A boat hitchhiker's guide to survival: Cabomba caroliniana desiccation resistance and survival ability

Cabomba caroliniana is a submersed macrophyte that has become a serious invader. Cabomba predominantly spreads by stem fragments, in particular through unintentional transport on boat trailers ('hitch hiking'). Desiccation resistance affects the potential dispersal radius. Therefore, knowledge of maximum survival times allows predicting future dispersal. Experiments were conducted to assess desiccation resistance and survival ability of cabomba fragments under various environmental scenarios. Cabomba fragments were highly tolerant of desiccation. However, even relatively low wind speeds resulted in rapid mass loss, indicating a low survival rate of fragments exposed to air currents, such as fragments transported on a boat trailer. The experiments indicated that cabomba could survive at least 3 h of overland transport if exposed to wind. However, even small clumps of cabomba could potentially survive up to 42 h. Thus, targeting the transport of clumps of macrophytes should receive high priority in management. The high resilience of cabomba to desiccation demonstrates the risk of continuing spread. Because of the high probability of fragment viability on arrival, preventing fragment uptake on boat trailers is paramount to reduce the risk of further spread. These findings will assist improving models that predict the spread of aquatic invasive macrophytes.

The effects of emotional awareness training on teachers' ability to manage the emotions of preschool children: an experimental study

This article describes the process and results of a Randomized Controlled Trial (RCT) on teachers' ability to manage the emotions of preschool children during a constrained play activity. Thirty early childhood education teachers participated in the study. Half of the participants were taught strategies to enhance their own emotional competence. The control group was provided with standard information on child development. The experimental group was trained in active strategies on emotion coaching, emotional schemas, reflective practice focused on emotions, and mindfulness training. The teachers' outcomes were assessed in situ during a pretend play session with small groups of preschoolers. The dependent variables were observed occurrences of different components of emotional competence in teachers. Significant statistical differences were found between the two groups across the three different emotional competence skills (regulation, expression, and knowledge) demonstrated by the early childhood teachers during a game situation. This experimental study highlights the processes through which teachers support the emotional competence of young children, and the importance of the role of early childhood teachers' own emotional competence on the socialisation of children's emotions. Most importantly, it provides evidence, based on the influence of emotion-focused teacher-training and reflective practices, that teachers' emotional skills should be supported such that they can optimally meet the emotional needs of young children.

Variation of cutting rooting ability in cultivated and wild species of Macadamia

In Australia, macadamia trees are commonly propagated by germinating rootstock seed and grafting when seedlings reach a suitable size. The production of grafted trees is a protracted and complex process, however, propagation of macadamia via cuttings represents a simpler and faster method of multiplication. Macadamias have traditionally proven difficult to propagate from cuttings, and while recent developments in the process have improved success rates, substantial variation in rooting ability between cultivars and species has been reported. The cultivar 'Beaumont' (Macadamia integrifolia × M. tetraphylla) is commonly propagated by cutting for use as a rootstock, and is relatively easy to strike while other cultivars are more difficult. There is speculation that Hawaiian cultivars are more difficult to strike from cuttings than Australian cultivars due to species and genetic composition. In this experiment, cuttings of 32 genotypes were evaluated for rooting ability. Each genotype's species profile was estimated using historical data, and used to determine species effects on survival (percentage) and rooting ability (rating 0-2). M. jansenii (100%), M. tetraphylla (84%) and M. integrifolia/tetraphylla hybrids (79%) had the highest success rates while M. integrifolia (54%) and M. ternifolia (43%) had the lowest survival. Rooting ability of M. jansenii (1.75) was significantly higher than M. ternifolia (0.49) but not significantly higher than M. tetraphylla × M. integrifolia with (1.09), M. tetraphylla (1.03) or M. integrifolia (0.88).

The Application of a Dendric Cell Algorithm to a Robotic Classifier

The dendritic cell algorithm is an immune-inspired technique for processing time-dependant data. Here we propose it as a possible solution for a robotic classification problem. The dendritic cell algorithm is implemented on a real robot and an investigation is performed into the effects of varying the migration threshold median for the cell population. The algorithm performs well on a classification task with very little tuning. Ways of extending the implementation to allow it to be used as a classifier within the field of robotic security are suggested.

