In the era of branding, the design of plush toy brands often faces a contradiction with the needs of target user groups. Addressing the brand transformation challenges faced by small and micro enterprises in the plush toy industry, this paper proposes a method for generating creative design schemes for plush toy brands based on extension theory. This method involves introducing the theory of primitives, utilizing extension primitives to construct problem models, employing extension diamond thinking for ideation and divergence, and using extension analysis for a comprehensive description of brand design elements. Subsequently, the method involves transforming these elements through extension transformation to generate innovative brand design schemes.
This study introduces novel aggregation operators aimed at enhancing data analysis and decision-making processes through the induction of confidence levels into complex polytopic fuzzy systems. Specifically, the induced confidence complex polytopic fuzzy ordered weighted averaging aggregation (ICCPoFOWAA) operator and the induced confidence complex polytopic fuzzy hybrid averaging aggregation (ICCPoFHAA) operator are proposed. By integrating confidence levels into the aggregation process, these operators facilitate a more nuanced interpretation of fuzzy data, allowing for the incorporation of expert judgment and uncertainty in decision-making frameworks. A practical demonstration is provided to validate the efficacy and proficiency of these innovative techniques. Through a comprehensive example, the ability of the ICCPoFOWAA and ICCPoFHAA operators to enhance decision-making accuracy and reliability is substantiated, showcasing their potential as powerful tools in the realms of data analysis and complex decision-making scenarios. The incorporation of confidence levels into fuzzy aggregation processes represents a significant advancement in the field, offering a sophisticated approach to handling uncertainty and expert opinions in multi-criteria decision-making problems. This work not only introduces groundbreaking aggregation operators but also sets a new standard for research in fuzzy decision-making, underscoring the importance of confidence levels in the analytical process.
The regulatory system for hazardous materials is complex, with poor inter-departmental communication and low levels of data sharing, making effective regulation challenging. Blockchain technology, known for its decentralization, traceability, and secure and trustworthy information, is widely applied in data sharing. Concurrently, attribute-based encryption (ABE), a novel encryption technique, offers high security and fine-grained access control, providing technical support for secure data access and privacy protection. However, existing attribute encryption algorithms do not consider the hierarchical relationship of access structures among data files during data sharing. Moreover, the immutable nature of blockchain means that access policies stored on it cannot be altered, leading to a lack of flexibility in data sharing. To address these issues, this paper proposes a blockchain and attribute-based dynamic layered access scheme for hazardous materials circulation data sharing. By constructing a Linear Secret-Sharing Scheme (LSSS) matrix, layered access control is achieved, allowing data decryption related to the matching parts of a user's attributes with the access structure. Additionally, through the design of a policy update algorithm, the blockchain structure is organized into transaction blocks and policy blocks, storing the encrypted symmetric keys separately to enable dynamic updates of access policies. Security analysis and experimental comparisons demonstrate the scheme's effectiveness and security in hazardous materials circulation data sharing.
The T-spherical fuzzy set (T-SFS), an advancement over the spherical fuzzy set (SFS), offers a refined approach for addressing contradictions and ambiguities in data. In this context, similarity measures (SMs) serve as critical tools for quantifying the resemblance between fuzzy values, traditionally relying on the calculation of distances between these values. Nevertheless, existing methodologies often encounter irrational outcomes due to certain characteristics and complex operations involved. To surmount these challenges, a novel parametric similarity measure is proposed, grounded in three adjustable parameters. This enables decision-makers to tailor the SM to suit diverse decision-making styles, thereby circumventing the aforementioned irrationalities. An analytical comparison with existing SM reveals the superiority of the proposed measure through mathematical validation. Furthermore, the utility of this measure is demonstrated in the resolution of multi-attribute decision-making (MADM) problems, highlighting its efficacy over several existing approaches within the domain of T-SFS. The implementation of the proposed SM not only enhances the precision of similarity assessment in fuzzy sets but also significantly contributes to the optimization of decision-making processes.
To facilitate early intervention and control efforts, this study proposes a soybean leaf disease detection method based on an improved Yolov5 model. Initially, image preprocessing is applied to two datasets of diseased soybean leaf images. Subsequently, the original Yolov5s network model is modified by replacing the Spatial Pyramid Pooling (SPP) module with a simplified SimSPPF for more efficient and precise feature extraction. The backbone Convolutional Neural Network (CNN) is enhanced with the Bottleneck transformer (BotNet) self-attention mechanism to accelerate detection speed. The Complete Intersection over Union (CIoU) loss function is replaced by EIoU-Loss to increase the model's inference speed, and Enhanced Intersection over Union (EIoU)-Non-Maximum Suppression (NMS) is used instead of traditional NMS to optimize the handling of prediction boxes. Experimental results demonstrate that the modified Yolov5s model increases the mean Average Precision (mAP) value by 4.5% compared to the original Yolov5 network model for the detection and identification of soybean leaf diseases. Therefore, the proposed method effectively detects and identifies soybean leaf diseases and can be validated for practicality in actual production environments.
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