Comparison of supports for the direct synthesis of hydrogen peroxide from H2 and O2 using Au–Pd catalysts

The direct synthesis of hydrogen peroxide from H₂ and O₂ using a range of supported Au–Pd alloy catalysts is compared for different supports using conditions previously identified as being optimal for hydrogen peroxide synthesis, i.e. low temperature (2 °C) using a water–methanol solvent mixture and short reaction time. Five supports are compared and contrasted, namely Al₂O₃, -Fe₂O₃, TiO₂, SiO₂ and carbon. For all catalysts the addition of Pd to the Au only catalyst increases the rate of hydrogen peroxide synthesis as well as the concentration of hydrogen peroxide formed. Of the materials evaluated, the carbon-supported Au–Pd alloy catalysts give the highest reactivity. The results show that the support can have an important influence on the synthesis of hydrogen peroxide from the direct reaction. The effect of the methanol–water solvent is studied in detail for the 2.5 wt% Au–2.5 wt% Pd/TiO₂ catalyst and the ratio of methanol to water is found to have a major effect on the rate of hydrogen peroxide synthesis. The optimum mixture for this solvent system is 80 vol.% methanol with 20 vol.% water. However, the use of water alone is still effective albeit at a decreased rate. The effect of catalyst mass was therefore also investigated for the water and water–methanol solvents and the observed effect on the hydrogen peroxide productivity using water as a solvent is not considered to be due to mass transfer limitations. These results are of importance with respect to the industrial application of these Au–Pd catalysts.     

Developing Mature Internet Strategies

Although the banking industry plays a central role in the world economy and has traditionally been an innovative user of information technology, banks are struggling to develop mature Internet strategies. A model used to analyze the type of Internet activities among a sample of banks as well as their level of sophistication and customization is useful not only for revealing unexploited opportunities in the banking sector but also for guiding the strategy-building process of organizations in other industries.     

Response time variability

Response time variability is a new optimization problem with a broad range of applications and a distinctive number of theoretic flavour. The problem occurs whenever events, jobs, clients or products need to be sequenced so as to minimize the variability of time for which they wait for the next turn in obtaining the resources necessary for their advance. The problem has numerous real-life applications. We study its computational complexity, present efficiency, polynomial time algorithms for some cases, and the NP-hardness proof for a general problem. We propose a position exchange heuristic and apply it to improve the total response time variability of an initial sequence. The latter is the optimum bottleneck sequence, Webster or Jefferson sequence of the apportionment, or a random sequence. We report on computational experiments with the heuristic.     

Nonlinear black-box modeling in system identification: a unified overview

A nonlinear black-box structure for a dynamical system is a model structure that is prepared to describe virtually any nonlinear dynamics. There has been considerable recent interest in this area, with structures based on neural networks, radial basis networks, wavelet networks and hinging hyperplanes, as well as wavelet-transform-based methods and models based on fuzzy sets and fuzzy rules. This paper describes all these approaches in a common framework, from a user's perspective. It focuses on what are the common features in the different approaches, the choices that have to be made and what considerations are relevant for a successful system-identification application of these techniques. It is pointed out that the nonlinear structures can be seen as a concatenation of a mapping form observed data to a regression vector and a nonlinear mapping from the regressor space to the output space. These mappings are discussed separately. The latter mapping is usually formed as a basis function expansion. The basis functions are typically formed from one simple scalar function, which is modified in terms of scale and location. The expansion from the scalar argument to the regressor space is achieved by a radial- or a ridge-type approach. Basic techniques for estimating the parameters in the structures are criterion minimization, as well as two-step procedures, where first the relevant basis functions are determined, using data, and then a linear least-squares step to determine the coordinates of the function approximation. A particular problem is to deal with the large number of potentially necessary parameters. This is handled by making the number of ‘used’ parameters considerably less than the number of ‘offered’ parameters, by regularization, shrinking, pruning or regressor selection.     

Isolation of Shigella dysenteriae serotypes 11, 12, and 13 from patients with diarrhea in Bangladesh

Nine isolates of bacteria biochemically resembling Shigella dysenteriae but not belonging to the 10 recognized serotypes were isolated from patients with diarrhea in Bangladesh. Further studies suggested that two, one, and six isolates belonged to the recently recognized S. dysenteriae serotypes 11, 12, and 13, respectively.     

Comments on Debra Hull's concept of rationality.

D. B. Hull (1988) misinterprets "rationality" as used in rational-emotive therapy (RET) in her critique of the present author's (1987) article. As used in RET, rational not only means derived from reason, but also means efficiently aiding human happiness. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Partial-predicate logic in computer science

Summary In A Logic Covering Undefinedness in Program Proofs [1], Barringer, Cheng and Jones present a 3-valued logic designed for Program-Verification and Computer Science applications in general. In this paper we want to add some the oretical background to their arguments for the need for such a system.     

Polycyclic Aromatic Hydrocarbons Activate the Aryl Hydrocarbon Receptor and the Constitutive Androstane Receptor to Regulate Xenobiotic Metabolism in Human Liver Cells

Polycyclic aromatic hydrocarbons (PAHs) are environmental pollutants produced by incomplete combustion of organic matter. They induce their own metabolism by upregulating xenobiotic-metabolizing enzymes such as cytochrome P450 monooxygenase 1A1 (CYP1A1) by activating the aryl hydrocarbon receptor (AHR). However, previous studies showed that individual PAHs may also interact with the constitutive androstane receptor (CAR). Here, we studied ten PAHs, different in carcinogenicity classification, for their potential to activate AHR- and CAR-dependent luciferase reporter genes in human liver cells. The majority of investigated PAHs activated AHR, while non-carcinogenic PAHs tended to activate CAR. We further characterized gene expression, protein abundancies and activities of the AHR targets CYP1A1 and 1A2, and the CAR target CYP2B6 in human HepaRG hepatoma cells. Enzyme induction patterns strongly resembled the profiles obtained at the receptor level, with AHR-activating PAHs inducing CYP1A1/1A2 and CAR-activating PAHs inducing CYP2B6. In summary, this study provides evidence that beside well-known activation of AHR, some PAHs also activate CAR, followed by subsequent expression of respective target genes. Furthermore, we found that an increased PAH ring number is associated with AHR activation as well as the induction of DNA double-strand breaks, whereas smaller PAHs activated CAR but showed no DNA-damaging potential.     

Immune Checkpoint Inhibitors: A Promising Treatment Option for Metastatic Castration-Resistant Prostate Cancer?

Since 2010, several treatment options have been available for men with metastatic castration-resistant prostate cancer (mCRPC), including immunotherapeutic agents, although the clinical benefit of these agents remains inconclusive in unselected mCRPC patients. In recent years, however, immunotherapy has re-emerged as a promising therapeutic option to stimulate antitumor immunity, particularly with the use of immune checkpoint inhibitors (ICIs), such as PD-1/PD-L1 and CTLA-4 inhibitors. There is increasing evidence that ICIs may be especially beneficial in specific subgroups of patients with high PD-L1 tumor expression, high tumor mutational burden, or tumors with high microsatellite instability/mismatch repair deficiency. If we are to improve the efficacy of ICIs, it is crucial to have a better understanding of the mechanisms of resistance to ICIs and to identify predictive biomarkers to determine which patients are most likely to benefit. This review focuses on the current status of ICIs for the treatment of mCRPC (either as monotherapy or in combination with other drugs), mechanisms of resistance, potential predictive biomarkers, and future challenges in the management of mCRPC.