How does a great power ally's demonstration of toughness toward a common adversary influence the sense of security among its weaker ally's citizens? The literature on the effects of standing firm against adversaries has significantly evolved in recent years, but empirical evidence is scarce, especially at the mass level. By taking advantage of the unique opportunity of the 2019 North Korea–US Hanoi summit, we examine the conditions under which a powerful alliance partner's firm and uncompromising posture toward a common adversary reassures the citizens of a junior ally. Based on alliance literature, we hypothesize that a patron's hawkish policy reassures its protégé when the protégé fears abandonment more than entrapment and that the more a citizen of the protégé fears abandonment, the more reassuring the patron's hawkish policy is to the individual. Our analysis of online surveys conducted before and after the summit using a quota sample of the Japanese electorate shows that the level of threat perception is significantly lower in the post-summit sample than in the pre-summit one, especially among the respondents who fear abandonment by the US. We thus conclude that President Trump's decision not to compromise with North Korea on its nuclear programs alleviated Japanese citizens’ concerns about US alliance commitments to Japan, improving their security perception. This study is significant in showing that a patron state's policy toward a common adversary can greatly influence the perception of its ally's citizens as well as its government.
This chapter considers the impact that the gendered aspects of the conflict in Ukraine may have on international criminal law, international humanitarian law, and international human rights law, focusing on two issues: (1) clarification of the scope of the crime against humanity of gender-based persecution, in light of reported targeting of the LGBTQ+ community in areas occupied by Russian forces, and (2) the question of whether states owe any duty under international law to provide appropriate protective gear to women soldiers serving in active combat, in light of the problems experienced in this area by servicewomen in the Ukrainian military.
We have performed measurement of core losses in metal composite toroidal cores over 10 MHz region using a measurement system where capacitors are connected in series with coils wound around sample cores and the measurement is conducted at LC resonant frequencies. In this paper, we introduce this “Resonant Method” and show the core loss measurement of metal composite toroidal cores up to 40 MHz. Furthermore, we examine the accuracy of the Resonant Method by comparing the equivalent resistance evaluated from the energy loss and the real part of the impedance measured by network analyzer.
In the construction work, flat plates for building materials are transported by a suspension mechanism using wires in air flow. Then, a flutter could be caused to the suspended plates due to the interaction between plate motion and fluid flow. The violent vibration due to flutter loses the efficiency and safety of the operation. To improve the efficiency and safety of the operation, a detailed understanding of the flutter characteristics is crucial. In this paper, the flutter analysis and experiments of a flat plate suspended by wires in air flow are carried out. In the flutter analysis, the Doublet-point method based on the unsteady lifting surface theory is used to calculate the unsteady fluid force acting on the plate surface. The equation of motion of the plate suspended by wires considering restoring force due to gravity is derived based on the Lagrange equation. The flat plate is modeled as a rigid body. The flutter velocity and frequency are examined through the root locus of the characteristic equation of the system with changing the flow velocity. Moreover, wind - tunnel experiments are conducted to verify the analytical results. The influence of a suspending angle on the flutter velocity, frequency and mode are investigated in detail. Finally, the work done by the fluid force acting on the plate surface is examined.
Cross-border mergers and acquisitions (M&As) have grown rapidly in recent years and are a major part of foreign direct investment (FDI). However, M&A distribution is highly skewed, with most of the activity concentrated in certain countries and even in certain cities. The fact that only a handful of cities account for most M&As raises a research question as to what city attributes attract foreign investment. Unlike many previous studies that have relied on a gravity model approach using the bilateral volume of FDI, this study examines the determinants of cross-border M&As by applying an FDI gravity model to inter-city investment flows. The results based on panel data of M&A flow across 44 major cities in the world from 2010 to 2017, reveal that the gravity model fits well for even the inter-city data, and show that besides the basic attributes used in conventional gravity models, such as market size and distance between origin city and destination city, and urban-specific attributes such as the agglomeration of the world’s top-ranked firms and international accessibility are positively associated with cross-border inward M&As.
The paper proposes a novel method for conducting keyword-based movie searches using user-generated rankings and reviews, by utilizing the BERT language model for task-specific fine-tuning. The model was trained on paired titles and reviews, enabling it to predict the likelihood of a movie appearing in a ranking that includes a particular keyword. An experiment using data from a reputable Japanese movie review site demonstrated that the method outperformed existing similarity-based approaches. However, some aspects, such as pooling methods, could be improved for accuracy.
This paper proposes a method to analyze product reviews to identify other products that can achieve the product’s intended use. When people want to achieve the purpose of “getting up early in the morning,” they generally tend to look only for an “alarm clock.” In contrast, there are several products such as “smart curtains” that can directly achieve the purpose and “sleeping pills” that can indirectly achieve the purpose by replacing “getting up early in the morning” with “going to sleep early at night.” The proposed method constructs a bipartite graph comprising product and uses purpose information from product review data. Then, the random walk with restart technique is employed to rank other products that can satisfy the use purpose of the input product. The proposed method was evaluated in subjective experiments, and the results suggest that the method is both accurate and useful in terms of identifying alternative products that satisfy similar use purposes.
