NUCLEAR PLANT

The structure of a nuclear power plant in many aspects resembles to that of a conventional thermal power station, since in both cases the heat produced in the boiler (or reactor) is transported by some coolant and used to generate steam. The steam then goes to the blades of a turbine and by rotating it, the connected generator will produce electric energy. The steam goes to the condenser, where it condenses, i.e. becomes liquid again. The cooled down water afterwards gets back to the boiler or reactor, or in the case of PWRs to the steam generator.


Several nuclear power plant (NPP) types are used for energy generation in the world. The different types are usually classified based on the main features of the reactor applied in them. The most widespread power plant reactor types are:

•Light water reactors: both the moderator and coolant are light water (H2O). To this category belong the pressurized water reactors (PWR) and boiling water reactors (BWR).

•Heavy water reactors (CANDU): both the coolant and moderator are heavy water (D2O).

•Graphite moderated reactors: in this category there are gas cooled reactors (GCR) and light water cooled reactors (RBMK).

•Exotic reactors (fast breeder reactors and other experimental installations).

•New generation reactors: reactors of the future.

DATA WAREHOUSE

A data warehouse is a relational database that is designed for query and analysis rather than for transaction processing. It usually contains historical data derived from transaction data, but it can include data from other sources. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources.
In addition to a relational database, a data warehouse environment includes an extraction, transportation, transformation, and loading (ETL) solution, an online analytical processing (OLAP) engine, client analysis tools, and other applications that manage the process of gathering data and delivering it to business users.

Dimensional Data Model

Dimensional data model is most often used in data warehousing systems. This is different from the 3rd normal form, commonly used for transactional (OLTP) type systems. As you can imagine, the same data would then be stored differently in a dimensional model than in a 3rd normal form model.
To understand dimensional data modeling, let's define some of the terms commonly used in this type of modeling:

Dimension:

A category of information. For example, the time dimension.

Attribute:

A unique level within a dimension. For example, Month is an attribute in the Time Dimension.

Hierarchy:

The specification of levels that represents relationship between different attributes within a dimension. For example, one possible hierarchy in the Time dimension is Year → Quarter → Month → Day.

Fact Table:

A fact table is a table that contains the measures of interest. For example, sales amount would be such a measure. This measure is stored in the fact table with the appropriate granularity. For example, it can be sales amount by store by day. In this case, the fact table would contain three columns: A date column, a store column, and a sales amount column.

Lookup Table:

The lookup table provides the detailed information about the attributes. For example, the lookup table for the Quarter attribute would include a list of all of the quarters available in the data warehouse. Each row (each quarter) may have several fields, one for the unique ID that identifies the quarter, and one or more additional fields that specifies how that particular quarter is represented on a report (for example, first quarter of 2001 may be represented as "Q1 2001" or "2001 Q1").
A dimensional model includes fact tables and lookup tables. Fact tables connect to one or more lookup tables, but fact tables do not have direct relationships to one another. Dimensions and hierarchies are represented by lookup tables. Attributes are the non-key columns in the lookup tables.
In designing data models for data warehouses / data marts, the most commonly used schema types are Star Schema and Snowflake Schema.
Star Schema: In the star schema design, a single object (the fact table) sits in the middle and is radially connected to other surrounding objects (dimension lookup tables) like a star. A star schema can be simple or complex. A simple star consists of one fact table; a complex star can have more than one fact table.

Snowflake Schema:

The snowflake schema is an extension of the star schema, where each point of the star explodes into more points. The main advantage of the snowflake schema is the improvement in query performance due to minimized disk storage requirements and joining smaller lookup tables. The main disadvantage of the snowflake schema is the additional maintenance efforts needed due to the increase number of lookup tables.
Whether one uses a star or a snowflake largely depends on personal preference and business needs. Personally, I am partial to snowflakes, when there is a business case to analyze the information at that particular level.

How to counter pirates of the seas

The scourge of piracy on the high seas is almost as old as the history of mankind,while maritime terrorism is a comparatively new phenomenon.It is a fact that there are some areas where piracy and terrorism have some commonality,in that both are the actions of non-state actors who threaten life and economic activities at sea or in ports.There are some differences also,in that pirates,motivated by economics,prefer to avoid publicity and use violence as a last resort,while maritime terrosists seek maximum publicity with maximum violence.

In recent times,with the spread of global terrorism,governments have become increasingly worried about not only the economic consequences of rampant piracy-like insurance rates going up,goods becoming costlier areas - but also the possibility that a hijacked ship could be sold by pirates to maritime terrorists.