Combination of freeze drying and molecular inclusion techniques improves the bioaccessibility of microencapsulated anthocyanins from black rice (Oryza sativa L.) and lavender (Lavandula angustifolia L.) essential oils in a model food system

Black rice and lavender are promising sources of bioactives, in terms of anthocyanins and essential oils. Their bioaccessibility were improved by microencapsulation, followed by mixing in order to benefit both from colour and flavour, along with radical scavenging and biological properties. The mixed powder showed a satisfactory anthocyanins of 2.55 mg g⁻¹ DW, leading to a radical scavenging activity of 65.14 mmol g⁻¹ DW. The powder displayed an inhibitory effect against α-glucosidase (~49%) and α-amylase (39%), respectively, with a controlled release in intestinal environment. To further examine the functional properties, the powder was added to a food model system. During storage, a release in anthocyanins and flavonoids were found, leading to an increase in radical scavenging activity. The sensorial analysis showed that supplemented biscuits were appreciated for colour and lavender aroma. The obtained results were promising in tailoring the health benefits of secondary metabolites, underutilised in human’s nutrition due to their low stability and bioavailability.     

Quantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data

This research establishes a methodological framework for quantifying community resilience based on fluctuations in a population''s activity during a natural disaster. Visits to points-of-interests (POIs) over time serve as a proxy for activities to capture the combined effects of perturbations in lifestyles, the built environment and the status of business. This study used digital trace data related to unique visits to POIs in the Houston metropolitan area during Hurricane Harvey in 2017. Resilience metrics in the form of systemic impact, duration of impact, and general resilience (GR) values were examined for the region along with their spatial distributions. The results show that certain categories, such as religious organizations and building material and supplies dealers had better resilience metrics—low systemic impact, short duration of impact, and high GR. Other categories such as medical facilities and entertainment had worse resilience metrics—high systemic impact, long duration of impact and low GR. Spatial analyses revealed that areas in the community with lower levels of resilience metrics also experienced extensive flooding. This insight demonstrates the validity of the approach proposed in this study for quantifying and analysing data for community resilience patterns using digital trace/location-intelligence data related to population activities. While this study focused on the Houston metropolitan area and only analysed one natural hazard, the same approach could be applied to other communities and disaster contexts. Such resilience metrics bring valuable insight into prioritizing resource allocation in the recovery process.     

Explanations for over-constrained problems using QuickXPlain with speculative executions

Conflict detection is used in various scenarios ranging from interactive decision making (e.g., knowledge-based configuration) to the diagnosis of potentially faulty models (e.g., using knowledge base analysis operations). Conflicts can be regarded as sets of restrictions (constraints) causing an inconsistency. Junker’s QuickXPlain is a divide-and-conquer based algorithm for the detection of preferred minimal conflicts. In this article, we present a novel approach to the detection of such conflicts which is based on speculative programming. We introduce a parallelization of QuickXPlain and empirically evaluate this approach on the basis of synthesized knowledge bases representing feature models. The results of this evaluation show significant performance improvements in the parallelized QuickXPlain version.

     

SaBRe: load-time selective binary rewriting

Binary rewriting consists in disassembling a program to modify its instructions. However, existing solutions suffer from shortcomings in terms of soundness and performance. We present SaBRe, a load-time system for selective binary rewriting. SaBRe rewrites specific constructs—particularly system calls and functions—when the program is loaded into memory, and intercepts them using plugins through a simple API. We also discuss the theoretical underpinnings of disassembling and rewriting. We developed two backends—for x86_64 and RISC-V—which were used to implement three plugins: a fast system call tracer, a multi-version executor, and a fault injector. Our evaluation shows that SaBRe imposes little overhead, typically below 3%.

     

Multidimensional modeling of biofilm development and fluid dynamics in a hydrogen-based,membrane biofilm reactor (MBfR)

A two-dimensional, particle-based biofilm model coupled with mass transport and computational fluid dynamics was developed to simulate autotrophic denitrification in a spiral-wound membrane biofilm reactor (MBfR), where hydrogen is supplied via hollow-fiber membrane fabric. The spiral-wound configuration consists of alternating layers of plastic spacer net and membrane fabric that create rows of flow channels, with the top and bottom walls comprised of membranes. The transversal filaments of the spacer partially obstruct the channel flow, producing complex mixing and shear patterns that require multidimensional representation. This study investigated the effect of hydrogen and nitrate concentrations, as well as spacer configuration, on biofilm development and denitrification fluxes. The model results indicate that the cavity spacer filaments, which rest on the bottom membranes, cause uneven biofilm growth. Most biofilm resided on the bottom membranes, only in the wake of the filaments where low shear zones formed. In this way, filament configuration may help achieve a desired biofilm thickness. For the conditions tested in this study, the highest nitrate fluxes were attained by minimizing the filament diameter and maximizing the filament spacing. This lowered the shear stress at the top membranes, allowing for more biofilm growth. For the scenarios studied, biomass limitation at the top membranes hindered performance more significantly than diffusion limitation in the thick biofilms at the bottom membranes. The results also highlighted the importance of two-dimensional modeling to capture uneven biofilm growth on a substratum with geometrical complexity.