The crypto space offers numerous opportunities for users to grow their wealth through trading, lending, and borrowing activities. However, these opportunities come with inherent risks that need to be carefully managed to protect your assets and maximize returns. By understanding the risks associated with wallets and depository services, trading, lending, and borrowing, users can make informed decisions and enjoy the benefits of the rapidly evolving world of cryptocurrencies. This review paper analyses 43 papers for the period of 2019–2023 and proposes recommendations for policy makers. The results confirm that international regulators expect national authorities to implement a regulatory framework for digital assets comparable to those that already exist for traditional finance. For national authorities, this means having and using the powers, tools and resources to regulate and oversee a growing market. Authorities should cooperate and coordinate with each other, at the national and international levels, to encourage consistency and knowledge sharing. Market operators (exchanges), service providers, exchanges and wallets, create effective risk management structures, as well as reliable mechanisms for collecting, storing, protecting and reporting data.

Increasingly, U.S. cities are focusing on transit-oriented development (TOD) policies to expand the stock of higher-density, mixed-use development near public transit stations within the context of a transit corridor and, in most cases, a regional metropolis. A TOD zone relies on a regulatory and institutional environment, public and private participation and investment, and development incentives to create vibrant, people-oriented communities and mobility options and to support business development. TODs provide local governments with more tax revenues due to increased property values (and, as applicable, income and sales tax revenues), but most planning for TODs ignores the non-transit infrastructure costs of increasing development density. This study focused on determining the water and sewer infrastructure costs for TOD zones along a rail line in southeast Florida. The finding was that millions of dollars in funds are needed to meet those water and sewer needs and that few are currently planned as a part of community capital improvement programs.

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.