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This study aims to design and develop teaching materials for Basic Digital Engineering material in the Basics of Electronics Engineering Subject at the Mechatronics Department of SMKN 4 Malang. Obstacles in learning Basic Digital Engineering, lack of practicum facilities and learning resources, encourage the development of digital teaching materials as a structured solution. Autodesk Tinkercad platform is used to support offline practicum implementation. The purpose of using Autodesk-based learning to encourage students to collaborate and continue to carry out practicum independently outside the school environment. This research includes the development of job sheets consisting of teacher’s handbook and student’s handbook, covering basic logic gates, advanced logic gates, and introduction to microcontrollers. In addition, a guidebook was also developed to optimize the use of Autodesk Tinkercad. This research involves testing the feasibility of job sheets and guidebooks in learning Basic Digital Engineering Materials using the Autodesk Tinkercad platform. The results of this study can contribute to improving the quality of learning and conclude that the use of digital teaching materials with the support of Autodesk Tinkercad can increase student independence in Basic Digital Engineering Materials.

The development of science and technology has experienced rapid progress which has a significant impact on people’s lives. One of the affected fields is education, where the utilization of science and technology can strengthen the learning process. In this context, the development of the right strategy and the use of supporting components are very effective. This research aims to analyze and develop the needs of teaching materials in the subject of Fundamentals of Electronic Engineering, focusing on Analog Electronics material. The method used is the development of teaching materials in the form of V-Lab-based jobsheets using the tinkercad website platform, as well as the preparation of guidebooks for teachers and students of Mechatronics Engineering in the subject of Basics of Electronics Engineering, especially on Analog Electronics material. Furthermore, the feasibility of V-Lab-based teaching materials using the Tinkercad website platform was tested. The results show that the use of the Tinkercad platform makes it easy to simulate electronic circuits without the need to install additional software. This platform helps students in better understanding the analog electronics material, as it can be accessed flexibly. Based on these findings, it can be concluded that the development of V-Lab-based teaching materials through the Tinkercad platform has great potential in improving the quality of analog electronics learning.

Methods for evaluating the fluctuation of texture patterns that are essentially regular have been proposed in the past, but the best method has not been determined. Here, as an attempt at this, we propose a method that applies AI technology (learning EfficientNet, which is widely used as a classification problem solving method) to determine when the fluctuation exceeds the tolerable limit and what the acceptable range is. We also apply this to clarify the tolerable limit of fluctuation in the "Kurume Kasuri" pattern, which is unique to the Chikugo region of Japan, and devise a method to evaluate the fluctuation in real time when weaving the Kasuri and keep it within the acceptable range. This study proposes a method for maintaining a unique faded pattern of woven textiles by utilizing EfficientNet for classification, fine-tuned with Optuna, and LightGBM for predicting subtle misalignments. Our experiments show that EfficientNet achieves high performance in classifying the quality of unique faded patterns in woven textiles. Additionally, LightGBM demonstrates near-perfect accuracy in predicting subtle misalignments within the acceptable range for high-quality faded patterns by controlling the weaving thread tension. Consequently, this method effectively maintains the quality of Kurume Kasuri patterns within the desired criteria. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to investigate the direct and indirect effects of managerial coaching on critical reflection mediated through learning goal orientation. Design/methodology/approach: The authors conducted a questionnaire survey of 169 employees in 53 teams at an IT firm in Japan. The data were examined using multi-level analyses. Findings: Managerial coaching has a direct positive effect on critical reflection, and the relationship is mediated by learning goal orientation. Research limitations/implications: The characteristics of the sample may limit the generalization of the findings. Future research should verify the model in other industries and countries. Practical implications: The organizations should note that managerial coaching is useful not only for facilitating employees' problem-solving but also for enhancing their critical reflection. It should also be noted that employees can reflect critically on their beliefs and work routines when they have learning goals. Originality/value: Although few quantitative studies have investigated the determinants of critical reflection, the present research reveals the overlooked functions of managerial coaching in promoting employees' learning.

Drawing on positive psychology and self-determination theories, this study investigates the direct and indirect effects of empowering leadership on meaningful work of employees, particularly through the mediation of learning goal orientation. Longitudinal survey data of 388 employees in medical institutions were used to test the hypotheses. The results support the direct positive relationship between empowering leadership and meaningful work, but also emphasize the mediating effect of learning goal orientation within this relationship. This study is among the first to examine the effect of mediation by learning goal orientation upon the relationship between empowering leadership and meaningful work.

we are facing global environmental issues such as global warming, ozone depletion, deforestation, air pollution, ocean pollution, desertification, etc. Homeostasis among geosphere, atmosphere, cryosphere, hydrosphere, biosphere has to be maintained. In order for Global Sustainability, global environmental monitoring from space is needed. The monitoring system does work for not only global environmental monitoring, but also disaster mitigation such as Sulawesi Big Earthquake and Tsunami which was occurred on 28 September 2018. Other than environmental monitoring from space, modeling and simulation, big data analysis, Artificial intelligence are also needed.

