Specific Maltose Derivatives Modulate the Swarming Motility of Nonswarming Mutant and Inhibit Bacterial Adhesion and Biofilm Formation by Pseudomonas aeruginosa

We have demonstrated that specific synthetic maltose derivatives activate the swarming motility of a Pseudomonas aeruginosa nonswarming mutant (rhlA) at low concentration, but inhibit it at high concentration. Although these molecules are not microbicidal, active maltose derivatives with bulky hydrocarbon groups inhibited bacterial adhesion, and exhibited biofilm inhibition and dispersion (IC₅₀ ～20 μM and DC₅₀ ～30 μM , respectively). Because the swarming motility of the rhlA mutant is abolished by the lack natural rhamnolipids, the swarming activation suggests that maltose derivatives are analogues of rhamnolipids. Together, these results suggest a new approach of controlling multiple bacterial activities (bacterial adhesion, biofilm formation, and swarming motility) by a set of disaccharide‐based molecules.     

Gelatin and amylopectin-based phase-separated hydrogels: An in-depth analysis of the swelling,mechanical, electrical and drug release properties

The current study delineates the development of gelatin–amylopectin-based phase-separated hydrogels for drug delivery applications. Gelatin and amylopectin were used as the representative protein and polysaccharide phases, respectively. The hydrogels were prepared by adding different proportions of amylopectin to gelatin solutions and subsequently cross-linking the mixture using glutaraldehyde. Microscopic analysis showed formation of phase-separated hydrogels. Secondary structure of gelatin was conserved within the hydrogels. The presence of amylopectin drastically reduced the rate of water absorption by the hydrogels. Viscoelastic analysis using stress relaxation study suggested an increase in the viscous component of the hydrogels with the increase in the amylopectin content. After incorporating amylopectin within the gelatin hydrogel, even though the bulk resistance remained unaltered, there was a corresponding variation in the capacitive elements of the equivalent electrical models. The release of the drug from the hydrogels was diffusion mediated. Suitable mathematical models were used for the analysis of the swelling (Peleg’s model), viscoelastic (Weichert model), electrical (RQQ model) and drug release (Korsmeyer–Peppas and Peppas–Sahlin models) properties. The drug retained its antimicrobial activity within the hydrogels. An analysis of the results suggested that the developed hydrogels may be explored as matrices for controlled drug delivery applications.     

Three of a kind [UPS topologies, IEC standard]

With uninterruptible power supplies now used in critical applications across every industrial sector, a standard terminology to describe types and performance measures has become essential. The author outlines how the international standards community has responded to this need by introducing IEC 62040-3 which distinguishes three types of UPS topology: passive standby, line interactive, and double conversion. These three topologies are described. For low power ratings (less than 2 kVA) all three topologies, as defined by the standards, are used. For medium to high power ratings, the case for the passive standby and the line interactive is, at best, marginal, in spite of their significantly lower cost. The vast majority of sales to medium to high-power rating applications are represented by double conversion UPSs (95% from a few kVA upwards; 98% above 10 kVA). The dominance of the double-conversion topology is attributable to its almost total lack of weaknesses, combined with the capacity to meet the needs of sensitive loads at these power ratings-a consequence of UPS being connected in series with the utility power sources     

Inactivation of Salmonella Typhimurium in orange juice containing antimicrobial agents by pulsed electric field

Combinations of different hurdles, including moderately high temperatures (<60 degrees C), antimicrobial compounds, and pulsed electric field (PEF) treatment, to reduce Salmonella in pasteurized and freshly squeezed orange juices (with and without pulp) were explored. Populations of Salmonella Typhimurium were found to decrease with an increase in pulse number and treatment temperature. At a field strength of 90 kV/cm, a pulse number of 20, and a temperature of 45 degrees C, PEF treatment did not have a notable effect on cell viability or injury. At and above 46 degrees C, however, cell death and injury were greatly increased. Salmonella numbers were reduced by 5.9 log cycles in freshly squeezed orange juice (without pulp) treated at 90 kV/cm, 50 pulses, and 55 degrees C. When PEF treatment was carried out in the presence of nisin (100 U/ml of orange juice), lysozyme (2,400 U/ml), or a mixture of nisin (27.5 U/ml) and lysozyme (690 U/ml), cell viability loss was increased by an additional 0.04 to 2.75 log cycles. The combination of nisin and lysozyme had a more pronounced bactericidal effect than did either nisin or lysozyme alone. An additional Salmonella count reduction of at least 1.37 log cycles was achieved when the two antimicrobial agents were used in combination. No significant difference (P > 0.05) in cell death was attained by lowering the pH value; only cell injury increased. Inactivation by PEF was significantly more extensive (P < 0.05) in pasteurized orange juice than in freshly squeezed orange juice under the same treatment conditions. This increase might be due to the effect of the chemical composition of the juices.     

