Images captured under poor lighting conditions often suffer from low brightness, low contrast, color distortion, and noise. The function of low-light image enhancement is to improve the visual effect of such images for subsequent processing. Recently, deep learning has been used more and more widely in image processing with the development of artificial intelligence technology, and we provide a comprehensive review of the field of low-light image enhancement in terms of network structure, training data, and evaluation metrics. In this paper, we systematically introduce low-light image enhancement based on deep learning in four aspects. First, we introduce the related methods of low-light image enhancement based on deep learning. We then describe the low-light image quality evaluation methods, organize the low-light image dataset, and finally compare and analyze the advantages and disadvantages of the related methods and give an outlook on the future development direction.

Recent studies have advocated that digital technologies (DTs) positively affect the transition of a linear economy model to a circular economy (CE) model and facilitate enterprises in implementing circular strategies. Despite this general statement, the literature still overlooks how enterprises should apply various DTs of Industry 4.0 across the entire product lifecycle to operationalize CE-related strategies. To fill this gap, this paper proposes a conceptual framework exploring DTs in terms of CE operationalization from the perspective of the product lifecycle. Based on insights gained through a systematic literature review, we clarify how DTs can facilitate CE performance objectives through the three stages of the product lifecycle: product design, product use, and product recovery or recycling. Furthermore, we study how various Industry 4.0 DTs, such as the Internet of things, big data, and cloud computing, are utilized to operationalize the transition toward a CE. DTs applied to the service-oriented product-service system contributes innovation into circular business models to make full use of idle resources and provide high-quality personalized services. We have adopted three performance objectives: using fewer materials and resources, extending product lifespan, and closing the loop to evaluate the effects of DTs in promoting CE development. By investigating how DTs affect CE performance objectives, the conceptual framework developed in this paper advances the knowledge regarding the role of DTs as an enabler of CE from the product lifecycle. Our findings provide a practical reference enabling researchers and managers to harness the potential of DTs to support CE transition.

Introduction: Efficient and accurate varietal classification of wheat grains is crucial for maintaining varietal purity and reducing susceptibility to pests and diseases, thereby enhancing crop yield. Traditional manual and machine learning methods for wheat grain identification often suffer from inefficiencies and the use of large models. In this study, we propose a novel classification and recognition model called SCGNet, designed for rapid and efficient wheat grain classification.Methods: Specifically, our proposed model incorporates several modules that enhance information exchange and feature multiplexing between group convolutions. This mechanism enables the network to gather feature information from each subgroup of the previous layer, facilitating effective utilization of upper-layer features. Additionally, we introduce sparsity in channel connections between groups to further reduce computational complexity without compromising accuracy. Furthermore, we design a novel classification output layer based on 3-D convolution, replacing the traditional maximum pooling layer and fully connected layer in conventional convolutional neural networks (CNNs). This modification results in more efficient classification output generation.Results: We conduct extensive experiments using a curated wheat grain dataset, demonstrating the superior performance of our proposed method. Our approach achieves an impressive accuracy of 99.56%, precision of 99.59%, recall of 99.55%, and an F₁-score of 99.57%.Discussion: Notably, our method also exhibits the lowest number of Floating-Point Operations (FLOPs) and the number of parameters, making it a highly efficient solution for wheat grains classification.

This paper interprets an image space accelerating branch and bound algorithm for globally solving a class of multiplicative programming problems (MP). In this algorithm, in order to obtain the global optimal solution, the problem (MP) is transformed into an equivalent problem (P2) by introducing new variables. By utilizing new linearizing relaxation technique, the problem (P2) can be converted into a series of linear relaxation programming problems, which provide the reliable lower bound in the branch and bound search. Meanwhile, an image space accelerating method is constructed to improve the computational performance of the algorithm by deleting the subintervals which have no global optimal solution. Furthermore, the global convergence of the algorithm is proved. The computational complexity of the algorithm is analyzed, and the maximum iterations of the algorithm are estimated. Finally, numerical experimental results show that the algorithm is robust and efficient.