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Vitamin B12 deficiency causes a variety of symptoms in the hematologic, gastrointestinal tract, and neurologic deficit. We report a rare case of progressive gait disturbance, bradykinesia and dysarthria which was that vitamin B12 deficiency was diagnosed as the cause, and symptoms improved after intramuscular cobalamin treatment. Vitamin B12, homocysteine, and methylmalonic acid tests are recommended for patients with progressive gait disturbance, even in the absence of obvious anemia symptoms.

Central obesity in midlife is a risk factor of cognitive decline and dementia, and also one of the factors that make cognitive functions deteriorate rapidly. The objective of this study is to investigate the relationship between truncal body composition (fat and muscle) and cognitive impairment in patients with dementia. A total of 81 female over 60 years of age with probable Alzheimer’s disease were recruited between November 2014 and September 2015. The Mini-Mental State Examination, Global Deterioration Scale, and Clinical Dementia Rating Scale were used to assess the cognitive functions. Both truncal fat and muscle mass were measured using body dual-energy X-ray absorptiometry and used as a percentage of body weight (TMM% and TFM%). Correlations between truncal composition and cognitive status were assessed by simple correlation analysis, which was followed by partial correlation analysis with age and educational years. TFM% was not related to cognitive impairment. In contrast, TMM% had a significantly negative correlation with all three cognitive assessment scores. After further adjusting for age, educational years, and vascular factors, there was still a relationship between TMM% and cognitive functions. Unlike truncal fat mass that showed no relevance with cognitive functions, the truncal muscle mass was negatively correlated with cognitive status. The truncal muscle mass is thought to affect cognitive status in dementia patients somehow.

Background/Aims: Alzheimer’s disease (AD) is the most common cause of dementia worldwide. Interestingly, muscle mass (MM) and muscle strength (MS) are related to AD. In addition to the muscle profile, brain atrophy is also a prominent feature of AD. There is substantial evidence showing an association between muscle profile and dementia, but the role of the muscle profile and cerebral cortical atrophy within this association is less well understood. The objective of this study was to determine if there is any association between muscle profile and brain regional volume in AD. A secondary objective was to determine whether this relationship continues as the clinical stage of AD progresses. Methods: We recruited 28 patients with probable AD without weakness. We assessed the patients’ basic demographic characteristics, Mini-Mental State Examination score, and brain magnetic resonance images. MM was measured using body dual-energy X-ray absorptiometry. MS was assessed in Nm/kg with an isokinetic knee extensor using an isokinetic device at an angular velocity of 60°/s. An automatic analysis program was used for brain regional volumetric measurements. Dementia was divided into two stages: mild and moderate. Results: MS was related to left hippocampal volume ratio. After adjusting for age and cognitive status, the relationship remained. MS did not demonstrate any relationship to any brain regional volume ratio in the mild stage; however, in the moderate stage, it was positively related to both the right and the left hippocampal volume ratio. Conclusions: Our findings imply a shared underlying pathology relating MS and brain volume and suggest cognitive functional declines through the muscle-brain axis. Further longitudinal studies are needed to find possible and related causes of reduced MS and cortical atrophy in patients with dementia.

Recently, combinations of 2D van der Waals (2D vdW) materials and organic materials have attracted attention because they facilitate the formation of various heterojunctions with excellent interface quality owing to the absence of dangling bonds on their surface. In this work, a double negative differential resistance (D‐NDR) characteristic of a hybrid 2D vdW/organic tunneling device consisting of a hafnium disulfide/pentacene heterojunction and a 3D pentacene resistor is reported. This D‐NDR phenomenon is achieved by precisely controlling an NDR peak voltage with the pentacene resistor and then integrating two distinct NDR devices in parallel. Then, the operation of a controllable‐gain amplifier configured with the D‐NDR device and an n‐channel transistor is demonstrated using the Cadence Spectre simulation platform. The proposed D‐NDR device technology based on a hybrid 2D vdW/organic heterostructure provides a scientific foundation for various circuit applications that require the NDR phenomenon., In this work, a double negative differential resistance (D‐NDR) characteristic of a hybrid 2D van der Waals (2D vdW)/organic tunneling device consisting of a hafnium disulfide/pentacene heterojunction and a 3D pentacene resistor is reported. Then the operation of a controllable‐gain amplifier configured with the D‐NDR device and an n‐channel transistor is demonstrated using the Cadence Spectre simulation platform.

