This study, rooted in extension theory and the principles of knowledge engineering, explores and formulates a novel method for generating sports protective gear designs. Given the critical role of sports protective gear in safeguarding athletes from injuries, coupled with escalating demands for product quality, the aim is to uncover a more effective approach to innovative design. This method involves formalizing modeling of various elements in the design process and representing this information in the elemental form of knowledge engineering. Through the related analysis, divergent analysis, as well as permutation and conduction transformations of these elements, innovative design schemes for sports protective gear are generated. This process not only optimizes design schemes in depth but also ventures into new design methods and processes. The objective is to offer a novel perspective in integrating extension theory and knowledge engineering in the design of sports protective gear, aspiring to provide more effective strategies to enhance existing design workflows. The goal of this new design method is to produce sports protective gear that is both practical and innovative, thereby enhancing the safety and enjoyment of athletes.

Twitter, a predominant platform for instantaneous communication and idea dissemination, is often exploited by cybercriminals for victim harassment through sexism, racism, hate speech, and trolling using pseudony-mous accounts. The propagation of racially charged online discourse poses significant threats to the social, political, and cultural fabric of many societies. Monitoring and prompt eradication of such content from social media, a breeding ground for racist ideologies, are imperative. This study introduces an advanced hybrid forecasting model, utilizing convolutional neural networks (CNNs) and long-short-term memory (LSTM) neural networks, for the efficient and accurate detection of racist and hate speech in English on Twitter. Unlabelled tweets, collated via the Twitter API, formed the basis of the initial investigation. Feature vectors were extracted from these tweets using the TF-IDF (Term Frequency-Inverse Document Frequency) feature extraction technique. This research contrasts the proposed model with existing intelligent classification algorithms in supervised learning. The HateMotiv corpus, a publicly available dataset annotated with types of hate crimes and ideological motivations, was employed, emphasizing Twitter as the primary social media context. A novel aspect of this study is the introduction of a revised artificial hummingbird algorithm (AHA), supplemented by quantum-based optimization (QBO). This quantum-based artificial hummingbird algorithm (QAHA) aims to augment exploration capabilities and reveal potential solution spaces. Employing QAHA resulted in a detection accuracy of approximately 98%, compared to 95.97% without its application. The study's principal contribution lies in the significant advancements achieved in the field of racism and hate speech detection in English through the application of hybrid deep learning methodologies.

Recent advancements in non-destructive testing methodologies have significantly propelled the efficiency of bearing defect detection, vital for maintaining optimal final quality standards. This study introduces a novel approach, integrating an Optimized Continuous Wavelet Transform (OCWT) and a Non-Local Convolutional Block Attention Module (NCBAM), to elevate fault diagnosis in motor bearings. The OCWT, central to this methodology, undergoes fine-tuning through a newly formulated metaheuristic algorithm, the Skill Optimization Algorithm (SOA). This algorithm bifurcates into two critical components: the acquisition of expertise (exploration) and the enhancement of individual capabilities (exploitation). The NCBAM, proposed for classification, adeptly captures long-range dependencies across spatial and channel dimensions. Furthermore, the model employs a learning matrix, adept at synthesizing spatial, channel, and temporal data, thus effectively balancing diverse data contributions by extracting intricate interrelations. The model's efficacy is rigorously validated using a gearbox dataset and a motor bearing dataset. The outcomes reveal superior performance, with the model achieving an average accuracy of 94.17% on the bearing dataset and 95.77% on the gearbox dataset. These results demonstrably surpass those of existing alternatives, underscoring the model's potential in enhancing fault diagnosis accuracy in motor bearings.