Broken Kramers Degeneracy in Altermagnetic MnTe.

Altermagnetism is a newly identified fundamental class of magnetism with vanishing net magnetization and time-reversal symmetry broken electronic structure. Probing the unusual electronic structure with nonrelativistic spin splitting would be a direct experimental verification of an altermagnetic phase. By combining high-quality film growth and in situ angle-resolved photoemission spectroscopy, we report the electronic structure of an altermagnetic candidate, α-MnTe. Temperature-dependent study reveals the lifting of Kramers degeneracy accompanied by a magnetic phase transition at T_{N}=267 K with spin splitting of up to 370 meV, providing direct spectroscopic evidence for altermagnetism in MnTe.

Predictors of Medical and Dental Clinic Closure by Machine Learning Methods: Cross-Sectional Study Using Empirical Data.

BACKGROUND: Small clinics are important in providing health care in local communities. Accurately predicting their closure would help manage health care resource allocation. There have been few studies on the prediction of clinic closure using machine learning techniques. OBJECTIVE: This study aims to test the feasibility of predicting the closure of medical and dental clinics (MCs and DCs, respectively) and investigate important factors associated with their closure using machine running techniques. METHODS: The units of analysis were MCs and DCs. This study used health insurance administrative data. The participants of this study ran and closed clinics between January 1, 2020, and December 31, 2021. Using all closed clinics, closed and run clinics were selected at a ratio of 1:2 based on the locality of study participants using the propensity matching score of logistic regression. This study used 23 and 19 variables to predict the closure of MCs and DCs, respectively. Key variables were extracted using permutation importance and the sequential feature selection technique. Finally, this study used 5 and 6 variables of MCs and DCs, respectively, for model learning. Furthermore, four machine learning techniques were used: (1) logistic regression, (2) support vector machine, (3) random forest (RF), and (4) Extreme Gradient Boost. This study evaluated the modeling accuracy using the area under curve (AUC) method and presented important factors critically affecting closures. This study used SAS (version 9.4; SAS Institute Inc) and Python (version 3.7.9; Python Software Foundation). RESULTS: The best-fit model for the closure of MCs with cross-validation was the support vector machine (AUC 0.762, 95% CI 0.746-0.777; P<.001) followed by RF (AUC 0.736, 95% CI 0.720-0.752; P<.001). The best-fit model for DCs was Extreme Gradient Boost (AUC 0.700, 95% CI 0.675-0.725; P<.001) followed by RF (AUC 0.687, 95% CI 0.661-0.712; P<.001). The most significant factor associated with the closure of MCs was years of operation, followed by population growth, population, and percentage of medical specialties. In contrast, the main factor affecting the closure of DCs was the number of patients, followed by annual variation in the number of patients, year of operation, and percentage of dental specialists. CONCLUSIONS: This study showed that machine running methods are useful tools for predicting the closure of small medical facilities with a moderate level of accuracy. Essential factors affecting medical facility closure also differed between MCs and DCs. Developing good models would prevent unnecessary medical facility closures at the national level.

Human-to-Robot Handover Based on Reinforcement Learning.

This study explores manipulator control using reinforcement learning, specifically targeting anthropomorphic gripper-equipped robots, with the objective of enhancing the robots' ability to safely exchange diverse objects with humans during human-robot interactions (HRIs). The study integrates an adaptive HRI hand for versatile grasping and incorporates image recognition for efficient object identification and precise coordinate estimation. A tailored reinforcement-learning environment enables the robot to dynamically adapt to diverse scenarios. The effectiveness of this approach is validated through simulations and real-world applications. The HRI hand's adaptability ensures seamless interactions, while image recognition enhances cognitive capabilities. The reinforcement-learning framework enables the robot to learn and refine skills, demonstrated through successful navigation and manipulation in various scenarios. The transition from simulations to real-world applications affirms the practicality of the proposed system, showcasing its robustness and potential for integration into practical robotic platforms. This study contributes to advancing intelligent and adaptable robotic systems for safe and dynamic HRIs.

Transformable Gaussian Reward Function for Socially Aware Navigation Using Deep Reinforcement Learning.

