Conveyer-belt accidents in mining and metallurgy

The dynamic interaction between the mechanical and electrical drive parameters when damage suddenly appears in the mechanical part of a conveyer belt is investigated. The possibility of using the change in the motor’s electrical parameters as a diagnostic signal is assessed. A model for investigating the dynamic processes in the system consisting of the grid, the induction motor, and the conveyer belt is proposed. The grid and induction motor are described by a model of fourth-order state space, whose output is the torque at the motor shaft. The moments of inertia and pliability of the links between the elements in the mechanical part of the conveyer belt are determined. By gradually identifying the elements with the minimum moment of inertia and distributing their moments of inertia and pliabilities among the adjacent elements, we obtain a three-mass system simulating the mechanical part of the conveyer belt. This dynamic model is solved by means of Matlab Simulink software. The energy characteristics of the drive are determined in dynamic processes following a mechanical accident.     

Collection efficiency of liquid‐based samplers for fungi in indoor air

This study assessed the collection efficiency (CE) of two popularly used sampling devices (BioSampler and Coriolis sampler) for fungal aerosols. Phosphate‐buffered saline (PBS) supplemented with or without surfactant (Tween‐20, Tween‐80, or Triton X‐100) and antifoam agent was prepared and used as collection liquids. The agar impactor (BioStage) was simultaneously operated with liquid‐based samplers to collect fungi from seven sites located at a university building, public library, and animal farming. Fungal concentrations determined by liquid samplers were divided by those by BioStage, and the ratio values represented CE. Results indicate that the CE of BioSampler was superior to that of Coriolis (P = 0.0001) and the PBS containing surfactant collected fungi better than that without surfactant (P < 0.0001), whereas antifoam agent showed no influence (P = 0.8). Moreover, fungal concentrations determined by BioSampler with surfactant‐added PBS were statistically indifferent from those by BioStage (P > 0.05) with a Spearman correlation coefficient of 0.81‐0.83 (P < 0.01). In addition to sampler and collection liquid, sampling location was also identified as a significant CE factor (P = 0.006), implying potential influences by fungal genera in the studied fields. Overall, BioSampler with surfactant‐supplemented PBS (eg, Triton X‐100) is recommended considering the great CE and compatibility with a variety of analytical assays.     

On Application of Carbon-Containing Electrode Materials in Technology of Electrospark Alloying: Part 1. Peculiarities of Coating Formation Using Electrospark Treatment of Titanium Alloy OT4-1

Electrospark treatment of OT4-1 titanium alloy was performed sequentially with a STIM-20N hard-alloy electrode (TiC–20% Ni) and carbon-containing material (graphite and carbon-based composite materials). Kinetics of the mass transfer of the hard-alloy electrode was studied. The cathode mass loss during the first minute of the treatment was established. The kinetics results were processed using the methods of mathematical statistics. The erosion resistance of the applied carbon-containing materials was determined. Phase composition and relief of the coatings formed were analyzed. It was found that the application of the carbon-containing material increases the content of refractory phases in the coatings. Increase in the time of the treatment using the carbon-containing materials decreases the roughness of the coatings.     

Automatic breast tumor detection in ABVS images based on convolutional neural network and superpixel patterns

Breast cancer is one of the most common female malignancies, as well as the second leading cause of mortality for women. Early detection and treatment can dramatically decrease the mortality rate. Recently, automated breast volume scanner (ABVS) has become one of the most frequently used diagnose methods for breast tumor screening because of its operator-independent and reproducible advantages. However, it is a challenging job to obtain the tumors’ accurate locations and shapes by reviewing hundreds of ABVS slices. In this paper, a novel computer-aided detection (CADe) system is developed to reduce clinicians’ reading time and improve the efficiency. The CADe system mainly contains three parts: tumor candidate acquisition, false-positive reduction and tumor segmentation. Firstly, a local phase-based approach is built to obtain breast tumor candidates for further recognition. Subsequently, a convolutional neural network (CNN) is applied to reduce false positives (FPs). The introduction of CNN can help to avoid complicated feature extraction as well as elevate the accuracy and efficiency. Finally, superpixel-based segmentation is used to outline the breast tumor. Here, superpixel-based local binary pattern (SLBP) is proposed to assist the segmentation, which improves the performance. The methods were evaluated on a clinical ABVS dataset whose abnormal cases were manually labeled by an experienced radiologist. The experiment results were mainly composed of two parts. At the FP reduction stage, the proposed CNN achieved 100% and 78.12% sensitivity with FPs/case of 2.16 and 0. At the segmentation stage, our SLBP obtained 82.34% true positive, 15.79% false positive and 83.59% Dice similarity. In summary, the proposed CADe system demonstrated promising potential to detect and outline breast tumors in ABVS images.