Abdul Momin¹, Amana Khatoon^1,*, Wajahat Khan¹, Dilsat Bozdoğan Konuşkan², Muhammad Mudasar Aslam³, Muhammad Jamil⁴, Shafiq Ur Rehman⁵, Baber Ali⁶, Alevcan Kaplan⁷, Sana Wahab⁸, Muhammad Nauman Khan^9,*, Sezai Ercisli^10,11, Mohammad Khalid Al-Sadoon¹²

Phyton-International Journal of Experimental Botany, Vol.93, No.6, pp. 1117-1128, 2024, DOI:10.32604/phyton.2024.048994

Abstract Sprouts are ready-to-eat and are recognized worldwide as functional components of the human diet. Recent advances in innovative agricultural techniques could enable an increase in the production of healthy food. The use of light-emitting diode (LED) in indoor agricultural production could alter the biological feedback loop, increasing the functional benefits of plant foods such as wheat and lentil sprouts and promoting the bioavailability of nutrients. The effects of white (W), red (R), and blue (B) light were investigated on the growth parameters and nutritional value of wheat and lentil sprouts. In the laboratory, seeds were… More >

Guangfei Jia^*, Yanchao Meng, Zhiying Qin

Structural Durability & Health Monitoring, Vol.18, No.4, pp. 445-463, 2024, DOI:10.32604/sdhm.2024.049298

Abstract The vibration signals of rolling bearings exhibit nonlinear and non-stationary characteristics under the influence of noise. In intelligent fault diagnosis, unprocessed signals will lead to weak fault characteristics and low diagnostic accuracy. To solve the above problem, a fault diagnosis method based on parameter optimization feature mode decomposition and improved deep belief networks is proposed. The feature mode decomposition is used to decompose the vibration signals. The parameter adaptation of feature mode decomposition is implemented by improved whale optimization algorithm including Levy flight strategy and adaptive weight. The selection of activation function and parameters is More > Graphic Abstract

Zhang Zhi¹, Haiyu Huang², Wei Xiong², Yijia Zhou³, Mingyu Yan^3,*, Shaolian Xia², Baofeng Jiang², Renbin Su², Xichen Tian⁴

Energy Engineering, Vol.121, No.6, pp. 1557-1576, 2024, DOI:10.32604/ee.2024.047401

Abstract Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existing scenario clustering technique for stochastic unit commitment cannot accurately select representative scenarios, which threatens the robustness of stochastic unit commitment and hinders its application. This paper provides a stochastic unit commitment with dynamic scenario clustering based on multi-parametric programming and Benders decomposition. The stochastic unit commitment is solved via the Benders decomposition, which decouples the primal problem into the master problem and two types of subproblems. In the master problem, the committed generator is determined, while the feasibility and… More >

Jianfeng He^1,2, Haowei Ye¹, Jie Ning¹, Hui Zhou^1,2,*, Bo She^3,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3355-3372, 2024, DOI:10.32604/cmc.2024.048706

Abstract In addressing the challenge of motion artifacts in Positron Emission Tomography (PET) lung scans, our study introduces the Triple Equivariant Motion Transformer (TEMT), an innovative, unsupervised, deep-learning-based framework for efficient respiratory motion correction in PET imaging. Unlike traditional techniques, which segment PET data into bins throughout a respiratory cycle and often face issues such as inefficiency and overemphasis on certain artifacts, TEMT employs Convolutional Neural Networks (CNNs) for effective feature extraction and motion decomposition.TEMT’s unique approach involves transforming motion sequences into Lie group domains to highlight fundamental motion patterns, coupled with employing competitive weighting for More >

Bin Chen¹, Ziyang Li¹, Shipeng Li¹, Qingzhou Zhao¹, Xingdou Liu^2,*

Energy Engineering, Vol.121, No.5, pp. 1291-1307, 2024, DOI:10.32604/ee.2024.046374

