Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of cart providing constant steerability regardless of loading weight or position : 2nd Report: Proposal of the operational interface

In this study, we aim at development of the cart providing constant steerability. The steerability of a cart is changed by the loading weight and position. Thus, user feels uncomfortable, and usability is decreased. To solve this problem, we have developed an active steering caster being attached on the cart and provides a constant steerability. Two proposed active steering casters and two conventional casters are attached on the cart in diagonal position. The moving direction is determined by controlling the steering angles of the active steering casters. In this paper, we propose an operational interface that measures the force and the moment of force at the handlebar. The direction of the force and the moment of force determine the moving directions of the cart as user intention. In order to measure the force and the moment of force, strain gauges are glued on both handle posts of a cart. Therefore, additional component is not needed. Moreover, this interface is simple structure. The experiment has been conducted to verify the feasibility of measuring force and torque at the handlebar in order to estimate user’s intention for moving direction.
Some Statistical Features of the Long-Term Variation of the Global and Regional Seismic Activity

The temporal variation of the shallow earthquake activity of the entire world and the regional area around Japan is statistically investigated on the basis of two catalogs of earthquakes: one is Abe's catalog for global earthquakes with magnitude Ms t 7 in the period 1897 through 1980, and the other is Utsu's catalog for the earthquakes in and around Japan with magnitude M, > 6 in the period 1885 through 1980. The occurrence rate of earthquakes in both the areas is high in the period from 1920's through 1940's and low in the last 30 years. This persistent fluctuation or trend in appearance can be related to a long-range statistical dependence of the time series of earthquake occurrence. Indices measuring the strength of such dependence are calculated for each catalog to compare with statistical models. Although Perez & Scholz (1984) have claimed the inhomogeneity of the earthquake catalogs under the postulate of constant rate of earthquake occurrence for the world through the century, their rationale is not always guaranteed in view of the long-range dependence nature of earthquake occurrence. The synchronous variation of seismic frequency in the high latitude area of the world and in the regional area around Japan obtained from the independent catalogs is suggestive of an external effect such as a large-scale motion of the earth rather than the presupposed inhomogeneity of the catalogs.
Prediction of thermo-mechanical properties of PCB conductive layers using convolutional neural networks

Deriving the effective material properties of printed circuit board (PCB) layers is a particular challenge due to the intricate patterns of the conductive artwork. In this paper, we develop a convolutional neural network model to predict the effective orthotropic thermo-mechanical properties of the copper conductive layers of PCBs. To ensure the necessary level of detail for the material modeling, the images of the copper-resin distributions are extracted from the real electronic design files as patches of preselected pixel sizes. For each image, the corresponding homogenized material properties are computed using finite element analyses. After the training process on the formed dataset is completed, the developed model is validated using data unseen during training. The obtained results indicate that the considered approach is suitable for the prediction of the effective orthotropic properties of copper-resin patterns, and moreover, is promising for facilitating present-day time-to-market objectives in the industry.
Research on Speed-Flow Relationship Model of the Urban Roadways

Greenshields speed-flow model and Re-Greenshields speed-flow model are established to study the speed-flow relationship which suits Chinese urban roadways.With the discontinuous characteristics on the urban roadways,G-LN speed-flow model is established by using subsection modeling method.Greenshields model and Re-Greenshields model are compared by criterion R_(sq),Re-Greenshields speed-flow model adn G-LN speed-flow model are compared by criterion S.The result shows that G-LN speed-flow model can describe the relationship better.Finally the model parameter is analyzed and the value of capacity of each lane of G-LN speed-flow model is obtained by section iteration mediod.
Decreased Interoceptive Awareness as a Risk Factor for Moderate to Severe Pain in Japanese Full-Time Workers: A Longitudinal Cohort Study

