The purpose of this study is to analyze the achievement standards for grades 3-4 of the 2022 revised science curriculum and identify the goals of science education for grades 3-4 of the 2022 revised curriculum, as well as provide implications for the development of the science textbooks for grades 3-4 and the direction of teaching for teachers in the field. For this purpose, 57 achievement standards of the Science Department 2022 revised curriculum for grades 3-4 were analyzed as to their knowledge dimensions and cognitive processes according to Bloom's Taxonomy of the New Educational Objectives. In cases where an achievement standard is a double sentence or combines two or more knowledge dimensions or cognitive process dimensions, we separated the sentences after having consulted with a group of experts and divided the achievement standards into 57 sentences. We then analyzed the frequency of the categorization of concepts and descriptors by comparing them with the previously studied elementary science standards from the 2015 revised curriculum. The main findings of the study are as follows. First, in the knowledge dimension, the "factual knowledge" accounted for 50 items (86%), compared to "conceptual knowledge" (10%), and "procedural knowledge" (4%), and "metacognitive knowledge" was not analyzed at all. Second, in terms of the cognitive processes, "Understanding" was the highest at 60% with 34 items. It was followed by "applying" with 11%, "creating" with 19%, "evaluating" with 15%, and "analyzing" and "remembering" with 6%. Third, when analyzing the descriptors, "I can explain" was the highest with 9%, followed by "comparison" with 6%, and "practice" and "classification" with 5%. Fourth, compared to the 2015 revised curriculum, "conceptual knowledge" was reduced and "factual knowledge" was overwhelmingly increased. Fifth, in the cognitive process dimension, "understanding,' has increased significantly, while the other cognitive process dimensions have decreased. Conclusions and implications based on these findings are as follows: the focus of the Science Department for grades 3-4 in the 2022 revised curriculum is heavily weighted toward the "factual knowledge," with "understanding" dominating the cognitive process dimensions. As a result, many concepts and applications have been reduced. Based on the results of the comparison of the descriptors with the results of the 2015 revised curriculum, the implications for the development of the science textbooks for grades 3-4 of the 2022 revised curriculum were discussed, and so were the implications of the curriculum for the field.
Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.
Background: The immediate hoot response to LPS is the production of proinflammatory cytokines that act as intercellular mediators in inflammatory reactions, including acute lung injury. These "early response" cytokines transmit signals from recognition cells to target or effector cells. This host response is further amplified by the expression of leukocyte chemoattractants, growth factors, and adhesion molecules, resulting in an array of proinflammatory events. This experiment was performed to define the lung origin of proinflammatory cytokines, such as TNF-
Purpose: The aim of this study was to determine an appropriate energy level of breakfast with less risk of chronic disease for Korean adults. Methods: Using data from the 2007~2009 Korean National Health & Nutrition Examination Survey, from a total of 12,238 adults aged 19~64, the final 7,769 subjects were analyzed except subjects who were undergoing treatment for cancer or metabolic disorder. According to the percent of breakfast energy intake versus their estimated energy requirement (EER), the subjects were divided into four groups: < 10% (very low, VL), 10~20% (low, L), 20~30% (moderate, M),
This study attempts to examine the influence that negative WOM (NWOM) has in an online context. It specifically focuses on the impact of the service failure description and the perceived intention of the communication provider on consumer evaluations of firm competence, attitude toward the firm, positive word of mouth and behavioral intentions. Studies of communication persuasiveness focus on "who says what; to whom; in which channel; with what effect (Chiu 2007)." In this research study, we examine electronic web posting, particularly focusing on two aspects of "what": the level of service failure communicated and perceived intention of the individual posting. It stands to reason electronic NWOM that appears to be trying to damage a product’s or firm's reputation will be viewed as more biased and will thus be considered as less credible. According to attribution theory, people search for the causes of events especially those that are negative and unexpected (Weiner 2006). Hennig-Thurau and Walsh (2003) state "since the reader has only limited knowledge and trust of the author of an online articulation the quality of the contribution could be expected to serve as a potent moderator of the articulation-behavior relationship. We therefore posit the following hypotheses: H1. Subjects exposed to electronic NWOM describing a high level of service failure will provide lower scores on measures of (a) firm competence, (b) attitude toward the firm, (c) positive word of mouth, and (d) behavioral intention than will subjects exposed to electronic NWOM describing a low level of service failure. H2. Subjects exposed to electronic NWOM with a warning intent will provide lower scores on measures of (a) firm competence, (b) attitude toward the firm, (c) positive word of mouth, and (d) behavioral intention than will subjects exposed to electronic NWOM with a vengeful intent. H3. Level of service failure in electronic NWOM will interact with the perceived intention of the electronic NWOM, such that there will be a decrease in mean response