtained via the incompressibility constraint without extra computational cost. Unlike the traditional numerical methods, NSFnets inherit the properties of neural networks (NNs), hence the total error is composed of the approximation, the optimization, and the generalization errors. Here, we empirically attempt to quantify these errors by varying the sampling (“residual”) points, the iterative solvers, and the size of the NN architecture. For the laminar flow solutions, we show that both the VP and the VV formulations are comparable in accuracy but their best performance corresponds to different NN architectures. The initial convergence rate is fast but the error eventually saturates to a plateau due to the dominance of the optimization error. For the turbulent channel flow, we show that NSFnets can sustain turbulence at Reτ∼1,000, but due to expensive training we only consider part of the channel domain and enforce velocity boundary conditions on the subdomain boundaries provided by the DNS data base. We also perform a systematic study on the weights used in the loss function for balancing the data and physics components, and investigate a new way of computing the weights dynamically to accelerate training and enhance accuracy. In the last part, we demonstrate how NSFnets should be used in practice, namely for ill-posed problems with incomplete or noisy boundary conditions as well as for inverse problems. We obtain reasonably accurate solutions for such cases as well without the need to change the NSFnets and at the same computational cost as in the forward well-posed problems. We also present a simple example of transfer learning that will aid in accelerating the training of NSFnets for different parameter settings.
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