Robot navigation has transitioned from avoiding static obstacles to adopting socially aware navigation strategies for coexisting with humans. Consequently, socially aware navigation in dynamic, human-centric environments has gained prominence in the field of robotics. One of the methods for socially aware navigation, the reinforcement learning technique, has fostered its advancement. However, defining appropriate reward functions, particularly in congested environments, holds a significant challenge. These reward functions, crucial for guiding robot actions, necessitate intricate human-crafted design due to their complex nature and inability to be set automatically. The multitude of manually designed reward functions contains issues such as hyperparameter redundancy, imbalance, and inadequate representation of unique object characteristics. To address these challenges, we introduce a transformable Gaussian reward function (TGRF). The TGRF possesses two main features. First, it reduces the burden of tuning by utilizing a small number of hyperparameters that function independently. Second, it enables the application of various reward functions through its transformability. Consequently, it exhibits high performance and accelerated learning rates within the deep reinforcement learning (DRL) framework. We also validated the performance of TGRF through simulations and experiments.

Social Type-Aware Navigation Framework for Mobile Robots in Human-Shared Environments.

As robots become increasingly common in human-populated environments, they must be perceived as social beings and behave socially. People try to preserve their own space during social interactions with others, and this space depends on a variety of factors, such as individual characteristics or their age. In real-world social spaces, there are many different types of people, and robots need to be more sensitive, especially when interacting with vulnerable subjects such as children. However, the current navigation methods do not consider these differences and apply the same avoidance strategies to everyone. Thus, we propose a new navigation framework that considers different social types and defines appropriate personal spaces for each, allowing robots to respect them. To this end, the robot needs to classify people in a real environment into social types and define the personal space for each type as a Gaussian asymmetric function to respect them. The proposed framework is validated through simulations and real-world experiments, demonstrating that the robot can improve the quality of interactions with people by providing each individual with an adaptive personal space. The proposed costmap layer is available on GitHub.

Growth and Electronic Structure of Copper Oxide Monolayer Epitaxial Films.

Copper-based high-temperature superconductors share a common feature in their crystal structure, which is the presence of a CuO2 plane, where superconductivity takes place. Therefore, important questions arise as to whether superconductivity can exist in a single layer of the CuO2 plane and, if so, how such superconductivity in a single CuO2 plane differs from that in a bulk cuprate system. To answer these questions, studies of the superconductivity in cuprate monolayers are necessary. In this study, we constructed a heterostructure system with a La2-xSrxCuO4 (LSCO) monolayer containing a single CuO2 plane and measured the resulting electronic structures. Monolayer LSCO has metallic and bulk-like electronic structures. The hole doping ratio of the monolayer LSCO is found to depend on the underlying buffer layer due to the interface effect. Our work will provide a platform for research into ideal two-dimensional cuprate systems.

Mutanolysin-Digested Peptidoglycan of Lactobacillus reuteri Promotes the Inhibition of Porphyromonas gingivalis Lipopolysaccharide-Induced Inflammatory Responses through the Regulation of Signaling Cascades via TLR4 Suppression.

Periodontitis is an oral infectious disease caused by various pathogenic bacteria, such as Porphyromonas gingivalis. Although probiotics and their cellular components have demonstrated positive effects on periodontitis, the beneficial impact of peptidoglycan (PGN) from probiotic Lactobacillus remains unclear. Therefore, our study sought to investigate the inhibitory effect of PGN isolated from L. reuteri (LrPGN) on P. gingivalis-induced inflammatory responses. Pretreatment with LrPGN significantly inhibited the production of interleukin (IL)-1ß, IL-6, and CCL20 in RAW 264.7 cells induced by P. gingivalis lipopolysaccharide (LPS). LrPGN reduced the phosphorylation of PI3K/Akt and MAPKs, as well as NF-κB activation, which were induced by P. gingivalis LPS. Furthermore, LrPGN dose-dependently reduced the expression of Toll-like receptor 4 (TLR4), indicating that LrPGN inhibits periodontal inflammation by regulating cellular signaling cascades through TLR4 suppression. Notably, LrPGN exhibited stronger inhibition of P. gingivalis LPS-induced production of inflammatory mediators compared to insoluble LrPGN and proteinase K-treated LrPGN. Moreover, MDP, a minimal bioactive PGN motif, also dose-dependently inhibited P. gingivalis LPS-induced inflammatory mediators, suggesting that MDP-like molecules present in the LrPGN structure may play a crucial role in the inhibition of inflammatory responses. Collectively, these findings suggest that LrPGN can mitigate periodontal inflammation and could be a useful agent for the prevention and treatment of periodontitis.

Sign-tunable anomalous Hall effect induced by two-dimensional symmetry-protected nodal structures in ferromagnetic perovskite thin films.

