In order to survive in a competitive environment, getting placed in a reputed company after graduation is not the end of education. Within a company also a person needs to keep re-skilling and up-skilling oneself to stay relevant and successful. A Recommender system would help new employees to identify the best choice (course) of their interest. In this paper, we have proposed a recommender system that would assist new employees by predicting for them the choices of certifications and skills that they should acquire to advance in their careers, given their personal histories (education and already acquired skills and certifications). We have employed a relatively new technique, Compact Prediction Trees, for this task. CPTs have shown convincing results in sequence prediction, performing better and faster than many traditional algorithms.

The experiment was conducted to observe the callus induction ability of Brassica species. Plantlets were regenerated from cotyledon and stem explants of Brassica napus, Brassica campestris and Brassica juncea through direct organogenesis. The experiments were conducted in a Completely Randomized Design (CRD) with 4 replications. The highest frequency of callus formation was recorded in MS containing 2.0 mgl-1 BAP, 0.5 mgl-1 NAA and 2.0 mgl-1 AgNO3 in both stem and cotyledon explants. Among these explants, stem was found to be better responsive in callus induction than cotyledon. Among the genotypes used, BINA Sarisha-4 induced the highest percentage (100.00%) of callus from stem explants which was followed by BINA Sarisha-5 (100.00%) and Sampad (83.35%). On the other hand, BINA Sarisha-4 induced callus from 91.67% cotyledon explants, followed by BINA Sarisha-5 (75.00%) and Sampad (66.67%). Similarly, the highest percentage of shoot regeneration (58.34%) from stem explants of BINA Sarisha-4 was observed in MS medium supplemented with combination of hormone and silver nitrate concentrations. The highest percentage of root induction was 66.67 and 58.33% in plantlets derived from stem and cotyledon explants, respectively in ½ MS medium supplemented with 2.0 mgl-1 IBA and 0.5 mgl-1 of NAA. The highest survival rate was found after acclimatization of plants derived from stem (77.78%) and cotyledon (64.28%) explants of BINA Sarisha-4 in pot and 64.33 and 55.55%, respectively in field.The Agriculturists 2017; 15(2) 01-10

Use of renewable energy sources for electricity generation is gaining prominence due to the increasing importance of sustainable development. Many of the electricity producers using renewable energy resources have very small generating capacities and it is a challenge to integrate them with the grid. Smart grids can play an important role in facilitating integration of these small distributed electricity producers to the power grid. The concept of virtual power plants (VPPs) is one of the important approaches used in smart grid for integrating distributed energy resources. The VPP aggregates electricity generated from many distributed energy sources such as wind turbines, small hydro, etc. The energy sources within a VPP are run by their individual owners and the VPP planner helps to aggregate and deliver the power to the grid. In this paper, we model the virtual power plant (VPP) formation problem with strategic producers (suppliers) using renewable energy sources as a combinatorial auction problem. We state and prove the necessary and sufficient condition for incentive compatible and individually rational auction mechanism in the presence of strategic suppliers. We also show that the dominant strategy for a supplier in our mechanism is to bid truthfully and improve her reputation.

Background: The overexpression of oestrogen-related receptor- β (ERR β ) in breast cancer patients is correlated with improved prognosis and longer relapse-free survival, and the level of ERR β mRNA is inversely correlated with the S-phase fraction of cells from breast cancer patients. Methods: Chromatin immunoprecipitation (ChIP) cloning of ERR β transcriptional targets and gel supershift assays identified breast cancer amplified sequence 2 ( BCAS2 ) and Follistatin (FST) as two important downstream genes that help to regulate tumourigenesis. Confocal microscopy, co-immunoprecipitation (CoIP), western blotting and quantitative real-time PCR confirmed the involvement of ERR β in oestrogen signalling. Results: Overexpressed ERR β induced FST-mediated apoptosis in breast cancer cells, and E-cadherin expression was also enhanced through upregulation of FST. However, this anti-proliferative signalling function was challenged by ERR β -mediated BCAS2 upregulation, which inhibited FST transcription through the downregulation of β -catenin/TCF4 recruitment to the FST promoter. Interestingly, ERR β -mediated upregulation of BCAS2 downregulated the major G1-S transition marker cyclin D1, despite the predictable oncogenic properties of BCAS2. Interpretation: Our study provides the first evidence that ERR β , which is a coregulator of ER α also acts as a potential tumour-suppressor molecule in breast cancer. Our current report also provides novel insights into the entire cascade of ERR β signalling events, which may lead to BCAS2-mediated blockage of the G1/S transition and inhibition of the epithelial to mesenchymal transition through FST-mediated regulation of E-cadherin. Importantly, matrix metalloprotease 7, which is a classical mediator of metastasis and E-cadherin cleavage, was also restricted as a result of ERR β -mediated FST overexpression.

