A Comprehensive Context-Free Grammar for the Arabic Language: Including Non- Fundamentalist Phrases

Dixon's assertion regarding the idiosyncratic nature of natural languages initiates an investigation into the unique characteristics of the Arabic language. Contrary to Dixon's viewpoint, some scholars suggest the presence of regularity within Arabic, attributable to its extensive array of syntactic rules and formulations. Yet, the copious volume of terminal vocabulary in Arabic poses significant challenges to grammar development. While annotations have offered partial solutions, they bring forth additional difficulties due to the necessity of retrieving data from the annotated corpora. To mitigate these issues, an innovative study was executed that utilized an annotated taxonomy of syntactic roles, coupled with an examination of both fundamentalist and non-fundamentalist phrases. A codification method was applied to a knowledge base employing the Subsumption Hierarchical Attribute (SHA), enabling the integration of Arabic word classes based on their potential syntactic roles. The SHA acts as an annotation method for deriving a grammar class 02, where classes are coded as terminal vocabulary. Its primary objectives are twofold: to moderate the complexity of the parsing system and to automate the generation of over 1490 distinct possible sentence structures. The study culminated in the development of a novel context-free grammar (CFG) for Arabic, broadening the horizons of language processing techniques

Optimizing condition monitoring of ball bearings: An integrated approach using decision tree and extreme learning machine for effective decision-making

This article presents a study on condition monitoring and predictive maintenance, highlighting the importance of tracking ball bearing condition to estimate their Remaining Useful Life (RUL). The study proposes a methodology that combines three algorithms, namely Variational Mode Decomposition (VMD), Decision Tree (DT), and Extreme Learning Machine (ELM), to extract pertinent features and estimate RUL using vibration signals. To improve the accuracy of the method, the VMD algorithm is used to reduce noise from the original vibration signals. The DT algorithm is then employed to extract relevant features, which are fed into the ELM algorithm to estimate the RUL of the ball bearings. The effectiveness of the proposed approach is evaluated using ball bearing data sets from the PRONOSTIA platform. Overall, the results demonstrate that the suggested methodology successfully tracks the ball bearing condition and estimates RUL using vibration signals. This study provides valuable insights into the development of predictive maintenance systems that can assist decision-makers in planning maintenance activities. Further research could explore the potential of this methodology in other industrial applications and under different operating conditions