Magnetism and spin-orbit coupling are two quintessential ingredients underlying topological transport phenomena in itinerant ferromagnets. When spin-polarized bands support nodal points/lines with band degeneracy that can be lifted by spin-orbit coupling, the nodal structures become a source of Berry curvature, leading to a large anomalous Hall effect. However, two-dimensional systems can possess stable nodal structures only when proper crystalline symmetry exists. Here we show that two-dimensional spin-polarized band structures of perovskite oxides generally support symmetry-protected nodal lines and points that govern both the sign and the magnitude of the anomalous Hall effect. To demonstrate this, we performed angle-resolved photoemission studies of ultrathin films of SrRuO3, a representative metallic ferromagnet with spin-orbit coupling. We show that the sign-changing anomalous Hall effect upon variation in the film thickness, magnetization and chemical potential can be well explained by theoretical models. Our work may facilitate new switchable devices based on ferromagnetic ultrathin films.

Navigation Path Based Universal Mobile Manipulator Integrated Controller (NUMMIC).

As the demand for service robots increases, a mobile manipulator robot which can perform various tasks in a dynamic environment attracts great attention. There are some controllers that control mobile platform and manipulator arm simultaneously for efficient performance, but most of them are difficult to apply universally since they are based on only one mobile manipulator model. This lack of versatility can be a big problem because most mobile manipulator robots are made by connecting a mobile platform and manipulator from different companies. To overcome this problem, this paper proposes a simultaneous controller which can be applied not only to one model but also to various types of mobile manipulator robots. The proposed controller has three main characteristics, which are as follows: (1) establishing a pose that motion planning can be carried out in any position, avoiding obstacles and stopping in a stable manner at the target coordinates, (2) preventing the robot from collision with surrounding obstacles while driving, (3) defining a safety area where the manipulator does not hit the obstacles while driving and executing the manipulation accordingly. Our controller is fully compatible with Robot Operating System (ROS) and has been used successfully with three different types of mobile manipulator robots. In addition, we conduct motion planning experiments on five targets, each in two simulation worlds, and two motion planning scenarios using real robots in real-world environments. The result shows a significant improvement in time compared to existing control methods in various types of mobile manipulator and demonstrates that the controller works successfully in the real environment. The proposed controller is available on GitHub.

Observation of Spin-Dependent Dual Ferromagnetism in Perovskite Ruthenates.

We performed in situ angle-resolved photoemission spectroscopy (ARPES) and spin-resolved ARPES (SARPES) experiments to investigate the relationship between electronic band structures and ferromagnetism in SrRuO_{3} (SRO) thin films. Our high quality ARPES and SARPES results show clear spin-lifted band structures. The spin polarization is strongly dependent on momentum around the Fermi level, whereas it becomes less dependent at high-binding energies. This experimental observation matches our dynamical mean-field theory results very well. As temperature increases from low to the Curie temperature, spin-splitting gap decreases and band dispersions become incoherent. Based on the ARPES study and theoretical calculation results, we found that SRO possesses spin-dependent electron correlations in which majority and minority spins are localized and itinerant, respectively. Our finding explains how ferromagnetism and electronic structure are connected, which has been under debate for decades in SRO.

Development of a Single Leg Knee Exoskeleton and Sensing Knee Center of Rotation Change for Intention Detection.

In this study, we developed a single leg knee joint assistance robot. Commonly used exoskeletons have a left-right pair, but when only one leg of the wearer is uncomfortable, it is effective to wear the exoskeleton on only the uncomfortable leg. The designed exoskeleton uses a lightweight material and uses a wire-driven actuator, which reduces the weight of the driving section that is attached on the knee directly. Therefore, proposed exoskeleton reduces the force of inertia that the wearer experiences. In addition, the lower frame length of the exoskeleton can be changed to align with the complex movement of the knee. Furthermore, the length between the knee center of rotation and the ankle (LBKA) is measured by using this structure, and the LBKA values are used as the data for intention detection. These value helps to detect the intention because it changes faster than a motor encoder value. A neural network was trained using the motor encoder values, and LBKA values. Neural network detects the intention of three motions (stair ascending, stair descending, and walking), Training results showed that intention detection was good in various environments.

Intention Detection Using Physical Sensors and Electromyogram for a Single Leg Knee Exoskeleton.

In this paper, we present a knee exoskeleton. Due to the complicated structure of the knee, an exoskeleton can limit the wearer's movement (e.g., when completely sitting down). To prevent this, the proposed exoskeleton is designed to move the ankle part prismatically, so the movement of the wearer is not limited. In addition, the developed exoskeleton could be worn on only one leg, but in this case, it is difficult to detect the intention because the relative relationship information of the two legs is unknown. For this purpose, the length between the knee center of rotation and the ankle (LBKA) was measured and used for intention detection. Using a physical sensor-an encoder and an LBKA sensor, the success rate of intention detection was 82.1%. By additionally using an electromyogram (EMG) sensor, the success rate of intention detection was increased to 92%, and the intention detection was also 27.1 ms faster on average.

