Chemical composition and antifungal effects of three species of Satureja (S. hortensis, S. spicigera, and S. khuzistanica) essential oils on the main pathogens of strawberry fruit

Due to an increasing risk of chemical contamination upon the application of synthetic fungicides to preserve fresh fruits and vegetables, essential oils are gaining increasing attentions. In this research, besides chemical analysis of the essential oils of three Satureja species (S. hortensis, S. spicigera, and S. khuzistanica) by GC-MS, their fungicidal and/or fungistatic effects on postharvest pathogens of strawberry were investigated. Essential oils were extracted by means of hydro-distillation and afterwards GC/MS analysis was performed to identify their components. Carvacrol, γ-terpinene and p-cymene were detected as the repeating main constituents of the spices, while thymol and carvacrol methyl ether were found as major components only in S. spicigera oil. In vitro results showed that at the maximum concentration, the essential oils did not possess fungicidal effects on Aspergillus niger but they exhibited fungicidal activities against Penicillium digitatum, Botrytis cinerea and Rhizopus stolonifer. However, S. khuzistanica was the strongest oil in fungicidal activity. S. hortensis oil was more effective than S. spicigera against B. cinerea whereas S. spicigera oil showed stronger fungicidal activity against R. stolonifer. In conclusion, essential oils isolated from three savory species could be suitable for applications in the food industry to control molds and improve the safety of fruits and vegetables. © 2015 Elsevier B.V

Sustainability in construction projects: A systematic literature review

This paper aims to identify the major research concepts studied in the literature of sustainability in construction projects. Two bibliometric analysis tools—(a) BibExcel and (b) Gephi, were used to analyze the bibliometrics indices of papers and visualize their interrelations as a network, respectively. Therefore, a research focus parallelship network (RFPN) analysis and keyword co-occurrence network (KCON) analysis were performed to uncover the primary research themes. The RFPN analysis clustered the studies into three major categories of evaluating sustainability, project management for sustainability, and drivers of sustainable construction. The KCON analysis revealed that while each paper had a different focus, the underlying concept of all clusters was sustainability, construction, and project management. We found that while ‘sustainability’ was the leading keyword in the first cluster, i.e., evaluating sustainability, it was the second top keyword with the eigenvector centrality of over 0.94 in the other two clusters. We also found that the concept of sustainability should be included in construction projects from the early stages of design and feasibility studies and must be monitored throughout the project life. This review showed that previous researchers used a variety of statistical and mathematical techniques such as structural equation modelling and fuzzy decision-making methods to study sustainability in construction projects. Using an integrated approach to identifying the research gaps in this area, this paper provides researchers with insights on how to frame new research to study sustainability in construction projects

Laminar flow of two miscible fluids in a simple network

When a fluid comprised of multiple phases or constituents flows through a network, non-linear phenomena such as multiple stable equilibrium states and spontaneous oscillations can occur. Such behavior has been observed or predicted in a number of networks including the flow of blood through the microcirculation, the flow of picoliter droplets through microfluidic devices, the flow of magma through lava tubes, and two-phase flow in refrigeration systems. While the existence of non-linear phenomena in a network with many inter-connections containing fluids with complex rheology may seem unsurprising, this paper demonstrates that even simple networks containing Newtonian fluids in laminar flow can demonstrate multiple equilibria. The paper describes a theoretical and experimental investigation of the laminar flow of two miscible Newtonian fluids of different density and viscosity through a simple network. The fluids stratify due to gravity and remain as nearly distinct phases with some mixing occurring only by diffusion. This fluid system has the advantage that it is easily controlled and modeled, yet contains the key ingredients for network non-linearities. Experiments and 3D simulations are first used to explore how phases distribute at a single T-junction. Once the phase separation at a single junction is known, a network model is developed which predicts multiple equilibria in the simplest of networks. The existence of multiple stable equilibria is confirmed experimentally and a criteria for their existence is developed. The network results are generic and could be applied to or found in different physical systems

A fully hardware-based memristive multilayer neural network

Memristive crossbar arrays promise substantial improvements in computing throughput and power efficiency through in-memory analog computing. Previous machine learning demonstrations with memristive arrays, however, relied on software or digital processors to implement some critical functionalities, leading to frequent analog/digital conversions and more complicated hardware that compromises the energy efficiency and computing parallelism. Here, we show that, by implementing the activation function of a neural network in analog hardware, analog signals can be transmitted to the next layer without unnecessary digital conversion, communication, and processing. We have designed and built compact rectified linear units, with which we constructed a two-layer perceptron using memristive crossbar arrays, and demonstrated a recognition accuracy of 93.63% for the Modified National Institute of Standard and Technology (MNIST) handwritten digits dataset. The fully hardware-based neural network reduces both the data shuttling and conversion, capable of delivering much higher computing throughput and power efficiency

Speed and Accuracy of Static Image Discrimination by Rats

When discriminating dynamic noisy sensory signals, human and primate subjects achieve higher accuracy when they take more time to decide, an effect attributed to accumulation of evidence over time to overcome neural noise. We measured the speed and accuracy of twelve freely behaving rats discriminating static, high contrast photographs of real-world objects for water reward in a self-paced task. Response latency was longer in correct trials compared to error trials. Discrimination accuracy increased with response latency over the range of 500-1200ms. We used morphs between previously learned images to vary the image similarity parametrically, and thereby modulate task difficulty from ceiling to chance. Over this range we find that rats take more time before responding in trials with more similar stimuli. We conclude that rats' perceptual decisions improve with time even in the absence of temporal information in the stimulus, and that rats modulate speed in response to discrimination difficulty to balance speed and accuracy

