An experimental investigation of peer influences on hostile attributions in adolescents

Aggression in young people has been associated with a bias towards attributing hostile intent to others; however, little is known about the origin of biased social information processing. The current study explored the potential role of peer contagion in the emergence of hostile attribution in adolescents. 134 adolescents were assigned to one of two manipulated ‘chat-room’ conditions, where they believed they were communicating with online peers (e-confederates) who endorsed either hostile or benign intent attributions. Adolescents showed increased hostile attributions following exposure to hostile e-confederates and reduced hostility in the benign condition. Further analyses demonstrated that social anxiety was associated with a reduced tendency to take on hostile peer attitudes. Neither gender nor levels of aggression influenced individual susceptibility to peer influence, but aggressive adolescents reported greater affinity with hostile e-confederates.

Virtual landmarking for locality aware peer IDs

In Peer-to-Peer (P2P) networks, it is often desirable to assign node IDs which preserve locality relationships in the underlying topology. Node locality can be embedded into node IDs by utilizing a one dimensional mapping by a Hilbert space filling curve on a vector of network distances from each node to a subset of reference landmark nodes within the network. However this approach is fundamentally limited because while robustness and accuracy might be expected to improve with the number of landmarks, the effectiveness of 1 dimensional Hilbert Curve mapping falls for the curse of dimensionality. This work proposes an approach to solve this issue using Landmark Multidimensional Scaling (LMDS) to reduce a large set of landmarks to a smaller set of virtual landmarks. This smaller set of landmarks has been postulated to represent the intrinsic dimensionality of the network space and therefore a space filling curve applied to these virtual landmarks is expected to produce a better mapping of the node ID space. The proposed approach, the Virtual Landmarks Hilbert Curve (VLHC), is particularly suitable for decentralised systems like P2P networks. In the experimental simulations the effectiveness of the methods is measured by means of the locality preservation derived from node IDs in terms of latency to nearest neighbours. A variety of realistic network topologies are simulated and this work provides strong evidence to suggest that VLHC performs better than either Hilbert Curves or LMDS use independently of each other.

The global distribution of phytoplankton size spectrum and size classes from their light-absorption spectra derived from satellite data

The absorption spectra of phytoplankton in the visible domain hold implicit information on the phytoplankton community structure. Here we use this information to retrieve quantitative information on phytoplankton size structure by developing a novel method to compute the exponent of an assumed power-law for their particle-size spectrum. This quantity, in combination with total chlorophyll-a concentration, can be used to estimate the fractional concentration of chlorophyll in any arbitrarily-defined size class of phytoplankton. We further define and derive expressions for two distinct measures of cell size of mixed populations, namely, the average spherical diameter of a bio-optically equivalent homogeneous population of cells of equal size, and the average equivalent spherical diameter of a population of cells that follow a power-law particle-size distribution. The method relies on measurements of two quantities of a phytoplankton sample: the concentration of chlorophyll-a, which is an operational index of phytoplankton biomass, and the total absorption coefficient of phytoplankton in the red peak of visible spectrum at 676 nm. A sensitivity analysis confirms that the relative errors in the estimates of the exponent of particle size spectra are reasonably low. The exponents of phytoplankton size spectra, estimated for a large set of in situ data from a variety of oceanic environments (~ 2400 samples), are within a reasonable range; and the estimated fractions of chlorophyll in pico-, nano- and micro-phytoplankton are generally consistent with those obtained by an independent, indirect method based on diagnostic pigments determined using high-performance liquid chromatography. The estimates of cell size for in situ samples dominated by different phytoplankton types (diatoms, prymnesiophytes, Prochlorococcus, other cyanobacteria and green algae) yield nominal sizes consistent with the taxonomic classification. To estimate the same quantities from satellite-derived ocean-colour data, we combine our method with algorithms for obtaining inherent optical properties from remote sensing. The spatial distribution of the size-spectrum exponent and the chlorophyll fractions of pico-, nano- and micro-phytoplankton estimated from satellite remote sensing are in agreement with the current understanding of the biogeography of phytoplankton functional types in the global oceans. This study contributes to our understanding of the distribution and time evolution of phytoplankton size structure in the global oceans.

Knowledge-based design cost estimation through extending industry foundation classes

In order to overcome divergence of estimation with the same data, the proposed digital costing process adopts an integrated design of information system to design the process knowledge and costing system together. By employing and extending a widely used international standard, industry foundation classes, the system can provide an integrated process which can harvest information and knowledge of current quantity surveying practice of costing method and data. Knowledge of quantification is encoded from literatures, motivation case and standards. It can reduce the time consumption of current manual practice. The further development will represent the pricing process in a Bayesian Network based knowledge representation approach. The hybrid types of knowledge representation can produce a reliable estimation for construction project. In a practical term, the knowledge management of quantity surveying can improve the system of construction estimation. The theoretical significance of this study lies in the fact that its content and conclusion make it possible to develop an automatic estimation system based on hybrid knowledge representation approach.

