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Zusammenfassung Die öffentliche Theologie geht von der Kritik an der Privatisierung der Theologie aus und richtet sich auf das aktive Teilnehmen an den öffentlichen Diskursen der Gesellschaft für das gemeinschaftliche Wohl. Der vorliegende Aufsatz fasst den Ausgangs- sowie Zielpunkt der öffentlichen Theologie unter dem Begriff Entprivatisierungsthese zusammen. Während öffentliche Theologie auf die Überwindung von Säkularisierung und Individualisierung zielt, die unter dem Begriff von Privatisierung mitbegriffen sind, sind hier die positiven öffentlichen Bedeutungen von Säkularisierung und Individualisierung verdeutlicht. Ausgehend von Harnacks privatisierter theologischer Auffassung vom Evangelium und seiner Teilnahme an politischen und sozialen Fragen wird herausgestellt, dass die öffentliche Dimension der Theologie nicht in der Exklusion von Säkularisierung und Individualisierung, sondern in ihrer positiven Aufnahme zurückzugewinnen ist.

Reinforced retaining walls are structures constructed horizontally to resist earth pressure by leveraging the frictional force imparted by the backfill. Reinforcements are employed because they exhibit excellent safety and economic efficiency. However, insufficient reinforcement can lead to collapse, and excessive reinforcement reduces economic efficiency. Therefore, it is important to select the appropriate type, length, and spacing of reinforcements. However, in actual sites, although the stress and fracture mechanisms in the straight and curved sections of reinforced soil retaining walls differ, the same amount of reinforcements are typically installed. Such an approach can lead to wall collapse or reduce economic feasibility. Therefore, in this study, the behaviours of straight and curved sections fortified with reinforcements of various lengths (1, 3, 5, and 7 m) are predicted through a three-dimensional numerical analysis. The retaining walls are of the same height, but the reinforcement variations in the aforementioned sections influence the wall behaviour differently. Based on the results, the optimum reinforcement lengths for the straight and curved parts were selected. By installing reinforcements of different lengths in these sections, the maximum reinforcing effect with minimum reinforcement was derived. This study further found that the curved section of the wall required more reinforcements, and the reinforcement lengths for the curved and straight sections should be separately optimized.

The process used by engine manufacturers for the development of a new engine includes the planning and conceptual design phases, followed by the detailed design phase, in which the design target specifications are met. In the conceptual design phase, a rough specification of the target engine is presented to facilitate a detailed design and the additional cost of modification is reduced exponentially. In the conceptual design phase, however, not only is there no real engine. but there are also no 1D and 3D models present, so it is impossible to test and simulate them. Therefore, at this stage, a model that can predict emission and performance only according to the specifications and operating conditions of the engine would be very useful. Previous studies developed an EGR prediction model that can be used in the 0-D NOx prediction using a deep learning method. In this study, a NOx prediction model with high accuracy using only the operating conditions as input variables, without ECU data, was developed using deep neural networks. The developed model has high accuracy with an R-square of 0.988. The feature of this model is that all the input parameters for the deep neural network come from the operating conditions of the engine. Therefore, this model can be used in the early stages of the development of new engines when testing and simulation cannot be performed because they do not exist. The designer can set the range of the operating conditions such that they do not exceed the NOx limits at the specific operating point (specific rpm and BMEP). This variable operating design methodology is expected to be useful in the development of new engines for automobile manufacturers with various engine data.

Most of the parameters needed to predict Nitrogen Oxides (NOx) emissions, for example, combustion temperature, oxygen concentration and in-cylinder composition ratio, can be predicted by phenomenological 0-D prediction model if accurate EGR rates are provided. However, it is difficult to predict the EGR rate itself accurately by the model, so the EGR rate is predicted by the temperature measurement method. Although this method predicts EGR rates very accurately and quickly, there are some problems such as thermocouple failures and the difficulty in applying to mass production engines, so it is necessary to predict EGR rates by another method. The deep learning method follows an inductive methodology that extracts common characteristics of data based on a lot of data themselves. Therefore, although it requires a lot of experimental data, it has an advantage of high accuracy that can be obtained without any feature engineering. In this study, the EGR rate, which was difficult to predict in the past, was predicted by making various models using the deep learning method. Finally, EGR rate was predicted with a high accuracy of R-square 0.9994 and root mean squared error 0.0692 using a deep learning method at 1500 rpm and bmep 4, 6 and 8 bar. This study can be used as a basic study to predict EGR rates in transient and RDE conditions.

