Influence of Unit Water Content Control on Concrete Performance in the Ready-Mixed Concrete Production Process.

This study examined the effects of increasing concrete unit water content and artificially controlling water content on concrete performance in the production process of ready-mixed concrete. Results showed that changes in the unit water content of 20 concrete mix proportions without air-entraining significantly reduced concrete compressive strength, increased porosity, and in-creased occurrence of bleeding. A unit water content increase of 25 kg/m3 or more may reduce the compressive strength of concrete below the design standard and significantly affect the occurrence of bleeding water. Moreover, an extra unit water content of at least 25 kg/m3 could significantly affect the diffusion of chloride ions in the concrete. The carbonation depth of concrete was extremely high with the increase in unit water content and water addition. In the production of concrete requiring at least normal strength or durability, the extra water change to total unit water content should be maintained at 15 kg/m3 or less. And a water-cement ratio of 48% or less and a unit water content of 155 kg/m3 or less are considered effective for management of concrete quality. Considering the aggregate type, absorption rate, and moisture state, the management of unit water content error in concrete production processes requires greater.

Development of a Prediction Model for Demolition Waste Generation Using a Random Forest Algorithm Based on Small DataSets.

Recently, artificial intelligence (AI) technologies have been employed to predict construction and demolition (C&D) waste generation. However, most studies have used machine learning models with continuous data input variables, applying algorithms, such as artificial neural networks, adaptive neuro-fuzzy inference systems, support vector machines, linear regression analysis, decision trees, and genetic algorithms. Therefore, machine learning algorithms may not perform as well when applied to categorical data. This article uses machine learning algorithms to predict C&D waste generation from a dataset, as a way to improve the accuracy of waste management in C&D facilities. These datasets include categorical (e.g., region, building structure, building use, wall material, and roofing material), and continuous data (particularly, gloss floor area), and a random forest (RF) algorithm was used. Results indicate that RF is an adequate machine learning algorithm for a small dataset consisting of categorical data, and even with a small dataset, an adequate prediction model can be developed. Despite the small dataset, the predictive performance according to the demolition waste (DW) type was R (Pearson's correlation coefficient) = 0.691-0.871, R2 (coefficient of determination) = 0.554-0.800, showing stable prediction performance. High prediction performance was observed using three (for mortar), five (for other DW types), or six (for concrete) input variables. This study is significant because the proposed RF model can predict DW generation using a small amount of data. Additionally, it demonstrates the possibility of applying AI to multi-purpose DW management.

Prediction Model for Dry Eye Syndrome Incidence Rate Using Air Pollutants and Meteorological Factors in South Korea: Analysis of Sub-Region Deviations.

Here, we develop a dry eye syndrome (DES) incidence rate prediction model using air pollutants (PM10, NO2, SO2, O3, and CO), meteorological factors (temperature, humidity, and wind speed), population rate, and clinical data for South Korea. The prediction model is well fitted to the incidence rate (R2 = 0.9443 and 0.9388, p < 2.2 × 10-16). To analyze regional deviations, we classify outpatient data, air pollutant, and meteorological factors in 16 administrative districts (seven metropolitan areas and nine states). Our results confirm NO2 and relative humidity are the factors impacting regional deviations in the prediction model.

Deicing Concrete Pavements and Roads with Carbon Nanotubes (CNTs) as Heating Elements.

Existing deicing technologies involving chloride and heating wires have limitations such as reduced durability of roads and surrounding structures, and high labor requirements and maintenance costs. Hence, in this study, we performed indoor experiments, numerical analyses, and field tests to examine the efficiency of deicing using carbon nanotubes (CNTs) to overcome these limitations. For indoor experiments, a CNT was inserted into the center of a concrete sample and then heated to 60 °C while maintaining the ambient and internal temperatures of the sample at -10 °C using a refrigeration chamber. Numerical analysis considering thermal conductivity was performed based on the indoor experimental results. Using the calculation results, field tests were conducted, and the thermal conduction performance of the heating element was examined. Results showed that the surface temperature between the heating elements exceeded 0 °C. Moreover, we found that the effective heating distance of the heating elements should be 20-30 cm for effective thermal overlap through the indoor experiments. Additionally, the numerical analysis results indicated that the effective heating distance increased to 100 cm when the heating element temperature and experiment time were increased. Field test results showed that 62 cm-deep snow melted between the heating elements (100 cm), thus, verifying the possibility of deicing.

Analysis of Waste Generation Characteristics during New Apartment Construction-Considering the Construction Phase.

The waste generation rate (WGR) is used to predict the generation of construction and demolition waste (C&DW) and has become a prevalent tool for efficient waste management systems. Many studies have focused on deriving the WGR, but most focused on demolition waste rather than construction waste (CW). Moreover, previous studies have used theoretical databases and thus were limited in showing changes in the generated CW during the construction period of actual sites. In this study, CW data were collected for recently completed apartment building sites through direct measurement, and the WGR was calculated by CW type for the construction period. The CW generation characteristics by type were analyzed, and the results were compared with those of previous studies. In this study, CW was classified into six types: Waste concrete, waste asphalt concrete, waste wood, waste synthetic resin, waste board, and mixed waste. The amount of CW generated was lowest at the beginning of the construction period. It slowly increased over time and then decreased again at the end. In particular, waste concrete and mixed waste were generated throughout the construction period, while other CWs were generated in the middle of the construction period or towards the end. The research method and results of this study are significant in that the construction period was considered, which has been neglected in previous studies on the WGR. These findings are expected to contribute to the development of efficient CW management systems.

