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Maize Hybrids Performance Evaluation with Data Fusion by Matrix Factorization Algorithm

ABSTRACT Crop breeders often face challenges due to limited data availability when making crucial decisions, such as selecting top-performing varieties/hybrids for further experiments, registration, and commercialization. Evaluating all varieties/hybrids across all fields is impractical due to high experimental and time costs, as well as the limited number of locations for planting. This article aims to evaluate the performance of various maize hybrids in untested locations using historical data. The problem is approached through a matrix framework, where hybrids and fields correspond to rows and columns, respectively, with entries representing the yield of a specific hybrid at a given location. As this matrix is typically sparse, the task is to fill in missing data. Agronomists are primarily interested in the performance of top hybrids at specific locations for smart seed selection. To address this, we introduce a novel application of the Data Fusion by Matrix Factorization (DFMF) algorithm for predicting crop yields using maize data from the 2019 Syngenta Crop Challenge. The DFMF results are compared with the Random Forest (RF) algorithm as a benchmark, focusing on model performance for smart seed selection. Our analysis highlights the advantages of the DFMF approach over the traditional RF method in this context.

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Intelligent Design Method and System of Tin Spraying Steel Mesh for Complex Combiner

ABSTRACT In the process of spraying tin into the combiner cavity, employing the Tin Spraying Steel Mesh (TSSM) is crucial. This operation is akin to covering a complex, maze-like area with multiple islands and branches. Manual operation is labor-intensive, time-consuming, and challenging to ensure high-quality results. Therefore, this paper presents an intelligent design method and system for the TSSM, based on graphic processing. Initially, isolated islands are used as nodes to establish the topology structure of complex paths. Then, an improved Delaunay triangulation method is utilized to extract intermediate paths for regions with branching paths, resulting in the division into multiple single path regions. Following this, the island regions undergo equal arc length segmentation, while the single path regions are subjected to random bidirectional segmentation to obtain the final TSSM. Finally, a TSSM intelligent segmentation system is developed by seamlessly integrating this method into the AutoCAD software platform using ObjectARX technology. The TSSM obtained by this method meets the quality requirements, demonstrating consistent segmentation direction and uniform gaps. Furthermore, the system can adjust segmentation parameters to cater to different combiner specifications, thereby significantly improving design efficiency and establishing itself as an indispensable auxiliary tool in the automated design of combiners.

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Enhancing Multimodal Emotional Information Extraction in Film and Television through Adaptive Feature Fusion with DenseNe, Transformer, and 3D CNN Models

ABSTRACT The extraction of multimodal emotional information enables a more nuanced representation of the emotional subtleties embedded in film and television works. However, conventional approaches that independently extract features from images and text fail to capture the rich semantic interplay between these modalities, impeding the feature learning process within each modality. To address this, this paper presents an innovative model for extracting emotional information from multimodal film and television content. The model utilizes DenseNe for image feature extraction, enhancing network depth via the MSC module. Text feature extraction is achieved through a Transformer encoder, while video feature extraction employs a 3D CNN model. Refinements are made to the number and placement of convolutional layers, planar convolution size, and 3D convolution depth. Moreover, a multi-head scaled dot-product attention mechanism is incorporated into the interaction module to compute the similarity between each image block within the sequence and every word in the text sequence. Experimental evaluations on the CMU-MOSEI and CMU-MOSI datasets showcase superior performance compared to the baseline model, achieving accuracy and F1 scores of 0.708 and 0.698, respectively. Noteworthy is the proposed adaptive feature fusion module, which enriches the expressiveness of pivotal emotional features while eliminating redundant data.

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Quality Parameter Adaptive Optimization for Spinning Process Using Dynamic Non-Dominated Sorting Algorithm

ABSTRACT Intelligent textile equipment can discover potential patterns in the production process through data mining, and utilize these patterns through intelligent optimization, ultimately achieving intelligent and automated textile production. This paper focuses on the spinning process parameters optimization under changing spinning conditions and proposes a dynamic non-dominant ranking parameter quality adaptive optimization algorithm. The factors of spinning process condition changes are transformed into mathematical dynamic constraints and constructing an adaptive optimization model for spinning parameter quality. Based on this, the response mechanism of spinning environment is established to readjust the optimization direction according to the change of spinning conditions, and the DNSGA-II is used to solve the quality adaptive optimization model. A case study is designed to validate the effectiveness, results show that for different usage periods of wire rings, the optimal breaking strength is 5.6, and the number of details is 33.3, 31.1, and 41.6 respectively. In some degree, the proposed algorithm can effectively adapt to the quality optimization problem of spinning process parameters under different spinning conditions, which could provide corresponding parameter optimization combinations for different spinning conditions.

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Integration of warrior artificial intelligence and leadership reflexivity to enhance decision-making

ABSTRACT In the emerging literature on artificial intelligence (AI) and leadership, there is increasing recognition of the importance played by advanced technologies in decision-making. AI is viewed as the next frontier to improve decision-making processes and as a result enhance human decision-making in general. However, existing literature lacks studies on how AI, operating as a “warrior” or innovator in business, can in turn enhance leadership reflexivity, and thereby improve decision-making outcomes. This study is aimed at addressing this gap by drawing on the reflexivity perspective and existing research on AI and leadership to examine the integration of the concepts of warrior AI with leadership reflexivity to improve decision-making. The study used a systematic literature review to identify and map articles using specified inclusion and exclusion criteria to achieve this. Selected articles were included for in-depth analysis to address the issue under investigation. The study explored the potential benefits of blending advanced AI with reflective leadership strategies, offering insights into how organizations can optimize their decision-making processes through this innovative approach. A comprehensive literature review was thus the foundation for our investigation into how warrior AI may enhance human decision-making especially under high-stress conditions by providing real-time data analysis capabilities, pattern recognition skills, and predictive simulations. Our work emphasizes how leadership reflexivity plays a critical role in assessing AI-driven recommendations to ensure ethical soundness and contextual appropriateness of the decisions being taken. Based on our findings, we suggest that integrating AI capabilities with reflective leadership practices can lead to more effective and adaptable decision-making frameworks, particularly when swift yet well-informed action is necessary. This study adds to the existing body of knowledge by illustrating that, with the aid of a flow diagram, the integration of warrior AI into the reflective process can potentially amplify the benefits of AI, offering data-driven insights for leaders to reflect upon, thereby reinforcing the decision-making process with a more rigorous, ethical, and nuanced approach in alignment with organizational objectives and societal values. It is recommended that leadership actively engage in discussions regarding ethical AI use, ensuring alignment with organizational values and ethics. Ultimately, this study contributes valuable insights to discussions around AI and leadership by underscoring the significance of maintaining a balanced relationship between machine efficiency and human wisdom.

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