Neurodegenerative disease is one of the main contributing factors affecting muscle atrophy. However, this intriguing brain-muscle axis has been explained by the unsubstantial mechanisms. Although there have been several studies that have evaluated the muscle profile and its relation to cognition in patients with dementia, there is still lack of data using standardized methods and only few published studies on Korean populations. The objective of this study is to evaluate the relationship of muscle mass and strength to cognition in patients with Alzheimer’s disease dementia (AD). We recruited 91 patients with probable AD without weakness. We assessed patients’ basic demographic characteristics, vascular risk, body mass index, and global cognitive assessment scores. Muscle mass was measured using body dual-energy X-ray absorptiometry. Muscle strength was assessed by isokinetic knee extensor using an isokinetic device at an angular velocity of 60°/s in nm/kg. The muscle mass and strength were not related to each other in both male and female groups. Only muscle strength, but not muscle mass, was negatively related to cognition. After adjusting for covariates, the relationship between muscle strength and cognition still remained in the male group, however, was attenuated in the female group. In patients with AD dementia, abundant muscle mass did not mean strong power. The simple lower-extremity muscle strength assessment is more effective in predicting cognition than a muscle mass measure in male patients.

This article aims at developing an art-based STEAM educational program that would help elementary school students to develop their abilities to solve scientific problems and artistic sensibilities by using an educational robot. In addition, this article investigates whether the program could be applied in the field. In order to achieve the purpose of this article, ‘mobile' and ‘abstraction' were selected as the subjects of the activities and the STEAM educational program which allowed students to learn knowledge regarding science, technology and mathematics in a comprehensive manner through the course in order to experience and create works of art. Also, the level of satisfaction and effectiveness were confirmed by applying the program to the class targeting students in the 4th and 6th grades.

Cognitive impairment is a common complication observed after a stroke. Currently there are no definitively proven pharmacologic therapies for recovery from post-stroke cognitive impairment and vascular dementia. In this meta-analysis, we evaluated the efficacy and safety of cholinesterase inhibitors in their improvement of cognition in patients with post-stroke cognitive impairment and vascular dementia. We conducted a meta-analysis using seven eligible studies from 305 published articles. We investigated the differences in Mini–Mental State Examination (MMSE) and Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) scores, before and after cholinergic augmentation in patients with post-stroke cognitive impairment and vascular dementia. MMSE and ADAS-cog scores were also compared during the subsequent follow-up periods. MMSE score of patients with post-stroke cognitive impairment was increased after cholinergic augmentation throughout the 24 weeks with mean differences [MD] of 3.000, 1.732, 1.578 1.516, and 1.222, at 4, 4–8, 8–12, 12–18, and 18–24 weeks, respectively. In addition, ADAS-cog scores decreased at 6, 12, 18, and 24 weeks by pharmaceutical augmentation, but not with placebo with mean differences [MD] of -2.333, -2.913, -2.767, -2.416, and -1.859, respectively. This meta-analysis shows that acetylcholinesterase inhibitors maintain a stable pattern of improved cognitive function in patients with post stroke cognitive impairment and vascular dementia without the increased risk of side effects.

Introduction: Many factors are known to affect the rate of cognitive decline; however, studies on clinical outcomes are rare. Muscle profile and their relationship to dementia trajectories have not been extensively investigated. We investigated factors that affect the rate of clinical decline and the usefulness of muscle profiles for predicting the clinical outcomes of patients with Alzheimer’s dementia (AD). Objective: Sixty-nine subjects with probable AD were included and several factors that are known to affect the rate of cognitive decline were evaluated. Methods: Over a period of 3 years, each subject received an annual evaluation that included a clinical interview and an assessment of their cognitive status as measured by a clinical dementia rating-sum of boxes (CDR-SOB) score. Linear mixed-effects models were used to test for associations between each factor and the ­CDR-SOB score over time. These analyses were repeated in a multivariate linear mixed-effects model after adjusting the covariates. Results: Age, diabetes mellitus, and baseline dementia severity were identified as potential covariates that influence clinical progression. However, a subject’s muscle profile was not found to predict dementia progression. Conclusions: We expect that early screening and intervention, as well as new drugs with mechanisms of action similar to those of antidiabetic medications, will help patients with dementia maintain their clinical status.

