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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.

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.

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.

This work presents the mathematical modelling and simulation of complex plate heat exchanger arrangements under milk fouling, using detailed dynamic models. A complex fouling model based on a reaction/mass transfer scheme is coupled with a general thermal dynamic model of plate heat exchangers. All the important factors affecting milk heat treatment are formally quantified. The final model comprises a set of partial differential, integral and algebraic equations. Parameter estimation analysis is performed based on the solution of a dynamic optimization problem. The simulation results are in a good agreement with available experimental work. Three different configurations with complex flow arrangements are considered to illustrate aspects of fouling behaviour. The simulation results provide significant insight into the key factors affecting milk fouling.

This paper addresses the problem of cyclic cleaning and energy scheduling in special classes of heat exchanger networks (HENs). A salient characteristic of this problem is that the performance of each heat exchanger decreases with time and can then be restored to its initial state by performing cleaning operations. Due to the cyclic nature of the schedule, some operations may span successive cycles (wrap-around) which is taken into account in the mathematical model. A tight mixed integer linear programming (MILP) model is presented which is solved to global optimality. A detailed objective function is used to account for cleaning cost and energy requirements. The formulations can model serial and parallel HENs, as well as network arrangements arising from the combination of these basic cases. The optimization algorithm determines simultaneously: (i) the number of cleaning operations required along with their corresponding timings and (ii) the optimal utility utilization profile over time. A complex heat exchanger network example is presented to illustrate the applicability of the proposed model.

This paper addresses the problem of short-term cleaning scheduling in a special class of heat-exchanger networks (HENs). A salient characteristic of this problem is that the performance of each heat exchanger decreases with time and can then be restored to its initial state by performing cleaning operations. Because of its practical importance, a specific problem has been considered here involving decaying equipment performance due to milk fouling. A mixed-integer nonlinear-programming (MINLP) model is first presented incorporating general fouling profiles. This model is then linearized to a tight mixed-integer linear-programming (MILP) model which can be solved to global optimality. A detailed objective function is used to account for cleaning cost and energy requirements. The formulations can model serial and parallel HENs as well as network arrangements arising from the combination of these basic cases. The optimization algorithm determines simultaneously: (i) the number of cleaning operation tasks required along with their corresponding timings and (ii) the optimal utility utilization profile over time. A number of complex heat-exchanger networks examples are presented to illustrate the applicability of the proposed models together with comparative performance results between the MINLP and MILP models.

The generation of a good layout is an important stage in the design of a new plant or the retrofit of an existing facility. Layout decisions affect piping, electrics, instrumentation and therefore have a great impact on the total plant cost. Moreover, layout has a large impact on the safety, operability and maintainability of any chemical plant. This paper presents a general mathematical programming approach for addressing the problem of allocating items of equipment in a given two or three dimensional space. The problem is formulated as a mixed integer linear programming model where equipment of various sizes and geometries are taken into account. The objective function to be minimized accounts for the total transport, connection, land and floor construction cost. This optimization procedure results in the coordinates of each unit (location), the total piping length, and the land occupied. Three case studies are presented to illustrate the applicability of the proposed approach.

This work addresses the problem of cyclic cleaning scheduling in heat exchanger networks (HENs). A salient characteristic of this problem is that the performance of each heat exchanger decreases with time which can then be restored to its initial state by performing cleaning operations. Due to the cyclic nature of the schedule, some operations may span successive cycles (wrap-around) which should be taken into account in the mathematical models. A tight mixed integer linear programming (MILP) model is proposed minimising cleaning cost and energy requirements. A complex heat exchanger network example is presented to illustrate the applicability of the proposed model.

Results of an experimental and numerical investigation of shallow foundation displacements behind cantilever sheet pile wall excavations in sand, are presented. A series of model tests have been carried out, in which the effect of sand density, excavation depth, foundation load and the distance between the foundation and the excavation edge on foundation settlement and horizontal displacement were investigated. Based on the model test results and the results of a parametric finite element analysis, shallow foundation displacements have been related to: a) the lateral sheet pile wall movement, b) the ratio of the distance between foundation and wall to the excavation depth, and c) the safety factor against bearing capacity failure of the foundation.

