Measurement of implicit leadership theories and their effect on leadership processes and outcomes

The leadership categorisation theory suggests that followers rely on a hierarchical cognitive structure in perceiving leaders and the leadership process, which consists of three levels; superordinate, basic and subordinate. The predominant view is that followers rely on Implicit Leadership Theories (ILTs) at the basic level in making judgments about managers. The thesis examines whether this presumption is true by proposing and testing two competing conceptualisations; namely the congruence between the basic level ILTs (general leader) and actual manager perceptions, and subordinate level ILTs (job-specific leader) and actual manager. The conceptualisation at the job-specific level builds on context-related assertions of the ILT explanatory models: leadership categorisation, information processing and connectionist network theories. Further, the thesis addresses the effects of ILT congruence at the group level. The hypothesised model suggests that Leader-Member Exchange (LMX) will act as a mediator between ILT congruence and outcomes. Three studies examined the proposed model. The first was cross-sectional with 175 students reporting on work experience during a 1-year industrial placement. The second was longitudinal and had a sample of 343 students engaging in a business simulation in groups with formal leadership. The final study was a cross-sectional survey in several organisations with a sample of 178. A novel approach was taken to congruence analysis; the hypothesised models were tested using Latent Congruence Modelling (LCM), which accounts for measurement error and overcomes the majority of limitations of traditional approaches. The first two studies confirm the traditional theorised view that employees rely on basic-level ILTs in making judgments about their managers with important implications, and show that LMX mediates the relationship between ILT congruence and work-related outcomes (performance, job satisfaction, well-being, task satisfaction, intragroup conflict, group satisfaction, team realness, team-member exchange, group performance). The third study confirms this with conflict, well-being, self-rated performance and commitment as outcomes.

Independent modulation of engagement and connectivity of the facial network during affect processing by CACNA1C and ANK3 risk genes for bipolar disorder

IMPORTANCE Genome-wide association studies (GWASs) indicate that single-nucleotide polymorphisms in the CACNA1C and ANK3 genes increase the risk for bipolar disorder (BD). The genes influence neuronal firing by modulating calcium and sodium channel functions, respectively. Both genes modulate ?-aminobutyric acid-transmitting interneuron function and can thus affect brain regional activation and interregional connectivity. OBJECTIVE To determine whether the genetic risk for BD associated with 2 GWAS-supported risk single-nucleotide polymorphisms at CACNA1C rs1006737 and ANK3 rs10994336 is mediated through changes in regional activation and interregional connectivity of the facial affect-processing network. DESIGN, SETTING, AND PARTICIPANTS Cross-sectional functional magnetic resonance imaging study at a research institute of 41 euthymic patients with BD and 46 healthy participants, all of British white descent. MAIN OUTCOMES AND MEASURES Blood oxygen level-dependent signal and effective connectivity measures during the facial affect-processing task. RESULTS In healthy carriers, both genetic risk variants were independently associated with increased regional engagement throughout the facial affect-processing network and increased effective connectivity between the visual and ventral prefrontal cortical regions. In contrast, BD carriers of either genetic risk variant exhibited pronounced reduction in ventral prefrontal cortical activation and visual-prefrontal effective connectivity. CONCLUSIONS AND RELEVANCE Our data demonstrate that the effect of CACNA1C rs1006737 and ANK3 rs10994336 (or genetic variants in linkage disequilibrium) on the brain converges on the neural circuitry involved in affect processing and provides a mechanism linking BD to genome-wide genetic risk variants.

Network density and R&D spillovers

This paper models how the structure and function of a network of firms affects their aggregate innovativeness. Each firm has the potential to innovate, either from in-house R&D or from innovation spillovers from neighboring firms. The nature of innovation spillovers depends upon network density, the commonality of knowledge between firms, and the learning capability of firms. Innovation spillovers are modelled in detail using ideas from organizational theory. Two main results emerge: (i) the marginal effect on innovativeness of spillover intensity is non-monotonic, and (ii) network density can affect innovativeness but only when there are heterogeneous firms.

The effect of a curing agent on the thermal degradation of fire retardant brominated epoxy resins

The diglycidyl ether of tetrabromobisphenol A, the diglycidyl ether of bisphenol A and their mixture was cured by 4,4'-diaminodiphenyl methane. The pyrolysis of the obtained epoxy resins was studied by TG, DSC, TG/FTIR as well as FTIR characterization of pyrolysis residues. The gaseous and high boiling pyrolysis products were collected, characterized by GC/MS and their formation is discussed. The brominated epoxy resins are thermally less stable than the non-brominated ones. This effect is caused by the amine-containing hardener. The degradation initiation reaction is associated with the formation of hydrogen bromide which further destabilizes the epoxy network. The effect of the curing agent can be used in recycling of epoxy resins to separate brominated pyrolysis products from non-brominated ones.

