Hospital waste is categorized as a very dangerous waste type as it possesses many pathogenic elements and it can cause harmful human health effects. This research shows the survey and field investigation conducted at government hospitals located in Faisalabad. The quantitative waste analysis showed that 55% non-infectious, 15% infectious, 25% bio-hazardous, 2% sharps and 3% chemical waste is generation in these hospitals. Few main hospitals have their own incinerator but still either they are not working efficiency or insufficient to handle waste. However, this paper deals with the design of incinerator for small scale hospitals. The proposed incinerator has three chambers: primary chamber (temperature range 500-7000c), secondary chamber (temperature range 700-900oc), and tertiary chamber (temperature range 900-12000c). Situation analysis confirmed the earlier view that a hospital waste management project should be implemented at this hospital and an intensive training programmed, at all levels, and is required to deal effectively with this problem.

Bottled water is perceived by many people of Pakistan and other countries as well a safer alternative to other sources of water such as tap water. This may be attributed to consumers disliking the taste of tap water or it's organoleptic. Bottled water usage has increased even in countries where clean tap water is present such as in Germany, Kuwait and Saudi Arabia etc. The objective of present study was to compare the drinkable bottled water quality between different countries along with ZamZam water. Eleven different brands of bottled water including one un-bottled water (zamzam) collected from Pakistan, U.A.E, Kuwait, Germany and Saudi Arabia. They were analyzed for physiochemical and microbiological parameters for estimation of quality of water. For physio-chemical analysis, the pH meter, EC meter, Flame photometer and UV-Spectrophotometer were used respectively. For bacteriological parameters like Total Coliforms, Fecal Coliform and E. coli, filtration technique is adopted. In this study, comparison of mineral contents with labeled values mentioned on different brands of the countries like Rawdatain(Kuwait), Al-Ain(UAE), and Masafi (UAE) which have difference in actual measured values and labelled but all within WHO and IBWA standards. Bottle water companies claim, the water from different sources such as spring, deep ground and natural water but no doubt it may be taken from different natural sources and they are saying it a mineral water when it was not pass through any treatment processes. These bottles water are actually processed/treated water which does not contain sufficient amount of minerals necessary for the health.

Stock markets and relevant entities generate enormous amounts of data on a daily basis and are accessible from various channels such as stock exchange, economic reviews, and employer monetary reports. In recent times, machine learning techniques have proven to be very helpful in making better trading decisions. Machine learning algorithms use complex logic to observe and learn the behavior of stocks using historical data which can be used to predict future movements of the stock. Technical indicators such as rolling mean, momentum, and exponential moving average are calculated to convert the data into meaningful information. Furthermore, this information can be used to build machine learning prediction models that learn different patterns in the data and make future predictions for accurate financial forecasting. Additional factors that are being used for stock prediction include social media influences and daily news on trading stocks. Considering these qualitative and quantitative features at the same time result in improved prediction models.

The rapid growth of population and development of industrial field is contributing to massive waste pollution in which plastic waste is considered as most difficult waste to deal with due to its non-biodegradable nature. This lead to various impact on environment as well as all living things. Many of the waste produced due to human activity will remain in the environment for long period of time leading to various environmental problem. To overcome this problem a study is conducted in which plastic waste is effectively utilized with bitumen a binding material in construction of flexible pavements to enhance its stability and water resistant capacity. Cleaned plastic waste is shredded to a size of 2.76mm-4mm is mixed with hot bitumen mix and then this waste plastic added bitumen is mixed with hot aggregate and the end product is used as laying material for road construction. The objective of this study is to give an innovative idea for effective use of plastic waste and recommend a favorable percentage of plastic for replacement of bitumen. This paper also includes some tests performed on aggregate and bitumen.

Stock markets and relevant entities generate enormous amounts of data on a daily basis and are accessible from various channels such as stock exchange, economic reviews, and employer monetary reports. In recent times, machine learning techniques have proven to be very helpful in making better trading decisions. Machine learning algorithms use complex logic to observe and learn the behavior of stocks using historical data which can be used to predict future movements of the stock. Technical indicators such as rolling mean, momentum, and exponential moving average are calculated to convert the data into meaningful information. Furthermore, this information can be used to build machine learning prediction models that learn different patterns in the data and make future predictions for accurate financial forecasting. Additional factors that are being used for stock prediction include social media influences and daily news on trading stocks. Considering these qualitative and quantitative features at the same time result in improved prediction models.