The optimal configuration and workload allocation problem in flexible manufacturing systems

In this article we consider the problem of determining the minimum cost configuration (number of machines and pallets) for a flexible manufacturing system with the constraint of meeting a prespecified throughput, while simultaneously allocating the total workload among the machines (or groups of machines). Our procedure allows consideration of upper and lower bounds on the workload at each machine group. These bounds arise as a consequence of precedence constraints among the various operations and/or limitations on the number or combinations of operations that can be assigned to a machine because of constraints on tool slots or the space required to store assembly components. Earlier work on problems of this nature assumes that the workload allocation is given. For the single-machine-type problem we develop an efficient implicit enumeration procedure that uses fathoming rules to eliminate dominated configurations, and we present computational results. We discuss how this procedure can be used as a building block in solving the problem with multiple machine types.     

Lateralization of olfaction in the honeybee Apis mellifera

Lateralization of function is a well-known phenomenon in humans. The two hemispheres of the human brain are functionally specialized such that certain cognitive skills, such as language or musical ability, conspecific recognition, and even emotional responses, are mediated by one hemisphere more than the other [1, 2]. Studies over the past 30 years suggest that lateralization occurs in other vertebrate species as well [3-11]. In general, lateralization is observed in different sensory modalities in humans as well as vertebrates, and there are interesting parallels (reviewed in [12]). However, little is known about functional asymmetry in invertebrates [13, 14] and there is only one investigation in insects [15]. Here we show, for the first time, that the honeybee Apis mellifera displays a clear laterality in responding to learned odors. By training honeybees on two different versions of the well-known proboscis extension reflex (PER) paradigm [16, 17], we demonstrate that bees respond to odors better when they are trained through their right antenna. To our knowledge, this is the first demonstration of asymmetrical learning performance in an insect.     

Molecular characterization of native isolates of lactic acid bacteria,bifidobacteria and yeasts for beneficial attributes

In a changing scenario of food habits being associated with wellness factors through the concepts of probiotics and prebiotics, an attempt has been made to characterize on molecular basis, the desirable benefits associated with natural isolates of lactic acid bacteria, bifidobacteria, and yeasts. From a diverse range of foods and related samples, based on conventional microbiological protocols, three well-characterized natural isolates of Lactobacillus plantarum MTCC 5422, Bifidobacterium adolescentis MTCC 5423 and Saccharomyces cerevisiae MTCC 5421 were selected. The cultures of L. plantarum and B. adolescentis showed positive polymerase chain reaction (PCR) amplification with oligonucleotide primers targeting genus-specific 16 S rRNA for Lactobacillus and fructose-6-phosphate phosphoketolase for Bifidobacterium. Similarly, species-specific positive amplification in PCR was observed with primers of phytase (acid phosphatase) in S. cerevisiae and α-d-galactosidase and bile salt hydrolase in L. plantarum and B. adolescentis. The cultures of L. plantarum and B. adolescentis exhibited a broad spectrum antibacterial activity against selected foodborne pathogenic bacterial species and tolerance to acid and bile. Gene sequence of respective PCR-amplified products confirmed the genetic identity of the isolated cultures as L. plantarum and B. adolescentis showing 99% homology with the documented sequence of established gene bank.     

Honeybees Learn Odour Mixtures via a Selection of Key Odorants

Background

The honeybee has to detect, process and learn numerous complex odours from her natural environment on a daily basis. Most of these odours are floral scents, which are mixtures of dozens of different odorants. To date, it is still unclear how the bee brain unravels the complex information contained in scent mixtures.

Methodology/Principal Findings

This study investigates learning of complex odour mixtures in honeybees using a simple olfactory conditioning procedure, the Proboscis-Extension-Reflex (PER) paradigm. Restrained honeybees were trained to three scent mixtures composed of 14 floral odorants each, and then tested with the individual odorants of each mixture. Bees did not respond to all odorants of a mixture equally: They responded well to a selection of key odorants, which were unique for each of the three scent mixtures. Bees showed less or very little response to the other odorants of the mixtures. The bees'' response to mixtures composed of only the key odorants was as good as to the original mixtures of 14 odorants. A mixture composed of the other, non-key-odorants elicited a significantly lower response. Neither an odorant''s volatility or molecular structure, nor learning efficiencies for individual odorants affected whether an odorant became a key odorant for a particular mixture. Odorant concentration had a positive effect, with odorants at high concentration likely to become key odorants.