This paper proposes a method of enabling users to memorize important information obtained from daily Web browsing by letting them manage their browsing history as cards. People always encounter a lot of information on websites, but most of it is forgotten even if needed later. Therefore, we implemented a memory retention support system based on card creation and management. This system allows users to make cards semi-manually using their website browsing history. The system displays the cards in an easy-to-view manner and provides management functions. By creating and organizing the cards that summarize their daily browsing activities and reviewing the cards they collected, users can realize what they value and recall necessary information more easily. The results of the user study in which the participants used the system for a Web search task demonstrated that the proposed semi-manual card creation has positive effects on memory retention after four days.
This paper proposes a method for identifying an aspect highlighted in a sentence from a movie review, utilizing a generative language model. For example, the aspect “SFX Techniques” is identified for the sentence “The explosions in cosmic space were realistic.” Classically, aspects are commonly estimated in the field of opinion mining within product reviews with classification or extraction approaches. However, because the aspects of movie reviews are diverse and innumerable, they cannot be listed in advance. Thus, we propose a generation-based approach using a generative language model to identify the aspect of a review sentence. We adopt T5 (Text-to-Text Transfer Transformer), a modern generative language model, providing additional pre-training and fine-tuning to reduce the training data. To verify the effectiveness of the learning techniques thus adopted, we conducted an experiment incorporating reviews of Yahoo! movies. Manual labeling of the correctness and diversity of the aspect names generated shows that our method can generates a variety of fine-grained aspect names using little training data.
The performance and reliability of power apparatus are affected by the degradation of electrical insulation, often caused by voids in resin‐molded components. To address this issue, we investigated the use of machine learning to estimate the degree of degradation due to void discharges in epoxy resin. We prepared samples with artificial void defects and collected partial discharge data up to the dielectric breakdown, applying long‐term AC voltage. Utilizing this data, a machine learning model was developed to estimate degradation levels. The results showed that the accuracy was 55% and the false estimation rate which underestimates the degradation level was as high as 31%, which was a significant problem for practical use. Therefore, for regions of data that were difficult to classify, we constructed a weighted SVM model that predicted higher levels of degradation by weighting the data according to their degradation labels. With this approach, the accuracy remained at 57%, but the rate of underestimation of the degree of degradation was reduced to only 4%. Consequently, we were able to develop an effective estimation model that is practical for the maintenance of power apparatus. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.
Various artificial oligodeoxynucleotides (ODNs) that contribute to gene regulation have been developed and their diversity and multifunctionality have been demonstrated. However, few artificial ODNs are actively transported to the cell nucleus, despite the fact that gene regulation also takes place in both the cell nucleus and the cytoplasm. In this study, to prepare ODNs with the ability to accumulate in the cell nucleus, we introduced Hoechst molecules into ODNs that act as carriers of functional molecules to the cell nucleus (Hoe‐ODNs). We synthesized Hoe‐ODNs and confirmed that they bound strongly to DNA duplexes. When single‐stranded Hoe‐ODNs or double‐stranded ODNs consisting of Hoe‐ODNs and its complementary strand were administered into living cells, both ODNs were efficiently accumulated in the cell nucleus. In addition, antisense ODNs, which were tethered with Hoechst unit, were delivered into the cell nucleus and efficiently suppressed the expression of their target RNA. Thus, we constructed a delivery system that enables the transport of ODNs into cell nucleus.
The importance of surface finishing processes and accurate surface quality prediction models has increased in response to the growing demand for improved surface finish in ultra-precision applications. To enhance process efficiency and develop accurate predictive models, numerous studies have investigated the monitoring and prediction of surface roughness. However, existing mathematical approaches encounter challenges in establishing the correlation between input and output variables and providing real-time surface status monitoring. Therefore, this study aimed to monitor and predict surface roughness in real-time for the rotational electro-magnetic finishing (REMF) process using acoustic emission (AE) signals. First, a total of 72 fundamental experiments were conducted based on the mixed orthogonal array L18(21\(\times\) 34) to determine the optimal configuration for achieving a high-quality surface. The results revealed that the best combination was achieved with an abrasive length of 3.0 mm, an abrasive diameter of 0.7 mm, a total abrasive weight of 2.0 kg, a rotational speed of 1800 rpm, and a working time of 10 min. To analyze signal features and develop an accurate surface prediction model, a convolutional neural network (CNN) was suggested, utilizing scalogram images as time–frequency characteristics of AE signals. The suggested model demonstrated outstanding quantitative results compared to those of the regression model, with training coefficient of determination (R2), mean squared error (MSE), and F-test of 0.986, 0.19\(\times\) 10−3, and 99%, and testing R2, MSE, and F-test of 0.951, 2.23\(\times\) 10−3, and 99%, respectively. In addition, the suggested model showed good generalization ability with a relatively lower mean MSE of 0.003 through verification experiments. These results demonstrated that the sensory data and image-driven model were effective in real-time monitoring and surface roughness prediction in the REMF process with high accuracy and reliability.
