Search

Myriocin is an inhibitor of serine palmitoyltransferase involved in the initial biosynthetic step for sphingolipids, and causes potent growth inhibition in eukaryotic cells. In budding yeast, Rsb1, Rta1, Pug1, and Ylr046c are known as the Lipid-Translocating Exporter (LTE) family and believed to contribute to export of various cytotoxic lipophilic compounds. It was reported that Rsb1 is a transporter responsible for export of intracellularly accumulated long-chain bases, which alleviate the cytotoxicity. In this study, it was found that LTE family genes are involved in determination of myriocin sensitivity in yeast. Analyses of effects of deletion and overexpression of LTE family genes suggested that all LTEs contribute to suppression of cytotoxicity of myriocin. It was confirmed that RSB1 overexpression suppressed reduction in complex sphingolipid levels caused by myriocin treatment, possibly exporting myriocin to outside of the cell. These results suggested that LTE family genes function as a defense mechanism against myriocin.

Simultaneous localization and mapping (SLAM) based on light detection and ranging (LiDAR) is a trending research direction. This paper proposes a tightly coupled LiDAR inertial odometry (LIO) system that integrates geometric and textural information from the environment, aiming to address the key challenge of autonomous robot navigation. Most existing methods only consider geometric information and assume that reflective surfaces are ideal Lambertian surfaces, neglecting the variable reflective properties of surfaces at different angles and distances. Innovatively, we propose a new LiDAR intensity model that compensates for distortions in intensity values caused by incident angles and distances. Furthermore, we use intensity discontinuities as line features to reflect texture changes in the environment. These points are utilized to calculate residuals of line features, combined with surface features derived from geometric information, and integrated into the observation equations as part of an error-state Kalman filter sensor fusion scheme, thus constructing a tightly coupled LiDAR-inertial odometry system. Compared with the classic LOAM algorithm based on feature extraction and a state-of-the-art algorithm FAST-LIO, the results demonstrate that our system improves trajectory accuracy by approximately 16.1% with LOAM, 15.8% with LOAM+IMU and 11.2% with FAST LIO, rotation accuracy error precision by approximately 15.9% with LOAM, 15.3% with LOAM+IMU and 8.2% with FAST-LIO, demonstrate that proposed method significantly outperforms these methods in terms of localization accuracy and path generation precision.

Although significant efforts have been made to enhance industrial air conditioning systems, there are still efficiency and transient response issues in vehicle air conditioning systems using IPMSM compressors. This paper focuses on the neural network compensator-based control in an interior permanent-magnet synchronous motor (IPMSM) to address the occurrence of reduced power copper loss efficiency and degraded velocity response in the motor system when confronted with periodic dynamic disturbances of step signals. This paper encompasses two main objectives: the first objective is to introduce a neural network (NN) compensator to improve the power copper loss efficiency. The NN compensator is developed using the velocity loop and current loop control model equation of an IPMSM, and trained to implement optimal compensation control based on the back propagation algorithm. The second objective is to optimize the dynamic performance of velocity response compared to the traditional maximum torque per ampere (MTPA) current control method under step disturbance and dynamic control conditions by building an experimental system for validation, incorporating both hardware and simulation. Another significant advantage is the low computational load introduced by the neural network compensator, rendering it well-suited for implementation within low-order DSP systems. The results indicate that the neural network compensator surpasses conventional MTPA control method in both simulation and hardware-based implementations concerning power copper loss and velocity response in an IPMSM control system.

An AC (alternative current) power integration of distributed energies faces multi-fold challenges such as synchronization and weak grid-induced instability. In this study, a multi-terminal DC transformer is proposed for renewable energy clusters grid connection. The DC transformer provides multiple DC input ports for renewable energy collection, while the load port is connected to the medium-voltage DC grid via a modular multilevel converter (MMC). The multi-port topology enables flexible power transfer between multiple input sources to the load without additional components. The carrier layer modulation strategy is implemented to balance the MMC module voltage; bidirectional power transmission between multiple input sources is achieved through the phase shift modulation (PSM) method. First, we provided a detailed introduction of the proposed topology and working principle. A simulation model was built using the SIMULINK, and the simulation results verified the effectiveness of the proposed converter modulation strategy and phase shift modulation method. A corresponding hardware experimental platform was designed and built, and the modulation and voltage equalization functions of modular multilevel rectifiers were presented as well as the measured results of power transmission modulation of the converter under single-input and multi-input conditions. The results indicate that the proposed transformer can achieve multiple DC inputs and has multi-channel power transmission capabilities, making it suitable for renewable energy clusters grid connection.