Effect of cooling rate on intercritically reheated microstructure and toughness in high strength low alloy steel

High strength low alloy steels have been shown to be adversely affected by the existence of regions of poor impact toughness within the heat affected zone (HAZ) produced during multipass welding. One of these regions is the intercritically reheated coarse grained HAZ or intercritical zone. Since this region is generally narrow and discontinuous, of the order of 0.5 mm in width, weld simulators are often employed to produce a larger volume of uniform microstructure suitable for toughness assessment. The steel usedfor this study was a commercial quenched and tempered steel of 450 MN m -2 yield strength. Specimen blanks were subjected to a simulated welding cycle to produce a coarse grained structure of upper bainite during the first thermal cycle, followed by a second thermal cycle where the peak temperature T p2 was controlled. Charpy tests carried out for T p2 values in the range 650-850°C showed low toughness for T p2 values between 760 and 790°C, in the intercritical regime. Microstructural investigation of the development of grain boundary martensite-retained austenite (MA) phase has been coupled with image analysis to measure the volume fraction of MAformed. Most of the MA constituent appears at the prior austenite grain boundaries during intercritical heating, resulting in a 'necklace' appearance. For values of T p2 greater than 790°C the necklace appearance is lost and the second phase areas are observed throughout the structure. Concurrent with this is the development of the fine grained, predominantly ferritic structure that is associated with the improvement in toughness. At this stage the microstructure is transforming from the intercritical regime structure to the supercritically reheated coarse grained HAZ structure. The toughness improvement occurs even though the MA phase is still present, suggesting that the embrittlement is associated with the presence of a connected grain boundary network of the MA phase. The nature of the second phase particles can be controlled by the cooling rate during the second cycle and variesfrom MA phase at high cooling rates to a pearlitic structure at low cooling rates. The lowest toughness of the intercritical zone is observed only when MA phase is present. The reason suggested for this is that only the MA particles debond readily, a number of debonded particles in close proximity providing sufficient stress concentration to initiate local cleavage. © 1993 The Institute of Materials.

Altered resting-state EEG source functional connectivity in schizophrenia:the effect of illness duration

Despite the increasing body of evidence supporting the hypothesis of schizophrenia as a disconnection syndrome, studies of resting-state EEG Source Functional Connectivity (EEG-SFC) in people affected by schizophrenia are sparse. The aim of the present study was to investigate resting-state EEG-SFC in 77 stable, medicated patients with schizophrenia (SCZ) compared to 78 healthy volunteers (HV). In order to study the effect of illness duration, SCZ were divided in those with a short duration of disease (SDD; n = 25) and those with a long duration of disease (LDD; n = 52). Resting-state EEG recordings in eyes closed condition were analyzed and lagged phase synchronization (LPS) indices were calculated for each ROI pair in the source-space EEG data. In delta and theta bands, SCZ had greater EEG-SFC than HV; a higher theta band connectivity in frontal regions was observed in LDD compared with SDD. In the alpha band, SCZ showed lower frontal EEG-SFC compared with HV whereas no differences were found between LDD and SDD. In the beta1 band, SCZ had greater EEG-SFC compared with HVs and in the beta2 band, LDD presented lower frontal and parieto-temporal EEG-SFC compared with HV. In the gamma band, SDD had greater connectivity values compared with LDD and HV. This study suggests that resting state brain network connectivity is abnormally organized in schizophrenia, with different patterns for the different EEG frequency components and that EEG can be a powerful tool to further elucidate the complexity of such disordered connectivity.

Performance evaluation of adaptive context aware rate selection algorithm (ACARS) for road safety applications in vehicular network

One of the major drawbacks for mobile nodes in wireless networks is power management. Our goal is to evaluate the performance power control scheme to be used to reduce network congestion, improve quality of service and collision avoidance in vehicular network and road safety application. Some of the importance of power control (PC) are improving spatial reuse, and increasing network capacity in mobile wireless communications. In this simulation we have evaluated the performance of existing rate algorithms compared with context Aware Rate selection algorithm (ACARS) and also seen the performance of ACARS and how it can be applied to road safety, improve network control and power management. Result shows that ACARS is able to minimize the total transmit power in the presence of propagation processes and mobility of vehicles, by adapting to the fast varying channels conditions with the Path loss exponent values that was used for that environment which is shown in the network simulation parameter. Our results have shown that ACARS is a very robust algorithm which performs very well with the effect of propagation processes that is prone to every transmitted signal in mobile networks. © 2013 IEEE.

