125 research outputs found
FREE INTERACTOR MATRIX METHOD FOR CONTROL PERFORMANCE ASSESSMENT OF MULTI-VARIATE SYSTEMS
In this paper, an alternative method for the assessment of multi-vitiate control loop performance with consider twocircumstances. First, known time delays between each pair of inputs and outputs, and second, without relying on any a priori knowledge about the process model or timedelays. The performance of the control loop is calculated from data driven autoregressive moving average (ARMA) and prediction error model. It is clear that the limited data in scalar measure used for performance assessment results tends to steady-state as time tends to infinity, but large number of samples gives risen in scalar measures and tends to infinity as time samples tends to infinity and therefore it becomes difficult to calculate the performance index. In this paper, the later problem is solved by considering initial part of scalar measures with steady value for next-to-next time samples to calculate the control-loop performance index which would be utilized to decide healthy working of the control loop. Simulation example is included to show the performance index of multi-variate control loop
Application of selection hyper-heuristics to the simultaneous optimisation of turbines and cabling within an offshore windfarm
Global warming has focused attention on how the world produces the energy required to power the planet. It has driven a major need to move away from using fossil fuels for energy production toward cleaner and more sustainable methods of producing renewable energy. The development of offshore windfarms, which harness the power of the wind, is seen as a viable approach to creating renewable energy but they can be difficult to design efficiently. The complexity of their design can benefit significantly from the use of computational optimisation. The windfarm optimisation problem typically consists of two smaller optimisation problems: turbine placement and cable routing, which are generally solved separately. This paper aims to utilise selection hyper-heuristics to optimise both turbine placement and cable routing simultaneously within one optimisation problem. This paper identifies and confirms the feasibility of using selection hyper-heuristics within windfarm optimisation to consider both cabling and turbine positioning within the same single optimisation problem. Key results could not identify a conclusive advantage to combining this into one optimisation problem as opposed to considering both as two sequential optimisation problems.</p
Modelling and solving the combined inventory routing problem with risk consideration
This work proposes a multi-objective extension of a real-world inventory routing problem (IRP), a generalisation of the classical vehicle routing problem (VRP) with vendor managed inventory (VMI) replenishment. While many mathematical formulations and solution models already exist, this study incorporates business related and risk considerations that makes it unique. It is known that a significant volume of hazardous materials travels every day. Consideration of risks arising from the transportation of hazardous materials as a criterion for selecting distribution routes could potentially reduce the likelihood of accidents and/or the expected consequences of accident
Evaluation of targetable biomarkers for chimeric antigen receptor T-cell (CAR-T) in the treatment of pancreatic cancer:a systematic review and meta-analysis of preclinical studies
One of the cutting edge techniques for treating cancer is the use of the patient's immune system to prevail cancerous disease. The versatility of the chimeric antigen receptor (CAR) T-cell approach in conjugation with promising treatments in haematological cancer has led to countless cases of research literature for the treatment of solid cancer. A systematic search of online databases as well as gray literature and reference lists of retrieved studies were carried out up to March 2019 to identify experimental animal studies that investigated the antigens targeted by CAR T-cell for pancreatic cancer treatment. Studies were evaluated for methodological quality using the SYstematic Review Center for Laboratory Animal Experimentation bias risk tool (SYRCLE's ROB tool). Pooled cytotoxicity ratio/percentage and 95% confidence intervals were calculated using the inverse-variance method while random-effects meta-analysis was used, taking into account conceptual heterogeneity. Heterogeneity was assessed with the Cochran Q statistic and quantified with the I2 statistic using Stata 13.0. Of the 485 identified studies, 56 were reviewed in-depth with 16 preclinical animal studies eligible for inclusion in the systematic review and 11 studies included in our meta-analysis. CAR immunotherapy significantly increased the cytotoxicity assay (percentage: 65%; 95% CI: 46%, 82%). There were no evidence for significant heterogeneity across studies [P = 0.38 (Q statistics), I2 = 7.14%] and for publication bias. The quality assessment of included studies revealed that the evidence was moderate to low quality and none of studies was judged as having a low risk of bias across all domains. CAR T-cell therapy is effective for pancreatic cancer treatment in preclinical animal studies. Further high-quality studies are needed to confirm our finding and a standard approach of this type of studies is necessary according to our assessment.</p