Aiming at the problem of accurate AC series arc fault detection, this paper proposes a low voltage AC series arc fault intelligent detection model based on deep learning. According to the GB/T 31143—2014 standard, an experimental platform was established. This system comprises a lower computer (slave computer) and an upper computer (master computer). It facilitates the acquisition of experimental data and the detection of arc faults during the data acquisition process. Based on a one-dimensional Convolutional Neural Network (CNN), Residual model (Res) and RIME optimization algorithm (RIME) are introduced to optimize the CNN. The current signals collected using high-frequency current, low-frequency coupled current, and high-frequency coupled current are used to construct an arc fault feature set for training of the necessary detection model. The experimental results indicate that the RIME optimization algorithm delivers the best performance when optimizing a one-dimensional CNN detection model with an introduced Res. This model achieves a detection accuracy of 99.42% ± 0.13% and a kappa coefficient of 95.69% ± 0.96%. For collection methods, high-frequency coupled current signals are identified as the optimal choice for detecting low-voltage AC series arc faults. Regarding feature selection, random forest-based feature importance ranking proves to be the most effective method.

Brain–computer interfaces (BCIs) allow information to be transmitted directly from the human brain to a computer, enhancing the ability of human brain activity to interact with the environment. In particular, BCI-based control systems are highly desirable because they can control equipment used by people with disabilities, such as wheelchairs and prosthetic legs. BCIs make use of electroencephalograms (EEGs) to decode the human brain’s status. This paper presents an EEG-based facial gesture recognition method based on a self-organizing map (SOM). The proposed facial gesture recognition uses α, β, and θ power bands of the EEG signals as the features of the gesture. The SOM-Hebb classifier is utilized to classify the feature vectors. We utilized the proposed method to develop an online facial gesture recognition system. The facial gestures were defined by combining facial movements that are easy to detect in EEG signals. The recognition accuracy of the system was examined through experiments. The recognition accuracy of the system ranged from 76.90% to 97.57% depending on the number of gestures recognized. The lowest accuracy (76.90%) occurred when recognizing seven gestures, though this is still quite accurate when compared to other EEG-based recognition systems. The implemented online recognition system was developed using MATLAB, and the system took 5.7 s to complete the recognition flow.

In commercial electrical equipment, interior permanent magnet (IPM) synchronous motors undergo variations in temperature and parameters under different operating conditions. Conventional maximum torque per ampere (MTPA) control using fixed parameters lacks accurate motor parameter information, which can negatively impact the operating efficiency of electrical equipment. To address the above problem, this paper proposes a simple online MTPA control method without motor parameters. To approximate the interface structure of the conventional MTPA controller, a back propagation neural network (BP-NN) adjuster with a space vector decomposer is designed as the MTPA controller. Moreover, by leveraging the similarity between the loss function of the BP-NN and the stator current representing the efficiency of the IPM motor, the teacher signal is cleverly chosen to update the parameters of the BP-NN online. In the discrete domain, the motion mechanism of the proposed method is analyzed and characterized by the perturb & observe technique. Compared to the conventional MTPA control using fixed parameters, the feasibility and high efficiency of the proposed method for copper-loss-minimization control were validated in the simulation. The feasibility of the proposed method was further validated in simulated scenarios involving sudden changes in torque and speed. Furthermore, the transient characteristics of the output stator current were analyzed. Overall, the proposed method can achieve true online MTPA control without motor parameters.