Interoceptive awareness, the conscious perception of internal bodily states, is a key construct of mind-body interaction. Decreases in interoceptive awareness, as measured by the Multidimensional Assessment of Interoceptive Awareness (MAIA), are found in chronic pain patients. In this study, we explored whether a specific aspect of interoceptive awareness is a risk for the onset and chronicity of pain. A longitudinal cohort study was conducted in 2018 and 2020 among a sample of full-time workers in an industrial manufacturing company in Japan. Participants completed a questionnaire on pain intensity, MAIA, exercise habits, kinesiophobia, psychological distress and work stress. Principal component analyses using the MAIA identified two principal components: self-control and emotional stability. Low emotional stability was associated with the prevalence of moderate to severe pain in 2020 among people with mild or no pain in 2018 (p < 0.01). Lack of exercise habits were associated with the prevalence of moderate to severe pain in 2020 among people with pain in 2018 (p < 0.01). Furthermore, exercise habits were associated with reduction in kinesiophobia among people with moderate to severe pain in 2018 (p = 0.047). Overall, these findings indicate that low emotional stability may be a risk for the onset of moderate to severe pain; lack of exercise habits may sustain kinesiophobia and be a risk for the chronicity of pain.
Spacetime stereo and its applications

A long-standing challenge in computer vision is to recover 3D shapes from images, especially when the shapes change over time. Among the numerous existing shape acquisition methods, very few are able to accurately measure time-varying shapes at both high spatial and temporal resolution. Furthermore, some previous methods have strict assumptions on surface color and texture and do not work robustly for complex object shapes. In this dissertation, we address these limitations and present a new approach, spacetime stereo, for dynamic shape acquisition. Spacetime stereo formulates stereo matching as a 3D window warping problem in video volumes. This new formulation incorporates the temporal variations of image pixels as a cue for better shape reconstruction, and is a general framework for different variants of triangulation-based shape reconstruction methods, namely, passive stereo, active stereo, one-shot structured light, and multi-shot structured light. Under this framework, we develop algorithms that accurately reconstruct deforming objects at high spatial and temporal resolution. Spacetime stereo is also effective for a class of natural scenes, such as waving trees and flowing water, which have repetitive textures and chaotic behaviors and are challenging for existing stereo algorithms. To demonstrate an application of spacetime stereo, we build a dynamic face capture system that consists of off-the-shelf cameras, projectors, and computers. We also propose a new template fitting and tracking method that computes a sequence of 3D face meshes with vertex-to-vertex correspondence by combining shape and optical flow estimation. The resulting system is able to continuously (20Hz) capture high resolution (about 20K points) facial motion simultaneously from multiple viewpoints without the need to paint markers on the face. Finally, we develop data-driven facial animation tools that take advantage of the captured 3D face sequence. These tools enable untrained users to produce realistic facial expressions and animations.
Stability of the extragalactic VLBI reference frame

The qualication and the maintenance of the International Celestial Reference Frame (ICRF) source directions are currently based on global statistics regarding the complete data set of VLBI observations of extra- galactic radio sources. The founding hypothesis in the selection of extragalactic objects to access a quasi-inertial reference system is that their directions are xed in space. Therefore the study of the time variability of the sources is an important step in the process of checking and improving the reliability of the ICRF, in view of high precision applications such as Earth rotation studies or the unication with future space astrometry results. In this paper we investigate the systematic and random behaviours in time series of individual determinations of coordinates for several hundred sources over 1987{1999. New criteria for the qualication of sources in a future revision of the ICRF are proposed.
Simultaneous Statistical Inference: With Applications in the Life Sciences

The problem of simultaneous inference. - Part I General Theory. - Some theory of p-values. - Classes of multiple test procedures. - Simultaneous test procedures. - Stepwise rejective multiple tests. - Multiple testing and binary classification. - Multiple testing and model selection. - Software solutions for multiple hypotheses testing. - Part II From Genotype to Phenotype. - Genetic association studies. - Gene expression analyses. - Functional magnetic resonance imaging. - Part III Further Applications in the Life Sciences. - Further life science applications.
Employing Latent Categories of Entities for Knowledge Graph Embeddings With Contrastive Learning