Conclusions/Significance

Our study suggests that the brain processes complex scent mixtures by predominantly learning information from selected key odorants. Our observations on key odorant learning lend significant support to previous work on olfactory learning and mixture processing in honeybees.     

Microbial diversity and complexity in hypersaline environments: A preliminary assessment

The microbial communities in solar salterns and a soda lake have been characterized using two techniques: BIOLOG, to estimate the metabolic potential, and amplicon length heterogeneity analysis, to estimate the molecular diversity of these communities. Both techniques demonstrated that the halophilic Bacteria and halophilic Archaea populations in the Eilat, Israel saltern are dynamic communities with extensive metabolic potentials and changing community structures. Halophilic Bacteria were detected in Mono Lake and the lower salinity ponds at the Shark Bay saltern in Western Australia, except when the crystallizer samples were stressed by exposure to Acid Green Dye #9899. At Shark Bay, halophilic Archaea were found only in the crystallizer samples. These data confirm both the metabolic diversity and the phylogenetic complexity of the microbial communities and assert the need to develop more versatile media for the cultivation of the diversity of bacteria in hypersaline environments. Journal of Industrial Microbiology & Biotechnology (2002) 28, 48–55 DOI: 10.1038/sj/jim/7000175 Received 20 May 2001/ Accepted in revised form 15 June 2001     

The effect of optic flow cues on honeybee flight control in wind

To minimize the risk of colliding with the ground or other obstacles, flying animals need to control both their ground speed and ground height. This task is particularly challenging in wind, where head winds require an animal to increase its airspeed to maintain a constant ground speed and tail winds may generate negative airspeeds, rendering flight more difficult to control. In this study, we investigate how head and tail winds affect flight control in the honeybee Apis mellifera, which is known to rely on the pattern of visual motion generated across the eye—known as optic flow—to maintain constant ground speeds and heights. We find that, when provided with both longitudinal and transverse optic flow cues (in or perpendicular to the direction of flight, respectively), honeybees maintain a constant ground speed but fly lower in head winds and higher in tail winds, a response that is also observed when longitudinal optic flow cues are minimized. When the transverse component of optic flow is minimized, or when all optic flow cues are minimized, the effect of wind on ground height is abolished. We propose that the regular sidewards oscillations that the bees make as they fly may be used to extract information about the distance to the ground, independently of the longitudinal optic flow that they use for ground speed control. This computationally simple strategy could have potential uses in the development of lightweight and robust systems for guiding autonomous flying vehicles in natural environments.     

