Modeling and off-design performance of a 1 kWe HT-PEMFC (high temperature-proton exchange membrane fuel cell)-based residential micro-CHP (combined-heat-and-power) system for Danish single-family households

A novel proposal for the modeling and operation of a micro-CHP (combined-heat-and-power) residential system based on HT-PEMFC (High Temperature-Proton Exchange Membrane Fuel Cell) technology is described and analyzed to investigate its commercialization prospects. An HT-PEMFC operates at elevated temperatures, as compared to Nafion-based PEMFCs and therefore can be a significant candidate for cogeneration residential systems. The proposed system can provide electric power, hot water, and space heating for a typical Danish single-family household. A complete fuel processing subsystem, with all necessary BOP (balance-of-plant) components, is modeled and coupled to the fuel cell stack subsystem. The micro-CHP system is simulated in LabVIEW™ environment to provide the ability of Data Acquisition of actual components and thereby more realistic design in the future. A part-load study has been conducted to indicate performance characteristics at off-design conditions. The system is sized to provide realistic dimensioning of the actual system.     

Resource-Aware Compiler Prefetching for Fine-Grained Many-Cores

Super-scalar, out-of-order processors that can have tens of read and write requests in the execution window place significant demands on Memory Level Parallelism (MLP). Multi- and many-cores with shared parallel caches further increase MLP demand. Current cache hierarchies however have been unable to keep up with this trend, with modern designs allowing only 4?C16 concurrent cache misses. This disconnect is exacerbated by recent highly parallel architectures (e.g. GPUs) where power and area per-core budget favor numerous lighter cores with less resources, further reducing support for MLP on a per-core basis. Support for hardware and software prefetch increases MLP pressure since these techniques overlap multiple memory requests with existing computation. In this paper, we propose and evaluate a novel Resource-Aware Prefetching (RAP) compiler algorithm that is aware of the number of simultaneous prefetches supported, and optimized for the same. We implemented our algorithm in a GCC-derived compiler and evaluated its performance using an emerging fine-grained many-core architecture. Our results show that the RAP algorithm outperforms a well-known loop prefetching algorithm by up to 40.15% in run-time on average across benchmarks and the state-of-the art GCC implementation by up to 34.79%, depending upon hardware configuration. Moreover, we compare the RAP algorithm with a simple hardware prefetching mechanism, and show run-time improvements of up to 24.61%. To demonstrate the robustness of our approach, we conduct a design-space exploration (DSE) for the considered target architecture by varying (i) the amount of chip resources designated for per-core prefetch storage and (ii) off-chip bandwidth. We show that the RAP algorithm is robust in that it improves performance across all design points considered. We also identify the Pareto-optimal hardware-software configuration which delivers 53.66% run-time improvement on average while using only 5.47% more chip area than the bare-bones design.     

Designed spiro-bicyclic analogues targeting the ribosomal decoding center

The bacterial ribosome represents the confirmed biological target for many known antibiotics that interfere with bacterial protein synthesis. Aminoglycosides represent a lead paradigm in RNA molecular recognition and constitute ideal starting points for the design and synthesis of novel RNA binders. Previous rational design approaches of RNA-targeting small molecules have been mainly concentrated on direct functionalization of aminoglycosidic substructures. Herein, we successfully designed and synthesized rigid spirocyclic scaffolds locked in a predicted ribosome-bound "bioactive" conformation. These analogues are able to mimic many of the interactions of the natural products for the A-site, as proven by their obtained binding affinities. The development of an optimized approach for their synthesis and their potential to inhibit protein production in vitro are presented. Our results could be further utilized for the development of analogues with improved antibiotic profiles.     

