Energy is a major input for overall socio-economic development. With fossil fuels expected to get exhausted in another 40 years or so, renewable sources of energy have emerged as an alternative to fossil fuels. India with an area of almost 328 million hectares is the 7th largest country in the world producing 450-500 million tonnes of biomass per year as per EAI. As per a recent report almost 200 million tonnes of household and agro processing waste annually generated in India are disposed-off in a dispersed manner. Also, there are about 63 million ha waste land in the country, out of which about 40 million ha area can be developed for use. Additionally Urban, municipal and industrial wastes alone have a roughly 1700 MW potential of cheap and affordable power generation. The research work illustrates a specific case for North East India as to how this can be achieved through an innovative entrepreneurial approach for generation of green energy from biomass waste.

As Internet use has increased, customers have become more active at sharing their travel opinions through social media regarding their experiences with service organizations. Social media has become a ubiquitous tool that enables customers to share their travel experiences. In particular, members of Generation Y are more likely to be active on social media and more likely to share their experiences online. Understanding Generation Y’s online customer engagement preferences on different social media platforms may help to enhance brand loyalty. Customer engagement (CE) has been attracting the attention of both practitioners and academics because it may help to enhance both brand loyalty and purchase decisions. Social media platforms have become a significant communication tool for both customers and service providers, creating an opportunity to engage with customers. Interacting with active customers on the right social media platform can increase direct bookings, building brand loyalty. Therefore, the purpose of this research is to examine Generation Y’s brand loyalty preferences through its members’ engagement with social media. The results of this research will expand understanding of Generation Y customers’ online engagement through social media. This research may also suggest how hotels are able to utilize social media platforms in order to encourage online engagement with Generation Y by building brand loyalty.

The culinary business in Indonesia has become attractive since it gives 41.40% contribution to total creative economy GDP in 2016 (BEKRAF-BPS, 2016). The high contribution of the culinary industry could not separate from the fact that Indonesia has plenty of local foods variety from a different area in Indonesia. Since 2016, on the scope of ASEAN Economy Community, the role of Indonesian millennial is essential. The total of ASEAN population is 625 million. 65 percent of them who was born in 1980 is around 375 million. Indonesia has approximately 84 million millennial which means 23% of the youth in ASEAN is Indonesia citizen. The current research aims to analyzed millennial behaviors and how they consumed local foods based on the online and offline appearance of the local food. Based on the survey to 121 millennial, this study found that online and offline appearance includes social media, sensory appeal, health and weight control, price, habit, and sociability and social image, have a significant influence to local food consumption among millennial. This study may provide a new perspective on how local food entrepreneurs should manage their online and offline appearance of local food to increase the product’s consumption.

The demand for cosmetic products is generally declining globally, but growing among female Generation Y (Gen Y). Gen Y (18-34 years) are large in size and disposable income and are high users of various social media platforms. Thus, cosmetic companies are competing to capture this market segment. However, the type of social media platforms, which can best attract and induce cosmetic products interest among this fickle and notoriously disloyal market segment is unknown. This study therefore employed the AIDA model to examine the effectiveness of YouTube, Instagram and Facebook in igniting female Gen Y South Africans‟ interest in cosmetic products. Data was collected from 220 respondents. Structural equation modeling results revealed that the cosmetic products interest is ignited by YouTube and Instagram ads and not Facebook ads. Implications are provided.