Bacteria on leaves: a previously unrecognised source of N2O in grazed pastures

Nitrous oxide (N₂O) emissions from grazed pastures are a product of microbial transformations of nitrogen and the prevailing view is that these only occur in the soil. Here we show this is not the case. We have found ammonia-oxidising bacteria (AOB) are present on plant leaves where they produce N₂O just as in soil. AOB (Nitrosospira sp. predominantly) on the pasture grass Lolium perenne converted 0.02–0.42% (mean 0.12%) of the oxidised ammonia to N₂O. As we have found AOB to be ubiquitous on grasses sampled from urine patches, we propose a ‘plant'' source of N₂O may be a feature of grazed grassland.In terms of climate forcing, nitrous oxide (N₂O) is the third most important greenhouse gas (Blunden and Arndt, 2013). Agriculture is the largest source of anthropogenic N₂O (Reay et al., 2012) with about 20% of agricultural emissions coming from grassland grazed by animals (Oenema et al., 2005).Grazed grassland is a major source of N₂O because grazers harvest nitrogen (N) from plants across a wide area but recycle it back onto the pasture, largely as urine, in patches of very high N concentration. The N in urine patches is often in excess of what can be used by plants resulting in losses through leaching as nitrate, as N₂O and through volatilisation as ammonia (NH₃) creating a high NH₃ environment in the soil and plant canopy; an important point that we will return to later. The established wisdom is that N₂O is generated exclusively by soil-based microbes such as ammonia-oxidising bacteria (AOB). This soil biology is represented in models designed to simulate N₂O emissions and the soil is a target for mitigation strategies such as the use of nitrification inhibitors.We have previously shown that pasture plants can emit N₂O largely through acting as a conduit for emissions generated in the soil, which are themselves controlled to some degree by the plant (Bowatte et al., 2014). In this case the origin of the emission is still the soil microbes. However, AOB have been found on the leaves of plants, for example, Norway spruce (Papen et al., 2002; Teuber et al., 2007) and weeds in rice paddies (Bowatte et al., 2006), prompting us to ask whether AOB might be present on the leaves of pasture species and contribute to N₂O emissions as they do in soil.We looked for AOB on plants in situations where NH₃ concentrations were likely to be high, choosing plants from urine patches in grazed pastures and plants from pastures surrounding a urea fertiliser manufacturing plant. DNA was extracted from the leaves (including both the surface and apoplast) and the presence of AOB tested using PCR. AOB were present in all the species we examined—the grasses Lolium perenne, Dactylis glomerata, Anthoxanthum odoratum, Poa pratensis, Bromus wildenowii and legumes Trifolium repens and T. subterraneum.To measure whether leaf AOB produce N₂O, we used intact plants of ryegrass (L. perenne) lifted as cores from a paddock that had been recently grazed by adult sheep. The cores were installed in a chamber system designed to allow sampling of above- and belowground environments separately (Bowatte et al., 2014). N₂O emissions were measured from untreated (control) plants and from plants where NH₃ was added to the aboveground chamber and leaves were either untreated or sterilised by wiping twice with paper towels soaked in 1% hypoclorite (Sturz et al., 1997) and then with sterile water. We tested for the presence and abundance of AOB on the leaves by extracting DNA and using PCR and real-time PCR targeting the ammonia monoxygenase A (amoA) gene, which is characteristic of AOB. AOB identity was established using cloning and DNA sequencing. Further details of these experiments can be found in the Supplementary Information.The addition of NH₃ to untreated plants significantly stimulated N₂O emissions (P<0.001) compared with the controls; by contrast, the plants with sterilised leaves produced significantly less N₂O than controls (P<0.001) even with NH₃ added (Figure 1) providing strong evidence for emissions being associated with bacteria on the leaves. Control plants did emit N₂O suggesting there was either sufficient NH₃ available for bacterially generated emissions and/or other plant-based mechanisms were involved (Bowatte et al., 2014).Open in a separate windowFigure 1Effect of an elevated NH₃ atmosphere and surface sterilisation of leaves on leaf N₂O emissions measured over 1-h periods on three occasions during the day. Values are means (s.e.m.), where n=7.The major AOB species identified was Nitrosospira strain III7 that has been previously shown to produce N₂O (Jiang and Bakken, 1999). We measured 10⁹ AOB cells per m² ryegrass leaf, assuming a specific leaf area of 250 cm² g⁻¹ leaf.The rate of production of N₂O (0.1–0.17 mg N₂O-N per m² leaf area per hour) can be translated to a field situation using the leaf area index (LAI)—1 m² leaf per m² ground would be an LAI of 1. LAI in a pasture can vary from <1 to >6 depending on the management (for example, Orr et al., 1988). At LAI of 1, the AOB leaf emission rate would equate to a N₂O emission rate of about 0.1–0.3 mg N₂O-N per m² ground per hour. By comparison, the emission rates measured after dairy cattle urine (650 kg N ha⁻¹) was applied to freely and poorly drained soil were 0.024–1.55 and 0.048–3.33 mg N₂O-N per m² ground per hour, respectively (Li and Kelliher, 2005).The fraction of the NH₃ that was converted to N₂O by the leaf AOB was 0.02–0.42% (mean 0.12%). The mean value is close to that measured for Nitrosospira strains including strain III7 isolated from acidic, loamy and sandy soils where values ranged from 0.07 to 0.10% (Jiang and Bakken, 1999). This is good evidence that the AOB on leaves have the capacity to produce N₂O at the same rate as AOB in soils. We do not suggest that leaf AOB will produce as much N₂O as soil microbes; however, because leaf AOB have access to a source of substrate—volatilised NH₃—that is unavailable to soil microbes and may constitute 26% (Laubach et al., 2013) to 40% (Carran et al., 1982) of the N deposited in the urine, N₂O emissions from these aboveground AOB are additional to soil emissions. Further research is required to identify the situations in which leaf AOB contribute to total emissions and to quantify this contribution.     

