Hosei University

The purpose of this study was to evaluate the effects of different attentional focuses, specifically trunk rotation, on shuttlecock velocity and smash movements in badminton. A total of 13 experts (all male, mean age: 19.9±0.8years, height: 172.2±5.1cm, weight: 66.2±3.9kg, years of experience: 12.4±1.9years, all at the national competition level, all right-handed) participated, executing smashes under three different attentional conditions: arm internal focus (AIF), trunk internal focus (TIF), and external focus (EF). This study analysed 1) shuttlecock velocity, 2) joint velocity, 3) upper joint and trunk rotational angle, 4) angle velocity, and 5) trunk rotational range. On comparing the effect of attentional focus in terms of shuttlecock velocity, TIF was observed to be significantly higher than AIF. In hand joint maximum velocity, TIF was significantly higher than the other conditions. The maximum angle velocity of the trunk (lower torso) rotation was significantly higher in TIF than the other conditions. Trunk (lower torso) rotational angle velocity was also higher in TIF than the other conditions at each phase. The trunk (lower torso) rotational range showed a significant difference between TIF and AIF. However, there were no differences observed in upper joint angles. These results indicated that instructing an internal focus of attention may improve performance when focusing on larger muscles (proximal muscles) rather than smaller ones (distal muscles). Furthermore, directing focus on the twisting of the trunk proves effective as a strategy for improving shuttlecock velocity and hand joint maximum velocity during the smash motion in badminton.

Aim The role of environmental factors that shape the large‐scale distribution of eukaryotic microbes remains understudied. We aimed to disentangle the impacts of latitudinal and longitudinal gradients on the distribution of Sphagnum‐ dwelling testate amoebae in mires and to understand the influence of environmental factors related to both local habitats (hummock—lawn—hollows) and regional climates. Location A range from temperate to subarctic and from the European part to the Far East of Russia (51°–70°N, 32°–158°E). Taxon Testate amoeba (Arcellinida, Euglyphida, and Amphitremida). Methods We analysed the testate amoeba (TA) composition and abundance data from 816 samples collected in 75 peatlands. Linear mixed‐effects models and redundancy analysis were applied to determine the likely environmental drivers of TA α ‐ and β ‐diversity. Results We identified a significant reversed latitudinal gradient in α ‐diversity which negatively correlated with the mean annual temperature. This gradient is microhabitat‐specific, being prominent in lawn and hollow microhabitats, but not in hummocks. Longitude, which corresponds mainly to a gradient of precipitation seasonality, was a significant predictor of TA β ‐diversity, especially in hollows. Main Conclusions Our findings identify climatic factors (e.g., mean annual temperature and precipitation seasonality) as likely shaping the continental‐scale TA α ‐ and β ‐diversity patterns, emphasising the microhabitat‐specific nature of these relationships. The absence of pattern in hummocks is interpreted as evidence for a predominant microhabitat stress (i.e., low moisture and pH) in this habitat.

This study numerically evaluated the flexural capacity and fracture mechanism of precast reinforced concrete beams connected with in situ horizontal loop connections employing the three-dimensional rigid body spring model (3D-RBSM) for modeling of concrete and beam elements (BEs) for modeling of steel, respectively. The research analyzed the performance of continuous horizontal loop connections with and without transverse reinforcement, considering varying vertical spacing between loops and transverse steel ratios. The numerical model effectively captured the test load displacement relationships by incorporating the model parameters like Young's modulus, tensile strength, fracture energy, compressive strength, cohesion, and angle of internal friction for concrete along with mechanical properties of steel and revealed that loop joints without transverse reinforcement exhibited loop type failure. In contrast, specimens with transverse reinforcement improved the peak load and caused compression failure. It was discovered that transverse rebars on the continuous side of the joint experience more strain; particularly, the maximum strain occurred in the corner rebars within the curved sections of U-rebars. Moreover , the increased vertical spacing between the loop rebars increased the diagonal crack propagation and demonstrated the loop-type failure. Further, a larger circumferential area of transverse rebar offered greater bond strength and confinement to the concrete core and reproduced the maximum deformation capacity, ductility, and compression failure.

