Efficacy Of Processes Research Articles

Failure Evaluation of Thermal Spray Coating Using High-Velocity Oxy-Fuel (HVOF) on Steam Turbine Shaft 940-PT 2 A/B

The repair of worn steel shaft surfaces in power transmission systems is made possible by thermal spray procedures, which have become an essential technology in many sectors. With this novel approach, worn shaft components may be recycled effectively since an API 687-compliant wear-resistant coating designed for specialty rotating equipment repairs is applied. The process's efficacy stems from its capacity to extend the functional life of essential mechanical components, therefore decreasing replacement expenses and downtime. The variety of factors that affect the result highlights the intricacy of thermal spray techniques. Finding the ideal mix of these crucial process variables becomes essential to provide the required degree of wear resistance. The coated shafts perform better as a result of this careful tuning, which also guarantees dependability under challenging working conditions. The coated shafts' surface hardness data in this investigation showed that the initial coating hardness value was below the predetermined goals. Several parts that interact with the shaft were redesigned strategically in response to these discoveries. These adjustments were mainly motivated by economic considerations, to optimize effectiveness while resolving the noted hardness deficiencies. Furthermore, a thorough analysis of failure data showed important correlations between different operational parameters and how they interacted, offering a further understanding of how these factors affected wear resistance. This research allowed for the identification of crucial parameters that required change and emphasized the precise balance essential for optimum thermal spray application. All things considered, this study emphasizes how critical it is to use a methodical approach to improve thermal spray processes, guarantee successful shaft component restoration, and ultimately increase the operational longevity of such components in challenging applications.

Innovative ionic liquid pretreatment followed by wet disk milling treatment provides enhanced properties of sugar palm nano-fibrillated cellulose

In order to accommodate the increased demand for innovative materials, intensive research has focused on natural resources. In pursuit of advanced substances that exhibit functionality, sustainability, recyclability, and cost-effectiveness, the present work attempted an alternative study on cellulose nanofibers derived from sugar palm fiber. Leveraging an innovative approach involving ionic liquid (IL) pre-treatment, bleaching, and wet disc mill technique, nano-fibrillated cellulose (NFC) was successfully obtained from the sugar palm fiber source. Remarkably, 96.89% of nanofibers were extracted from the sugar palm fiber, demonstrating the process's efficacy and scalability. Further investigation revealed that the sugar palm nano-fibrillated cellulose (SPNFC) exhibited a surface area of 3.46 m2/g, indicating a significant interface for enhanced functionality. Additionally, the analysis unveiled an average pore size of 4.47 nm, affirming its suitability for various applications that necessitate precise filtration. Moreover, the surface charge densities of SPNFC were found to be −32.1 mV, offering opportunities for surface modification and enhanced interactions with various materials. The SPNFC exhibit remarkable thermal stability, enduring temperatures of up to 360.5 °C. Additionally, the isolation process is evident in a significant rise in the crystallinity index, escalating from 50.97% in raw fibers to 61.62% in SPNFC. These findings shed light on the vast potential and distinct features of SPNFC, opening the path for its application in a wide array of industries, including but not limited to advanced materials, biomedicine, and environmental engineering.

Research on fruit shape database mining to support fruit class classification using the shuffled frog leaping optimization (SFLO) technique

<abstract><p>Association rule mining (ARM) is a technique for discovering meaningful associations within databases, typically handling discrete and categorical data. Recent advancements in ARM have concentrated on refining calculations to reveal connections among various databases. The integration of shuffled frog leaping optimization (SFLO) processes has played a crucial role in this pursuit. This paper introduces an innovative SFLO-based method for performance analysis. To generate association rules, we utilize the apriori algorithm and incorporate frog encoding within the SFLO method. A key advantage of this approach is its one-time database filtering, significantly boosting efficiency in terms of CPU time and memory usage. Furthermore, we enhance the optimization process's efficacy and precision by employing multiple measures with the modified SFLO techniques for mining such information.The proposed approach, implemented using MongoDB, underscores that our performance analysis yields notably superior outcomes compared to alternative methods. This research holds implications for fruit shape database mining, providing robust support for fruit class classification.</p></abstract>

Automatic Detection of Object-Based Video Forgery Using Various Groups of Pictures (GOP)

In recent years, there has been a lot of interest in detecting object-based video forgeries. There has been a lack of satisfactory performance with object-based forgery detectors until recently since a majority of them are still based on handcrafted features. There has been a great deal of interest in passive video forensics in recent years. Forgery of video encoded with advanced codec frameworks remains one of the biggest challenges in object-based forgery research. An object-based forgery detection approach is presented in this paper. To evaluate the proposed method, a derived test dataset of variable video lengths and frame sizes is also used in addition to the SYSU-OBJFORG dataset. This process's efficacy is verified by comparing its results with other methods. When tested on datasets with degraded-quality videos, the proposed framework performed better in real-life scenarios.

Feature based-Learning with Data Increasing for video Recommendation and Computing

Image content analysis is crucial for determining the reliability of a link between two videos. Video characteristics are increasingly being used in image and video representation as custom pre-trained picture and video convolutional neural networks become generally available. People also have limited access to video editing tools for a variety of reasons, such as ownership and privacy concerns. You don't need to go back to the source video data to use the refined features again. An affine transformation, for instance, can be used to map a well-studied function onto an unfamiliar domain. To do this, we use a unique triplet failure in conjunction with the re-learning strategy. We propose a contemporary data augmentation method that may be applied to functionality on various frames for videos as an alternative to employing specific motion data. Extensive testing on the well-known Hulu content-based Video Relevance challenge demonstrates the process's efficacy and provides solid evidence of state-of-the-art performance.