Azobenzene moiety variation directing self-assembly and photoresponsive behavior of azo-surfactants

The effect of varying the position of the azobenzene group within two comparable photoresponsive amphiphiles on their capability to form lyotropic liquid crystals (LLCs) was investigated in detail in this study. Two photoresponsive amphiphiles having comparable structures were designed and synthesized consisting of hydrophilic oligooxyethylene units, a hydrophobic alkyl chain and a light-sensitive azobenzene moiety. When the azobenzene group was located in the middle of the hydrophobic alkyl chain, multiple LLC phases were observed at various water contents in the azo-surfactant–water binary system. In contrast, when the azobenzene group was directly attached to the hydrophilic domain, the azo-surfactant–water binary system exhibited only lamellar phases. The temperature dependence of these self-organised nanostructures was also investigated by the combination of small angle X-ray scattering (SAXS), differential scanning calorimetry (DSC), and rheology. Under alternating UV and visible light irradiation, reversible trans–cis photoisomerization of the azobenzene group occurred efficiently in dilute solution for both azo-surfactants. However, only photoisomerization of the surfactant possessing the azobenzene group localized in the middle of the alkyl chain induced significant changes in the self-assembled structure and its bulk properties. This study demonstrates that self-assembly and photoresponsive behaviour of photosensitive amphiphiles is extremely sensitive to the position of the photoactive moiety within the surfactant molecular architecture.

Deficient cognitive control fuels children's exuberant false allegations.

In eyewitness studies as in actual investigations, a minority of children generate numerous false (and sometimes incredulous) allegations. To explore the characteristics of these children, we reinterviewed and administered a battery of tasks to 61 children (ages 4-9 years) who had previously participated in an eyewitness study where a man broke a "germ rule" twice when he tried to touch them. Performance on utilization, response conflict (Luria tapping), and theory of mind tasks predicted the number of false reports of touching (with age and time since the event controlled) and correctly classified 90.16% of the children as typical witnesses or exuberant (more than 3) false reporters. Results of a factor analysis pointed to a common process underlying performance on these tasks that accounted for 49% of the variability in false reports. Relations between task performance and testimony confirmed that the mechanisms underlying occasional intrusions are different from those that drive persistent confabulation and that deficient cognitive control fuels young children's exuberant false reports.

Joint resource allocation for max-min throughput in multicell networks

We investigate the resource-allocation problem in multicell networks targeting the max-min throughput of all cells. A joint optimization over power control, channel allocation, and user association is considered, and the problem is then formulated as a nonconvex mixed-integer nonlinear problem (MINLP). To solve this problem, we proposed an alternating-optimization-based algorithm, which applies branch-and-bound and simulated annealing in solving subproblems at each optimization step. We also demonstrate the convergence and efficiency of the proposed algorithms by thorough numerical experiments. The experimental results show that joint optimization over all resources outperforms the restricted optimization over individual resources significantly.

Regulation of HbPIP2;3, a latex-abundant water transporter, is associated with latex dilution and yield in the rubber tree (Hevea brasiliensis Muell. Arg.)

Rubber tree (Hevea brasiliensis) latex, the source of natural rubber, is synthesised in the cytoplasm of laticifers. Efficient water inflow into laticifers is crucial for latex flow and production since it is the determinant of the total solid content of latex and its fluidity after tapping. As the mature laticifer vessel rings are devoid of plasmodesmata, water exchange between laticifers and surrounding cells is believed to be governed by plasma membrane intrinsic proteins (PIPs). To identify the most important PIP aquaporin in the water balance of laticifers, the transcriptional profiles of ten-latex-expressed PIPs were analysed. One of the most abundant transcripts, designated HbPIP2;3, was characterised in this study. When tested in Xenopus laevis oocytes HbPIP2;3 showed a high efficiency in increasing plasmalemma water conductance. Expression analysis indicated that the HbPIP2;3 gene was preferentially expressed in latex, and the transcripts were up-regulated by both wounding and exogenously applied Ethrel (a commonly-used ethylene releaser). Although regular tapping up-regulated the expression of HbPIP2;3 during the first few tappings of the virginal rubber trees, the transcriptional kinetics of HbPIP2;3 to Ethrel stimulation in the regularly tapped tree exhibited a similar pattern to that of the previously reported HbPIP2;1 in the virginal rubber trees. Furthermore, the mRNA level of HbPIP2;3 was associated with clonal yield potential and the Ethrel stimulation response. Together, these results have revealed the central regulatory role of HbPIP2;3 in laticifer water balance and ethylene stimulation of latex production in Hevea.

