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Congenital disease of the hip (CDH) is a common reason for the development of secondary osteoarthritis at the hip joint and the need for total hip arthroplasty (THA). The distorted femoral anatomy in patients with CDH in combination with soft tissue considerations and leg length discrepancy complicate the procedure of THA and this sometimes precludes the implantation of classical industry designed femoral stems. In such cases a customised femoral implant must be used in order to optimise the fit of the stem to the femur, to improve strain distribution and to reconstruct hip biomechanics. The present study reviews the preoperative planning, the design and material selection of custom-made implants, the surgical techniques and the reported clinical results of the published literature on the use of custom-made femoral implants in patients with CDH.

This work studies three differential equation models of the leukemia cell cycle: a population balance model (PBM) using intracellular protein expression levels as state variables representing phase progress; a delay differential equation model (DDE) with temporal phase durations as delays; and an ordinary differential equation model (ODE) of phase-to-phase progression. In each type of model, global sensitivity analysis determines the most significant parameters while parameter estimation fits experimental data. To compare models based on the output of their structural properties, an expected behavior was defined, and each model was coupled to a pharmacokinetic/pharmacodynamic model of chemotherapy delivery. Results suggest that the particular cell cycle model chosen highly affects the simulated treatment outcome, given the same steady state kinetic parameters and drug dosage/scheduling. The manuscript shows how cell cycle models should be selected according to the complexity, sensitivity, and parameter av...

Acute myeloid leukaemia is characterized by marked inter- and intra-patient heterogeneity, the identification of which is critical for the design of personalized treatments. Heterogeneity of leukaemic cells is determined by mutations which ultimately affect the cell cycle. We have developed and validated a biologically relevant, mathematical model of the cell cycle based on unique cell-cycle signatures, defined by duration of cell-cycle phases and cyclin profiles as determined by flow cytometry, for three leukaemia cell lines. The model was discretized for the different phases in their respective progress variables (cyclins and DNA), resulting in a set of time-dependent ordinary differential equations. Cell-cycle phase distribution and cyclin concentration profiles were validated against population chase experiments. Heterogeneity was simulated in culture by combining the three cell lines in a blinded experimental set-up. Based on individual kinetics, the model was capable of identifying and quantifying cellular heterogeneity. When supplying the initial conditions only, the model predicted future cell population dynamics and estimated the previous heterogeneous composition of cells. Identification of heterogeneous leukaemia clones at diagnosis and post-treatment using such a mathematical platform has the potential to predict multiple future outcomes in response to induction and consolidation chemotherapy as well as relapse kinetics.

Mammalian cell cultures are intrinsically heterogeneous at different scales (molecular to bioreactor). The cell cycle is at the centre of capturing heterogeneity since it plays a critical role in the growth, death, and productivity of mammalian cell cultures. Current cell cycle models use biological variables (mass/volume/age) that are non-mechanistic, and difficult to experimentally determine, to describe cell cycle transition and capture culture heterogeneity. To address this problem, cyclins—key molecules that regulate cell cycle transition—have been utilized. Herein, a novel integrated experimental-modelling platform is presented whereby experimental quantification of key cell cycle metrics (cell cycle timings, cell cycle fractions, and cyclin expression determined by flow cytometry) is used to develop a cyclin and DNA distributed model for the industrially relevant cell line, GS-NS0. Cyclins/DNA synthesis rates were linked to stimulatory/inhibitory factors in the culture medium, which ultimately affect cell growth. Cell antibody productivity was characterized using cell cycle-specific production rates. The solution method delivered fast computational time that renders the model’s use suitable for model-based applications. Model structure was studied by global sensitivity analysis (GSA), which identified parameters with a significant effect on the model output, followed by re-estimation of its significant parameters from a control set of batch experiments. A good model fit to the experimental data, both at the cell cycle and viable cell density levels, was observed. The cell population heterogeneity of disturbed (after cell arrest) and undisturbed cell growth was captured proving the versatility of the modelling approach. Cell cycle models able to capture population heterogeneity facilitate in depth understanding of these complex systems and enable systematic formulation of culture strategies to improve growth and productivity. It is envisaged that this modelling approach will pave the model-based development of industrial cell lines and clinical studies., Author Summary The cell cycle is a complex regulatory network that influences not only growth and division, but also other relevant cellular events (e.g. death, productivity, etc.). The development of biologically accurate cell cycle models can help to systematically study mammalian cell cultures. However, the inclusion of segregation in biological systems usually displays a computationally intensive nature. We propose a combined experimental and mathematical framework that allows capturing the heterogeneity in computationally fast and biologically accurate cell cycle models. Using multiparameter flow cytometry a cyclin blueprint is derived to support the model development. Further, the mathematical formulation is reduced to provide a fast solution, allowing its use for sensitivity analysis and model-based parameter estimation. The simulation results are compared to experimental data to test the accuracy and predictive power of the model. This approach can easily be extended to other culture systems, as well as to include further biological detail. The significance of this approach is not limited to industrially relevant cell lines but its application extends to cell cycle relevant systems such as clinical problems (tumours, cancer treatments, etc.).

