Biofilm control in water by advanced oxidation process (AOP) pre-treatment: effect of natural organic matter (NOM)

The main goal of this study was to examine the influence of natural organic matter (NOM) on the efficiency of H?O?/UV advanced oxidation process (AOP) as a preventive treatment for biofilm control. Pseudomonas aeruginosa PAO1 biofilm-forming bacteria were suspended in water and exposed to various AOP conditions with different NOM concentrations, and compared to natural waters. H?O?/UV prevented biofilm formation: (a) up to 24 h post treatment - when residual H?O? was neutralized; (b) completely (days) - when residual H?O? was maintained. At high NOM concentrations (i.e. 25 mg/L NOM or 12.5 mg/L DOC) an additive biofilm control effect was observed for the combined H?O?/UV system compared to UV irradiation alone, after short biofilm incubation times (<24 h). This effect was H?O? concentration dependent and can be explained by the high organic content of these water samples, whereby an increase in NOM could enhance (?)OH production and promote the formation of additional reactive oxygen species. In addition, maintaining an appropriate ratio of bacterial surviving conc.: residual H?O? conc. post-treatment could prevent bacterial regrowth and biofilm formation.     

Embedded-TM: Energy and complexity-effective hardware transactional memory for embedded multicore systems

We investigate how transactional memory can be adapted for embedded systems. We consider energy consumption and complexity to be driving concerns in the design of these systems and therefore adapt simple hardware transactional memory (HTM) schemes in our architectural design. We propose several different cache structures and contention management schemes to support HTM and evaluate them in terms of energy, performance, and complexity. We find that ignoring energy considerations can lead to poor design choices, particularly for resource-constrained embedded platforms. We conclude that with the right balance of energy efficiency and simplicity, HTM will become an attractive choice for future embedded system designs.     

ON CAPACITY MODELING FOR PRODUCTION PLANNING WITH ALTERNATIVE MACHINE TYPES

Analyzing the capacity of production facilities in which manufacturing operations may be performed by alternative machine types presents a seemingly complicated task. In typical enterprise-level production planning models, capacity limitations of alternative machine types are approximated in terms of some single artificial capacitated resource. In this paper we propose procedures for generating compact models that accurately characterize capacity limitations of alternative machine types. Assuming that processing times among alternative machine types are identical or proportional across operations they can perform, capacity limitations of the alternative machine types can be precisely expressed using a formulation that is typically not much larger than the basic linear programming formulation that does not admit alternative resource types. These results have important implications for industrial practice, suggesting that in the case that processing times are nearly proportional among alternatives, the prevalent approximation that involves using a single, capacitated, artificial resource may be dropped in favor of our formulation incorporating the approximation that processing times among the alternatives are proportional. Another advantage is that the set of capacity constraints we formulate can be used to check the feasibility of suggested production schedules or demands simply by plugging mem into the constraints, without need to develop values for allocation variables.     

Fault-tolerant data management in the gaston peer-to-peer file system

Gaston is a peer-to-peer large-scale file system designed to provide a fault-tolerant and highly available file service for a virtually unlimited number of users. Data management in Gaston disseminates and stores replicas of files on multiple machines to achieve the requested level of data availability and uses a dynamic tree-topology structure to connect replication schema members. We present generic algorithms for replication schema creation and maintenance according to file user requirements and autonomous constraints that are set on individual nodes. We also show specific data object structure as well as mechanisms for secure and efficient update propagation among replicas with data consistency control. Finally, we introduce a scalable and efficient technique improving fault-tolerance of the tree-topology structure connecting replicas.     

Enhancing the Energy Storage Capabilities of Ti3C2Tx MXene Electrodes by Atomic Surface Reduction

MXenes are a large class of 2D materials that consist of few-atoms-thick layers of transition metal carbides, nitrides, or carbonitrides. The surface functionalization of MXenes has immense implications for their physical, chemical, and electronic properties. However, solution-phase surface functionalization often leads to structural degradation of the MXene electrodes. Here, a non-conventional, single-step atomic surface reduction (ASR) technique is adopted for the surface functionalization of MXene (Ti₃C₂T_x) in an atomic layer deposition reactor using trimethyl aluminum as a volatile reducing precursor. The chemical nature of the modified surface is characterized by X-ray photoelectron spectroscopy and nuclear magnetic resonance techniques. The electrochemical properties of the surface-modified MXene are evaluated in acidic and neutral aqueous electrolyte solutions, as well as in conventional Li-ion and Na-ion organic electrolytes. A considerable improvement in electrochemical performance is obtained for the treated electrodes in all the examined electrolyte solutions, expressed in superior rate capability and cycling stability compared to those of the non-treated MXene films. This improved electrochemical performance is attributed to the increased interlayer spacing and modified surface terminations after the ASR process.     

Unusual migration of a foreign body from the gut to a vertebral body

We report a unique case in which a needle was accidentally swallowed and migrated into the vertebral body. Plain films and CT of the spine revealed fragmented, linear, metallic-density material in the L3 vertebral body. The possible mechanisms of the migration are discussed.     

The Coral Protein CARP3 Acts from a Disordered Mineral Surface Film to Divert Aragonite Crystallization in Favor of Mg‐Calcite

Stony corals construct their aragonite skeleton by calcium carbonate precipitation, in a process recently suggested to be biologically controlled. Amorphous calcium carbonate and small amounts of calcite are also reported recently, however, their functional role is unknown. Coral acid‐rich proteins (CARPs) are extracted from the coral skeleton and are shown to be active in calcium carbonate precipitation in vitro. However, individual function of these proteins in coral mineralization is not known. Here, the regulatory activity of the aspartate‐rich CARP3 protein is examined. The whole protein and two peptides representing its acidic domain and its variable domain are used in CaCO₃ precipitation reactions from Mg‐rich solutions. The biomolecules alter crystallization pathways, promoting Mg‐calcite in place of aragonite, with the acidic peptide capable of eradicating aragonite formation. The activity of CARP3 and its representative peptides is exerted from disordered CaCO₃ mineral phases, coating the crystals formed, as shown by 2D ¹H–¹³C heteronuclear correlation nuclear magnetic resonance (NMR) measurements, localizing organic protons in atomic proximity to disordered carbonate carbons. The structures of the protein and individual domains as derived from NMR measurements and folding calculations and their amino acid compositions are discussed in the context of their observed activity and its implication to mineralization in hard corals.     

CoralSeg: Learning coral segmentation from sparse annotations

Robotic advances and developments in sensors and acquisition systems facilitate the collection of survey data in remote and challenging scenarios. Semantic segmentation, which attempts to provide per‐pixel semantic labels, is an essential task when processing such data. Recent advances in deep learning approaches have boosted this task's performance. Unfortunately, these methods need large amounts of labeled data, which is usually a challenge in many domains. In many environmental monitoring instances, such as the coral reef example studied here, data labeling demands expert knowledge and is costly. Therefore, many data sets often present scarce and sparse image annotations or remain untouched in image libraries. This study proposes and validates an effective approach for learning semantic segmentation models from sparsely labeled data. Based on augmenting sparse annotations with the proposed adaptive superpixel segmentation propagation, we obtain similar results as if training with dense annotations, significantly reducing the labeling effort. We perform an in‐depth analysis of our labeling augmentation method as well as of different neural network architectures and loss functions for semantic segmentation. We demonstrate the effectiveness of our approach on publicly available data sets of different real domains, with the emphasis on underwater scenarios—specifically, coral reef semantic segmentation. We release new labeled data as well as an encoder trained on half a million coral reef images, which is shown to facilitate the generalization to new coral scenarios.