Introduction This research will investigate the advancement of cognitive computing and how it can be applied through „Dynamic Marketing Capabilities‟ (Bruni and Verona, 2009) to raise the bar of personalizing services and amenities provided to the luxury watch market loyal customer. Through intuitive digital applications, new levels of interactive systems can focus on explicitly the next generation of hyper-connected luxury customers. Theoretical Development The purpose of this research is to investigate how to personalize the communication process in the luxury market segment through cognitive computing and address the high expectation of the new affluent consumer of the digital age. The next generation of affluent luxury consumers is accustomed to interactive systems and personalized interfaces that enable computers to get more intuitive of the customer(s) to enable them to personal the individual‟s needs. This level of personalization undoubtedly raises the bar on the luxury customer‟s journey from the tactile in-person luxury shopping experience currently found in brick-and-mortar locations, to a decidedly more interactive and increasingly immersive online customer experience. Abbott (1955) and Alderson (1957) focused on the notion that “what people desire are not products but satisfying experiences” (Abbot 1955, p. 40). The fundamentals of cognitive computing are to recognizing trends and behaviors that enable companies to utilize Artificial Intelligence to make proper predictions and give insight to intuitively give consumers what they need before they have to request it. The idea behind this research is to take the traditional luxury market sector of Swiss watches and combine it with the intuitive software provided by cognitive computing. Research Design According to the Federation of the Swiss Watch Industry (FHS) in 2017, Switzerland occupies only 3% of the global market regarding the quantity of watches. As for value, Switzerland represents 54% of global sales that is 21 billion USD. Thus, about 95% of luxury watches with price starting from 1,000 USD are stamped "Swiss Made.” Thus, the Swiss watch industry has become an integral part of the luxury universe. However, it‟s not an easy task to get a place in this luxury market of reference. According to the estimation made by the Institute of Watch Marketing, there are approximately 200 active Swiss independent watch brands on the market today. Under conditions of the highly competitive market, the challenge concerns not only market share, but also competitive advantage as well as customer relationships or brand equity. We situate this research within the context of the Swiss luxury watchmaking industry and focus on the power of the website to increase customer loyalty. We suggest ways to utilize a brand‟s electronic (desk, mobile, tablet) touch points to aggregate data to gain a deeper understanding of their loyalist. Armed with knowledge, luxury watch brands can connect to their customers through the power of artificial intelligence. Affluent “digital native” consumers have “hyper-connected” instincts, and increasingly expect more from their chosen brands through next-level personalization. The continued evolution of consumers‟ online behavior, attitude, and expectations from brands currently exceeds what is possible for a single human to process. It is, therefore, becoming increasingly necessary to incorporate both the power of cognitive computing and the information gleaned from large data sets (big data) to produce more intuitive and personalized experiences. This information enhances the brand‟s ability to uncover behavioral patterns and begin to incorporate “machine learning,” (a subset of AI) a calculated algorithm that can facilitate the process of personalization. The speed at which data can now be processed, analyzed, clustered and contextualized has increased the value of machine learning in the world of the consumer experience. Personalization of luxury branded communication that utilize artificial intelligence (AI) to help them connect intuitively with their audience are more apt to meet the needs of the next generation of affluent consumers on a more personalized level. By focusing on the shift in adaptive interactive systems, we highlight the power of cognitive computing to help offer more intuitive luxury personalization for their loyal customers (owners of the brand‟s watches). Achieving this stage of customization requires computers to mimic human intelligence using logic and insight. This research will explore new opportunities to help identify the independent luxury watch industry to capture the attention of the next generation of customers in cyber-space. The customer‟s journey no longer ends at the front door of the traditional brick-andmortar location – it continues into the digital space and even starts from it. Offline Swiss luxury watch brands have mastered the role of personalization through “white glove” in-person customer service and installations exhibiting their expert craftsmanship. This level of customer service is still best accomplished through cultivating and curating the boutique shopping experience, although AI is rapidly changing this dynamic. Currently, the online experiences of luxury watch brands have proven to be less than satisfying for their discriminating clientele because most still shy away from creating a fully-realized digital landscape including an e-commerce presence. Independent watch brands need to push beyond the generic expectations and curate rich aesthetic experiences that set a crucial dimension of the luxury sector (Berthon et al. 2009). This research begins to address how Swiss luxury watch brands can fully embrace the digital evolution and strategically utilize the valued subset of AI including cognitive computing, machine learning, and adaptive interactive systems. AI machine learning will ensure the level of personalization to which the discerning luxury customer has grown accustomed. The next section details how watchmakers can accomplish this integration. Conceptual Framework Over the past years, researchers have increased conceptual understanding of the role of marketing in enabling firms to create and sustain competitive advantage and superior value (Ramaswami et al., 2009). By potential to improve business performance, some studies (Bruni and Verona, 2009), have introduced the term „Dynamic Marketing Capabilities‟ (DMCs hereafter). In fact, DMCs are focused explicitly on releasing and integrating the market knowledge that helps firms evolve. The strategic position of marketing to absorb market knowledge allows Swiss luxury watch brands to provide accurate insight into brand equity and distinctive experience. DMCs are capabilities that use market knowledge to adapt firms‟ resources and capabilities (Day, 1994; Slater and Naver, 1998). In this research will explore the characteristics and uses of market-based resources, such as building brands, relationships, and knowledge and apply to digital solutions through interactive systems and personalized interfaces. This market-based perspective suggests that marketing research increasingly focuses on intangible, complementary resources, whose effects on the firm‟s sustained competitive advantage (SCA) and performance may be greater than the impact of tangible resources (Srivastava et al.1998). As much as 70% of a firm‟s market value may come from its intangible resources (Capraro and Srivastava 1997), and organizational performance increasingly seems tied to intangible resources, such as customer relationships or brand equity (Lusch and Harvey 1994). The abundance of active users globally on the internet, smartphones, laptops, tablets, and desktops