A firm that can no longer pay its creditors-its bankers and suppliers-is illiquid and technically bankrupt, a situation that no manager wishes to face. Managers must make decisions that do not endanger their firm's liquidity-a term that refers to the firm's ability to meet its recurrent cash obligations toward various creditors. A firm's liquidity is driven by the structure of its balance sheet, namely, by the nature and composition of its assets and the way they are financed. To finance these investments, the firm uses a combination of short-term and long-term sources of funds. One way a firm can manage its balance sheet and enhance its liquidity is by using the matching strategy. This strategy requires that long-term investments be financed with long-term funds and short-term investments with short-term funds. We show in this chapter that the matching principle helps explain how a firm's liquidity should be measured and how liquidity is affected by managerial decisions.

Volatile organic compounds (VOCs) are main type of indoor pollutants which are found in many workstation materials. In multi-layer work surface panels, different VOCs have different chemical and physical properties which will affect their transport properties and storage properties. Therefore, the VOC emission process and emission rate are generally different and depend on the type of VOCs. The effect of the initial condition, such as temperature, relative humidity, air change rate, initial VOC concentration and pollutant type are evaluated separately. This paper uses a build environment simulation (BES) program to investigate the effect of these factors on the VOC concentration in the air. Based on the theoretic analysis, several means can be adopted to reduce the pollutant emission and improve the indoor air quality (IAQ).

This paper investigates the increasing use of artificial intelligence in the field of investments today. It seeks to explore how artificial intelligence strategies can help investors make better investment decisions as well as highlight the risks posed by the use of artificial intelligence. In the digital era, transformational technology is powering new forms of automation that are more universal and smarter than ever before. The technology has not only changed significant operational aspects such as logistics, manufacturing, and warehousing but also the investment industry at large. Technologies associated with artificial intelligence help in performing tasks commonly associated with human beings such as logical operations, visual operations, speech recognition, visual perceptions, language translation and decision-making. Today, Computer technology has the capabilities of trouncing human intelligence in solving complex computations in shorter periods. The same technique can be replicated in investment in problem solving, decision-making and risk management. For instance, artificial intelligence tools helps in making faster precise assessment of potential borrowers at minimal cost which accounts for better informed and data backed decision that reduces human bias. Keywords: Artificial intelligence; Investments