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Despite growing signs of urban shrinkage in countries such as Korea, Japan and China, few studies have examined the generalisable pattern of urban shrinkage and its relationship to the characteristics of housing abandonment in the East Asian context. This study explores five major paths that may explain the emergence of vacant houses in declining inner-city areas, based on empirical observations in the city of Incheon, South Korea. The paths are: (1) strong government-led new built-up area development plans (pull factor for population movement); (2) delay and cancellation of indiscriminate redevelopment projects (push factor for population movement); (3) initial poor development and concentration of substandard houses; (4) aging of the elderly population; and (5) the outflow of infrastructure and services. These paths, also found in Japan or China, are expected to be combined in a local context, leading to more serious housing abandonment. This study suggests that it is important to take appropriate countermeasures based on the identification of the paths causing vacant houses. 尽管在韩国、日本和中国等国家，城市收缩的迹象越来越明显，但很少有研宄考察城市收 缩的普遍模式及其与东亚地区弃房特征的关系。本研宄基于在韩国仁川市的经验观察，探 索了五条主要路径，这五条路径可以解释衰退的市中心地区空置房屋的出现。这五条途径 是：（I)强有力的政府主导的新建成区发展计划（人口流动的拉动因素）；（2)拖延和取消 无差别再开发项目（人口流动的推动因素）；（3)初始开发不良，不合格房屋集中；（4)老 年人口老龄化；（5)基础设施和服务外流。这些途径在日本或中国也可以找到，预计将与 当地情况结合导致更严重的房屋遗弃。本研宄表明，在确定空置房屋产生路径的基础上采 取适当的对策十分重要。

Although shrinking neighbourhoods are places where urban citizens experience negative emotions, some evidence suggests that people in some shrinking neighbourhoods feel less negative emotions than in other areas. Nevertheless, empirical studies that analyse environmental and personal elements that affect people’s emotions in a shrinking neighbourhood remain insufficient. This is rather surprising, considering an increasing interest in the effects of negative emotions on individuals’ health. Thus, this study used electroencephalography (EEG) to examine the impacts of environmental and personal characteristics on people’s emotional levels in a shrinking area of Seoul, South Korea. A multilinear regression model was used to analyse emotional valence levels between sites with different urban designs and management levels. The results revealed that people felt positive emotions at sites where both urban design factors and their management were both satisfactory at appropriate levels. The results also found that people who had lived or worked in the neighbourhood for a long time and were women experienced more positive emotions than visitors and men. This finding implies that a shrinking neighbourhood can maintain a sense of satisfaction as long as the area is carefully managed. Revealing the emotional effects of environmental and personal characteristics in a shrinking neighbourhood can be used for planning practices and policy-making to create healthy and liveable urban neighbourhoods.

Vacant houses have been regarded, in terms of the broken windows theory, as one of the signs of neighborhood disorder inducing prevalent violent crimes. Previous studies have indicated that vacant houses not only endanger residents’ physical health but also worsen their mental health. However, little is known about residents’ experiences and interpretations of vacant houses in declining districts. In this study, the perceptions of vacant houses in shrinking inner-city neighborhoods of Incheon, South Korea, were investigated utilizing a semi-structured questionnaire and the photo-elicitation method. Residents' impressions of vacant houses were influenced by their level of understanding and responsibility for their neighborhoods, as well as their knowledge regarding those houses. The photo-elicitation demonstrated that the fear of vacant houses arose not just from the visible buildings, especially their poor management, but also from invisible wrongdoers or outsiders. The findings suggest that appropriate vacant house management and utilization measures reflecting the perspectives of residents remaining in shrinking cities should be established.

We propose a practical Bayesian optimization method over sets, to minimize a black-box function that takes a set as a single input. Because set inputs are permutation-invariant, traditional Gaussian process-based Bayesian optimization strategies which assume vector inputs can fall short. To address this, we develop a Bayesian optimization method with set kernel that is used to build surrogate functions. This kernel accumulates similarity over set elements to enforce permutation-invariance, but this comes at a greater computational cost. To reduce this burden, we propose two key components: (i) a more efficient approximate set kernel which is still positive-definite and is an unbiased estimator of the true set kernel with upper-bounded variance in terms of the number of subsamples, (ii) a constrained acquisition function optimization over sets, which uses symmetry of the feasible region that defines a set input. Finally, we present several numerical experiments which demonstrate that our method outperforms other methods.

This study examined the spatial patterns and transformation of the socialist collective housing areas (KTTs). The areas experienced physical and functional changes with Hanoi's urbanization after the country's reforms. The KTTs were originally built on the outskirt of Hanoi during the 1960s-1980s. The development was influenced by the state-led mass housing model originated from the micro-district concept of the former Soviet Union. With the urban expansion of Hanoi, the KTTs have become situated in the city's central area. The study attempted to analyze the location of Hanoi's KTTs based on the distance from the inner-city area. The outcome of physical transformation, the use of spaces, housing prices, and conditions of the surrounding areas of the KTTs were investigated based on a resident survey of 240 households and field studies in six KTTs sites. Changes in the use of spaces on the ground-level, addition of self-extended structure from existing housing units, and wholesale redevelopment were observed from the field studies. The degree and pattern of changes were different by locational types. The KTTs located near the inner-city area with good accessibility was the most actively transformed. The study showed that the living conditions of the KTTs were influenced by the location as well as social and economic factors such as transitional living culture and an increase in the residents' income. Furthermore, the study found that Hanoi's KTTs play an essential role as adequate urban housing due to their locational advantages, the presence of an intimate community, and affordable housing prices.