This paper presents developments toward a unified framework for incorporating both process flexibility and robustness (in terms of product quality) criteria in process optimization under uncertainty. A robust design methodology, known as the Taguchi approach, is discussed in the context of uncertainty, and some limitations are identified. Taguchi's method is then extended to take into account process constraints and probabilistic uncertainty. A framework for establishing the interactions and synergistic benefits between the two operability objectives is proposed, based on an expected measure, where product quality losses are taken into account explicitly. Tradeoffs between stochastic flexibility and a robust criterion are explored in order to depict optimal operating policies in the presence of uncertainty; extensions toward design optimization are also briefly discussed. A number of examples is presented to illustrate the applicability of the proposed framework.

This article presents a procedure for the simultaneous optimization of the design and operation of heat exchangers under milk fouling. This scheme is based on a highly accurate dynamic model described by integral, partial differential, and algebraic equations. Design and operating parameters determined by the dynamic optimization include the length and diameter of the exchanger, the control policy, and the timing of the key operating steps. An economic objective function is used to account for the important factors related to milk heat treatment, and many operating constraints are imposed. Three heat exchanger configurations are considered to study the effect of fouling on the optimal plant design. Optimization indicates that the cost factor due to the interruption of production is dominant, while an increase in energy consumption due to fouling is not very important. The constant wall temperature configuration proved to be the most economical. The economic impact of different control structures was also explored.

This paper presents results of finite-element analyses and an experimental investigation of cantilever sheet-pile walls in sand, demonstrating the effect of berms on the behaviour of the wall. Based on these results, a procedure for assessing the effect of berm geometry, excavation depth, wall stiffness and sand density on the reduction of wall movements and bending moments is proposed. (A)

This work presents recent developments for the incorporation of process flexibility and robustness (in terms of product quality) in process optimization under uncertainty. The Taguchi robust design methodology is far extended to take into account of process constraints and probabilistic uncertainty. Trade-offs between stochastic flexibility and a robust criterion are then explored based on which a framework for studying the interactions and synergistic benefits between the two operability objectives is established with the objective to maximize an expected profit criterion while explicitly accounting for product quality losses. Extensions for design optimization are also presented to derive the optimal design featuring an optimal degree of flexibility and robustness. A simplified process flowsheet problem is presented to illustrate the basic concepts and the applicability of the proposed integrated framework.

A mathematical model for single shell and tube heat exchangers under milk fouling is presented. A fouling model based on a reaction/mass-transfer scheme is detailed in which the main factors during milk heat treatment are quantified in a formal way. This model is coupled with a detailed dynamic model of a shell-and-tube heat exchanger where both radial and axial domains are taken into account. An analytical procedure for the calculation of key parameters provides the means to achieve more accuracy. The simulation results agree well with available experimental work. Four different heat exchanger arrangements are then considered to illustrate their impact on the fouling behavior. The results are encouraging enough to validate current operating industrial techniques for fouling mitigation. For a given thermal duty, short heat exchangers are more prone to fouling due to high-temperature requirements and milk should be heated 11s gradually as possible to minimize fouling. The results show that there are main tradeoffs between design and operation issues.

This paper presents the detailed mathematical modelling and simulation of complex plate heat exchangers under milk fouling, using computational fluid dynamics models. A complex fouling model based on a reaction/mass transfer scheme is described where all the important factors during milk heat treatment are quantified in a formal way. This model is coupled with detailed dynamic models of plate heat exchangers (PHEs). The final model comprises a set of partial differential, integral and algebraic equations. Parameter estimation analysis is performed based on the solution of a dynamic optimization problem. The simulation results have been compared with available experimental work and a satisfactory agreement has been found. Three different industrial configurations with complex flow arrangement are considered to illustrate aspects of fouling behaviour.

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.