Machined surface layer, which consist of a fine grained layer and plastically deformed layer, has significant effects on the fatigue strength on the austenitic stainless steel. Therefore, the mechanical properties of the machined surface layer are important for accurate prediction of the fatigue life. However, slight thickness of the machined surface layer makes difficult to evaluate the mechanical properties of the machined surface layer. The objective of this paper is to investigate the mechanical properties of thin machined surface layer. For this purpose, we proposed the method for evaluating the stress-strain relationship of the machined surface layer by using the surface residual axial and circumferential stresses of the as-machined round specimen obtained by X-ray diffraction. Our measurement revealed that reducing grain size and thickness of the machined surface layer make the strength higher. The increase rate in the strength of the machined surface layer is inversely proportional to the cutting speed.
Circularly polarized luminescence (CPL) has significantly increased the interest in biological fields. In this research, a water-soluble Eu(iii) complex with a helical complex structure, EuLCOOH, was incorporated in chiral DNA in aqueous solutions. The photoluminescence performance of this DNA/EuLCOOH hybrid system was investigated. Compared to EuLCOOH alone, emission intensity and emission lifetime were effectively improved in the presence of DNA. The major binding between EuLCOOH and DNA was proven to be the electrostatic interaction. Owing to this interaction, the chiral environment provided by DNA successfully induced CPL from EuLCOOH.
Detailed measurements of the spectral structure of cosmic-ray electrons and positrons from 10.6 GeV to 7.5 TeV are presented from over 7 years of observations with the CALorimetric Electron Telescope (CALET) on the International Space Station. The instrument, consisting of a charge detector, an imaging calorimeter, and a total absorption calorimeter with a total depth of 30 radiation lengths at normal incidence and a fine shower imaging capability, is optimized to measure the all-electron spectrum well into the TeV region. Because of the excellent energy resolution (a few percent above 10 GeV) and the outstanding e/p separation (105), CALET provides optimal performance for a detailed search of structures in the energy spectrum. The analysis uses data up to the end of 2022, and the statistics of observed electron candidates has increased more than 3 times since the last publication in 2018. By adopting an updated boosted decision tree analysis, a sufficient proton rejection power up to 7.5 TeV is achieved, with a residual proton contamination less than 10%. The observed energy spectrum becomes gradually harder in the lower energy region from around 30 GeV, consistently with AMS-02, but from 300 to 600 GeV it is considerably softer than the spectra measured by DAMPE and Fermi-LAT. At high energies, the spectrum presents a sharp break around 1 TeV, with a spectral index change from −3.15 to −3.91, and a broken power law fitting the data in the energy range from 30 GeV to 4.8 TeV better than a single power law with 6.9 sigma significance, which is compatible with the DAMPE results. The break is consistent with the expected effects of radiation loss during the propagation from distant sources (except the highest energy bin). We have fitted the spectrum with a model consistent with the positron flux measured by AMS-02 below 1 TeV and interpreted the electron+positron spectrum with possible contributions from pulsars and nearby sources. Above 4.8 TeV, a possible contribution from known nearby supernova remnants, including Vela, is addressed by an event-by-event analysis providing a higher proton-rejection power than a purely statistical analysis.
This study investigates the accurate measurements of high-frequency core loss using a capacitive cancellation method for low-permeability magnetic material, which leads to high leakage flux during measurement. We observe that the core loss is underestimated with the lower coupling of the core-under-test. To solve the issue, we propose a new correction method in which the effective permeability observed during the measurement is measured, and the core loss is corrected with that permeability. The core loss is corrected by the proposed method even when high leakage flux exists.
In this paper, a low loss micro-strip line (MSL) on thin flexible substrate was realized using defected ground structure (DGS). DGS allows thinner MSL than conventional MSL maintaining insertion loss and far-end crosstalk. MSL with DGS on the flexible substrate with a thickness 25 μm was realized the same insertion loss and far-end crosstalk in comparison with conventional MSL on the substrate with a thickness 50 ßm while analyzing by 3D electromagnetic simulation. The MSL with DGS was fabricated and evaluated to confirm the simulated results. Excellent agreement with the measurement results was demonstrated over the broad bandwidth of 1–40 GHz.
In recent years, as electronic devices have become more sophisticated, there has been a growing demand for highly efficient heat transport devices. In this research, as a new heat transport device, we have created a mechanism in which soft matter slides through a pipe. This sliding is expected to make the temperature boundary layer thinner in the pipe and to transport more heat than a dream pipe due to heat conduction with the pipe wall. In this experiment, the PIV method, LIF method and temperature measurements using thermocouples were used to elucidate the heat transport phenomena in sliding. The experimental results showed that heat conduction between the pipe and the soft-matter and the fluid flow behind the soft-matter caused the temperature change due to sliding. The temperature change of the fluid flow behind the soft matter was more dominant than that of the pipe contact.
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