This work presents a generic mixed integer linear programming (MILP) model that integrates the unit commitment problem (UCP) (daily energy planning) within the long-term generation expansion planning (GEP) framework. The model has been tested on an illustrative case study of the Greek power system. Our approach aims to provide useful insights into the strategic and challenging decisions to be determined by investors and/or policy makers at a national and/or regional level by providing the optimal energy roadmap according to specific assumptions and projections (e.g., electricity demand, fuel prices, and investment costs).

A modelling framework for the separation of gas mixtures using multibed Pressure Swing Adsorption (PSA) and Vacuum Swing Adsorption (VSA) flowsheets has been developed. The core of the modelling framework represents a detailed multi-scale adsorbent bed model relying on a coupled set of mixed algebraic and partial differential equations for mass, heat and momentum balance at both bulk gas and particle level, equilibrium isotherm equations and boundary conditions according to the operating steps. The proposed modelling equations have been implemented in the gPROMS™ modelling environment. The modelling framework has been applied on the capture of post-combustion carbon dioxide (CO 2 ) from dry flue gas by PSA/VSA process in order to validate its predictive power against experimental and simulation data available from the literature. The simulation results in terms of process performance indicators are in good agreement with data available from the literature. The results of sensitivity analysis illustrate the typical trade-offs between process performance indicators.

In this paper, we present a framework which incorporates runtime adaptation for BPEL scenarios. The adaptation is based on (a) the quality of service parameters of available services, allowing for tailoring their execution to the diverse needs of individual users and (b) collaborative filtering techniques, allowing clients to further refine the adaptation process by considering service selections made by other clients, in the context of the same business processes. The proposed framework also caters maintaining the transactional semantics that invocations to multiple services offered by the same provider may bear. © 2014 Elsevier B.V.

The cell cycle is the biological process used by cells to replicate their genetic material and give birth to new cells that are in turn eligible to proliferate. It is highly regulated by the timed expression of proteins which trigger cell cycle events such as the start of DNA replication or the commencement of mitosis (when the cell physically divides into two daughter cells). Mathematical models of the cell cycle have been widely developed both at the intracellular (protein kinetics) and macroscopic (cell duplication) levels. Due to the cell cycle specificity of most chemother-apeutic drugs, these models are increasingly being used for the study and simulation of cellular kinetics in the area of cancer treatment. In this work, we present a population balance model (PBM) of the cell cycle in leukemia that uses intracellular protein expression as state variable to represent phase progress. Global sensitivity analysis highlighted cell cycle phase durations as the most significant parameters; experiments were performed to extract them and the model was validated. Our model was then tested against other differential cell cycle models (ODEs, delay differential equations (DDEs)) in their ability to fit experimental data and oscillatory behavior. We subsequently coupled each of them with a pharmacokinetic/pharmacodynamic model of chemotherapy delivery that was previously developed by our group. Our results suggest that the particular cell cycle model chosen highly affects the outcome of the simulated treatment, given the same steady-state kinetic parameters and drug dosage/scheduling, with our PBM appearing to be the most sensitive under the same dose.

The cell cycle is a biologically timed process by which cells duplicate. It consists of 4 phases, during which cells undergo different mandatory transformations. Modelling the cell cycle therefore requires capturing the evolution of those processes inside of each phase. A specific three stage biologically supported population balance model employed to simulate evolution of several cell cultures is studied here in detail. The three governing equations of this model are composed by growth and transition terms. A one equation analogue of the multistage model is formulated and it is solved analytically in the self-similarity domain. The effect of initial conditions at the system evolution is studied numerically. The three model equations are then considered by using asymptotic and numerical techniques. It is shown that in the case of sharp interstage transition the discontinuities of the initial conditions are preserved during cell growth leading to eternal oscillations whereas for distributed transition the cell distribution converges to a self-similar (long time asymptote) shape. It is also illustrated that the closer the initial condition to the self similar distribution the faster the convergence to self-similarity and the smaller the oscillations of the total cell number. Exact results are given for the growth parameters of the population balance and lumped models.