Screening target microorganisms from a mutated recombinant library plays a crucial role in advancing synthetic biology and metabolic engineering. However, conventional screening tools have several limitations regarding throughput, cost, and labor. Here, we used the fluid array platform to conduct high-throughput screening (HTS) that identified Escherichia coli 'TesA thioesterase mutants producing elevated yields of free fatty acids (FFAs) from a large (106) mutant library. A growth-based screening method using a TetA-RFP fusion sensing mechanism and a reporter-based screening method using high-level FFA producing mutants were employed to identify these mutants via HTS. The platform was able to cover >95% of the mutation library, and it screened target cells from many arrays of the fluid array platform so that a post-analysis could be conducted by gas chromatography. The 'TesA mutation of each isolated mutant showing improved FFA production in E. coli was characterized, and its enhanced FFA production capability was confirmed.

PurposeData envelopment analysis (DEA) is based on the production possibility set that involves the process of converting resources or inputs to outputs. Accordingly, most DEA models include endogenous variables and need an additional step to find the influence of exogenous variables on the process. The purpose of this paper is to examine the relationship between the efficiency scores of DEA and the exogenous variables using truncated regression analysis with double bootstrapping along with two additional methods.Design/methodology/approachFirst, the authors employ DEA for benchmarking the comparative efficiency of the health care institutes. Next, the authors run and compare truncated, ordinary least square (OLS) and Tobit regression analysis using the double bootstrapping algorithm for finding the influence of exogenous variables on the efficiency of the health care institutes.FindingsThe authors confirmed the amount of bias for the Tobit and OLS regression models, which was caused by serially correlated errors. Accordingly, the authors chose results from the truncated regression model with double bootstrapping for examining the influence of exogenous or environment variables on the efficiency scores.Research limitations/implicationsThe study includes cross-sectional data on health care institutes in the state of Washington, USA. Collecting data in various states or regions over time is left for future studies.Practical implicationsIn this study, three exogenous variables such as Medicaid revenues, locations of health care institutes and ownership types are significant for explaining the relationship between the efficiency scores and a group of the exogenous variables. Managers and policy makers need to pay attention to these variables along with endogenous variables for promoting the sustainability of the health care institutes.Originality/valueThe study demonstrates the usefulness of the truncated regression analysis with double bootstrapping for confirming the relationship between the efficiency scores of DEA and a group of exogenous variables, which is rare in the DEA literature.

Vital underground structures such as sewers, power transmission lines, subways, and underpasses are potentially vulnerable to adverse effects from aboveground construction. In this study, the influence of pile installation on nearby existing tunnels was investigated. Both a laboratory model test and finite-element numerical analysis were conducted. Twelve different combinations of horizontal and vertical offsets between the pile and the tunnel were investigated. Different surcharge loads (allowable and ultimate) were also considered. In this way, the appropriate separation distance between the existing tunnel and the piles was established for sandy, medium-compaction soil. Although this study considers simple ground conditions, it facilitates safe construction by confirming the appropriate separation distance and comparing the areas that cannot be penetrated by the structures of each standard.

Background: Location of death has been used to examine an indicator for good death. This study aims to examine location of death among patients with three major cancers (gastric, liver, and lung) and other factors associated with location of death in South Korea. Methods: We selected the medical and pharmacy claims data for health services and location of death among the 42,596 decedents with cancer (lung 16,632, liver 15,872, gastric 10,092) from 2009 to 2013. We used logistic regressions to identify factors associated with home death. Outcome measures are locations of death (hospital, outpatient clinics or emergency room and home). Results: Only 8.9% died at home whereas 46.5% died in hospital as inpatients. Patients with more than one comorbid cancer or receivers for any supportive care were significantly more likely to die in hospital. Female and younger than 55 years old liver cancer patients were associated with home death. Patients living in metropolitan area, or paying more insurance premium, or being public aid beneficiaries, were associated with home death. Conclusions: The supportive care service use prior to death was significantly associated with increasing odds to hospital death. Being older than 75, or having multiple cancers was significant factors associated with hospital death, whereas living in metropolitan area, lower income or emergency visit were significant factors with home death. These findings are opposite to what is found, as the palliative care and hospice is predominantly hospital-centered. The findings emphasize a need to available end-of-life care in community for dying patients.

