An allometric smoothing function to describe the relation between otolith and somatic growth over the lifespan of walleye pollock (Theragra chalcogramma)

We propose a new equation to describe the relation between otolith length (OL) and somatic length (fork length [FL]) of fish for the entire lifespan of the fish. The equation was developed by applying a mathematical smoothing method based on an allometric equation with a constant term for walleye pollock (Theragra chalcogramma) —a species that shows an extended longevity (>20 years). The most appropriate equation for defining the relation between OL and FL was a four-phase allometric smoothing function with three inflection points. The inflection points correspond to the timing of settlement of walleye pollock, changes in sexual maturity, and direction of otolith growth. Allometric smoothing functions describing the relation between short otolith radius and FL, long otolith radius and FL, and FL and body weight were also developed. The proposed allometric smoothing functions cover the entire lifespan of walleye pollock. We term these equations “allometric smoothing functions for otolith and somatic growth over the lifespan of walleye pollock.

Research on vibration-based early diagnostic system for excavator motor bearing using 1-D CNN

In mining, super-large machines such as rope excavators are used to perform the main mining operations. A rope excavator is equipped with motors that drive mechanisms. Motors are easily damaged as a result of harsh mining conditions. Bearings are important parts in a motor; bearing failure accounts for approximately half of all motor failures. Failure reduces work efficiency and increases maintenance costs. In practice, reactive, preventive, and predictive maintenance are used to minimize failures. Predictive maintenance can prevent failures and is more effective than other maintenance. For effective predictive maintenance, a good diagnosis is required to accurately determine motor-bearing health. In this study, vibration-based diagnosis and a one-dimensional convolutional neural network (1-D CNN) were used to evaluate bearing deterioration levels. The system allows for early diagnosis of bearing failures. Normal and failure-bearing vibrations were measured. Spectral and wavelet analyses were performed to determine the normal and failure vibration features. The measured signals were used to generate new data to represent bearing deterioration in increments of 10%. A reliable diagnosis system was proposed. The proposed system could determine bearing health deterioration at eleven levels with considerable accuracy. Moreover, a new data mixing method was applied

Use of a DNN-Based Image Translator with Edge Enhancement Technique to Estimate Correspondence between SAR and Optical Images

In this paper, the local correspondence between synthetic aperture radar (SAR) images and optical images is proposed using an image feature-based keypoint-matching algorithm. To achieve accurate matching, common image features were obtained at the corresponding locations. Since the appearance of SAR and optical images is different, it was difficult to find similar features to account for geometric corrections. In this work, an image translator, which was built with a DNN (deep neural network) and trained by conditional generative adversarial networks (cGANs) with edge enhancement, was employed to find the corresponding locations between SAR and optical images. When using conventional cGANs, many blurs appear in the translated images and they degrade keypoint-matching accuracy. Therefore, a novel method applying an edge enhancement filter in the cGANs structure was proposed to find the corresponding points between SAR and optical images to accurately register images from different sensors. The results suggested that the proposed method could accurately estimate the corresponding points between SAR and optical images

Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV

The use of drones in mining environments is one way in which data pertaining to the state of a site in various industries can be remotely collected. This paper proposes a combined system that employs a 6-bands multispectral image capturing camera mounted on an Unmanned Aerial Vehicle (UAV) drone, Spectral Angle Mapping (SAM), as well as Artificial Intelligence (AI). Depth possessing multispectral data were captured at different flight elevations. This was in an attempt to find the best elevation where remote identification of magnetite iron sands via the UAV drone specialized in collecting spectral information at a minimum accuracy of +/- 16 nm was possible. Data were analyzed via SAM to deduce the cosine similarity thresholds at each elevation. Using these thresholds, AI algorithms specialized in classifying imagery data were trained and tested to find the best performing model at classifying magnetite iron sand. Considering the post flight logs, the spatial area coverage of 338 m(2), a global classification accuracy of 99.7%, as well the per-class precision of 99.4%, the 20 m flight elevation outputs presented the best performance ratios overall. Thus, the positive outputs of this study suggest viability in a variety of mining and mineral engineering practices

Investigations of the radial propagation of blob-like structure in a non-confined electron cyclotron resonance heated plasma on Q-shu University Experiment with a Steady-State Spherical Tokamak

A study of radial propagation and electric fields induced by charge separation in blob-like structures has been performed in a non-confined cylindrical electron cyclotron resonance heating plasma on Q-shu University Experiment with a Steady-State Spherical Tokamak using a fast-speed camera and a Langmuir probe. The radial propagation of the blob-like structures is found to be driven by E × B drift. Moreover, these blob-like structures were found to have been accelerated, and the property of the measured radial velocities agrees with the previously proposed model [C. Theiler et al., Phys. Rev. Lett. 103, 065001 (2009)]. Although the dependence of the radial velocity on the connection length of the magnetic field appeared to be different, a plausible explanation based on enhanced short-circuiting of the current path can be proposed

Multiple liver metastases of pancreatic solid pseudopapillary tumor treated with resection following chemotherapy and transcatheter arterial embolization: A case report

