Broadband spin-controlled focusing via logarithmic-spiral nanoslits of varying width

This work presents analytical, numerical and experimental demonstrations of light diffracted through a logarithmic spiral (LS) nanoslit, which forms a type of switchable and focus-tunable structure. Owing to a strong dependence on the incident photon spin, the proposed LS-nanoslit converges incoming light of opposite handedness (to that of the LS-nanoslit) into a confined subwavelength spot, while it shapes light with similar chirality into a donut-like intensity profile. Benefitting from the varying width of the LS-nanoslit, different incident wavelengths interfere constructively at different positions, i.e., the focal length shifts from 7.5 μm (at λ = 632.8 nm) to 10 μm (at λ = 488 nm), which opens up new opportunities for tuning and spatially separating broadband light at the micrometer scale

Oxygen reduction reaction activity in non-precious single-atom (M–N/C ) catalysts-contribution of metal and carbon/nitrogen framework-based sites.

We examine the performance of a number of single-atom M-N/C electrocatalysts with a common structure in order to deconvolute the activity of the framework N/C support from the metal M-N4 sites in M-N/Cs. The formation of the N/C framework with coordinating nitrogen sites is performed using zinc as a templating agent. After the formation of the electrically conducting carbon-nitrogen metal-coordinating network, we (trans)metalate with different metals producing a range of different catalysts (Fe-N/C, Co-N/C, Ni-N/C, Sn-N/C, Sb-N/C, and Bi-N/C) without the formation of any metal particles. In these materials, the structure of the carbon/nitrogen framework remains unchanged-only the coordinated metal is substituted. We assess the performance of the subsequent catalysts in acid, near-neutral, and alkaline environments toward the oxygen reduction reaction (ORR) and ascribe and quantify the performance to a combination of metal site activity and activity of the carbon/nitrogen framework. The ORR activity of the carbon/nitrogen framework is about 1000-fold higher in alkaline than it is in acid, suggesting a change in mechanism. At 0.80 VRHE, only Fe and Co contribute ORR activity significantly beyond that provided by the carbon/nitrogen framework at all pH values studied. In acid and near-neutral pH values (pH 0.3 and 5.2, respectively), Fe shows a 30-fold improvement and Co shows a 5-fold improvement, whereas in alkaline pH (pH 13), both Fe and Co show a 7-fold improvement beyond the baseline framework activity. The site density of the single metal atom sites is estimated using the nitrite adsorption and stripping method. This method allows us to deconvolute the framework sites and metal-based active sites. The framework site density of catalysts is estimated as 7.8 × 1018 sites g-1. The metal M-N4 site densities in Fe-N/C and Co-N/C are 9.4 × 1018 sites-1 and 4.8 × 1018 sites g-1, respectively

A game player expertise level classification system using electroencephalography (EEG)

The success and wider adaptability of smart phones has given a new dimension to the gaming industry. Due to the wide spectrum of video games, the success of a particular game depends on how efficiently it is able to capture the end users' attention. This leads to the need to analyse the cognitive aspects of the end user, that is the game player, during game play. A direct window to see how an end user responds to a stimuli is to look at their brain activity. In this study, electroencephalography (EEG) is used to record human brain activity during game play. A commercially available EEG headset is used for this purpose giving fourteen channels of recorded EEG brain activity. The aim is to classify a player as expert or novice using the brain activity as the player indulges in the game play. Three different machine learning classifiers have been used to train and test the system. Among the classifiers, naive Bayes has outperformed others with an accuracy of 88%, when data from all fourteen EEG channels are used. Furthermore, the activity observed on electrodes is statistically analysed and mapped for brain visualizations. The analysis has shown that out of the available fourteen channels, only four channels in the frontal and occipital brain regions show significant activity. Features of these four channels are then used, and the performance parameters of the four-channel classification are compared to the results of the fourteen-channel classification. It has been observed that support vector machine and the naive Bayes give good classification accuracy and processing time, well suited for real-time applications

Vine Robots: Design, Teleoperation, and Deployment for Navigation and Exploration

A new class of continuum robots has recently been explored, characterized by tip extension, significant length change, and directional control. Here, we call this class of robots "vine robots," due to their similar behavior to plants with the growth habit of trailing. Due to their growth-based movement, vine robots are well suited for navigation and exploration in cluttered environments, but until now, they have not been deployed outside the lab. Portability of these robots and steerability at length scales relevant for navigation are key to field applications. In addition, intuitive human-in-the-loop teleoperation enables movement in unknown and dynamic environments. We present a vine robot system that is teleoperated using a custom designed flexible joystick and camera system, long enough for use in navigation tasks, and portable for use in the field. We report on deployment of this system in two scenarios: a soft robot navigation competition and exploration of an archaeological site. The competition course required movement over uneven terrain, past unstable obstacles, and through a small aperture. The archaeological site required movement over rocks and through horizontal and vertical turns. The robot tip successfully moved past the obstacles and through the tunnels, demonstrating the capability of vine robots to achieve navigation and exploration tasks in the field.Comment: IEEE Robotics and Automation Magazine, 2019. Video available at https://youtu.be/9NtXUL69g_

Saliency-directed prioritization of visual data in wireless surveillance networks

YesIn wireless visual sensor networks (WVSNs), streaming all imaging data is impractical due to resource constraints. Moreover, the sheer volume of surveillance videos inhibits the ability of analysts to extract actionable intelligence. In this work, an energy-efficient image prioritization framework is presented to cope with the fragility of traditional WVSNs. The proposed framework selects semantically relevant information before it is transmitted to a sink node. This is based on salient motion detection, which works on the principle of human cognitive processes. Each camera node estimates the background by a bootstrapping procedure, thus increasing the efficiency of salient motion detection. Based on the salient motion, each sensor node is classified as being high or low priority. This classification is dynamic, such that camera nodes toggle between high-priority and low-priority status depending on the coverage of the region of interest. High-priority camera nodes are allowed to access reliable radio channels to ensure the timely and reliable transmission of data. We compare the performance of this framework with other state-of-the-art methods for both single and multi-camera monitoring. The results demonstrate the usefulness of the proposed method in terms of salient event coverage and reduced computational and transmission costs, as well as in helping analysts find semantically relevant visual information.Supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2012904)

Lowering virus attack with improved yield and fiber quality in different cotton genotypes by early sown cotton (Gossypium hirsutum L.)