Rumor suppression is targeted at diminishing the impact of false and negative information within social networks by decreasing the prevalence of belief in such rumors among individuals, utilizing diverse strategies. Previous studies have broadly delineated rumor suppression strategies into two primary categories: targeting key nodes or edges for obstruction, and enlisting high-influence nodes to disseminate truth-related accurate information. Traditionally, employing a singular strategy involves utilizing a static algorithm throughout the rumor suppression endeavor. This method, however, encounters difficulties in adapting to fluctuating external conditions, rendering it less efficacious in the management of rumor proliferation. In response to these challenges, we introduce the concept of Adaptive Rumor Suppression (ARS), which aims to dynamically counter rumors by taking into account the nuances of propagation dynamics and the surrounding environmental context. We propose a multi-label state transition linear threshold model to more closely mirror the complex process of information diffusion across social networks. Furthermore, we advocate for a multi-round hybrid strategy that amalgamates blocking and clarification tactics to address the ARS problem within the confines of limited resource allocations. To navigate the complexities of ARS, we introduce the Hybrid Strategy of Each Round (HS-R) algorithm, which synergizes multiple strategies to effectively counter the spread of rumors. In extension, we present the Multi-Round Multi-Label (MRML) algorithm, designed to augment the efficiency of the HS-R algorithm. Experimental evaluations conducted on authentic social network datasets illustrate that our methodologies significantly outshine baseline algorithms, offering a more effective and adaptable solution to curb rumor propagation across varied environments.

A high breakdown voltage was achieved for a p–n junction diode grown by a hybrid epitaxial growth. Using a quartz-free hydride vapor-phase epitaxy, a thick and extremely high-purity n⁻-GaN drift layer was grown on a GaN substrate. A p-GaN layer was grown on the n⁻-GaN drift layer using metal-organic vapor-phase epitaxy (MOVPE). Before the MOVPE growth, inductively coupled plasma dry etching treatment using CF4 gas was performed on the regrowth surface to reduce the effect of Si contamination. The device with a reduced effective donor sheet concentration at the regrowth interface obtained by the CF4 treatment achieved a high breakdown voltage of 6.23 kV with good diode characteristics. A clear correlation was found between the breakdown voltage and the effective donor concentration at the p–n junction determined by C–V measurements. It is predicted that a breakdown voltage as high as 8 kV can be obtained if the effective donor concentration at the p–n junction is reduced to 1 × 10¹² cm⁻² or lower.

Pantoea ananatis is an emerging plant pathogen that causes center rot disease of onion. To identify genes related to virulence, we inoculated transposon mutants of P. ananatis NR 53 into onion bulbs and screened them for mutants with reduced virulence. One mutant with reduced virulence was obtained in which the transposon was inserted into the gla gene, which encodes UDP-galacturonic acid 4-epimerase. A gla mutant strain in which the gla gene was complemented with a plasmid caused the same symptoms as the wild-type strain. To exclude the polar effect of the transposon insertion mutation, we constructed a gla deletion mutant of P. ananatis CTB1135, which is also pathogenic to onions as NR 53. Deletion of the gla gene also reduced the virulence of P. ananatis CTB1135. To investigate why deletion of the gla gene reduces virulence, we analyzed the phenotype of the gla deletion mutant. In the gla deletion mutant, lipopolysaccharide (LPS) biosynthesis was inhibited, resulting in reduced drug resistance. Furthermore, flagella formation was inhibited, and motility was reduced. These findings suggest that the gla gene contributes to the virulence of P. ananatis through its involvement in overall virulence mechanisms, including LPS biosynthesis, drug resistance, and motility.