金沢大学医薬保健研究域医学系A 33-year-old female was diagnosed with a solid pseudopapillary tumor (SPT) of the pancreas and multiple liver metastases at the Department of Gastroenterological Surgery, Ishikawa Prefectural Central Hospital (Kanazawa, Japan). Distal pancreatectomy and postoperative systemic chemotherapy with gemcitabine (GEM) and S-1, an oral fluoropyrimidine derivative, was administered, however, liver metastases became enlarged and local recurrence occurred. Therefore, the patient was referred to the Department of Gastroenterologic Surgery at the Graduate School of Medicine (Kanazawa, Japan) for hepatic arterial infusion (HAI) chemotherapy. Oral S-1 (80 mg/m2) was administered as well as HAI chemotherapy with GEM (1,000 mg/standard liver volume). Following 18 cycles, tumor sizes were reduced and 18-fluorodeoxyglucose positron emission tomography (18FDG-PET) examination revealed obvious reduction of tumor FDG uptake. Transarterial tumor embolization (TAE) was performed for the previously unresectable right subphrenic liver tumor, and the other tumors were surgically resected. The resected tumors were diagnosed as liver metastases and a local recurrence of SPT in the postoperative pathological examination, which revealed that the resected tumors were composed of sheets of bland cells, which were positive for CD10, CD56, vimentin, neuron-specific enolase and α-antitrypsin. The postoperative course was uneventful, and the patient is currently under observation at an outpatient clinic; postoperative adjuvant chemotherapy with oral S-1 has continued, and additional TAE is planned. In the future, if the middle segment of the liver becomes enlarged, surgery for the residual right lobe tumor may be possible. This case demonstrates one method of SPT treatment: Preoperative HAI chemotherapy with GEM, plus oral S-1 and TAE. If complete resection can be achieved, the majority of patients with SPT have a favorable prognosis. In patients with unresectable metastases from SPT, it is crucial to conduct systematic multimodal treatment to maximize treatment success. © 2015, Spandidos Publications. All Rights Reserved.Embargo Period 6 month

Recent and future situation of Japan’s T&D system

Japan suffered from the Great East Japan Earthquake followed by the nuclear disaster. As a result, we experienced rolling outages for a few months in the Tokyo and Tohoku area. Japan’s power transmission system consists of 50 Hz AC and 60 Hz AC in eastern and western Japan respectively. When the nuclear disaster occurred in Fukushima, enough electricity hasn’t been supplied in eastern area. Power interchange capacity between east and west was small because of small redundant T&D system design. Based on this rolling outage and some present electricity supplying issues in Japan, METI (Ministry of Economy, Trade and Industry) has set the electricity system reform committee to improve this situation and make a good T&D system for the future. This committee reported the discussed issue to the Japanese METI and METI proposed policy on Electricity System Reform to the Japanese Cabinet. As a result, the Japanese Electricity System Reform policy was adopted. This future T&D system deals with redundant T&D systems between east and west, how to handle large amounts of renewable electrical energy, and how to fully de-regulate the distribution market. HVDC (VSC system) will be introduced between Hokkaido and Honshu as a subsea cable transmission system and HVDC transmission system between eastern area and western area. This paper describes recent and future Japan’s T&D systems. This will be helpful to understand how to solve the issues of Japan’s T&D system

High frequency short-circuit inductance for model transformer

Currently, the International Electrotechnical Commission (IEC) standard (IEC62271-100) requires that the transformer limited fault (TLF) interruption shall be verified in the test duty T10, the amplitude factor of which is defined as 1.7 for the transient recovery voltage (TRV). But, the TRV measurements for the TLF at various transformers show that the factor is mostly lower than 1.7. The reason for the low values is considered to be that the short-circuit inductance of transformer at the TRV frequency region is lower than that at the power frequency, and consequently the factor is lower than the standard value 1.7. But, the frequency-dependence of the short-circuit inductance of transformer has not been clarified yet. We measured the short-circuit inductances of model transformer with and without the iron core. The short-circuit inductance with the iron core becomes small and equals to one without the iron core at the TRV frequency region. We concluded that this is the reason for the phenomena of low amplitude factor. In addition, we showed that the frequency-dependence of short-circuit inductance could be explained by taking eddy current within an iron core and copper windings into consideration

Application of Bayesian Neural Network (BNN) for the Prediction of Blast-Induced Ground Vibration

Rock blasting is one of the most common and cost-effective excavation techniques. However, rock blasting has various negative environmental effects, such as air overpressure, fly rock, and ground vibration. Ground vibration is the most hazardous of these inevitable impacts since it has a negative impact not only on the environment of the surrounding area but also on the human population and the rock itself. The PPV is the most critical base parameter practice for understanding, evaluating, and predicting ground vibration in terms of vibration velocity. This study aims to predict the blast-induced ground vibration of the Mikurahana quarry, using Bayesian neural network (BNN) and four machine learning techniques, namely, gradient boosting, k-neighbors, decision tree, and random forest. The proposed models were developed using eight input parameters, one output, and one hundred blasting datasets. The assessment of the suitability of one model in comparison to the others was conducted by using different performance evaluation metrics, such as R, RMSE, and MSE. Hence, this study compared the performances of the BNN model with four machine learning regression analyses, and found that the result from the BNN was superior, with a lower error: R = 0.94, RMSE = 0.17, and MSE = 0.03. Finally, after the evaluation of the models, SHAP was performed to describe the importance of the models' features and to avoid the black box issue