This work presents a systematic approach for modelling, optimization and control of metal hydride beds used for hydrogen storage. A detailed 2-D mathematical model is developed and validated against experimental and theoretical literature results. Based on recent advances in dynamic optimization, the objective is then to find the optimal process design (e.g. cooling systems design) and operating strategy (e.g. cooling fluid profile over time) so as to minimize the storing time, while satisfying a number of operating constraints. Such constraints account for pressure drop limitations, cooling fluid availability, maximum tank temperature, etc. Optimization results indicate that almost 60% improvement of the storage time can be achieved, over the case where the system is not optimized, for a minimum storage capacity of 99% of the total bed capacity. Trade-offs between various objectives, alternative design options and optimal cooling control policies are systematically revealed illustrating the potential offered by modern optimization techniques.

This work presents a novel systematic approach for the optimal design and control of metal hydride beds used for hydrogen storage. A detailed 2-D mathematical model is developed and validated against experimental and theoretical literature results. Based on recent advances in dynamic optimization, the objective is then to find the optimal process design (e.g. cooling systems design) and operating strategy (e.g. cooling fluid profile over time, hydrogen charging profile, etc.) so as to minimize the storing time, while satisfying, a number of operating constraints. Such constraints account for pressure drop limitations, cooling fluid availability and maximum tank temperature. Optimization results indicate that almost 60% improvement of the storage time can be achieved, over the case where the system is not optimized, for a minimum storage capacity of 99% of the total bed capacity. Trade-offs between various objectives, alternative design options and optimal cooling control policies are systematically revealed illustrating the potential offered by modern optimization techniques.

The aim of the present study is the development of a multiscale modeling and optimization framework for hydrogen storage in carbon-based nanoporous adsorbents. The outlined methodology is generic and can be easily adapted to the storage of several gases of relevant importance and/or different physisorbing nanoporous materials. The results indicate clearly how operating constraints (such as temperature limitations arising from safety considerations) can affect the material design in terms of its pore size distribution and how material design constraints (such as those arising from manufacturing limitations) can effect the operation and efficiency of the process. The ultimate objective is to systematically reveal the strong and highly related synergistic benefits between process design/operation decisions and material design aspects so as to ensure an economically attractive, technically feasible, and safe process. © 2006 American Institute of Chemical Engineers AIChE J, 2006

This article presents the design and control of a reactive distillation system utilizing recent advances in mixed integer dynamic optimization. A high fidelity dynamic model is used to predict the behavior of the process under time-varying disturbances. Design and control decisions, involving both discrete and continuous variables, are simultaneously optimized leading to a more economically attractive and better controlled system than that obtained by following a sequential optimization approach. It is shown that the resulting design and control scheme can guarantee feasible operation under bounded uncertainty at a minimum total average cost, representing ∼17% savings over the original design.

This work presents the development and implementation of a production scheduling system for an electrical appliance manufacturer. Based on recent advances in optimisation-based scheduling approaches, two different software architectures based on two different scheduling formulations, namely the RTN and the STN, are proposed to integrate information available in the different production units and stages with formal algorithmic tools. Optimization results indicate that significant economic benefits can be achieved (e.g. minimization of total operating costs) while ensuring full customer satisfaction as opposed to normal practices followed in the company relying on human expertise. The work indicates that it is possible to solve real-life manufacturing problems using optimization-based approaches but the integration of information in a timely fashion seems to be a major factor in successfully implementing the system and fully realizing its benefits.

A Mixed-Integer Dynamic Optimization (MIDO) model and solution strategy to study the interactions between process design and process control/operability of an ethyl acetate reactive distillation system is presented. It is shown that the resulting design and control scheme can guarantee feasible operation tmder bounded uncertainty at a minimum total average cost, representing 17 to 20% savings over the original design.

This work presents the detailed mathematical modelling and advanced optimization of a number of zeolite membrane process structures for gas separations. First, a general model for the separation of binary gas mixtures is developed based on the generalized Maxwell–Stefan (GMS) approach. Accordingly, the model is used in the simulation of various process structures including co-current and counter-current operation, recycle modules and membrane cascades. The model is validated against available experimental data of methane-ethane and methane–propane mixtures on silicalite membranes. A non-linear programming approach is then employed to determine optimal design options for different process performance objectives. Various trade-offs between different optimization objectives are systematically revealed. The impact of the detailed GMS model on the optimization results is investigated through a comparison with corresponding results obtained using the single-file diffusion model, which ignores diffusional adsorbate–adsorbate interactions.