Active Data Collection Techniques to Understand Online Scammers and Cybercriminals

Nigerian scam, also known as advance fee fraud or 419 scam, is a prevalent form of online fraudulent activity that causes financial loss to individuals and businesses. Nigerian scam has evolved from simple non-targeted email messages to more sophisticated scams targeted at users of classifieds, dating and other websites. Even though such scams are observed and reported by users frequently, the community’s understanding of Nigerian scams is limited since the scammers operate “underground”. To better understand the underground Nigerian scam ecosystem and seek effective methods to deter Nigerian scam and cybercrime in general, we conduct a series of active and passive measurement studies. Relying upon the analysis and insight gained from the measurement studies, we make four contributions: (1) we analyze the taxonomy of Nigerian scam and derive long-term trends in scams; (2) we provide an insight on Nigerian scam and cybercrime ecosystems and their underground operation; (3) we propose a payment intervention as a potential deterrent to cybercrime operation in general and evaluate its effectiveness; and (4) we offer active and passive measurement tools and techniques that enable in-depth analysis of cybercrime ecosystems and deterrence on them. We first created and analyze a repository of more than two hundred thousand user-reported scam emails, stretching from 2006 to 2014, from four major scam reporting websites. We select ten most commonly observed scam categories and tag 2,000 scam emails randomly selected from our repository. Based upon the manually tagged dataset, we train a machine learning classifier and cluster all scam emails in the repository. From the clustering result, we find a strong and sustained upward trend for targeted scams and downward trend for non-targeted scams. We then focus on two types of targeted scams: sales scams and rental scams targeted users on Craigslist. We built an automated scam data collection system and gathered large-scale sales scam emails. Using the system we posted honeypot ads on Craigslist and conversed automatically with the scammers. Through the email conversation, the system obtained additional confirmation of likely scam activities and collected additional information such as IP addresses and shipping addresses. Our analysis revealed that around 10 groups were responsible for nearly half of the over 13,000 total scam attempts we received. These groups used IP addresses and shipping addresses in both Nigeria and the U.S. We also crawled rental ads on Craigslist, identified rental scam ads amongst the large number of benign ads and conversed with the potential scammers. Through in-depth analysis of the rental scams, we found seven major scam campaigns employing various operations and monetization methods. We also found that unlike sales scammers, most rental scammers were in the U.S. The large-scale scam data and in-depth analysis provide useful insights on how to design effective deterrence techniques against cybercrime in general. We study underground DDoS-for-hire services, also known as booters, and measure the effectiveness of undermining a payment system of DDoS Services. Our analysis shows that the payment intervention can have the desired effect of limiting cybercriminals’ ability and increasing the risk of accepting payments.

Enhanced engraftment and repairing ability of human adipose-derived stem cells, conveyed by pharmacologically active microcarriers continuously releasing HGF and IGF-1, in healing myocardial infarction in rats

International audience

'The application of Bayesian Optimization and Classifier Systems in Nurse Scheduling'

Abstract. Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person's assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, the Bayesian optimization algorithm builds a Bayesian network of the joint probability distribution of the rules used to construct solutions, while the adapted classifier system assigns each rule a strength value that is constantly updated according to its usefulness in the current situation. Computational results from 52 real data instances of nurse scheduling demonstrate the success of both approaches. It is also suggested that the learning mechanism in the proposed approaches might be suitable for other scheduling problems.

'Explicit Learning: an Effort towards Human Scheduling Algorithms'

Abstract Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem. In our previous indirect GAs, learning is implicit and is restricted to the efficient adjustment of weights for a set of rules that are used to construct schedules. The major limitation of those approaches is that they learn in a non-human way: like most existing construction algorithms, once the best weight combination is found, the rules used in the construction process are fixed at each iteration. However, normally a long sequence of moves is needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning processes. When a human scheduler is working, he normally builds a schedule step by step following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not completed yet, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this research we intend to design more human-like scheduling algorithms, by using ideas derived from Bayesian Optimization Algorithms (BOA) and Learning Classifier Systems (LCS) to implement explicit learning from past solutions. BOA can be applied to learn to identify good partial solutions and to complete them by building a Bayesian network of the joint distribution of solutions [3]. A Bayesian network is a directed acyclic graph with each node corresponding to one variable, and each variable corresponding to individual rule by which a schedule will be constructed step by step. The conditional probabilities are computed according to an initial set of promising solutions. Subsequently, each new instance for each node is generated by using the corresponding conditional probabilities, until values for all nodes have been generated. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the Bayesian network is updated again using the current set of good rule strings. The algorithm thereby tries to explicitly identify and mix promising building blocks. It should be noted that for most scheduling problems the structure of the network model is known and all the variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus learning can amount to 'counting' in the case of multinomial distributions. In the LCS approach, each rule has its strength showing its current usefulness in the system, and this strength is constantly assessed [4]. To implement sophisticated learning based on previous solutions, an improved LCS-based algorithm is designed, which consists of the following three steps. The initialization step is to assign each rule at each stage a constant initial strength. Then rules are selected by using the Roulette Wheel strategy. The next step is to reinforce the strengths of the rules used in the previous solution, keeping the strength of unused rules unchanged. The selection step is to select fitter rules for the next generation. It is envisaged that the LCS part of the algorithm will be used as a hill climber to the BOA algorithm. This is exciting and ambitious research, which might provide the stepping-stone for a new class of scheduling algorithms. Data sets from nurse scheduling and mall problems will be used as test-beds. It is envisaged that once the concept has been proven successful, it will be implemented into general scheduling algorithms. It is also hoped that this research will give some preliminary answers about how to include human-like learning into scheduling algorithms and may therefore be of interest to researchers and practitioners in areas of scheduling and evolutionary computation. References 1. Aickelin, U. and Dowsland, K. (2003) 'Indirect Genetic Algorithm for a Nurse Scheduling Problem', Computer & Operational Research (in print). 2. Li, J. and Kwan, R.S.K. (2003), 'Fuzzy Genetic Algorithm for Driver Scheduling', European Journal of Operational Research 147(2): 334-344. 3. Pelikan, M., Goldberg, D. and Cantu-Paz, E. (1999) 'BOA: The Bayesian Optimization Algorithm', IlliGAL Report No 99003, University of Illinois. 4. Wilson, S. (1994) 'ZCS: A Zeroth-level Classifier System', Evolutionary Computation 2(1), pp 1-18.

