Wei Cong, Yong Zhao^*, Bingxiao Du^*, Senlin Huo, Xianqi Chen

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 621-654, 2023, DOI:10.32604/cmes.2023.025143

Abstract The spacecraft equipment layout optimization design (SELOD) problems with complicated performance constraints and diversity are studied in this paper. The previous literature uses the gradient-based algorithm to obtain optimized non-overlap layout schemes from randomly initialized cases effectively. However, these local optimal solutions are too difficult to jump out of their current relative geometry relationships, significantly limiting their further improvement in performance indicators. Therefore, considering the geometric diversity of layout schemes is put forward to alleviate this limitation. First, similarity measures, including modified cosine similarity and gaussian kernel function similarity, are introduced into the layout optimization process. Then the optimization produces… More >

Roberto Altieri^1,2,*, Francesco Certo¹, Marco Garozzo¹, Giacomo Cammarata¹, Massimiliano Maione¹, Giuseppa Fiumanò³, Giuseppe Broggi⁴, Giada Maria Vecchio⁴, Rosario Caltabiano⁴, Gaetano Magro⁴, Giuseppe Barbagallo¹

Oncologie, Vol.24, No.4, pp. 693-706, 2022, DOI:10.32604/oncologie.2022.025393

Abstract Background: Intracranial lymphoma remains a challenging differential diagnosis in daily neurosurgical practice. We analyzed our early experience with a surgical series of frameless neuronavigated biopsies in Primary CNS Lymphomas (PCNSLs), highlighting the importance of using an intraoperative combined imaging protocol (5-ALA fluorescence, i-CT and ¹¹C-MET-PET) to overcome potential targeting errors secondary to tumor volume reduction after corticosteroid therapy. Materials and Methods: All patients treated for PCNLSs at our center in a 24-month period (1/1/2019 to 31/12/2020) were analyzed. Our cohort included 6 patients (4 males), with a median age of 67 years (59–82). A total of 45 samples were evaluated… More >

Ibrahim M. Almanjahie^1,2, Zahid Rasheed^3,4,*, Majid Khan⁵, Syed Masroor Anwar⁶, Ammara Nawaz Cheema⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2091-2118, 2023, DOI:10.32604/cmes.2023.022201

Abstract Nonparametric (distribution-free) control charts have been introduced in recent years when quality characteristics do not follow a specific distribution. When the sample selection is prohibitively expensive, we prefer ranked-set sampling over simple random sampling because ranked set sampling-based control charts outperform simple random sampling-based control charts. In this study, we proposed a nonparametric homogeneously weighted moving average based on the Wilcoxon signed-rank test with ranked set sampling () control chart for detecting shifts in the process location of a continuous and symmetric distribution. Monte Carlo simulations are used to obtain the run length characteristics to evaluate the performance of the… More >

Sunisa Junnumtuam, Sa-Aat Niwitpong^*, Suparat Niwitpong

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1229-1254, 2023, DOI:10.32604/cmes.2022.022098

Abstract A new three-parameter discrete distribution called the zero-inflated cosine geometric (ZICG) distribution is proposed for the first time herein. It can be used to analyze over-dispersed count data with excess zeros. The basic statistical properties of the new distribution, such as the moment generating function, mean, and variance are presented. Furthermore, confidence intervals are constructed by using the Wald, Bayesian, and highest posterior density (HPD) methods to estimate the true confidence intervals for the parameters of the ZICG distribution. Their efficacies were investigated by using both simulation and real-world data comprising the number of daily COVID-19 positive cases at the… More > Graphic Abstract

Qiang Qin^1,2,*, Xiaolei Cao¹, Shengpeng Zhang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1457-1480, 2023, DOI:10.32604/cmes.2023.022078

