Nowadays, artificial intelligence model approaches such as machine and deep learning have been widely used to predict variations of water quality in various freshwater bodies. In particular, many researchers have tried to predict the occurrence of cyanobacterial blooms in inland water, which pose a threat to human health and aquatic ecosystems. Therefore, the objective of this study were to: 1) review studies on the application of machine learning models for predicting the occurrence of cyanobacterial blooms and its metabolites and 2) prospect for future study on the prediction of cyanobacteria by machine learning models including deep learning. In this study, a systematic literature search and review were conducted using SCOPUS, which is Elsevier’s abstract and citation database. The key results showed that deep learning models were usually used to predict cyanobacterial cells, while machine learning models focused on predicting cyanobacterial metabolites such as concentrations of microcystin, geosmin, and 2-methylisoborneol (2-MIB) in reservoirs. There was a distinct difference in the use of input variables to predict cyanobacterial cells and metabolites. The application of deep learning models through the construction of big data may be encouraged to build accurate models to predict cyanobacterial metabolites.

This study was designed to develop and to qualify a coffee alternative beverage using a mixture of coffee beans and roasted black beans (Rhynchosia nulubilis). Therefore, the total isoflavone content (TIC), total phenol content (TPC), antioxidant activity, anti-inflammatory activity, NFATc1 (Nuclear factor of activated T-cells c1) expression in RANKL (receptor activator of nuclear factor kappa-B ligand)-stimulated RAW264.7 cells and sensory evaluation were measured for 5 different Cb (coffee bean)-RoS (roasted seomoktae) mixture extracts (Cb100RoS0, Cb75RoS25, Cb50RoS50, Cb25RoS75, and Cb0RoS100). Cb0RoS100 had the highest TIC (516.83±36.61 mg/100 g) and TPC (18.11±1.77 mg TAE/100 g) along with the highest antioxidant activity as measured by DPPH radical scavenging activity (73.55±8.11%) and ABTS radical scavenging activity (63.27±7.27%). Also, Cb0RoS100 showed the highest anti-inflammatory activity as measured by NO production (13.57±2.21 μM) and PGE2 production (3.25±0.21 ng/mL). The more the RoS ratio was increased in the mixtures of Cb-RoS, the more the NFATc1 protein expression was decreased in RANKL-stimulated RAW264.7 cells. In case of sensory evaluation, Cb50RoS50 had the highest scores for flavor, delicate flavor and overall quality, which were similar to those in Cb alone (Cb100RoS0). We suggest that the use of RoS replacement instead of Cb in/as a coffee alternative beverage may help to reduce the risk of caffeine-related bone loss and/or bone disease by effectively blocking NFATc1 expression in RANKLstimulated RAW264.7 cells compared with Cb alone.

본 연구는 국제기아 돕기를 촉구하는 설득 메시지의 효과에 영향을 미치는 죄책감 소구 수준과 공감적 개인성 향의 상호작용을 살펴보았다. 죄책감 소구 수준은 메시지가 수용자의 죄책감을 이끌어 내는 정도의 높고 낮음을 의미한다. 공감적 성향이란 타인의 경험에 동조하거나 관심을 갖는 개인의 경향을 지칭하며 본 연구에서는 개인 적 고통과 공감적 관심이라는 하위 차원을 이용하였다. 메시지의 실험은 2단계로 구성하였다. 1단계에서는 개인 성향을 측정하고 2단계에서는 죄책감 소구의 수준을 차별화한 메시지를 전달하였다. 전체 실험은 죄책감 소구 수준(2) ✕ 공감성향(2)으로 설계되었다. 그 결과, 죄책감 소구 수준은 개인적 고통과 상호작용하는 것으로 나타 났다. 그 상호작용은 주로 죄책감 수준이 높은 조건에서 개인적 고통이 높은 개인과 낮은 개인들의 차이에 의해 발현되었다. 공감적 관심이 높은 개인들은 낮은 개인들에 비해 죄책감 수준과 상관없이 돕기 메시지에 더 우호적인 것으로 나타났다.