A method for training and white boxing of deep learning (DL) binary decision trees (BDT), random forest (RF) as well as mind maps (MM) based on graph neural networks (GNN) is proposed. By representing DL, BDT, RF, and MM as graphs, these can be trained by GNN. These learning architectures can be optimized through the proposed method. The proposed method allows representation of the architectures with matrices because the learning architecture can be expressed with graphs. These matrices and graphs are visible, which makes the learning processes visible, and therefore, more accountable. Some examples are shown here to highlight the usefulness of the proposed method, in particular, for learning processes and for ensuring the accountability of DL together with improvement in network architecture. [ABSTRACT FROM AUTHOR]

With a spatial resolution of 50 m, a revisit time of three days, and a swath of 950 km, the coastal zone imager (CZI) offers great potential in monitoring coastal zone dynamics. Accurate atmospheric correction (AC) is needed to exploit the potential of quantitative ocean color inversion. However, due to the band setting of CZI, the AC over coastal waters in the western Pacific region with complex optical properties cannot be realized easily. This research introduces a novel neural network (NN) AC algorithm for CZI data over coastal waters. Total 100,000 match-ups of HY-1 C CZI-observed reflectance at the top-of-atmosphere and Operational Land Imager (OLI)-retrieved high-quality remote sensing reflectance (Rrs) at the CZI bands are built to train the NN model. These reflectance data are obtained from the standard AC algorithm in the SeaDAS. Results indicate that the distributions of the CZI retrieved Rrs were consistent with the quasi-synchronous OLI data, but the spatial information from the CZI is more detailed. Then, the accuracy of the CZI data for AC is evaluated using the multi-source in-situ data. Results further show that the NN-AC can successfully retrieve Rrs for CZI and the coefficients of determination in the blue, green, red, and near-infrared bands were 0.70, 0.77, 0.76, and 0.67, respectively. The NN algorithm does not depend on shortwave-infrared bands and runs very fast once properly trained. [ABSTRACT FROM AUTHOR]

The reflectance-based vicarious calibration approach has been applied to the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) that is on the Terra platform. The results from three separate groups operating at the same sites at the same time arc presented. The three groups show good agreement between each other, with differences between the groups being smaller than 4% in all bands ranging from 0.6 to 2.2 [micro]m and with larger differences being seen at shorter wavelengths and beyond 2.2 [micro]m. Comparisons between the groups and to the ASTER sensor are best at 1.66 [micro]m (band 4), with differences between each group being less than 0.5% and all of the groups agreeing with ASTER to better than 5%. Differences in the visible and near infrared (VNIR) are larger, particularly prior to August 2002 when an update to the calibration processing was performed. These differences exceed 10% in some bands. In addition, the vicarious results appear to show a different trend than the onboard calibration for the VNIR indicating a possible problem with the onboard calibrator for bands 1-3. Index Terms--Absolute radiometric calibration, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), vicarious calibration.

Radiometric performance of the Advanced Spectrometer for Thermal Emission and Reflection Radiometer (ASTER) is characterized by using acquired imagery data. Noise-equivalent reflectance and temperature, sensitivity (gain), bias (offset), and modulation transfer function (MTF) are determined for the visible and near-infrared (VNIR), the shortwave infrared (SWIR), and the thermal infrared (TIR) radiometers that constitute ASTER. The responsivity evaluated from onboard calibration (OBC) and from instrumented scenes show similar trends for the VNIR: the OBC data yield 2.7% to 5.5% a year for the VNIR. The SWIR response changed less than 2% in the 3.5 years following launch. The zero-radiance offsets of most VNIR and SWIR bands have increased about 1/2 digital number per year. The in-orbit noise levels, calculated by the standard deviation of dark (VNIR and SWIR) or ocean (TIR) scenes, show that all bands are within specification. The MTF at Nyquist and 1/2 Nyquist frequencies was determined for all bands using the Moon (VNIR and SWIR) or terrestrial scenes with lines of sharp thermal contrast. In-orbit performance along-track and cross-track is better than prelaunch for the VNIR and SWIR bands in nearly all cases; the TIR effectively meets specification in-orbit. Index Terms--Advanced Spectrometer for Thermal Emission and Reflection Radiometer (ASTER), gain and offset, modulation transfer function (MTF), noise-equivalent reflectance, noise-equivalent temperature, radiometric performance characterization.

