준정부조직 콴고(QUANGO: Quasi-Autonomous Non-Governmental Organization)는 자율성과 독립성을 갖춘 비국가적 행위자로 국제관계 속에서 네트워크 촉매 역할을 한다. 본 연구의 목적은 콴고를 공공외교 관점에서 살펴보고, 어떤 요인이 네트워크 참여를 추동하는지 밝히는 것 이다. 개별행위자의 차원에서 조직구성원, 일시적 구성원, 잠재적 구성원 을 대상으로 인터뷰 설문조사를 수행하고 참여관찰자의 관점에서 조직의 특징을 추적 관찰했다. 분석결과 다음의 다섯 가지 시사점을 얻을 수 있 었다. 첫째, 조직의 형태와 조직문화는 조직구성원의 관계성에 영향을 미 쳤고, 조직에 따라 네트워크 추동요인(상호통제, 헌신, 만족, 신뢰)의 일 부가 발현되거나 모두 작동할 수 있다. 둘째, 조직구성원-일시적 구성원 에서 중요하게 나타난 신뢰의 관계성은 종류와 정도에 따라 다른 양태를 보일 수 있다. 셋째, 조직구성원-잠재적 구성원에서는 신뢰와 만족이 추 동원인으로 작동했고, 콴고의 보편적 가치 추구가 중요한 네트워크 결정 요인이었다. 넷째, 국가성을 배제한 연대의 정체성이 성공요인으로 작용 했다. 마지막으로 문화 간 커뮤니케이션의 윤리성과 공공성의 충족이 네 트워크 형성과 확대에 지대한 영향을 미쳤다.

The global coffee market has undergone several structural changes with power shifting from the International Coffee Agreement and its member countries to international coffee traders, multinational coffee corporations, and ultimately end-consumers. Despite these changes, the chronic issue of income disparity between coffee-producing and coffee-consuming countries remains entrenched. Although various international organizations and individual companies have initiated diverse sustainability movements, these efforts have shown limitations. In this context, it is essential to identify and analyze successful examples of prioritizing the development of marginalized tribal coffee producers and their community in the process of globalization. It is also essential to generalize factors contributing to their success. This study aimed to analyze the Araku Coffee Project led by the Naandi Foundation in India through lens of the cultural-political economy of the Global Production Networks (GPN). The Naandi Foundation rooted in the philosophy of sustainability has worked to enlighten the indigenous people of the Araku region while cooperating and building trust with smallholder farmers, cooperative, European carbon-fund, and international buyers. During this process, not only a platform of international coffee sales but a regional coffee festival called ‘Gems of Araku’ was initiated while marketing efforts using the name of ‘Araku’ were made. At the same time, organizational strategies of the global production network were put into practice. As a result, coffee production showed both quantitative and qualitative growths, leading to an improvement in the quality of life for the indigenous people.

Abstract Handling imbalanced datasets in binary classification, especially in employment big data, is challenging. Traditional methods like oversampling and undersampling have limitations. This paper integrates TabNet and Generative Adversarial Networks (GANs) to address class imbalance. The generator creates synthetic samples for the minority class, and the discriminator, using TabNet, ensures authenticity. Evaluations on benchmark datasets show significant improvements in accuracy, precision, recall, and F1-score for the minority class, outperforming traditional methods. This integration offers a robust solution for imbalanced datasets in employment big data, leading to fairer and more effective predictive models.

본 연구에서는 구조물의 재료, 구조물의 단면, 지진 하중등의 불확실성을 고려한 저형 전단벽의 최대 전단력를 예측하는 뉴 런-네트워크 모델을 개발하였다. 이를 위해 실험 데이터를 통해 검증된 박스타입 저형 전단벽 수치해석 모델을 구축하였고, 가정된 분 포를 통해 200개의 구조물의 재료, 단면변수를 라틴 하이퍼 큐브 샘플링을 통해 추출하였다. 또한 이전 연구에서 사용된 인공지진파를 데이터를 기반으로 10개의 다른 PGA 레벨별 총 200개의 인공지진파 데이터를 구축하였다. 뉴런-네트워크 모델의 Training 및 testing을 위해 200개의 데이터셋에 상응 수치해석 모델을 구축하고 최대 전단력을 산출하였다. 이렇게 구축된 데이터셋을 이용하여 최종적으로 뉴런-네트워크 모델을 확정하였다. 마지막으로 구축된 모델로부터 얻어진 취약도와 기존에 사용되는 방법들로부터 얻은 취약도를 비교, 분석하여 본 연구에서 구축된 모델의 정확도를 보여주었다.