Enantioselective Self-Replicators

Self-replicating molecules provide a simple approach for investigating fundamental processes in scenarios of the emergence of life. Although homochirality is an important aspect of life and of how it emerged, the effects of chirality on self-replicators have received only little attention so far. Here, we report several self-assembled self-replicators with enantioselectivity that emerge spontaneously and grow only from enantiopure material. These require a relatively small number of chiral units in the replicators (down to eight) and in the precursors (down to a single chiral unit), compared to the only other enantioselective replicator reported previously. One replicator was found to incorporate material of its own handedness with high fidelity when provided with a racemic mixture of precursors, thus sorting (L)- and (D)-precursors into (L)- and (D)-replicators. Systematic studies reveal that the presence or absence of enantioselectivity depends on structural features (ring size of the replicator) that appear to impose constraints on its supramolecular organization. This work reveals new aspects of the little researched interplay between chirality and self-replication and represents another step toward the de novo synthesis of life.</p

SQG-Differential Evolution for difficult optimization problems under a tight function evaluation budget

In the context of industrial engineering, it is important to integrate efficient computational optimization methods in the product development process. Some of the most challenging simulation-based engineering design optimization problems are characterized by: a large number of design variables, the absence of analytical gradients, highly non-linear objectives and a limited function evaluation budget. Although a huge variety of different optimization algorithms is available, the development and selection of efficient algorithms for problems with these industrial relevant characteristics, remains a challenge. In this communication, a hybrid variant of Differential Evolution (DE) is introduced which combines aspects of Stochastic Quasi-Gradient (SQG) methods within the framework of DE, in order to improve optimization efficiency on problems with the previously mentioned characteristics. The performance of the resulting derivative-free algorithm is compared with other state-of-the-art DE variants on 25 commonly used benchmark functions, under tight function evaluation budget constraints of 1000 evaluations. The experimental results indicate that the new algorithm performs excellent on the 'difficult' (high dimensional, multi-modal, inseparable) test functions. The operations used in the proposed mutation scheme, are computationally inexpensive, and can be easily implemented in existing differential evolution variants or other population-based optimization algorithms by a few lines of program code as an non-invasive optional setting. Besides the applicability of the presented algorithm by itself, the described concepts can serve as a useful and interesting addition to the algorithmic operators in the frameworks of heuristics and evolutionary optimization and computing

The Effect of Geometric Nonlinearity on the Seismic Performance of Steel Plate Shear Wall (SPSW) Systems

Recently, steel plate shear wall (SPSW) systems have attracted a lot of attention to be used as reliable lateral load resisting systems in areas of high seismicity. The main problem associated with the analysis and design of SPWS systems, particularly in high-rise buildings, is that the structural model cannot be numerically converged due to the effects of geometric nonlinearity using thin-walled webs while experiencing significant membrane actions in shell elements. The present study examines whether neglecting the geometric nonlinearity on the numerical modeling of SPSW system affects the accuracy of the models. This study confirms that neglecting the geometric nonlinearity in the numerical models can significantly overestimate the seismic capacity of SPSW systems between 10% and 17% depending on the height of the building considered. This modeling issue can be proved extremely critical in modeling tall buildings equipped with SPSW systems while the geometric nonlinearity is ignored in order to help the large model to converge

Wafer-scale fabrication of 2D nanostructures via thermomechanical nanomolding

With shrinking dimensions in integrated circuits, sensors, and functional devices, there is a pressing need to develop nanofabrication techniques with simultaneous control of morphology, microstructure, and material composition over wafer length scales. Current techniques are largely unable to meet all these conditions, suffering from poor control of morphology and defect structure or requiring extensive optimization or post-processing to achieve desired nanostructures. Recently, thermomechanical nanomolding (TMNM) has been shown to yield single-crystalline, high aspect ratio nanowires of metals, alloys, and intermetallics over wafer-scale distances. Here, we extend TMNM for wafer-scale fabrication of 2D nanostructures. Using Cu, we successfully nanomold Cu nanoribbons with widths < 50 nm, depths ~ 0.5-1 microns and lengths ~ 7 mm into Si trenches at conditions compatible with back end of line processing. Through SEM cross-section imaging and 4D-STEM grain orientation maps, we show that the grain size of the bulk feedstock is transferred to the nanomolded structures up to and including single crystal Cu. Based on the retained microstructures of molded 2D Cu, we discuss the deformation mechanism during molding for 2D TMNM.Comment: 4 figure

Type specific Real time PCR for detection of human herpes virus 6 in schizophrenia and bipolar patients: A case control study

Background: Schizophrenia (SC) and bipolar disorder (BD) are among the most devastating diseases worldwide. There are several lines of evidence suggesting that viruses may play significant roles in the etiology of these mental disorders. The aim of this study was the detection of HHV-6A/B in the peripheral blood mononuclear cells (PBMC) of SC and BD patients versus the healthy control (HC) subjects using a new method of type-specific Real time PCR analysis. Methods: A type-specific Real time PCR was performed for simultaneous detection and typing of HHV-6A/B in the PBMCs of 120 SC and BD patients and 75 HCs. Results: Only one case of HHV-6B out of 120 (0.8 ) SC and BD patients and two cases of HHV-6A (2.7 ) in 75 HCs were detected. Conclusions: The low levels of HHV-6 detection in PBMCs, severely limited the capacity of this study to investigate the association between the presence of HHV-6 and BD or SC in this population, thus no conclusions can be drawn in this regard. Meanwhile this study introduces a Real time PCR based method for type specific detection of HHV-6A/B in clinical samples. © 2015 Yavarian et al