The column-averaged, dry air mole fractions of CO2 and CH4 (XCO2 and XCH4, respectively) were retrieved from short-wavelength infrared (SWIR) spectra observed by the Greenhouse gases Observing SATellite (GOSAT). Continuous measurements of SWIR spectra have been made via GOSAT since 2009, but there has been insufficient investigation of the effects of cirrus clouds and aerosols on the observations. In this work, we investigated the influences of aerosols and cirrus clouds on the differences between GOSAT observations and Total Carbon Column Observing Network (TCCON) data for XCO2 and XCH4 (ΔXCO2 and ΔXCH4) at three sites: Tsukuba and Saga in Japan, and Lauder in New Zealand. We used aerosol optical thickness (AOT), Angstrom exponents (AEs), and single scattering albedo (SSA), all obtained from sky radiometer observations, as well as vertical profiles of aerosols and thin cirrus clouds from lidar observations. Matchups were performed within ±0.1° latitude/longitude rectangular areas of each TCCON site, and within 30 min of the GOSAT overpass time. The results show a negative slope between ΔXCO2 and AOT at 500 nm determined from sky radiometer data at Tsukuba and Saga. The GOSAT XCO2 values tended to be lowered in the presence of cirrus clouds and dense boundary-layer aerosols. Moreover, a significant negative ΔXCO2 was observed at times of large AOTs that resulted from dust-like events. At Lauder, ΔXCO2 was negatively correlated with the AOT at 500 nm, although the AOT at this site was generally small. The mean ± standard deviation for ΔXCO2 and ΔXCH4 at Lauder are −0.80 ± 1.83 (ppm) and −5.27 ± 10.79 (ppb) with correlation coefficients r between GOSAT and TCCON of 0.94 and 0.83, respectively. Both ΔXCO2 and ΔXCH4 were significantly and negatively correlated with the AOT during Sep-Oct-Nov. In addition, stratospheric aerosols caused large negative biases of ΔXCO2 and ΔXCH4 at Lauder despite the small stratospheric aerosol optical depth at that site. [ABSTRACT FROM AUTHOR]

A total synthesis of resolvin T4 was achieved by connecting three intermediates by Wittig reactions. The enal in the C1–C10 part was constructed through reduction of a propargylic alcohol with Red-Al followed by oxidation. The enal moiety in the C11–C16 part was synthesized by a ring-opening reaction of a silyl epoxide followed by a Peterson elimination. The chiral centers at C7 and C13 were constructed by ruthenium-catalyzed asymmetric transfer hydrogenation. [ABSTRACT FROM AUTHOR]

Face recognition plays an important role in the identity recognition system, the color and geometry feature has been claimed able to be used as parameter for face recognition. This study aims to analize the performance of geometric features, color features, and both of them on the human face using Gaussian Naïve Bayes (GNB) and the other Machine Learning method. This study using various geometric features: the distance between the eyes, nose, mouth by using Euclidean distance, and classified using GNB, K-Nearest Neighbour (KNN), and Support Vector Machine (SVM). The result compared with color feature: normalized RGB values, mean of normalized RGB, and RGB Variant as color features. The feature values obtained are assembled and processed using GNB and the other ML method to classified and recognized the faces. The dataset obtained from Aberdeen faces the dataset, which has 687 color faces from Ian Craw at Aberdeen. Between 1 and 18 images of 90 individuals. Some variations in lighting, varied viewpoints, and the resolution have varied between 336x480 to 624x544. The experimental results show that the system successfully recognized the face based on the determined algorithm and based on three models, SVM reached nearly 74.83%, GNB reached nearly 74.67%, and KNN with K = 5 reached nearly 72.17%. [ABSTRACT FROM AUTHOR]

Drawing on positive psychology and self‐determination theories, this study investigates the direct and indirect effects of empowering leadership on meaningful work of employees, particularly through the mediation of learning goal orientation. Longitudinal survey data of 388 employees in medical institutions were used to test the hypotheses. The results support the direct positive relationship between empowering leadership and meaningful work, but also emphasize the mediating effect of learning goal orientation within this relationship. This study is among the first to examine the effect of mediation by learning goal orientation upon the relationship between empowering leadership and meaningful work. [ABSTRACT FROM AUTHOR]