Abstract Background Acute encephalopathy with biphasic seizures and late reduced diffusion (AESD) and mild encephalopathy associated with excitotoxicity (MEEX) are the most frequent acute encephalopathies in pediatric patients in Japan. AESD typically presents with biphasic seizures and delayed reduced diffusion in the subcortical area, called bright tree appearance (BTA), on radiological examination. In patients with AESD, arterial spin labeling (ASL) shows decreased cerebral blood flow (CBF) in the hyperacute stage and increased CBF in the acute stage, suggesting the usefulness of ASL for the early diagnosis of AESD. Additionally, proton magnetic resonance spectroscopy (MRS) shows elevated glutamate (Glu) and glutamine (Gln) in AESD. MEEX is a group of mild encephalopathies with transient elevation of Gln on MRS similar to that in AESD; however, MEEX does not include any clinical biphasic course or abnormalities, including BTA on diffusion-weighted imaging. Although the usefulness of ASL for AESD has been reported, there are no reports for patients with MEEX. In this study, we report our experience with a 4-year-old girl diagnosed with MEEX who showed unique findings on ASL. Case presentation The patient was a 4-year-old girl admitted to the emergency room with febrile status epilepticus. Considering the possibility of AESD, vitamin therapy was initiated. ASL-MR imaging (MRI) of the brain performed on the second day showed increased blood flow in the frontal, temporal, and occipital regions with spared central sulcus, which indicated AESD with central sparing. The patient was diagnosed with AESD, and the treatment included pulse steroid therapy and immunoglobulin therapy from day 3. The patient remained mildly unconscious but gradually became conscious by day 7 with no seizures. Brain MRI performed on day 8 did not show any characteristic AESD findings, such as BTA. Furthermore, MRS showed elevated Gln, which, along with the clinical course, led to the diagnosis of MEEX. The patient was discharged on day 16 without obvious sequelae. Conclusions ASL may be useful in the early diagnosis of MEEX as well as AESD, facilitating early intervention.

Autonomous mobile robots have become integral to daily life, providing crucial services across diverse domains. This paper focuses on path following, a fundamental technology and critical element in achieving autonomous mobility. Existing methods predominantly address tracking through steering control, neglecting velocity control or relying on path-specific reference velocities, thereby constraining their generality. In this paper, we propose a novel approach that integrates the conventional pure pursuit algorithm with deep reinforcement learning for a nonholonomic mobile robot. Our methodology employs pure pursuit for steering control and utilizes the soft actor-critic algorithm to train a velocity control strategy within randomly generated path environments. Through simulation and experimental validation, our approach exhibits notable advancements in path convergence and adaptive velocity adjustments to accommodate paths with varying curvatures. Furthermore, this method holds the potential for broader applicability to vehicles adhering to nonholonomic constraints beyond the specific model examined in this paper. In summary, our study contributes to the progression of autonomous mobility by harmonizing conventional algorithms with cutting-edge deep reinforcement learning techniques, enhancing the robustness of path following.

This paper presents the experimental magnetic levitation control development of Sanki Engineering airport luggage conveyor carts which have four magnetic levitation units working synchronously. With the PID controller, the state feedback controller and the zero-power controller refined by PID controller were implemented in the one magnetic levitation unit system and four-unit magnetic levitation system, and the displacement and the current were verified in a real-time system. The magnetic levitation unit had a fast response, and the control algorithms were easily implemented. The change of current and displacement were compared. In the one-unit system, the PID and state feedback controller react to the disturbance at the same speed and have similar power consumptions. For a disturbance on the zero-power controller, the system generates a transient current to deal with the load disturbance and finally settles to 0 A. The PID control for four magnetic levitation units of the conveyor cart has a better stable performance during synchronous operation. Under the control of state feedback controller, they can keep the cart statically stable with some oscillation. These characteristics are experimentally confirmed.

The potential of autonomous driving technology to revolutionize the transportation industry has attracted significant attention. Path following, a fundamental task in autonomous driving, involves accurately and safely guiding a vehicle along a specified path. Conventional path-following methods often rely on rule-based or parameter-tuning aspects, which may not be adaptable to complex and dynamic scenarios. Reinforcement learning (RL) has emerged as a promising approach that can learn effective control policies from experience without prior knowledge of system dynamics. This paper investigates the effectiveness of the Deep Deterministic Policy Gradient (DDPG) algorithm for steering control in ground vehicle path following. The algorithm quickly converges and the trained agent achieves stable and fast path following, outperforming three baseline methods. Additionally, the agent achieves smooth control without excessive actions. These results validate the proposed approach’s effectiveness, which could contribute to the development of autonomous driving technology.