creates a wealth of data, up to 80 percent of which is untapped and unstructured and not contextualized for use (Alexander, 2016). This unused data often referred to as “dark data,” includes web images, social media networks, emails, blogs, and videos (Alexander, 2016). An analysis of the online behavior of a brand‟s current customers contributes to the formation of stronger, more meaningful clusters based on current customer personas to reveal patterns of similar interests between groups of customers. This majority of available data can be analyzed by machine learning, and “the more data an algorithm can train on, the more accurate it will be” (Deep Learning, n/a). The sub-domain of machine learning, deep learning, which is itself a sub-domain of AI, breaks down tasks to make machine assistance possible (Copeland, 2016). Thus, deep learning provides insights, which can then be used to help curate a personalized experience through predictive analytics. Gathering customer attributes through insight for this research requires that we take a closer look at similar customer gathering registering their watches online (serial number required) making sure to capture necessary demographic and psychographic information, and through using clientele retail locations, which would then was mined for insights and more branded content. The reactive software would utilize deep learning algorithms to recognize moments, behavior and geo-location to offer realtime personalized mobile engagement. The increasingly common practice of merging of marketing teams enhances marketing deliverables through the User Interface (UI) and User Experience (UX). The interface, coupled with the experience, need to work synergistically to drive curiosity and encourage the user to explore and discover what will eventually become more personally-targeted curated content on the website. To further explain how these technologies can be employed for the luxury watchmaking companies, we examine several Swiss luxury watch brands. Describing how actionable data derived from cognitive computing can create a more intuitive customer experience, Vishal Katelia, Senior Manager, Global CRM at the luxury ecommerce website Mr. Porter provides an analogy from the luxury hotel world. He says that luxury hotels excel in many ways, “especially around the „surprise and delight‟ aspect of keeping track of small but important details that personalize their clientele‟s experience from the type of pillow they prefer to sleep on. Paying attention to these details can ensure future expertise, are as perfect as the hotel can offer (Miller, 2016). While machine learning focuses on building machines that replicate the human brain‟s cognitive capabilities to apply this knowledge from cognitive science to react in a intuitive way (Jones, 2017). Artificial intelligence refers to "a broad set of methods, algorithms and technologies that make software 'smart' in a way that may seem human-like to an outside observer," according to Lynne Parker, director of the division of Information and Intelligent Systems for the National Science Foundation (Noyes, K., 2016). Cognitive technologies are themselves products of artificial intelligence that perform human-like tasks such as speech recognition, natural language process, machine learning, computer vision (Schatsky D., Muraskin, Ragu Gurumurthy, R. 2015). Furthermore, it is important to look beyond AI to the most efficient way to personalize the customer‟s experience. By using cognitive technologies marketers can create intuitive experiences for customers offering services and experiences based on behavioral profiling through data clusters and affinity analysis. Result The actionable insight that can be gained through this research was presented in a two-pronged process. First, connecting data that would typically be collected from a brand‟ e-commerce customers‟ journey now would be used to produce more curated content. The ability to apply cognitive computing through tools such as Qubit (data science company) that was used for this study that offers gives us the opportunity to apply an affinity analysis to form insights, patterns, behaviors that previously were undiscovered. Insights gathered would help to identify and reward loyal customers beyond the standard loyalty programs with notifications, private activations on geolocation services. Second, understanding the UX and UI on the websites of the three pre-selected independent luxury watch brands enabled us to follow and eventually understand the user‟s journey both on mobile and desktop. The personalization of the interface allows for in-depth learning to better understand an individual‟s needs and create an intuitive experience for the user. This research is limited to the personalization of loyal users, not to the interested parties of the independent luxury watch brands. While for marketing purposes, most insight collected from a brand‟s website typically is obtained from e-commerce, the independent luxury watch brands are currently limited to UX to measure the movement and interests of the users. The practical application of utilizing untapped „dark data‟ through the process of deep learning personalizes the interface and further utilizing AI technology to offer unique immersive experiences raises the innovation of personalization. The behavioral insights provided by deep learning can contextualize actionable information, which can then be applied by the brand‟s marketing management, retail managers, digital marketing, and public relations (PR) teams. Collection Process & Insight 1. Discovery of Audience Clusters based on Interests 2. Selection Process of Interest-Sets 3. Profiling / Persona of Audience 4. Discovery patterns from AI‟s Subset of Deep Learning 5. Select Deliverables That Align with Brand Through Mobile Applications Further investigation and re-evaluation of changing audience should be measured through the new applied data collected from discovering of new AI subset of deep learning from the luxury consumer. The AI movement will continue to change the next generation of affluent consumer‟s expectations, and with a continued reliance on smartphone technology it is inevitable that the future of personalization will require further investigation. While we have specifically focused on these three independent luxury watchmakers and the four clusters of interests currently available on their websites, future research will need to more deeply examine how the continued evolution of deep learning measurements can be best utilized to match the organic interests of the next generation of loyal customers. The behavioral insights provided by deep learning can contextualize actionable information, which can then be applied throughout the luxury brand communication; marketing management, retail managers, digital marketing, and public relations (PR) teams. Conclusion In this paper, we aimed to revisit luxury customer‟s expectation in the digital age and discuss how the industry is in the midst of a revolution that is changing the level of expectations of personalization. There is no doubt that the new technology is shifting the levels of customization through interactive systems and personalized interface will continue to advance. The next generation of affluent consumers have a high demand for interactive visual content, and dynamic marketing capabilities concept helps to integrate data for creating the new value and competitive advantage for the company. Cognitive computing insight will continue to enable luxury Swiss watches makers to understand how to personalize for the next generation of affluent consumers; more research is required to continue to explore more actionable insight.

