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Abstract Topological phase transitions, influenced by magnetic fields, dopants, pressure, and temperature, create Berry curvature in band structures, challenging to detect due to resolution and scattering issues in spectroscopy and transport. Here, we propose nonlinear electrical transport phenomena as fingerprints of a topological phase transition in ZrTe5 under magnetic fields. Both a nonlinear longitudinal conductivity Δσ L in a magnetic-field-aligned electric field and a third-order nonlinear Hall (transverse) conductivity Δσ xy in a magnetic-field-perpendicular electric field arise below a characteristic temperature T *. The sensitivity of nonlinear transport to the band topology allows the detection of a subtle change in the band topology hidden in linear transport coefficients. Extending the previous scaling theory between linear transport coefficients (σ xx and σ xy ), we also propose scaling relations for both linear (σ xx and σ xy ) and nonlinear (Δσ L and Δσ xy ) transport coefficients. These scaling relations will help understand the interplay between the mechanisms of nonlinear transport coefficients and the influence of Berry curvature.

Board games like chess serve as an excellent testbed for human–robot interactions, where advancements can lead to broader human–robot cooperation systems. This paper presents a chess-playing robotic system to demonstrate controlled pick and place operations using a 3-DoF manipulator with image and speech recognition. The system identifies chessboard square coordinates through image processing and centroid detection before mapping them onto the physical board. User voice input is processed and transcribed into a string from which the system extracts the current and destination locations of a chess piece with a word error rate of 8.64%. Using an inverse-kinematics algorithm, the system calculates the joint angles needed to position the end effector at the desired coordinates actuating the robot. The developed system was evaluated experimentally on the 3-DoF manipulator with a voice command used to direct the robot movement in grasping a chess piece. Consideration was made involving both the own pieces as well as capturing the opponent’s pieces and moving the captured piece outside the board workspace.

Autonomous driving system (ADS) is anticipated to revolutionize travel by reclaiming lost time and improve safety on the roads. With automation, user-engagements that enhances road monitoring should be considered to maintain vigilance and safety. From the literature, virtual reality (VR) usage in cars offer productivity and increased privacy. This paper explores the efficacy of passenger use of VR headsets to enhance user-engagement during transit. User-engagement was quantified using physiological measures (pupillary response and electrodermal activity) during an in-car VR game/activity experiment. Further, the impacts of engaging with secondary tasks was evaluated using reaction time of pop-up objects. We designed a driving simulation with inbuilt entertaining activities, no-task, game-task, video-task, and mixed-task, played in a real car with a FOVE VR headset on the perimeter track of the Gifu University campus with 15 subjects (average 25.6 years, SD = 6.4). From reaction time, significant difference between tasks was found using one-way ANOVA (F(3,231) = 2.75, p = .0437). A post-hoc test revealed that game and mixed task reaction times were significantly different (p = .0126 and p = .016, respectively) suggesting that task design should consider hazard recognition in a real car. From physiological measures, an increased/sustained effect of user engagement was noted compared with baseline (no-task) suggesting effectiveness in maintaining vigilance. The results also reported a 10-fold improvement in sitting posture compared to baseline. The methodology employed is applicable as an indirect measure of engagement that would find use in productivity and vigilance study in an ADS.

The article is a systematic literature review of the use of automation for quality of service (QoS) in computer networks. It summarizes the research trends, datasets, and methods used in the field and provides an overview of the current state of the art. The focus of the review is on the use of automation for QoS management and improvement in computer networks, including the use of machine learning, artificial intelligence, and other computational techniques. The review highlights the need for further research and development in this area and provides insights into future directions for the field. The review covered a wide range of studies, including research papers and conference proceedings, and involved a comprehensive database search of the Scopus database covering journals and proceedings such as the Institute of Electrical and Electronics Engineers (IEEE) Xplore, Association for Computing Machinery (ACM) Digital Library, Springer, and ScienceDirect databases between 2017 and September 2022. From these databases, 1856 metadata were found, which eventually became seventy-three metadata after going through the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol.

This paper presents a 2-link, 2-DOF flexible manipulator control using an inverse feedforward controller and an adaptive notch filter with a direct strain feedback controller. In the flexible manipulator, transient and residue vibrations inhibit the full potential of the manipulator. Vibrations caused by abrupt changes in the direction of the links are referred to as transient vibrations, whereas residual vibrations occur when the arm takes too long to settle after engaging in the intended task. The feedforward adaptive notch filter will reduce transient vibration caused by the manipulator arm beginning and halting suddenly, while the strain feedback will assure the quick decay of leftover vibrations. Maple, Maplesim, and MATLAB tools were used to model the manipulator and create the inverse controller and adaptive notch filter. The experiments took place in the dSPACE control desk environment. The experimental results of the spectral power of strain resulting from the two strategies are compared. From the results, the adaptive notch filter control had over an 80% improvement in the reduction in resonant frequencies that contribute to vibration. The results confirmed the feasibility of the approach, characterized by very minimal transient vibrations and a quick settling of the end effector.