Functional astrocyte-neuron lactate shuttle in a human stem cell derived neuronal network

The development of stem cell-derived neuronal networks will promote experimental system development for drug screening, toxicological testing and disease modelling, providing that they mirror closely the functional competencies of their in vivo counterparts. The NT2 cell line is one of the best documented embryocarcinoma cell lines, and can be differentiated into neurons and astrocytes. Great focus has also been placed on defining the electrophysiological properties of these cells, and more recently we have investigated the functional properties of their associated astrocytes. We now show for the first time in a human stem cell derived co-culture model that these cultures are also metabolically competent and demonstrate a functional astrocyte neuron lactate shuttle (ANLS). The ANLS hypothesis proposes that during neuronal activity, glutamate released into the synaptic cleft is taken up by astrocytes and triggers glucose uptake which is converted into lactate and released via monocarboxylate transporters for neuronal use. Using mixed cultures of NT2 derived neurons and astrocytes we have shown that these cells modulate their glucose uptake in response to glutamate, an effect that was blocked by cytochalasin B and ouabain. Additionally we demonstrate that in response to increased neuronal activity and under hypoglycaemic conditions, co-cultures modulate glycogen turnover and increase lactate production. Similar results were also shown following treatment with glutamate, potassium, Isoproterenol and dbcAMP. Together these results demonstrate for the first time a functional ANLS in a human stem cell derived co-culture.

Resting state morphology predicts the effect of theta burst stimulation in false belief reasoning:ventrolateral prefrontal cortex in false belief reasoning

When required to represent a perspective that conflicts with one's own, functional magnetic resonance imaging (fMRI) suggests that the right ventrolateral prefrontal cortex (rvlPFC) supports the inhibition of that conflicting self-perspective. The present task dissociated inhibition of self-perspective from other executive control processes by contrasting belief reasoning-a cognitive state where the presence of conflicting perspectives was manipulated-with a conative desire state wherein no systematic conflict existed. Linear modeling was used to examine the effect of continuous theta burst stimulation (cTBS) to rvlPFC on participants' reaction times in belief and desire reasoning. It was anticipated that cTBS applied to rvlPFC would affect belief but not desire reasoning, by modulating activity in the Ventral Attention System (VAS). We further anticipated that this effect would be mediated by functional connectivity within this network, which was identified using resting state fMRI and an unbiased model-free approach. Simple reaction-time analysis failed to detect an effect of cTBS. However, by additionally modeling individual measures from within the stimulated network, the hypothesized effect of cTBS to belief (but, importantly, not desire) reasoning was demonstrated. Structural morphology within the stimulated region, rvlPFC, and right temporoparietal junction were demonstrated to underlie this effect. These data provide evidence that inconsistencies found with cTBS can be mediated by the composition of the functional network that is being stimulated. We suggest that the common claim that this network constitutes the VAS explains the effect of cTBS to this network on false belief reasoning. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.

Improving energy efficiency in a wireless sensor network by combining cooperative MIMO with data aggregation

In wireless sensor networks where nodes are powered by batteries, it is critical to prolong the network lifetime by minimizing the energy consumption of each node. In this paper, the cooperative multiple-input-multiple-output (MIMO) and data-aggregation techniques are jointly adopted to reduce the energy consumption per bit in wireless sensor networks by reducing the amount of data for transmission and better using network resources through cooperative communication. For this purpose, we derive a new energy model that considers the correlation between data generated by nodes and the distance between them for a cluster-based sensor network by employing the combined techniques. Using this model, the effect of the cluster size on the average energy consumption per node can be analyzed. It is shown that the energy efficiency of the network can significantly be enhanced in cooperative MIMO systems with data aggregation, compared with either cooperative MIMO systems without data aggregation or data-aggregation systems without cooperative MIMO, if sensor nodes are properly clusterized. Both centralized and distributed data-aggregation schemes for the cooperating nodes to exchange and compress their data are also proposed and appraised, which lead to diverse impacts of data correlation on the energy performance of the integrated cooperative MIMO and data-aggregation systems.

Dynamic Bayesian network for semantic place classification in mobile robotics

In this paper, the problem of semantic place categorization in mobile robotics is addressed by considering a time-based probabilistic approach called dynamic Bayesian mixture model (DBMM), which is an improved variation of the dynamic Bayesian network. More specifically, multi-class semantic classification is performed by a DBMM composed of a mixture of heterogeneous base classifiers, using geometrical features computed from 2D laserscanner data, where the sensor is mounted on-board a moving robot operating indoors. Besides its capability to combine different probabilistic classifiers, the DBMM approach also incorporates time-based (dynamic) inferences in the form of previous class-conditional probabilities and priors. Extensive experiments were carried out on publicly available benchmark datasets, highlighting the influence of the number of time-slices and the effect of additive smoothing on the classification performance of the proposed approach. Reported results, under different scenarios and conditions, show the effectiveness and competitive performance of the DBMM.