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This study aims to present the results of an experimental evaluation of low (M30), mid (M40) and high (M50) grade self-compacting concrete (SCC) with three nominal maximum aggregate sizes (NMAS), namely, 20 mm, 16 mm and 12.5 mm, with Bailey gradation (BG) in comparison with Indian standard gradation (ISG).

This study was conducted in a laboratory by testing the characteristics of fresh and hardened properties of self-compacting concrete.

Rheological and mechanical properties of SCC were evaluated in detail and according to the results, a concrete sample containing lower NMAS with BG demonstrated improvement in modulus of elasticity and compressive strength, while improving the rheological properties as well. Meanwhile, SCC demonstrated poor performance in split tensile and flexural strengths with lower NMAS gradations and a direct correlation was evident as the increase in NMAS caused an increase in the strength and vice-versa.

Upon comparison of BG with ISG, it was revealed that BG mixes succeeded to demonstrate superior performance. From the material optimization, rheological and mechanical performance study, it is recommended that BG with NMAS 16 mm can be used for conventional SCC.

This paper aims to examine the post-merger changes in the credit risk profile of merging bank holding companies and tests whether there is an increase in credit risk after a merger due to changes in the mix of loans in the portfolio.

The authors use the expected variability of the credit risk of a loan portfolio based on the mix of loan types in the portfolio and the variability of the industry credit losses of each type following the standard Markowitz procedure for finding the standard deviation of an investment portfolio. The authors then test to see whether there has been a significant change in the expected variability (the credit risk profile) after a merger.

The authors find that there are significant differences in both the level and variability of loan charge-offs and non-performing loans (NPL) among the various loan categories. The authors also find significant changes in the mix of loan categories in the loan portfolio after a merger. In addition, the authors find that the expected variability in both the charge-off rate and the NPL rate rises significantly after a merger.

This is the first of two papers looking at post-merger changes in credit risk based simply on the changes in the mix of loan types; it does not consider the actual post-merger credit performance of the specific mergers. That will be addressed in a subsequent paper.

Financial analysts evaluating banking merger announcements may wish to include the impact of the likely shifts in loan mix and credit risk shown in this paper as they project the likely impact of the merger.

This paper addresses an aspect of bank mergers that has not been addressed in the literature, the impact of mergers on credit risk. The results are likely to be useful to investors, financial analysts and regulators.

This paper aims to investigate the 28-day compressive strength of concrete produced with aggregates from different sources.

Coarse aggregates were crushed granite and natural local stones mined from Umunneochi, Lokpa and Uturu, Isuakwato, respectively, in Abia State, Nigeria. Fine aggregate (river sand) and another coarse aggregate (river stone) were dredged from Otammiri River in Owerri, Imo State, Nigeria. The nominal mix ratios were 1:1:2, 1:2:4 and 1:3:6, whereas the respective water–cement ratios were 0.45, 0.5, 0.55 and 0.6.

The compressive strength of granite concrete, river stone concrete and local stone concrete ranged 17.79-38.13, 15.37-34.57 and 14.17-31.96 N/mm², respectively. Compressive strength was found to increase with decreasing water–cement ratio and increasing cement content.

Granite concrete should be used in reinforced-concrete construction, especially when a cube compressive strength of 30 N/mm² or higher is required.

Granite concrete exceeded the target compressive strength for all the concrete specimens, whereas river stone concrete and local stone concrete failed to achieve the target strength for some mix proportions and water–cement ratios.

