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This paper developed two robust data-driven models, namely gene expression programming (GEP) and multivariate adaptive regression splines (MARS), for the estimation of the slump of concrete (SL). The main feature of the proposed data-driven methods is to provide explicit mathematical equations for estimating SL. The experimental data set contains five input variables, including the water-cement ratio (W/C), water (W), cement (C), river sand (Sa), and Bida Natural Gravel (BNG) used for the estimation of SL. Three common statistical indices, such as the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE), were used to evaluate the accuracy of the derived equations. The statistical indices revealed that the GEP formula (R=0.976, RMSE=19.143, and MAE=15.113) was more accurate than the MARS equation (R=0.962, RMSE=23.748, and MAE=16.795). However, the application of MARS, due to its simple regression equation for estimating SL, is more convenient for practical purposes than the complex formulation of GEP.

Introduction: The construction industry uses a large quantity of natural materials in the production of concrete. Although attempts to incorporate green materials in concrete began years ago, not every building uses such materials today, and roadways, particularly, still rely on unsustainable materials.Methods: Therefore, this study used alternative materials, including fly ash, manufactured sand aggregates, and different molarities of alkaline activators, to incorporate waste byproducts in a geopolymer concrete white-topping pavement layer. Recent developments have led to the emergence of geopolymers as distinct classes of materials. In the 1990s, fly ash-based geopolymers became more popular than other kinds, as they are more efficient compared to Portland cement concrete.Results: Aluminosilicate gel can be obtained by combining fly ash and alkaline solution. A comprehensive literature review of geopolymer concrete was performed in this study. It examines its critical design parameters, including alkaline solutions, curing temperatures, curing methods, workability, and compressive strength under various environmental conditions. This review provides a unique opportunity for researchers to understand how geopolymer concrete performs.Discussion: A range of conditions were investigated to determine how to enhance and use this material in a variety of ways. The fresh characteristics of different mixes were studied using slump and Vee-Bee tests, and the characteristics of the cured concrete mixes were determined using flexural, compressive, and flexural fatigue tests. The results indicated that the use of manufactured sand and fly ash with high-molarity alkaline activators results in a geopolymer concrete with an excellent maximum resistance of 5.1 N/mm2 workability, strength, and fatigue properties, making it suitable for use in roadway pavement.

(1) Background: The main objective of this research was to assess the clinical factors related to the condition of pediatric patients with congenital heart defects after they underwent intensive care unit surgery. The information was gathered from the Congenital Heart Disease Surgery Unit at the National Heart Foundation Hospital and Research Institute in Dhaka, Bangladesh. We gathered and examined data from 288 ICU patients. Patients under the age of twelve who required more than a 24-h ICU stay were selected. (2) Methods: The dependent and independent variables were chosen in advance based on expert opinion. The relationships between these pre-specified ICU parameters were determined using the Pearson correlation model and assessed through linear regression and ARIMA modeling to predict subsequent acute changes in the patients' ICU statuses. (3) Results: A statistically significant relationship (p value < 0.001) was found between CVP and BP (95% CI = 0.2113; 0.353 r = 0.2841249) and between PEEP and FiO2 (95% CI = 0.6992; 0.770 r = 0.7367744). Although the relationships between pH and PO2 were minor (95% CI = 0.161; 0.308 r = 0.2362575), they were statistically significant. The parameters considered statistically significant (p < 0.001) were chosen for forecasting. In this work, the linear regression model and the ARIMA model used the parameters BP, FiO2, and PO2 for prediction. We forecasted the patients' statuses for the next hour. It was found that the ARIMA model had a lower error rate than the linear regression model. (4) Conclusions: This study helps identify the important parameters for predicting and monitoring patients' statuses in the ICU, with the ultimate goal of providing physicians with an early warning system to anticipate deterioration in clinical and biochemical parameters. The ability to accurately forecast future patients' conditions can enable proactive, targeted interventions, potentially improving outcomes and reducing the risk of adverse events. [ABSTRACT FROM AUTHOR]

