A novel approach for efficient stance detection in online social networks with metaheuristic optimization

In the 19th and 20th centuries, social networks have been an important topic in a wide range of fields from sociology to education. However, with the advances in computer technology in the 21st century, significant changes have been observed in social networks, and conventional networks have evolved into online social networks. The size of these networks, along with the large amount of data they generate, has introduced new social networking problems and solutions. Social network analysis methods are used to understand social network data. Today, several methods are implemented to solve various social network analysis problems, albeit with limited success in certain problems. Thus, the researchers develop new methods or recommend solutions to improve the performance of the existing methods. In the present paper, a novel optimization method that aimed to classify social network analysis problems was proposed. The problem of stance detection, an online social network analysis problem, was first tackled as an optimization problem. Furthermore, a new hybrid metaheuristic optimization algorithm was proposed for the first time in the current study, and the algorithm was compared with various methods. The analysis of the findings obtained with accuracy, precision, recall, and F-measure classification metrics demonstrated that our method performed better than other methods.     

变分布的量子行为粒子群优化算法求解工程约束优化问题

针对工程形状设计领域中带有多个约束条件的非线性设计优化问题,提出了一种自适应的基于高斯分布的量子行为粒子群优化(AG-QPSO)算法。通过自适应地调整高斯分布,AG-QPSO算法能够在搜索的初始阶段有很强的全局搜索能力,随着搜索过程的进行,算法的局部搜索能力逐渐增强,从而满足了算法在搜索过程不同阶段的需要。为了验证算法的有效性,在压力容器和张弦设计问题这两个工程约束优化问题上进行50轮独立实验。实验结果表明,在满足所有约束条件的情况下,AG-QPSO算法在压力容器设计问题上取得了5890.9315的平均解和5885.3328的最优解,在张弦设计问题上取得了0.01096的平均解和0.01096的最优解,远优于标准粒子群优化(PSO)算法、具有量子行为的粒子群优化(QPSO)算法和高斯量子行为粒子群(G-QPSO)算法等现有的算法的结果,同时AG-QPSO算法取得的结果的方差较小,说明该算法具有很好的鲁棒性。     

Performance optimization of microbial electrolysis cell (MEC) for palm oil mill effluent (POME) wastewater treatment and sustainable Bio-H2 production using response surface methodology (RSM)

Microbial electrolysis cells (MECs) are a new bio-electrochemical method for converting organic matter to hydrogen gas (H₂). Palm oil mill effluent (POME) is hazardous wastewater that is mostly formed during the crude oil extraction process in the palm oil industry. In the present study, POME was used in the MEC system for hydrogen generation as a feasible treatment technology. To enhance biohydrogen generation from POME in the MEC, an empirical model was generated using response surface methodology (RSM). A central composite design (CCD) was utilized to perform twenty experimental runs of MEC given three important variables, namely incubation temperature, initial pH, and influent dilution rate. Experimental results from CCD showed that an average value of 1.16 m³ H₂/m³ d for maximum hydrogen production rate (HPR) was produced. A second-order polynomial model was adjusted to the experimental results from CCD. The regression model showed that the quadratic term of all variables tested had a highly significant effect (P < 0.01) on maximum HPR as a defined response. The analysis of the empirical model revealed that the optimal conditions for maximum HPR were incubation temperature, initial pH, and influent dilution rate of 30.23 ^°C, 6.63, and 50.71%, respectively. Generated regression model predicted a maximum HPR of 1.1659 m³ H₂/m³ d could be generated under optimum conditions. Confirmation experimentation was conducted in the optimal conditions determined. Experimental results of the validation test showed that a maximum HPR of 1.1747 m³ H₂/m³ d was produced.     

The optimization landscape of hybrid quantum–classical algorithms: From quantum control to NISQ applications

This review investigates the landscapes of hybrid quantum–classical optimization algorithms that are prevalent in many rapidly developing quantum technologies, where the objective function is computed by either a natural quantum system or an engineered quantum ansatz, but the optimizer is classical. In any particular case, the nature of the underlying control landscape is fundamentally important for systematic optimization of the objective. In early studies on the optimal control of few-body dynamics, the optimizer could take full control of the relatively low-dimensional quantum systems to be manipulated. Stepping into the noisy intermediate-scale quantum (NISQ) era, the experimentally growing computational power of the ansatz expressed as quantum hardware may bring quantum advantage over classical computers, but the classical optimizer is often limited by the available control resources. Across these different scales, we will show that the landscape’s geometry experiences morphological changes from favorable trap-free landscapes to easily trapping rugged landscapes, and eventually to barren-plateau landscapes on which the optimizer can hardly move. This unified view provides the basis for understanding classes of systems that may be readily controlled out to those with special consideration, including the difficulties and potential advantages of NISQ technologies, as well as seeking possible ways to escape traps or plateaus, in particular circumstances.     