Reduction of loss in a power distribution network is of utmost importance for any electrical utility. Recent years have seen development of various optimization techniques and computer technology which has made it possible to perform accurate analysis of distribution losses. The network reconfiguration of a distribution network is done by changing the status of sectionalizing switches by switching operation. It is done usually to minimize the loss and to avoid overloading. In this paper the objective is to find a method by which management of distribution system loss by network reconfiguration can be done so that all the loads receive power. The developed program is made flexible for checking many conditions at a time and compares them with the rated condition to find the best condition with minimum loss and identifies occurrence of any overload. A method using Smart wire is also suggested for congestion management under overload conditions.

Green or sustainable procurement is critical to any supply chain in the modern era. In this paper, we address the issue of selection of suppliers in order to ensure that the procurement process in a manufacturing or service supply chain selects suppliers so as to minimize carbon emissions. The specific problem we address pertains to that of an orchestrator or a procurement planner who wishes to put together a green procurement network consisting of strategic suppliers. Our approach decomposes the problem into two stages. In Stage 1 (information elicitation), the orchestrator uses a green budget to offer appropriate incentives to the suppliers to report their carbon emissions accurately. The incentives are determined using an approach based on proper scoring rules. Having obtained emissions data in Stage 1, the orchestrator identifies a pool of suppliers in Stage 2 (green supplier selection) to minimize the quantum of carbon emissions of the procurement process. The paper focuses on Stage 1 of the problem and develops an incentive compatible mechanism for elicitation of emission estimates. We illustrate the proposed mechanism with a stylized example and also with detailed simulation results.

Auction based mechanisms have become popular in industrial procurement settings. These mechanisms minimize the cost of procurement and at the same time achieve desirable properties such as truthful bidding by the suppliers. In this paper, we investigate the design of truthful procurement auctions taking into account an additional important issue namely carbon emissions. In particular, we focus on the following procurement problem: A buyer wishes to source multiple units of a homogeneous item from several competing suppliers who offer volume discount bids and who also provide emission curves that specify the cost of emissions as a function of volume of supply. We assume that emission curves are reported truthfully since that information is easily verifiable through standard sources. First we formulate the volume discount procurement auction problem with emission constraints under the assumption that the suppliers are honest (that is they report production costs truthfully). Next we describe a mechanism design formulation for green procurement with strategic suppliers. Our numerical experimentation shows that emission constraints can significantly alter sourcing decisions and affect the procurement costs dramatically. To the best of our knowledge, this is the first effort in explicitly taking into account carbon emissions in planning procurement auctions.

We discuss four problems that we have identified under the umbrella of carbon economics problems: carbon credit allocation (CCA), carbon credit buying (CCB), carbon credit selling (CCS), and carbon credit exchange (CCE). Because of the strategic nature of the players involved in these problems, game theory and mechanism design provides a natural way of formulating and solving these problems. We then focus on a particular CCA problem, the carbon emission reduction problem, where the countries or global industries are trying to reduce their carbon footprint at minimum cost. We briefly describe solutions to the above problem.

The problem addressed in this paper is concerned with an important issue faced by any green aware global company to keep its emissions within a prescribed cap. The specific problem is to allocate carbon reductions to its different divisions and supply chain partners in achieving a required target of reductions in its carbon reduction program. The problem becomes a challenging one since the divisions and supply chain partners, being autonomous, may exhibit strategic behavior. We use a standard mechanism design approach to solve this problem. While designing a mechanism for the emission reduction allocation problem, the key properties that need to be satisfied are dominant strategy incentive compatibility (DSIC) (also called strategy-proofness), strict budget balance (SBB), and allocative efficiency (AE). Mechanism design theory has shown that it is not possible to achieve the above three properties simultaneously. In the literature, a mechanism that satisfies DSIC and AE has recently been proposed in this context, keeping the budget imbalance minimal. Motivated by the observation that SBB is an important requirement, in this paper, we propose a mechanism that satisfies DSIC and SBB with slight compromise in allocative efficiency. Our experimentation with a stylized case study shows that the proposed mechanism performs satisfactorily and provides an attractive alternative mechanism for carbon footprint reduction by global companies.