The study aims to investigate the effect of post-reading question-generation activities on Korean middle school students’ English reading abilities with respect to cooperative learning. Two groups of students read the same reading materials; however, one group as an experimental group generated questions of three types, literal, inferential, and evaluative questions, while the other group as a control group answered comprehension questions. Each group was further divided into two sub-groups by cooperative and individual learning. A statistical analysis of the recall test scores reveals a positive effect of post-reading question-generation activities and cooperative learning on English reading abilities. The reading test scores by the three question types further illustrated variations across the question types: the experimental group outperformed the control group in the inferential and evaluative questions and individual learning was detected to be more effective than cooperative learning in the evaluative questions. Interactional effects were observed between post-reading activities and cooperative learning in the literal and evaluative questions. The findings suggest question-generation activities as a beneficial post-reading task, though their effectiveness can vary by question types and learning context.

저어새의 먹이생물을 파악하기 위해 2010년 6월부터 2014년 6월까지 인천 남동유수지에서 저어새의 토사물 시료를 채집하여 현미경 관찰 및 차세대염기서열 (NGS) 기법으로 분석 하였다. 저어새의 먹이생물은 어류, 갑각류, 다모류, 곤충류로 구성되어 있었으며, 주로 저어새는 어류와 갑각류를 섭이하는 것으로 나타났다. 최우점 먹이생물은 풀망둑 (Acanthogobius hasta)이었으며, 이 외에도 길게 (Macrophthalmus abbreviates), 징거미새우류 (Macrobrachium sp.), 칠게 (Macrophthalmus japonicus), 각시흰새우 (Exopalaemon modestus), 참 갯지렁이 (Neanthes japonica)가 우점 먹이생물로 출현하였다. 이들 먹이생물은 번식지 인근지역인 송도갯벌과 시화호에서 흔히 발견되며, 저어새는 채식지로써 이들 지역에 대한 의존도가 높을 것으로 판단된다. 현미경과 NGS로 분석한 일부 먹이생물에서 차이를 보였는데, 이는 토사물 내 먹이생물은 저어새의 위 내에서 분해되어 현미경 분석을 통한 형태학적 분류 특징을 찾기 어려웠던 반면, NGS 분석은 유전자를 통해 분류가 가능하기 때문에 형태학적 분석의 결과보다 높은 종 다양성을 보인 결과이다.