This paper describes the implementation and evaluation of an RC polyphase filter (RCPF) and circuitry for measuring its frequency characteristics. The integrated circuit is fabricated on a 0.6 μm CMOS process and its supply voltage is 5V. The integrated circuit includes a filter under test (FUT), a four-phase square wave generator, several analog switches and differential amplifiers to amplify the high-impedance signals of the RCPF. This FUT is a third-order complex RCPF whose passband is 8-40 MHz. This paper describes the design of these circuits in detail. This integrated circuit can select two measurement methods by means of external control signals. The first method uses a four-phase square wave. The frequency characteristics are measured by using an on-chip four-phase square wave generator driven by an external clock generator and by a spectrum analyzer tuned to the fundamental frequency. The second method uses an external network analyzer. Furthermore, this integrated circuit is equipped with a calibration circuit to measure the frequency characteristics of the circuitry except FUT by passing through the input / output signals of the FUT. As a result, the frequency characteristics of the FUT itself can be accurately measured. The effectiveness is confirmed through evaluation of the fabricated integrated circuit.

We evaluate the image quality of aerial images using Retroreflective Mirror Array (RMA) and propose a model for the misalignment of the glass plate during manufacturing that causes the degradation of the image quality of the aerial images. We evaluated RMA's resolution characteristics using the Modulation Transfer Function and modeled its cause of distortion of aerial imaging.

In this study, an ergodic sequential logic central pattern generator model was developed to demonstrate the capability of producing various gait patterns. A simple parameter search method enabled the model to acquire typical hexapod gaits such as tripod, tetrapod, and wave gaits. The model was then implemented in a field-programmable gate array (FPGA), and experiments validated that a hexapod robot equipped with the FPGA walked appropriately. It was also shown that the model could be implemented with fewer circuit elements and consumed less power for operation than the conventional CPG model. These results suggest that the model is suitable for gait controllers of neuromorphic robots.

Stealthy False Data Injection Attack (FDIA) that intentionally modifies measurement data of smart grid meters to bypass the traditional bad data detection module is one of menacing cyber attacks in smart grid. Due to requiring no costly labeling abnormal measurement data, deep neural networks (DNNs) based unsupervised FDIA detection has attracted great attentions. However, the existing schemes have two weaknesses. First, most schemes didn’t take into account the inherent spatial relationships between measurements in the grid. Second, for practical usage, the robustness and generalization of the trained FDIA detection scheme will be influenced by potential noisy measurement data. To address the issues above, based on spatial Graph Neural Network (GNN) architecture, a novel FDIA detection and localization scheme is proposed, named as Recursive Variational Graph Autoencoder (ReVGAE). Specifically, our contributions are following. The VGAE module in our proposed ReVGAE innovatively plays dual roles: data and topology reconstructor, and denoising module. The first role aims to simultaneously reconstruct both nodes’ temporal measurements and topological relationship between nodes. In the second role, the outputs of VGAE as the reconstructor (i.e., the reconstructed temporal measurements) are intentionally used as the artificially noisy samples, and recursively fed into VGAE as input to improve the model’s robustness. Then the residual between the finally reconstructed and the observed measurement data on each node is viewed as anomaly score to judge whether FDIA temporally happens on each node. Thorough experiments on a real grid system demonstrate that the proposed ReVGAE outperforms other VAE and GNN based FDIA anomaly detection schemes.

Modern transportation networks, with their complexity and dynamic nature, have a substantial demand for intelligent vehicles. Developing effective production strategies for smart vehicles is essential to reducing both production costs and energy consumption. Traditional vehicle production planning has largely depended on heuristic algorithms and solvers, which lack scalability and are susceptible to local optima. Furthermore, existing solutions do not concurrently address both dynamic and regular vehicle production planning. To overcome these limitations, this paper proposes an effective optimizing method for large-scale smart manufacturing within intelligent transportation networks using Federated Reinforcement Learning. In our proposal, the Gated Recurrent Unit and Asynchronous Advantage Actor Critic (A3C) reinforcement algorithms are employed to develop a Dynamic Optimizing Planning Module(DOPM), which can output an excellent solution of 1000 vehicles within 5 seconds. A High-Quality Processing Module(HQPM) is constructed by the Transformer with A3C, significantly enhancing the production plan’s quality. Finally, the proposed methods will integrate with Federated Learning (FL) to establish a scalable, privacy-preserving intelligent manufacturing scheduling framework for transportation networks. Experimental results demonstrate that our work significantly outperforms traditional solutions, achieving over a 93% improvement in solving speed and reducing constraint violations by more than 95%.