Plant layout is concerned with the spatial arrangement of processing equipment, storage vessels, and their interconnecting pipework. Deciding a good layout is an important activity in the design of chemical and process plants. A good layout will facilitate a correct operation of the plant. It will also provide an economic acceptable balance between the often conflicting constraints deriving from safety, environment, construction, maintenance, operation, space for future expansion, and process relationships such as those determined by gravity flow. This paper presents a mathematical formulation for addressing the problem of allocating items of equipment in a given two- or three-dimensional space. The objective function to be minimized is the total pumping, connection, and floor construction cost. Detailed cost factors are used to account for the flow direction between two connected units. The problem is formulated as a mixed integer linear programming model. Specific attention is paid to constructing a for...

Plant layout is concerned with the spatial arrangement of processing equipment, storage vessels and their interconnecting pipework. This is an important aspect in the design of chemical and process plants since a good layout will ensure that the plant functions correctly and will provide an economically acceptable balance between the many, often conflicting, design constraints. These constraints are derived from safety, environmental, construction, maintenance and operational considerations. Process relationships, for example the use of gravity flow, and issues such as the provision of space for future expansion must also be taken into account. Traditional methods for locating equipment within chemical plants are based on mixtures of process heuristic rules and exact-to-the-inch distance information. Such techniques are unsystematic and they do not make use of all the relevant and appropriate data. In this paper an optimization based approach is used to determine a good preliminary plant layout, subject to all of the above constraints. A novel mathematical formulation is presented which addresses the problem of locating items of equipment within a given two or three dimensional space. The objective function to be minimized is the sum of the relevant operation, connection and floor construction costs. Detailed cost factors are used to account for the flow direction between two connected units. The problem is formulated as a mixed integer linear programming problem. Specific attention is paid to constructing a formulation which is suitable for the solution of large scale problems. The method presents the rigorous solution of problems with around 30 process equipment and of essentially unlimited size problems when combined with single heuristic rules. The approach is demonstrated with several practical scale problems, including an industrial multi-purpose plant.

The presented procedure aims to establish an in-depth understanding of a derived mathematical model for drug distribution, pharmacokinetics, and drug effect, pharmacodynamics, during volatile anesthesia. A physiologically based, patient-specific model is derived, where the pharmacokinetic (PK) part consists of multiple blood and tissue compartmental models, each adjusted to the weight, height, gender, and age of the patient. The pharmacodynamic (PD) part is described by an effect site compartment and the Hill equation both linking the hypnotic effect measured by the Bispectral Index (BIS) to the arterial anesthetic concentration. Via a global sensitivity analysis the patient-specific PK and PD variables and parameters are analyzed regarding their influence on the measurable outputs, which are the end-tidal concentration of the volatile anesthetic and the BIS. Via this analysis, the uncertainty introduced by PD variability is identified to be more significant than the uncertainty introduced by PK variability. A case study of isoflurane-based anesthesia shows that the simulation results of the individualized PK variables are in good accordance with the measured end-tidal concentration. However, the PD parameters need to be estimated online to predict the hypnotic depth, measured by the BIS, correctly. As a result of this study, the aim should be to focus on the individual identification of the PD parameters before and during anesthesia with future application in safe and robust model predictive control.