Equipped to deal with uncertainty in climate and impacts predictions: lessons from internal peer review

The quantification of uncertainty is an increasingly popular topic, with clear importance for climate change policy. However, uncertainty assessments are open to a range of interpretations, each of which may lead to a different policy recommendation. In the EQUIP project researchers from the UK climate modelling, statistical modelling, and impacts communities worked together on ‘end-to-end’ uncertainty assessments of climate change and its impacts. Here, we use an experiment in peer review amongst project members to assess variation in the assessment of uncertainties between EQUIP researchers. We find overall agreement on key sources of uncertainty but a large variation in the assessment of the methods used for uncertainty assessment. Results show that communication aimed at specialists makes the methods used harder to assess. There is also evidence of individual bias, which is partially attributable to disciplinary backgrounds. However, varying views on the methods used to quantify uncertainty did not preclude consensus on the consequential results produced using those methods. Based on our analysis, we make recommendations for developing and presenting statements on climate and its impacts. These include the use of a common uncertainty reporting format in order to make assumptions clear; presentation of results in terms of processes and trade-offs rather than only numerical ranges; and reporting multiple assessments of uncertainty in order to elucidate a more complete picture of impacts and their uncertainties. This in turn implies research should be done by teams of people with a range of backgrounds and time for interaction and discussion, with fewer but more comprehensive outputs in which the range of opinions is recorded.

Design patterns for promoting peer interaction in discussion forums in MOOCs

Design patterns are a way of sharing evidence-based solutions to educational design problems. The design patterns presented in this paper were produced through a series of workshops, which aimed to identify Massive Open Online Course (MOOC) design principles from workshop participants’ experiences of designing, teaching and learning on these courses. MOOCs present a challenge for the existing pedagogy of online learning, particularly as it relates to promoting peer interaction and discussion. MOOC cohort sizes, participation patterns and diversity of learners mean that discussions can remain superficial, become difficult to navigate, or never develop beyond isolated posts. In addition, MOOC platforms may not provide sufficient tools to support moderation. This paper draws on four case studies of designing and teaching on a range of MOOCs presenting seven design narratives relating to the experience in these MOOCs. Evidence presented in the narratives is abstracted in the form of three design patterns created through a collaborative process using techniques similar to those used in collective autoethnography. The patterns: “Special Interest Discussions”, “Celebrity Touch” and “Look and Engage”, draw together shared lessons and present possible solutions to the problem of creating, managing and facilitating meaningful discussion in MOOCs through the careful use of staged learning activities and facilitation strategies.

Interactional dynamics in on-line and face-to-face peer-tutoring sessions for second language writers

This paper reports the results of a study comparing the interactional dynamics of face-to-face and on-line peer-tutoring in writing by university students in Hong Kong. Transcripts of face-to-face tutoring sessions, as well as logs of on-line sessions conducted by the same peer-tutors, were coded for speech functions using a system based on Halliday's functional-semantic view of dialogue. Results show considerable differences between the interactional dynamics in on-line and face-to-face tutoring sessions. In particular, face-to-face interactions involved more hierarchal encounters in which tutors took control of the discourse, whereas on-line interactions were more egalitarian, with clients controlling the discourse more. Differences were also found in the topics participants chose to focus on in the two modes, with issues of grammar, vocabulary, and style taking precedence in face-to-face sessions and more “global” writing concerns like content and process being discussed more in on-line sessions.

Questioning Kenneth N. Waltz (1924-2013)

Two little-known academics examine the doctoral thesis of a young theorist, Kenneth Waltz. They conclude that his work is important, despite its ambiguities. Some years later, they catch up with Professor Waltz between classes and explore how his ideas have developed.

Farmer attitudes and livestock disease: exploring citizenship behaviour and peer monitoring across two BVD control schemes in the UK

The eradication of BVD in the UK is technically possible but appears to be socially untenable. The following study explored farmer attitudes to BVD control schemes in relation to advice networks and information sharing, shared aims and goals, motivation and benefits of membership, notions of BVD as a priority disease and attitudes toward regulation. Two concepts from the organisational management literature framed the study: citizenship behaviour where actions of individuals support the collective good (but are not explicitly recognised as such) and peer to peer monitoring (where individuals evaluate other’s behaviour). Farmers from two BVD control schemes in the UK participated in the study: Orkney Livestock Association BVD Eradication Scheme and Norfolk and Suffolk Cattle Breeders Association BVD Eradication Scheme. In total 162 farmers participated in the research (109 in-scheme and 53 out of scheme). The findings revealed that group helping and information sharing among scheme members was low with a positive BVD status subject to social censure. Peer monitoring in the form of gossip with regard to the animal health status of other farms was high. Interestingly, farmers across both schemes supported greater regulation with regard to animal health, largely due to the mistrust of fellow farmers following voluntary disease control measures. While group cohesiveness varied across the two schemes, without continued financial inducements, longer-term sustainability is questionable

