The CHPED (combined heat and power economic dispatch) is a complex engineering optimization problem. The goal is to minimize the system production costs by taking into consideration different kind of ...constraints. This research investigates the first implementation of a prevailing bio-inspired metaheuristic called the cuckoo optimization algorithm which is powered by a penalty function (PFCOA) for solving the CHPED problem. Two case studies of the CHPED are presented and the results are compared to those obtained by several other optimization techniques applied in the literature. It has been proven that the implemented PFCOA is superior.
•Combined heat and power economic dispatch problem was investigated.•Cuckoo optimization algorithm was powered by a penalty function to solve the problem.•Two case studies were presented and solved using the proposed approach.•The results show the effectiveness of the proposed approach over other works.
Unconventional machining processes (communally named advanced or modern machining processes) are widely used by manufacturing industries. These advanced machining processes allow producing complex ...profiles and high quality-products. However, several process parameters should be optimized to achieve this end. In this paper, the optimization of process parameters of two conventional and four advanced machining processes is investigated: drilling process, grinding process, abrasive jet machining, abrasive water jet machining, ultrasonic machining, and water jet machining, respectively. This research employed two bio-inspired algorithms called the cuckoo optimization algorithm and the hoopoe heuristic to optimize the machining control parameters of these processes. The obtained results are compared with other optimization algorithms described and applied in the literature.
•The Reliability-Redundancy Allocation Problem (RRAP) is addressed.•The assumption of heterogeneous components is considered in the RRAP problem.•A new solution approach, called hosted cuckoo ...optimization algorithm, is proposed.•The proposed approach is applied to five case studies.•The results are compared with those obtained by four methods.
During the last decade, system reliability optimization has been widely investigated. New strategies have been introduced recently to improve the overall system reliability, such as the standby and heterogeneous redundant components. However, the problem formulations of these strategies are more complex. This paper addresses the system reliability-redundancy allocation problem (RRAP) with heterogeneous components. A new solution approach, called hosted cuckoo optimization algorithm (HO-COA), is proposed to effectively solve the problem. It is based on the latest researches on the cuckoos. The egg-laying recognition used in this paper is more realistic than the simple cuckoo optimization algorithm (COA). The effectiveness of the proposed approach is verified on five case studies and the application results are compared to those obtained in the literature, the simple cuckoo optimization algorithm (COA), the differential evolution method (DE), and the flower pollination algorithm (FPA). The fifth case study represents a large-scale system highlighting the superiority of the HO-COA.
Medications have been a part of space travel dating back to the Apollo missions. Currently, medical kits aboard the International Space Station (ISS) contain medications and supplies to treat a ...variety of possible medical events. As we prepare for more distant exploration missions to Mars and beyond, risk management planning for astronaut healthcare should include the assembly of a medication formulary that is comprehensive enough to prevent or treat anticipated medical events, remains safe and chemically stable, and retains sufficient potency to last for the duration of the mission. Emerging innovation and technologies in pharmaceutical development, delivery, quality maintenance, and validation offer promise for addressing these challenges. The present editorial will summarize the current state of knowledge regarding innovative formulary optimization strategies, pharmaceutical stability assessment techniques, and storage and packaging solutions that could enhance drug safety and efficacy for future exploration spaceflight missions.
Creating a successful small molecule drug is a challenging multiparameter optimization problem in an effectively infinite space of possible molecules. Generative models have emerged as powerful tools ...for traversing data manifolds composed of images, sounds, and text and offer an opportunity to dramatically improve the drug discovery and design process. To create generative optimization methods that are more useful than brute-force molecular generation and filtering via virtual screening, we propose that four integrated features are necessary: large, quantitative data sets of molecular structure and activity, an invertible vector representation of realistic accessible molecules, smooth and differentiable regressors that quantify uncertainty, and algorithms to simultaneously optimize properties of interest. Over the course of 12 months, Terray Therapeutics has collected a data set of 2 billion quantitative binding measurements of small molecules to therapeutic targets, which directly motivates multiparameter generative optimization of molecules conditioned on these data. To this end, we present contrastive optimization for accelerated therapeutic inference (COATI), a pretrained, multimodal encoder-decoder model of druglike chemical space. COATI is constructed without any human biasing of features, using contrastive learning from text and 3D representations of molecules to allow for downstream use with structural models. We demonstrate that COATI possesses many of the desired properties of universal molecular embedding: fixed-dimension, invertibility, autoencoding, accurate regression, and low computation cost. Finally, we present a novel metadynamics algorithm for generative optimization using a small subset of our proprietary data collected for a model protein, carbonic anhydrase, designing molecules that satisfy the multiparameter optimization task of potency, solubility, and drug likeness. This work sets the stage for fully integrated generative molecular design and optimization for small molecules.
System availability is a key element for any industry. System designers and operators try to do their best to maintain the required availability of the systems to avoid production stoppages. They set ...up and undertake different maintenances, and these interventions imply cost. Therefore, the goal is to minimize the cost, but considering the constraint of the availability requirement. The problem involves three main aspects: redundancy allocation, component failure rates, and repair rates. In this paper, a novel solution approach is proposed based on an efficient cuckoo optimization algorithm (EF‐COA). Two numerical case studies are solved, and the results confirm the effectiveness of the approach proposed.