The Navier‐Stokes equation and the species continuity equation have been solved numerically in a boundary fitted coordinate system comprising the geometry of a large scale industrial size tundish. The solution of the species continuity equation predicts the time evolution of the concentration of a tracer at the outlets of a six strand billet caster tundish. The numerical prediction of the tracer concentration has been made with six different turbulence models (the standard k‐ε, the k‐ε RNG, the Low Re number Lam‐Bremhorst model, the Chen‐Kim high Re number model (CK), the Chen‐Kim low Re number model (CKL) and the simplest constant effective viscosity model (CEV)) which favorably compares with that of the experimental observation for a single strand bare tundish. It has been found that the overall comparison of the k‐ε model, the RNG, the Lam‐Bremhorst and the CK model is much better than the CKL model and the CEV model as far as gross quantities like the mean residence time and the ratio of mixed to dead volume are concerned. However, the k‐ε model predicts the closest value to the experimental observation compared to all other models. The prediction of the transient behavior of the tracer is best done by the Lam‐Bremhorst model and then by the RNG model, but these models do not predict the gross quantities that accurately like the k‐ε model for a single strand bare tundish. With the help of the above six turbulence models mixing parameters such as the ratio of mix to dead volume and the mean residence time were computed for the six strand tundish for different outlet positions, height of advanced pouring box (APB) and shroud immersion depth. It was found that three turbulence models show a peak value in the ratio of mix to dead volume when the outlets were placed at 200 mm away from the wall. An APB was put on the bottom of the tundish surrounding the inlet jet when the outlets were kept at 200 mm away from the wall. It was also found that there exists an optimum height of the APB where the ratio of mix to dead volume and the mean residence time attain further peak values signifying better mixing in the tundish. At this optimum height of the APB, the shroud immersion depth was made to change from 0 to 400 mm. It was also observed that there exists an optimum immersion depth of the shroud where the ratio of mix to dead volume still attains another peak signifying still better mixing. However, all the turbulence models do not predict the same optimum height of the APB and the same shroud immersion depth as the optimum depth. The optimum height of the APB and the shroud immersion depth were decided when two or more turbulence models predict the same values.

The purpose of this paper is to highlight the sustainability benefits of using demolition and industrial wastes as a replacement for aggregates and cement in traditional concrete mixes.

Crushed concrete from demolition sites served as a replacement for fine and coarse aggregate in some of the mixes at various ratios. In addition, ground granulated blast furnace slag, metakaolin, silica fume, and fly ash each served as a cement replacement for cement content in the mixes tested in this research at various rates. Compression strength tests, permeability, and thermal expansion tests were performed on various mixes to compare their performance to that of traditional mixes with natural aggregate, and with no cement replacement.

The compressive strength results indicated the suitability of using such demolition wastes as replacements in producing green concrete (GC) without hindering its mechanical characteristics significantly. In addition, the results indicated an enhancement in the mechanical characteristics of GC when replacing cement with pozzolanic industrial wastes and byproducts.

The research assesses the utilization of sustainable GC using recycled waste aggregate and byproducts.

The purpose of this paper is to present a computational fluid dynamic simulation for the investigation of the particles segregation phenomenon in the gas–solid fluidized beds.

These particles have the same size and different densities. The k–ε model and multiphase particle-in-cell method have been utilized for modeling the turbulent fluid flow and solid particles behaviors, respectively. The coupled equations of the velocity and pressure have been solved by using a combination of SIMPLE and PISO algorithms. After validating the simulation, different mixing indices, with different calculation bases, have been investigated, and it has been found that the Lacey mixing index, which was defined based on statistical concepts, is suitable to investigate the segregation/mixing phenomena of this bed in different conditions. Finally, the effects of parameters such as velocity, particle density ratio, jetsam concentration, and initial arrangement on the segregation/mixing behaviors of the bed have been studied.

The results show that the increase in the superficial gas velocity decreases the mixing index to a minimum value and then increases this index in the beds with mixed initial condition, unlike the beds with separated initial condition. Moreover, an increase in the particle density ratio increases the minimum fluidization velocity of the bed, and also the amount of segregation, and increase in the jetsam concentration increases the value of the mixing index.

A computational fluid dynamics simulation has been presented for the particles segregation phenomenon in the gas–solid fluidized beds.

The Navier‐Stokes equation and the species continuity equation have been solved numerically in a boundary fitted coordinate system comprising the geometry of a large scale industrial size tundish. The solution of the species continuity equation predicts the time evolution of the concentration of a tracer at the outlet of a single strand bare tundish. The numerical prediction of the tracer concentration has been made with three different turbulence models; (a standard k‐ε, a k‐ε RNG and a Low Re number Lam‐Bremhorst model) which favorably compares with that of the experimental observation for a single strand bare tundish. It has been found that the overall comparison of k‐ε model with that of the experiment is better than the other two turbulence models as far as gross quantities like mean residence time and ratio of mixed to dead volume are concerned. However, it has been found that the initial transient development of the tracer concentration is best predicted by the Lam‐Bremhorst model and then by the RNG model. The k‐ε model predicts the tracer concentration much better than the other two models after the initial transience (t>40 per cent of mean residence time) and the RNG model lies in between the k‐ε and the Lam‐Bremhorst one. The numerical study has been extended to a multi strand tundish (having 6 outlets) where the effect of outlet positions on the ratio of mix to dead volume has been studied with the help of the above three turbulence models. It has been found that all the three turbulence models show a peak value for the ratio of mix to dead volume (a mixing parameter) when the outlets are placed 200 mm away from the wall (position‐2) thus signifying an optimum location for the outlets to get highest mixing in a given multi strand tundish.

