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Global climate models (GCMs) are used to analyze future climate change. However, the observed data of a specified region may differ significantly from the model since the GCM data are simulated on a global scale. To solve this problem, previous studies have used downscaling methods such as quantile mapping (QM) to correct bias in GCM precipitation. However, this method cannot be considered when certain variables affect the observation data. Therefore, the aim of this study is to propose a novel method that uses a convolution neural network (CNN) considering teleconnection. This new method considers how the global climate phenomena affect the precipitation data of a target area. In addition, various meteorological variables related to precipitation were used as explanatory variables for the CNN model. In this study, QM and the CNN models were applied to calibrate the spatial bias of GCM data for three precipitation stations in Korea (Incheon, Seoul, and Suwon), and the results were compared. According to the results, the QM method effectively corrected the range of precipitation, but the pattern of precipitation was the same at the three stations. Meanwhile, for the CNN model, the range and pattern of precipitation were corrected better than the QM method. The quantitative evaluation selected the optimal downscaling model, and the CNN model had the best performance (correlation coefficient (CC): 69% on average, root mean squared error (RMSE): 117 mm on average). Therefore, the new method suggested in this study is expected to have high utility in forecasting climate change. Finally, as a result of forecasting for future precipitation in 2100 via the CNN model, the average annual rainfall increased by 17% on average compared to the reference data.

Integrating renewable energy into public space is becoming more common as a climate change solution. However, this approach is often guided by the environmental pillar of sustainability, with less focus on the economic and social pillars. The purpose of this paper is to examine this issue in the speculative renewable energy propositions for Freshkills Park in New York City submitted for the 2012 Land Art Generator Initiative (LAGI) competition. This paper first proposes an optimal electricity distribution (OED) framework in and around public spaces based on relevant ecology and energy theory (Odum’s fourth and fifth law of thermodynamics). This framework addresses social engagement related to public interaction, and economic engagement related to the estimated quantity of electricity produced, in conjunction with environmental engagement related to the embodied energy required to construct the renewable energy infrastructure. Next, the study uses the OED framework to analyse the top twenty-five projects submitted for the LAGI 2012 competition. The findings reveal an electricity distribution imbalance and suggest a lack of in-depth understanding about sustainable electricity distribution within public space design. The paper concludes with suggestions for future research.

The work proposed a reliability demonstration test (RDT) process, which can be employed to determine whether a finite population is accepted or rejected. Bayesian and non-Bayesian approaches were compared in the proposed RDT process, as were lot and sequential sampling. One-shot devices, such as bullets, fire extinguishers, and grenades, were used as test targets, with their functioning state expressible as a binary model. A hypergeometric distribution was adopted as the likelihood function for a finite population consisting of binary items. It was demonstrated that a beta-binomial distribution was the conjugate prior of the hypergeometric likelihood function. According to the Bayesian approach, the posterior beta-binomial distribution is used to decide on the acceptance or rejection of the population in the RDT. The proposed method in this work could be used to select item providers in a supply chain, who guarantee a predetermined reliability target and confidence level. Numerical examples show that a Bayesian approach with sequential sampling has the advantage of only requiring a small sample size to determine the acceptance of a finite population.

This study investigates the performance of the “green refurbishment” of existing buildings. Two ordinary rooms in an existing building were chosen for examination. Refurbishment measures such as additional insulation, high-performance glazing, and air-tightening were applied to the control room. Temperature and electricity use were monitored to identify heating performance in winter and then compared with a baseline. The results of the field tests showed that green refurbishment significantly improved heating performance. Lowered heating load and electricity use with increased airtightness were also verified through building performance simulations. The empirical investigation suggests a predictive model to obtain indoor minimum temperatures as a function of outdoor temperature swings.

The tourism industry has received increasing attention as it has become one of the fastest developing business sectors around the world. Service quality in tourism has come to be regarded as an important impetus for economic growth; however, the focus on tourism service quality has not yet been satisfactorily or comprehensively reviewed. Therefore, we conducted a systematic literature review combining bibliometric, citation network and keyword network analysis. We selected the top five tourism journals from the SCOPUS database and then collected papers according to their keywords. Our results revealed the critical issues, topics, and changes over time regarding research on tourism service quality. The critical issues included the important impact of service quality on tourist behavior and service quality evaluation, and topics comprised (1) tourist satisfaction, (2) sustainable issues in tourism, (3) value of service quality for customers, (4) restaurant service quality, (5) customers’ perceptions of tourism, (6) service quality evaluation in tourism, and (7) service quality’s influence on customer behavior. Furthermore, the keyword network analysis results revealed the most influential keywords.

Sustainably feeding the world’s growing population represents one of our most significant challenges. Aquaculture is well positioned to make contributions towards this challenge. Yet, the translation of aquaculture production innovations into benefits for rural communities is constrained by a limited understanding of the social dynamics that influence the adoption of new agricultural practices. In this paper, we investigate the factors that shape the spread of small-scale tilapia aquaculture through rural Solomon Islands. Based on diffusion of innovation theory, we focus on three potentially influential factors: (i) socio-economic characteristics of adopters; (ii) the role of opinion leaders; and (iii) characteristics of the innovation. We find that farmers who were wealthier, older, and had more diverse livelihoods were most likely to be adopters. Opinion leaders facilitated the adoption of tilapia aquaculture, but lacked the capacity to provide fundamental knowledge necessary to realize its potential benefits to food security. The paper argues for more explicit attention to the poorest households and makes the case for a deeper engagement with the broader social and institutional contexts that shape the adoption process. Aquaculture interventions that account for these social dynamics are critical for translating production innovations into sustainable benefits to rural communities.

In recent years, big data has been widely used to understand consumers’ behavior and opinions. With this paper, we consider the use of big data and its effects in the problem of projecting the number of reverse mortgage subscribers in Korea. We analyzed web-news, blog post, and search traffic volumes associated with Korean reverse mortgages and integrated them into a Generalized Bass Model (GBM) as a part of the exogenous variables representing marketing effort. We particularly consider web-news volume as a proxy for marketer-generated content (MGC) and blog post and search traffic volumes as proxies for user-generated content (UGC). Empirical analysis provides some interesting findings: First, the GBM by incorporating big data is helpful for forecasting the sales of Korean reverse mortgages, and second, the UGC as an exogenous variable is more useful for predicting sales volume than the MGC. The UGC can explain consumers’ interest relatively well. Additional sensitivity analysis supports that the UGC is important for increasing sales volume. Finally, prediction performance is different between blog posts and search traffic volumes.