Abstract To reduce carbon emissions, clean energy is being integrated into the power system. Wind power is connected to the grid in a distributed form, but its high variability poses a challenge to grid stability. This article combines wind turbine monitoring data with numerical weather prediction (NWP) data to create a suitable wind power prediction framework for distributed grids. First, high-precision NWP of the turbine range is achieved using weather research and forecasting models (WRF), and Kriging interpolation locates predicted meteorological data at the turbine site. Then, a preliminary predicted power series is obtained based on More >

Kranthi Alluri, S. Gopikrishnan^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 943-961, 2024, DOI:10.32604/cmc.2024.048621

Abstract Secure authentication and accurate localization among Internet of Things (IoT) sensors are pivotal for the functionality and integrity of IoT networks. IoT authentication and localization are intricate and symbiotic, impacting both the security and operational functionality of IoT systems. Hence, accurate localization and lightweight authentication on resource-constrained IoT devices pose several challenges. To overcome these challenges, recent approaches have used encryption techniques with well-known key infrastructures. However, these methods are inefficient due to the increasing number of data breaches in their localization approaches. This proposed research efficiently integrates authentication and localization processes in such a… More >

XIAOYAN LIU^a,*, XU CHEN^a, HONGXING ZHANG^b, CHANGJUN ZHANG^a, SHIYUAN YANG^a, GUANGQUAN LI^a

Journal of Polymer Materials, Vol.36, No.2, pp. 121-132, 2019, DOI:10.32381/JPM.2019.36.02.2

Abstract In this work, three in situ impact polypropylene copolymer(IPC) samples were prepared through Ziegler-Natta catalyst only changing the feed composition (ethylene to ethylene and propylene molar ratio, C2/C2+C3) in gas-phase polymerization reactor. Polymer (IPC) were characterical by solvent classification, gel permeation chromatography (GPC), differential scanning calorimetry (DSC), successive self-nucleation and annealing (SSA), nuclear magnetic resonance(13C-NMR) and scanning electron microscopy(SEM). The mechanical properties of IPC samples were tested.The results indicate that with similar ethylene content, the feed composition which determines the content and structure of EPR and EbP component in IPC, further impacts the rubber phase More >

GOBI NALLATHAMBI, BHARGAVI RAM THIMMIAH^*

Journal of Polymer Materials, Vol.37, No.3-4, pp. 189-200, 2020, DOI:10.32381/JPM.2020.37.3-4.6

Abstract The aim of this study is to optimize the process parameters of alkali treated Saharan aloe vera cactus fibres using of Box-behnken experimental design. The Saharan aloe vera cactus fibres were treated with different concentration of NaOH, soaking time and temperature which affect the properties of fibres and plays main role in removal of lignin, hemicellulose, pectin and wax content. The chemical composition of untreated and treated fibres was analyzed by standard methods. XRD result shows the improvement in the crystallinity index of fibres due to alkali treatment. ATR-FTIR analysis shows that hemicellulose and lignin More >

Duy Quang Tran¹, Huy Q. Tran^2,*, Minh Van Nguyen³

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3585-3602, 2024, DOI:10.32604/cmc.2024.047760

Abstract With the advancement of artificial intelligence, traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality. Traffic volume is an influential parameter for planning and operating traffic structures. This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems. A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process. The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal… More >

Anas Bilal¹, Azhar Imran², Talha Imtiaz Baig^3,4, Xiaowen Liu^1,*, Haixia Long¹, Abdulkareem Alzahrani⁵, Muhammad Shafiq⁶

Computer Systems Science and Engineering, Vol.48, No.2, pp. 511-528, 2024, DOI:10.32604/csse.2023.039672

Abstract Artificial Intelligence (AI) is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy (VTDR), which is a leading cause of visual impairment and blindness worldwide. However, previous automated VTDR detection methods have mainly relied on manual feature extraction and classification, leading to errors. This paper proposes a novel VTDR detection and classification model that combines different models through majority voting. Our proposed methodology involves preprocessing, data augmentation, feature extraction, and classification stages. We use a hybrid convolutional neural network-singular value decomposition (CNN-SVD) model for feature extraction and selection and an improved SVM-RBF with a Decision Tree More >