본 논문은 Ni - Al2O3로 구성된 금속-세라믹 이종 입자복합재의 2차원 미세구조(microstructure) 생성과 미세구조 스케일 (scale)에 따라 정의되는 계층적 모델들의 역학적 특성 분석에 관한 내용이다. 이종 입자복합재의 미세구조는 수학적인 RMDF(random morphology description functions) 모델링기법을 복합재의 2차원 RVE(representative volume element) 영 역에 적용하여 생성하였다. 그리고 미세구조 생성에 필요한 가우스 함수들의 개수에 따라 미세구조의 계층적 모델을 정의하였다. 한편 임의 미세구조 내 금속과 세라믹 입자가 차지하는 체적분율(volume fraction)은 RMDF 함수의 레벨을 조정함으로서 설정하였다. RMDF기법에 의한 미세구조들은 가우스 함수들의 개수가 일정할지라도 랜덤하게 생성된다. 이렇게 랜덤 하게 생성되는 미세구조들을 2차원 보(beam) 모델에 적용하여 미세구조의 스케일에 따른 수직응력과 전단응력의 계층적 변 동을 수치 해석적으로 고찰하였다. 또한, 균열해석을 통해 RMDF의 랜덤성과 가우스 함수들의 개수가 균열선단에서의 응력 값에 미치는 영향을 고찰하였다.

In recent years, imbalanced data is one of the most important and frequent issue for quality control in industrial field. As an example, defect rate has been drastically reduced thanks to highly developed technology and quality management, so that only few defective data can be obtained from production process. Therefore, quality classification should be performed under the condition that one class (defective dataset) is even smaller than the other class (good dataset). However, traditional multi-class classification methods are not appropriate to deal with such an imbalanced dataset, since they classify data from the difference between one class and the others that can hardly be found in imbalanced datasets. Thus, one-class classification that thoroughly learns patterns of target class is more suitable for imbalanced dataset since it only focuses on data in a target class. So far, several one-class classification methods such as one-class support vector machine, neural network and decision tree there have been suggested. One-class support vector machine and neural network can guarantee good classification rate, and decision tree can provide a set of rules that can be clearly interpreted. However, the classifiers obtained from the former two methods consist of complex mathematical functions and cannot be easily understood by users. In case of decision tree, the criterion for rule generation is ambiguous. Therefore, as an alternative, a new one-class classifier using hyper-rectangles was proposed, which performs precise classification compared to other methods and generates rules clearly understood by users as well. In this paper, we suggest an approach for improving the limitations of those previous one-class classification algorithms. Specifically, the suggested approach produces more improved one-class classifier using hyper-rectangles generated by using Gaussian function. The performance of the suggested algorithm is verified by a numerical experiment, which uses several datasets in UCI machine learning repository.

As a fuel for ship propulsion, liquefied natural gas (LNG) is currently considered a proven and reasonable solution for meeting the IMO emission regulations, with gas engines for the LNG-fueled ship covering a broad range of power outputs. For an LNG-fueled ship, the LNG bunkering process is different from the HFO bunkering process, in the sense that the cryogenic liquid transfer generates a considerable amount of boil-off gas (BOG). This study investigated the effect of the temperature difference on boil-off gas (BOG) production during ship-to-ship (STS) LNG bunkering to the receiving tank of the LNG-fueled ship. A concept design was resumed for the cargo/fuel tanks in the LNG bunkering vessel and the receiving vessel, as well as for LNG handling systems. Subsequently, the storage tank capacities of the LNG were 4,500 m3 for the bunkering vessel and 700 m3 for the receiving vessel. Process dynamic simulations by Aspen HYSYS were performed under several bunkering scenarios, which demonstrated that the boil-off gas and resulting pressure buildup in the receiving vessel were mainly determined by the temperature difference between bunkering and the receiving tank, pressure of the receiving tank, and amount of remaining LNG.