Background The rapid development in big data analytics and the data-rich environment of intensive care units together provide unprecedented opportunities for medical breakthroughs in the field of critical care. We developed and validated a machine learning-based model, the Pediatric Risk of Mortality Prediction Tool (PROMPT), for real-time prediction of all-cause mortality in pediatric intensive care units. Methods Utilizing two separate retrospective observational cohorts, we conducted model development and validation using a machine learning algorithm with a convolutional neural network. The development cohort comprised 1445 pediatric patients with 1977 medical encounters admitted to intensive care units from January 2011 to December 2017 at Severance Hospital (Seoul, Korea). The validation cohort included 278 patients with 364 medical encounters admitted to the pediatric intensive care unit from January 2016 to November 2017 at Samsung Medical Center. Results Using seven vital signs, along with patient age and body weight on intensive care unit admission, PROMPT achieved an area under the receiver operating characteristic curve in the range of 0.89–0.97 for mortality prediction 6 to 60 h prior to death. Our results demonstrated that PROMPT provided high sensitivity with specificity and outperformed the conventional severity scoring system, the Pediatric Index of Mortality, in predictive ability. Model performance was indistinguishable between the development and validation cohorts. Conclusions PROMPT is a deep model-based, data-driven early warning score tool that can predict mortality in critically ill children and may be useful for the timely identification of deteriorating patients.

Contemporary cities are home to an increasing number of cyclists. The gaze behavior of cyclists has an important impact upon cyclist safety and experience. Yet this behavior has not been studied to access its potential implications for urban design. This study aims to identify the eye-gaze pattern of cyclists and to examine its potential relationships with urban environmental characteristics, such as a raised cycle track, physical separation, land use, and number of pedestrian. This study measured and analyzed 40 cyclist’s gaze patterns using an eye tracker; the results were as follows. First, cyclists presented a T-shaped gaze pattern with two spots of frequent eye fixation points; the pattern suggests that it may benefit cyclists with greater safety and better readiness of road situation to avoid crashes. Second, more active horizontal gaze dispersion within the T-shaped gaze pattern was observed when participants cycled on a shared and non-raised bikeway. This indicates that there is a more suitable gaze behavior with different gaze limitations depending on the environmental characteristics. Therefore, bicycle facilities need to be constructed according to the consideration of the T-shaped gaze area and the change in cyclists’ gaze behavior in each environment to increase the effectiveness of bicycle facilities.

Intelligent packaging technologies are rapidly gaining interest in the agriculture and food industries. Intelligent packaging for agricultural and food products has great potential to improve the shelf life and safety of agricultural and food products apart from its basic functions of keeping the products clean and protecting against unwanted physical and chemical changes. Intelligent packaging components are not limited to radio frequency identification (RFID) sensors, time-temperature indicators, ripeness indicators, and biosensors. Carbon dioxide, oxygen gas sensors and nanobiosensor can be used for real-time monitoring of freshness or quality for agricultural and food products. In this review, details of different sensors that are primarily used for carbon dioxide or oxygen gas sensing and their possible potential to be incorporated into agricultural and food packaging for product quality monitoring are discussed. In addition, special emphasis is placed on detailing the importance of analytical signals and their transformation, because these aspects play crucial role in monitoring the quality and freshness of agricultural and food products via intelligent packaging systems. Signal transducers contribute to the establishment of communication between the product quality sensor and the communication components such as RFID sensors in smart packaging systems by converting a signal in one form of energy to another form.

Semarang is one of big cities in Indonesia contains of multy ethnics. They traditionally settled down inside a group of villages. Kauman is the cultural heritage of Muslim settlement in Semarang. The peculiarity of local Muslims in Java’s coastline and the strong social cohesion colour the people’s daily life. Seochon in Seoul is a historic area and is the home for more than 670 hanoks. In 2008, Seoul Metropolitan Government issued a conservation plan and recruited a team of architects and academics to observe and investigate Seochon’s condition and discover the possibilities of conservation there. It turned out that Seochon has a great potential for revitalization. Nowadays, Seochon has become a tourism destination having both traditional and contemporary cultural value. This research aims to understand the efforts of preservation done by the government and public participation for the sake of preservation. This research used primary and secondary data and comparative study methods. Seochon village has been successful in developing preservation and preservation placed as the best example. The result of research showed that the conservation and preservation of Kauman needs a workable rules to manage and investigate the potential and resources. The result of this research could be used to any other cases similar to Kauman.

Clusters facing a crisis could have devastating effects on the economic conditions of the regions. Therefore, it is important to study how resilience works in the lives of clusters. The purpose of the current study is to more quantitatively understand the life path of the growth and decline of industrial clusters by verifying actual patterns. Also, it is to explain why these patterns were formed by qualitatively analyzing the process of utilizing resilience. The main contribution to the field of the lifecycle of clusters would be proving the theoretical concepts with data of the entire official industrial clusters in South Korea for 2 decades. Although previous works have attempted to define life paths by classifying the groups, most of their cases only dealt with one or two cases, making it difficult to generalize to a theory that can explain all types of clusters. This research used South Korean data as representative data for classification by analyzing the 1375 industrial clusters for 20 years. The trend of their life paths was calculated using a classic time-series decomposition method, and dynamic time series warping was adopted to measure the similarity between the paths. The k-medoids method from an unsupervised machine learning technique was adopted to classify the data. They were classified into three types: Malmo-type, Silicon Valley-type, and Detroit-type. The same classification method can be applied to other countries. Through this classification, the necessary or weak determinants of resilience in their clusters can be found. By making up for these shortcomings, continuous growth can be achieved.