To resolve a mismatch between the needs required at industrial sites and school curriculum, the government created National Competency Standards(NCS) that facilitates the organization of industrial demands and introduced NCS to be applied to schools. 3D printing technology has changed the paradigm of traditional industries, the industries that contribute to the next generation of new creative industries and high value as well as the industry. This study aims to develop convergence curriculum in Technical Received (January 19, 2016), Review Result (February 2, 2016) Accepted (February 11, 2016), Published (March 31, 2016) Social Policy Building, Sejong National Research Complex, 370, Sicheong-daero, Sejong-si, 30147 Korea email: jwkim@krivet.re.kr Graduate School of Korea National University of Education, Cheongju, Chungbuk, 28173 Korea email: jjok@naver.com (Corresponding Author) Dept. of Technology Education, Korea Nat’l Univ. of Education, Cheongju, Chungbuk, 28173, Korea email: jskim@knue.ac.kr The Development of Convergence Curriculum in Technical College Using 3D Printing Based on National Competency Standards Copyright c 2016 HSST 312 colleges using 3D printing technology that train experts in 3D modeling and 3D printing sectors required from the industry according to NCS-based curriculum guidelines presented by the Ministry of Education. To achieve the aim, based on the trend of the 3D printing market and the analysis of external circumstances, NCS unit(element), which is required at industrial sites, was determined, and liberal arts and major subjects were selected. The curriculum organization was presented by connecting the finally selected liberal arts and major subjects to NCS. The composition of curriculum included a total of 36 subjects: 4 liberal arts, 10 major subjects of theory, 22 major subjects of practice. It is highly prospected that Technical colleges can operate the convergence curriculum using 3D printing technology, so they contribute to fostering experts who have the ability to use 3D modeling and 3D printing needed for working industrial design sectors and for implementing related works.

Recently, triboelectric nanogenerators (TENGs) have been widely utilized to address the energy demand of portable electronic devices by harvesting electrical energy from human activities or immediate surroundings. To increase the surface charge and surface area of negative TENGs, previous studies suggested several approaches such as micro-patterned arrays, porous structures, multilayer alignment, ion injections, ground systems and mixing of high dielectric constant materials. However, the preparation processes of these nanocomposite TENGs have been found to be complex and expensive. In this work, we report a simple, efficient and inexpensive modification of poly(dimethylsiloxane) (PDMS) using graphene nanoplatelets (GNPs) fillers and a Na2CO3 template. This GNP-PDMS was chemically bonded using 3-aminopropylethoxysilane (APTES) as a linker with an electrode multilayer made by layer-by-layer deposition of polyvinyl alcohol (PVA) and poly(4-styrene-sulfonic acid) (PSS)-stabilized GNP (denoted as [PVA/GNP-PSS]n). A 33 wt.% Na2CO3 and 0.5 wt.% of GNP into a PDMS-based TENG gives an open-circuit voltage and short-circuit current density of up to ~270.2 V and ~0.44 &mu, A/cm2, which are ~8.7 and ~3.5 times higher than those of the pristine PDMS, respectively. The higher output performance is due to (1) the improved surface charge density, 54.49 &mu, C/m2, from oxygen functional moieties of GNP, (2) high surface roughness of the composite film, ~0.399 &mu, m, which also increased the effective contact area, and (3) reduced charge leakage from chemical bonding of GNP-PDMS and [PVA/GNP-PSS]3 via APTES. The proposed TENG fabrication process could be useful for the development of other high-performance TENGs.