The ride quality (i.e. smoothness) is a key factor for evaluating the construction quality of expressway asphalt pavement. Conventionally, three paving devices are widely used to control the surface layer thickness: leveling sensor (i.e. LS), short-range-surfacing-contact-ski (i.e. SSCS) and long-range-surfacing-contact-ski (i.e. LSCS). However, each of these levelling tools presents one major drawback. In the case of LS, if the original sub-layer evenness is poor, the final asphalt pavement surface and its smoothness will be negatively affected. The SSCS cannot assure satisfactory smoothness when relatively long paving section (in the order of 10 km) are paved. While the LSCS would reduce the drawback of the SSCS, its weight on the one hand and its length on the other discourage its use in the paving site especially for curved sections. In this paper, a next generation pavement smoothness leveling equipment, known as non-contact-digital-ski (i.e. NCDS) was implemented, evaluated and compared to the conventional equipment leveling device. The international Roughness Index (IRI m/km) was measured on sections paved with and without NCDS and the results visually and statistically compared. In addition, for the same sections, the modulus of the pavement layers was computed and compared by means of Falling Weight Deflectometer (i.e. FWD). It was observed that when NCDS is used for asphalt pavement overlay of existing concrete pavement, significant improvement in IRI (i.e. IRI<1.0m/km) and consistently uniform elastic modulus could be achieved compared to the conventional levelling and paving method.

Historically, the two major aspects of road design have been (i) The design principles – like AASHTO 1993 Empirical Design or lately, Mechanistic Empirical Pavement Design Method (MEPDM) (ii) The materials & technologies of pavement construction The fundamental design principles have not undergone major changes, however, the advancement in materials and technologies have improved tremendously over last few decades and this makes it necessary to revisit some of the conventional concepts in road design. The new technology now challenges the conventional wisdom and has brought us to the threshold of an era of all new sustainable green roads of tomorrow. AASHTO 1993 Empirical Pavement Design is the basis for pavement design today; in most parts of the world. In some parts of the world, there is a movement towards Mechanistic Empirical Pavement Design Guideline (MEPDG), but the movement is slow and gradual as this approach is expensive and heavily dependent on software programs due to its inherent computational complexities. The concept of Structural Number and Layer Coefficients of pavement layers under AASHTO 1993 Empirical Pavement Design was derived from AASHO road test carried out in Ottawa, Illinois between 1958 & 1960. The conventional Layer Coefficient value of 0.44 which is used today was recommended considering the strength of the construction materials available at that time. But, in view of the new technology available now, this needs to be revisited. Secondly, AASHTO 1993 Empirical Pavement Design provides for assuming certain values for Drainage Coefficients, ranging between 0.4 to 1.4, based on certain criteria. It is quite common to assume a value of 1 for drainage coefficient, in most parts of the world. Now, with the advent of new nanotechnology for waterproofing of road layers, it is possible to consider higher values for drainage coefficients. The new nanotechnology for soil stabilization can make subgrade soils well bonded, strong and flexible, allowing the designer to assume higher values of Resilient Moduli in the AASHTO 1993 design equation, which may bring the required structural number down and allow a lighter design of cross-section of structural layers on top of the subgrade. Indicative calculations for a typical 100 MSA, two lane (10 m wide) highway show that the new technology, while remaining within the AASHTO 1993 design guidelines, enables designing a pavement that is 3 times better (it will now be a 300 MSA pavement instead of 100 MSA) with a cost reduction of about USD 114000 per km. This paper takes an overview of latest trends in USA regarding pavement design approaches. It also puts forth, the opportunities presented to a pavement designer, by the new nanotechnology and proposes a new design approach, for optimized pavement design - green, sustainable & economical; while remaining within the AASHTO 1993 guidelines or MEPDG.

가솔린, 플라스틱, 섬유 등 수많은 일상 소재들의 원재료를 저에너지 및 저탄소 공정으로 생산하는 것은 석유화학 회사들의 초미의 관심사라고 할 수 있다. 특히 우리나라는 원유를 해외에서 수입하고 이를 분리 및 정제 하여 다양한 고부가 가치를 창출하는데 여러 집약된 기술에 의존하고 있다. 이와 같은 석유화학 원재료들이 복합적으로 섞여있는 혼합물로부터 비슷한 종류의 성분을 분리하는 공정에 전 세계적으로 막대한 양의 열에너지가 소비 된다. 본 발표에서는 석유화학 에너지 비용을 낮출 수 있는 멤브레인 기반 상온 액상 탄화수소 역삼투 분리 공정에 대해 소개하고자 한다. 특히 탄소 분자체 기반 분리막의 제조와 이의 응용에 대한 내용을 다루고자 한다.