Constraints from the old quasar APM 08279+5255 on two classes of Lambda(t)-cosmologies

The viability of two different classes of Lambda(t)CDM cosmologies is tested by using the APM 08279+5255, an old quasar at redshift z = 3.91. In the first class of models, the cosmological term scales as Lambda(t) similar to R(-n). The particular case n = 0 describes the standard Lambda CDM model whereas n = 2 stands for the Chen and Wu model. For an estimated age of 2 Gyr, it is found that the power index has a lower limit n > 0.21, whereas for 3 Gyr the limit is n > 0.6. Since n can not be so large as similar to 0.81, the Lambda CDM and Chen and Wu models are also ruled out by this analysis. The second class of models is the one recently proposed by Wang and Meng which describes several Lambda(t)CDM cosmologies discussed in the literature. By assuming that the true age is 2 Gyr it is found that the epsilon parameter satisfies the lower bound epsilon > 0.11 while for 3 Gyr, a lower limit of epsilon > 0.52 is obtained. Such limits are slightly modified when the baryonic component is included.

Proteomics of the neurotoxic fraction from the sea anemone Bunodosoma cangicum venom: Novel peptides belonging to new classes of toxins

In contrast to the many studies on the venoms of scorpions, spiders, snakes and cone snails, tip to now there has been no report of the proteomic analysis of sea anemones venoms. In this work we report for the first time the peptide mass fingerprint and some novel peptides in the neurotoxic fraction (Fr III) of the sea anemone Bunodosoma cangicum venom. Fr III is neurotoxic to crabs and was purified by rp-HPLC in a C-18 column, yielding 41 fractions. By checking their molecular masses by ESI-Q-Tof and MALDI-Tof MS we found 81 components ranging from near 250 amu to approximately 6000 amu. Some of the peptidic molecules were partially sequenced through the automated Edman technique. Three of them are peptides with near 4500 amu belonging to the class of the BcIV, BDS-I, BDS-II, APETx1, APETx2 and Am-II toxins. Another three peptides represent a novel group of toxins (similar to 3200 amu). A further three molecules (similar to similar to 4900 amu) belong to the group of type 1 sodium channel neurotoxins. When assayed over the crab leg nerve compound action potentials, one of the BcIV- and APETx-like peptides exhibits an action similar to the type 1 sodium channel toxins in this preparation, suggesting the same target in this assay. On the other hand one of the novel peptides, with 3176 amu, displayed an action similar to potassium channel blockage in this experiment. In summary, the proteomic analysis and mass fingerprint of fractions from sea anemone venoms through MS are valuable tools, allowing us to rapidly predict the occurrence of different groups of toxins and facilitating the search and characterization of novel molecules without the need of full characterization of individual components by broader assays and bioassay-guided purifications. It also shows that sea anemones employ dozens of components for prey capture and defense. (C) 2008 Elsevier Inc. All rights reserved.

A hybrid approach to learn with imbalanced classes using evolutionary algorithms

There is an increasing interest in the application of Evolutionary Algorithms (EAs) to induce classification rules. This hybrid approach can benefit areas where classical methods for rule induction have not been very successful. One example is the induction of classification rules in imbalanced domains. Imbalanced data occur when one or more classes heavily outnumber other classes. Frequently, classical machine learning (ML) classifiers are not able to learn in the presence of imbalanced data sets, inducing classification models that always predict the most numerous classes. In this work, we propose a novel hybrid approach to deal with this problem. We create several balanced data sets with all minority class cases and a random sample of majority class cases. These balanced data sets are fed to classical ML systems that produce rule sets. The rule sets are combined creating a pool of rules and an EA is used to build a classifier from this pool of rules. This hybrid approach has some advantages over undersampling, since it reduces the amount of discarded information, and some advantages over oversampling, since it avoids overfitting. The proposed approach was experimentally analysed and the experimental results show an improvement in the classification performance measured as the area under the receiver operating characteristics (ROC) curve.

Evolutionary model trees for handling continuous classes in machine learning

Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating the optimal model tree is an NP-Complete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. In this paper, we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model trees in order to improve the convergence to globally near-optimal solutions. We call our new approach evolutionary model tree induction (E-Motion). We test its predictive performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning applications. (C) 2010 Elsevier Inc. All rights reserved.