Magnetorheological Shear Thickening Polishing Characteristics for Ti-6Al-4V using Multi-Pole Coupling Magnetic Field: A Comprehensive Experimental Study
Magnetorheological shear thickening polishing (MRSTP) demonstrates significant potential for the precision processing of titanium alloys. However, prior studies have primarily focused on surface roughness, while comprehensive experimental investigations remain limited. Moreover, the underlying mechanisms have yet to be explored. To address these research gaps, comprehensive MRSTP experiments are conducted on Ti-6Al-4V using a multi-pole coupling magnetic field, considering surface roughness, polishing force, and material removal rate. The optimal parameters are determined, including a carrier fluid concentration of 20 wt%, an abrasive concentration of 7.5 wt%, an abrasive size of 13 μm, a carbonyl iron particle size of 50 μm, a magnetic flux density of 140 mT, a feed rate of 6000 mm/min, and a spindle speed of 100 r/min. After 60 min of MRSTP under these conditions, the surface roughness is reduced from approximately 280 to 24 nm, achieving a mirror-like appearance. The normal and tangential forces are measured to be 4.04 and 1.74 N, respectively, with a corresponding material removal rate of 24.3 mg/h. The formation of enhanced particle clusters explains the underlying mechanisms for these variations. This study provides valuable insights into tailoring MRSTP strategies for specific difficult-to-machine materials and into understanding the MRSTP mechanism. 1 | Introduction Titanium alloys, particularly Ti-6Al-4V, have attracted significant attention in advanced ma
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Small non-coding RNAs as diagnostic, prognostic and predictive biomarkers of gynecological cancers: an update
Introduction: Non-coding RNAs (ncRNAs) comprise a heterogeneous cluster of RNA molecules. Emerging evidence suggests their involvement in various aspects of tumorigenesis, particularly in gynecological malignancies. Notably, ncRNAs have been implicated as mediators within tumor signaling pathways, exerting their influence through interactions with RNA or proteins. These findings further highlight the hypothesis that ncRNAs constitute therapeutic targets and point out their clinical potential as stratification biomarkers. Areas Covered: The review outlines the use of small ncRNAs, including miRNAs, tRNA-derived small RNAs, PIWI-interacting RNAs and circular RNAs, for diagnostic, prognostic, and predictive purposes in gynecological cancers. It aims to increase our knowledge of their functions in tumor biology and their translation into clinical practice. Expert Opinion: By leveraging interdisciplinary collaborations, scientists can decipher the riddle of small ncRNA biomarkers as diagnostic, prognostic and predictive biomarkers of gynecological tumors. Integrating small ncRNA-based assays into clinical practice will allow clinicians to provide cure plans for each patient, reducing the likelihood of adverse responses. Nevertheless, addressing challenges such as standardizing experimental methodologies and refining diagnostic assays is imperative for advancing small ncRNA research in gynecological cancer. ARTICLE HISTORY Received 7 May 2024 Accepted 22 September
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Real-Time Rice Crop Disease Monitoring: YOLOv11-Powered System with Voice Alerts and Health Scoring
This paper presents a cost-efficient, AI-driven framework for real-time detection and alerting of rice crop diseases using YOLOv11. The proposed system addresses the limitations of existing solutions by leveraging existing low-cost field cameras or drone/UAV-captured images with geotagged metadata, eliminating the need for new camera infrastructure and reducing costs significantly. The YOLOv11 model is employed for multi-class detection of Bacterial Blight, Rice Blast, and Brown Spot, achieving a Box Precision (P) of 0.649, Box Recall (R) of 0.569, mAP50 of 0.626. The system integrates with a localized Interactive Voice Response (IVR) system for voice alerts and includes a field health scoring mechanism to provide actionable insights to farmers. The web-based dashboa rd health scores, upcoming risks like disease and alert logs, enabling farmers to monitor crop health remotely. Simulated case studies demonstrate the system's effectiveness in generating relevant alerts and tracking health scores over time. Future work will focus on improving model recall and mAP50-95 through further training with augmented datasets and expanding detection capabilities to include more diseases. This framework represents a significant advancement in AI-driven agricultural solutions for small-scale farmers, offering a powerful tool to enhance rice crop management and improve food security.
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