유기용매 나노여과(OSN, organic solvent nanofiltration) 분리막은 폴리이마이드(PI)나 폴리벤질이미디아졸(PBI)과 같은 특수 고분자의 개발, 상업화가 이루어지고 강한 유기용매에 견딜 수 있도록 가교를 통해 분리막의 내구성이 급격하게 향상되면서 저분자량의 합성, 정제 및 농축을 필요로하는 의약, 바이오, 식품산업에 획기적이고 효율적인 분리막 공정으로 주목 받고 있다. 하지만 여전히 고가의 고분자, 가교를 위한 복잡한 프로세스, 다량의 강한 용매 폐수 발생등 상업화를 이루는데 여러 가지 문제점들이 산재하고 있다. 본 연구는 기존 제막방식에서 벗어나 무독성의 용제를 사용하여 단일공정으로 유기용매 나노여과 분리막을 제막하고 그 특성을 연구하였다.

In a solar coronagraph, the most important component is an occulter to block the direct light from the disk of the sun. Because the intensity of the solar outer corona is 10−6 to 10−10 times of that of the solar disk (I⊙), it is necessary to minimize scattering at the optical elements and diffraction at the occulter. Using a Fourier optic simulation and a stray light test, we investigated the performance of a compact coronagraph that uses an external truncated-cone occulter without an internal occulter and Lyot stop. In the simulation, the diffracted light was minimized to the order of 7.6 × 10−10 I⊙ when the cone angle c was about 0.39◦. The performance of the cone occulter was then tested by experiment. The level of the diffracted light reached the order of 6 × 10−9 I⊙ at c = 0.40◦. This is sufficient to observe the outer corona without additional optical elements such as a Lyot stop or inner occulter. We also found the manufacturing tolerance of the cone angle to be 0.05◦, the lateral alignment tolerance was 45 μm, and the angular alignment tolerance was 0.043◦. Our results suggest that the physical size of coronagraphs can be shortened significantly by using a cone occulter.