The outdoor recreation fever that is drastically increasing in Korea could be the result of the interaction between mass media and popular culture as well as increase of leisure time and disposable income. Thus, the purpose of this study is to specify how the exposure of outdoor recreation through mass media influences potential outdoor recreationists. In order to specify this decision-making process, playfulness, which is the intrinsic characteristic of adults` fun and play, was added to the theory of planned behavior, which has high explanation power regarding human behavior. As a result of the study, it turned out that playfulness significantly influences attitude, subjective norm, and perceived behavioral control, and that attitude, subjective norm, and perceived behavioral control also significantly influence behavioral intention. These results specify that playfulness is a factor that has a great effect on outdoor recreation that seeks deviant behavior.

Various literatures related computing of information processing have been recently shown the researches inspired from the remarkably excellent human capabilities which recognize and categorize very complex visual patterns such as body motions and facial expressions. Applied from human’s outstanding ability of perception, the classification function of visual sequences without context information is specially crucial task for computer vision to understand both the coding and the retrieval of spatio-temporal patterns. This paper presents a biological process based action recognition model of computer vision, which is inspired from visual information processing of human brain for action recognition of visual sequences. Proposed model employs the structure of neural fields of bio-inspired visual perception on detecting motion sequences and discriminating visual patterns in human brain. Experimental results show that proposed recognition model takes not only into account several biological properties of visual information processing, but also is tolerant of time-warping. Furthermore, the model allows robust temporal evolution of classification compared to researches of action recognition. Presented model contributes to implement bio-inspired visual processing system such as intelligent robot agent, etc.Keywords:Bio-Inspired Visual Information Processing, Action Recognition, Spatio-Temporal Correlation, Recognition of Visual Sequence

Making good predictions about the outcome of one’s actions woul d seem to be essential in the context of social interaction and decision-making. This paper proposes a computational model for learning articulated motion patterns for action recognition, wh ich mimics biological-inspired visual perception processing of huma n brain. Developed model of cortical architecture for the unsup ervised learning of motion sequence, builds upon neurophysiological kno wledge about the cortical sites such as IT, MT, STS and specifi c neuronal representation which contribute to articulated motion perception. Experiments show how the model automatically selects significant motion patterns as well as meaningful static snapsh ot categories from continuous video input. Such key poses corre spond to articulated postures which are utilized in probing the train ed network to impose implied motion perception from static views. We also present how sequence selective representations are learned in STS by fusing snapshot and motion input and how learned feedback connections enable making predictions about future inp ut sequence. Network simulations demonstrate the computational capacity of the proposed model for motion recognition.

Early researches in human action recognition have focused on tracking and classifying articulated body motions. Such methods required accurate segmentation of body parts, which is a sticky task, particularly under realistic imaging conditions. Recent trends of work have become popular towards the use of more and low-level appearance features such as spatio-temporal interest points. Given the great progress in pose estimation over the past few years, redefined views about pose-based approach are needed. This paper addresses the issues of whether it is sufficient to train a classifier only on low-level appearance features in appearance approach and proposes effective pose-based approach with pose estimation for emotional action recognition. In order for these questions to be solved, we compare the performance of pose-based, appearance-based and its combination-based features respectively with respect to scenario of various emotional action recognition. The experiment results show that pose-based features outperform low-level appearance-based approach of features, even when heavily spoiled by noise, suggesting that pose-based approach with pose estimation is beneficial for the emotional action recognition.

Virtual human used as HCI in digital contents expresses his var ious emotions across modalities like facial expression and body posture. However, few studies considered combinations of such nonverbal multimodal in emotion perception. Computational engine models have to consider how a combination of nonverbal modal like facial expression and body posture will be perceived by users to implement emotional virtual human, This paper proposes the impa cts of nonverbal multimodal in design of emotion expressed virtual human. First, the relative impacts are analysed between differe nt modals by exploring emotion recognition of modalities for vi rtual human. Then, experiment evaluates the contribution of the facia l and postural congruent expressions to recognize basic emotion categories, as well as the valence and activation dimensions. M easurements are carried out to the impact of incongruent expres sions of multimodal on the recognition of superposed emotions which are known to be frequent in everyday life. Experimental results show that the congruence of facial and postural expression of virtua l human facilitates perception of emotion categories and catego rical recognition is influenced by the facial expression modality, fu rthermore, postural modality are preferred to establish a judge ment about level of activation dimension. These results will be used to im plementation of animation engine system and behavior syncroniza tion for emotion expressed virtual human.

