National Institute of Technology, Ube College

Climate change increasingly threatens global food security through disrupted precipitation patterns and extreme weather events, requiring resilient systems for assessing agricultural vulnerability. This study developed and compared machine learning approaches for predicting cropland vulnerability using Earth Observation data, operationalized through NDVI anomalies as a defensible biophysical metric. We employed both a dual-stream deep learning architecture and a Random Forest model to predict 2023 NDVI anomalies across Bangladesh croplands using a 22-year time series (2001–2023) of MODIS vegetation indices, ERA5 climate variables, and static environmental covariates. A spatially aware block cross-validation strategy ensured rigorous, independent performance evaluation. Results demonstrated that the Random Forest model (R2 = 0.70, RMSE = 197.03) substantially outperformed the deep learning architecture (R2 = 0.02, RMSE = 357.57), explaining 70% of cropland stress variance and enabling early detection of vulnerable areas three months before harvest. Feature importance analysis identified recent climate variables, March precipitation, February NDVI, and vapor pressure deficit as primary vulnerability drivers. Spatial analysis revealed distinct vulnerability patterns, with Natore and Magura districts exhibiting elevated stress consistent with 2023 drought conditions, threatening the productivity of the region’s critical irrigation-dependent rice cultivation. These findings demonstrate that simpler, interpretable models can sometimes outperform complex architectures while providing useful information for early warning systems and precision targeting of climate adaptation interventions.

Recently, the application of digital twins is gaining traction to act as a replica for preemptively detecting the issues for the real-world system and giving feedback to improve the operations. Nevertheless, the main barrier for the unaffiliated researchers to implement digital twins for the real-world smart grids is that the configurations and algorithms used are often kept secret due to security concerns. Without the necessary information to create the simulation for serving as a digital twin, researchers cannot assess the impact on pricing when proposing the new electricity forecasting method. To address the black-box problem, this paper employed Granger causality to identify the most suitable openly available data and utilized a temporal convolutional network-based encoder model as the digital twin to emulate the locational marginal pricing mechanism without needing access to confidential information. In addition to achieving an average mean absolute error of 4.850 in simulating pricing in New York City, the proposed method demonstrates that electricity load forecasting accuracy affects the locational marginal pricing.

Electromagnetic wave (EMW)-absorbing materials have received significant attention in recent years owing to their important role in mitigating electromagnetic pollution caused by the increased use of wireless communication devices. SiC is expected to be one of the EMW absorbers because of its excellent properties such as chemical stability, mechanical stability, and high hardness; however, SiC alone does not have high EMW absorption performance. SiC-carbon composites were prepared as high-performance EMW-absorbing materials by thermal decomposition of 6H-SiC(0001) substrates. The surface morphology and chemical composition of the pure and annealed samples were evaluated using scanning electron microscopy and X-ray photoelectron spectroscopy, respectively. Carbon clusters with sizes of several micrometers were formed on the SiC surface after annealing, which formed in an interfacial structure between SiC and carbon and also increased the surface area of carbon. These microstructural changes improved the electromagnetic absorption of the SiC-carbon composite, which was confirmed by transmission attenuation measurements.

Cirrhosis is a liver disease resulting from abnormal continuation of fibrosis, and ultrasound imaging is widely used for cirrhosis diagnosis because of its non‐invasiveness. However, due to unclear appearances of cirrhosis on ultrasound images, diagnoses are difficult and individual results possibly differ depending on the physician's experience. Recently, computer‐aided diagnostic systems using image processing and machine learning have been developed to help physicians detect cirrhosis as a ‘Second opinion’. Some related studies have focused on a scenario where physicians set ROIs (Region of Interests) manually because selecting reliable ROIs for training a classifier and classification of patients is indispensable. But, the accuracy of such systems depends inherently on the quality of ROIs, and thus the workloads of physicians increase. In this paper, we propose a reliability evaluation method (REM) for each ROI based on its posterior probability and relationship to peripheral ROIs. The assumption of our proposal is that reliable regions of cirrhosis and normal can be observed in certain regions predominantly. We evaluated the effectiveness of the REM and its optimization for practical use. Experimental results showed that our proposed method curated reliable ROIs and improved classification performance in terms of AUC (Area Under the Curve). © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