Improving the toughness and electrical conductivity of epoxy nanocomposites by using aligned carbon nanofibres

There is an increasing demand for high performance composites with enhanced mechanical and electrical properties. Carbon nanofibres offer a promising solution but their effectiveness has been limited by difficulty in achieving directional alignment. Here we report the use of an alternating current (AC) electric field to align carbon nanofibres in an epoxy. During the cure process of an epoxy resin, carbon nanofibres (CNFs) are observed to rotate and align with the applied electric field, forming a chain-like structure. The fracture energies of the resultant epoxy nanocomposites containing different concentrations of CNFs (up to 1.6wt%) are measured using double cantilever beam specimens. The results show that the addition of 1.6wt% of aligned CNFs increases the electrical conductivity of such nanocomposites by about seven orders of magnitudes to 10^-2S/m and increases the fracture energy, G<inf>Ic</inf>, by about 1600% from 134 to 2345J/m². A modelling technique is presented to quantify this major increase in the fracture energy with aligned CNFs. The results of this research open up new opportunities to create multi-scale composites with greatly enhanced multifunctional properties.

Synchronization by low-amplitude light-dark cycles of 24-hour pineal and plasma melatonin rhythms of hatchling European starlings (Sturnus vulgaris)

In young European starlings, as in other avian species, high-amplitude 24-hr rhythms in plasma and pineal melatonin are already present around the time of hatching. In chickens this rhythmicity results at least partly from the light sensitivity of the melatonin-producing and -secreting system. In contrast to the chicken, the starling is a hole-nesting bird, and it seemed questionable whether the low light intensities in the nest are sufficient to synchronize perinatal melatonin rhythms. We therefore exposed starling eggs to light cycles roughly simulating those measured in nest-boxes, i.e., an 11-hr phase of complete darkness and a 13-hr phase consisting of 15 min of dim light (10 lux) alternating with 30 min of darkness. For one group the photophase lasted from 0600 to 1900 hr; for the other group the photophase lasted from 1800 to 0700 hr. In approximately 10-hr-old hatchlings of both groups, plasma and pineal melatonin concentrations were high during the dark phase and low during the light phase. We conclude that perinatal low-amplitude light intensity changes of the kind experienced by hatching starlings in the field are sufficient for synchronizing the melatonin-producing and -secreting system in the pineal and possibly other organs.

Fear of self, doubt and obsessive compulsive symptoms

BACKGROUND AND OBJECTIVES: Following observations in the literature that obsessions often contain or imply negative evaluative information about the self, Aardema et al. (2013) recently developed a measure of feared-self relevant to OCD. The current study aimed to provide further examination of the relevance of such feared self-beliefs to obsessive compulsive processes - in particular whether they partially underlie doubt in OCD-relevant situations. METHOD: Nonclinical participants (N = 463; 291 females; Mage = 25.17, SD = 7.47), were presented with three vignettes, related to washing, checking and non-OCD relevant themes, which assessed doubt through providing alternating sensory and possibility-based information. RESULTS: Higher levels of OCD symptoms and feared-self beliefs both significantly predicted both higher baseline levels of doubt and greater fluctuation in levels of doubt in both the contamination and checking scenarios, and to a much lesser extent in the control scenario. Feared-self beliefs did not predict fluctuation in doubt over-and-above OCD symptoms, consistent with a mediation model. LIMITATIONS: The main limitation was the use of a non-clinical sample, although this allowed sufficient participant numbers to test hypotheses. CONCLUSIONS: The findings provided further experimental support for reasoning processes in OCD, and suggested that feared self-beliefs may make individuals vulnerable to experiencing doubt. Additionally, these results suggested that individuals with high OCD symptoms and those with high feared self-beliefs are unable to recognise the improbable nature of possibility-based statements. Implications for treatment and theory are discussed.

Stable feature selection with support vector machines

The support vector machine (SVM) is a popular method for classification, well known for finding the maximum-margin hyperplane. Combining SVM with l1-norm penalty further enables it to simultaneously perform feature selection and margin maximization within a single framework. However, l1-norm SVM shows instability in selecting features in presence of correlated features. We propose a new method to increase the stability of l1-norm SVM by encouraging similarities between feature weights based on feature correlations, which is captured via a feature covariance matrix. Our proposed method can capture both positive and negative correlations between features. We formulate the model as a convex optimization problem and propose a solution based on alternating minimization. Using both synthetic and real-world datasets, we show that our model achieves better stability and classification accuracy compared to several state-of-the-art regularized classification methods.

