The rivers are one of the main sources of water supply for different usage like agriculture, domestic and industrial. Therefore, according to the recent droughts in the country and also urban and rural development it is important to conserve these resources. In the basin of Sefidroud river ther is a small river that named Siyahroud. The rivers after passing from the eastern part of Rasht City reach to Pirbazar River and at the end to Anzali Lagoon. A lot of wastewater from factories, urban and agricultural activities discharge directly to the river and put the river in danger. In this Case study the pollution resources and the quality parameters of the river analyzed and compared with quality standard of Iran Department of Environment. Furthermore the discharge of the river during 40 years period analyzed and the months with lowest discharge determined. Then the pollution trend during the year compared with the discharge of the river and at the end some suggestion presented to joint of a section Sefidroud river to Syahroud for reduce the impacts of pollution.

Bergmann’s rule is an empirical generalisation concerning body size in endothermic species. It states that within a species, body size varies such that individuals occupying colder environments tend to be larger than individuals who live in warmer environments. To test this law in domestic Bos species, we performed a systemic review of the literature on 60 Zebu (Bos indicus) and Zebu-derivative crossbreeds from Africa and Asia. The following data were obtained via a literature metareview for each breed: wither height, live weight, conformation of dewlap and hump, latitude, longitude, and Köppen-Geiger climatic data. The body mass index (BMI, body weight divided by the square of height) was obtained to assess body size. No trend in BMI was found along latitude or between BMI and Köppen-Geiger data. BMI was not clearly explained by the development of dewlap and hump, either, but it tended to increase with body mass. Although the validity of Bergmann’s rule for Zebu breeds seems to be highly idiosyncratic and partially dependent on the study design, this study provides a new vision about the ecogeographical distribution of Zebu breeds.

The Tonga is one of the recognized breeds of the Zambian cattle. This is a multi-purpose breed found in the southern part of the country. The present study was undertaken to study the different body measurements and relationships among different body measurements and to develop unobservable factors (latent) to define which of these measurements best represent body conformation in cows of this breed. The two extracted factors factors which accounted for 54.4% of total variance represented the body and the distal conformation of the cow. The communalities estimates indicated that cephalic conformation did not contribute effectively to explain body conformation, while the remaining traits contributed effectively, and these traits could be considered to explain the body conformation of the Tonga cow. The result suggests that the principal component analysis could be used in breeding programs with a drastic reduction in the number of biometric traits needed to explain the body conformation.

This paper reports the cost-benefit analysis of the triple-layer hermetic bag, Jute and Polypropylene bags in storing maize infested with the Larger Grain Borer, Prostephanus truncatus (Horn) and the maize weevil, Sitophilus zeamais (Mot). The three bags were used to store three maize varieties (Obatanpa, Abrodenkye and Kamangkpong) for six (6) months. A factorial experiment was conducted involving 5 kg of each maize variety with moisture content between12.5-14%. These were stored in the various bags at laboratory conditions of 32 ±2 oC and 58-88% r.h. A destructive sampling was done monthly to determine weight loss, moisture content etc. The cost benefit analysis was conducted using the cost-benefit ratio (BCR). The results show that the triple-layer hermetic bag has highest cost-benefit ratio of 1.5:1 followed by the polypropylene and the jute trailing with 1.3:1 and 1.2:1, respectively.

Big Data is basically vast amount of data which cannot be effectively processed, captured, and analyzed by traditional database and search tools in reasonable amount of time. Big Data information explosion is mainly due to the vast amount of data generated by social media platform, data input from omni-channels, various mobile devices, user agents, multimedia data, and so on. Overall it is an expanding “Digital Universe”. Big Data predominately revolve around 3V’s: Volume, Velocity, Variety. Big data plays a major role in oil and gas industries. The purpose of this research is to analyze the various problems faced by the Oil & Gas industries in monitoring the vast amount of data received from their various units and to provide a suitable solution for monitoring the data. Oil and Gas (O&G) companies – both the operator companies as well as oil field service providers, now have more upstream data than they are processed before. They are at the verge of managing the vast amount of data generated from exploration , drilling or production works. Management of this is essential for effective, productive, and on demand data insight is critical, for decision making within the organization. The major problem faced by industries is the maintenance of data in concern with Exploration and Production processes. In the oil and gas industry, organizations must apply new technologies and processes that will capture and transform raw data into actionable insight to improve asset value and yield while enhancing safety and protecting the environment. Well and field operations are instrumented to capture a holistic view of equipment performance and well productivity data including reservoir, well, facilities and export data. Leading, analytics-driven oil and gas organizations are connecting people with trusted information to predict business outcomes, and to make real-time decisions that help them outperform their competitors. But for many, these breakaway results remain out of reach. Despite the wealth of data and content available today, decision makers are often starved for true insight. IBM’s big data platform provides a scalable, easy to use, secure information management and analytics platform for complex and large-scale analysis and economical storage of drilling production data. Successfully harnessing big data unleashes the potential to achieve three critical objectives: 1. Enhance exploration and production 2. Improve refining and manufacturing efficiency & 3. Optimize global operations With the help of IBM frameworks the O&G companies can adopt Hadoop enabled Big Data solutions for creating integrated digital Oil Field strategy. Hadoop based solutions allow storing, processing, and analyzing these humongous logs on near real time basis. The crux of solution involves processing raw data in its native format to create aggregated views along with understanding of its relationships and patterns and thereby derive meaningful insight for quick decision-making related to reservoir and optimizing the data exploitation using Map Reduce paradigm. HiveQL is a scalable Data Warehouse solution available on Hadoop, which is similar to SQL syntax. Hive internally generates map-reduce jobs that can be executed on Hadoop clusters. Hive in-turn allows overcoming the learning curve associated with the Map-Reduce code generation. With the help of Hadoop and HiveQL framework the Oil & Gas industries can manage the Big data from various units of oil refineries thereby generating a platform Glob. J. Mul. App. Sci., 1 (2): 52-57, 2013 53 for efficient utilization of the required data related to the companies for creating the excellence of customers in the global market.