Low molecular weight chitosan--preparation with the aid of pepsin, characterization, and its bactericidal activity

Pepsin (EC 3.4.4.1) from porcine stomach mucosa caused depolymerization of a chitosan sample (a copolymer of glucosamine and N-acetylglucosamine linked by beta-1-4-glycosidic bonds). N-terminal sequence and zymogram analyses confirmed dual (proteolytic and chitosanolytic) activities of pepsin. Optimum depolymerization occurred at pH 5.0 and 45 degrees C with an activity of 4.98 U. Low molecular weight chitosan (LMWC), the major depolymerization product, was obtained in a yield of 75-82%, the degree of polymerization of which depended on reaction time. The LMWC showed a nearly 10-14-fold decrease in the molecular mass as compared to native chitosan, which was also confirmed by GPC and HPLC analyses. IR and 13C NMR spectra indicated a decrease in the degree of acetylation (DA, approximately 13.4-18.8%) as compared to native chitosan (approximately 25.7%), which was in accordance with the CD analysis. Native chitosan had a crystallinity index (CrI) of approximately 70%, whereas there was a decrease in the CrI of LMWC (approximately 61%). The latter showed a better bactericidal activity toward both Bacillus cereus and Escherichia coli, which was more toward the former. The bactericidal activity was essentially due to the lytic and not static effect of LMWC, as evidenced by the pore formation on the bacterial cell surface when observed under SEM. This study suggests the possible use of pepsin in place of chitosanase, which is expensive and unavailable in bulk quantities for the production of LMWC of desired molecular mass that has diversified applications in various fields.     

Visual scanning behaviour in honeybees

Freely flying bees were rewarded with sugar solution on a variety of black-and-white shapes as well as on coloured gratings in various training situations. In subsequent dual-choice tests, the bees' discrimination between the various shapes was measured. In addition, the bees were video-filmed while flying in front of the shapes. The scanning patterns thus obtained were then quantified in order to characterize scanning behaviour and its relationship to the geometrical parameters of the scanned shapes, investigate whether scanning plays a role in pattern discrimination and examine the influence of training on the characteristics of scanning. The scanning patterns clearly mirror the contours of the scanned shape in all cases, i.e. the bees fly along the contours contained in the shape. This behaviour does not depend on whether the scanned shape is one that was previously rewarded, or one that is completely novel to the bees. Comparison of the results of quantifying the scanning patterns with the results of dual-choice tests reveals that scanning behaviour is independent of discrimination performance. On the average, horizontal scanning directions occur more often than vertical directions. Variations of the training situation produce measurable differences in scanning behavior. However, except in the case of vertical scanning on a vertical grating, these differences are quite small, indicating that following contours is a largely stereotyped behaviour. Horizontal gratings are very well discriminated from vertical ones even if they offer contrast to only one receptor type, i.e. blue or green, demonstrating that the direction of contours is visible to the pattern recognition system even under these conditions. However, vertical and horizontal coloured gratings offering only blue-contrast do not elicit contour-following, whereas gratings offering only green-contrast do. Thus, the bees' scanning behaviour is colour-blind and most probably governed by the green receptors. We suggest that contour-following is the by-product of a behavioural mode which serves to prevent retinal image movement during flight in front of a contoured visual pattern.