Search Results (9)

Shoho Castle Maps are maps of castle towns throughout Japan drawn by Kano School painters on the order of the shogun in 1644. The Shoho Castle Map of Takada, Joetsu City, Niigata Prefecture was used to visualize local distortions in historical maps and to scrutinize the mapmaking process. A novel method, geographically weighted bidimensional regression, was developed and applied to visualize the local distortions of the map. Exaggerated expressions by mapmakers that have not been identified in previous studies were revealed. That is, in addition to the castle being drawn enlarged, the town where the merchants and artisans lived was drawn larger than the castle. Therefore, the Takada Shoho Castle Map reflects mapmakers’ intentions, besides enlarging military facilities, which appear to have emphasized the pictorial composition of the map by placing the main gate to the castle at the center and drawing the map area evenly from the center in a well-balanced layout. Full article

Background: Current guideline-based implantable cardioverter-defibrillator (ICD) implants fail to meet the demands for precision medicine. Machine learning (ML) designed for survival analysis might facilitate personalized risk stratification. We aimed to develop explainable ML models predicting mortality and the first appropriate shock and compare these to standard Cox proportional hazards (CPH) regression in ICD recipients. Methods and Results: Forty-five routine clinical variables were collected. Four fine-tuned ML approaches (elastic net Cox regression, random survival forests, survival support vector machine, and XGBoost) were applied and compared with the CPH model on the test set using Harrell’s C-index. Of 887 adult patients enrolled, 199 patients died (5.0 per 100 person-years) and 265 first appropriate shocks occurred (12.4 per 100 person-years) during the follow-up. Patients were randomly split into training (75%) and test (25%) sets. Among ML models predicting death, XGBoost achieved the highest accuracy and outperformed the CPH model (C-index: 0.794 vs. 0.760, p < 0.001). For appropriate shock, survival support vector machine showed the highest accuracy, although not statistically different from the CPH model (0.621 vs. 0.611, p = 0.243). The feature contribution of ML models assessed by SHAP values at individual and overall levels was in accordance with established knowledge. Accordingly, a bi-dimensional risk matrix integrating death and shock risk was built. This risk stratification framework further classified patients with different likelihoods of benefiting from ICD implant. Conclusions: Explainable ML models offer a promising tool to identify different risk scenarios in ICD-eligible patients and aid clinical decision making. Further evaluation is needed. Full article

(1) Objective: To design an artificial intelligence system for prostate cancer prediction using the data obtained by shear wave elastography of the prostate, by comparing it with the histopathological exam of the prostate biopsy specimens. (2) Material and methods: We have conducted a prospective study on 356 patients undergoing transrectal ultrasound-guided prostate biopsy, for suspicion of prostate cancer. All patients were examined using bi-dimensional shear wave ultrasonography, which was followed by standard systematic transrectal prostate biopsy. The mean elasticity of each of the twelve systematic biopsy target zones was recorded and compared with the pathological examination results in all patients. The final dataset has included data from 223 patients with confirmed prostate cancer. Three machine learning classification algorithms (logistic regression, a decision tree classifier and a dense neural network) were implemented and their performance in predicting the positive lesions from the elastographic data measurements was assessed. (3) Results: The area under the curve (AUC) results were as follows: for logistic regression—0.88, for decision tree classifier—0.78 and for the dense neural network—0.94. Further use of an upsampling strategy for the training set of the neural network slightly improved its performance. Using an ensemble learning model, which combined the three machine learning models, we have obtained a final accuracy of 98%. (4) Conclusions: Bi-dimensional shear wave elastography could be very useful in predicting prostate cancer lesions, especially when it benefits from the computational power of artificial intelligence and machine learning algorithms. Full article

In the current industrial landscape, increasingly pervaded by technological innovations, the adoption of optimized strategies for asset management is becoming a critical key success factor. Among the various strategies available, the “Prognostics and Health Management” strategy is able to support maintenance management decisions more accurately, through continuous monitoring of equipment health and “Remaining Useful Life” forecasting. In the present study, convolutional neural network-based deep neural network techniques are investigated for the remaining useful life prediction of a punch tool, whose degradation is caused by working surface deformations during the machining process. Surface deformation is determined using a 3D scanning sensor capable of returning point clouds with micrometric accuracy during the operation of the punching machine, avoiding both downtime and human intervention. The 3D point clouds thus obtained are transformed into bidimensional image-type maps, i.e., maps of depths and normal vectors, to fully exploit the potential of convolutional neural networks for extracting features. Such maps are then processed by comparing 15 genetically optimized architectures with the transfer learning of 19 pretrained models, using a classic machine learning approach, i.e., support vector regression, as a benchmark. The achieved results clearly show that, in this specific case, optimized architectures provide performance far superior (MAPE = 0.058) to that of transfer learning, which, instead, remains at a lower or slightly higher level (MAPE = 0.416) than support vector regression (MAPE = 0.857). Full article

Little is known about psychological distress and its associated factors during the COVID-19 pandemic among nursing students, especially during the initial spread. The present study aimed to investigate the prevalence of psychological distress among Japanese nursing students after the first semester of 2020 with shifted classes and practice online. Furthermore, we examined whether factors such as health issues and resilience were associated with psychological distress. The data were obtained from a cross-sectional, self-administered questionnaire survey at a Japanese nursing college from September to November 2020 (n = 289). We collected data such as sociodemographic variables, medical history, insomnia, and resilience (Bidimensional Resilience Scale). Psychological distress was measured using the K6 scale (scores ≥ 5). The prevalence of psychological distress was 58.5%. The multivariate Poisson regression analysis found a negative association between innate resilience factors, and positive associations between medical history, insomnia, and psychological distress. The present study showed that more than half of the study participants experienced high levels of psychological distress. Our results suggest the importance of monitoring mental health among nursing students, focusing not only on health issues, but also personality components such as innate resilience during the prolonged COVID-19 pandemic. Full article