N-Containing Carbons Derived from Microporous Coordination Polymers for Use in Post-Combustion Flue Gas Capture

Herein, novel carbons that, owing to a high density of micropores (up to 79%) and N-content (up to 14.9%), offering exciting potential for post-combustion CO₂ capture are reported. Given that little is known about how starting materials impact the structure and performance of carbons, three different microporous materials are pyrolyzed. These include a Co-(metal-organic framework) (MOF), a Co-MOF-polymer composite, and a coordination polymer derived from the same monomer and cobalt ions. Notably, the cobalt, which is required to drive the polymerization, is subsequently leached from the carbons via acid for its reuse in MOF synthesis. Next, various metrics including CO₂ capacity, selectivity, isosteric heat of adsorption, breakthrough time and cyclability are assessed. The acid treated carbons adsorb 0.21, 0.99, and 1.11 mmol CO₂ g⁻¹, respectively, (313 K, 0.15 bar) with CO₂/N₂ selectivity ranging from 37 to 52. Due to superior capacity, the polymer-derived carbons also reveal impressive breakthrough times in simulated flue gas mixtures (15% CO₂/85% N₂, 80% RH, 313 K) ranging from 33 to 40 min g⁻¹. Similar performance is also observed under dry conditions and after pre-saturation with water for 1.5 h. Remarkably, no loss in working capacity is observed after 100 CO₂ TSA cycles (313 K/393 K).     

Disruptive infrared image sensors enabled by quantum dots

Sensors based on quantum dot photodiodes promise quality and accessibility improvement of infrared imaging. We demonstrate miniaturization by sub-2-μm pixel pitch arrays. Functionality is confirmed with external quantum efficiencies above 40% at 1450 nm. Monolithic integration enables high throughput and wide deployment of short-wave infrared (SWIR) imagers in applications that previously could not afford them.     

Using online search data to forecast new product sales

Search engines are rapidly emerging to be the “go-to” sites for consumers to learn more about a product, concept or a term of interest, irrespective of the initial channel in which the interest originated — text, radio, TV, multi-media channels, word of mouth, etc. In this paper we argue that data on the search terms used by consumers can provide valuable measures and indicators of consumer interest in a product, concept or a term. Such data can be particularly valuable to managers in gauging potential product interest in a new product launch context or consumption interest in the post-release context. Based on this premise, we develop a model of pre-launch search activity and link the pre-launch search behavior and product characteristics to early sales of the product, thus providing a useful forecasting tool. Applying the model in the context of motion pictures, we find that search term usage follows rather predictable patterns in the pre-launch and post-launch periods and the model provides significant power in forecasting release week sales as a function of pre-release search activity. With advertising data included in the model, we find that the pre-release search data offers additional explanatory and forecasting power, thus highlighting the ability of the search data to capture other factors, such as possibly word-of-mouth, in impacting early sales. We offer specific insights into how managers can use search volume data and the model to plan their new product release.     

Modeling product-mix planning for batches of melt under multiple objectives in a small scale iron foundry

Product-mix of castings of different types/sizes can be produced in a heat (batch of melt) if all these castings require similar raw material composition. However, inappropriate product-mix may lead to under utilization of furnace capacity or failure in timely delivery or overproduction of castings or may call for deployment of excess resources for packing of molds to enable starting of the pouring operation in time. Some objectives, again, can be conflicting in nature. This paper presents a weighted integer goal programming model for the product-mix planning, developed in the context of a small scale iron foundry. Implementation of the model is illustrated using real life data from an Indian foundry.     

Model-driven optimal resource scaling in cloud

Cloud computing offers the flexibility to dynamically size the infrastructure in response to changes in workload demand. While both horizontal scaling and vertical scaling of infrastructure are supported by major cloud providers, these scaling options differ significantly in terms of their cost, provisioning time, and their impact on workload performance. Importantly, the efficacy of horizontal and vertical scaling critically depends on the workload characteristics, such as the workload’s parallelizability and its core scalability. In today’s cloud systems, the scaling decision is left to the users, requiring them to fully understand the trade-offs associated with the different scaling options. In this paper, we present our solution for optimizing the resource scaling of cloud deployments via implementation in OpenStack. The key component of our solution is the modeling engine that characterizes the workload and then quantitatively evaluates different scaling options for that workload. Our modeling engine leverages Amdahl’s Law to model service timescaling in scale-up environments and queueing-theoretic concepts to model performance scaling in scale-out environments. We further employ Kalman filtering to account for inaccuracies in the model-based methodology and to dynamically track changes in the workload and cloud environment.