This paper presents a new mathematical programming formulation for the problem of determining the optimal manner in which several product rolls of given sizes are to be cut out of raw rolls of one or more standard types. The objective is to perform this task so as to maximize the profit taking account of the revenue from the sales, the costs of the original rolls, the costs of changing the cutting pattern and the costs of disposal of the trim. A mixed integer linear programming (MILP) model is proposed which is solved to global optimality using standard techniques. A number of example problems, including an industrial case study, are presented to illustrate the efficiency and applicability of the proposed model.

A new mathematical formulation for scheduling multipurpose continuous processes is presented. The formulation is based on the state-task network representation, coupled with an event-driven representation of time, resulting in a mixed-integer linear programming model. Event points are defined by the end of task execution for all continuous tasks in the process. Timing constraints are applied to continuous tasks involving the same material state to ensure feasibility of rate-based material balances. The formulation allows for unit-dependent variable processing rates, sequence-dependent changeovers, and dedicated and flexible intermediate storage requirements. Several variants of a medium to large scale continuous manufacturing process are examined to illustrate the applicability and efficiency of the method. The formulation is shown to compare favorably with existing continuous-time models; a new optimal solution on the finite intermediate storage case of the process is also established.

A new continuous-time formulation for scheduling short-term multipurpose batch processes is presented. The formulation gives rise to a mixed-integer linear programming (MILP) model. The state−task network (STN) representation forms the basis of the proposed approach. A number of event points is prepostulated, which is the same for all tasks in the process. Event times are defined by the ends of task execution, and they are generally different for different tasks of the process, giving rise to a nonuniform time grid. The necessary time monotonicity for single tasks is guaranteed by means of simple duration constraints. Suitable sequencing constraints, applicable to batch tasks involving the same state, are also introduced, so that state balances are properly posed in the context of the nonuniform time grid. The expression of duration and sequencing constraints is greatly simplified by hiding all unit information within the task data. Three benchmark problems are used to illustrate the efficiency and applic...

Leukemia is an immediately life-threatening cancer wherein immature blood cells are overproduced, accumulate in the bone marrow (BM) and blood and causes immune and blood system failure. Treatment with chemotherapy can be intensive or nonintensive and can also be life-threatening since only relatively few patient-specific and leukemia-specific factors are considered in current protocols. We have already presented a mathematical model for one intensive chemotherapy cycle with intravenous (IV) daunorubicin (DNR), and cytarabine (Ara-C) [1] . This model is now extended to nonintensive subcutaneous (SC) Ara-C and for a standard intensive chemotherapy course (four cycles), consistent with clinical practice. Model parameters mainly consist of physiological patient data, indicators of tumor burden and characteristics of cell cycle kinetics. A sensitivity analysis problem is solved and cell cycle parameters are identified to control treatment outcome. Simulation results using published cell cycle data from two acute myeloid leukemia patients [2] are presented for a course of standard treatment using intensive and nonintensive protocols. The aim of remission–induction therapy is to debulk the tumor and achieve normal BM function; by treatment completion, the total leukemic population should be reduced to at most 10 $^{9}$ cells, at which point BM hypoplasia is achieved. The normal cell number should be higher than that of the leukemic, and a 3-log reduction is the maximum permissible level of population reduction. This optimization problem is formulated and solved for the two patient case studies. The results clearly present the benefits from the use of optimization as an advisory tool for treatment design.

Type I Diabetes Mellitus is a lifelong disease characterized by elevated blood glucose levels due to lack of insulin, resulting from autoimmune mediated destruction of the insulin-producing beta cells of the pancreas. Insulin regulates blood glucose levels by permitting glucose to enter the human cells to provide them with energy. Patients with type I diabetes require exogenous insulin administration to regulate their blood glucose concentration. Optimization of insulin dosing minimizes the risk of possible hypoglycaemia (over-dosing) and avoids hyperglycaemia (under-dosing). Rigorous optimization studies are performed for 10 patients with type 1 DM on an insulin pump, using the UVa/Padova T1DM Simulator as the process model. The insulin bolus, given to compensate for food consumption, is optimized in terms of time to peak maximum effect and also basal and bolus dosing balance during a meal is considered. These results are compared with conventional insulin dosing obtained in the literature and finally the insulin regimen that normalizes the glucose curve more effectively – maintain blood glucose concentration within the normal range – is determined. Additionally, an alternative to bolus insulin dosing is evaluated and the two dosing types are compared, again in terms of their effect on glucose concentration. This study intends to identify the most effective dosing strategy to be further used as a background guideline in closed loop studies.