Barium monoferrite and M−type hexaferrite with generic formulae BaFe2O4 and BaFe12O19 were synthesized using the sol–gel method by annealing at 1000 °C. To confirm the phase and compositions, XRD and FTIR analyses were employed, and the formation of orthorhombic crystal structure of BaFe2O4 and hexagonal crystal structure of BaFe12O19 were observed. The surface morphology of the samples was investigated through the use of the scanning electron micrograph (SEM) technique. Energy dispersive X-ray spectroscopy (EDS) confirmed the chemical compositions of the samples. At room temperature, the real part of the dielectric constant (ɛʹ), the imaginary part of the dielectric constant (ɛʹʹ), the dielectric loss tangent (tanδ), and AC conductivity (σAC) were measured at the specific frequencies (1, 1.5, and 2 MHz), and they were increased with both temperature and relative humidity (RH). Barium ferrite humidity sensors were created. The resistance of the sensor goes down as the RH% goes up, and the BaFe12O19 humidity sensor is 1.82 times more sensitive than the BaFe2O4 sensor.

Spinel ferrite nanocomposites of Ni1–xZnxFe2O4 (x = 0.25 and 0.75) were synthesized by sol-gel auto-combustion and annealed between 250°C and 1000°C. A single-phase spinel structure was found through X-ray diffraction (XRD). The crystallite size is in the range of 17.55–66.98 nm, and lattice parameters are in the range of 8.351–8.434 Å. X-ray analysis revealed a slight shift of the peaks towards shorter angles when the zinc concentration increased from 0.25 to 0.75. XRD measurements revealed the metal ion distribution in the spinel ferrite system. For each sample, XRD data were used to compute structural characteristics such as lattice spacing, lattice constant, crystallite size, oxygen position parameter, tetrahedral and octahedral ionic radii, and bond lengths. Energy dispersive spectroscopy (EDS) spectra and field emission-electron scanning microscope (FESEM) were used to evaluate the elemental content and morphology. EDS analysis confirmed the presence of expected elements in the samples and confirmed the high doping rate of more than 180% of Zn ions in Ni ferrite. The evaluated particle sizes were determined to be 79.2 and 118.4 nm for zinc content of 0.25 and 0.75, respectively. The nearly spherical shape of the nanoparticles was shown in the transmission electron microscope (TEM). The magnetic moment, remanent, coercivity, and saturation magnetization were calculated by using vibrating sample magnetometer (VSM) results. The saturation magnetization magnitudes showed the influence of cation distribution.

This paper developed two robust data-driven models, namely gene expression programming (GEP) and multivariate adaptive regression splines (MARS), for the estimation of the slump of concrete (SL). The main feature of the proposed data-driven methods is to provide explicit mathematical equations for estimating SL. The experimental data set contains five input variables, including the water-cement ratio (W/C), water (W), cement (C), river sand (Sa), and Bida Natural Gravel (BNG) used for the estimation of SL. Three common statistical indices, such as the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE), were used to evaluate the accuracy of the derived equations. The statistical indices revealed that the GEP formula (R=0.976, RMSE=19.143, and MAE=15.113) was more accurate than the MARS equation (R=0.962, RMSE=23.748, and MAE=16.795). However, the application of MARS, due to its simple regression equation for estimating SL, is more convenient for practical purposes than the complex formulation of GEP. [ABSTRACT FROM AUTHOR]

Membrane application in wastewater is gaining significant popularity. Selecting the right membrane and filtration technique is an important consideration to ensure a successful system development and long term performance. A new type of membrane filtration technology known as ‘Vibratory Shear Enhanced Process’ (VSEP) is introduced in this paper with some test results that has been conducted with VSEP pilot unit to recycle starch wastewater. Conventional cross flow membrane process used in wastewater application always led to rapid fouling. This loss in throughput capacity is primarily due to the formation of a layer that builds up naturally on the membranes surface during the filtration process. In addition to cutting down on the flux performance of the membrane, this boundary or gel layer acts as a secondary membrane reducing the native design selectivity of the membrane in use. This inability to handle the buildup of solids has also limited the use of membranes to low-solids feed streams. In a VSEP system, an additional shear wave produced by the membrane’s vibration cause solids and foulants to be lifted off the membrane surface and remixed with the bulk material flowing through the membrane stack. This high shear processing exposes the membrane pores for maximum throughput that is typically between 3 to10 times the throughput of conventional cross-flow systems. The short term results with raw starch wastewater shows very stable flux rate of 110 lmh using the VSEP system and selecting the PVDF ultrafiltration membrane with no pre-filtration.