Terrain plays an important role in the formation, development and distribution of local precipitation and is a major factor leading to locally abnormal weather in weather systems. Although small-scale topography has little influence on the spatial distribution of precipitation, it interferes with precipitation fitting. Due to the arbitrary combination of small, medium and large-scale terrain, complex terrain distribution is formed, and small-scale terrain cannot be clearly defined and removed. Based on the idea of bidimensional empirical mode decomposition (BEMD), this paper extracts small-scale terrain data layer by layer to smooth the terrain and constructs a macroterrain model for different scales in Central China. Based on the precipitation distribution model using multiple regression, precipitation models (B0, B1, B2 and B3) of different scales are constructed. The 18-year monthly average precipitation data of each station are compared with the precipitation simulation results under different scales of terrain and TRMM precipitation data, and the influence of different levels of small-scale terrain on the precipitation distribution is analysed. The results show that (1) in Central China, the accuracy of model B2 is much higher than that of TRMM model A and monthly precipitation model B0. The comprehensive evaluation indexes are increased by 3.31% and 1.92%, respectively. (2) The influence of different levels of small-scale terrain on the precipitation distribution is different. The first- and second-order small-scale terrain has interference effects on precipitation fitting, and the third-order small-scale terrain has an enhancement effect on precipitation. However, the effect of small-scale topography on the precipitation distribution is generally reflected as interference. Full article

(1) Background: Coal mining operations caused severe land subsidence and altered the distributions of soil nutrients that influenced by multiple environmental factors at different scales. However, the prediction performances for soil nutrients based on their scale-specific relationships with influencing factors remains undefined in the coal mining area. The objective of this study was to establish prediction models of soil nutrients based on their scale-specific relationships with influencing factors in a coal mining area. (2) Methods: Soil samples were collected based on a 1 × 1 km regular grid, and contents of soil organic matter, soil available nitrogen, soil available phosphorus, and soil available potassium were measured. The scale components of soil nutrients and the influencing factors collected from remote sensing and topographic factors were decomposed by two-dimensional empirical mode decomposition (2D-EMD), and the predictions for soil nutrients were established using the methods of multiple linear stepwise regression or partial least squares regression based on original samples (MLSR_Ori or PLSR_Ori), partial least squares regression based on bi-dimensional intrinsic mode function (PLSR_BIMF), and the combined method of 2D-EMD, PLSR, and MLSR (2D-EMD_PM). (3) Results: The correlation types and correlation coefficients between soil nutrients and influencing factors were scale-dependent. The variances of soil nutrients at smaller scale were stochastic and non-significantly correlated with influencing factors, while their variances at the larger scales were stable. The prediction performances in the coal mining area were better than those in the non-coal mining area, and 2D-EMD_PM had the most stable performance. (4) Conclusions: The scale-dependent predictions can be used for soil nutrients in the coal mining areas. Full article

Increasing energy demand and economic performance can be observed in emerging markets and, in parallel, their share in world energy use and in global GDP is growing as well. It causes significant spatial shifts and calls attention for a new geography of energy demand. The main purpose of this study is to reveal the spatial distribution of energy use and economic growth focusing on the link between them. Developing gravity models, we identify the economic and energy centres of gravity in the world and on different continents and reveal their movements between 1990 and 2015, in particular, the directions of the shifts. Bi-dimensional regression analysis and the method of standard distance are applied to compare these movements. The study utilizes cartograms to visualize how the space is changed and distorted by the field of force. It can be stated that the economic and energy centre of gravity can be found in the Mediterranean Basin, but a slow and gradual shift to the east can be observed. Currently it reflects the dominance of the north, but it marks the position loss of the northern hemisphere and the greater importance of developing economies (in the southern hemisphere). Full article

The growing need to monitor changes in the surface of the Earth requires a high-quality, accessible Digital Elevation Model (DEM) dataset, whose development has become a challenge in the field of Earth-related research. The purpose of this paper is to improve the overall accuracy of public domain DEMs by data fusion. Multi-scale decomposition is an important analytical method in data fusion. Three multi-scale decomposition methods—the wavelet transform (WT), bidimensional empirical mode decomposition (BEMD), and nonlinear adaptive multi-scale decomposition (N-AMD)—are applied to the 1-arc-second Shuttle Radar Topography Mission Global digital elevation model (SRTM-1 DEM) and the Advanced Land Observing Satellite World 3D—30 m digital surface model (AW3D30 DSM) in China. Of these, the WT and BEMD are popular image fusion methods. A new approach for DEM fusion is developed using N-AMD (which is originally invented to remove the cycle from sunspots). Subsequently, a window-based rule is proposed for the fusion of corresponding frequency components obtained by these methods. Quantitative results show that N-AMD is more suitable for multi-scale fusion of multi-source DEMs, taking the Ice Cloud and Land Elevation Satellite (ICESat) global land surface altimetry data as a reference. The fused DEMs offer significant improvements of 29.6% and 19.3% in RMSE at a mountainous site, and 27.4% and 15.5% over a low-relief region, compared to the SRTM-1 and AW3D30, respectively. Furthermore, a slope position-based linear regression method is developed to calibrate the fused DEM for different slope position classes, by investigating the distribution of the fused DEM error with topography. The results indicate that the accuracy of the DEM calibrated by this method is improved by 16% and 13.6%, compared to the fused DEM in the mountainous region and low-relief region, respectively, proving that it is a practical and simple means of further increasing the accuracy of the fused DEM. Full article