Marketing the 21st century library and information organization to its new age customers using Web 2.0 tools is a hot topic. These proceedings focus on the marketing applications and (non- technical) aspects of Web 2.0 in library and information set ups. The papers in English and French are exploring and discussing the following aspects: General concepts of Web 2.0 and marketing of library and information organizations; How libraries are adopting Web 2.0 marketing strategies; Marketing libraries to clients in using Web 2.0 tools; International trends and Interesting cases of marketing through Web 2.0 tools.

Regulation A(+): How the JOBS Act Creates Opportunities for Entrepreneurs and Investors explains how to raise money under new provisions in the recently enacted JOBS Act. It will guide and advise executives of emerging growth companies, entrepreneurs, financial advisers, venture capitalists, investment bankers, securities lawyers, finance and MBA students, and others on how to raise up to $50 million a year through streamlined regulations. Signed by President Obama on April 5, 2012, Title IV of the JOBS Act amends the 1930s-era Regulation A, making it far easier for businesses to raise growth capital through public offerings. It is, in effect, a new type of IPO but with much less regulation and cost. Regulation A(+): How the JOBS Act Creates Opportunities for Entrepreneurs and Investors spells out new processes that can and will have a dramatic impact on how companies obtain growth capital to create new jobs and bolster returns for investors. Some financial gurus believe that the new law, dubbed Regulation A+ due to the enhancements, will usher in a revolutionary period of growth and innovation comparable to our largest past economic expansions.To date, much of the commentary on the JOBS Act has focused on Title III, which allows broader use of crowdfunding to raise up to $1 million per year. However, many entrepreneurs and economists believe that new changes to Regulation A will have a much greater impact on innovation and job creation. The best part? Regulation A+ lifts many constraints on soliciting funds and trading new stock issues. Among other things, readers of this book will learn how to take advantage of these provisions: * Regulation A+ permits companies to raise up to $50 million, a tenfold increase over the old limit of $5 million, and much more than the crowdfunding provisions of the JOBS Act ($1 million). * Regulation A+ allows companies to market IPOs to more people than just accredited investors and makes it easier to get the word out on offerings. * Regulation A+ allows certain companies to avoid the SEC periodic reporting regimen (Form 10-K, Form 10-Q, Form 8-K, and proxy statements), provided that the number of shareholders is kept below revised thresholds. * Regulation A+ exempts certain companies from many onerous and costly compliance requirements, including Sarbanes-Oxley.In short, Regulation A+ greatly simplifies the capital-raising process, making it easier to grow companies, create jobs, and reward investors. What you'll learn * How Title IV of the JOBS Act amends Regulation A, making it easier for you to raise up to $50 million in expansion capital while avoiding burdensome regulations. * How raising funds through Regulation A might now be a better and less costly choice for raising capital than current options (like loans or venture capital). * How to use Regulation A to gain liquidity for your business, your employees, and your investors--while maintaining control. * How to abide by Regulation A rules before, during, and after an IPO. * What kinds of businesses can take part in Regulation A offerings * How and where to trade shares after the IPO. Who this book is for Executives of emerging growth companies, entrepreneurs, financial advisers, venture capitalists, investment bankers, securities lawyers, finance and MBA students, and others.

Since the late 1940s, linear programming models have been used for many different purposes. Airline companies apply these models to optimize their use of planes and staff. NASA has been using them for many years to optimize their use of limited resources. Oil companies use them to optimize their refinery operations. Small and medium-sized businesses use linear programming to solve a huge variety of problems, often involving resource allocation. In my study, a typical product-mix problem in a manufacturing system producing two products (each product consists of two sub-assemblies) is solved for its optimal solution through the use of the latest versions of MATLAB having the command simlp, which is very much like linprog. As analysts, we try to find a good enough solution for the decision maker to make a final decision. Our attempt is to give the mathematical description of the product-mix optimization problem and bring the problem into a form ready to call MATLAB's simlp command. The objective of this study is to find the best product mix that maximizes profit. The graph obtained using MATLAB commands, give the shaded area enclosed by the constraints called the feasible region, which is the set of points satisfying all the constraints. To find the optimal solution we look at the lines of equal profit to find the corner of the feasible region which yield the highest profit. This corner can be found out at the farthest line of equal profit, which still touches the feasible region. The most critical part is the sensitivity analysis, using Excel Solver, and Parametric Analysis, using computer software, which allows us to study the effect on optimal solution due to discrete and continuous change in parameters of the LP model including to identify bottlenecks. We have examined other options like product outsourcing, one-time cost, cross training of one operator, manufacturing of hypothetical third product on under-utilized machines and optimal sequencing of jobs on machine

Master's Thesis from the year 2013 in the subject Engineering - Industrial Engineering and Management, grade: Good, LMU Munich (Dr. B R Ambedkar National Institute of Technology, Jalandhar), course: Industrial Engg., language: English, abstract: Since the late 1940s, linear programming models have been used for many different purposes. Airline companies apply these models to optimize their use of planes and staff. NASA has been using them for many years to optimize their use of limited resources. Oil companies use them to optimize their refinery operations. Small and medium-sized businesses use linear programming to solve a huge variety of problems, often involving resource allocation. In my study, a typical product-mix problem in a manufacturing system producing two products (each product consists of two sub-assemblies) is solved for its optimal solution through the use of the latest versions of MATLAB having the command simlp, which is very much like linprog. As analysts, we try to find a good enough solution for the decision maker to make a final decision. Our attempt is to give the mathematical description of the product-mix optimization problem and bring the problem into a form ready to call MATLAB's simlp command. The objective of this paper is to find the best product mix that maximizes profit. The graph obtained using MATLAB commands, give the shaded area enclosed by the constraints called the feasible region, which is the set of points satisfying all the constraints. To find the optimal solution we look at the lines of equal profit to find the corner of the feasible region which yield the highest profit. This corner can be found out at the farthest line of equal profit which still touches the feasible region. The most critical part is the sensitivity analysis using Excel Solver and Parametric Analysis using computer software which allows us to study the effect on optimal solution due to discrete and continuous change in parameters of the LP model including