Preparation and performance optimization of La0.7Sr0.3Co0.8-xFe0.2MxO3 (M = Cu, Ni, Y) perovskite

PurposeThis paper aims to deal with the performance modeling and optimization for the stock preparation unit of a paper plant using genetic algorithm. It provides the optimum unit availability level for different combinations of failure and repair rates of the subsystems of the stock preparation unit of the paper plant concerned.Design/methodology/approachEfforts have been made to develop the performance model based on a real situation for the stock preparation unit. The performance in terms of availability has been evaluated on the basis of Markov birth‐death process. After that, the performance optimization using genetic algorithm is performed, which gives the optimum unit availability levels for different combinations of failure and repair rates of the subsystems of stock preparation unit for enhancing the overall performance of the paper plant.FindingsThe effect of genetic algorithm parameters such as number of generations, population size and crossover probability on the unit performance, i.e. availability, has been analyzed and discussed with the concerned paper plant management. It is found that these results are highly beneficial to the maintenance engineers for the purpose of the effective maintenance planning to enhance the overall performance (availability) of stock preparation unit of the paper plant.Originality/valueMost other researchers have confined their work to the development and analysis of theoretical models which has little practical significance. To fulfill this deficiency, efforts have been made in the present work to develop a model based on real situation for stock preparation unit.

This comprehensive article explores the critical aspects of mobile application performance optimization, focusing on the relationship between technical efficiency and user satisfaction. The article examines various performance metrics, including latency, frame rates, memory usage, and cold start times, while analyzing their impact on user engagement and business outcomes. Through detailed explanation of common performance challenges and optimization strategies, the article provides insights into effective development practices, testing methodologies, and resource management approaches. The article combines empirical data from multiple studies to demonstrate the significance of systematic performance optimization in mobile application development, offering practical solutions for developers and stakeholders to enhance application performance across different operational scenarios.

MapReduce plays an critical role in finding insights in Big Data. The performance optimization of MapReduce programs is challenging because it requires a comprehensive understanding of the whole system including both hardware layers (processors, storages, networks and etc), and software stacks (operating systems, JVM, runtime, applications and etc). However, most of the existing performance tuning and optimization are based on empirical and heuristic attempts. It remains a blank on how to build a systematical framework which breaks the boundary of multiple layers for performance optimization. In this paper, we propose a performance evaluation framework by correlating performance metrics from different layers, which provides insights to efficiently pinpoint the performance issue. This framework is composed of a series of predefined patterns. Each pattern indicates one or more potential issues. The behavior of a MapReduce program is mapped to the corresponding resource utilization. The framework provides a holistic approach which allows users at different levels of experience to conduct MapReduce program performance optimization. We use Terasort benchmark running on a 10-node Power7R2 cluster as a real case to show how this framework improves the performance. By this framework, we finally get the Terasort result improved from 47 mins to less than 8 mins. In addition to the best practice on performance tuning, several key findings are summarized as valuable workload analysis for JVM, MapReduce runtime and application design.

The present review paper provides a systematic insight into the studies published so far when it comes to the research on the cost and performance optimisation in the parcel delivery phase. Globalisation, as well as the new trends, such as selling online, directly influences the demands for the delivery of goods. Demand for the delivery of goods proportionally affects the transport prices. A great majority of deliveries is carried out in densely populated urban areas. In terms of costs, the greatest part in the courier organisations costs is observed in the technological phase of parcel delivery, which is at the same time the least efficient. For that reason, significant improvement of performance and cost optimisation in the very delivery phase is a rather challenging field for the researchers. New algorithm-based technologies, innovations in the logistics and outsourcing of individual technological phases are ways by means of which one strives to enhance the delivery efficiency, to improve performance and quality, but also - to optimise the costs in the last phase of delivery. The aim of the present paper is to offer a systematic review into the most recent research in the field of technology, innovations and outsourcing models with the aim of reducing the cost and enhancing the productivity and quality in parcel delivery.

MapReduce plays an critical role in finding insights in Big Data. The performance optimization of MapReduce programs is challenging because it requires a comprehensive understanding of the whole system including both hardware layers (processors, storages, networks and etc), and software stacks (operating systems, JVM, runtime, applications and etc). However, most of the existing performance tuning and optimization are based on empirical and heuristic attempts. It remains a blank on how to build a systematical framework which breaks the boundary of multiple layers for performance optimization. In this paper, we propose a performance evaluation framework by correlating performance metrics from different layers, which provides insights to efficiently pinpoint the performance issue. This framework is composed of a series of predefined patterns. Each pattern indicates one or more potential issues. The behavior of a MapReduce program is mapped to the corresponding resource utilization. The framework provides a holistic approach which allows users at different levels of experience to conduct MapReduce program performance optimization. We use Terasort benchmark running on a 10-node Power7R2 cluster as a real case to show how this framework improves the performance. By this framework, we finally get the Terasort result improved from 47 mins to less than 8 mins. In addition to the best practice on performance tuning, several key findings are summarized as valuable workload analysis for JVM, MapReduce runtime and application design.