Understanding agent's intent is an essential component of the human-computer interaction of ubiquitous computing. Because correct inference of subject's intention in ubiquitous computing system helps particularly to understand situations that invol ve collaboration among multiple agents or detection of situations that can pose a particular activity. This paper, inspired by people have a mechanism for interpreting one another's actions and for infe rring the intentions and goals that underlie action, proposes an approach that allows a computing system to quickly recognize the intent of agents based on experience data acquired through prior capabilities of activities recognition. To proceed intention re cognition, proposed method uses formulations of Hidden Markov Models (HMM) to model a system's prior experience and agents' action change, then makes for system infer intents in advance before the agent's actions are finalized while taking the perspective of the agent whose intent should be recognized. Quantitative validation of experimental results, while presenting an accurate rate, an early detection rate and a correct duration rate with detecting the intent of several people performing various activities, shows that proposed research contributes to implement effective intent recognition system.

Research into the visual perception of human emotion to recognize an intention has traditionally focused on emotions of facial expression. Recently researchers have turned to the more challenging field of emotional expressions through body posture or activity. Proposed work approaches recognition of basic emotional categories from body postures using neural model applied visual perception of neurophysiology. In keeping with information processing models of the visual cortex, this work constructs a biologically plausible hierarchy of neural detectors, which can discriminate 6 basic emotional states from static views of associated body postures of activity. The proposed model, which is tolerant to parameter variations, presents its possibility by evaluating against human test subjects on a set of body postures of activities.

Elsholtzia hallasanensis, E. springia, and E. splendens var. fasciflora (Lamiaceae) were reported recently as new species or new varieties of E. splendens according to their morphological characteristics. To reappraise the taxonomic status of these additional taxa and to determine the relationships between all Korean Elsholtzia taxa except E. saxatilis, which is distributed in North Korea, molecular studies based on the nrDNA (ITS) and cpDNA (rpl16, and trnH-psbA) sequences of seven taxa of Elsholtzia and one outgroup were carried out. The molecular data support that E. angustifolia and E. minima are distinct species from E. splendens and E. ciliata, respectively, because they have several private marker genes and show monophyly. The molecular data also support that E. splendens has a very close taxonomic relationship with both E. hallasanensis and E. springia. We found that E. splendens var. fasciflora, with multiple inflorescence, was based on several private marker genes and on the monophyly of its trees, suggesting that it can be considered as a variety. Elsholtzia springia, with the same sequences and the same morphological characteristics with E. hallasanensis after transplanting, should be treated as a synonym of E. hallasanensis. Moreover, we consider the taxonomic status of E. hallasanensis as E. splendens var. hallasanensis (Y. Lee) N.S. Lee & C.S. Lee, stat. nov.

This paper introduces a new model based method for facial expression recognition that uses facial grid angles as feature space. In order to be able to recognize the six main facial expression, proposed method uses a grid approach and therefore it establishes a new feature space based on the angles that each gird's edge and vertex form. The way taken in the paper is robust against several affine transformations such as translation, rotation, and scaling which in other approaches are considered very harmful in the overall accuracy of a facial expression recognition algorithm. Also, this paper demonstrates the process that the feature space is created using angles and how a selection process of feature subset within this space is applied with Wrapper approach. Selected features are classified by SVM, 3-NN classifier and classification results are validated with two-tier cross validation. Proposed method shows 94% classification result and feature selection algorithm improves results by up to 10% over the full set of feature.Keywords:Facial Expression Recognition, Features Space Generation, Wrapper Approach, Multi-Tier Cross Validation

This paper proposes an approach of a novel facial expression recognition to deal with different intensities to improve a performance of a facial expression recognition. Various expressions and intensities of each person make an affect to decrease the performance of the facial expression recognition. The effect of different intensities of facial expression has been seldom focused on. In this paper, a face expression template and an expression-intensity distribution model are introduced to recognize different facial expression intensities. These techniques, facial expression template and expression-intensity distribution model contribute to improve the performance of facial expression recognition by describing how the shift between multiple interest points in the vicinity of facial parts and facial parts varies for different facial expressions and its intensities. The proposed method has the distinct advantage that facial expression recognition with different intensities can be very easily performed with a simple calibration on video sequences as well as still images. Experimental results show a robustness that the method can recognize facial expression with weak intensities.