Road traffic accidents (RTA) are a prevalent cause of fatality with African countries having the highest fatality index (25–34 per quota). The World Health Organization estimates Kenya's fatality rate due to RTA at 28 per quota. From literature, the country's fatality and injuries have increased by 26% and 46.5%, respectively, since the year 2015. The country is faced with incomplete RTA data capturing, hindering effective planning and policy adjustments to curb the menace. In this paper, we scrapped user-generated data (Twitter) and national transport and safety authority's (NTSA) reports to shed light on traffic safety, practices, and cultures in the country. To this end, we gathered 1,000,000 tweets and 8000 speeding entries between 2015 and 2021 and performed natural language processing (NLP) and quantitative study of the data. We applied NLP and n-gram search of keywords to categorize data into 8 topics: traffic, public service vehicle (PSVs), policing, accident, infrastructure, recklessness, robbery, and corruption. From the data, policing, which touches on all police and law-enforcement-related activity was found to be highly correlated with PSVs, recklessness, accidents, traffic congestion, robbery, infrastructure, and corruption with indices of r(76) = 0.92, 0. 91, 0.87, 0.82, 0.81, 0.76, and 0.70, respectively with p < 0.001. The topic modeling confirmed the identified topics to be the latent discussion issues affecting the public. From the study, PSVs, policing and traffic flow were isolated as key issues that ought to be addressed immediately. The research recommended the integration of driver monitoring systems to strengthen policing. The research, which utilized unstructured data, points to the utility of data mining which would greatly benefit traffic research, particularly African-based studies, that suffer from data inadequacy.

Welfare robots, as a category of robotics, seeks to improve the quality of life of the elderly and patients by availing a control mechanism to enable the participants to be self-dependent. This is achieved by using man-machine interfaces that manipulate certain external processes like feeding or communicating. This research aims to realize a man-machine interface using brainwave combined with object recognition applicable to patients with locked-in syndrome. The system utilizes a camera with pretrained object-detection system that recognizes the environment and displays the contents in an interface to solicit a choice using P300 signals. Being a camera-based system, field of view and luminance level were identified as possible influences. We designed six experiments by adapting the arrangement of stimuli (triangular or horizontal) and brightness/colour levels. The results showed that the horizontal arrangement had better accuracy than the triangular method. Further, colour was identified as a key parameter for the successful discrimination of target stimuli. From the paper, the precision of discrimination can be improved by adopting a harmonized arrangement and selecting the appropriate saturation/brightness of the interface.

Japan has continued to experience population decline which adversely affect working-age group (15-64 years). As a remedy to this social issue, advancements in robotics and human-machine cooperation is proposed to make up for the declining labor force. To this end, design of robots which can work in constrained (indoor) workspace is desirable. A coaxial two-wheeled robot with an appended robot arm aimed at transporting objects is proposed in this paper. The robot is designed with center of gravity below the axle to make it statically stable at rest. It is combined with a robot arm with two links, two degrees of freedom. The goal is to maintain equilibrium of the arm tip during motion with the robot-arm is inclined at 0-, 3-, and 120-degree. In this study, simulations to combine a stable coaxial two-wheel robot with the robot arm is performed to confirm the effectiveness of the designed LQ, and LQI controller. From the results, all the controllers are able to maintain the robot-arm tip at 0-degrees. For 120-degrees, LQI performs better than LQ controller in stabilizing the rotation speed of the wheels by 1.7 seconds. In the future, the proposed controller model will be incorporated in the actual robot to confirm the performance for object transportation.

Text classification is the task of estimating the genre of a document based on information such as word co-occurrence and frequency of occurrence. Text classification has been studied by various approaches. In this study, we focused on text classification using graph structure data. Conventional graph-based methods express relationships between words and relationships between words and documents as weights between nodes. Then, a graph neural network is used for learning. However, there is a problem that conventional methods are not able to represent the relationship between documents on the graph. In this paper, we propose a graph structure that considers the relationships between documents. In the proposed method, the cosine similarity of document vectors is set as weights between document nodes. This completes a graph that considers the relationship between documents. The graph is then input into a graph convolutional neural network for training. Therefore, the aim of this study is to improve the text classification performance of conventional methods by using this graph that considers the relationships between document nodes. In this study, we conducted evaluation experiments using five different corpora of English documents. The results showed that the proposed method outperformed the performance of the conventional method by up to 1.19%, indicating that the use of relationships between documents is effective. In addition, the proposed method was shown to be particularly effective in classifying long documents.

Disability is one of a person's physical and mental conditions that can inhibit normal daily activities. One of the disabilities that can be found in disability is speech without fingers. Persons with disabilities have obstacles in communicating with people around both verbally and in writing. Communication tools to help people with disabilities without finger fingers continue to be developed, one of them is by creating a virtual keyboard using a Leap Motion sensor. The hand gestures are captured using the Leap Motion sensor so that the direction of the hand gesture in the form of pitch, yaw, and roll is obtained. The direction values are grouped into normal, right, left, up, down, and rotating gestures to control the virtual keyboard. The amount of data used for gesture recognition in this study was 5400 data consisting of 3780 training data and 1620 test data. The results of data testing conducted using the Artificial Neural Network method obtained an accuracy value of 98.82%. This study also performed a virtual keyboard performance test directly by typing 20 types of characters conducted by 15 respondents three times. The average time needed by respondents in typing is 5.45 seconds per character.

Due to impaired mobility caused by aging, it is very important to employ early detection and monitoring of gait parameters to prevent the inevitable huge amount of medical cost at a later age. For gait training and potential tele-monitoring application outside clinical settings, low-cost yet highly reliable gait analysis systems are needed. This research proposes using a single LiDAR system to perform automatic gait analysis with polynomial fitting. The experimental setup for this study consists of two different walking speeds, fast walk and normal walk, along a 5-m straight line. There were ten test subjects (mean age 28, SD 5.2) who voluntarily participated in the study. We performed polynomial fitting to estimate the step length from the heel projection cloud point laser data as the subject walks forwards and compared the values with the visual inspection method. The results showed that the visual inspection method is accurate up to 6 cm while the polynomial method achieves 8 cm in the worst case (fast walking). With the accuracy difference estimated to be at most 2 cm, the polynomial method provides reliability of heel location estimation as compared with the observational gait analysis. The proposed method in this study presents an improvement accuracy of 4% as opposed to the proposed dual-laser range sensor method that reported 57.87 cm ± 10.48, an error of 10%. Meanwhile, our proposed method reported ±0.0633 m, a 6% error for normal walking.