A three year study with the objective of exploring the possible role of different sowing dates and cotton genotypes on seed cotton yield, fiber quality and virus attack was conducted at the Cotton Research Station (CRS), Multan, Pakistan during three consecutive years (2006, 2007 and 2008). Two cotton genotypes namely: MNH-6070 and CIM-496 were sown on five different sowing dates (15th April, 1st May, 15th May, 1st June and 15th June) during the three consecutive years (2006, 2007 and 2008). The analyzed data indicated that early sown cotton (15th April) resulted in low virus attack (21.06%) and enhanced seed cotton yield (1575%), together with yield components (number of bolls per plant and boll weight), and improved fiber quality (staple length and micronaire) during all the three years as compared with late sown crop (15th June). Likewise, MNH-6070 also resulted in low virus attack (45.79%) and higher seed cotton yield (117.19%), as well as yield components. Regarding fiber quality, MNH-6070 resulted in higher micronarie, while CIM-496 resulted in higher staple length. Early sowing and cotton genotype MNH-6070 also resulted in maximum ginning out turn (GOT). Nonetheless, seed cotton yield and fiber quality were both negatively affected due to late sowing (1st and 15th of June) in both cotton genotypes. In crux, early sowing enhanced seed cotton yield due to increased number of bolls per plant, boll weight and low virus attack. Similarly, cotton genotype MNH-6070 also resulted to higher seed cotton yield, GOT and more resistance against virus attack due to its better genetic makeup. In summary, cotton genotype MNH-6070 should be sown on 15th April in order to obtain maximum seed cotton yield under agro-climatic conditions of Multan, Pakistan.Key words: Sowing time, seed cotton yield, staple length, micronaire

Machine Learning based Energy Management Model for Smart Grid and Renewable Energy Districts

The combination of renewable energy sources and prosumer-based smart grid is a sustainable solution to cater to the problem of energy demand management. A pressing need is to develop an efficient Energy Management Model (EMM) that integrates renewable energy sources with smart grids. However, the variable scenarios and constraints make this a complex problem. Machine Learning (ML) methods can often model complex and non-linear data better than the statistical models. Therefore, developing an ML algorithm for the EMM is a suitable option as it reduces the complexity of the EMM by developing a single trained model to predict the performance parameters of EMM for multiple scenarios. However, understanding latent correlations and developing trust in highly complex ML models for designing EMM within the stochastic prosumer-based smart grid is still a challenging task. Therefore, this paper integrates ML and Gaussian Process Regression (GPR) in the EMM. At the first stage, an optimization model for Prosumer Energy Surplus (PES), Prosumer Energy Cost (PEC), and Grid Revenue (GR) is formulated to calculate base performance parameters (PES, PEC, and GR) for the training of the ML-based GPR model. In the second stage, stochasticity of renewable energy sources, load, and energy price, same as provided by the Genetic Algorithm (GA) based optimization model for PES, PEC, and GR, and base performance parameters act as input covariates to produce a GPR model that predicts PES, PEC, and GR. Seasonal variations of PES, PEC, and GR are incorporated to remove hitches from seasonal dynamics of prosumers energy generation and prosumers energy consumption. The proposed adaptive Service Level Agreement (SLA) between energy prosumers and the grid benefits both these entities. The results of the proposed model are rigorously compared with conventional optimization (GA and PSO) based EMM to prove the validity of the proposed model

Factor structure of Urdu version of the flourishing scale

Background: A great deal of research has been carried out on the assessment of the eudaimonic perspective of psychological well-being and the hedonic perspective of subjective well-being. The Flourishing Scale (FS) has been extensively used in research and practice, as it assesses the fundamental aspects of social psychological functioning. Nevertheless, the psychometric properties of Urdu versions of eudaimonic measures, such as the FS, have not yet been ascertained. The translation and validation of the FS in the Urdu language was not available, and hence this study was planned with the aim to validate the Urdu version of the FS. Methods: We assessed the psychometric properties of the FS in a sample of adults aged 18 years and above in Pakistan (N = 130) using exploratory factor analysis based on principal component analysis with varimax rotation and confirmatory factor analysis. Results: The exploratory factor analysis confirmed the unidimensional nature of the 8-item FS. We assessed that the Urdu version of the FS showed a high internal consistency reliability (α = 0.914) with a significant intraclass correlation coefficient (ICC), p < 0.001). In our study, the Kaiser–Mayer–Olkin value was 0.915 with a chi-square test value (χ2) of 637.687, and Bartlett's test of sphericity was significant (df = 28, p < 0.001). The intraclass correlation coefficients (ICCs) at test–retest for all domains were statistically significant (p < 0.001) and showed excellent agreement for all the items. The revised confirmatory factor analysis revealed a good-fit model, but with item 8—“People respect me”—removed due to its lower factor loading. Conclusions: The findings suggest that the FS is a psychometrically sound instrument for assessing social psychological functioning among adults in Pakistan. Therefore, the validated Urdu version of the FS may be used in future studies of well-being in clinical psychology and positive psychology