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The paper aims to investigate the influence of waste tire rubber and silica fume on the fresh and hardened properties of self-compacting concrete (SCC) and to design multivariate regression models for the prediction of the mechanical properties of self-compacting rubberized concrete (SCRC). For this purpose, 21 concrete mixtures were designed. Crumb rubber derived from end-of-life tires (grain size 0.5–3.5 mm) was replaced fine aggregate by 0%, 5%, 10%, 15%, 20%, 25%, and 30% of total aggregate volume. Silica fume was replaced cement by 0%, 5%, and 10% of the total cement mass. The optimal replacement level of both materials was investigated in relation to the values of the fresh properties and mechanical properties of self-compacting concrete. Tests on fresh and hardened self-compacting concrete were performed according to the relevant European standards. Furthermore, models for predicting the values of the compressive strength, modulus of elasticity, and flexural strength of SCRC were designed and verified with the experimental results of 12 other studies. According to the obtained results, mixtures with up to 15% of recycled rubber and 5% of silica fume, with 28 days compressive strength above 30 MPa, were found to be optimal mixtures for the potential future investigation of reinforced self-compacting rubberized concrete structural elements.

Cohesin is a chromosome-bound, multisubunit adenosine triphosphatase complex. After loading onto chromosomes, it generates loops to regulate chromosome functions. It has been suggested that cohesin organizes the genome through loop extrusion, but direct evidence is lacking. Here, we used single-molecule imaging to show that the recombinant human cohesin-NIPBL complex compacts both naked and nucleosome-bound DNA by extruding DNA loops. DNA compaction by cohesin requires adenosine triphosphate (ATP) hydrolysis and is force sensitive. This compaction is processive over tens of kilobases at an average rate of 0.5 kilobases per second. Compaction of double-tethered DNA suggests that a cohesin dimer extrudes DNA loops bidirectionally. Our results establish cohesin-NIPBL as an ATP-driven molecular machine capable of loop extrusion.

In this study, the complex interactions between soil types, compaction, and moisture on nitrogen (N) transformation processes such as ammonia (NH 3 ) volatilization, ammonification, nitrification, and denitrification were examined over a 30-day period using a simulated column approach. Two soil types: loam, and sandy loam, were subjected to three compaction treatments—control, surface, and sub-surface compaction—and two moisture regimes, dry and wet. Liquid urea ammonium nitrate (32-0-0) was used as the N fertilizer source at a rate of 200 kg N ha -1 . Key indicators of N transformations were measured, including residual concentrations of ammonium (NH 4 -N) and nitrate (NO 3 -N), NO 3 -N leaching, NH 3 volatilization, and nitrous oxide (N 2 O) emissions. Findings revealed that compaction significantly increased residual NH 4 -N concentrations in deeper soil profiles, with the highest 190.80 mg kg -1 recorded in loam soil under sub-surface compaction and dry conditions. Nitrification rates decreased across both soil types due to compaction, evidenced by elevated residual NH 4 -N levels. Increased NO 3 -N leaching was observed in loam soil (178.06 mg L -1 ), greater than sandy loam (81.11 mg L -1 ), due to initial higher residual NO 3 - in loam soil. The interaction of compaction and moisture most affected N 2 O emissions, with the highest emissions in control treatments during dry weather at 2.88 kg ha -1 . Additionally, higher NH 3 volatilization was noted in moist sandy loam soil under control conditions at 19.64 kg ha -1 . These results highlight the necessity of considering soil texture, moisture, and compaction in implementing sustainable N management strategies in agriculture and suggest recommendations such as avoiding broadcast application in moist sandy loam and loam soil to mitigate NH 3 volatilization and enhance N use efficiency, as well as advocating for readjustment of fertilizer rate based on organic matter content to reduce potential NO 3 -N leaching and N 2 O emissions, particularly in loam soil.

Producing high-strength self-compacting concrete (SCC) requires a low water-cement ratio (W/C). Hence, using a superplasticizer is necessary to attain the desired self-compacting properties at a fresh state. The use of low W/C results in very brittle concrete with a low deformation capacity. This research aims to investigate the influence of crumb rubber (CR), fly ash (FA), and nanosilica (NS) on SCC’s workability and mechanical properties. Using response surface methodology (RSM), 20 mixes were developed containing different levels and proportions of FA (10–40% replacement of cement), CR (5–15% replacement of fine aggregate), and NS (0–4% addition) as the input variables. The workability was assessed through the slump flow, T500, L-box, and V-funnel tests following the guidelines of EFNARC 2005. The compressive, flexural, and tensile strengths were determined at 28 days and considered as the responses for the response surface methodology (RSM) analyses. The results revealed that the workability properties were increased with an increase in FA but decreased with CR replacement and the addition of NS. The pore-refining effect and pozzolanic reactivity of the FA and NS increased the strengths of the composite. Conversely, the strength is negatively affected by an increase in CR, however ductility and deformation capacity were significantly enhanced. Response surface models of the mechanical strengths were developed and validated using ANOVA and have high R2 values of 86–99%. The optimization result produced 36.38%, 4.08%, and 1.0% for the optimum FA, CR, and NS replacement levels at a desirability value of 60%.