Abstract The present study develops a general framework for weak antilocalization (WAL) in a three-dimensional (3D) system, which can be applied for a consistent description of longitudinal resistivity $$\rho_{xx} \left( B \right)$$ ρ xx B and Hall resistivity $$\rho_{xy} \left( B \right)$$ ρ xy B over a wide temperature (T) range. Compared to the previous approach Vu et al. (Phys Rev B 100:125162, 2019), which assumes infinite phase coherence length (l ϕ ) and a zero spin–orbit scattering length (l SO), the present framework is more general, covering high T and the intermediate spin–orbit coupling strength. Based on the new approach, the $$\rho_{xx} \left( B \right)$$ ρ xx B and $$\rho_{xy} \left( B \right)$$ ρ xy B of the Dirac semimetal Bi0.97Sb0.03 was analyzed over a wide T range from 1.7 to 300 K. The present framework not only explains the main features of the experimental data but also enables one to estimate l ϕ and l SO at different temperatures. The l ϕ has a power-law T dependence at high T and saturates at low T. In contrast, the l SO shows negligible T dependence. Because of the different T dependence, a crossover occurs from the l SO-dominant low-T to the l ϕ -dominant high-T regions. Accordingly, the hallmark features of weak antilocalization (WAL) in $$\rho_{xx} \left( B \right)$$ ρ xx B and $$\rho_{xy} \left( B \right)$$ ρ xy B are gradually suppressed across the crossover with increasing T. The present theory describes both low-T and high-T regions successfully, which is impossible in the previous approximate approach. This work offers insights for understanding quantum electrical transport phenomena and their underlying physics, particularly when multiple WAL length scales are competing.

Introduction Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated systems are RNA-mediated adaptive immune systems that actagainst invading genetic elements such as phages or plasmids. CRISPR/Cas systems exist in nearly half of bacteria. Mycoplasma salivarium is a commensal species of the oropharynx. The American Type Culture Collection maintains five M. salivarium strains: ATCC 14277, 23064, 23557, 29803, and 33130. The genome sequence of ATCC 23064 revealed that it has an incomplete CRISPR/Cas system. However, the genome sequences of the remaining strains have not been analyzed. Methods We performed polymerase chain reaction-amplicon sequencing and de novo genome sequencing to evaluate the presence of the CRISPR/Cas system in four strains. Results Only ATCC 29803 possessed cas1, cas2, cas9, and csn2 genes, a CRISPR array, and tracrRNA. The sequences of most components were identical between the CRISPR/Cas systems of ATCC 29803 and ATCC 23064, whereas the spacer sequences and a region of the cas9 gene were different. Unlike the CRISPR/Cas system of ATCC 23064, the cas9 gene of ATCC 29803 was not disrupted by the presence of stop codons. Conclusion ATCC 29803 possesses genomic components required to express the type II-A CRISPR/Cas system, which potentially functions as an RNA-guided endonuclease.

In recent years, surface Electromyography (sEMG) signals have been effectively applied in various fields such as control interfaces, prosthetics, and rehabilitation. We propose a neck rotation estimation from EMG and apply the signal estimate as a game control interface that can be used by people with disabilities or patients with functional impairment of the upper limb. This paper utilizes an equation estimation and a machine learning model to translate the signals into corresponding neck rotations. For testing, we designed two custom-made game scenes, a dynamic 1D object interception and a 2D maze scenery, in Unity 3D to be controlled by sEMG signal in real-time. Twenty-two (22) test subjects (mean age 27.95, std 13.24) participated in the experiment to verify the usability of the interface. From object interception, subjects reported stable control inferred from intercepted objects more than 73% accurately. In a 2D maze, a comparison of male and female subjects reported a completion time of 98.84 s. ± 50.2 and 112.75 s. ± 44.2, respectively, without a significant difference in the mean of the one-way ANOVA (p = 0.519). The results confirmed the usefulness of neck sEMG of sternocleidomastoid (SCM) as a control interface with little or no calibration required. Control models using equations indicate intuitive direction and speed control, while machine learning schemes offer a more stable directional control. Control interfaces can be applied in several areas that involve neck activities, e.g., robot control and rehabilitation, as well as game interfaces, to enable entertainment for people with disabilities.

In recent years, industries have increasingly emphasized the need for high-speed, energy-efficient, and cost-effective solutions. As a result, there has been growing interest in developing flexible link manipulator robots to meet these requirements. However, reducing the weight of the manipulator leads to increased flexibility which, in turn, causes vibrations. This research paper introduces a novel approach for controlling the vibration and motion of a two-link flexible manipulator using reinforcement learning. The proposed system utilizes trust region policy optimization to train the manipulator’s end effector to reach a desired target position, while minimizing vibration and strain at the root of the link. To achieve the research objectives, a 3D model of the flexible-link manipulator is designed, and an optimal reward function is identified to guide the learning process. The results demonstrate that the proposed approach successfully suppresses vibration and strain when moving the end effector to the target position. Furthermore, the trained model is applied to a physical flexible manipulator for real-world control verification. However, it is observed that the performance of the trained model does not meet expectations, due to simulation-to-real challenges. These challenges may include unanticipated differences in dynamics, calibration issues, actuator limitations, or other factors that affect the performance and behavior of the system in the real world. Therefore, further investigations and improvements are recommended to bridge this gap and enhance the applicability of the proposed approach.