Ezetimibe Treatment Lowers Indicators of Oxidative Stress in Hypercholesterolemic Subjects with High Oxidative Stress

Ezetimibe effectively reduces low-density lipoprotein cholesterol (LDL-C). In this study, we tested the hypothesis that ezetimibe monotherapy may also decrease markers of oxidative stress in subjects with hypercholesterolemia. Subjects with hypercholesterolemia and no evidence of cardiovascular disease were randomly allocated to open-label ezetimibe monotherapy 10 mg/day (EZT group) or therapeutic lifestyle changes (TLC group). At baseline and 12 weeks post-treatment serum lipoprotein and apolipoprotein levels as well as oxidative stress parameters, including oxidized LDL (ox-LDL), 8-isoprostanes (8-epiPGF2a) and reactive oxygen metabolites (d-ROMs) levels, were blindly determined. A total of 60 patients were included; 30 in each group. Despite a significant decrease in ox-LDL levels (by 20.8%, p < 0.001 vs. baseline; p < 0.001 vs. TLC group) in the EZT group no change in the ratio ox-LDL to LDL-C was noticed following ezetimibe treatment. No significant change in 8-epiPGF2a and d-ROMs levels was observed in the EZT group. Of note, a significant decrease in 8-epiPGF2a and d-ROMs levels (by 20.4% and 18.2%, respectively, p < 0.01 vs. baseline for both), was noted among patients in the EZT group who exhibited ‘high oxidative stress’ at baseline. No change in any of oxidative stress parameters was noted in the TLC group. Ezetimibe may decrease markers of oxidative stress in hypercholesterolemic subjects. This benefit may be more profound among patients who exhibit ‘high oxidative stress’ at baseline.     

Structural optimization of thin-walled tubular trusses using a virtual strain energy density approach

The present paper deals with the weight minimization of tubular trusses subjected to multiple loads under size, stress and buckling constraints. The applied optimization procedure is based on a virtual strain energy density approach developed by the first two authors, already tested in plane and space truss structures. The key point of the method is the activation of at least one of the imposed displacement constraints. In case where such limitations are absent, a dummy displacement constraint is introduced instead, which iteratively sustains corrections until convergence is achieved within the desirable tolerance. The efficiency and practicability of the proposed method was tested in typical cases of tubular truss structures. For reasons of comparison, the same cases were also optimized using Sequential Quadratic Programming (SQP), which is a powerful mathematical programming optimization method. The results revealed that the proposed method performs very well in terms of convergence, of required number of iterations and of optimum tracing, while the value of the introduced dummy displacement constraint has insignificant effect on the optimization procedure.     

Evaluation of Differing Subsurface Ground Conditions in Construction Contracts

Subsurface ground conditions present a big factor of uncertainty in a construction project. It is difficult to determine subsurface ground conditions even with a prebid site investigation. Nevertheless, such conditions must be included in the bid price. Due to this uncertainty, claims arise during the construction of a project. Many claims end up in litigation. This paper presents an analysis of legal cases dealing with differing subsurface ground conditions in construction contracts. By creating a database containing salient features from legal cases related to subsurface ground conditions, this research identifies the most important factors influencing the outcome of legal cases pertaining to subsurface ground conditions. Further, the most commonly used arguments by the litigating parties and the precedent cases supporting them are identified and categorized.     

Processing and classification of protein mass spectra

Among the many applications of mass spectrometry, biomarker pattern discovery from protein mass spectra has aroused considerable interest in the past few years. While research efforts have raised hopes of early and less invasive diagnosis, they have also brought to light the many issues to be tackled before mass-spectra-based proteomic patterns become routine clinical tools. Known issues cover the entire pipeline leading from sample collection through mass spectrometry analytics to biomarker pattern extraction, validation, and interpretation. This study focuses on the data-analytical phase, which takes as input mass spectra of biological specimens and discovers patterns of peak masses and intensities that discriminate between different pathological states. We survey current work and investigate computational issues concerning the different stages of the knowledge discovery process: exploratory analysis, quality control, and diverse transforms of mass spectra, followed by further dimensionality reduction, classification, and model evaluation. We conclude after a brief discussion of the critical biomedical task of analyzing discovered discriminatory patterns to identify their component proteins as well as interpret and validate their biological implications.