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Low-adhesive surfaces have been highlighted due to the potentials to mitigate fouling issues by preventing unwanted substances from adhering. Realizing superhydrophobicity with 3D surface structures/chemical modifiers or fabricating lubricant-assisted slippery surfaces has been demonstrated to realize low-adhesive surfaces. However, they still need to overcome the transition to Wenzel from Cassie states of droplets on 3D surface structures or the lubricant depletion issues of slippery surfaces for sustainable operations. Herein, we report the fabrication of low-adhesive polymeric surfaces, neither assisted by 3D surface structures/chemical modifiers nor lubricants, which is realized by embedding the interconnected pore networks underneath the top smooth surface using a water steaming method. The fabricated silicone surfaces exhibit low-adhesive properties due to the stress concentration effects generated by the subsurface-structured pores, favorable for easy detachment of the adherent from the surface. Our platform can be exploited to lower adhesion of superhydrophilic surfaces or to achieve ultralow-adhesive properties upon combination with superhydrophobicity. Finally, scale precipitation tests reveal 4.2 times lower scale accumulation of our low-adhesive polymeric surfaces than that in control samples.

Li+ electrolyte-gated transistors (EGTs) have attracted significant attention as artificial synapses because of the fast response of Li+ ion, low operating voltage, and applicability to flexible electronics. Due to the inherent nature of Li+ ion, Li+ EGTs show, however, limitations, such as poor long-term synaptic plasticity and nonlinear/nonsymmetric conductance update, which hinder the practical applications of artificial synapses. Herein, Li+ EGTs integrated with poly(vinylidene fluoride-co-trifluoroethylene) (PVDF-TrFE) ferroelectric polymer as a channel–electrolyte interlayer are presented. Owing to the polarized domains of PVDF-TrFE, the transport of Li+ ions at the channel–electrolyte interface is accelerated, and Li+ ions effectively penetrate the channel. Moreover, the self-diffusion of Li+ ions from the channel to the electrolyte is suppressed by the downward polarized domains. Li+ EGTs, therefore, successfully demonstrate synaptic characteristics, including excitatory postsynaptic current, short-/long-term synaptic plasticity, and paired-pulse facilitation. Also, conductance update in Li+ EGTs shows a dynamic range ( Gmax/ Gmin) of 92.42, high linearity, and distinct stability over 100 cycles. Based on their synaptic characteristics, inference simulations using a convolution neural network for the CIFAR-10 dataset imply that Li+ EGTs are suitable as artificial synapses with an inference accuracy of 89.13%. The new methodological approach addressing modulation of ion dynamics at the interface is introduced for developing practical synaptic devices.

This article describes a novel approach to classification of whole-body motions from estimated human postures in 2D camera images and subsequent generation of their relevant descriptions. The motions are encoded into stochastic motion models referred to as motion primitives. Words are connected to the motion primitives, and word n-grams are represented stochastically. More specifically, the motion observation is classified into the motion primitive, from which its relevant words are generated. These words are arranged in a grammatically correct order to make the descriptions for the observation. This approach was tested on actions performed by older adults in the care facility and its validity was demonstrated.

Background In arthroscopic shoulder surgery, a mechanical fluid-irrigation system is used to wash out blood from the joint. If used at high pressure, it can cause side effects such as fluid extravasation, leading to airway obstruction after surgery. Desflurane is prone to increasing sympathetic nerve activity and plasma catecholamine release more than another inhalation anesthetics. The objective of this study was to determine whether desflurane could increase in the irrigation pump pressure than sevoflurane in shoulder arthroscopy. Methods Patients were randomized into a sevoflurane group (group S) and a desflurane group (group D). Each included 20 patients. For group S, sevoflurane 1.2 MAC and intravenous remifentanil were administered for anesthesia maintenance. Group D received desflurane 1.2 MAC and intravenous remifentanil. Starting at 20 mmHg of pump pressure, the surgeon estimated the visibility of the surgical field (grade I-IV). After that, the pressure was freely adjusted by the surgeon to obtain clear vision with the arthroscope during the surgery. Results The maximum pressure of the mechanical water pump was higher in group D than group S (54.0 ± 6.8 mmHg vs. 48.9 ± 5.7 mmHg, P = 0.017), but the difference was not statistically significant at a significance level of 0.01. The arthroscopic visibility at the surgical site did not differ significantly between the two groups (P = 0.284). Conclusions When desflurane is used in arthroscopic shoulder surgery, it does not require more pressure from the irrigating-fluid pump to secure a clear vision of the surgical site, compared to sevoflurane.

