This research investigates the impact of specific emotions in electronic word-of-mouth (eWOM) on the monthly donations received by a non-profit organization (NPO). We employ the empathy-helping (empathy-altruism) hypothesis as a theoretical foundation, proposing that donation motivations should inform eWOM fundraising appeals. To do so, we analyzed 71,462 tweets about a charity from 23,430 users, categorizing them as either marketer-generated content (MGC) or user-generated content (UGC). To automatically detect six distinct emotions in the text, we utilized a transformer-transfer learning approach for emotion detection. This model was trained in a sequential manner, starting with self-reported emotions in over 3.6 million tweets and progressing to socially agreed-upon emotion datasets to mimic social-emotional development stages. Our findings revealed that emotions prompting empathy (such as sadness in MGC) and positive empathy (like joy in UGC) positively influence donation amounts in line with the empathy-helping hypothesis. We offer insights on how social media marketers can leverage these results to create and manage tweets that boost donations. This study contributes to marketing research and practice in three ways: (1) by being the first, to our knowledge, to examine the effect of specific emotions in eWOM on donation decisions, (2) by introducing a novel machine learning model capable of detecting emotions in large-scale eWOM, and (3) by providing actionable recommendations for NPOs to increase donations via emotionally driven social media messaging. As a result, marketing managers can more effectively use social media platforms to foster emotional connections between NPOs and donors.

We conducted diagnostic investigations to analyze the causes of abortions (46 cases, 65.7%), deaths (22 cases, 31.4%) and muscular lesions (2 cases, 2.9%) occurred after foot and mouth disease (FMD) vaccination in livestock farms in Korea. Bacterial culture, enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR) were performed to detect the causative agents of abortion in bovine and caprine. The diagnostic results showed that 36 (51.4%) cases, referring as “Identified”, were occurred by influence of underlying disease including bovine viral diarrhea (12 cases, 17.1%), neosporosis (7 cases, 10.0%), septicemic colibacillosis (5 cases, 7.1%), Q fever (4 cases, 5.7%) and other abnormal conditions (8 cases, 11.4%) not by vaccination. Other 2 (3.0%) cases were suspected to be vaccine-associated adverse reaction on the basis of pathological findings (shock lung, oil-component-induced granuloma) and clinical symptoms (dyspnea with pulmonary edema). The other 32 (45.7%) cases were determined “Unknown” because any pathogens and pathological changes were not identified. However, many of the “Unknown” cases were presumptive to be the vaccine-related adverse reaction based on epidemiological investigation, especially, the cases which showed the clinical signs within 2 days after the vaccination. It is important to conduct pathological, microbilogical and epidemiological investigation to diagnose whether the cases are from vaccine-associated adverse reaction or not.

We report a massive outbreak of human Q fever cases, which occurred at totally 11 humans. The occurrence was related to a goat farm where Coxiella burnetii infection was diagnosed from goat tissues and environmental specimens. From January of 2018, continuous abortions from 6 goats occurred. Laboratory tests from 77 goat specimens for C. burnetii showed that 54 (70.1%) and 63 (81.8%) goats were positive by polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA), respectively. The infection was also confirmed from the farmer, his wife and 9 persons from all 16 veterinary officials who had visited the farm for protective measures and preparing goat specimens for laboratory tests. The farm was found to be extensively contaminated with C. burnetii from the examination to the environmental specimens and epidemiological inspections, which might be the main source of C. burnetii infection to humans. The extensive contamination to the farm was derived from the uncareful handling of postpartum animal tissues or discharges by the farm owner. This report will contribute to the establishment of educational system on the biosecurity to novice farmers.

Field research was undertaken for recovering mosquito larval mermithid parasite, Romanomermis species in rice fields in 54 different areas in period of May through October, 2009. Of 54 area rice fields surveyed, Romanomermis sp. recoveries were made from 4 areas. 32 Anopheles mosquito larvae, malaria disease vector were infected and died from samples collected in Pochon area, and a total of 10 Culex mosquito larvae, house frequenting mosquito were infected to death in 3 different areas, Gimpo, Sangju and Gurae, respectively. On the basis of positive natural infection record, an intensive host-parasite occurrences and/or population study was surveyed in foci area of Pochon in small pond (P) and Rice paddies (A and B) during 5 months till end of October. The natural mermithid infection was continuously occurred from June through October in weekly samples, however the rate of infection was appeared higher in June-July, thereafter the rate gradually decreased in progress of the season. The highest natural infection rate was observed from the Pond 9.1% followed by Rice paddy "A" 5.2%, Rice paddy "B" 2.4%, respectively. Including 2 other Rice paddies "C" and "D", a total mosquito larvae collected was 3,270, an overall average natural infection rate was recorded as 3.7% (121 Anopheles mosquito larvae infected).

This paper introduces a design of multi-dimensional complex emotional model for various complex emotional expression. It is a novel approach to design an emotional model by comparison with conventional emotional model which used a three-dimensional emotional space with some problems; the discontinuity of emotions, the simple emotional expression, and the necessity of re-designing the emotional model for each robot. To solve these problems, we have designed an emotional model. It uses a multi-dimensional emotional space for the continuity of emotion. A linear model design is used for reusability of the emotional model. It has the personality for various emotional results although it gets same inputs. To demonstrate the effectiveness of our model, we have tested with a human friendly robot.

In face recognition, learning speed of face is very important since the system should be trained again whenever the size of dataset increases. In existing methods, training time increases rapidly with the increase of data, which leads to the difficulty of training with a large dataset. To overcome this problem, we propose SVDD (Support Vector Domain Description)-based learning method that can learn a dataset of face rapidly and incrementally. In experimental results, we show that the training speed of the proposed method is much faster than those of other methods. Moreover, it is shown that our face recognition system can improve the accuracy gradually by learning faces incrementally at real environments with illumination changes.