Observation of partial reduction of manganese in the lithium rich layered oxides, 0.4Li2MnO3-0.6LiNi1/3Co1/3Mn1/3O2, during the first charge.

Lithium-rich layered oxides show promise as high-energy harvesting materials due to their large capacities. However, questions remain regarding the large irreversible loss in capacities for the first charge-discharge cycle due to oxygen removal in lattices related to layered Li2MnO3. Herein we present detailed studies on Li-rich Mn-based layered oxides of 0.4Li2MnO3-0.6LiNi1/3Co1/3Mn1/3O2 (Li-rich NCM) electrochemically activated between 2.5 V and 4.3 or 4.7 V vs. Li+/Li. Electron energy loss spectroscopy (EELS) and X-ray absorption spectroscopy (XAS) revealed unusual manganese reduction after the first charge up to a high voltage of 4.7 V. Moreover, the electronic structure did not fully recover to the original pristine of Mn4+ state after discharge. Interestingly, these phenomena were not limited to a single particle, but were observed across the entire electrode. High-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) images and electron dispersive spectra (EDS) also showed a dramatic decline in oxygen content with highly porous morphologies, associated with oxygen vacancy formation following oxidation of O2- ions to O2. Our analysis suggests that an unstable manganese valence state with severe defects due to oxygen vacancies may lead to large irreversible capacity loss during the first charge-discharge cycle of Li-rich layered oxides.

Inspection Robot Based Mobile Sensing and Power Line Tracking for Smart Grid.

Smart sensing and power line tracking is very important in a smart grid system. Illegal electricity usage can be detected by remote current measurement on overhead power lines using an inspection robot. There is a need for accurate detection methods of illegal electricity usage. Stable and correct power line tracking is a very prominent issue. In order to correctly track and make accurate measurements, the swing path of a power line should be previously fitted and predicted by a mathematical function using an inspection robot. After this, the remote inspection robot can follow the power line and measure the current. This paper presents a new power line tracking method using parabolic and circle fitting algorithms for illegal electricity detection. We demonstrate the effectiveness of the proposed tracking method by simulation and experimental results.

Guiding protractor for accurate freehand placement of ventricular catheter in ventriculoperitoneal shunting.

BACKGROUND: While frameless stereotaxis can be used for shunt ventricular catheter placement in patients with smaller ventricles, the ventricular catheter is still commonly placed based on the surface anatomy of the head for patients with larger ventricles. Thus, surgical techniques and guides facilitating accurate and reliable freehand placement of the ventricular catheter still need to be devised. METHODS: With the patient in a supine position and the axis of their head maintained horizontally, the guiding protractor is placed horizontally in the frontal burrhole at Kocher's point. Using the guiding angle between the head axis and the frontal horn of the lateral ventricle based on coronal head computed tomography (CT) or magnetic resonance (MR) images, the ventricular catheter is then placed in the catheter guide within the guiding protractor. RESULTS: In 20 hydrocephalic patients with a bicaudate index >0.2 or bifrontal distance >25 mm, the ideal guiding angle ranged from 17 to 23° (mean ± standard deviation [SD], 19.6° ± 1.6°). In all these patients, ventricular catheterization was successfully achieved with only one pass of the catheter, and postoperative CT scans showed satisfactory placement of the catheter in the ipsilateral frontal horn of the lateral ventricles. CONCLUSIONS: The proposed surgical technique using a guiding protractor facilitates accurate freehand placement of a ventricular catheter for patients with a bicaudate index >0.2 or bifrontal distance >25 mm.

Visualizing the Low-Energy Electronic Structure of Prototypical Hybrid Halide Perovskite through Clear Band Measurements.