The Nitrogen Oxides (NOx) from engines aggravate natural environment and human health. Institutional regulations have attempted to protect the human body from them, while car manufacturers have tried to make NOx free vehicles. The formation of NOx emissions is highly dependent on the engine operating conditions and being able to predict NOx emissions would significantly help in enabling their reduction. This study investigates advanced method of predicting vehicle NOx emissions in pursuit of the sensorless engine. Sensors inside the engine are required to measure the operating condition. However, they can be removed or reduced if the sensing object such as the engine NOx emissions can be accurately predicted with a virtual model. This would result in cost reductions and overcome the sensor durability problem. To achieve such a goal, researchers have studied numerical analysis for the relationship between emissions and engine operating conditions. Also, a Deep Neural Network (DNN) is applied recently as a solution. However, the prediction accuracies were often not satisfactory where hyperparameter optimization was either overlooked or conducted manually. Therefore, this study proposes a virtual NOx sensor model based on the hyperparameter optimization. A Genetic Algorithm (GA) was adopted to establish a global optimum with DNN. Epoch size and learning rate are employed as the design variables, and R-squared based user defined function is adopted as the object function of GA. As a result, a more accurate and reliable virtual NOx sensor with the possibility of a sensorless engine could be developed and verified.

Reinforced soil retaining wall are ground structures that can be readily seen all around us. The development of reinforcements to these walls and their demand have increased rapidly. These walls are advantageous because they can be used not only in simple construction compared with reinforced concrete retaining walls but also when the height of the wall needs to be higher. However, unlike reinforced concrete retaining walls, in which the walls are integrated and resist the earth pressure on the back, the block-type reinforced earth retaining wall method secures its structural stability by frictional force between the buried land and reinforcements. A phenomenon in which a block is cracked or dropped owing to deformation has been frequently reported. In particular, this phenomenon is concentrated at the curved parts of a reinforced soil retaining wall and is mainly known as a stress concentration. However, to date, the design of reinforced soil retaining walls has been limited by the two-dimensional plane strain condition and has not considered the characteristics of the curved part. There is a lack of research on curved part. Therefore, this research determines the behavioural characteristics of curved-part reinforced soil retaining walls with regard to the shape (convex or concave) and angle (60&deg, 90&deg, 120&deg, and 150&deg, ). The displacement generated in the straight part and the curved part was analysed through an Laboratory Scale Test. The results showed that the horizontal displacement of the curved part increases as a convex angle becomes smaller, and the horizontal displacement of the curved part decreases as a concave angle becomes smaller. At the center (D and H have the same length, but H represents the height and D represents the separation distance from the center of the curved part) of the convex curve, the horizontal displacement of the 0.5 D section decreased to 13.8%, it decreased to 41.0% in the 1.0 D section. For concave angles, it was revealed that the horizontal displacement from the center 0.0 D to the 0.5 D section of the curved part increased by 25%, and from the 1.0 D section, by 75%. It was confirmed that the displacement difference was largely based on the value of 0.5 D. It was judged that this can be used as basic data for the design and construction guidelines for reinforced soil retaining wall of reinforced soil retaining walls.

A study on the habitat use and diet of Iksookimia hugowolfeldi, which is endemic to Korea, was carried out focusing on feeding and resting behaviour in the wild. This study focused on a valley stream in a forest on Geogeum Island (Eojeon-ri, Geumsanmyeon, Goheung-gun, Jeollanam-do, Korea) with a large population. The bottom substrate of this stream was mud ( 256 mm, 10 %), with an Aa-Bb river type and a slow current (0.1 m/s). The food sources analyzed based on the index of relative importance (IRI) included Diptera (70.98 %), Phryganeidae (10.46 %), Branchiopoda (9.74 %), Harpacticidae (8.33 %), Ephemeroptera (0.47 %) and other (0.02 %). As a diurnal and benthic filter feeder, two main feeding behaviours were observed: feeding on surface (sand, cobble) and on sand-cobble digging. Resting behaviour predominantly occurred on the surface of sand and cobbles, and sometimes while buried in the sand. Daily feeding behaviour was more active at the highest water temperature of 22.1– 23.7 °C from 14:00 to 15:00, and feeding behaviour occurred more frequently in July to August, at temperatures as high as 22–24 °C. Meanwhile, while the water temperature is below 8 °C, they are getting around in the water by being hid in or buried in the substrate. In that season, the empty stomach rate was higher than in other seasons.