PurposeThe purpose of this paper is to deal with the performance modeling and optimization for the stock preparation unit of a paper plant using genetic algorithm. It provides the optimum unit availability level for different combinations of failure and repair rates of the subsystems of the stock preparation unit of the paper plant concerned.Design/methodology/approachIn this paper, efforts have been made to develop performance models based on real situations for the stock preparation unit. The performance in terms of availability has been evaluated on the basis of Markov birth‐death process. After that, the performance optimization using genetic algorithm is done, which gives the optimum unit availability levels for different combinations of failure and repair rates of the subsystems of stock preparation units for enhancing the overall performance of the paper plant.FindingsThe effect of genetic algorithm parameters, namely number of generations, population size and crossover probability on the unit performance i.e. availability has been analyzed and discussed with the concerned paper plant management. It is found that these results are highly beneficial to the maintenance engineers for the purpose of effective maintenance planning to enhance the overall performance (availability) of the stock preparation unit of the paper plant.Originality/valueMost of the researchers have confined their work to the development and analysis of theoretical models which has little practical significance. To fulfill this deficiency, efforts have been made in the present work to develop a model based on real situations for the stock preparation unit.

Platinum-nickel (Pt-Ni) nanowires were developed as fuel cell electrocatalysts, and were optimized for the performance and durability in the oxygen reduction reaction. Spontaneous galvanic displacement was used to deposit Pt layers onto Ni nanowire substrates. The synthesis approach produced catalysts with high specific activities and high Pt surface areas. Hydrogen annealing improved Pt and Ni mixing and specific activity. Acid leaching was used to preferentially remove Ni near the nanowire surface, and oxygen annealing was used to stabilize near-surface Ni, improving durability and minimizing Ni dissolution. These protocols detail the optimization of each post-synthesis processing step, including hydrogen annealing to 250 °C, exposure to 0.1 M nitric acid, and oxygen annealing to 175 °C. Through these steps, Pt-Ni nanowires produced increased activities more than an order of magnitude than Pt nanoparticles, while offering significant durability improvements. The presented protocols are based on Pt-Ni systems in the development of fuel cell catalysts. These techniques have also been used for a variety of metal combinations, and can be applied to develop catalysts for a number of electrochemical processes.

Study on thermal storage performance of a novel conical spiral tube heat storage system

Energy proportionality is the key design goal followed by architects of multicore processors. One of its implications is that optimization of an application for performance will also optimize it for energy.In this work, we show that energy proportionality does not hold true for multicore processors. This finding creates the opportunity for bi-objective optimization of applications for energy and performance. We propose and study a novel application-level bi-objective optimization method for energy and performance for multithreaded dataparallel applications. The method uses two decision variables, the number of identical multithreaded kernels (threadgroups) executing the application and the number of threads per threadgroup, with a given workload partitioned equally between the threadgroups.We experimentally demonstrate the efficiency of the method using four popular and highly optimized multithreaded data-parallel applications, two employing two-dimensional fast Fourier transform and the other two, dense matrix multiplication. The experiments performed on four modern multicore processors show that the optimization for performance alone results in increase in dynamic energy consumption by up to 89% and optimization for dynamic energy alone results in performance degradation by up to 49%. By solving the bi-objective optimization problem, the method determines up to 11 Pareto-optimal solutions.Finally, we propose a qualitative dynamic energy model employing performance events as variables to explain the discovered energy nonproportionality. The model shows that the energy nonproportionality on our experimental platforms for the two data-parallel applications is due to disproportionately energy expensive activity of the data translation lookaside buffer.

