A suite of novel allenes from Australian melolonthine scarab beetles. Structure, synthesis, and stereochemistry

A suite of allenic hydrocarbons, previously unknown as a molecular class from insects, has been characterized from several Australian melolonthine scarab beetles. The allenes are represented by the formula CH3(CH2)nCH=.=CH(CH2)(7)CH3 with n being 11-15, 17 and 19, and thus, all have Delta(9,10)-unsaturation. These structures have been confirmed by syntheses and comparisons of spectral and chromatographic properties with those of the natural components. The enantiomers of (+/-)-Delta(9,10)-tricosadiene and Delta(9,10)-pentacosadiene were separable on a modified beta-cyclodextrin column (gas chromatography), and the natural Delta(9,10)-tricosadiene (n = 11) and Delta(9,10)-pentacosadiene (n = 13) were shown to be of >85% ee. Syntheses of nonracemic allenes of known predominating chirality were acquired using both organotin chemistry and sulfonylhydrazine intermediates, and comparisons then demonstrated that the natural allenes were predominantly (R)-configured.

Do increases in xylem sap pH and/or ABA concentration mediate stomatal closure following nitrate deprivation?

Stomatal conductance (g(s)) of pepper (Capsicum annuum L.) plants decreased during the second photoperiod (day 2) after withholding nitrate (N). Stomatal closure of N-deprived plants was not associated with a decreased shoot water potential (Psi(shoot)); conversely Psi(shoot) was lower in N-supplied plants. N deprivation transiently (days 2 and 3) alkalized (0.2-0.3 pH units) xylem sap exuded from de-topped root systems under root pressure, and xylem sap expressed from excised shoots by pressurization. The ABA concentration of expressed sap increased 3-4-fold when measured on days 2 and 4. On day 2, leaves detached from N-deprived and N-supplied plants showed decreased transpiration rates when fed an alkaline (pH 7) artificial xylem (AX) solution, independent of the ABA concentration (10-100 nM) supplied. Thus changes in xylem sap composition following N deprivation can potentially close stomata. However, the lower transpiration rate of detached N-deprived leaves relative to N-supplied leaves shows that factors residing within N-deprived leaves also mediate stomatal closure, and that these factors assume greater importance as the duration of N deprivation increases.

The Brazilian Business World: the difficult adaptation to globalization

The article deals with the internationalization of Brazilian businesses in the current decade. In the 1990s, Brazil embraced economic neoliberalism and promoted a huge opening up of its economy. At that time, Brazilian companies had to adapt rapidly. Twenty years later, the country has reinforced its presence in Latin America and has ensured a better position in the global markets, especially by through agricultural exports.

On the discovery of business processes orchestration patterns

COORDINSPECTOR is a Software Tool aiming at extracting the coordination layer of a software system. Such a reverse engineering process provides a clear view of the actually invoked services as well as the logic behind such invocations. The analysis process is based on program slicing techniques and the generation of, System Dependence Graphs and Coordination Dependence Graphs. The tool analyzes Common Intermediate Language (CIL), the native language of the Microsoft .Net Framework, thus making suitable for processing systems developed in any .Net Framework compilable language. COORDINSPECTOR generates graphical representations of the coordination layer together with business process orchestrations specified in WSBPEL 2.0

The perceived potential of business social networking sites

Business social networking is a facilitator of several business activities, such as market studies, communication with clients, and identification of business partners. This paper traduces the results of a study undertaken with the purpose of getting to know how the potential of networking is perceived in the promotion of business by participants of the LinkedIn network, and presents two main contributions: (1) to disseminate within the business community which is the relevance given to social networking; and (2) which are the social networks best suitable to the promotion of business, to support the definition of strategies and approaches accordingly. The results confirm that LinkedIn is the most suitable network to answer the needs of those that look for professional contacts and for the promotion of business, while innovation is the most recognized factor in the promotion of business through social networking. This study contributes to a better understanding of the potential of different business social networking sites, to support organizations and professionals to align their strategies with the perceived potential of each network.

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.