Generating a Visual Language of Performance-Driven Configurations for the Principal Façade of a Prototype Sustainable House

A performance-driven application of shape grammars is presented. A parametric shape grammar that generates a language of pattern designs for the principal façade of a prototype house—featuring a 5 × 20 matrix of electrochromic windows—based equally on performance and aesthetic criteria is described. The adjustment of the chromatism and light transmittance of each individual windowpane on the façade enables the adjustment of solar radiation at the house interior. The novel aspect of the grammar is that it encodes performance constraints of interior daylight illuminance and associates them to visual, symmetry principles of two-dimensional pattern generation. Twelve parametric rules account for the generation of the façade pattern language and five subclasses account for the symmetry of the patterns in the language.     

Colloidal Quantum Dot Infrared Lasers Featuring Sub-Single-Exciton Threshold and Very High Gain

The use of colloidal quantum dots (CQDs) as a gain medium in infrared laser devices has been underpinned by the need for high pumping intensities, very short gain lifetimes, and low gain coefficients. Here, PbS/PbSSe core/alloyed-shell CQDs are employed as an infrared gain medium that results in highly suppressed Auger recombination with a lifetime of 485 ps, lowering the amplified spontaneous emission (ASE) threshold down to 300 µJ cm⁻², and showing a record high net modal gain coefficient of 2180 cm⁻¹. By doping these engineered core/shell CQDs up to nearly filling the first excited state, a significant reduction of optical gain threshold is demonstrated, measured by transient absorption, to an average-exciton population-per-dot 〈N^th〉_g of 0.45 due to bleaching of the ground state absorption. This in turn have led to a fivefold reduction in ASE threshold at 〈N^th〉_ASE = 0.70 excitons-per-dot, associated with a gain lifetime of 280 ps. Finally, these heterostructured QDs are used to achieve near-infrared lasing at 1670 nm at a pump fluences corresponding to sub-single-exciton-per-dot threshold (〈N^th〉_Las = 0.87). This work brings infrared CQD lasing thresholds on par to their visible counterparts, and paves the way toward solution-processed infrared laser diodes.     

Customized kernel execution on reconfigurable hardware for embedded applications

To conserve space and power as well as to harness high performance in embedded systems, high utilization of the hardware is required. This can be facilitated through dynamic adaptation of the silicon resources in reconfigurable systems in order to realize various customized kernels as execution proceeds. Fortunately, the encountered reconfiguration overheads can be estimated. Therefore, if the scheduling of time-consuming kernels considers also the reconfiguration overheads, an overall performance gain can be obtained. We present our policy, experiments, and performance results of customizing and reconfiguring Field-Programmable Gate Arrays (FPGAs) for embedded kernels. Experiments involving EEMBC (EDN Embedded Microprocessor Benchmarking Consortium) and MiBench embedded benchmark kernels show high performance using our main policy, when considering reconfiguration overheads. Our policy reduces the required reconfigurations by more than 50% as compared to brute-force solutions, and performs within 25% of the ideal execution time while conserving 60% of the FPGA resources. Alternative strategies to reduce the reconfiguration overhead are also presented and evaluated.     

Behavioral adaptation of information systems through goal models

Customizing software to perfectly fit individual needs is becoming increasingly important in information systems engineering. Users want to be able to customize software behavior through reference to terms familiar to their diverse needs and experience. We present a requirements-driven approach to behavioral customization of software systems. Goal models are constructed to represent alternative behaviors that users can exhibit to achieve their goals. Customization information is then added to restrict the space of possibilities to those that fit specific users, contexts, or situations. Meanwhile, elements of the goal models are mapped to units of source code. This way, customization preferences posed at the requirements level are directly translated into system customizations. Our approach, which we apply to an on-line shopping cart system and an automated teller machine simulator, does not assume adoption of a particular development methodology, platform, or variability implementation technique and keeps the reasoning computation overhead from interfering with the execution of the configured application.     

A sparse nonparametric hierarchical Bayesian approach towards inductive transfer for preference modeling

In this paper, we present a novel methodology for preference learning based on the concept of inductive transfer. Specifically, we introduce a nonparametric hierarchical Bayesian multitask learning approach, based on the notion that human subjects may cluster together forming groups of individuals with similar preference rationale (but not identical preferences). Our approach is facilitated by the utilization of a Dirichlet process prior, which allows for the automatic inference of the most appropriate number of subject groups (clusters), as well as the employment of the automatic relevance determination (ARD) mechanism, giving rise to a sparse nature for our model, which significantly enhances its computational efficiency. We explore the efficacy of our novel approach by applying it to both a synthetic experiment and a real-world music recommendation application. As we show, our approach offers a significant enhancement in the effectiveness of knowledge transfer in statistical preference learning applications, being capable of correctly inferring the actual number of human subject groups in a modeled dataset, and limiting knowledge transfer only to subjects belonging to the same group (wherein knowledge transferability is more likely).     

