Fares Abdulhafidh Dael^1,*, Ömer Çağrı Yavuz², Uğur Yavuz¹

Computer Systems Science and Engineering, Vol.47, No.1, pp. 19-35, 2023, DOI:10.32604/csse.2023.037903

Abstract The key indication of a nation’s economic development and strength is the stock market. Inflation and economic expansion affect the volatility of the stock market. Given the multitude of factors, predicting stock prices is intrinsically challenging. Predicting the movement of stock price indexes is a difficult component of predicting financial time series. Accurately predicting the price movement of stocks can result in financial advantages for investors. Due to the complexity of stock market data, it is extremely challenging to create accurate forecasting models. Using machine learning and other algorithms to anticipate stock prices is an interesting area. The purpose of… More >

Shehab Abdulhabib Alzaeemi¹, Kim Gaik Tay^1,*, Audrey Huong¹, Saratha Sathasivam², Majid Khan bin Majahar Ali²

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1163-1184, 2023, DOI:10.32604/csse.2023.038912

Abstract Radial Basis Function Neural Network (RBFNN) ensembles have long suffered from non-efficient training, where incorrect parameter settings can be computationally disastrous. This paper examines different evolutionary algorithms for training the Symbolic Radial Basis Function Neural Network (SRBFNN) through the behavior’s integration of satisfiability programming. Inspired by evolutionary algorithms, which can iteratively find the near-optimal solution, different Evolutionary Algorithms (EAs) were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation (SRBFNN-2SAT). The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA),… More >

Shehab Khan Noor¹, Arif Mawardi Ismail¹, Mohd Najib Mohd Yasin^1,*, Mohamed Nasrun Osman¹, Thennarasan Sabapathy¹, Shakhirul Mat Salleh², Ping Jack Soh³, Ali Hanafiah Rambe⁴, Nurulazlina Ramli⁵

Computer Systems Science and Engineering, Vol.47, No.1, pp. 537-551, 2023, DOI:10.32604/csse.2023.037381

Abstract This paper presents a flexible and wearable textile array antenna designed to generate Orbital Angular Momentum (OAM) waves with Mode +2 at 3.5 GHz (3.4 to 3.6 GHz) of the sub-6 GHz fifth-generation (5G) New Radio (NR) band. The proposed antenna is based on a uniform circular array of eight microstrip patch antennas on a felt textile substrate. In contrast to previous works involving the use of rigid substrates to generate OAM waves, this work explored the use of flexible substrates to generate OAM waves for the first time. Other than that, the proposed antenna was simulated, analyzed, fabricated, and… More >

Hassan Shokouhandeh¹, Mehrdad Ahmadi Kamarposhti^2,*, William Holderbaum³, Ilhami Colak⁴, Phatiphat Thounthong⁵

Computer Systems Science and Engineering, Vol.47, No.1, pp. 809-822, 2023, DOI:10.32604/csse.2023.035827

Abstract The widespread penetration of distributed energy sources and the use of load response programs, especially in a microgrid, have caused many power system issues, such as control and operation of these networks, to be affected. The control and operation of many small-distributed generation units with different performance characteristics create another challenge for the safe and efficient operation of the microgrid. In this paper, the optimum operation of distributed generation resources and heat and power storage in a microgrid, was performed based on real-time pricing through the proposed gray wolf optimization (GWO) algorithm to reduce the energy supply cost with the… More >

Dongsheng Li, Chunsheng Li^*, Kejia Zhang, Tao Liu, Fang Liu, Jingsong Yin, Mingyue Liao

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1209-1231, 2023, DOI:10.32604/csse.2023.034395

Abstract Intelligent identification of sandstone slice images using deep learning technology is the development trend of mineral identification, and accurate mineral particle segmentation is the most critical step for intelligent identification. A typical identification model requires many training samples to learn as many distinguishable features as possible. However, limited by the difficulty of data acquisition, the high cost of labeling, and privacy protection, this has led to a sparse sample number and cannot meet the training requirements of deep learning image identification models. In order to increase the number of samples and improve the training effect of deep learning models, this… More >