Background and purpose: Although the latest recommendations suggest that carotid endarterectomy (CEA) should be performed in symptomatic carotid artery stenosis (sCAS) patients within 2 weeks of the index event, only a minority of patients undergo surgery within the recommended time-frame. The aim of this international multicenter study was to prospectively evaluate the safety of early CEA in patients with sCAS in everyday clinical practice settings. Methods: Consecutive patients with non-disabling acute ischaemic stroke (AIS) or transient ischaemic attack (TIA) due to sCAS (≥70%) underwent early (≤14 days) CEA at five tertiary-care stroke centers during a 2-year period. Primary outcome events included stroke, myocardial infarction (MI) or death occurring during the 30-day follow-up period and were defined according to the International Carotid Stenting Study criteria. Results: A total of 165 patients with sCAS [mean age 69 ± 10 years; 69% men; 70% AIS; 6% crescendo TIA; 8% with contralateral internal carotid artery (ICA) occlusion] underwent early CEA (median elapsed time from symptom onset 8 days). Urgent CEA (≤2 days) was performed in 20 cases (12%). The primary outcomes of stroke and MI were 4.8% [95% confidence interval (CI) 1.5%-8.1%] and 0.6% (95% CI 0%-1.8%). The combined outcome event of non-fatal stroke, non-fatal MI or death was 5.5% (95% CI 2.0%-9.0%). Crescendo TIA, contralateral ICA occlusion and urgent CEA were not associated (P > 0.2) with a higher 30-day stroke rate. Conclusions: Our findings indicate that the risk of early CEA in consecutive unselected patients with non-disabling AIS or TIA due to sCAS is acceptable when the procedure is performed within 2 weeks (or even within 2 days) from symptom onset. Click here for the corresponding questions to this CME article. © 2014 EAN.

This paper presents a mathematical programming model for the optimal design of Generalized Supply Chain Networks (GSCNs) that incorporates strategic flexibility in network’s configuration. The model is formulated as a deterministic Mixed-Integer Linear Programming (MILP) problem and solved to global optimality using standard branch-and-bound techniques. Optimality is assessed in terms of SCN’s overall cost while its applicability, benefits, and robustness are illustrated by using a real case study.

This work presents an optimization approach for the optimal design and operational planning of an urban energy network based on combined heat and power generators. The model is formulated as linear Mixed Integer Programming (MIP) and solved to optimality using standard branch-and-bound techniques. Optimality is assessed in terms of total system cost, while the applicability of the proposed model is illustrated using an illustrative example.

This work presents a systematic mathematical framework for scheduling the operation of multipurpose plants involving heat-integrated unit operations under fouling considerations. A salient characteristic of this problem is that the performance of each heat-integrated unit, which decreases with time due to fouling, can be restored to its initial state by performing cleaning operations. Based on a uniform time discretization, the overall problem is formulated as a mixed integer non-linear programming (MINLP) model. An iterative procedure is proposed for the solution of the resulting non-convex MINLP model, involving the solution of a series of mixed integer linear programming and non-linear programming subproblems. The optimization algorithm determines simultaneously: (i) the production schedule, (ii) the number of cleaning operations required along with their corresponding timings, (iii) the optimal utility utilization profile and (iv) the heat transfer medium flow rate profiles over time. An example problem is presented to illustrate the applicability of the proposed approach. It is proved that fouling considerations can significantly affect the production schedule as well as heat integration opportunities.

A practical instance of a one-dimensional cutting stock problem arising frequently in the paper industry is considered. Given a set of raw paper rolls of known length and width, a set of product paper rolls of known length (equal to the length of raw paper rolls) and width, practical cutting constraints on a single cutting machine, and demand orders for all products, the problem requires the determination of an optimal cutting schedule to maximize the overall cutting process profitability while satisfying all demands and cutting constraints. A purely mixed-integer linear programing (MILP) model is developed that does not require the a priori determination of all feasible cutting combinations. A complex objective function including trim loss, overproduction, knife (pattern) changeover costs, and format (raw material type) changeover costs is optimized. A salient feature of the model is that intermediate demand orders are taken into consideration as an integral part of the formulation. A number of example p...

This work presents a new mathematical programming formulation for the problem of scheduling cutting operations on multiple parallel slitting machines. A Mixed-Integer Linear Programming (MILP) model is proposed, solved to optimality using standard techniques. A continuous time representation is used to avoid unnecessary time intervals and to limit the number of variables in the formulation. One important feature of the model is the explicit treatment of change-over times as an important part of a multiple objective cost function, which may be adapted to minimizing makespan, or total flow time, or any weighted combination of the two. An industrial case study from the paper-converting industry is presented to illustrate the applicability and efficiency of the proposed model.