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Purpose: The purpose of this paper is to propose a model for product recommendation to improve the accuracy of recommendation based on the current search engines used in e-commerce platforms like Tmall.com. Design/methodology/approach: First, the proposed model comprehensively considers price, trust and online reviews, which all represent critical factors in consumers' purchasing decisions. Second, the model introduces the quantization methods for these criteria incorporating fuzzy theory. Third, the model uses a distance measure between two single valued neutrosophic sets based on the prioritized average operator to consolidate the influences of positive, neutral and negative comments. Finally, the model uses multi-criteria decision-making methods to integrate the influences of price, trust and online reviews on purchasing decisions to generate recommendations. Findings: To demonstrate the feasibility and efficiency of the proposed model, a case study is conducted based on Tmall.com. The results of case study indicate that the recommendations of our model perform better than those of current search engines of Tmall.com. The proposed model can significantly improve the accuracy of product recommendations based on search engines. Originality/value: The product recommendation method can meet the critical challenge from the search engines on e-commerce platforms. In addition, the proposed method could be used in practice to develop a new application for e-commerce platforms. [ABSTRACT FROM AUTHOR]

The mild slope equation (MSE) has been widely used to describe combined wave refraction and diffraction in the field of coastal and offshore engineering owing to its applicability for a wide range of wave frequencies. In this paper, a meshless numerical algorithm, based on the generalized finite difference method (GFDM), is firstly proposed to efficiently and accurately solve the MSE. As a newly-developed domain-type meshless method, the GFDM can truly get rid of time-consuming meshing generation and numerical quadrature. The partial differential terms of the MSE for each point in the computational domain can be discretized into linear combinations of nearby function values with the moving-least-squares method of the GFDM, so the numerical implementation is very convenient and efficient. To evaluate the accuracy and capability of the proposed scheme for MSE, a series of numerical tests were conducted, covering a range of complexity that included propagation and transformation of waves due to a parabolic shoal, a circular island mounted on a paraboloidal shoal and elliptic shoal situated on a slope, as well as breakwater gap. The results were compared with experimental data, analytical solutions and other numerical methods, and reasonable agreements have been achieved. [ABSTRACT FROM AUTHOR]

RSA algorithm is one of the most popular and secure public key cryptographic algorithms and has been widely used in many real-life applications. The security of the RSA algorithm lies in the difficulty of factoring large integers efficiently and the General Number Field Sieve (GNFS) algorithm is the most efficient algorithm for factoring integers greater than 110 digits at present. In this paper, targeted to speed up the factorization process of RSA, we discuss the current research about solving large and sparse linear systems over GF(2), which is one of the most time-consuming steps of the GNFS algorithm. With that, we propose an improved parallel block Lanczos (IBL) algorithm to reduce the communication cost of solving large and sparse linear systems over GF(2). More specifically, we firstly re-implement the parallel block Lanczos algorithm from the BSP paradigm to Open MPI. To further improve the performance, we then reorganize and redesign the algorithm to reduce the synchronization and communication costs during the outer product step. After this, we integrate the improved parallel block Lanczos algorithm with the GNFS algorithm. Finally, theoretical and experimental results demonstrate that the IBL algorithm greatly enhances the performance of GNFS compared with previous parallel block Lanczos (PBL) algorithm, in terms of both execution time and speedup. [ABSTRACT FROM AUTHOR]

• A new method for multi-scale smoke detection combining CNN and transformer is proposed. • A multiple receptive Fields(MRF) module is designed to improve the ability to extract features from smoke at different scales. • A enhanced pool Former(EPF) module is designed to improve the expression ability of smoke features. • A dataset of multi-scale smoke detection was constructed. Smoke identification in photos has long been difficult because of the intricate variations in smoke's texture, color, and shape. This article suggests a Transformer network and YOLOv5-based smoke detector to solve this problem. In particular, we suggest using the Enhanced Pool Former (EPF) structure to improve the expression ability of smoke features by obtaining greater global information. Additionally, to improve the recognition capacity of slight smoke, we strive to prevent the loss of information by increasing the number of detecting heads and introducing more feature fusion. We do this by using the NWDLoss to compensate for the sensitivity of IoU to the position difference of small objects. In addition, we develop the Multiple Receptive Fields (MRF) module to improve the ability to extract features from smoke at different scales. Our solution outperforms current methods on our custom dataset for AP and AP50, making it well-suited for smoke detection. [ABSTRACT FROM AUTHOR]