The potential of WHR/batch and cullet preheating for energy efficiency in the EU ETS glass industry and the related energy incentives

The European Emissions Trading Scheme (EU ETS) covers approximately 45% of European greenhouse gases (GHGs), 11,000 stationary installations as well as aircraft operators. The EU ETS particularly affects the energy-intensive industries while it imposes a significant risk of “carbon leakage,” i.e., the risk of EU industry departing to countries with weaker restraints on GHG emissions. The EU glass industry, being capital intensive and also requiring long investment cycles, is the world’s largest glass producer with a market share close to one third of global production. Therefore, it is of significant importance to view the position of the EU ETS glass industry in terms of energy conservation possibilities. The present paper utilizes a vertical approach to provide information on both ETS market evolution and specific technical information to support technological innovation to the glass industry. EU ETS glass industry is analyzed regarding the balance between allocated European Union emission allowances (EUAs), verified CO₂ emissions, and potential shortfall in allowances so as to determine the situation of glass industries and the extent of urge for energy-saving activities towards the strengthening of their position within the requirements of the EU ETS phase III. The replication potential of waste heat recovery (WHR) through batch preheating is specifically addressed since it is considered a promising technology according to the latest Best Available Techniques (BAT) reference document for the glass industry under Directive IED 2010/75/EU. A case study for a container glass furnace based on simulation results is presented investigating the impact of different operating and design configurations on specific energy consumption and CO₂ emissions.     

Synthesis and Biopharmaceutical Evaluation of Imatinib Analogues Featuring Unusual Structural Motifs

A convenient synthesis of imatinib, a potent inhibitor of ABL1 kinase and widely prescribed drug for the treatment of a variety of leukemias, was devised and applied to the construction of a series of novel imatinib analogues featuring a number of non‐aromatic structural motifs in place of the parent molecule's phenyl moiety. These analogues were subsequently evaluated for their biopharmaceutical properties (e.g., ABL1 kinase inhibitory activity, cytotoxicity). The bicyclo[1.1.1]pentane‐ and cubane‐containing analogues were found to possess higher themodynamic solubility, whereas cubane‐ and cyclohexyl‐containing analogues exhibited the highest inhibitory activity against ABL1 kinase and the most potent cytotoxicity values against cancer cell lines K562 and SUP‐B15. Molecular modeling was employed to rationalize the weak activity of the compounds against ABL1 kinase, and it is likely that the observed cytotoxicity of these agents arises through off‐target effects.     

Chemical investigation and antimicrobial properties of mastic water and its major constituents

Mastic water is a commercial flavouring obtained during the steam distillation of mastic resin (the resin of Pistacia lentiscus var. chia) for the production of mastic oil. The mastic water extracts were analysed by GC−MS. The major compounds identified were verbenone, α-terpineol, linalool and trans-pinocarveol. Overall the composition was found to be very different from that of mastic oil. Additional GC−MS revealed the enantiomeric ratio of the chiral constituents of mastic water. The antimicrobial activity of mastic water extract, as well as that of its major constituents, was examined against Escherichia coli, Staphylococcus aureus and Candida spp. including ATCC wild clinical and food-borne strains. Linalool and α-terpineol were found to be the most potent antimicrobial constituents. Finally the stability of mastic water at different temperatures was studied, showing no change in the GC−MS profile of the organic extract for a period of 4 months at storage temperatures up to 4 °C.     

Optimization of Power Consumption in 4G LTE Networks Using a Novel Barebones Self-adaptive Differential Evolution Algorithm

The power consumption of wireless access networks is an important issue. In this paper, the power consumption of Long Term Evolution (LTE) base stations is optimized. We consider the city of Ghent, Belgium with 75 possible LTE base station locations. We optimize the network towards two objectives: the coverage maximization and the power consumption minimization. We propose a new Barebones Self-adaptive Differential Evolution. The results of the proposed method indicate the advantages and applicability of our approach.     

Synthesis and Biological Evaluation of Novel Epothilone B Side Chain Analogues

The design, synthesis, and biological evaluation of a series of epothilone analogues with novel side chains equipped with an amino group are described. Their design facilitates potential conjugation to selective drug delivery systems such as antibodies. Their synthesis proceeded efficiently via Stille coupling of a readily available vinyl iodide and heterocyclic stannanes. Cytotoxicity studies and tubulin binding assays revealed two of these analogues to be more potent than epothilones A–D and the anticancer agent ixabepilone, currently in clinical use.