Knowledge graph embedding aims to represent entities and relations as low-dimensional vectors, which is an effective way for robotics to learn and reason about semantic knowledge. It is crucial for knowledge graph embedding models to infer various relation patterns, such as symmetry/antisymmetry. However, most existing approaches ignore the latent semantic categories information of entities. For example, when robots use a faucet with relation OperatesOn, its tail entity category should be washable objects, such as apple, bowl, rather than non-washable objects, such as mobile phone, computer. To address such issues, we propose a novel and simple framework for knowledge graph embedding, which utilizes contrastive learning to cluster entities based on the constraints arising from relations, and enhances the discriminative ability for entities with the latent same category. Experimental results demonstrate the effectiveness of our proposed method on four standard knowledge graph completion benchmarks. It is noteworthy that our method can yield some new state-of-the-art results, achieving 88.2% MRR, 84.9% Hits@1 on the AI2Thor dataset, and 39.8% MRR, 30.0% Hits@1 on the FB15k-237 dataset.
Design of Frequency-Reconfigurable Branch-Line Crossover Using Rectangular Dielectric Channels

This paper presents an efficient yet straightforward passive reconfiguration technique to tune the operating frequency of a branch-line crossover (BLCO). The underlying principle is to fill rectangular dielectric channels (RDCs) prepared beforehand with either air or materials of different relative permittivity. Two configurations (one RDC and three RDCs in each arm) of the branch-line crossover are employed to estimate the tunability range of the operating frequency. The introduction of RDCs packed with different materials in the branch lines modifies the effective permittivity of the dielectric medium, resulting in an alteration of the operating frequency. The size and the positions of the RDCs are optimized using full-wave electromagnetic simulations to achieve maximum tunability range while ensuring reasonable bandwidth. A lumped circuit model (LCM) is developed to analyze the working principle of the proposed technique. To validate computational models, two prototypes of the branch-line crossover are realized, fabricated, and experimentally demonstrated. The first BLCO packed with seven RDCs exhibits a frequency tuning range of 15.8%, whereas the second prototype filled with twenty-one RDCs features a tuning range of 36.9%.
Welding Defect Detection in Steel Reinforcing Bars using Deep Learning

Weld connection is an important process in on-site reinforcing bar engineering, which should finally be visually inspected by construction workers to ensure that the quality of the joint meets the code requirements. Manual inspection is mostly time-consuming and laborious. The development of deep learning has led to great advances in the field of industrial defect detection. However, there are some challenges to considering both accuracy and efficiency in the recognition of rebar welding defects. Hence, in this study, a defect detector has been proposed, based on YOLOv3. In the proposed network, we integrate the dilated convolution with different dilation rates to increase the receptive field of the backbone. Moreover, CIoU loss is utilized, which accelerates the bounding box regression and improves the accuracy of defect detection. Furthermore, Focal loss is indeed applied to solve the class imbalance problem. The experiments are run on a real-world rebar welding dataset that mAP improves by 6.83%, compared with YOLOv3. Meanwhile, the proposed detector also meets the real-time requirements of the rebar welding defect detection task.
Regularized Multichannel Blind Deconvolution Using Alternating Minimization

Regularized Blind Deconvolution is a problem applicable in degraded images in order to bring the original image out of blur. Multichannel blind Deconvolution considered as an optimization problem. Each step in the optimization is considered as variable splitting problem using an algorithm called Alternating Minimization Algorithm. Each Step in the Variable splitting undergoes Augmented Lagrangian method (ALM) / Bregman Iterative method. Regularization is used where an ill posed problem converted into a well posed problem. Two well known regularizers are Tikhonov class and Total Variation (TV) /L2 model. TV can be isotropic and anisotropic, where isotropic for L2 norm and anisotropic for L1 norm. Based on many probabilistic model and Fourier Transforms Image deblurring can be solved. Here in this paper to improve the performance, we have used an adaptive regularization filtering and isotropic TV model Lp norm. Image deblurring is applicable in the areas such as medical image sensing, astrophotography, traffic signal monitoring, remote sensors, case investigation and even images that are taken using a digital camera /mobile cameras.
APPcache+: An STT-MRAM-Based Approximate Cache System With Low Power and Long Lifetime