Manufacturing requires various machining processes. Nowadays, machining implies advanced technologies in order to meet more exacting process performance criteria. This paper addresses the ...optimization of four conventional and nonconventional machining processes: drilling, grinding, water jet machining (WJM), and wire electrical discharge machining (EDM). The input process parameters are: cutting speed, feed rate, cutting environment, depth of cut, grit size, water jet pressure, diameter of water jet nozzle, traverse rate of the nozzle, stand-off-distance, ignition pulse current, pulse-off time, pulse duration, servo reference mean voltage, servo speed variation, wire speed, wire tension, and injection pressure. The multi-objective EDM optimization problem is converted to a single-objective problem using the weighted-sum method. Two nature-inspired algorithms of artificial intelligence (AI) are implemented for solving these problems, namely the particle swarm optimization (PSO) and the flower pollination algorithm (FPA). Penalty functions are introduced to handle the constraints and to enhance the algorithms for better results. The machining outputs, required number of function evaluations, CPU time, and standard deviations are the performance metrics. The results obtained are compared and show better performance than that already documented in the literature.
Sorafenib has been the standard of care for patients with advanced hepatocellular carcinoma and although immunotherapeutic approaches are now challenging this position, it retains an advantage in ...HCV-seropositive patients. We aimed to quantify the rate of tumour progression in patients receiving sorafenib and relate this figure to survival, both overall, and according to viral status.
Using serial data from an international clinical trial we applied a joint model to combine survival and progression over time in order to estimate the rate of tumour growth as assessed by tumour burden and serum alpha-fetoprotein, and the impact of treatment on liver function.
High tumour burden at baseline was associated with an increased risk of death. In patients still alive at the end of the study, the progression in relation to tumour burden was very low compared to those who died within the study. Overall, the change in mean tumour burden was 0.12 mm per day or an absolute growth rate of 3.6 mm/month. Median doubling time was 665 days. For those who progressed above 0.12 mm per day or the 12% rate, median survival was 234 days compared to 384 days if the rate was below 12%. Tumour growth rate and serum alpha-fetoprotein rise were significantly lower in those who were HCV seropositive as was the rate of decline in liver function. These results were replicated in 2 independent patient groups.
Our analysis suggests that sorafenib treatment is associated with improved survival in patients with advanced hepatocellular carcinoma mainly by decreasing the rate of tumour growth and liver function deterioration among patients with HCV infection.
Among patients receiving sorafenib for advanced hepatocellular carcinoma the rate of tumour growth (as assessed by changes in tumour size and the biomarker alpha-fetoprotein) and the deterioration of liver function is less in those who have the hepatitis C virus, than in those who do not.
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Among patients receiving sorafenib for advanced hepatocellular carcinoma:•The overall rate of increase in tumour burden was 12%.•Above the 12% rate median survival was 234 days, compared to 384 days if below.•High tumour burden at baseline was associated with an increased risk of death.•Tumour growth rate, AFP rises and rate of decline in liver function were lower in those who were HCV seropositive.
•Combined heat power economic dispatch problem is investigated.•A discussion on the results of two papers is given.•Feasible operating regions and production cost are claimed.
This discussion ...concerns the results and comments recently published in the paper “E. Davoodi et al. A GSO-based algorithm for combined heat and power dispatch problem with modified scrounger and ranger operators, Applied Thermal Engineering, Vol. 120, pp. 36–48, 2017.” The goal is to clarify some points from the results provided by the cuckoo optimization algorithm with penalty function (PFCOA) and those obtained by the MGSO for the numerical application on the combined heat and power economic dispatch problem (CHPED) containing four units by considering three scenarios.
Purpose The glomerular filtration rate (GFR) is essential for carboplatin chemotherapy dosing; however, the best method to estimate GFR in patients with cancer is unknown. We identify the most ...accurate and least biased method. Methods We obtained data on age, sex, height, weight, serum creatinine concentrations, and results for GFR from chromium-51 (
Cr) EDTA excretion measurements (
Cr-EDTA GFR) from white patients ≥ 18 years of age with histologically confirmed cancer diagnoses at the Cambridge University Hospital NHS Trust, United Kingdom. We developed a new multivariable linear model for GFR using statistical regression analysis.
Cr-EDTA GFR was compared with the estimated GFR (eGFR) from seven published models and our new model, using the statistics root-mean-squared-error (RMSE) and median residual and on an internal and external validation data set. We performed a comparison of carboplatin dosing accuracy on the basis of an absolute percentage error > 20%. Results Between August 2006 and January 2013, data from 2,471 patients were obtained. The new model improved the eGFR accuracy (RMSE, 15.00 mL/min; 95% CI, 14.12 to 16.00 mL/min) compared with all published models. Body surface area (BSA)-adjusted chronic kidney disease epidemiology (CKD-EPI) was the most accurate published model for eGFR (RMSE, 16.30 mL/min; 95% CI, 15.34 to 17.38 mL/min) for the internal validation set. Importantly, the new model reduced the fraction of patients with a carboplatin dose absolute percentage error > 20% to 14.17% in contrast to 18.62% for the BSA-adjusted CKD-EPI and 25.51% for the Cockcroft-Gault formula. The results were externally validated. Conclusion In a large data set from patients with cancer, BSA-adjusted CKD-EPI is the most accurate published model to predict GFR. The new model improves this estimation and may present a new standard of care.
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