Synaptic devices that mimic biological neurons have attracted much attention for brain-inspired neuromorphic computing. Especially, synaptic thin-film transistors (TFTs) have emerged with simultaneous signal processing and information storage advantages. However, the analysis of excitatory postsynaptic current (EPSC) relies on an empirical model such as a serial RC circuit, which limits a systematic and in-depth study of synaptic devices in terms of material and electrical properties. Herein, the single-pulse-driven synaptic EPSC (SPSE) model, including capacitive effect and information of the synaptic window, is analytically proposed. The SPSE model can simulate EPSC of synaptic devices at given TFT-operating conditions. EPSC with the SPSE model can be characterized with quantified parameters for the capacitive effects and the synaptic windows, which also depend on the electrical condition applied to TFTs. Various kinds of synaptic-TFTs with different gate insulators (e.g., SiO2 and ion-gel) are used to confirm the performance of the SPSE model. For example, the SPSE model can capture the long-term robustness of ion-gel-based TFTs with specific quantified parameters. In addition, the SPSE model enables the estimation of energy consumption, which can potentially be leveraged to compare the energy cost of EPSC fairly. The SPSE model can provide a guideline to understand the physical properties of synaptic TFTs.

Background: This study was investigated the effects of dexmedetomidine in combination with fentanyl-based intravenous patient-controlled analgesia (IV-PCA) on pain attenuation in patients undergoing open gastrectomy in comparison with conventional thoracic epidural patient-controlled analgesia (E-PCA) and IV-PCA. Methods: One hundred seventy-one patients who planned open gastrectomy were randomly distributed into one of the 3 groups: conventional thoracic E-PCA (E-PCA group, n = 57), dexmedetomidine in combination with fentanyl-based IV-PCA (dIV-PCA group, n = 57), or fentanyl-based IV-PCA only (IV-PCA group, n = 57). The primary outcome was the postoperative pain intensity (numerical rating scale) at 3 hours after surgery, and the secondary outcomes were the number of bolus deliveries and bolus attempts, and the number of patients who required additional rescue analgesics. Mean blood pressure, heart rate, and adverse effects were evaluated as well. Results: One hundred fifty-three patients were finally completed the study. The postoperative pain intensity was significantly lower in the dIV-PCA and E-PCA groups than in the IV-PCA group, but comparable between the dIV-PCA group and the E-PCA group. Patients in the dIV-PCA and E-PCA groups needed significantly fewer additional analgesic rescues between 6 and 24 hours after surgery, and had a significantly lower number of bolus attempts and bolus deliveries during the first 24 hours after surgery than those in the IV-PCA group. Conclusions: Dexmedetomidine in combination with fentanyl-based IV-PCA significantly improved postoperative analgesia in patients undergoing open gastrectomy without hemodynamic instability, which was comparable to thoracic E-PCA. Furthermore, this approach could be clinically more meaningful owing to its noninvasive nature.

Although the application of positive end-expiratory pressure (PEEP) can alter cardiopulmonary physiology during one-lung ventilation (OLV), these changes have not been clearly elucidated. This study assessed the effects of different levels of PEEP on biventricular function, as well as pulmonary oxygenation during OLV. Thirty-six lung cancer patients received one PEEP combination of six sequences, consisting of 0 (PEEP_0), 5 (PEEP_5), and 10 cmH2O (PEEP_10), using a crossover design during OLV. The ratio of arterial oxygen partial pressure to inspired oxygen fraction (P/F ratio), systolic and diastolic echocardiographic parameters were measured at 20 min after the first, second, and third PEEP. P/F ratio at PEEP_5 was significantly higher compared to PEEP_0 (p = 0.014), whereas the P/F ratio at PEEP_10 did not show significant differences compared to PEEP_0 or PEEP_5. Left ventricular ejection fraction (LV EF) and right ventricular fractional area change (RV FAC) at PEEP_10 (EF, p &lt, 0.001, FAC, p = 0.001) were significantly lower compared to PEEP_0 or PEEP_5. RV E/E&rsquo, (p = 0.048) and RV myocardial performance index (p &lt, 0.001) at PEEP_10 were significantly higher than those at PEEP_0 or PEEP_5. In conclusion, increasing PEEP to 10 cmH2O decreased biventricular function, especially on RV function, with no further improvement on oxygenation compared to PEEP 5 cmH2O during OLV.