The secondary growth model for Salmonella was developed based on the artificial neural network (ANN) with data collected from ComBase and FoodData Central. In addition to the existing secondary model variables (temperature, pH, Na+, and water contents), more input variables (sugar, carbohydrate, lipid, and protein contents) were considered. The output variables were microbial growth parameters (lag phase duration [l] and maximum growth rate [mmax]). A commercial ANN program (NeuralWorks Predict) was utilized with training at 80%, validation at 10%, and test data at 10%. ANN models were created using all data and cleansed data. Using the cleansed data, the training/testing root mean square error (RMSE) for mmax improved from 0.14/0.16 to 0.11/0.14, whereas the RMSE for l was still not acceptable, from 11.94/33.03 to 7.09/4.18. The l data were divided into two ranges with high and low goodness of fit, whereas the ANN model for each field was built, resulting in an optimally low RMSE.

This study deals with the application of an artificial neural network (ANN) model to predict power consumption for utilizing seawater source heat pumps of recirculating aquaculture system. An integrated dynamic simulation model was constructed using the TRNSYS program to obtain input and output data for the ANN model to predict the power consumption of the recirculating aquaculture system with a heat pump system. Data obtained from the TRNSYS program were analyzed using linear regression, and converted into optimal data necessary for the ANN model through normalization. To optimize the ANN-based power consumption prediction model, the hyper parameters of ANN were determined using the Bayesian optimization. ANN simulation results showed that ANN models with optimized hyper parameters exhibited acceptably high predictive accuracy conforming to ASHRAE standards.

Aqueous Zn-ion batteries (ZIBs) are very attractive owing to their high safety and low cost. Among various cathode materials, organic materials-based electrodes incorporating various redox functional groups have gained significant attention in the field of ZIBs due to their benefits of a tunable structural design, facility, eco-friendly, and possibility of multivalent energy storage. Herein, we demonstrate the nanostructured organic active materials deposited onto the CNT networks (HyPT@ CNT) for flexible ZIBs. This HyPT nanorods were obtained reassemblying the herringbone structured 3,4,9,10-tetracarboxylic dianhydride through a hydrothermal process in the presence of acid. These HyPT@CNT hybrids were electronically conductive and redox active, as well as could be fabricated into a flexible electrode achieving flexibility from mechanical integrity of robust networked structure. The as-fabricated flexible ZIBs delivered the high capacity of 100 Ah g− 1 at a current density of 0.1 A g− 1 and long-term cycling performance exceeding 5000 cycles. Consequently, these electrochemical performances are associated with the redox reactivity of carbonyl groups as verified by spectroscopic and electrochemical characterizations and the hybridization of HyPT nanorods with CNT networks.

최근에 선박을 안전하게 설계 및 운항하기 위해 인공지능으로 운동성능을 예측하는 연구가 늘고 있다. 하지만 일반적인 선박 에 비해 소형 어선에 대한 연구는 부족한 실정이다. 본 논문에서는 소형 어선의 운동성능 계산에 필수적인 운동응답을 심층신경망으로 추정하는 모델을 제안한다. 15척의 소형 어선에 대하여 유체동역학 해석을 수행하였으며 이를 통해 데이터베이스를 구축하였다. 환경 조 건과 주요 제원을 입력 데이터로, 단위 파고에 대한 운동응답(Response Amplitude Operator)을 출력 데이터로 설정하였다. 훈련된 심층신경 망 모델을 통해 예측된 운동응답은 유체동역학 해석 결과와 유사한 경향을 보이며 고주파 성분을 가진 운동응답 함수를 낮은 오차로 근 사하는 결과를 보여준다. 본 연구의 결과를 바탕으로 어선의 선형 특성 고려한 심층신경망 모델로 확장하여 연구 결과의 활용도를 넓히 고자 한다.

해양수산부는 2016년부터 2020년까지 국제사회의 이내비게이션 도입에 선제적으로 대응하고 어선 등 소형선박의 해사안전 증 진을 위해 “초고속 해상무선통신망(LTE-M)” 구축을 포함한 한국형 이내비게이션 구축사업을 추진하였으나, 초고속 해상무선통신망의 활용 관점에서 특정 목적에 한정하는 등의 한계점이 식별되었다. 이에 따라 통신망의 활용성 증대를 위해 사용자를 대상으로 설문 조사 및 인터뷰를 수행한 결과, 망 활용의 범위 확대, 망 활용 대상 확장, 망 활용 방식 다각화, 그리고 규제 완화 측면에서의 법·제도적 개선 사항을 확인할 수 있었다. 본 연구에서 도출한 사용자 요구사항을 기반으로 하여 향후 관련 법제 정비방안에 기여할 수 있을 것으로 기 대한다.

Tomato is one of the major widely cultivated crops around the world. The leaf area is directly related to the total amount of photosynthesis, which affects the yield and quality of the fruit. Traditional methods of measuring the leaf area are time-consuming and can cause damage to the leaves. To address these problems, various studies are being conducted for measuring the leaf area. In this study, we introduced a model to estimate the leaf area using images of tomatoes. Using images captured by a camera, we measured the leaf length and width and used linear regression analysis to derive the leaf area estimation formula. Furthermore, we used a Neural Network (NN) for additional analysis to compare the accuracy of the models. Initially, to verify the reliability of the image data, we conducted a correlation analysis between the actual measurement data and the image data, which showed a high positive correlation. The leaf area estimation model presented 23 estimation formulas. We used regression analysis to estimate the coefficients of each model and also used employed an artificial neural network analysis to derive high R-squared (R2) values and low Root Mean Square Error (RMSE) values. Among the estimation formulas, the ninth model showed the highest reliability with an R-squared value of 0.863. We conducted a verification experiment to confirm the accuracy of the selected model, and the R-squared value was 0.925. This study confirmed the reliability of data measured from images and the reliability of the leaf area estimation model using image data. These methods are expected to be an important tool in agriculture, using imaging equipment for measuring and monitoring the crop growth.