Fang Yu¹, Wenbin Bi², Ning Cao^3,4,*, Hongjun Li¹, Russell Higgs⁵

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1-17, 2023, DOI:10.32604/csse.2023.018349

Abstract In view of the fact that the prediction effect of influential financial customer churn in the Internet of Things environment is difficult to achieve the expectation, at the smart contract level of the blockchain, a customer churn prediction framework based on situational awareness and integrating customer attributes, the impact of project hotspots on customer interests, and customer satisfaction with the project has been built. This framework introduces the background factors in the financial customer environment, and further discusses the relationship between customers, the background of customers and the characteristics of pre-lost customers. The improved Singular Value Decomposition (SVD) algorithm and… More >

Ali Jaber Abdulhamed^1,*, Aws Al-Akam¹, Ahmed A. Abduljabbar², Mohammed H. Alkhafaji³

Energy Engineering, Vol.120, No.8, pp. 1729-1746, 2023, DOI:10.32604/ee.2023.029069

Abstract Thermoelectric generators (TEGs) are considered promising devices for waste heat recovery from various systems. The Seebeck effect can be utilized to generate power using the residual heat emitted by the filter dryer receiver (FDR) of an air conditioning (A/C) system, which would otherwise go to waste. The study aims to build a set of thermoelectric generators (TEG) to collect the waste heat of the FDR and generate low-power electricity. A novel electrical circuit with two transformers is designed and fabricated to produce a more stable voltage for operation and charging. The thermoelectric generator (TEGs) was installed on the FDR of… More >

Liqing Yan¹, Jiang Liu^1,2,*, Guangwei Ying³, Ning Zhang⁴

Energy Engineering, Vol.120, No.8, pp. 1919-1938, 2023, DOI:10.32604/ee.2023.027546

Abstract Recovery of waste heat from boiler flue gas is an effective way to improve energy utilization efficiency. Taking a heating station heating project as an example, the existing heating system of this heating station was analyzed for its underutilized flue gas waste heat and low energy utilization rate. Rankine cycle is an effective waste heat recovery method, and a steam boiler organic Rankine cycle (ORC) cogeneration waste heat utilization method is proposed. The system model simulation is constructed and verified. First, a thermodynamic model was constructed in MATLAB and five suitable work gases were selected to analyze the effects of… More > Graphic Abstract

Ming Li^1,*, Cairen Furifu¹, Chengyang Ge², Yunping Zheng¹, Shunfu Lin², Ronghui Liu²

Energy Engineering, Vol.120, No.8, pp. 1775-1801, 2023, DOI:10.32604/ee.2023.027215

Abstract As an effective carrier of integrated clean energy, the microgrid has attracted wide attention. The randomness of renewable energies such as wind and solar power output brings a significant cost and impact on the economics and reliability of microgrids. This paper proposes an optimization scheme based on the distributionally robust optimization (DRO) model for a microgrid considering solar-wind correlation. Firstly, scenarios of wind and solar power output scenarios are generated based on non-parametric kernel density estimation and the Frank-Copula function; then the generated scenario results are reduced by K-means clustering; finally, the probability confidence interval of scenario distribution is constrained… More >

Daniela Galatro^a,*, Maan Al-Zareer^a , Carlos Da Silva^a , David A. Romero^a , Cristina H. Amon^a

Frontiers in Heat and Mass Transfer, Vol.14, No.1, pp. 1-18, 2020, DOI:10.5098/hmt.14.17

Abstract This work presents a succinct review of the thermal behavior of lithium-ion batteries (LIBs) and its relationship with aging, heat generation, thermal management and thermal failure. This work focuses on the temperature effects that promote the main aging mechanisms in the anode and compare these effects among different cell chemistries for calendar and cycling aging modes. We review the strategies to mitigate aging, including the design of the battery thermal management system (BTMS), best practices of battery users to minimize the effect of stress factors, and the appropriate selection of the anode material. We discuss the heat generation and surface… More >