Due to high static power and low scalability, the traditional SRAM-based cache is not a good solution for image processing applications. Emerging spin transfer torque magnetic RAM (STT-MRAM) is a promising candidate for cache due to its low leakage power and high density. However, STT-MRAM suffers from high write energy. Therefore, by making use of the ability of tolerating minor errors in image processing applications, this work presents an STT-MRAM-based APProximate cache architecture (APPcache+) to write/read approximate data, which can largely reduce the cache energy and improve the STT-MRAM lifetime. APPcache+ includes three main designs. First, we find that there are many similar elements (e.g., pixels in images) in cache lines. Therefore, APPcache+ presents several lightweight similarity-based encoding techniques to remove redundant elements, thus, shortening the data size and reducing the energy of STT-MRAM cache. Second, we design a partial read scheme to reduce the read energy of the STT-MRAM cache. In the traditional decompression process, the whole line is fetched into the decompressor, leading to unnecessary read energy. The partial read scheme can largely reduce read energy while keeping the overhead low. Third, we observe the encoding schemes may lead to bit write imbalance. Therefore, we propose a lightweight Ping-Pong intraline wear-leveling scheme to improve the lifetime. Compared with the baseline, extensive evaluation results show that our APPcache+ can largely reduce the overall energy by 32.58%, improve lifetime by 40.7% with only 2.2% performance degradation, and 1.86% output quality loss.
Evaluation of Phytosterols as an Alternative to Cholesterol in Practical Diets on Growth and Nonspecific Immunity of Litopenaeus vannamei

This study is aimed at evaluating the effectiveness of phytosterols as an alternative to cholesterol in practical diets of Pacific white shrimp Litopenaeus vannamei from the perspective of growth and nonspecific immunity. Five diets were formulated to contain different sterol sources and levels. Two diets were supplemented with 1 g/kg cholesterol (LC (low cholesterol)) or phytosterol (LP (low phytosterol)). Other three experimental diets were supplemented with 2 g/kg cholesterol (HC (high cholesterol)), 2 g/kg phytosterol (HP (high phytosterol)), or mixed sterol source (CP, 1 g/kg cholesterol + 1 g/kg phytosterol), respectively. A total of 750 healthy and uniform-sized shrimp (0.52 ± 0.008 g) were randomly distributed into 5 groups with 3 replicates and fed with the five experimental diets for 60 days. Results showed that the growth performance of shrimp was influenced by the sterol levels and supplementation with 2 g/kg sterol level facilitated the growth of shrimp. The inclusion of phytosterol has a cholesterol-lowering effect on shrimp, as evidenced by a reduction in hemolymph cholesterol and triglyceride contents in the HP group. Besides, supplementation with 2 g/kg phytosterol or mixed sterol sources had positive effects on the hemolymph superoxide dismutase, phenol oxidase, and lysozyme as well as hepatopancreas alkaline phosphatase activities, demonstrating that the nonspecific immunity and antioxidative capacity were improved. In conclusion, phytosterols could be an appropriate alternative to partially replace dietary cholesterol in shrimp feeds. This study preliminarily revealed the effects of different sterol sources and levels on the growth and nonspecific immunity of shrimp and provided a basis for further exploration of the mechanism of phytosterol.
Supernovae driven galactic outflows

Abstract Outflows from galaxies play a crucial role in the evolution of galaxies and also affect the surrounding medium. The standard scenario of explaining these outflows with the help of supernovae driven wind has recently come under criticism, and other processes such as radiation pressure and cosmic-rays have been invoked. We examine the relative importance of supernovae as the driving mechanism of galactic outflows in light of these competing processes.
Polyethylene laser welding based on optical absorption variations