This paper presents the angle of the shoulder joint as basic research for developing a machine interface using EEG. The raw EEG voltage signals and power density spectrum of the voltage value were used as the learning feature. Hebbian learning was used on a multilayer perceptron network for pattern classification for the estimation of joint angles 0o, 90o and 180o of the shoulder joint. Experimental results showed that it was possible to correctly classify up to 63.3% of motion using voltage values of the raw EEG signal with the neural network. Further, with selected electrodes and power density spectrum features, accuracy rose to 93.3% with more stable motion estimation.

The purpose of this paper is to quickly and stably achieve grasping objects with a 3D robot arm controlled by electrooculography (EOG) signals. A EOG signal is a biological signal generated when the eyeballs move, leading to gaze estimation. In conventional research, gaze estimation has been used to control a 3D robot arm for welfare purposes. However, it is known that the EOG signal loses some of the eye movement information when it travels through the skin, resulting in errors in EOG gaze estimation. Thus, EOG gaze estimation is difficult to point out the object accurately, and the object may not be appropriately grasped. Therefore, developing a methodology to compensate, for the lost information and increase spatial accuracy is important. This paper aims to realize highly accurate object grasping with a robot arm by combining EMG gaze estimation and the object recognition of camera image processing. The system consists of a robot arm, top and side cameras, a display showing the camera images, and an EOG measurement analyzer. The user manipulates the robot arm through the camera images, which can be switched, and the EOG gaze estimation can specify the object. In the beginning, the user gazes at the screen’s center position and then moves their eyes to gaze at the object to be grasped. After that, the proposed system recognizes the object in the camera image via image processing and grasps it using the object centroid. The object selection is based on the object centroid closest to the estimated gaze position within a certain distance (threshold), thus enabling highly accurate object grasping. The observed size of the object on the screen can differ depending on the camera installation and the screen display state. Therefore, it is crucial to set the distance threshold from the object centroid for object selection. The first experiment is conducted to clarify the distance error of the EOG gaze estimation in the proposed system configuration. As a result, it is confirmed that the range of the distance error is 1.8–3.0 cm. The second experiment is conducted to evaluate the performance of the object grasping by setting two thresholds from the first experimental results: the medium distance error value of 2 cm and the maximum distance error value of 3 cm. As a result, it is found that the grasping speed of the 3 cm threshold is 27% faster than that of the 2 cm threshold due to more stable object selection.

Gut microbiota play many important roles, such as the regulation of immunity and barrier function in the intestine, and are crucial for maintaining homeostasis in living organisms. The disruption in microbiota is called dysbiosis, which has been associated with various chronic inflammatory conditions, food allergies, colorectal cancer, etc. The gut microbiota is also affected by several other factors such as diet, antibiotics and other medications, or bacterial and viral infections. Moreover, there are some reports on the oral-gut-liver axis indicating that the disruption of oral microbiota affects the intestinal biota. Non-alcoholic fatty liver disease (NAFLD) is one of the systemic diseases caused due to the dysregulation of the oral-gut-liver axis. NAFLD is the most common liver disease reported in the developed countries. It includes liver damage ranging from simple steatosis to nonalcoholic steatohepatitis (NASH), cirrhosis, and cancer. Recently, accumulating evidence supports an association between NAFLD and dysbiosis of oral and gut microbiota. Periodontopathic bacteria, especially Porphyromonas gingivalis, have been correlated with the pathogenesis and development of NAFLD based on the clinical and basic research, and immunology. P. gingivalis was detected in the liver, and lipopolysaccharide from this bacteria has been shown to be involved in the progression of NAFLD, thereby indicating a direct role of P. gingivalis in NAFLD. Moreover, P. gingivalis induces dysbiosis of gut microbiota, which promotes the progression of NAFLD, through disrupting both metabolic and immunologic pathways. Here, we review the roles of microbial dysbiosis in NAFLD. Focusing on P. gingivalis, we evaluate and summarize the most recent advances in our understanding of the relationship between oral-gut microbiome symbiosis and the pathogenesis and progression of non-alcoholic fatty liver disease, as well as discuss novel strategies targeting both P. gingivalis and microbial dysbiosis.

Models trained with Deep Reinforcement learning (DRL) have been deployed in various areas of robotics with varying degree of success. To overcome the limitations of data gathering in the real world, DRL training utilizes simulated environments and transfers the learned policy to real-world scenarios, i.e., sim–real transfer. Simulators fail to accurately capture the entire dynamics of the real world, so simulation-trained policies often fail when applied to reality, termed a reality gap (RG). In this paper, we propose a search (mapping) algorithm that takes in real-world observation (images) and maps them to the policy-equivalent images in the simulated environment using a convolution neural network (CNN) model. The two-step training process, DRL policy and a mapping model, overcomes the RG problem with simulated data only. We evaluated the proposed system with a gripping task of a custom-made robot arm in the real world and compared the performance against a conventional DRL sim–real transfer system. The conventional system achieved a 15–57% success rate in gripping operation depending on the position of the target object while the mapping-based sim–real system achieved 100%. The experimental results demonstrated that the proposed DRL with mapping method appropriately corresponded the real world to the simulated environment, confirming that the scheme can achieve high sim–real generalization at significantly low training costs.