Due mainly to the privatization and commercialization of airline companies and deregulation of the aviation rules, the demand for air transport has continuously been increasing. Airport authorities state that transfer passengers, who contribute to the large portion of the airports’ profits, are gaining much more importance, particularly in the Northeast Asia region where the air transport industry is very vital. Therefore, this study aims to investigate the competitiveness of IIA (Incheon International Airport) with other major airports located in Northeast Asia in passenger transfers made between Southeast Asia and China to North America using Conjoint Analysis. Results have indicated that airport brand is the most important attribute for the competitiveness of airport, followed by cost, connectivity and duty free shops. In further analysis focusing on brand value of the three airports measured by the use of transfer passengers, it was revealed that IIA needs more effort in developing their brand identity to become the leading transfer hub airport. Based on the results, recommendations for increasing the brand value have also been suggested.

Over the past three decades, urban congestion has become more costly in terms of time, money, and fuel. For the top 101 largest US urban centers, congestion generated 4.8 billion hours of travel delays in 2011, up from 1.1 billion hours in 1982. Congestion also required 8.419 million cubic meters of excess fuel consumption in 2011, up from 1.73 million cubic meters in 1982. Finally, the excess CO 2 emitted from congestion amounted to 19.524 billion kilograms in 2011, up from 3.94 billion kilograms in 1982. We examined the scaling relationships between the population sizes of urban centers and traffic congestion for four subgroups of urban centers with varying population sizes. We found that scaling relationships were superlinear for most subgroups. However, for the subgroup comprising mega cities with populations of over 3 million people, the relationship was linear. The varying scaling relationships we found for cities with different population sizes may resolve the contradictory scaling results reported in the extant literature. Several other implications from our findings are also discussed.

As the regulations on vehicle emissions have become more stringent internationally and real-driving emissions (RDE) have been established, the on-road characteristics of emissions have gained importance in vehicle research and development. The results of the fuel consumption levels and emissions from on-road tests are affected by many factors, such as driving conditions, routes and environmental conditions. Therefore, more research and analysis are needed for the effects of environmental factors and driving conditions according to RDE phase on the NOx emissions. In this study, RDE tests were conducted by season to analyze the on-road NOx emission characteristics of lean NOx trap (LNT)- and selective catalytic reduction (SCR)-equipped diesel vehicles corresponding to the Euro 6b regulation prior to the application of the RDE regulation. The purpose of this study is to analyze the effects of seasonal factors and phases of the RDE routes on the NOx emission and NOx conversion efficiency of catalyst. In spring/autumn and summer, the engine-out and tail-pipe NOx emissions were higher 1.3-5.9 times for vehicle A and 1.3-28.4 times for vehicle B in the urban phase than in other phases. In the urban phase, the engine bay temperature was probable to rise owing to frequent stops and low-speed driving, leading to a high intake air temperature, which causes excessive NOx emission, particularly in summer. The average air filter temperature in urban phase was 11-15 °C higher than the environment temperature for vehicle A. The NOx conversion efficiency of the LNT was highest at 54.1% on motorway and the efficiency was dependent on the phase of the test route. The NOx conversion efficiency of the SCR, which is dependent on the catalyst temperature, was highest at 98.7% in spring motorway and the efficiency was affected by the combined factors of season and phases.

For tunneling in urban areas, understanding the interaction and behavior of tunnels and the foundation of adjacent structures is very important, and various studies have been conducted. Superstructures in urban areas are designed and constructed with piled rafts, which are more effective than the conventional piled foundation. However, the settlement of a piled raft induced by tunneling mostly focuses on raft settlement. In this study, therefore, raft and pile settlements were obtained through 3D numerical analysis, and the change rate of settlement along the pile length was calculated by linear assumption. Machine learning was utilized to develop prediction models for raft and pile settlement and change rate of settlement along the pile length due to tunneling. In addition, raft settlement in the laboratory model test was used for the verification of the prediction model of raft settlement, derived through machine learning. As a result, the change rate of settlement along the pile length was between 0.64 and −0.71. In addition, among features, horizontal offset pile tunnel had the greatest influence, and pile diameter and number had relatively little influence.