Evaluation of targetable biomarkers for chimeric antigen receptor T-cell (CAR-T) in the treatment of pancreatic cancer:a systematic review and meta-analysis of preclinical studies
An analysis of the Taguchi method for tuning a memetic algorithm with reduced computational time budget
Determining the best initial parameter values for an algorithm, called parameter tuning, is crucial to obtaining better algorithm performance; however, it is often a time-consuming task and needs to be performed under a restricted computational budget. In this study, the results from our previous work on using the Taguchi method to tune the parameters of a memetic algorithm for cross-domain search are further analysed and extended. Although the Taguchi method reduces the time spent finding a good parameter value combination by running a smaller size of experiments on the training instances from different domains as opposed to evaluating all combinations, the time budget is still larger than desired. This work investigates the degree to which it is possible to predict the same good parameter setting faster by using a reduced time budget. The results in this paper show that it was possible to predict good combinations of parameter settings with a much reduced time budget. The good final parameter values are predicted for three of the parameters, while for the fourth parameter there is no clear best value, so one of three similarly performing values is identified at each time instant
Performance of selection hyper-heuristics on the extended HyFlex domains
Selection hyper-heuristics perform search over the space of heuristics by mixing and controlling a predefined set of low level heuristics for solving computationally hard combinatorial optimisation problems. Being reusable methods, they are expected to be applicable to multiple problem domains, hence performing well in cross-domain search. HyFlex is a general purpose heuristic search API which separates the high level search control from the domain details enabling rapid development and performance comparison of heuristic search methods, particularly hyper-heuristics. In this study, the performance of six previously proposed selection hyper-heuristics are evaluated on three recently introduced extended HyFlex problem domains, namely 0–1 Knapsack, Quadratic Assignment and Max-Cut. The empirical results indicate the strong generalising capability of two adaptive selection hyper-heuristics which perform well across the ‘unseen’ problems in addition to the six standard HyFlex problem domains
The data set development for the National Spinal Cord Injury Registry of Iran (NSCIR-IR): progress toward improving the quality of care
STUDY DESIGN: Descriptive study. OBJECTIVES: The aim of this manuscript is to describe the development process of the data set for the National Spinal Cord Injury Registry of Iran (NSCIR-IR). SETTING: SCI community in Iran. METHODS: The NSCIR-IR data set was developed in 8 months, from March 2015 to October 2015. An expert panel of 14 members was formed. After a review of data sets of similar registries in developed countries, the selection and modification of the basic framework were performed over 16 meetings, based on the objectives and feasibility of the registry. RESULTS: The final version of the data set was composed of 376 data elements including sociodemographic, hospital admission, injury incidence, prehospital procedures, emergency department visit, medical history, vertebral injury, spinal cord injury details, interventions, complications, and discharge data. It also includes 163 components of the International Standards for the Neurologic Classification of Spinal Cord Injury (ISNCSCI) and 65 data elements related to quality of life, pressure ulcers, pain, and spasticity. CONCLUSION: The NSCIR-IR data set was developed in order to meet the quality improvement objectives of the registry. The process was centered around choosing the data elements assessing care provided to individuals in the acute and chronic phases of SCI in hospital settings. The International Spinal Cord Injury Data Set was selected as a basic framework, helped by comparison with data from other countries. Expert panel modifications facilitated the implementation of the registry process with the current clinical workflow in hospitals
A stochastic local search algorithm with adaptive acceptance for high-school timetabling
Automating high school timetabling is a challenging task. This problem is a well known hard computational problem which has been of interest to practitioners as well as researchers. High schools need to timetable their regular activities once per year, or even more frequently. The exact solvers might fail to find a solution for a given instance of the problem. A selection hyper-heuristic can be defined as an easy-to-implement, easy-to-maintain and effective 'heuristic to choose heuristics' to solve such computationally hard problems. This paper describes the approach of the team hyper-heuristic search strategies and timetabling (HySST) to high school timetabling which competed in all three rounds of the third international timetabling competition. HySST generated the best new solutions for three given instances in Round 1 and gained the second place in Rounds 2 and 3. It achieved this by using a fairly standard stochastic search method but significantly enhanced by a selection hyper-heuristic with an adaptive acceptance mechanism. © 2014 Springer Science+Business Media New York
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