With increasing population and motorization, Kenya as well as other African countries are faced with a tragic road traffic accidents (RTA). This paper looks at 5-year (2015–2020) data downloaded from National Transport and Safety Authority (NTSA) website, to identify trends and review progress of the traffic accidents in the country. The objective is to assess the prevalence of accidents within affected groups and location to identify trends and generalized causative agency from the reported data. From literature review, research activity focused on RTA in the country is minimal compared to the social significance accidents poses. The data were extracted and classified using Latent Dirichlet Allocation, a machine learning algorithm modelled in Matlab to group reported accident briefs into general categories/topic which are closely related. Four categories were identified as leading causes of fatality in the country: Knocking down victims, hit-and-run, losing control and head on collision. The identified causes point to preventable driver’s errors which agrees with other researchers. From trend analysis, fatalities and injuries have increased by 26% and 46.5%, respectively since January 2015 to January 2020. This paper found that injuries in vulnerable road users: pedestrians, pillion passengers and motorcyclist, has seen a foldfold increment compared to 2015 data. From the discussion, urgent fine-tuning of policing to protect vulnerable road user as well as curb the overly decried driver behavior is needed. The paper recommends fine-tuning of data collection, capturing details of accident that will be useful in modeling and data analysis for future planning.

Abstract Flexible manipulators have numerous advantages such as lightweight, high operation speed, and low power consumption. However, they suffer from link vibrations, especially when operated at high speeds followed by sudden stops. This limitation has been addressed using techniques such as adaptive filters, adaptive strain feedback gain, state feedback control, etc. This article presents a filtered inverse controller for the mitigation of link vibrations in a multi-link flexible manipulator. To this end, the plant model, developed and linearized in Maple/Maplesim was inverted in MATLAB. The internal dynamics of the inverse model were stabilized using the state feedback technique. For safe and high-speed operations, the inverse model was augmented with a low pass filter to form the filtered inverse which was used as feedforward controller. Practical experiments were carried out in the dSPACE environment. Results show that filtered inverse controller yield not only faster response but relatively minimal link vibration when compared with the manipulator without vibration controller.

Porphyromonas gingivalis is the most common microorganism associated with adult periodontal disease, causing inflammation around the subgingival lesion. In this study, we investigated tryptophanyl tRNA synthase (WRS) production by THP-1 cells infected with P. gingivalis. Cytokine production, leukocyte adhesion molecules, and low-density lipoprotein receptor (LDLR) expressions in cultured cells were examined. WRS was detected in THP-1 cell culture supernatants stimulated with P. gingivalis from 1 to 24 h, and apparent production was observed after 4 h. No change in WRS mRNA expression was observed from 1 to 6 h in THP-1 cells, whereas its expression was significantly increased 12 h after stimulation with P. gingivalis. Lactate dehydrogenase (LDH) activity was observed from 4 to 24 h. The TNF-α, IL-6, IL-8, and CXCL2 levels of THP-1 cells were upregulated after treatment with recombinant WRS (rWRS) and were significantly reduced when THP-1 cells were treated with C29. The MCP-1, ICAM-1, and VCAM-1 levels in human umbilical vein endothelial cells were upregulated following treatment with rWRS, and TAK242 suppressed these effects. Additionally, unmodified LDLR, macrophage scavenger receptor A, and lectin-like oxidized LDLRs were upregulated in THP-1 cells treated with rWRS. These results suggest that WRS from macrophages infected with P. gingivalis is associated with atherosclerosis.

Lactic acid (LA) is short-chain fatty acid, such as butyric acid and propionic acid, that is produced as a metabolite of lactic acid bacteria, including periodontopathic bacteria. These short-chain fatty acids have positive effects on human health but can also have negative effects, such as the promotion of periodontal disease (PD), which is caused by periodontal pathogens present in the gingival sulcus. PD is characterized by apical migration of junctional epithelium, deepening of pockets, and alveolar bone loss. Thus, the junctional epithelial cells that form the bottom of the gingival sulcus are extremely important in investigating the pathophysiology of PD. The aim of this study was to investigate the effect of LA on wound healing, cell growth, cell cycle kinetics, and gene expression of cultured junctional epithelium cells. The results showed that stimulation with 10 mM LA slowed wound healing of the junctional epithelial cell layer and arrested the cell cycle in the G0/G1 (early cell cycle) phase, thereby inhibiting cell growth. However, cell destruction was not observed. LA also enhanced mRNA expression of integrin α5, interleukin (IL)-6, IL-8, intercellular adhesion molecule-1, and receptor activator of nuclear factor kappa-B ligand. The results of this study suggest that stimulation of junctional epithelial cells with high concentrations of LA could exacerbate PD, similarly to butyric acid and propionic acid.

Background: As Automated Driving Systems (ADS) technology gets assimilated into the market, the driver’s obligation will be changed to a supervisory role. A key point to consider is the driver’s engagement in the secondary task to maintain the driver/user in the control loop. This paper aims to monitor driver engagement with a game and identify any impacts the task has on hazard recognition. Methods: We designed a driving simulation using Unity3D and incorporated three tasks: No-task, AR-Video, and AR-Game tasks. The driver engaged in an AR object interception game while monitoring the road for threatening road scenarios. Results: There was a significant difference in the tasks (F(2,33) = 4.34, p = 0.0213), identifying the game-task as significant with respect to reaction time and ideal for the present investigation. Game scoring followed three profiles/phases: learning, saturation, and decline profile. From the profiles, it is possible to quantify/infer drivers’ engagement with the game task. Conclusion: The paper proposes alternative monitoring that has utility, i.e., entertaining the user. Further experiments with AR-Games focusing on the real-world car environment will be performed to confirm the performance following the recommendations derived from the current test.