Facial expression, which changes face geometry, usually has an adverse effect on the performance of a face recognition system. To improve the face recognition rate, we propose a normalization method of facial expression to diminish the difference of facial expression between probe and gallery faces. Two approaches are used to facial expression modeling and normalization from single still images using a generic facial muscle model without the need of large image databases. The first approach estimates the geometry parameters of linear muscle models to obtain a biologically inspired model of the facial expression which may be changed intuitively afterwards. The second approach uses RBF(Radial Basis Function) based interpolation and warping to normalize the facial muscle model as unexpressed face according to the given expression. As a preprocessing stage for face recognition, these approach could achieve significantly higher recognition rates than in the un-normalized case based on the eigenface approach, local binary patterns and a grey-scale correlation measure.

This study aims to set the effects of technology commercializing capabilities (Acquisition / Internalization, market-oriented innovation, and exploitational innovation) on business performances (financial performance, non-financial performance, and innovational performance) as a primary model; examine the moderating effects of open innovation; build up the foundation to promote small and medium enterprises located in industrial complexes in Daegu; and lay groundwork for regional industrial strategies and national policy projects. We examined the relations between variables by conducting correlation measurement with only those variables that went through the above process. And hierarchical regression analysis was done to confirm our research model and hypothesis test.The empirical analysis results of the research are as follows: First, we found that acquisition/internalization affected greatgly the firm’s financial performance and innovation performance (the speed of commercialization, the number of new product developments). Second, technological exploitation has positive effects on on their financial performance and innovation performance (the speed of commercialization, and the number of new product developments). Third, market exploitation also influenced strongly financial and innovative performances. This is because small and medium-sized companies in Korea produce and deliver products that higher level companies order rather than they develop their own products and improve the management performance by selling them to the market. Fourth, small and medium-sized firms seek to overcome the drawbacks coming from geographic proximity by means of open innovation during the process of commercializing the goods with their transferred techniques.

Motion transition is a useful method for reusing existing motion data. It generates a seamless intermediate motion with two short motion sequences. In this paper, the Uniform Posture Map (UPM) is proposed to perform motion transitions. The UPM is organized through the quantization of various postures with an unsupervised learning algorithm; it places the output neurons with similar postures in adjacent positions. Using this property, an intermediate posture of two applied postures is generated; the generating posture is used as a key-frame to make an interpolating motion. The UPM has lower computational costs in comparison with other motion transition algorithms. It provides a control parameter; an animator can not only control the motion simply by adjusting this parameter, but can also produce animation interactively. The UPM prevents the generating of invalid output neurons to present unrealistic postures in the learning phase; thus, it makes more realistic motion curves. Finally, it contributes to the making of more natural motions. The motion transition algorithm proposed in this paper can be applied to various fields such as real-time 3D games, virtual reality applications, and web 3D applications.

Biometric systems are forms of technology that use unique human physical characteristics to automatically identify individuals. They have sensors to pick up physical characteristics, convert them into digital patterns, and compare them with patterns stored for individual identification. However, lip print recognition has been less developed than the recognition of other human physical attributes such as the fingerprint, voice patterns, retinal blood vessel patterns, or the face. Lip print recognition by a CCD camera has the merit of being linked with other recognition systems such as the retinal/iris eye and the face. A new method using multi-resolution architecture is proposed to recognize a lip print from pattern kernels. A set of pattern kernels is a function of some local lip print masks. This function converts the information from a lip print into digital data. Recognition in the multi-resolution system is more reliable than recognition in the single-resolution system. Multi-resolution architecture allows us to reduce the false recognition rate from 15 to 4.7%. This paper shows that a lip print is sufficiently used by the measurements of biometric systems.