We discuss four problems that we have identified under the umbrella of carbon economics problems: carbon credit allocation (CCA), carbon credit buying (CCB), carbon credit selling (CCS), and carbon credit exchange (CCE). Because of the strategic nature of the players involved in these problems, game theory and mechanism design provides a natural way of formulating and solving these problems. We then focus on a particular CCA problem, the carbon emission reduction problem, where the countries or global industries are trying to reduce their carbon footprint at minimum cost. We briefly describe solutions to the above problem.

We present an optimal combinatorial auction mechanism for the initial commitment decision problem (ICDP) in virtual organizations for rational agents. ICDP determines how a virtual organization (VO) planner can allocate tasks to supplier agents forming a virtual organization. We take into consideration the reputation of agents in the auction formulation. The reputation of agents is formed over time by their behavior of completing the tasks assigned to them. This is very important, since some of the agents (in real time) might be assigned other (more profitable) tasks in addition to the VO tasks and they can decide not to complete the tasks allocated to them by the VO planner.

The femoral region of the thigh is utilised for various clinical procedures, both open and closed, particularly in respect to arterial and venous cannulations. A rare vascular pattern was observed during the dissection of the femoral region on both sides of the intact formaldehyde-preserved cadaver of a 42-year-old Indian man from West Bengal. The relationships and patterns found were contrary to the belief that the femoral vein is always medial to the artery, just below the inguinal ligament and the common femoral artery. The femoral artery crossed the vein just deep to the inguinal ligament so that the femoral vein was lying deep to the artery at the base of the femoral triangle. Just deep to the inguinal ligament, the profunda femoris artery (deep femoral artery) arose from the femoral artery, and the long saphenous vein drained into the femoral vein. The embryological and clinical correlations are discussed.

Combinatorial exchanges are double sided marketplaces with multiple sellers and multiple buyers trading with the help of combinatorial bids. The allocation and other associated problems in such exchanges are known to be among the hardest to solve among all economic mechanisms. In this paper, we develop computationally efficient iterative auction mechanisms for solving combinatorial exchanges. Our mechanisms satisfy Individual-rationality (IR) and budget-nonnegativity (BN) properties. We also show that our method is bounded and convergent. Our numerical experiments show that our algorithm produces good quality solutions and is computationally efficient.

Combinatorial exchanges are double sided market-places with multiple sellers and multiple buyers trading with the help of combinatorial bids. The allocation and other associated problems in such exchanges are known to be among the hardest to solve among all economic mechanisms. In this paper, we develop computationally efficient iterative auction mechanisms for solving combinatorial exchanges. Our mechanisms satisfy Individual rationality (IR) and budget-nonnegativity (BN) properties. We also show that the exchange problem can be reduced to combinatorial auction problem when either the buyers or the sellers are single minded. Our numerical experiments show that our algorithm produces good quality solutions and is computationally efficient.

A business cluster is a co-located group of micro, small, medium scale enterprises. Such firms can benefit significantly from their co-location through shared infrastructure and shared services. Cost sharing becomes an important issue in such sharing arrangements especially when the firms exhibit strategic behavior. There are many cost sharing methods and mechanisms proposed in the literature based on game theoretic foundations. These mechanisms satisfy a variety of efficiency and fairness properties such as allocative efficiency, budget balance, individual rationality, consumer sovereignty, strategyproofness, and group strategyproofness. In this paper, we motivate the problem of cost sharing in a business cluster with strategic firms and illustrate different cost sharing mechanisms through the example of a cluster of firms sharing a logistics service. Next we look into the problem of a business cluster sharing ICT (information and communication technologies) infrastructure and explore the use of cost sharing mechanisms.