This paper examines the effect of instructions on parts' positions during an assembly task having numerous parts for someone to remember where they are located. Hypothesizing that instructions relating to the positions of the parts would enhance work efficiency and reduce workload, we designed an experimental method to test this theory. In this experiment using educational blocks, visual instructions were given by illuminating the space where the parts are kept, while auditory instructions were provided to aid locating the parts to compare the efficiency of visual and auditory instructions. Experiments with six participants showed that the visual instruction significantly shortened not only the search time but also the assembly time (which was one way in which efficiency was assessed in this study). The use of parts' positions instructions tended to reduce the workload as evaluated with NASA-TLX compared with the cases without instructions.

Abstract Enteropeptidase, localized into the duodenum brush border, is a key enzyme catalyzing the conversion of pancreatic trypsinogen proenzyme to active trypsin, thereby regulating protein digestion and energy homeostasis. We report the discovery and pharmacological profiles of SCO‐792, a novel inhibitor of enteropeptidase. A screen employing fluorescence resonance energy transfer was performed to identify enteropeptidase inhibitors. Inhibitory profiles were determined by in vitro assays. To evaluate the in vivo inhibitory effect on protein digestion, an oral protein challenge test was performed in rats. Our screen identified a series of enteropeptidase inhibitors, and compound optimization resulted in identification of SCO‐792, which inhibited enteropeptidase activity in vitro, with IC50 values of 4.6 and 5.4 nmol/L in rats and humans, respectively. In vitro inhibition of enteropeptidase by SCO‐792 was potentiated by increased incubation time, and the calculated Kinact/KI was 82 000/mol/L s. An in vitro dissociation assay showed that SCO‐792 had a dissociation half‐life of almost 14 hour, with a calculated koff rate of 0.047/hour, which suggested that SCO‐792 is a reversible enteropeptidase inhibitor. In normal rats, a ≤4 hour prior oral dose of SCO‐792 effectively inhibited plasma elevation of branched‐chain amino acids in an oral protein challenge test, which indicated that SCO‐792 effectively inhibited protein digestion in vivo. In conclusion, our new screen system identified SCO‐792 as a potent and reversible inhibitor against enteropeptidase. SCO‐792 slowly dissociated from enteropeptidase in vitro and inhibited protein digestion in vivo. Further study using SCO‐792 could reveal the effects of inhibiting enteropeptidase on biological actions.

Purpose: To elucidate the effects of butyric acid (BA), a metabolite of bacteria involved in periodontitis, and a possible enhancer of the junctional epithelial cells. Methods: A murine junctional epithelial cell line, JE-1, was used to assess the effects of sodium butyrate (NaB) as BA. Cell proliferation, migration and attachment were analyzed. Additionally, gene and promoter expression analysis was performed, i.e., cap analysis of gene expression (CAGE) and gene ontology (GO) term enrichment analysis. Results: NaB affected junctional epithelial cell proliferation, migration and attachment. A high concentration of NaB caused cell death and a low concentration tended to promote migration and adhesion. CAGE analysis revealed 75 upregulated and 96 downregulated genes in the cells after 0.2 mM NaB stimulation for 3 h. Regarding GO term enrichment, the genes upregulated >4-fold participated predominantly in cell migration and proliferation. The results of this study suggest that BA produced from periodontopathic bacteria is involved in periodontal tissue destruction at high concentrations. Furthermore, at low concentrations, BA potentially participates in periodontal disease progression by increasing proliferation, migration and attachment of the junctional epithelium and thereby increasing epithelial down-growth.

This research focuses on the minimum process of classifying three upper arm movements (elbow extension, shoulder extension, combined shoulder and elbow extension) of humans with three electromyography (EMG) signals, to control a 2-degrees of freedom (DoF) robotic arm. The proposed minimum process consists of four parts: time divisions of data, Teager–Kaiser energy operator (TKEO), the conventional EMG feature extraction (i.e., the mean absolute value (MAV), zero crossings (ZC), slope-sign changes (SSC), and waveform length (WL)), and eight major machine learning models (i.e., decision tree (medium), decision tree (fine), k-Nearest Neighbor (KNN) (weighted KNN, KNN (fine), Support Vector Machine (SVM) (cubic and fine Gaussian SVM), Ensemble (bagged trees and subspace KNN). Then, we compare and investigate 48 classification models (i.e., 47 models are proposed, and 1 model is the conventional) based on five healthy subjects. The results showed that all the classification models achieved accuracies ranging between 74–98%, and the processing speed is below 40 ms and indicated acceptable controller delay for robotic arm control. Moreover, we confirmed that the classification model with no time division, with TKEO, and with ensemble (subspace KNN) had the best performance in accuracy rates at 96.67, recall rates at 99.66, and precision rates at 96.99. In short, the combination of the proposed TKEO and ensemble (subspace KNN) plays an important role to achieve the EMG classification.

A compact superconducting magnetic energy storage system (SMES) produced by Si micro fabrication technologies has been proposed to improve electricity storage volume density, w, in the sub-Wh/L range of conventional SMESs and to produce them at a low cost by mass production. In parallel with the experimental development reported previously, a series of trials was performed to estimate a feasible value of w based on the calculation of the magnetic field generated by the compact SMES by improving the calculation models step by step. In this work, the experimentally obtained magnetic flux density dependence of superconductive critical current density was taken into consideration for the first time in this series of trials, together with the additional improvement of the calculation models. The results of the estimation indicated that a compact SMES produced by the proposed concept can attain a w in the Wh/L range or more, ranking with or surpassing that of presently used capacitors.