Multifunctional properties of epoxy nanocomposites reinforced by aligned nanoscale carbon

The present paper compares improvements to the fracture energy and electrical conductivity of epoxy nanocomposites reinforced by one-dimensional carbon nanofibres (CNFs) or two-dimensional graphene nanoplatelets (GNPs). The focus of this investigation is on the effects of the shape, orientation and concentration (i.e. 0.5, 1.0, 1.5 and 2.0 wt%) of nanoscale carbon reinforcements on the property improvements. Alignment of the nano-reinforcements in the epoxy nanocomposites was achieved through the application of an alternating current (AC) electric-field before gelation and curing of the epoxy resin. Alignment of the nano-reinforcements increased the electrical conductivity and simultaneously lowered the percolation threshold necessary to form a conductive network in the nanocomposites. Nano-reinforcement alignment also increased greatly the fracture energy of the epoxy due to a higher fraction of the nano-reinforcement participating in multiple intrinsic (e.g. interfacial debonding and void growth) and extrinsic (e.g. pull-out and bridging) toughening mechanisms. A mechanistic model is presented to quantify the contributions from the different toughening mechanisms induced by CNFs and GNPs to the large improvements in fracture toughness. The model results show that one-dimensional CNFs are more effective than GNPs at increasing the intrinsic toughness of epoxy via void growth, whereas two-dimensional GNPs are more effective than CNFs at improving the extrinsic toughness via crack bridging and pull-out.

Visual object clustering via mixed-norm regularization

Many vision problems deal with high-dimensional data, such as motion segmentation and face clustering. However, these high-dimensional data usually lie in a low-dimensional structure. Sparse representation is a powerful principle for solving a number of clustering problems with high-dimensional data. This principle is motivated from an ideal modeling of data points according to linear algebra theory. However, real data in computer vision are unlikely to follow the ideal model perfectly. In this paper, we exploit the mixed norm regularization for sparse subspace clustering. This regularization term is a convex combination of the l1norm, which promotes sparsity at the individual level and the block norm l2/1 which promotes group sparsity. Combining these powerful regularization terms will provide a more accurate modeling, subsequently leading to a better solution for the affinity matrix used in sparse subspace clustering. This could help us achieve better performance on motion segmentation and face clustering problems. This formulation also caters for different types of data corruptions. We derive a provably convergent algorithm based on the alternating direction method of multipliers (ADMM) framework, which is computationally efficient, to solve the formulation. We demonstrate that this formulation outperforms other state-of-arts on both motion segmentation and face clustering.

Exploiting feature relationships towards stable feature selection

Feature selection is an important step in building predictive models for most real-world problems. One of the popular methods in feature selection is Lasso. However, it shows instability in selecting features when dealing with correlated features. In this work, we propose a new method that aims to increase the stability of Lasso by encouraging similarities between features based on their relatedness, which is captured via a feature covariance matrix. Besides modeling positive feature correlations, our method can also identify negative correlations between features. We propose a convex formulation for our model along with an alternating optimization algorithm that can learn the weights of the features as well as the relationship between them. Using both synthetic and real-world data, we show that the proposed method is more stable than Lasso and many state-of-the-art shrinkage and feature selection methods. Also, its predictive performance is comparable to other methods.

Orientation dependence of the deformation microstructure in a Fe-30Ni-Nb model austenitic steel subjected to hot uniaxial compression

The present work was aimed at a detailed investigation of the orientation dependence of the microstructure characteristics in a Fe-30Ni-Nb austenitic model steel subjected to hot uniaxial compression at 1198 K (925 °C) at a strain rate of 1 s−1 to several strain levels up to 1.0. The quantification of the substructure evolution as a function of strain was performed for the stable 〈011〉 oriented grains. Other grain orientations were also investigated in detail at a strain of 0.2. The 〈110〉 oriented grains contained self-screening arrays of “microbands” (MBs) aligned with high Schmid factor {111} slip planes. The MB crystallographic alignment was largely maintained up to a strain of 1.0, which suggests that the corresponding boundaries kept continuously rearranging themselves during straining and did not follow the sample shape change. The mean MB spacing decreased and misorientation angle increased with strain towards saturation, indicating the operation of the “repolygonization” dynamic recovery mechanism. The non-〈011〉 oriented grains displayed a strong tendency to split during deformation into deformation bands having alternating orientations and being mutually rotated by large angles. The bands were separated by transition regions comprising arrays of closely spaced, extended sub-boundaries collectively accommodating large misorientations across very small distances.