BackgroundMobile health apps can support the self-management of pediatric asthma. Previous studies on mobile apps for children aged >7 years with asthma are limited, and most reports on asthma apps do not consider interactions between the children and their caregivers. Therefore, we developed an asthma app for children aged 0-12 years and their caregivers based on the results of our previous study regarding user needs. ObjectiveThe aim of this study was to evaluate the feasibility of a developed mobile app for children with asthma and their caregivers and to modify and complete the app according to the feasibility results. MethodsWe recruited children diagnosed with persistent asthma by an allergy specialist at 2 children’s hospitals, 1 university hospital, 2 general hospitals, and 1 pediatric clinic. Thereafter, the app usage was assessed, and questionnaires were administered. This study used convergent mixed methods, including providing user feedback about the pediatric asthma app, completing questionnaire surveys regarding preferences, and obtaining quantitative data about app usage. Quantitative data were analyzed based on the ratings provided for the app features used by the participants, and the usage of the app features was analyzed using descriptive statistics. Qualitative data were analyzed via a descriptive qualitative research analysis and were used to identify codes from the content-characteristic words. ResultsIn total, 30 pairs of children aged 2-12 years and their caregivers responded to the 3-month survey, and 20 pairs of children aged 4-12 years and their caregivers responded to the 6-month survey. In the 3- and 6-month surveys, “record” was the most commonly used feature by both caregivers and children. The average access logs per month among the 20 pairs ranged from 50 to 79 in the 6-month survey. The number of access logs decreased over time. In the qualitative results, app utilization difficulties were identified for 6 categories: record, preparing, alert settings, change settings, mobile phone owner, and display and motivation. Regarding app feasibility, 60% (12/20) of the caregivers strongly agreed or agreed for all evaluation items, while 63% (7/11) of the children strongly agreed or agreed for 6 items, excluding satisfaction. In the qualitative results, feasibility evaluation of the app was classified into 3 categories: high feasibility of the app, improvement points for the app, and personal factors preventing app utilization. Based on the results of the feasibility analysis, the final version of the app was modified and completed. ConclusionsThe app feasibility among children with asthma and their caregivers was generally good. Children aged 7-12 years used elements such as record, quiz, and manga. This app can support the continuous self-management of pediatric asthma. However, efforts must be taken to maintain and improve the app quality. Trial RegistrationUMIN Clinical Trials Registry UMIN000039058; https://tinyurl.com/3na9zyf8

Abstract Background Neurogenic pulmonary edema is a rare but serious complication of febrile status epilepticus in children. Comprehensive screening for viral pathogens is seldomly performed in the work-up of febrile children. Case presentation A 22-month-old girl presented with her first episode of febrile status epilepticus, after which she developed acute pulmonary edema and respiratory failure. After the termination of seizure activity, the patient was intubated and managed on mechanical ventilation in the emergency room. The resolution of respiratory failure, as well as the neurological recovery, was achieved 9 h after admission, and the patient was discharged 6 days after admission without any complications. Molecular biological diagnostic methods identified the presence of human coronavirus HKU1, influenza C virus, and human parainfluenza virus 2 from the patient’s nasopharyngeal specimens. Conclusions Neurogenic pulmonary edema following febrile status epilepticus was suspected to be the etiology of our patient’s acute pulmonary edema and respiratory failure. Timely seizure termination and rapid airway and respiratory intervention resulted in favorable outcomes of the patient. Molecular biological diagnostic methods identified three respiratory viruses; however, their relevance and association with clinical symptoms remain speculative.