Abstract In this work, an improved active kriging method based on the AK-IS and truncated importance sampling (TIS) method is proposed to efficiently evaluate structural reliability. The novel method called AWK-TIS is inspired by AK-IS and RBF-GA previously published in the literature. The innovation of the AWK-TIS is that TIS is adopted to lessen the sample pool size significantly, and the whale optimization algorithm (WOA) is employed to acquire the optimal Kriging model and the most probable point (MPP). To verify the performance of the AWK-TIS method for structural reliability, four numerical cases which are utilized as benchmarks in literature and… More >

DIEGO MASONE^1,2,*

BIOCELL, Vol.47, No.1, pp. 1-14, 2023, DOI:10.32604/biocell.2023.024146

Abstract The inflexible concept of membrane curvature as an independent property of lipid structures is today obsolete. Lipid bilayers behave as many-body entities with emergent properties that depend on their interactions with the environment. In particular, proteins exert crucial actions on lipid molecules that ultimately condition the collective properties of the membranes. In this review, the potential of enhanced molecular dynamics to address cell-biology problems is discussed. The cases of membrane deformation, membrane fusion, and the fusion pore are analyzed from the perspective of the dimensionality reduction by collective variables. Coupled lipid-protein interactions as fundamental determinants of large membrane remodeling events… More >

Hong Zhang¹, Lukai Song^1,2,*, Guangchen Bai¹

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1871-1897, 2023, DOI:10.32604/cmes.2022.021880

Abstract

The reliability and sensitivity analyses of stator blade regulator usually involve complex characteristics like high-nonlinearity, multi-failure regions, and small failure probability, which brings in unacceptable computing efficiency and accuracy of the current analysis methods. In this case, by fitting the implicit limit state function (LSF) with active Kriging (AK) model and reducing candidate sample pool with adaptive importance sampling (AIS), a novel AK-AIS method is proposed. Herein, the AK model and Markov chain Monte Carlo (MCMC) are first established to identify the most probable failure region(s) (MPFRs), and the adaptive kernel density estimation (AKDE) importance sampling function is constructed to… More >

Waleed Albattah, Saleh Albahli^*

Computer Systems Science and Engineering, Vol.45, No.1, pp. 531-544, 2023, DOI:10.32604/csse.2023.021548

Abstract The world of information technology is more than ever being flooded with huge amounts of data, nearly 2.5 quintillion bytes every day. This large stream of data is called big data, and the amount is increasing each day. This research uses a technique called sampling, which selects a representative subset of the data points, manipulates and analyzes this subset to identify patterns and trends in the larger dataset being examined, and finally, creates models. Sampling uses a small proportion of the original data for analysis and model training, so that it is relatively faster while maintaining data integrity and achieving… More >

Pramote Charongrattanasakul¹, Wimonmas Bamrungsetthapong^2,*, Poom Kumam³

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4611-4626, 2022, DOI:10.32604/cmc.2022.030856

Abstract The design of a new adaptive version of the multiple dependent state (AMDS) sampling plan is presented based on the time truncated life test under the Weibull distribution. We achieved the proposed sampling plan by applying the concept of the double sampling plan and existing multiple dependent state sampling plans. A warning sign for acceptance number was proposed to increase the probability of current lot acceptance. The optimal plan parameters were determined simultaneously with nonlinear optimization problems under the producer’s risk and consumer’s risk. A simulation study was presented to support the proposed sampling plan. A comparison between the proposed… More >

Jyoti Arora¹, Meena Tushir², Keshav Sharma¹, Lalit Mohan¹, Aman Singh^3,*, Abdullah Alharbi⁴, Wael Alosaimi⁴

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4801-4817, 2022, DOI:10.32604/cmc.2022.025960

Abstract Datasets with the imbalanced class distribution are difficult to handle with the standard classification algorithms. In supervised learning, dealing with the problem of class imbalance is still considered to be a challenging research problem. Various machine learning techniques are designed to operate on balanced datasets; therefore, the state of the art, different under-sampling, over-sampling and hybrid strategies have been proposed to deal with the problem of imbalanced datasets, but highly skewed datasets still pose the problem of generalization and noise generation during resampling. To over-come these problems, this paper proposes a majority clustering model for classification of imbalanced datasets known… More >