PURPOSES : This study aims to determine whether machine learning techniques based on the results of chemical analysis experiments can be rationally applied to evaluate the aging of various asphalt binders used throughout the country. METHODS : We conducted chemical experiments such as FT-IR, H-NMR, C- NMR, and GPC for the three-stage aging levels of eight types of asphalt binders used in the country and utilized two artificial neural network models to determine valid chemical experimentation and conditions for the use of neural modeling through predictions. RESULTS : The M-prop model, which combined the findings from each neural network model into a single artificial neural network model, demonstrated superior predictive performance compared with the M-base model, which assessed aging using two cluster layers. In addition, the minimum amount of data required to achieve 100% predictive accuracy for the target asphalt binders, regardless of the artificial neural network model, was 18, and the amount of training data decreased to less than 50%. CONCLUSIONS : The predictive accuracy of the aging of asphalt binders was significantly enhanced when GPC data was used, indicating that GPC should be prioritized in evaluating the aging of asphalt binders.

본 논문에서는 소형어선의 운동 응답을 예측하기 위해 딥러닝 모델을 구축하였다. 크기가 다른 두 소형어선을 대상으로 유체 동역학 성능을 평가하여 데이터세트를 확보하였다. 딥러닝 모델은 순환 신경망 기법의 하나인 장단기 메모리 기법(LSTM, Long Short-Term Memory)을 사용하였다. 딥러닝 모델의 입력 데이터는 6 자유도 운동 및 파고의 시계열 데이터를 사용하였으며, 출력 라벨로는 6 자유도 운동의 시계열 데이터로 선정하였다. 최적 LSTM 모델 구축을 위해 hyperparameter 및 입력창 길이의 영향을 평가하였다. 구축된 LSTM 모 델을 통해 입사파 방향에 따른 시계열 운동 응답을 예측하였다. 예측된 시계열 운동 응답은 해석 결과와 전반적으로 잘 일치함을 확인 할 수 있었다. 시계열의 길이가 길어짐에 따라서 예측값과 해석 결과의 차이가 발생하는데, 이는 장기 데이터에 따른 훈련 영향도가 감 소 됨에 따라 나타난 것으로 확인할 수 있다. 전체 예측 데이터의 오차는 약 85% 이상의 데이터가 10% 이내의 오차를 보였으며, 소형어 선의 시계열 운동 응답을 잘 예측함을 확인하였다. 구축된 LSTM 모델은 소형어선의 모니터링 및 경보 시스템에 활용될 수 있을 것으로 기대한다.

With the expanding availability of market data, firms are increasingly reliant on analytical capabilities as a source of competitive advantage, a trend that is reflected in the rising budgets allocated to data and analytics (The CMO Survey, 2022). Analytical capabilities denote firms' capacity to define and extract insights from the available data and link these insights to decision-making (e.g., Cao et al., 2019; Penttinen & Frösén, 2022). Despite the growing influence of the capability perspective on marketing analytics, which is rooted in the resource-based view (Barney, 1991), the understanding of analytical capabilities as a source of performance disparities remains in its early stages. In particular, although analytical capabilities of firms constitute (1) capabilities internal to the firm, as well as (2) those shared within the firm’s broader business network, the vast majority of previous research focuses on the development and use of internal analytical capabilities only (Gupta & George, 2016; Wedel & Kannan, 2016). As a result, there remains limited understanding regarding shared analytical capabilities that assume close collaboration between business partners, such as suppliers, buyers, and third parties, in developing and sharing data and insights (Alinaghian & Razmdoost, 2018; Penttinen & Frösén, 2022) and extend beyond the boundaries of individual firms.

Political conflicts and trade tensions affect entrepreneurial activities. This paper qualitatively evaluates the success factors of a B2B company’s marketing management strategies within the context of trade policy changes. Results indicate that a strong brand, personal ties to customers, retailers and competitors, and international manufacturing sites reduce the risk. Companies not only face the challenge of disruptive innovation caused by global digitalization activities. In addition, disruptions in the macro-environment are actually increasing. One example that impedes the growth of industrial activities is the current, still escalating, US-China trade war. Unusual forms of marketing coalitions and networks in trans-organizational systems are considered key constellations to ensure future company success (Achrol, 1991). What are the success factors for a B2B company’s marketing management strategies within the context of disruptive economic market and industry conditions, e.g., international trade policy changes?