Floral expression and pollen germination ability in productive mountain papaya ( Vasconcellea pubescens A.DC.) orchards

Mountain papaya ( Vasconcellea pubescens A.DC.) is described as trioecious in the centers of origin of Ecuador, Colombia, and Peru. However, under cultivation conditions in La Serena (30° S, 70° W), Chile, it is found to be dioecious and monoecious. The objective was to learn about the variations in floral expression of mountain papaya. Flowers from monoecious and dioecious plants were therefore identified and quantified during two seasons. In vitro pollen germination ability was also evaluated based on the factors of site, season, and plant sex. Monoecious plant inflorescences are polygamous; female and male flowers are observed, as well as bisexual flowers that are usually deformed. This condition allows them to be classified as an ambisexual plant. The existence of flowers of different sexes appears to depend on the season; the female dioecious plant is maintained as such, independently of climatic conditions. Pollen from male flowers, from both ambisexual and male plants, germinates 75% in summer, while germination decreases to 56% in spring (P ≤ 0.05). Flowering of female plants coincides with the permanent occurrence of male flowers in ambisexual plants, which ensures pollination without the need for male plants as pollinators in orchards. Based on this information, some management practices and possible lines of research about this species are proposed.

Competitive ability of cultivated rice against weedy rice biotypes – A review

Weedy rice has been identified as a threat to rice production worldwide. Its phenotypic and genotypic diversity and its potential to compete against rice in all development stages from germination to maturity have resulted in a loss of rice yield and grain quality, which is remarkably high in directseeded rice cultivation. Weedy rice dormancy varies, it has a higher germination rate, and tolerates deeper germination depth compared to rice cultivars. Interactions of weedy rice with cultivars often reflect early vigor, more tillering, nutrient utilization ability for shoot development with respect to rice cultivars even though the latter also show an improvement in shoot development under competition. An exponential relationship has been reported between cultivated rice loss and weedy rice density: this is true for all rice cultivars. The degree of loss is dependent on the competitive ability of the rice cultivar being studied, and each weedy rice biotype also interacts differently. Hence, the need for a comprehensive study of the biology of various weedy rice variants. Difficulties arise in the management of weedy rice due to its physiological, anatomical, and morphological similarities to cultivated rice. The manipulation of the environment to improve cultivated rice production and suppress the emergence of weedy rice variants is important in the management of weedy rice, as well as other cultural practices and use of pesticides. The development of herbicide-resistant rice cultivars is necessary to totally eliminate the weedy rice variants. This review provides information on the competitive ability of weedy rice against rice cultivars; this information is essential to create management options to control weedy rice.

The Application of a Dendric Cell Algorithm to a Robotic Classifier

The dendritic cell algorithm is an immune-inspired technique for processing time-dependant data. Here we propose it as a possible solution for a robotic classification problem. The dendritic cell algorithm is implemented on a real robot and an investigation is performed into the effects of varying the migration threshold median for the cell population. The algorithm performs well on a classification task with very little tuning. Ways of extending the implementation to allow it to be used as a classifier within the field of robotic security are suggested.

The Application of a Dendric Cell Algorithm to a Robotic Classifier

The dendritic cell algorithm is an immune-inspired technique for processing time-dependant data. Here we propose it as a possible solution for a robotic classification problem. The dendritic cell algorithm is implemented on a real robot and an investigation is performed into the effects of varying the migration threshold median for the cell population. The algorithm performs well on a classification task with very little tuning. Ways of extending the implementation to allow it to be used as a classifier within the field of robotic security are suggested.