Application of Magnetic Nanoparticles for Rapid Detection and In Situ Diagnosis in Clinical Oncology

Screening, monitoring, and diagnosis are critical in oncology treatment. However, there are limitations with the current clinical methods, notably the time, cost, and special facilities required for radioisotope-based methods. An alternative approach, which uses magnetic beads, offers faster analyses with safer materials over a wide range of oncological applications. Magnetic beads have been used to detect extracellular vesicles (EVs) in the serum of pancreatic cancer patients with statistically different EV levels in preoperative, postoperative, and negative control samples. By incorporating fluorescence, magnetic beads have been used to quantitatively measure prostate-specific antigen (PSA), a prostate cancer biomarker, which is sensitive enough even at levels found in healthy patients. Immunostaining has also been incorporated with magnetic beads and compared with conventional immunohistochemical methods to detect lesions; the results suggest that immunostained magnetic beads could be used for pathological diagnosis during surgery. Furthermore, magnetic nanoparticles, such as superparamagnetic iron oxide nanoparticles (SPIONs), can detect sentinel lymph nodes in breast cancer in a clinical setting, as well as those in gallbladder cancer in animal models, in a surgery-applicable timeframe. Ultimately, recent research into the applications of magnetic beads in oncology suggests that the screening, monitoring, and diagnosis of cancers could be improved and made more accessible through the adoption of this technology.

Impact of the Field Winding Interturn Short-Circuit Position on Rotor Vibration Properties in Synchronous Generators

This paper investigates the effect of the field winding interturn short-circuit (FWISC) position on the rotor vibration properties in turbo generators. Different from the previous studies which focused on the influence of the short-circuit degree, this work pays much attention to the impact of the short-circuit position on the rotor unbalanced magnetic pull (UMP) properties and vibration characteristics. The theoretical UMP model is firstly deduced based on the analysis of the magnetic flux density (MFD) variation. Then, the finite element analysis (FEA) is performed to calculate the UMP data. Finally, the rotor vibrations are tested on a CS-5 prototype generator which has two poles and a rated capacity of 5 kVA. It is shown that the occurrence of FWISC will greatly increase the UMP as well as the rotor vibration. In addition to the short-circuit degree, the short-circuit position will also affect the UMP and vibration. The nearer the short-circuit position is to the big rotor teeth, the larger the UMP and vibration will be. The proposed study in this paper will be beneficial for the monitoring and diagnosis of FWISC faults.

Health Monitoring and Diagnosis System for a Small H-Type Darrieus Vertical-Axis Wind Turbine

As the wind power market grows rapidly, the importance of technology for real-time monitoring and diagnosis of wind turbines is increasing. However, most of the developed technologies and research mainly focus on large horizontal-axis wind turbines, and research conducted on small- and medium-sized wind turbines is rare. In this study, a novel low-cost and real-time health monitoring and diagnosis system for the small H-type Darrieus vertical axis wind turbine is proposed. Turbine operating conditions were classified into parked/idle and power production. In the case of the power production condition, abnormality diagnosis was performed using key monitoring parameters, including vibration, fundamental frequency, the bending stress of the tower and generator vibration. The turbine abnormalities were diagnosed in two stages by applying the alert and alarm limits, determined by referring to international standards and material properties and the long-term measurement data together.

Monitoring and Diagnosis System of Downhole Tubing Leakage

The downhole tubing in a gas well is affected by many factors such as high pressure erosion, gas lift operation, sand production at the bottom of the well and engineering construction, etc., which can easily lead to leakage of the threaded joints of the tubing and the pipe body, and the leaked natural gas will invade Annulus, making the annulus under pressure. The annular pressure caused by oil pipe leakage is a major safety hazard in oil and gas production. Therefore, the accurate diagnosis the degree of leakage of downhole tubing is of great significance to preventing the occurrence of production accidents effectively. To this end, a set of downhole tubing leak monitoring and diagnosis system has been developed by integrating fluid monitoring, acoustic wave detection and tracer detection technology, and the developed tubing leak monitoring and diagnosis system was used for leak detection tests on offshore platforms. The test results show that the developed tubing leakage monitoring and diagnosis system can meet the need of offshore gas well diagnosis, and realize the holographic diagnosis of the leakage degree of the downhole tubing without moving the downhole tubing string.

Emphasizing privacy and security of edge intelligence with machine learning for healthcare

PurposeArtificial Intelligence (AI) has surpassed expectations in opening up different possibilities for machines from different walks of life. Cloud service providers are pushing. Edge computing reduces latency, improving availability and saving bandwidth.Design/methodology/approachThe exponential growth in tensor processing unit (TPU) and graphics processing unit (GPU) combined with different types of sensors has enabled the pairing of medical technology with deep learning in providing the best patient care. A significant role of pushing and pulling data from the cloud, big data comes into play as velocity, veracity and volume of data with IoT assisting doctors in predicting the abnormalities and providing customized treatment based on the patient electronic health record (EHR).FindingsThe primary focus of edge computing is decentralizing and bringing intelligent IoT devices to provide real-time computing at the point of presence (PoP). The impact of the PoP in healthcare gains importance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients. The impact edge computing of the PoP in healthcare gains significance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients.Originality/valueThe utility value of sensors data improves through the Laplacian mechanism of preserved PII response to each query from the ODL. The scalability is at 50% with respect to the sensitivity and preservation of the PII values in the local ODL.

Vibration Analysis for Machine Monitoring and Diagnosis: A Systematic Review

Untimely machinery breakdown will incur significant losses, especially to the manufacturing company as it affects the production rates. During operation, machines generate vibrations and there are unwanted vibrations that will disrupt the machine system, which results in faults such as imbalance, wear, and misalignment. Thus, vibration analysis has become an effective method to monitor the health and performance of the machine. The vibration signatures of the machines contain important information regarding the machine condition such as the source of failure and its severity. Operators are also provided with an early warning for scheduled maintenance. Numerous approaches for analyzing the vibration data of machinery have been proposed over the years, and each approach has its characteristics, advantages, and disadvantages. This manuscript presents a systematic review of up-to-date vibration analysis for machine monitoring and diagnosis. It involves data acquisition (instrument applied such as analyzer and sensors), feature extraction, and fault recognition techniques using artificial intelligence (AI). Several research questions (RQs) are aimed to be answered in this manuscript. A combination of time domain statistical features and deep learning approaches is expected to be widely applied in the future, where fault features can be automatically extracted from the raw vibration signals. The presence of various sensors and communication devices in the emerging smart machines will present a new and huge challenge in vibration monitoring and diagnosing.