Diamond-like Nanocomposites (DLN) is a newly member in amorphous carbon (a:C) family. It consists of two or more interpenetrated atomic scale network structures. The amorphous silicon oxide (a:SiO) is incorporated within diamond-like carbon (DLC) matrix i.e. a:CH and both the network is interpenetrated by Si-C bond. Hence, the internal stress of deposited DLN film decreases remarkably compare to DLC. The diamond-like properties have come due to deform tetrahedral carbon with sp³ configuration and high ratio of sp³ to sp² bond. The DLN has excellent mechanical, electrical, optical and tribological properties. Those properties of DLN could be varied over a wide range by changing deposition parameters, precursor and even post deposition treatment also. The range of properties are: Resistivity 10^-4 to 10¹⁴ Ωcm, hardness 10–22 GPa, coefficient of friction 0.03-0.2, wear factor 0.2-0.4 10^-7mm³/Nm, transmission Vis-far IR, modulus of elasticity 150-200 GPa, residual stress 200-300 Mpa, dielectric constant 3-9 and maximum operating temperature 600^°C in oxygen environment and 1200^°C in O₂ free air. Generally, the PECVD method is used to synthesize the DLN film. The most common procedures used for investigation of structure and composition of DLN films are Raman spectroscopy, Fourier transformed infrared spectroscopy (FTIR), HRTEM, FESEM and X-ray photo electron spectroscopy (XPS). Interest in the coating technology has been expressed by nearly every industrial segment including automotive, aerospace, chemical processing, marine, energy, personal care, office equipment, electronics, biomedical and tool and die or in a single line from data to beer in all segment of life. In this review paper, characterization of diamond-like nanocomposites is discussed and subsequently different application areas are also elaborated.

This research examines three data mining approaches employing cost management datasets from 391 Thai contractor companies to investigate the predictive modeling of construction project failure with nine parameters. Artificial neural networks, naive bayes, and decision trees with attribute selection are some of the algorithms that were explored. In comparison to artificial neural network’s (91.33%) and naive bays’ (70.01%) accuracy rates, the decision trees with attribute selection demonstrated greater classification efficiency, registering an accuracy of 98.14%. Finally, the nine parameters include: 1) planning according to the current situation; 2) the company’s cost management strategy; 3) control and coordination from employees at different levels of the organization to survive on the basis of various uncertainties; 4) the importance of labor management factors; 5) the general status of the company, which has a significant effect on the project success; 6) the cost of procurement of the field office location; 7) the operational constraints and long-term safe work procedures; 8) the implementation of the construction system system piece by piece, using prefabricated parts; 9) dealing with the COVID-19 crisis, which is crucial for preventing project failure. The results show how advanced data mining approaches can improve cost estimation and prevent project failure, as well as how computational methods can enhance sustainability in the building industry. Although the results are encouraging, they also highlight issues including data asymmetry and the potential for overfitting in the decision tree model, necessitating careful consideration.

Functions are the core of algebra, and the teaching of function concepts is also the main task of high school mathematics Students' learning of functions and their concepts shifts from understanding specific quantitative relationships to understanding abstract quantitative relationships The monotonicity of functions, as the property of the first function that students learn in high school, lays a certain foundation for learning function related knowledge in the future.

The semiclassical boron–boron interatomic pair potential is constructed in an integral form allowing its converting into the analytical one. It is an ab initio B–B potential free of any semiempirical adjusting parameters, which would serve as an effective tool for the theoretical characterization of all-boron and boron-rich nanomaterials.