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AbstractCellulose nanofibers (CNFs) are the newly introduced plant-based materials in the construction industry to ensure sustainable development. The use of artificial intelligence (AI) techniques especially machine learning (ML) models has assisted to economized civil engineering. This research aims to determine the compressive strength of cellulose nanofibers reinforced concrete by using supervised regression machine learning techniques for analysis before adopting to utilize. To achieve this task, the machine learning models: Random Forest (RF), Linear Regression (LR), Support Vector Regressor (SVR), Gradient Boosting Regressor (GBR), Ada Boosting Regressor (ABR), K-Neighbor Regressor (KNN), Bagging Regressor (BR), XG Boost Regressor (XGBR), Decision Tree (DT), and Pruned Decision Tree (PDT) were implemented. An experimental-based dataset containing 695 data points was prepared and split into two categories (Training dataset = 70%, Testing dataset = 30%) for the evolution of ML models. There were seven independent variables: cement (kg/m3), water (kg/m3), CNFs (kg/m3), superplasticizer (kg/m3), fine aggregate (kg/m3), coarse aggregate (kg/m3), and age (Day) variables as an input and one dependent variable: compressive strength fc of CNFs reinforced concrete (MPa) as an output. The following metrics were employed to gauge the ability of the model: R2, MAPE, MAE, MSE, and RMSE. The findings specified that seven out of ten models (RF, BR, XGBR, DT, GBR, ABR, and KNN) to predict the compressive strength of CNFs concrete had a firm capability (R2 >0.72, MAPE ≤ 0.1, and MAE ≤ 5) confirming to the standard of R2 value greater than 0.60 and metrics values very less, close to one. According to the sensitivity analysis of Random Forest model, water and cement were the factors with the biggest effects on the prediction of CNFs reinforced concrete, while the smallest effecting variable was coarse aggregate. It was concluded that the RF, BR, and DT were the premier predicting models.

Asphalt mixtures and cement concrete are an important material in the construction of roads, highways and buildings, and there has been a lot of research about the improvement of their performance. Among them, fibers are commonly used in the construction industry because of their superior properties as reinforcing materials that can provide a proper interfacial action between the fibers and the substrate. This review classifies fibers into natural fibers, inorganic fibers and polymer fibers according to their sources and properties. It summarizes and compares the characteristics, modification methods, usage requirements and research status of each type of fiber in asphalt and cement construction materials, and analyzes the problems and challenges faced by fibers in their applications. The evaluation results show that various types of fibers can enhance the fracture resistance, tensile strength and rutting resistance of asphalt to a certain extent, improve the high temperature performance and viscoelasticity of asphalt, and have a certain effect on the fatigue resistance and road water resistance of asphalt mixes. The fibers also provide better tensile, compressive and abrasion resistance to cement concrete and improve the brittleness and crack resistance of ordinary cement. Besides, for some defects of various types of fibers in construction materials, such as biodegradability, dispersibility and surface inertness of fibers, the targeted modification of fibers is introduced based on physical and chemical modification methods to improve the performance impact of modified fibers in various conditions of application.

For efficient biodiesel production, an acid-activated clay (AC) modified by calcium hydroxide and sodium hydroxide (CaNa/AC) was prepared as a catalyst. CaNa/AC and Na/AC were characterized by Hammett indicators, CO2-TPD, FT-IR, XRD, and N2 adsorption techniques. The influence of catalyst dose, reaction temperature, methanol/oil molar ratio, and reaction time on the transesterification of Jatropha oil was studied. Due to the introduction of calcium, CaNa/AC displayed a higher activity and stability, thereby achieving an oil conversion of 97% under the optimal reaction conditions and maintaining over 80% activity after five successive reuses. The reaction was accelerated as the temperature rose, and the apparent activation energy of CaNa/AC was 75.6 kJ·mol−1. The enhanced biodiesel production by CaNa/AC was ascribed to the increase in active sites and higher basic strength. This study presents a facile and practical method for producing biodiesel on large-scale operation.

This study explored the factors influencing the application of Information and Communication Technologies (ICTs) among farmers in the Punjab province of Pakistan. The Punjab province is divided into three agro-ecological zones viz Northern, Central and Southern. In this study, one district from each zone was randomly selected. Total sample size comprises of 150 farmers. Farmers were interviewed using a self-administered interview schedule comprising of open and close-ended questions. Respondents’ access to ICTs was low level. Chi-square test revealed a significant association between the age of respondents and adoption of ICTs. Young respondents were leading users of ICTs in the study area. Size of landholding and income level of the respondents had a significant association with the frequency of use of ICTs in agriculture. Among various ICTs tools, mobile/cell phone attracted 47.3% of respondents to access agricultural information. Factors militating use of ICTs include; electricity/power issues (x̄=2.95/3.00), language barriers (x̄=2.93/3.00), limited knowledge and skills about ICTs (x̄=2.89/3.00), high cost (x̄=2.87/3.00) and limited availability of ICTs (x̄=2.85/3.00). Extent of ICTs use to access agricultural information can surge with the unit income level rise, enhanced access to ICTs and enhanced skill level to use ICTs.

In the process of preparing high-purity MnSO4 from industrial MnSO4 solution, it is difficult to remove Ca2+ and Mg2+ due to their closely similar properties. In this study, thermodynamic software simulation and experimental procedures were combined to remove Ca2+ and Mg2+ from industrial MnSO4 solution to obtain high-purity MnSO4. The simulation model was applied to predict the trend of the crystallization of different ions in the solution upon the addition of H2SO4, which revealed that, at a volume ratio of H2SO4 to MnSO4 solution of more than 0.2, MnSO4 started to crystallize and precipitate. The experimental results further verified the simulation results, and the yield of MnSO4 increased with the increasing ratio of H2SO4, while the removal rate of Ca2+ and Mg2+ decreased gradually. Keeping the economic aspect in mind, the 0.3 ratio of H2SO4 was selected at which the yield of MnSO4 reached 86.44%. The removal rate of Ca2+ and Mg2+ by recrystallization reached 99.68% and 99.17% respectively after six consecutive cycles. The recrystallized sample was washed twice with anhydrous ethanol (volume ratio of ethanol to MnSO4 solution of 0.5) and dried for 6 h at 120 °C, and the purity of MnSO4·H2O reached the battery grade requirements with the final yield as high as 80.54%. This study provides important guideline information for the purification of MnSO4·H2O from industrial MnSO4 solution via a cost-effective, simple and facile approach.

Raw bentonite (RB) was chemically modified by citric acid (CA) to obtain a low-cost and environment-friendly citric acid incorporated bentonite (CAB) adsorbent, which was applied for the adsorptive removal of Congo Red (CR). The effect of adsorbent dosage, contact time, ionic strength, surfactant, and pH on adsorption was investigated. Adsorption equilibrium data fitted well with Langmuir model while the Langmuir adsorption capacity of CR on CAB reached up to 384 mg·g−1. Furthermore, CR adsorption on CAB followed pseudo-second kinetic model while intra-particle diffusion was not the only rate-limiting step as determined from intra-particle diffusion model investigation. RB and CAB were characterized by XRD, FT-IR, and BET techniques. A proposed mechanism for the adsorption of CR over CAB suggested the chemical adsorption phenomenon is mainly controlled by chelation, hydrogen bonding, and fixing.