A new approach to measure dwell position inaccuracy in HDR ring applicators - quantification and corrective QA.

As part of quality assurance (QA) in high dose rate brachytherapy, it is necessary to verify that the source dwell positions correspond to the radiographic markers used in simulation and treatment planning. The procedure is well established for linear tandem applicators. However, with the advent of ring applicators, this has become more critical and challenging. This work describes a new approach to determine positional inaccuracies for ring applicators in which the dummy markers are imaged just once and their dwell positions characterized with respect to an applicator-defined axis. The radiograph serves as a reference for dummy markers for comparison with all subsequent measurements in which the active sources are autoradiographed at different offsets - thus obviating the back-and-forth transferring of setup between afterloader and simulator. The method has been used specifically to characterize the Varian GammaMed 60degrees ring applicator, but it may be adapted to any other applicator. The results show that an offset of 1-2 mm minimizes the overall inaccuracy to within 2 mm

Role of ergonomics in the selection of stemming plugs for surface mining operations

Stemming plugs are one of the widely used accessory in surface mining operations. Stemming plugs assist conventional stemming material in gas retention and help in better fragmentation and explosive utilization. Effective use of the stemming plugs results in economic benefits and enhance the efficacy of the project. Economic and productive viability of stemming plugs have been conducted in depth by different researchers. Addition of stemming plugs to a new system requires ergonomic challenges for operators conducting drilling and blasting operation. Induction of a newer product in already established system is subject to overall positive feedback. This work investigates ergonomics of three different stemming plugs introduced to a limestone quarry in Pakistan. The stemming plugs were evaluated based on extra time needed, workers feedback, failures during operation, recovery time after failure and number of extra equipment required to carry out the operation. Points based matrix was established with likeliness of each plug and based on overall scores stemming plug 1 was most acceptable followed by stemming plug 3. Stemming plug 2 was disliked by operation and did not reach the level of acceptability of operators. This work will help stemming plug making industry in adapting to best practices by incorporating ergonomics of plugs in designing. Literature shows no previous work on ergonomics of stemming plugs

Impact of Shear Zone on Rockburst in the Deep Neelum-Jehlum Hydropower Tunnel: A Numerical Modeling Approach

Rockburst is a hazardous phenomenon in deep tunnels influenced by geological structural planes like faults, joints, and shear planes. Small-scale shear-plane-like structures have damaging impact on the boundaries of the tunnel, which act as barrier and accumulate high stresses. A shear plane combined with high stress conditions is very dangerous in deep excavations. Such a shear plane exposed in the side wall of the right headrace tunnel in the Neelum-Jehlum Hydropower Project. This project is constructed in the tectonically active Himalayas under high stress conditions. The influence of a shear zone on rockburst occurrence near the tunnel is studied. The FLAC3D explicit code simulated the shear zone in the right tunnel, revealing that the stresses are concentrated near the shear zone, while no such stress concentration is present in the left tunnel. The Rock mass got displaced near this shear zone. Modeling results confirm that the presence of shear zone in side wall of the right tunnel has a major influence on rockburst occurrence. A shear slip along this plane released huge amounts of accumulated energy, causing human fatalities and property damage. A comparison of numerical simulation with empirical rockburst criteria validates the actual conditions and help us to understand the phenomenon of stress concentration near the shear zone and its impact during deep tunneling

Transfer Learning-Based Model for Diabetic Retinopathy Diagnosis Using Retinal Images

Diabetic retinopathy (DR) is a visual obstacle caused by diabetic disease, which forms because of long-standing diabetes mellitus, which damages the retinal blood vessels. This disease is considered one of the principal causes of sightlessness and accounts for more than 158 million cases all over the world. Since early detection and classification could diminish the visual impairment, it is significant to develop an automated DR diagnosis method. Although deep learning models provide automatic feature extraction and classification, training such models from scratch requires a larger annotated dataset. The availability of annotated training datasets is considered a core issue for implementing deep learning in the classification of medical images. The models based on transfer learning are widely adopted by the researchers to overcome annotated data insufficiency problems and computational overhead. In the proposed study, features are extracted from fundus images using the pre-trained network VGGNet and combined with the concept of transfer learning to improve classification performance. To deal with data insufficiency and unbalancing problems, we employed various data augmentation operations differently on each grade of DR. The results of the experiment indicate that the proposed framework (which is evaluated on the benchmark dataset) outperformed advanced methods in terms of accurateness. Our technique, in combination with handcrafted features, could be used to improve classification accuracy

A Microwave Ring-Resonator Sensor for Non-Invasive Assessment of Meat Aging

The assessment of moisture loss from meat during the aging period is a critical issue for the meat industry. In this article, a non-invasive microwave ring-resonator sensor is presented to evaluate the moisture content, or more precisely water holding capacity (WHC) of broiler meat over a four-week period. The developed sensor has shown significant changes in its resonance frequency and return loss due to reduction in WHC in the studied duration. The obtained results are also confirmed by physical measurements. Further, these results are evaluated using the Fricke model, which provides a good fit for electric circuit components in biological tissue. Significant changes were observed in membrane integrity, where the corresponding capacitance decreases 30% in the early aging (0D-7D) period. Similarly, the losses associated with intracellular and extracellular fluids exhibit changed up to 42% and 53%, respectively. Ultimately, empirical polynomial models are developed to predict the electrical component values for a better understanding of aging effects. The measured and calculated values are found to be in good agreement

Patient Monitoring by Abnormal Human Activity Recognition Based on CNN Architecture

Human action recognition has emerged as a challenging research domain for video understanding and analysis. Subsequently, extensive research has been conducted to achieve the improved performance for recognition of human actions. Human activity recognition has various real time applications, such as patient monitoring in which patients are being monitored among a group of normal people and then identified based on their abnormal activities. Our goal is to render a multi class abnormal action detection in individuals as well as in groups from video sequences to differentiate multiple abnormal human actions. In this paper, You Look only Once (YOLO) network is utilized as a backbone CNN model. For training the CNN model, we constructed a large dataset of patient videos by labeling each frame with a set of patient actions and the patient’s positions. We retrained the back-bone CNN model with 23,040 labeled images of patient’s actions for 32 epochs. Across each frame, the proposed model allocated a unique confidence score and action label for video sequences by finding the recurrent action label. The present study shows that the accuracy of abnormal action recognition is 96.8%. Our proposed approach differentiated abnormal actions with improved F1-Score of 89.2% which is higher than state-of-the-art techniques. The results indicate that the proposed framework can be beneficial to hospitals and elder care homes for patient monitoring