• Propose a multipopulation cooperative particle swarm optimization (MPCPSO). • The dynamic segment-based mean learning strategy (DSMLS), which is employed to construct learning exemplars, achieves information sharing and coevolution between populations. • The multidimensional comprehensive learning strategy (MDCLS) is employed to speed up the convergence tendency and solution accuracy of MPCPSO. • A difference mutation operator is introduced to increase the population diversity and enhance the global exploration ability of MPCPSO. The traditional particle swarm optimization algorithm learns from the two best experiences: the best position previously learned by the particle itself and the best position learned by the entire population to date. This learning strategy is simple and ordinary, but when addressing high-dimensional optimization problems, it is unable to quickly find the global optimal solution due to its low efficiency. This paper proposes a multipopulation cooperative particle swarm optimization (MPCPSO) algorithm with a dynamic segment-based mean learning strategy and a multidimensional comprehensive learning strategy. In MPCPSO, the dynamic segment-based mean learning strategy (DSMLS), which is employed to construct learning exemplars, achieves information sharing and coevolution between populations. The multidimensional comprehensive learning strategy (MDCLS) is employed to speed up convergence and improve the accuracy of MPCPSO solutions. Additionally, a differential mutation operator is introduced to increase the population diversity and enhance the global exploration ability of MPCPSO. Sixteen benchmark functions and seven well-known PSO variants are employed to verify the advantages of MPCPSO. The comparison results indicate that MPCPSO has a faster convergence speed, obtains more accurate solutions, and is more robust. [ABSTRACT FROM AUTHOR]

Wave breaking often happens in the process of wave propagation to shallow water near the shore, which causes sharp attenuation of wave height and dissipation of wave energy. In this paper, an extended mild-slope equation (EMSE), including the effect of dissipation caused by wave breaking, is adopted to establish a numerical model. The current work develops a meshless technique, based on the local radial basis function-based method coupled with the differential quadrature method (local RBF-DQM) to solve the EMSE. Due to the influence of obstacles on wave propagation, the selection of the shape of the local support domain is improved to promote the accuracy of local RBF-DQM. For the numerical simulation, a modified iterative method based on previous research is suggested to estimate the energy dissipation term herein, which effectively solves the numerical oscillation problem. Two classical numerical cases are simulated to evaluate the validity and accuracy of local RBF-DQM in solving the EMSE. The results are in broad agreement with other numerical and experimental data. Finally, the numerical technique is used to study the influence of the factors of shore-parallel breakwater on wave breaking. [ABSTRACT FROM AUTHOR]

As part of our search for new cytotoxic and antimicrobial natural products from endolichenic fungi, 19 compounds including 1 new 10-member lactone (2), 1 new polyacetylene glycoside (3), 1 new brasilane-type sesquiterpenoid glycoside (4), and 2 isobenzofuran-1(3H)-one derivatives (5 and 6) were isolated from the solid culture of the endolichenic fungus Hypoxylon fuscum. Their structures were unambiguously elucidated by NMR spectroscopic data, MS, ECD (electronic circular dichroism) calculation, and chemical methods. The cytotoxic effects on K562, SW480, and HEPG2 cell lines and the antimicrobial activity against Bacillus subtilis, Staphylococcus aureus, Escherichia coli , and Candida albicans were assessed. Compounds 1, 2 , and 5 exhibited moderate cytotoxicity against K562, SW480, and HEPG2 cell lines while compounds 1, 9 , and 11 displayed weak antibacterial activity against S. aureus. [ABSTRACT FROM AUTHOR]