Determining the Construction Costs for Basic Type to Estimate the Sale Prices of New Multi-Family Housing Projects

Over the past two decades, the South Korean government has been regulating the supply and prices of multi-family housing (MFH) projects to stabilize the national population. Recently, active research has been conducted on the construction costs for basic type (CCBT) calculation to formulate appropriate policies. However, related previous studies have focused on improving the predictability of the construction cost in early stages based on existing house sale prices. In contrast, the CCBT calculation approach mainly requires policy implementation in practical fields, without considering the requirements of academics. Therefore, it is necessary to academically discuss a different approach for the estimation of sale prices of new MFH in the construction stage. This study aimed to calculate the CCBT to determine the appropriate sale price for new MFH. We selected four sample projects to calculate the CCBT, and a weighted average method was applied to correct regional deviations. Case application, which is a comparison between the CCBT-based sale price and actual case-based sale price, produced cost values in the range of 98–104%, and they included additional expenses. The results of this study demonstrate an extremely high level of cost estimation accuracy according to the Association for the Advancement of Cost Engineering study. Furthermore, this study can facilitate the stabilization of national housing by determining an appropriate sale price and can contribute to cost management research conducted during the construction phase

Development of a Model for Predicting Probabilistic Life-Cycle Cost for the Early Stage of Public-Office Construction

Decisions made in the early stages of construction projects significantly influence the costs incurred in subsequent stages. Therefore, such decisions must be based on the life-cycle cost (LCC), which includes the maintenance, repair, and replacement (MRR) costs in addition to construction costs. Furthermore, as uncertainty is inherent during the early stages, it must be considered in making predictions of the LCC more probabilistic. This study proposes a probabilistic LCC prediction model developed by applying the Monte Carlo simulation (MCS) to an LCC prediction model based on case-based reasoning (CBR) to support the decision-making process in the early stages of construction projects. The model was developed in two phases: first, two LCC prediction models were constructed using CBR and multiple-regression analysis. Through k-fold validation, one model with superior prediction performance was selected; second, a probabilistic LCC model was developed by applying the MCS to the selected model. The probabilistic LCC prediction model proposed in this study can generate probabilistic prediction results that consider the uncertainty of information available at the early stages of a project. Thus, it can enhance reliability in actual situations and be more useful for clients who support both construction and MRR costs, such as those in the public sector

Plant growth promoting bacteria increases biomass, effective constituent, and modifies rhizosphere bacterial communities of Panax ginseng

Purpose: The main aim of this study was to introduce and explore plant growth-promoting bacteria (PGPB) indigenous to ginseng, and to evaluate their ability to improve production and quality, and effect on rhizosphere niche in ginseng. Materials and methods: Endophytic bacteria were isolated from root, stem, and leaf of ginseng from different sites and genotype in China and Korea, screened based on their beneficial properties as PGPB. Nine bacterial isolates were selected according to their plant growth properties including soluble phosphate and potassium, ammonia, auxin and siderophore producing, ACC deaminase, and antagonistic pathogen as well. Changes in ginseng after PGPB inoculation were evaluated with respect to the non-inoculated control. Results and Conclusions: The PGPB isolates were identified as genera Bacillus, Lysinibacillus, Rhizobium, Stenotrophomonas, Erwinia, Ochrobactrum, Enterobacter and Pantoea based on 16S rRNA sequences. Inoculation of G209 and G119 increased not only plant height, root length, fresh weight, and dry weight, but also root activity and the amount of ginsenosides significantly. In particular, using the Illumina Miseq platform, the native bacterial community of rhizospheric soil maintained high community diversity and increased abundance of specific bacteria. Therefore, they may be play a crucial role in sustainable ginseng cultivating in farmland

Genetic Basis of Tiller Dynamics of Rice Revealed by Genome-Wide Association Studies

A tiller number is the key determinant of rice plant architecture and panicle number and consequently controls grain yield. Thus, it is necessary to optimize the tiller number to achieve the maximum yield in rice. However, comprehensive analyses of the genetic basis of the tiller number, considering the development stage, tiller type, and related traits, are lacking. In this study, we sequence 219 Korean rice accessions and construct a high-quality single nucleotide polymorphism (SNP) dataset. We also evaluate the tiller number at different development stages and heading traits involved in phase transitions. By genome-wide association studies (GWASs), we detected 20 significant association signals for all traits. Five signals were detected in genomic regions near known candidate genes. Most of the candidate genes were involved in the phase transition from vegetative to reproductive growth. In particular, HD1 was simultaneously associated with the productive tiller ratio and heading date, indicating that the photoperiodic heading gene directly controls the productive tiller ratio. Multiple linear regression models of lead SNPs showed coefficients of determination (R2) of 0.49, 0.22, and 0.41 for the tiller number at the maximum tillering stage, productive tiller number, and productive tiller ratio, respectively. Furthermore, the model was validated using independent japonica rice collections, implying that the lead SNPs included in the linear regression model were generally applicable to the tiller number prediction. We revealed the genetic basis of the tiller number in rice plants during growth, By GWASs, and formulated a prediction model by linear regression. Our results improve our understanding of tillering in rice plants and provide a basis for breeding high-yield rice varieties with the optimum the tiller number