This study analyzes the relationship between college quality and obtaining first job after graduation among new graduates in the Japanese labor market. Although lively debate prevails in Western countries regarding the economic effects of college quality, no consensus has been reached concerning the effects of college quality on labor market outcomes. Additionally, few studies have examined the effects of college quality on labor market outcomes in Japan owing to data limitations. Therefore, this study analyzes the correlation between college quality and obtaining first job after graduation in Japan using newly available data that accurately match individual-level survey data and institutional-level data. This study’s empirical results indicate that college quality has a limited relationship with first job after graduation in the Japanese labor market—similar to previous studies’ findings. College characteristics do not appear to be a determining factor for recent graduates in obtaining first job after graduation in the Japanese labor market.

This paper describes a graph-based SLAM approach using wall detection and floor plan constraints without relying on loop closure. In SLAM, loop closure is widely used to address cumulative errors. Although loop closure helps maintain the map’s relative consistency, it does not ensure the accuracy of absolute positions. Therefore, we focus on floor plans that accurately depict the environmental geometry and propose a SLAM method that leverages this information. However, floor plans do not depict semi-static objects such as bookshelves and other fixtures. Thus, our study aims to build accurate maps based on floor plans and represent actual environments. The proposed method achieves this goal by integrating wall detection and floor plan constraints within the framework of graph-based SLAM. We evaluated the proposed method based on qualitative assessments of mapping results and quantitative evaluations of robot trajectories and processing time. Experiments were conducted using datasets obtained from both simulation and real-world environments. The results demonstrate that the proposed method can build a map with accurate absolute positions in a low processing time by leveraging wall detection and floor plan constraints.

In this paper, aiming at the development of a low-cost surgical support robot system that can present haptic sensations, a forceps robot with a robotic forceps that can detect a gripping force and a pressing force, and a laparoscopic robot for the suspended surgical support robot system were designed and built. Then, effectiveness of the built forceps robot, robotic forceps and laparoscopic robot was confirmed through some verification experiments.

In this paper, we propose a method to project semantic segmentation results from monocular camera images onto 3D lidar point clouds. Our method uses mmsegmentation trained models for semantic segmentation and image geometry for projection. In our experiments, we used NVIDIA Isaac Sim, and evaluated the semantic segmentation and the projection of the label information in multiple environments. We compared several models of semantic segmentation and confirmed that the label information was correctly projected onto the point clouds.

Wearable assistive devices have now been developed to reduce various work burdens. However, wearable assistive devices have problems such as low mobility, complicated mounting and dismounting, dead weight of the device, comfort, high installation costs and low ease of use. In addition, most arm assistive devices during upward work assist the horizontal upper arm, and there are few devices that assist the upper arm near the vertical axis, and none that solve the above problems. This study proposes an exoskeleton-type assistive device using an inflatable structure that is supported by an inflatable pouch structure by injecting fluid, to solve the problems of handling and cost, which are problems of wearable assistive devices for vertical work.

In our previous study, an elbow support power assist suit for underwater work has been developed. In this study, a shoulder support power assist suit for underwater work was developed so as to extend an assist function of the underwater assist suit. In order to evaluate an assist effect of the developed power assist suit, experiments of the movements such as rolling, carrying, and holding a heavy object, were conducted to measure surface electromyograms of the biceps brachii muscles. As a result, a reduction of the burden on the biceps brachii muscles under wearing the shoulder support power assist suit for underwater work was confirmed. Thus, the effectiveness of the developed power assist suit was verified.

This paper describes fan breezing appliance for relaxation applying the structural principle of bird wing. The purpose of this paper is to apply the structure of the remiges to switch the air flow, and to provide relaxation through the physical sensation of wind with periodic strength and the visual sensation of organic movement. The research and development include the selection of the mechanism that realizes the movement, verification of the selected locking mechanism and offset crank mechanism, the shape of the wing and counterweights that smooth the movement, and the exterior of the design.