In this analytical investigation, preheated palm oil was used in the direct injection diesel engine with various optimization methods. The main purpose of the optimization was to get better results than the conventional engine. Raw palm oil was heated using the heat exchange process to reduce the density and viscosity. The relationship between the output process and factors response was evaluated in the design of experiment methods. The Taguchi method is an important method for optimization of the output response performance and emission characteristics of a diesel engine. Two important factors—output and input—were calculated. The input factors considered were preheated palm biodiesel blend, torque, injection pressure, compression ratio, and injection timing. The output factors calculated were smoke opacity, carbon monoxide emission, and brake-specific fuel consumption by using the signal-to-noise (S/N) ratio and analysis of variance. Carbon monoxide was most impacted by torque conditions through injection timing and injecting pressure, and opacity of smoke emission. Among them, injection timing had a higher impact. Different biodiesel blends were prepared: B10 (90% diesel + 10% oil), B20 (80% diesel + 20% oil), B30 (70% diesel + 30% oil) and B40 (60% diesel + 40% oil). Silver nanoparticles (50 ppm) were constantly mixed with the various biodiesel blends. The smoke opacity emission for the biodiesel blend B30 + 50 ppm silver nanoparticle showed the lowest S/N ratio and achieved better optimum results compared with the other blends. The blend B30 + 50 ppm silver nanoparticle showed the lowest S/N ratio value of 9.7 compared with the other blends. The smoke opacity, carbon monoxide emission, and brake-specific fuel consumption of all the response optimal factors were found to be 46.77 ppm, 0.32%, and 0.288 kg/kW·h, respectively.

Optimization of the performance of flotation circuits using a genetic algorithm oriented by process-based rules

Optimization of performance and stability of quantum dot sensitized solar cells by manipulating the electrical properties of different metal sulfide counter electrodes

The aim of the present scoping review was to present the image-based analysis methods that have being utilized to assist Track and Field coaches to observe the technique of their athletes aiming towards the optimization of performance. From the era of early photography till the current innovation of markerless motion analysis, sports and track and field athletes in particular are the field where the technological advances in image capturing and analysis are implemented for both practical, as well as for scientific purposes. The outcome of the blending of technology and sports science is the better understanding of human motion, the exploitation of its movement abilities, and its ideal segmentation when teaching sport techniques that has led to the optimization of sport performance and the identification of the unique prospects of human performance as presented by elite athletes. The chapter is comprised by a short description of the evolution in motion analysis methods, its contribution in the understanding sport techniques, its exploitation to create tools to effectively teach sport technique, and the presentation of the technological innovations that will assist track and field coaches in the future.

This paper describes the methodology and the results of a successful partnership between operator and service companies that improved the drilling performance and economics for the development of the North Field in Qatar. Fifteen high rate gas wells were delivered within designated directional targets, ahead of schedule and under budget. The systematic use of a new methodology for bit selection, linked with an excellent level of communication and confidence between different parties involved in the drilling operations, was a major reason for the drilling success. The different sections drilled were mainly made up of limestone, marl, and shale, but also anhydrite in the top portions of the well. In the lower sections, hard and soft limestone alternates withhighly compacted shales and dolomites. Penetration rates were greatly enhanced and bit performance improved by more than 100% in some instances. The optimization of the performance took into consideration three aspects:A very tight schedule, where the optimization phase had to be performed within a limited time frame: Four wells were planned to be drilled back to back with two jack up rigs from two production platforms. After this short phase, batch drilling was expected to be performed for more than 20 deviated wells.The formations to be drilled presented a high complexity and heterogeneity where hard stringers alternate with a soft or a very compacted rock, and where in the lower section of the hole, a typical pseudo plastic behavior of the rock has to be anticipated.The well designs, casing depths, and bit types had to be in harmony with the drilling environment, the formation, and provide effective performance. New bit designs, more adapted to the technology available had to be developed or identified. The use of roller cone bits and motors was planned and used in the top hole sections. The use of PDC bits associated with extended steerable motors and/or turbines was considered as potentially providing the best performance in the intermediate and bottom sections. The early bit designs were modified based on wear patterns of dull bits. These enhancements and modifications were one of the major reasons in improved drilling performance and helped reduce the total drilling time from 75 days to 56 days per well. The challenges were met successfully, the most economical options selected and earned out ahead of time and under budget.

Platinum-nickel (Pt-Ni) nanowires were developed as fuel cell electrocatalysts, and were optimized for the performance and durability in the oxygen reduction reaction. Spontaneous galvanic displacement was used to deposit Pt layers onto Ni nanowire substrates. The synthesis approach produced catalysts with high specific activities and high Pt surface areas. Hydrogen annealing improved Pt and Ni mixing and specific activity. Acid leaching was used to preferentially remove Ni near the nanowire surface, and oxygen annealing was used to stabilize near-surface Ni, improving durability and minimizing Ni dissolution. These protocols detail the optimization of each post-synthesis processing step, including hydrogen annealing to 250 °C, exposure to 0.1 M nitric acid, and oxygen annealing to 175 °C. Through these steps, Pt-Ni nanowires produced increased activities more than an order of magnitude than Pt nanoparticles, while offering significant durability improvements. The presented protocols are based on Pt-Ni systems in the development of fuel cell catalysts. These techniques have also been used for a variety of metal combinations, and can be applied to develop catalysts for a number of electrochemical processes.