The Front Cover shows a gear representing [18]crown-6 and a series of dominoes embodying [Ni(dmit)2]− anions (dmit2−=2-thioxo-1,3-dithiole-4,5-dithiolate). A crystal composed of 9-triptycylammonium, [18]crown-6, and [Ni(dmit)2]− exhibits an irreversible phase transition at 381 K involving the rotation of [18]crown-6 and rearrangement of [Ni(dmit)2]−. The rotation of the gear mirrors that of [18]crown-6, while the falling dominoes symbolize the reorientation of the [Ni(dmit)2]−. This significant phase transition causes a change in the magnetic system, increasing conductivity by four orders of magnitude. Notably, this is a very rare example of an irreversible phase transition driven by molecular rotation. These results pave the way for the development of innovative materials with irreversible phase transition characteristics. The artwork was designed by the first author, J. Manabe. More information can be found in the Research Article by S. Nishihara and co-workers.

ABSTRACT An integrative conceptual model developed tested the relative influence of servant leadership and narcissism in relation to strategic variables including adaptation and cooperation on competitive advantage and firm performance. Data were collected using structured on-site interviews conducted with 140 and 95 key respondents of randomly selected manufactuarer-export houses located in U.S. and China, respectively. Empirical results revealed that both strategic variables and leadership variables were positively related to competitive advantage, which, in turn, influenced firm performance. The influence of societal culture was evident in our results in that relationships among antecedents of firm performance were denser for U.S. than they were for Chinese firms. Based on these findings, insights for administrative guidance and implications for influencing and managing a firms ISA between manufacturers and their principal international distribution channel alliance partners were discussed. Research drawbacks are identified and directions for future studies are proffered.

Today, there are two main types of hula: the classical hula "kahiko" and the modern hula "auana".Music, costumes, and accessories are essential to these hula.Auana, in particular, is danced to music played on Western instruments and expresses the mythology, history, and various aspects of Hawaiian culture.The costumes and accessories worn by the dancers are designed to match the music.Therefore, we thought that it would be possible to design costumes and accessories more in line with the image of the dancers if we could freely design them using a personal computer and simulate how they would look when finished.In this study, we focused on accessories and conducted basic research to develop a CAD system for the design of "leis," one of the accessories.

In Japan, the declining birth rate, aging population, and shrinking domestic market pose challenges for local economies, especially the agricultural economy. Therefore, branding is essential for agricultural and industrial products to remain sustainable in local areas. This study focuses on the brand name, an element that forms a brand. As previous studies have not sufficiently examined the brand names of Japanese agricultural products, this study examines Japanese staple food, rice. Three hundred and fourteen brand names were surveyed to determine whether they are suggestive or nonsuggestive and contain words describing the properties of rice or related to production. The study analyzed the names through unique Japanese letters, such as Hiragana, Katakana, Kanji, and English. The results found that most Japanese rice brand names are nonsuggestive, and 141 brands are named in combinations of letters that do not follow conventional rules. Furthermore, as the domestic market situation necessitates devising export methods, more unique names and marketing promotions are required. If rice is shipped to the overseas market using a nonsuggestive name, as in Japan, it would be difficult as Japanese words may not convey the product’s image. Local consumers require words that are easily understood while retaining the Japanese notation and packaging. This study contributes to the field of brand naming, particularly for Japanese agricultural products.

This study examined the effect of an imposed equibiaxial pre-stress (EBPS) on the evaluation of mechanical properties, using the depth-sensing indentation method with a point-sharp indenter, through a numerical analysis of indentations simulated with the 3D finite element method. The predicted elastic modulus, E*, and yield stress, Y*, were used as elastic and plastic deformation resistances under the indentation, respectively. It was found that both increased nominally with the increase in compressive EBPS and decreased with the increase in tensile EBPS, even though the induced change in the piling-up or sinking-in around the indentations was not significant. The effect of EBPS on E* was described by the Hooke’s law for an isotropic elastoplastic material, whereas that on Y* was accounted for by the change in the von Mises stress due to EBPS.