Polymeric materials, both pure and containing nanostructures, can be prepared as thin sheets in order to produce joints with an interface between an optically transparent sheet and an optically absorbent substrate to be welded by infrared pulsed laser irradiation. The Laser Transmission Welding (LTW) technique has been successfully applied in order to join two or more thermoplastic polymeric sheets that must have a similar chemical composition. In this research work, polymeric joints of Ultra High Molecular Weight Polyethylene sheets were realized, characterized and welded. Some polymer sheets were doped, at different concentrations, with carbon nano-particles absorbent the laser radiation. A pulsed laser operating in the wavelength region 532 nm with intensity of the order of 109 Watt/cm2 was employed to be transmitted by the transparent polymer and to be absorbed by the carbon enriched surface. At the interface of the two polymers the released energy induces melting, that is assisted by pressure, producing a fast and resistant welding zone. Mechanical and optical characterizations and surface analyses are presented and discussed.
The Wave Finite Element Method applied to a one-dimensional linear elastodynamic problem with unilateral constraints

The Wave Finite Element Method (WFEM) is implemented to accurately capture travelling waves propagating at a finite speed within a bouncing rod system and induced by unilateral contact collisions with a rigid foundation; friction is not accounted for. As opposed to the traditional Finite Element Method (FEM) within a time-stepping framework, potential discontinuous deformation, stress and velocity wave fronts are accurately described, which is critical for the problem of interest.A one-dimensional benchmark with an analytical solution is investigated. The WFEM is compared to two time-stepping solution methods formulated on a FEM semi-discretization in space: (1) an explicit technique involving Lagrange multipliers and (2) a non-smooth approach implemented in the Siconos package. Attention is paid to the Gibb’s phenomenon generated during and after contact occurrences together with the time evolution of the total energy of the system.It is numerically found that the WFEM outperforms the FEM and Siconos solution methods because it does not induce any spurious oscillations or dispersion and diffusion of the shock wave. Furthermore, energy is not dissipated over time. More importantly, the WFEM does not require any impact law to close the system of governing equations.Copyright © 2015 by ASME
Berry Dietary Interventions in Metabolic Syndrome: New Insights

Metabolic Syndrome (MetS) is characterized by a group of dysmetabolic conditions, including abdominal obesity, dyslipidemia, glucose intolerance and/or insulin resistance, and hypertension. Generally, MetS is accompanied by an exacerbation of oxidative stress, inflammation, and vascular dysfunction. Increasing evidence suggests that berries and berry bioactives could play a potential role in the prevention and mitigation of the risk factors associated with MetS. The present systematic review summarizes the more recently available evidence deriving from human intervention studies investigating the effect of berries in subjects with at least three out of five MetS parameters. The PubMed, Scopus, and Embase databases were systematically searched from January 2010 until December 2022. A total of 17 human intervention trials met the inclusion criteria. Most of them were focused on blueberry (n = 6), cranberry (n = 3), and chokeberry (n = 3), while very few or none were available for the other berries. If considering MetS features, the main positive effects were related to lipid profile (low and high-density lipoproteins, cholesterol, and triglycerides) following blueberries and chokeberries, while conflicting results were documented for anthropometric parameters, blood pressure, and fasting blood glucose levels. Other markers analyzed within the studies included vascular function, oxidative stress, and inflammation. Here, the main positive effects were related to inflammation with a reduction in interleukin 6 and tumor necrosis factor-alpha following the intake of different berries. In conclusion, although limited, the evidence seems to support a potential role for berries in the modulation of lipid profile and inflammation in subjects with MetS. Furthermore, high-quality intervention trials are mandatory to demonstrate the role of berries in reducing risk factors for MetS and related conditions. In the future, such a demonstration could bring the adoption of berries as a potential dietary strategy to prevent/counteract MetS and related risk factors.