Comparative Study on Corrosion Protection of Reinforcing Steel by Using Amino Alcohol and Lithium Nitrite Inhibitors.

In this study, the ability of lithium nitrite and amino alcohol inhibitors to provide corrosion protection to reinforcing steel was investigated. Two types of specimens-reinforcing steel and a reinforced concrete prism that were exposed to chloride ion levels resembling the chloride attack environment-were prepared. An autoclave accelerated corrosion test was then conducted. The variables tested included the chloride-ion concentration and molar ratios of anti-corrosion ingredients in a CaOH2-saturated aqueous solution that simulated a cement-pore solution. A concentration of 25% was used for the lithium nitrite inhibitor LiNO2, and an 80% solution of dimethyl ethanolamine ((CH3)2NCH2CH2OH, hereinafter DMEA) was used for the amino alcohol inhibitor. The test results indicated that the lithium nitrite inhibitor displayed anti-corrosion properties at a molar ratio of inhibitor of ≥0.6; the amino alcohol inhibitor also displayed anti-corrosion properties at molar ratios of inhibitor greater than approximately 0.3.

Regulation of maternal gene expression by MEK/MAPK and MPF signaling in porcine oocytes during in vitro meiotic maturation.

Mitogen-activated protein kinase (MAPK) and maturation/M phase promoting factor (MPF) play crucial roles in oocyte meiotic maturation in mammals. However, the underlying molecular mechanisms have not been addressed. In this study, the effects of the MEK/MAPK pathway inhibitor U0126 and the MPF inhibitor roscovitine on meiotic maturation and maternal gene expression in pig cumulus-oocyte complexes (COCs) and denuded oocytes (DOs) were investigated. Both inhibitors can reversibly block the resumption of meiosis in pig oocytes. COCs or DOs initially cultured in drug-free medium for 24 h and then transferred to medium containing U0126 showed normal progress to the Metaphase II stage (MII); (90.38 vs. 92.16% control group). In contrast, roscovitine treatment from 24 to 44 h significantly inhibited maturation of COCs and DOs. To explore the underlying molecular mechanisms, expression patterns and polyadenylation states of five representative maternal transcripts, cyclin B1, Cdc2, C-mos, GDF9 and BMP15, were examined by real-time PCR and poly(A)-test PCR (PAT assay). U0126 treatment resulted in aberrant expression of Cdc2 and GDF9, while roscovitine significantly maintained all five maternal transcripts at very high levels in treated COCs compared with the control group. The polyadenylation of these mRNAs increased as well. Furthermore, the experiments were repeated in DOs, and the results also indicated that both Cdc2 and GDF9 showed significantly higher expression in both mRNA and polyadenylation levels in the drug treatment groups. Together, these results provide the first demonstration in a mammalian system that MAPK and MPF play important roles in regulation of maternal gene expression during oocyte maturation.

Web-based applications for building, managing and analysing kinetic models of biological systems.

Mathematical modelling and computational analysis play an essential role in improving our capability to elucidate the functions and characteristics of complex biological systems such as metabolic, regulatory and cell signalling pathways. The modelling and concomitant simulation render it possible to predict the cellular behaviour of systems under various genetically and/or environmentally perturbed conditions. This motivates systems biologists/bioengineers/bioinformaticians to develop new tools and applications, allowing non-experts to easily conduct such modelling and analysis. However, among a multitude of systems biology tools developed to date, only a handful of projects have adopted a web-based approach to kinetic modelling. In this report, we evaluate the capabilities and characteristics of current web-based tools in systems biology and identify desirable features, limitations and bottlenecks for further improvements in terms of usability and functionality. A short discussion on software architecture issues involved in web-based applications and the approaches taken by existing tools is included for those interested in developing their own simulation applications.

An alpha-numeric code for representing N-linked glycan structures in secreted glycoproteins.

Advances in high-throughput techniques have led to the creation of increasing amounts of glycome data. The storage and analysis of this data would benefit greatly from a compact notation for describing glycan structures that can be easily stored and interpreted by computers. Towards this end, we propose a fixed-length alpha-numeric code for representing N-linked glycan structures commonly found in secreted glycoproteins from mammalian cell cultures. This code, GlycoDigit, employs a pre-assigned alpha-numeric index to represent the monosaccharides attached in different branches to the core glycan structure. The present branch-centric representation allows us to visualize the structure while the numerical nature of the code makes it machine readable. In addition, a difference operator can be defined to quantitatively differentiate between glycan structures for further analysis. The usefulness and applicability of GlycoDigit were demonstrated by constructing and visualizing an N-linked glycosylation network.