PURPOSE The purpose of this study was to evaluate a revised version of the Incontinence-Associated Skin Damage Severity instrument (IASD.D.2) using 3 different groups of nursing staff. Revisions to the instrument included renumbering 1 body area where incontinence-associated dermatitis (IAD) occurs into 2 areas (right and left), which raised the total possible score from 52 to 56, and defining the borders of the body areas. DESIGN Observational, evaluative design. SAMPLE AND SETTING Five clinical experts certified in wound, ostomy, and/or continence (WOC) nursing evaluated content validity. Evaluators were attendees at the WOC Nurses (WOCN) Society 2014 conference, hospital nurses, and nursing staff at a nursing home. Evaluators were attendees at the WOCN Society's 2014 National Conference, hospital nurses at a community hospital with Magnet designation, and nursing staff at a skilled nursing home in the Midwestern United States. The evaluator group comprised 198 conference attendees (all nurses; age 53 ± 8.2 years, mean ± SD), 67 hospital nurses (age 37 ± 11 years), and 34 nursing home nursing staff (age 45 ±13.8 years). The majority of evaluators (>75%) in each of the groups were female. METHODS Clinical experts evaluated the content validity of the revised instrument. Evaluators scored 5 to 9 photographic cases using the revised instrument. Four of the cases were scored by all evaluators. The agreement of case scores among all evaluators was analyzed to assess interrater reliability. The scores of evaluators grouped by evaluators' self-identified skin color or nursing experience (

There seem to be modifiable components of diabetes in terms of glycemic control to improve surgical outcome. The aim of the study was to evaluate impact of preoperative glycated hemoglobin (HbA1c) level in patients with diabetes mellitus undergoing off-pump coronary bypass (OPCAB) on perioperative glycemic variability and short-term outcome.Medical records of 703 patients with diabetes mellitus who underwent OPCAB from 2005 to 2017 were reviewed. The perioperative goal of glycemic control was180 mg/dL. Patients were divided into 2 groups according to HbA1c level (7.0% or ≥7.0%). Glycemic variability during surgery and up to 24 hours after surgery was assessed by coefficient of variation (CV). Primary outcome was defined as composite of postoperative permanent stroke, prolonged ventilation, deep sternal wound infection, renal failure, reoperation, and 30-day mortality.Incidence of composite of postoperative morbidity and mortality endpoints was greater in patients with HbA1c ≥7.0% (21% vs 15%, P = .041). Perioperative time-weighted average glucose concentration was also greater in patients with HbA1c ≥7.0%, whereas the CV did not show any difference. Multivariable logistic regression analysis revealed that congestive heart failure, chronic kidney disease, moderate mitral regurgitation or greater, preoperative hemoglobin level, and preoperative HbA1c ≥7.0% were independently associated with composite of postoperative morbidity and mortality, but perioperative CV and time-weighted average glucose ≥200 mg/dL were not.Increased preoperative HbA1c (≥7.0%) level reflecting long-term glycemic control seems to exert an adverse influence on outcome, whereas the influence of perioperative glycemic variables appear to be abrogated using a target glucose level of180 mg/dL in patients with diabetes mellitus undergoing OPCAB.

When robot comes to our daily life, sharing knowledge is a key factor to realize the symbiosis between human and robot. In previous research, Robot Technology (RT) ontology was proposed as a knowledge base to help robot understands human’s intention in daily activities. However, the method to build that ontology was not discussed. This paper presents our approach to build RT ontology based on basic-level knowledge. We proposed a new structure for RT ontology with Where, What, and How layers based on 4W1H. As for input data, we used educational books and MIT’s ConceptNet. Our method is able to build RT ontology automatically by extracting objects and human activities from these data sources. We also implemented a weighting mechanism for the new ontology. Result shows that our method achieved better accuracy than conventional approach using Internet data.

The authors developed cloud based chat robot for exhilarating elderly people. Our robot is designed to keep users from being bored. Conventional chat robot systems use huge phrase database in advance. However, fixed phrase involves an immense amount of time and effort to make. The proposed system uses dialogue histories which are collected from users. This paper reports the effect of updating the phrase database by using histories.

Recently,reinforcement learning (RL) methods have been used for learning problems in environments with embedded hidden states. However, conventional RL methods have been limited to handlingMarkov decision process problems. In order to overcome hidden states, several algorithms were proposed, but these need an extreme amount of memory of past sequences which represent historical state transitions. The aim of this work was to extend our previously proposed algorithm for hidden states in an environment, calledlabeling Q-learning (LQ-learning), which reinforces incompletely observed perception by labeling. In LQ-learning, the agent has a perception structure which consists of pairs of observations and labels. From these pairs, the agent can distinguish more exactly hidden states which look the same but are actually different each other. Labeling is carried out by labeling functions. Numerous labeling functions can be considered, but here we introduce some labeling functions based on the sequence of only the last and the current observations. This extended LQ-learning is applied to grid-world problems which have hidden states. The results of these simulations show the availability of LQ-learning.