도시 녹지는 도시 내 기후완화, 공기정화 등의 역할 뿐만 아니라 자연과는 거리가 먼 환경에서 생활하는 도시민의 자연 접촉과 자연환경에서의 활동으로 정신적 안정을 찾고 질병 예방 및 치유하는 역할을 하고 있다. 녹지는 음이온, 피톤치드와 같은 인체에 긍정적인 영향을 미치는 물질을 생성하는 공간으로 도시민의 육체적·정신적 쾌적성에 큰 영향을 미치고 있다. 특히 음이온에 대한 연구는 1960년대 이후로 꾸준히 진행되어 왔으며, 전기적 특성에 의한 공기 정화효과와 인체에 긍정적 영향을 미쳐 신진대사가 촉진되는 효과가 입증되어 왔다. 이에 본 연구는 대표적인 도심지 내 산지형 공원인 경주 남산에서 인간건강에 긍정적인 영향을 미치는 대표적 인자인 음이온을 지형구조 및 해발고도에 따른 발생량 분포실태를 분석하고 각 유형에 따른 기상인자 및 음이온과의 상관 성을 분석하여, 향후 지형구조 및 해발고도에 따른 기상인 자 및 음이온 상관관계의 기초자료로 제시하고자 하였다. 연구 대상지는 경주에 위치한 남산을 대상으로 진행하였다. 연구 시기는 11월 가을철이며 광합성량이 가장 높은 시간대인 11시~15시 사이에 측정을 진행하였다. 측정 지점 은 총 26지점으로 능선부 13지점, 계곡부 13지점으로 선정 하였고 해발고도 80m부터 320m까지 매 20m 마다 측정을 진행하였다. 계곡부, 능선부로 나누어 현존식생을 조사한 후 측정지점마다 기온, 상대습도, 일사량, 풍속, 음이온 발생 량을 각각 5반복 측정하였다. 통계분석은 SPSS 18.0 프로 그램을 활용하여 분석하였다. 현존식생유형 분석 결과, 소나무림과 혼효림이 나타났으 며, 총 26개 측정지점 중 계곡부에서 소나무림 6개소, 혼효림 7개소가 나타났고, 혼효림의 경우 소나무, 참나무 식생이 었다. 능선부에서 소나무림 12개소, 혼효림 1개소가 나타났고, 혼효림의 경우 소나무, 신갈나무 등이 나타났다. 계곡부 측정지점의 수고의 경우 8~16m, 평균 수고는 11.92m로 나타났으며, 흉고직경의 경우 15~46cm, 평균 흉고직경은 26.69cm로 나타났다. 능선부 측정지점의 수고의 경우 8~15m, 평균 수고는 10.52m로 나타났으며, 흉고직경의 경우 15~30cm, 평균 흉고직경은 22.95cm로 나타났다. 지형구조 및 해발고도에 따른 기상요소 분석의 경우 기온은 해발고도가 높아질수록 낮아지는 경향을 보였고 평균 기온은 능선부(9.82℃) > 계곡부(8.44℃)이었다. 상대습도 는 해발고도가 높아질수록 감소하는 경향을 보였고 평균 상대습도는 계곡부(59.01%) > 능선부(58.64%) 순으로 높은 경향을 보였다. 풍속은 경향을 보이지 않았으며 평균 풍 속은 능선부(0.63m/s) > 계곡부(0.37m/s) 순으로, 평균 일사 량은 능선부(5120lux) > 계곡부(1831lux) 순으로 높은 경향 을 보였고 해발고도 300m지점에서 역전현상이 나타났다. 이는 해발고도에 따른 식생의 변화로 사료되었다. 지형구조 및 해발고도 차이에 따른 음이온 발생량 분석결과, 능선부에서 580.04ea/cm3, 계곡부에서 636.81ea/cm3로 계곡부가 더 높은 음이온 발생량을 보였다. 계곡부와 능선 부 모두 해발고도가 증가할수록 음이온 발생량이 증가하는 경향을 보였다. 이는 해발고도가 증가할수록 낮아지는 기온 과 높아지는 상대습도 때문에 나온 결과로 사료되었다. 음이온 발생량과의 상관성 분석을 실시한 결과 계곡부의 경우, 풍속, 상대습도, 일사량, 해발고도와는 정의 상관관계 가 있는 것으로 분석되었고, 기온과는 부의 상관관계에 있는 것으로 분석되었다. 능선부의 경우 상대습도, 일사량, 해 발고도와는 정의 상관관계가 있는 것으로 분석되었고, 기온 과는 부의 상관관계가 있는 것으로 분석되었다. 계곡부와 능선부의 분석 결과가 다른 이유는, 선행 연구 고찰 결과 음이온 발생에 다양한 요소가 영향을 미치는데 지형구조가 달라지면서 그에 따라 기상 및 기후 또한 달라지기 때문인 것으로 판단되었다.

본 연구의 목적은 생애사적 접근을 통해 조선족 1세대들이 몸소 체험하고 자신의 언어로 재구성한 문화 적응 과정을 심층적으로 이해하는 것이다. 연구의 초점은 이주의 동기, 문화적응 양상, 문화적응 맥락을 탐색하는 것이다. 연구 대상자들은 중국에 현재 거주하고 있는 이민 1세대로 직접 중국에 이주한 사람들이다. 분석결과 조선족 1세대의 이주동기는 대부분이 생활고와 차이나 드림에 기인한 것이었다. 그리고 조선족 1세대들의 문화적응 유형은 분리양상에 가까웠다. 중요한 점은, 이들이 수동적 타자로서가 아니 라 적극적 주체로서 스스로 중국 사회와 거리를 둔 것으로 나타났다. 조선족 1세대들은 중국에 거주하면 서도 본인들은 뼛속까지 조선족이라는 의식이 강했으며, 조선족 집성촌을 만들어 중국 사회와 거리를 두었고, 조선어만 사용하였다. 이런 분리의 양상에 영향을 미치는 맥락적 요인으로는 높은 민족적 우월감, 높은 민족 교육, 임시 체류자, 이중차별, 경쟁의식, 언어적 어려움으로 나타났다.