on measures of (a) firm competence, (b) attitude toward the firm, (c) positive word of mouth, and (d) behavioral intention from electronic NWOM with a warning intent to a vengeful intent. The main study involved a2 (service failure severity) x2 (NWOM with warning versus vengeful intent) factorial experiment. Stimuli were presented to subjects online using a mock online web posting. The scenario described a service failure associated with non-acceptance of a gift card in a brick-and-mortar retail establishment. A national sample was recruited through an online research firm. A total of 113 subjects participated in the study. A total of 104 surveys were analyzed. The scenario was perceived to be realistic with 92.3% giving the scenario a greater than average response. Manipulations were satisfactory. Measures were pre-tested and validated. Items were analyzed and found reliable and valid. MANOVA results found the multivariate interaction was not significant, allowing our interpretation to proceed to the main effects. Significant main effects were found for post intent and service failure severity. The post intent main effect was attributable to attitude toward the firm, positive word of mouth and behavioral intention. The service failure severity main effect was attributable to all four dependent variables: firm competence, attitude toward the firm, positive word of mouth and behavioral intention. Specifically, firm competence for electronic NWOM describing high severity of service failure was lower than electronic NWOM describing low severity of service failure. Attitude toward the firm for electronic NWOM describing high severity of service failure was lower than electronic NWOM describing low severity of service failure. Positive word of mouth for electronic NWOM describing high severity of service failure was lower than electronic NWOM describing low severity of service failure. Behavioral intention for electronic NWOM describing high severity of service failure was lower for electronic NWOM describing low severity of service failure. Therefore, H1a, H1b, H1c and H1d were all supported. In addition, attitude toward the firm for electronic NWOM with a warning intent was lower than electronic NWOM with a vengeful intent. Positive word of mouth for electronic NWOM with a warning intent was lower than electronic NWOM with a vengeful intent. Behavioral intention for electronic NWOM with a warning intent was lower than electronic NWOM with a vengeful intent. Thus, H2b, H2c and H2d were supported. However, H2a was not supported though results were in the hypothesized direction. Otherwise, there was no significant multivariate service failure severity by post intent interaction, nor was there a significant univariate service failure severity by post intent interaction for any of the three hypothesized variables. Thus, H3 was not supported for any of the four hypothesized variables. This study has research and managerial implications. The findings of this study support prior research that service failure severity impacts consumer perceptions, attitude, positive word of mouth and behavioral intentions (Weun et al. 2004). Of further relevance, this response is evidenced in the online context, suggesting the need for firms to engage in serious focused service recovery efforts. With respect to perceived intention of electronic NWOM, the findings support prior research suggesting reader's attributions of the intentions of a source influence the strength of its impact on perceptions, attitude, positive word of mouth and behavioral intentions. The implication for managers suggests while consumers do find online communications to be credible and influential, not all communications are weighted the same. A benefit of electronic WOM, even when it may be potentially damaging, is it can be monitored for potential problems and additionally offers the possibility of redress.
In collaborative tagging systems such as Delicious.com and Flickr.com, users assign keywords or tags to their uploaded resources, such as bookmarks and pictures, for their future use or sharing purposes. The collection of resources and tags generated by a user is called a personomy, and the collection of all personomies constitutes the folksonomy. The most significant need of the folksonomy users Is to efficiently find useful resources or experts on specific topics. An excellent ranking algorithm would assign higher ranking to more useful resources or experts. What resources are considered useful In a folksonomic system? Does a standard superior to frequency or freshness exist? The resource recommended by more users with mere expertise should be worthy of attention. This ranking paradigm can be implemented through a graph-based ranking algorithm. Two well-known representatives of such a paradigm are Page Rank by Google and HITS(Hypertext Induced Topic Selection) by Kleinberg. Both Page Rank and HITS assign a higher evaluation score to pages linked to more higher-scored pages. HITS differs from PageRank in that it utilizes two kinds of scores: authority and hub scores. The ranking objects of these pages are limited to Web pages, whereas the ranking objects of a folksonomic system are somewhat heterogeneous(i.e., users, resources, and tags). Therefore, uniform application of the voting notion of PageRank and HITS based on the links to a folksonomy would be unreasonable, In a folksonomic system, each link corresponding to a property can have an opposite direction, depending on whether the property is an active or a passive voice. The current research stems from the Idea that a graph-based ranking algorithm could be applied to the folksonomic system using the concept of mutual Interactions between entitles, rather than the voting notion of PageRank or HITS. The concept of mutual interactions, proposed for ranking the Semantic Web resources, enables