Commonsense is one of the keys to enable human-robot communication in daily life scenarios. It is very difficult for a robot to do tasks ordered by a human without having some basic knowledge to understand the human's commands. This paper proposes a method to automatically build commonsense knowledge for the “Tidy-up” service, in which a robot is asked to take objects such as books, cups, dishes on a table to appropriate places automatically. We defined three object classes that are necessary for the service, namely “Washable”-objects that need to be washed, “Reusable”-objects that need to be stored for reuse, and “Trashable”-objects that need to be disposed of. For each object, multiple attributes were extracted from both the ConceptNet knowledge base and the Google search engine, and fed to classifiers to classify the object into the appropriate class. To evaluate the proposed method, output from classifiers were compared with the result from actual human. The result showed that the proposed approach is efficient in classifying objects and in providing object type as commonsense knowledge, hence, helping the robots to understand human intention and to provide intuitive service.

In this paper, we focus on the “Tidy-up” robotic service which needs extremely complex and enormous kinds of knowledge about objects, environments, places, and user’s status. Especially, we have tried to find the way for specifying of appropriate places of daily-life objects for the “Tidy-up” robotic service. As the first phase, we categorized daily-life objects into three categories: Trashable, Reusable, and Washable. In the specification of an appropriate place, there are two methods: object category based and individual object trajectory based. For the first method, object state machine has been provided for each object category. In the second method, the object trajectories in term of places have been collected. Based on these historical trajectories, we could recognize the trajectory pattern and use it to provide and appropriate place for individual object.

This paper proposes our approach to disambiguate human pointing gesture by using commonsense knowledge. Pointing gesture is a natural way to specify the object that a user needs in the “Bring something” service. However, error in gesture recognition makes robot difficult to select the correct candidate object when there are multiple objects located closely together. To solve this problem, Robot Technology (RT) Ontology was used to make assumption about human activity and necessary objects for the activity. By combining this commonsense reasoning with the spatial constrain from human's pointing direction, robot is able to find appropriate object. Experiment showed the successful rate of object selection was improved, and therefore robotic service becomes more intuitive.

Sharing commonsense knowledge is one key to realize symbiosis between human and robot. However, as human knowledge is so complicated, it is difficult to extract appropriate information for robot. This paper presents our approach to extract the core part from Basic-level Knowledge Network (BKN) to be used for intuitive “Bring something” robotic service. A two-step filtering method was implemented based on the weighting mechanism of BKN. The first step used one weighting parameter to filter the most irrelevant data. The second step combined all weighting parameters from BKN with user response data from a questionnaire to estimate commoness of object-activity connections. Result showed that the proposed approach is efficient to extract the most common activities related to each object in BKN, therefore to help robot understands human intention and provide intuitive service.

In order to realize a human-like intuitive communication between human and robot, a robot should understand the human's basic level knowledge which is usually hidden in communication. However, as human's basic level knowledge has been unconsciously built during our development, it is very hard to build the knowledge network as the computational system. In this paper, we propose the approach to build the basic level knowledge network through different types of corpus including websites, books, and other available common sense knowledge bases. As for the main part, we build our Basic level Knowledge Network system semantic elements, namely concepts and connections. Each connection is weighted by a set of weights which represents the commonness of the semantic element. We implemented our system onto iPad to realize a human-robot interface device for the scenario of “Bring something”.

In this paper, we propose a new type of LQ-learning to solve POMDP. In the POMDP environment, the agent cannot observe the environment directly. In the LQ-learning, in order to discriminate partially observed states, the agent attaches label to each observation which perceived as the same ones. Unlike our previous LQ-learning, we make preparations of knowledge about the environment in advance. The knowledge is automatically acquired by Kohenen's Self-Organizing Map (SOM), which provides the knowledge about state transitions to the agent. Then, LQ-learning agent attaches labels to observations with reference to a map obtained by SOM.

The most widely used reinforcement learning (RL) algorithms, such as Q-learning and TD (/spl lambda/) are limited to Markovian environments. Recent research on reinforcement learning algorithms has concentrated on partially observable Markov decision process (POMDP). The only way to overcome partial observability is to use memory to estimate state. In this paper, we present a new memory architecture of RL algorithms to solve certain type of POMDPs. Our algorithm, which we call labeling Q-learning (LQ-learning), is applied to test problems of simple mazes taken from recent literature. The results demonstrate LQ-learning's ability to work well in near optimal manner.

Sharing commonsense knowledge would be one of the most important keys for interaction between human and service robot. However, the complication of human commonsense makes extracting appropriate information for service robot difficult. This paper presents our approach to build the basic-level knowledge network (BKN) as a commonsense and how it is used for robotic services. In this paper, we focused on two services. One is ‘bring something’ and the other is ‘tidy things up’. We found that for ‘bring something’, relationship between object-activity is important, and for ‘tidy things up’ service, knowledge about relationship between object-place is more important. To perform these services at the practical level, many relative knowledge should be prepared. Moreover, we would like to introduce how BKN is built and show the demonstration result in which how BKN is useful for actual services in real environment.