Organic-inorganic hybrid perovskites (OIHPs) are a promising class of materials that rival conventional semiconductors in various optoelectronic applications. However, unraveling the precise nature of their low-energy electronic structures continues to pose a significant challenge, primarily due to the absence of clear band measurements. Here, we investigate the low-energy electronic structure of CH3NH3PbI3 (MAPI3) using angle-resolved photoelectron spectroscopy combined with ab initio density functional theory. We successfully visualize the electronic structure of MAPI3 near the bulk valence band maximum by using a laboratory photon source (He Iα, 21.2 eV) at low temperature and explore its fundamental properties. The observed valence band exhibits a highly isotropic and parabolic band characterized by small effective masses of 0.20-0.21 me, without notable spectral signatures associated with a large polaron or the Rashba effect, subjects that are intensely debated in the literature. Concurrently, our spin-resolved measurements directly disprove the giant Rashba scenario previously suggested in a similar perovskite compound by establishing an upper limit for the Rashba parameter (αR) of 0.28 eV Å. Our results unveil the unusually complex nature of the low-energy electronic structure of OIHPs, thereby advancing our fundamental understanding of this important class of materials.

Cyber infrastructure for Fusarium: three integrated platforms supporting strain identification, phylogenetics, comparative genomics and knowledge sharing.

The fungal genus Fusarium includes many plant and/or animal pathogenic species and produces diverse toxins. Although accurate species identification is critical for managing such threats, it is difficult to identify Fusarium morphologically. Fortunately, extensive molecular phylogenetic studies, founded on well-preserved culture collections, have established a robust foundation for Fusarium classification. Genomes of four Fusarium species have been published with more being currently sequenced. The Cyber infrastructure for Fusarium (CiF; http://www.fusariumdb.org/) was built to support archiving and utilization of rapidly increasing data and knowledge and consists of Fusarium-ID, Fusarium Comparative Genomics Platform (FCGP) and Fusarium Community Platform (FCP). The Fusarium-ID archives phylogenetic marker sequences from most known species along with information associated with characterized isolates and supports strain identification and phylogenetic analyses. The FCGP currently archives five genomes from four species. Besides supporting genome browsing and analysis, the FCGP presents computed characteristics of multiple gene families and functional groups. The Cart/Favorite function allows users to collect sequences from Fusarium-ID and the FCGP and analyze them later using multiple tools without requiring repeated copying-and-pasting of sequences. The FCP is designed to serve as an online community forum for sharing and preserving accumulated experience and knowledge to support future research and education.

Merkel Cell Carcinoma of the Trunk: Two Case Reports and Imaging Review.

Merkel cell carcinoma (MCC) is a rare malignant cutaneous tumor primarily located in the head and neck. We report the imaging features of pathologically confirmed MCC in the trunk. On US, MCC showed heterogeneous echogenicity with perpendicular hypoechoic linear bands that resembled "columns of smoke" in the skin and subcutaneous layers as well as prominent vascularity. On MRI, the tumor showed hypointensity on T1-weighted images and hyperintensity on proton density and T2-weighted images with linear low-signal bands in the skin and subcutaneous layers as well as intense enhancement on T1-enhanced images. Although MCC has nonspecific imaging features, these characteristics may be helpful for the early diagnosis of this disease.

Electric Control of 2D Van Hove Singularity in Oxide Ultra-Thin Films.

Divergent density of states (DOS) can induce extraordinary phenomena such as significant enhancement of superconductivity and unexpected phase transitions. Moreover, van Hove singularities (VHSs) lead to divergent DOS in 2D systems. Despite recent interest in VHSs, only a few controllable cases have been reported to date. In this work, by utilizing an atomically ultra-thin SrRuO3 film, the electronic structure of a 2D VHS is investigated with angle-resolved photoemission spectroscopy and transport properties are controlled. By applying electric fields with alkali metal deposition and ionic-liquid gating methods, the 2D VHS and the sign of the charge carrier are precisely controlled. Use of a tunable 2D VHS in an atomically flat oxide film could serve as a new strategy to realize infinite DOS near the Fermi level, thereby allowing efficient tuning of electric properties.

Attitudinal analysis of vaccination effects to lead endemic phases.

To achieve endemic phases, repeated vaccinations are necessary. However, individuals may grapple with whether to get vaccinated due to potential side effects. When an individual is already immune due to previous infections or vaccinations, the perceived risk from vaccination is often less than the risk of infection. Yet, repeated rounds of vaccination can lead to avoidance, impeding the establishment of endemic phases. We explore this phenomenon using an individual-based Monte Carlo simulation, validating our findings with game theory. The Nash equilibrium encapsulates individuals' non-cooperative behavior, while the system's optimal value represents the societal benefits of altruistic cooperation. We define the difference between these as the price of anarchy. Our simulations reveal that the price of anarchy must fall below a threshold of 12.47 for endemic phases to be achieved in a steady state. This suggests that for a basic reproduction number of 10, a consistent vaccination rate greater than 89% is required. These findings offer new insights into vaccination-related decision-making and can inform effective strategies to tackle infectious diseases.