the calculation of importance scores of various resources unaffected by link directions. The weights of a property representing the mutual interaction between classes are assigned depending on the relative significance of the property to the resource importance of each class. This class-oriented approach is based on the fact that, in the Semantic Web, there are many heterogeneous classes; thus, applying a different appraisal standard for each class is more reasonable. This is similar to the evaluation method of humans, where different items are assigned specific weights, which are then summed up to determine the weighted average. We can check for missing properties more easily with this approach than with other predicate-oriented approaches. A user of a tagging system usually assigns more than one tags to the same resource, and there can be more than one tags with the same subjectivity and objectivity. In the case that many users assign similar tags to the same resource, grading the users differently depending on the assignment order becomes necessary. This idea comes from the studies in psychology wherein expertise involves the ability to select the most relevant information for achieving a goal. An expert should be someone who not only has a large collection of documents annotated with a particular tag, but also tends to add documents of high quality to his/her collections. Such documents are identified by the number, as well as the expertise, of users who have the same documents in their collections. In other words, there is a relationship of mutual reinforcement between the expertise of a user and the quality of a document. In addition, there is a need to rank entities related more closely to a certain entity. Considering the property of social media that ensures the popularity of a topic is temporary, recent data should have more weight than old data. We propose a comprehensive folksonomy ranking framework in which all these considerations are dealt with and that can be easily customized to each folksonomy site for ranking purposes. To examine the validity of our ranking algorithm and show the mechanism of adjusting property, time, and expertise weights, we first use a dataset designed for analyzing the effect of each ranking factor independently. We then show the ranking results of a real folksonomy site, with the ranking factors combined. Because the ground truth of a given dataset is not known when it comes to ranking, we inject simulated data whose ranking results can be predicted into the real dataset and compare the ranking results of our algorithm with that of a previous HITS-based algorithm. Our semantic ranking algorithm based on the concept of mutual interaction seems to be preferable to the HITS-based algorithm as a flexible folksonomy ranking framework. Some concrete points of difference are as follows. First, with the time concept applied to the property weights, our algorithm shows superior performance in lowering the scores of older data and raising the scores of newer data. Second, applying the time concept to the expertise weights, as well as to the property weights, our algorithm controls the conflicting influence of expertise weights and enhances overall consistency of time-valued ranking. The expertise weights of the previous study can act as an obstacle to the time-valued ranking because the number of followers increases as time goes on. Third, many new properties and classes can be included in our framework. The previous HITS-based algorithm, based on the voting notion, loses ground in the situation where the domain consists of more than two classes, or where other important properties, such as "sent through twitter" or "registered as a friend," are added to the domain. Forth, there is a big difference in the calculation time and memory use between the two kinds of algorithms. While the matrix multiplication of two matrices, has to be executed twice for the previous HITS-based algorithm, this is unnecessary with our algorithm. In our ranking framework, various folksonomy ranking policies can be expressed with the ranking factors combined and our approach can work, even if the folksonomy site is not implemented with Semantic Web languages. Above all, the time weight proposed in this paper will be applicable to various domains, including social media, where time value is considered important.
Background : The signal pathways and their precise roles for acute respiratory distress syndrome caused by endotoxin (ETX) has not been established. Since there has been several in vitro experiments suggesting that activation of protein kinase C (PKC) pathway may be responsible for endotoxin-induced inflammatory reaction, we performed in vivo experiments in the rats with the hypothesis that PKC-inhibition can effectively prevent endotoxin-induced acute lung injury. Methods : We studied the role of PKC in ETX-induced ALI using PKC inhibitor (staurosporine, STP) in the rat Specific pathogen free male Sprague-Dawley weighted from 165 to 270g were used for the study. Animals were divided into the normal control (NC)-, vehicle control (VC)-, ETX-, PMA (phorbolmyristateacetate)-, STP+PMA-, and STP+ETX-group. PMA (50mg/kg) or ETX (7mg/kg) was instilled through polyethylen catheter after aseptic tracheostomy with and without STP (0.2mg/kg)-pretreatment STP was injected via tail vein 30min before intratracheal injection (IT) of PMA or ETX. Bronchoalveolar lavage (BAL) was done 3-or 6-hrs after IT of PMA or ETX respectively, to measure protein concentration, total and differential cell counts. Results : The results were as follows. The protein concentrations in BALF in the PMA- and ETX-group were very higher than that of VC-group (p<0.001). When animals were pretreated with STP, the %reduction of the protein concentration in BALF was
Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used