This paper gives a comprehensive overview of the state-of-the-art machine learning methods that can be used for estimating self-compacting rubberized concrete (SCRC) compressive strength, including multilayered perceptron artificial neural network (MLP-ANN), ensembles of MLP-ANNs, regression tree ensembles (random forests, boosted and bagged regression trees), support vector regression (SVR) and Gaussian process regression (GPR). As a basis for the development of the forecast model, a database was obtained from an experimental study containing a total of 166 samples of SCRC. Ensembles of MLP-ANNs showed the best performance in forecasting with a mean absolute error (MAE) of 2.81 MPa and Pearson’s linear correlation coefficient (R) of 0.96. The significantly simpler GPR model had almost the same accuracy criterion values as the most accurate model; furthermore, feature reduction is easy to combine with GPR using automatic relevance determination (ARD), leading to models with better performance and lower complexity.

The utilization of Self-compacting Concrete (SCC) has escalated worldwide due to its superior properties in comparison to normal concrete such as compaction without vibration, increased flowability and segregation resistance. Various other desirable properties like ductile behaviour, increased strain capacity and tensile strength etc. can be imparted to SCC by incorporation of fibres. Thus, this study presents a novel approach to predict 28-day compressive strength (C–S) of FR-SCC using Gene Expression Programming (GEP) and Multi Expression Programming (MEP) for fostering its widespread use in the industry. For this purpose, a dataset had been compiled from internationally published literature having six input parameters including water-to-cement ratio, silica fume, fine aggregate, coarse aggregate, fibre, and superplasticizer. The predictive abilities of developed algorithms were assessed using error metrices like mean absolute error (MAE), a20-index, and objective function (OF) etc. The comparison of MEP and GEP models indicated that GEP gave a simple equation having lesser errors than MEP. The OF value of GEP was 0.029 compared to 0.031 of MEP. Thus, sensitivity analysis was performed on GEP model. The models were also checked using some external validation checks which also verified that MEP and GEP equations can be used to forecast the strength of FR-SCC for practical uses.

In order to study the influence of metastable intermolecular composite (MIC) materials on the internal implosion properties of thermobaric explosive, the calorimetric bomb and closed chamber were used to measure the detonation heat, quasi-static pressure and energy impulse of five explosive formulations containing different MIC materials. Compared with the traditional aluminized warm compressed explosives, the energy release characteristics and output characteristics were analyzed. The results show that the explosive formula containing MIC material has lower detonation heat value in air and vacuum than that containing traditional Al powder; The quasi-static pressure and energy impulse of the former are higher than those of the latter, indicating that MIC materials can improve the output energy of thermobaric explosives. The results can be used to guide the formulation design of thermobaric explosives.

This study investigates mechanical properties, durability performance, non-destructive testing (NDT) characteristics, environmental impact evaluation, and advanced machine learning (ML) modelling techniques employed in the analysis of high-strength self-compacting concrete (HSSCC) incorporating varying supplementary cementitious materials (SCMs) to develop sustainable building construction. The findings from the fresh characteristics test indicate that mixes’ optimal flowability and passing qualities can be achieved using different concentrations of marble powder (MP) alongside a consistent amount of silica fume (SF) and fly ash (FA). Moreover, the incorporation of 10% MP along with 10% FA and 20% SF in HSSCC significantly improved the compressive strength by 14.68%, while the splitting tensile strength increased by 15.59% compared to the reference mix at 56 days. While random forest (RF), gradient boosting (GB), and their ensemble models exhibit strong coefficient correlation (R2) values, the GB model demonstrates more precision, indicating reliable predicted outcomes of the mechanical properties. Following subsequent testing, it has been demonstrated that incorporating SCMs improves the NDT properties of HSSCC and enhances its durability. The finer MP, SF, and FA particles enhanced microstructural performance by minimizing voids and cracks while improving the C–H–S bond. As noticed by its lower CO2-eq per MPa for SCMs, the HSSCC mix with up to 15% MP inclusion increased mechanical strength while reducing the environmental footprint, making it an eco-friendly concrete alternative.