3D printing technology: The new era for food customization and elaboration

BackgroundDigitalizing food using 3-Dimensional (3D) printing is an incipient sector that has a great potential of producing customized food with complex geometries, tailored texture and nutritional content. Yet, its application is still limited and the process utility is under the investigation of many researchers.Scope and approachThe main objective of this review was to analyze and compare published articles pertaining 3D food printing to ensure how to reach compatibility between the huge varieties of food ingredients and their corresponding best printing parameters. Different from previously published reviews in the same journal by Lipton et al. (2015) and Liu et al. (2017), this review focuses in depth on optimizing extrusion based food printing which supports the widest array of food and maintains numerous shapes and textures. The benefits and limitations of 3D food printing were critically reviewed from a different perspective while providing ample mechanisms to overcome those barriers.Key findings and conclusionsFour main obstacles hamper the printing process: ordinance and guidelines, food shelf life, ingredients restrictions and post processing. Unity and integrity between material properties and process parameters is the key for a best end product. For each group, specific criteria should be monitored: rheological, textural, physiochemical and sensorial properties of the material its self in accordance with the process parameters of nozzle diameter, nozzle height, printing speeds and temperature of printing. It is hoped that this paper will unlock further research on investigating a wider range of food printing ingredients and their influence on customer acceptability.     

Degradation rate quantification of solid oxide fuel cell performance with and without Al2TiO5 addition

Degradation rates of electrical current during constant voltage operation of SOFCs with anodes made using NiO precursor powders from two different manufacturers with and without the addition of aluminum titanate (ALT) added by either mechanical mixing or anode infiltration have been quantified using a novel MATLAB algorithm. Because the algorithm has been used to quantify degradation rates for many different SOFC tests, it is thought that the method can be applied to most measured SOFC data to quantify the instantaneous cell degradation rate as a function of time for the entire SOFC performance measurement. Degradation rates determined at different times have been plotted against varying concentrations of ALT addition, facilitating the estimation of optimum ALT concentration for SOFC anodes made with NiO from a specific manufacturer. The algorithm used to determine degradation rates is available upon request to the corresponding author.     

Inline Spectroscopy-Based Optimization of Chemical Reactions Considering Dynamic Process Conditions

The demand for sustainable energy sources, like biomass, solar energy, hydro and wind power, is connected to challenges like energy storage and fluctuating energy supply. Regarding the second challenge, industry has to evolve their existing processes from steady state processes to dynamic ones. This work is concerned with the conception of an inline spectroscopy-based optimization routine for chemical reactions under dynamic process conditions, which implements the search for a well applicable optimization algorithm. The studied reaction to reach this goal is a nitroaldol condensation.     

An ANFIS-RSM based modeling and multi-objective optimization of syngas powered dual-fuel engine

The present study investigates the possibility of increasing efficiency and lowering pollutant emissions from a syngas-powered engine by modulating operational parameters such as engine load and syngas composition. Although artificial intelligence-based technologies are becoming more common for modeling single-fuel mode engines, they are rarely employed to simulate a dual-fuel syngas/diesel engine. In the first of its kind Endeavor in the area of syngas fueled engines, a hybrid adaptive neuro-fuzzy inference system (ANFIS)-response surface methodology (RSM) is investigated. The performance of syngas (H₂+CO), a novel synthetic gaseous fuel, was studied in four different combinations. ANFIS and RSM-based prediction models were developed using engine performance and emissions data collected over the whole load range. While ANFIS surpassed RSM in model prediction, RSM was useful in establishing mathematical links between engine input and output. The ANFIS model developed had a good correlation between R (0.996–0.9998) and R² (0.992–0.9972), as well as low model errors as determined by the root means square error range (0.0086–5.936) and mean absolute percentage error range (0.0028–0.0194). Theil's U2 was used to calculate the model's uncertainty, which was estimated to be 0.0065–0.0439. The superior forecasting abilities of ANFIS models were proved by low errors and uncertainty. The performance of the syngas-powered engine was optimized using the desirability approach to achieve optimum efficiency while emitting the least amount of pollution. The ideal engine load and syngas compositions for maximum production were 67.99% engine load and 72.4:27.6 as H₂:CO syngas mix.     

Output voltage control of double chambers microbial fuel cell using intelligence-based optimized adaptive neuro fuzzy inference controller

Microbial fuel cell (MFC) has become a very important biotechnological tool to produce clean energy in recent years. It is very important to adjust the output voltage and power density in order to obtain the desired energy quickly and smoothly at the output of the MFC. In this study, an optimization-based neuro-fuzzy inference controller is proposed for improving voltage tracking performance of the MFC. A double-chambers MFC model including biochemical reactions, Butler-Volmer expressions and mass/charge balances was studied and Particle Swarm Optimization (PSO) and Improved Grey Wolf Optimization (IGWO) algorithms are used to adjust the parameters of the neuro-fuzzy controller. The results show that PSO and IGWO based controllers have efficient performances to follow the reference voltage pattern quickly and robustly against external load changes, distributions and parameter uncertainties. Moreover, it was observed that IGWO was a more stable and robust controller than PSO according to rise time, overshoot and peak time.     

车制乳化炸药深孔爆破参数优化

金堆城露天矿采用车制炸药后 ,对原用的爆破参数进行了调整和优化。本文介绍了孔网参数、炸药单耗、孔内起爆系统和微差时间等爆破参数的优化。此外 ,还论述了爆破震动监测 ,以及使用车制炸药和优化爆破参数后所产生的经济效益