The article contains sections titled: 1. Introduction 2. Process Synthesis and Design 2.1. Objective of Process Synthesis and Design 2.2. Generic Problem Definition 2.3. Process Synthesis–Design Methods 2.4. Challenges and Opportunities 3. Process Integration–Energy 3.1. Process Integration–Introduction to Key Concepts and Major Topics 3.1.1. Design of Heat Exchanger Networks (HENs) 3.1.2. Correct Heat Integration 3.1.3. Use of Optimization in Process Integration 3.1.4. Use of Exergy in Process Integration 3.1.5. Heat Recovery in Batch Processes 3.1.6. Expansions of the Pinch Concept 3.1.7. Concluding Remarks and Future Directions 3.2. Introduction to the Synthesis of Heat Exchanger Networks 3.2.1. Problem Statement 3.2.2. Minimum Utility Cost 3.2.2.1. Partition of Temperature Scale 3.2.2.2. Linear Programming Transshipment Model 3.2.2.3. Illustration 3.2.3. Minimum Number of Matches 3.2.4. Derivation of Optimal HEN Structure 3.2.4.1. Problem Statement 3.2.4.2. HEN superstructure 3.2.4.3. Mathematical Formulation of the HEN Superstructure 3.2.5. Simultaneous HEN Synthesis Approach 4. Process Intensification 4.1. Introduction 4.2. Definition of PI 4.3. Process Systems Engineering (PSE) and Process Intensification (PI) 4.3.1. Synthesis and Design 4.3.2. Modeling and Simulation 4.4. Analysis and Verification 4.5. Perspective 5. Reactive Distillation Processes 5.1. Graphical Design Methods 5.1.1. Element Balances and Equilibrium Condition 5.1.2. Design of Reactive Distillation Columns 5.1.2.1. Binary-Element Reactive Systems 5.1.2.2. The Reactive Equilibrium Curve 5.1.2.3. Constant Total Element Mass Overflow Assumption (CTEMO) 5.1.2.4. The Reactive McCabe–Thiele Diagram 5.1.2.5. Accounting Heat Effects: The Reactive Ponchon–Savarit Diagram 5.1.3. Application Examples 5.1.4. Conclusions 5.2. New Applications 5.2.1. Clean Fuels, Challenges and Opportunities 5.2.2. Methodology 5.2.3. Case Study: Production of Ultra-Clean Diesel 5.2.3.1. The Reaction System (Stage 1) 5.2.3.2. Model Formulation: Batch Reactive Distillation Process (Stage 2) 5.2.3.3. Conceptual design of a reactive distillation column (Stage 3) 5.2.3.4. Rigorous Simulation of Reactive Distillation Process (Stage 3) 5.2.4. Conclusions 6. Chemical Product Synthesis/Design 6.1. Introduction 6.2. Design, Synthesis, and Formulation 6.2.1. Mathematical Formulation of the Chemical Product Design Problem 6.2.2. Database Searches 6.2.3. Enumeration & Generation 6.2.4. Optimization 6.3. Tools for Product Design 6.3.1. Computer-Aided Mixture/Blend Design (CAMbD) 6.3.2. Computer-Aided Molecular Design (CAMD) 6.3.3. Design of Structured Chemical Products 6.4. Example: Design of an Insect Repellent 6.5. Conclusions and Future Directions 7. Approaches to Pharmaceutical Product Design 7.1. Introduction 7.2. General Concepts in Pharmaceutical Product Design 7.3. Design and Development of Active Pharmaceutical Ingredient 7.3.1. Overview 7.3.2. Ligand Screening 7.3.3. Structure-Based Drug Design 7.4. Pharmaceutical Formulation Design 7.4.1. Overview 7.4.2. Formulation Properties and Selection 7.4.3. Computer-Aided Molecular Design of Excipients 7.5. Example: Formulation Design to Minimize Aggregation of Protein Drugs 7.5.1. Overview 7.5.2. Prediction of Protein Aggregation 7.5.3. Design of Excipients for the Minimization of Aggregation 7.6. Conclusions and Future Directions 8. Acknowledgments

Purpose: The purpose of the present study was to investigate the acute effect of counter movement jumping (CMJ) or sprinting on hammer throwing performance in experienced hammer throwers. Methods: Six well-trained hammer throwers with best performance 61-74m, participated in the study. After standard warm-up they performed three hammer throwing attempts with maximum effort, separated with 1.5-min interval. Three minutes later, they performed three maximal consecutive CMJs. Immediately after the CMJs, they performed three hammer throwing attempts with maximum effort, separated with 1.5-min interval. One week later, they carried out an identical protocol but they performed a bout of 20m sprinting instead of the CMJs, in order to potentiate shot put performance (interventions were counterbalanced). Muscular strength (1RM in squat, snatch, clean and jerk) was also measured. Results: Hammer throwing performance was significantly increased after CMJs (62.92±4.43m vs. 64.42±5.13m, p=0.047) as well as after 20m sprinting (64.87±3.90m vs. 65.30±4.02m, p=0.013), although the increase in performance was not different between the two interventions (p=0.214). Conclusions: These results suggest that performing 3 CMJs or one bout of 20m sprint with maximum effort just before hammer throwing may be a useful method for acute increases in performance in experienced hammer throwers. © JPES.