The Navier‐Stokes equation and the species continuity equation have been solved numerically in a boundary fitted coordinate system comprising the geometry of a single strand bare tundish. The solution of the species continuity equation predicts the time evolution of the concentration of a tracer at the outlet of the tundish. The numerical prediction of the tracer concentration has been made with nine different turbulence models and has been compared with the experimental observation for the tundish. It has been found that the prediction from the standard k‐ε model, the k‐ε Chen‐Kim (ck) and the standard k‐ε with Yap correction (k‐ε Yap), matches well with that of the experiment compared to the other turbulence models as far as gross quantities like the mean residence time and the ratio of mixed to dead volume are concerned. It has been found that the initial transient development of the tracer concentration is best predicted by the low Reynolds number Lam‐Bremhorst model (LB model) and then by the k‐ε RNG model, while these two models under predict the mean residence time as well as the ratio of mixed to dead volume. The Chen‐Kim low Reynolds number (CK low Re) model (with and without Yap correction) as well as the constant effective viscosity model over predict the mixing parameters, i.e. the mean residence time and the ratio of mixed to dead volume. Taking the solution of the k‐ε model as a starting guess for the large eddy simulation (LES), a solution for the LES could be arrived after adopting a local refinement of the cells twice so that the near wall y⁺ could be set lower than 1. Such a refined grid gave a time‐independent solution for the LES which was used to solve the species continuity equation. The LES solution slightly over predicted the mean residence time but could predict fairly well the mixed volume. However, the LES could not predict both the peaks in the tracer concentration like the k‐ε, RNG and the Lam‐Bremhorst model. An analysis of the tracer concentration on the bottom plane of the tundish could help to understand the presence of plug and mixed flow in it.

Describes the complementary standard mixed loads concept (the standard mix concept in short), which allows for assembling mixed loads at an upstream echelon. These standard mixed loads are assigned to customer orders at an echelon downstream of the chain. Describes two applications of the use of the standard mix concept in order to identify up‐front the logistic advantages, both in inventory reduction and in handling reduction. Presents the basic principles of the concept and offers some mathematical modelling. Describes the effects on the inventory at the various points of the supply chain, and presents some simulation results. Concludes that the concept offers new perspectives for supply chain management and intermodal transport.

This study is conducted to determine the factors that affect profitability in Chinese listed companies (by using financial ratios). Four independent variables liquidity, intangible assets, working capital and company leverage were empirically tested for their relationships with profitability besides two control variables which are firm size and company efficiency.

This study used secondary data extracted manually from the annual reports of non-financial Chinese listed companies on the Shanghai stock exchange (http://www.szse.cn/); the data set covers 100 companies during the period of 2017–2019, and a random selection method was used in order to achieve credibility and fairness as much as possible.

The findings show firm size, working capital and intangible assets have positive and significant relationships with profitability [return on assets (ROA) and earnings per share (EPS)]. Positive working capital is important to lower the cost of capital and improve companies' profitability. Intangible assets are also an essential source to improve profitability due to their low costs. In addition, the findings display a negative and strong relationship between liquidity and profitability, meaning that companies suffer low profit due to inefficient use of liquid items. Interestingly, leverage, which is measured by debt ratio and leverage ratio, shows mixed results; debt ratio shows a positive and strong association with ROA but not with EPS; while leverage ratio displays a strong but negative association with ROA but not with EPS. These results confirm the inverted U-shape relationship between leverage and profitability, which depends on the balance between benefit and cost of debt.

Profitability is also important for employees and society where business organization provides sustainability and stability for both of them. Employees can then significantly contribute to achieve higher firm's profitability by efficiently using firm's resources.

This study differs than previous studies in number of aspects: First, this study focuses on financial ratios to explain profitability in Chinese companies. This study provides empirical results about the factors connected to profitability and help stakeholders to make their right decisions. Second, it examines the impact of four independent factors and two control variables that some of them are new in Chinese context such as intangible assets. Third previous studies focus on financial industry such as banks; however, this study focuses on non-financial industry.