Metabolic power method underestimates energy expenditure in field sport movements using a GPS tracking system

The purpose of this study was to assess the validity of a GPS tracking system to estimate energy expenditure (EE) during exercise and field sport locomotor movements. Twenty-seven participants each completed one 90 minute exercise session on an outdoor synthetic futsal pitch. During the exercise session participants wore a 5 Hz GPS unit interpolated to 15 Hz (SPI HPU, GPSports Pty Ltd, Australia) and a portable gas analyser (Metamax® 3B, Cortex Pty Ltd, Germany) which acted as the criterion measure of EE. The exercise session was comprised of alternating five minute exercise bouts of randomised walking, jogging, running or a field sport circuit (x3) followed by 10 minutes of recovery. One-way ANOVA showed significant (p<0.01) and very large underestimations between GPS metabolic power derived EE and VO2 derived EE for all field sport circuits (% difference ≈ -44%). No differences in EE were observed for the jog (7.8%) and run (4.8%) while very large overestimations were found for the walk (43.0%). The GPS metabolic power EE over the entire 90 minute session was significantly lower (p<0.01) than the VO2 EE, resulting in a moderate underestimation overall (-19%). The results of this study suggest that a GPS tracking system using the metabolic power model of EE does not accurately estimate EE in field sport movements or over an exercise session consisting of mixed locomotor activities interspersed with recovery periods; however is able to provide a reasonably accurate estimation of EE during continuous jogging and running.

Multi-view subspace clustering for face images

In many real-world computer vision applications, such as multi-camera surveillance, the objects of interest are captured by visual sensors concurrently, resulting in multi-view data. These views usually provide complementary information to each other. One recent and powerful computer vision method for clustering is sparse subspace clustering (SSC); however, it was not designed for multi-view data, which break down its linear separability assumption. To integrate complementary information between views, multi-view clustering algorithms are required to improve the clustering performance. In this paper, we propose a novel multi-view subspace clustering by searching for an unified latent structure as a global affinity matrix in subspace clustering. Due to the integration of affinity matrices for each view, this global affinity matrix can best represent the relationship between clusters. This could help us achieve better performance on face clustering. We derive a provably convergent algorithm based on the alternating direction method of multipliers (ADMM) framework, which is computationally efficient, to solve the formulation. We demonstrate that this formulation outperforms other alternatives based on state-of-The-Arts on challenging multi-view face datasets.

Estrutura organizacional do tipo network e a satisfação no trabalho

Muitos historiadores afirmam que estamos iniciando uma nova era, a era do conhecimento, da informação, a era digital. Surgem duas grandes armas estratégicas nesse novo ambiente global, para que as empresas sejam competitivas no século vinte e um: a criatividade e a integração. E muitas empresas estão adotando uma nova estrutura organizacional, a estrutura do tipo network, como solução para a gerência da criatividade e da integração. Essa estrutura não se preocupa com novas maneiras de manipular subordinados em vantagem própria. Ao contrário, ela nos desafia a repensar o básico: nossos valores, atitudes e considerações a respeito de liderança, trabalho e tempo. As estruturas hierárquicas convencionais não proporcionam a agilidade de resposta requerida pelo mercado atualmente, devido à burocracia por trás de todas as atividades. As pessoas especializam-se em pequenas atividades, perdendo o sentido do trabalho e a motivação intrínseca. E uma vez que as pessoas são crescentemente reconhecidas como o capital mais importante de qualquer empreendimento, a desmotivação se toma desastrosa para o futuro de qualquer negócio. A reciprocidade empresa-indivíduo é essencial. Esta dissertação pretende analisar o fator humano nos trabalhos realizados dentro da estrutura de network, traçando-se um paralelo entre as propostas dessa estrutura e as necessidades humanas, demonstrando a relação existente entre a estrutura organizacional da criatividade e da integração e a satisfação no trabalho. Iniciamente, apresenta-se uma revisão bibliográfica, sob três diferentes enfoques. Primeiro, explica-se como as transformações mundiais estão afetando a estratégia das empresas. Depois, mostra-se o impacto da estratégia do século vinte e um dentro da organização. Por fim, focaliza-se o lado psicológico do ser humano, suas necessidades, tais quais a autonomia, a competência e o relacionamento interpessoal, os fatores de satisfação intrínsecos e extrínsecos. Assim, pode-se avaliar o impacto de uma nova estrutura organizacional na motivação dos funcionários. A seguir, apresenta-se o projeto de uma pesquisa-piloto dos fatores de satisfação mais relevantes para as pessoas, confirmando-se a importância dos fatores de satisfação intrínsecos. Mostra-se também que os índices de satisfação são diretamente afetados pelo ambiente empresarial onde atuam, de acordo com seu grau de autonomia. Então, são mostradas as conclusões do trabalho e recomendações práticas para mudanças na estrutura organizacional dentro de uma empresa, seus custos e como elas devem ser administradas no longo prazo.