This article sought to address issues related to human-robot cooperation tasks focusing especially on robotic operation using bio-signals. In particular, we propose to develop a control scheme for a robot arm based on electromyography (EMG) signal that allows a cooperative task between humans and robots that would enable teleoperations. A basic framework for achieving the task and conducting EMG signals analysis of the motion of upper limb muscles for mapping the hand motion is presented. The objective of this work is to investigate the application of a wearable EMG device to control a robot arm in real-time. Three EMG sensors are attached to the brachioradialis, biceps brachii, and anterior deltoid muscles as targeted muscles. Three motions were conducted by moving the arm about the elbow joint, shoulder joint, and a combination of the two joints giving a two degree of freedom. Five subjects were used for the experiments. The results indicated that the performance of the system had an overall accuracy varying from 50% to 100% for the three motions for all subjects. This study has further shown that upper-limb motion discrimination can be used to control the robotic manipulator arm with its simplicity and low computational cost.

Conventional rigid actuators, such as DC servo motors, face challenges in utilizing them in artificial muscles and soft robotics. Dielectric elastomer actuators (DEAs) overcome all these limitations, as they exhibit complex and fast motions, quietness, lightness, and softness. Recently, there has been much focus on studies of the DEAs material’s non-linearity, the non-linear electromechanical coupling, and viscoelastic behavior of VHB and silicone-based conical DEAs having compliant electrodes that are based on graphite powder and carbon grease. However, the mitigation of overshoot that arises from fast response conical DEAs made with solid electrodes has not received much research focus. In this paper, we fabricated a conical configuration of multi-walled carbon nanotube/polydimethylsiloxane (MWCNT/PDMS) based DEAs with a rise time of 10 ms, and 50% peak overshoot. We developed a full feedback state-based linear-quadratic regulator (LQR) having Luenberger observer to mitigate the DEAs overshoot in both the voltage ON and OFF instances. The cone DEA’s model was identified and a stable and well-fitting transfer function with a fit of 94% was obtained. Optimal parameters Q = 70,000, R = 0.1, and Q = 7000, R = 0.01 resulted in the DEA response having a rise time value of 20 ms with zero overshoot, in both simulations and experiments. The LQR approach can be useful for the control of fast response DEAs and this would expand the potential use of the DEAs as artificial muscles in soft robotics.

Despite the promising characteristics of Dielectric Elastomel Actuator (DEA) as a practical soft actuator, the need of high voltage for its operation prevents the successful fabrication of a practical DEA, that is, the high voltage generation takes a bulky and costly power supply. Induction of complex shape change motion of DEA such as oscillatory shape change takes even a more bulky and costly multipurpose power supply. It is a serious practical issue to be overcome. In our latest study, however, we could build a simple DEA system which exhibited a relatively complex and autonomous oscillatory shape change merely under a constant voltage, though the voltage needed was high. This successful outcome must broaden the potential usefulness of DEA as a practical soft actuator.

This paper considers the balance control of a biped robot under a constant external force or on a sloped ground. We have proposed a control method with feedback of the ground reaction forces and have realized adaptive posture changes that ensure the stability of the robot. However, fast responses have not been obtained because effective control is achieved by an integral feedback that accompanies a time delay necessary for error accumulation. To improve this response, here, we introduce gravity compensation in a feedforward manner. The stationary state and its stability are analyzed based on dynamic equations, and the robustness as well as the response is evaluated using computer simulations. Finally, the adaptive behaviors of the robot are confirmed by standing experiments on the slope.

Performing some special tasks using electrooculography (EOG) in daily activities is being developed in various areas. In this paper, simple rotation matrixes were introduced to help the operator move a 2-DoF planar robot manipulator. The EOG sensor, NF 5201, has two output channels (Ch1 and Ch2), as well as one ground channel and one reference channel. The robot movement was the indicator that this system could follow gaze motion based on EOG. Operators gazed into five training target points each in the horizontal and vertical line as the preliminary experiments, which were based on directions, distances and the areas of gaze motions. This was done to get the relationships between EOG and gaze motion distance for four directions, which were up, down, right and left. The maximum angle for the horizontal was 46°, while it was 38° for the vertical. Rotation matrixes for the horizontal and vertical signals were combined, so as to diagonally track objects. To verify, the errors between actual and desired target positions were calculated using the Euclidian distance. This test section had 20 random target points. The result indicated that this system could track an object with average angle errors of 3.31° in the x-axis and 3.58° in the y-axis.

Biosignals will play an important role in building communication between machines and humans. One of the types of biosignals that is widely used in neuroscience are electrooculography (EOG) signals. An EOG has a linear relationship with eye movement displacement. Experiments were performed to construct a gaze motion tracking method indicated by robot manipulator movements. Three operators looked at 24 target points displayed on a monitor that was 40 cm in front of them. Two channels (Ch1 and Ch2) produced EOG signals for every single eye movement. These signals were converted to pixel units by using the linear relationship between EOG signals and gaze motion distances. The conversion outcomes were actual pixel locations. An affine transform method is proposed to determine the shift of actual pixels to target pixels. This method consisted of sequences of five geometry processes, which are translation-1, rotation, translation-2, shear and dilatation. The accuracy was approximately 0.86° ± 0.67° in the horizontal direction and 0.54° ± 0.34° in the vertical. This system successfully tracked the gaze motions not only in direction, but also in distance. Using this system, three operators could operate a robot manipulator to point at some targets. This result shows that the method is reliable in building communication between humans and machines using EOGs.