In this paper, the simulation and design of a power converter suitable for a low-voltage photovoltaic (PV) battery energy storage converter was investigated. The converter was suitable for sources and loads with near voltage levels and were aimed at efficiency improvement. The converter was called a series partial power converter (SPPC). A continuous current and a boost SPPC topology based on an isolated Cûk converter was constructed. The operation modes of the converter were analyzed. The efficiency verification test of the SPPC in a laboratory environment was completed using an outdoor start-up test, a photovoltaic full cover test and a steady-state operation test under different power levels. The results show that the SPPC can achieve 95–98% converter efficiency within the 87 W–242 W power range. The incremental conductance method based on the SPPC can realize the dynamic MPPT under constant illumination and changing meteorological conditions.

With the development of mobile payment, the Internet of Things (IoT) and artificial intelligence (AI), smart vending machines, as a kind of unmanned retail, are moving towards a new future. However, the scarcity of data in vending machine scenarios is not conducive to the development of its unmanned services. This paper focuses on using machine learning on small data to detect the placement of the spiral rack indicated by the end of the spiral rack, which is the most crucial factor in causing a product potentially to get stuck in vending machines during the dispensation. To this end, we propose a k-means clustering-based method for splitting small data that is unevenly distributed both in number and in features due to real-world constraints and design a remarkably lightweight convolutional neural network (CNN) as a classifier model for the benefit of real-time application. Our proposal of data splitting along with the CNN is visually interpreted to be effective in that the trained model is robust enough to be unaffected by changes in products and reaches an accuracy of 100%. We also design a single-board computer-based handheld device and implement the trained model to demonstrate the feasibility of a real-time application.

Abstract Background Streptococcus pyogenes (group A Streptococcus [GAS]) is a major human pathogen that causes a wide spectrum of clinical manifestations. Although invasive GAS (iGAS) infections are relatively uncommon, emm3/ST15 GAS is a highly virulent, invasive, and pathogenic strain. Global molecular epidemiology analysis has suggested that the frequency of emm3 GAS has been recently increasing. Case presentation A 14-year-old patient was diagnosed with streptococcal toxic shock syndrome and severe pneumonia, impaired renal function, and rhabdomyolysis. GAS was isolated from a culture of endotracheal aspirates and designated as KS030. Comparative genome analysis suggested that KS030 is classified as emm3 (emm-type) and ST15 (multilocus sequencing typing [MLST]), which is similar to iGAS isolates identified in the UK (2013) and Switzerland (2015). Conclusions We conclude that the global dissemination of emm3/ST15 GAS strain has the potential to cause invasive disease.

In a standard setup of conventional state estimation problems, the output signal of a dynamical system is sampled at every regular time interval. This paper addresses an estimation problem under event-triggered sampling: an irregular sampling rule in which the output signal is sampled only when a specified event occurs. In particular, this paper focuses on the Lebesgue sampling, which is a type of event-triggered sampling such that the output signal is sampled only when it crosses specific thresholds. In the proposed estimation method, not only information in the sampled points but also that in the inter-sample intervals are utilized to improve the estimation accuracy efficiently. The particle filter is applied to the estimation since the inter-sample information makes the distribution of the estimates non-Gaussian. Furthermore, the proposed estimator is combined with a linear-quadratic-regulator (LQR) type state-feedback controller. The derived control law can be an effective approach to an event-triggered control problem in which the Lebesgue-sampled output is utilized. The effectiveness of both of estimation and control methods is examined through numerical examples.

A switching sequence model predictive direct torque control (MPDTC) of IPMSMs for EVs in switch open-circuit fault-tolerant mode is studied in this paper. Instead of selecting one space vector from the possible four space vectors, the proposed MPDTC method selects an optimized switching sequence from two well-designed switching sequences, including three space vectors, according to a new designed cost function of which the control objectives have been transferred to the dq-axes components of the stator flux-linkage under the maximum-torque-per-ampere control. The calculation method of the durations of the adopted space vectors in the optimized switching sequence is studied to realize the stator flux-linkage reference tracking. In addition, the capacitor voltage balance method, by injecting a dc offset to the current of fault phase, is given. Compared with the conventional MPDTC method, the complicated weighting factors designing process is avoided and the electromagnetic torque ripples can be greatly suppressed. The experimental results prove the effectiveness and advantages of the proposed scheme.