Background: An excessive pronated foot is defined as a flattening or complete loss of the medial longitudinal arch. Excessive foot pronation is considered to have high risk factors of overuse injuries in the lower limb. Various treatments have been investigated in attempts to control excessive pronation. Objects: This meta-analysis identifies the effects of an anti-pronation taping technique using different materials. Methods: The electronic databases used include MEDLINE, the Physiotherapy Evidence Database (PEDro), Science Direct, the Korean Studies Information Service System (KISS), the Research Information Sharing Service (RISS), the Korea National Library, and the Korean Medical Database (studies published up to July 31, 2019). The database search used the following keywords: "foot drop" OR "foot arch" OR "foot pronation" OR "flat foot (pes planus)" AND "taping" OR "support." Eight eligible studies were analyzed to determine the effectiveness of anti-pronation taping in study and control groups. Results: The overall random effect size (Hedges’ g) of the anti-pronation taping technique was 0.147 (95% confidence interval [CI]: -.214 to .509). When the effect (Hedges’ g) was compared by the type of tape material, rigid tape (RT; Lowdye taping) was .213 (95% CI: -.278 to .704) and kinesiotape (KT; arch support taping) was -.014 (95% CI: -.270 to .242). Based on this meta-analysis, it was not possible to identify the extent to which anti-pronation taping was effective in preventing navicular drop, improving balance, or changing foot pressure. Only three of the eight eligible studies applied KT on excessive pronated feet, and the outcome measure areas were different to those of the RT studies. The KT studies used EMG data, overall foot posture index (FPI) scores, and rear foot FPI scores. In contrast, the RT studies measured navicular heights, various foot angles, and foot pressure. Conclusion: This review could not find any conclusive evidence about the effectiveness of any taping method for patients with pronated feet. Future studies are needed to develop the anti-pronation taping technique based on the clinical scientific evidence.

Piperlongumine (PL) is a natural product found in long pepper (Piper longum ). The pharmacological effects of PL are well known, and it has been used for pain, hepatoprotection, and asthma in Oriental medicine. No studies have examined the effects of PL on bone tissue or bone-related diseases, including osteoporosis. The current study investigated for the first time the inhibitory effects of PL on osteoclast differentiation, bone resorption, and osteoclastogenesis-related factors in RAW264.7 macrophages stimulated by the receptor activator for nuclear factor- κB ligand (RANKL). Cytotoxicity was examined using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, and osteoclast differentiation and bone resorption were confirmed by tartrate-resistant acid phosphatase (TRAP) staining and pit formation analysis. Osteoclast differentiation factors were confirmed by western blotting. PL exhibited toxicity in RAW264.7 macrophages, inhibiting osteoclast formation and bone resorption, in addition to inhibiting the expression of osteoclastogenesis-related factors, such as tumor necrosis factor receptor-associated factor 6 (TRAF6), c-Fos, and NFATc1, in RANKL-stimulated RAW264.7 macrophages. These findings suggest that PL is suitable for the treatment of osteoporosis, and it serves as a potential therapeutic agent for various bone diseases.

최근, 여러 분야에서의 AI가 빠르게 성장하였고 게임에서도 큰 발전이 있었다. 게임 AI에 대한 접근 방법은 여러 가지가 있다. 먼저 지도 학습 기반 접근 방법은 게임 플레이 데이터에서 학습하고, 플레이 행동을 흉내 낸다. 그러나, 지도 학습 기반 접근 방법은 입력 자질을 선형 조합하므로, 복잡한 문제에는 성능 향상에 한계가 있다. 선형 조합에 따른 성능 한계를 개선하기 위해, 딥 뉴럴 네트워크 기반 접근방법은 지역적 특성 및 전역적 특성을 개별적으로 각각 표현하기 위해 둘 이상의 뉴럴 네트워크를 사용한다. 그러나 딥 뉴럴 네트워크 기반 접근방법은 충분한 학습 집합이 필요하다. 학습 집합을 준비해야 하는 부담을 줄이기 위해서, 강화 학습 기반 접근 방식은 Agent가 먼저 Action을 하고 이에 따른 보상을 분석하여 학습한다. 즉, 이 접근방법은 Agent가 최대 보상을 받도록 학습한다. 본 논문에서는 강화 학습을 통해 여러 게임에서 학습하는 AI를 제안한다. 제안하는 AI 모델은 개별 게임에서 Local Agent가 플레이하고, 여러 Local Agent에서 Global Agent를 학습한다. 실험 결과, 한 게임에서 학습한 Agent는 학습했던 게임에서는 우수한 성능을 보여주었지만, 새로운 게임에서는 성능이 떨어졌다. 반면에, 두 게임에서 학습한 제안하는 Agent는 학습한 게임과 새로운 게임 모두에서 잘 적응했다.

This paper is an exploratory study to investigate the influence of characteristics of local government web-sites on user satisfaction and trust. The objective of this research is to identify the characteristics of local government web-sites influencing on user satisfaction and trust, and determine how they affect user satisfaction and trust. From the literature review, we identify four characteristics which are expected to influence on user satisfaction: technical, information, customer-oriented, and work-performing characteristics. In addition, we develop research model that the four characteristics are expected to affect user satisfaction and trust via users’ perceived values. The results of the research are as follows. The technical, customer-oriented, and work-performing characteristics significantly affects perceived values, which significantly affected user satisfaction and trust. Perceived value is partially mediated by user satisfaction in affecting user trust. The characteristics of local government web-sites affecting perceived values differed partially by the purpose of visitor. As for the personal goal, the technical, customer-oriented, and work-performing characteristics significantly affect perceived values, with the technical and customer-oriented characteristics having more significant effects. As for the business goal, the technical, information, work-performing characteristics significantly affected perceived values, with information characteristics having more significant effects.

최근 디지털 콘텐츠 중 E-Book과 웹 소설이 증가하고 있다. 삽화는 텍스트 콘텐츠에서 독자의 이해를 도울 수 있다. 따라서 텍스트 콘텐츠를 분석하여 자동으로 삽화를 생성하는 여러 접근방법이 등장했다. 먼저 규칙 기반 접근방법은 문장을 분석하는 규칙과 분석된 문장 구조를 삽화로 변환하는 규칙을 모두 활용하여 삽화를 생성한다. 그러나 정해진 규칙에서 벗어나면 삽화를 생성하지 못 할 수도 있다는 단점이 있다. 다음으로 통계 기반 접근방법은 통계를 기반으로 후보 중 가장 가능성이 높은 삽화를 생성한다. 그러나 통계 기반 접근방법은 사람이 추출한 정보에 강하게 의존한다는 단점이 있다. 본 연구는 사람이 아닌 시스템이 자동으로 정보를 추출하는 딥러닝을 활용한 삽화 생성 시스템을 제안한다. 제안하는 시스템은 형태소 분석기, 개체명 인식기, 의존 구문 구조 분석기로 이루어져있다. 동화 "빨간모자"를 통해 평가하였고, 총 99문장 중 삽화 생성이 가능한 문장이 28문장, 삽화 생성이 불가능한 문장이 71문장이었다. 삽화 생성이 불가능한 문장의 경우 대사가 41문장, 불명확한 주어가 2문장, 그리고 표현이 불가능한 서술어가 28문장이었다. 제안하는 시스템이 올바른 삽화를 생성한 문장은 23문장, 부적절한 삽화를 생성한 문장은 2문장, 삽화를 생성하지 않은 문장은 74문장으로 나타났다.