This paper sought to improve the precision of the Alternating Current Electro-Occulo-Graphy (AC-EOG) gaze estimation method. The method consisted of two core techniques: To estimate eyeball movement from EOG signals and to convert signals from the eyeball movement to the gaze position. In conventional research, the estimations are computed with two EOG signals corresponding to vertical and horizontal movements. The conversion is based on the affine transformation and those parameters are computed with 24-point gazing data at the calibration. However, the transformation is not applied to all the 24-point gazing data, but to four spatially separated data (Quadrant method), and each result has different characteristics. Thus, we proposed the conversion method for 24-point gazing data at the same time: To assume an imaginary center (i.e., 25th point) on gaze coordinates with 24-point gazing data and apply an affine transformation to 24-point gazing data. Then, we conducted a comparative investigation between the conventional method and the proposed method. From the results, the average eye angle error for the cross-shaped electrode attachment is x = 2.27 ° ± 0.46 ° and y = 1.83 ° ± 0.34 ° . In contrast, for the plus-shaped electrode attachment, the average eye angle error is is x = 0.94 ° ± 0.19 ° and y = 1.48 ° ± 0.27 ° . We concluded that the proposed method offers a simpler and more precise EOG gaze estimation than the conventional method.

The exhibition of significantly large bending is a remarkable characteristic of an ionic polymer-metal composite (IPMC). However, its inability to generate a high enough force is a major problem in achieving a practical IPMC actuator. The simultaneous enhancement of bending and force generation is needed for broadening the potential of the IPMC actuator as a practical engineering device. Corrosive materials as a flexible electrode of the IPMC is usually not preferred, whereas a noncorrosive material such as platinum is broadly used. Here, we used silver, a corrosive metal, as an IPMC electrode intentionally. The silver electrode exhibits a reversible redox reaction upon an external electric stimulation. That silver redox reaction resulted in the material characteristics change of the IPMC, and it consequently resulted in the simultaneous enhancement of the IPMC bending curvature and blocking force generation. It was further found that the thicker silver coating anchored into the far inside of the IPMC led to the occurrence of a significant silver redox reaction and it altered the material characteristics of the IPMC, consequently turning the IPMC into a greatly deformable and high force generative one.

This paper presents feedforward, feedback and two-degree-of-freedom control applied to an Ionic Polymer-Metal Composite (IPMC) actuator. It presents a high potential for development of miniature robots and biomedical devices and artificial muscles. We have reported in the last few years that dehydration treatment improves the electrical controllability of bending in Selemion CMV-based IPMCs. We tried to replicate this controllability in Nafion-based IPMC. We found that the displacement of a Nafion-based IPMC was proportional to the total charge imposed, just as in the Selemion-CMV case. This property is the basis of self-sensing controllers for Nafion-based IPMC bending behavior: we perform bending curvature experiments on Nafion-based IPMCs, obtaining the actuator's dynamics and transfer function. From these, we implemented self-sensing controllers using feedforward, feedback and two-degree-of-freedom techniques. All three controllers performed very well with the Nafion-based IPMC actuator.

Wireless driving of a micromirror device is achieved by electromagnetic excitation tuned to the mechanical resonance of the mirror rotation. The micromirror used is designed for low-voltage-driving. Static mirror rotation angle is 7.7 deg. at 12 V bias. The micromirror is connected to a coil for receiving the driving energy through electromagnetic induction. Magnetic field of 19.3 mT at the surface of the first coil induces 0.6 V at the second coil that is 24 mm apart from the first coil. An LC resonance circuit is incorporated in the second coil system to enhance the induced voltage. The LC resonance frequency is tuned to induce the mechanical resonance of the micromirror device. When the separation distance between the first and second coils is 24 mm, the voltage induced by the LC resonance is 1.43 V. Mirror rotation angle is 0.3 deg. for half stroke is achieved. It is expected that the rotation angle is expanded by increasing the Q factors of the electrical circuit and mechanical resonance of the micromirror. These will be achieved by reducing the loss in the electrical circuits and packaging the micromirror device in vacuum with hermetic seal.

In this work, a respiratory sensor is studied, measuring the capacitance constructed by attached electrodes on the abdomen. Based on previous findings, that skin thickness changes caused by respiration provides the signal, the fitting condition of the electrode on the skin is stabilized using a 7-μm-thick dressing film. This film can be comfortably worn for a long time, while maintaining the electrode’s position on the skin. This stabilized setup enables the detection of, not only respiration, as the cyclic capacitance change, but also of minute body volume changes over the daytime, as a change in the base line indicates the quality of the sensor signal. For this demonstration, the respiration signal is measured during the daily activity of exercise and 6-min walks.

Tactile sensing is required for assistant robots. A new stacked sensor head was proposed which detects forces and thermal sensations with a nerve-net LSI chip. To measure temperatures and heat flows, the sensor head required a thin and small heater to heat up the sensor head. The features of the polyimide heater are compact, low heat capacity, low power consumption and ease of attaching it to the LSI chip. The polyimide heater consisted of Au 10 nm/Cu 200 nm/Cr 20 nm thickness layers on a polyimide film. The surface of the sensor head heated up to 60 °C by the polyimide heater with 0.3 W. By the Cu thin film polyimide heater, the small stacked sensor head with the nerve-net LSI chip is enable to detect forces, temperatures, and heat flows, simultaneously.