In this article, we demonstrate how the instability of a polymeric thin film, when cast either as a mobile underlying support-layer of a bilayer assembly, as a mixture, or as a template, affects the self-assembly and arrangement of carbon nanotube (CNT) nanoparticles dispersed over/with it. The interplay of the different forces, like centrifugal, dewetting, Marangoni, electrostatic, assist the mobile nanoparticles in arranging themselves into circular ring-like structures. The congruence of the length scales of the self-assembled circular ring and the dewetted length scales of the thin polystyrene film reveals that the influence of dewetting dynamics is majorly responsible for the incipience of ring formations. However, the absence of coarsening and complete stoppage of movement of the rings towards the particulate region, reveals the presence of electrostatic repulsion. The nanoparticles not involved in circular-ring-self-assembly formation are driven by Marangoni force and engender nano-scratches over the polymeric surface. CNTs of smaller lengths deposit in the particulate zone where also, beyond a particular aspect ratio, the nanotubes are found to bend into circular rings. The bending of the carbon nanotubes is an energetically favored self-organization process controlled by the shape formation energy. When the underlying dewetted polystyrene surface acts as a template, the assembled CNT forms a circular ring-like interconnected relief structure. The circular rings of CNTs formed throughout the PS surface, fabricated by the dewetting of the mixture method, can be used to identify trace amounts of oil from an oil-water mixture by selective adsorption. The different self-assembled ring structures of CNTs are realized only for a specific concentration range of CNTs. The nanotubes assemble into random bundles/blobs devoid of any definite structures above this concentration.
This article provides a new tool for examining the efficiency and robustness of derivative-free optimization algorithms based on high-dimensional normalized data profiles that test a variety of performance metrics. Unlike the traditional data profiles that examine a single dimension, the proposed data profiles require several dimensions in order to analyze the relative performance of different optimization solutions. To design a use case, we utilize five sequences (solvers) of trigonometric simplex designs that extract different features of non-isometric reflections, as an example to show how various metrics (dimensions) are essential to provide a comprehensive evaluation about a particular solver relative to others. In addition, each designed sequence can rotate the starting simplex through an angle to designate the direction of the simplex. This type of features extraction is applied to each sequence of the triangular simplexes to determine a global minimum for a mathematical problem. To allocate an optimal sequence of trigonometric simplex designs, a linear model is used with the proposed data profiles to examine the convergence rate of the five simplexes. Furthermore, we compare the proposed five simplexes to an optimized version of the Nelder-Mead algorithm known as the Genetic Nelder-Mead algorithm. The experimental results demonstrate that the proposed data profiles lead to a better examination of the reliability and robustness for the considered solvers from a more comprehensive perspective than the existing data profiles. Finally, the high-dimensional data profiles reveal that the proposed solvers outperform the genetic solvers for all accuracy tests.
Introduction: Illness models, including illness recognition, perceived severity, and perceived nature can affect treatment-seeking behaviors. Vignettes are a leading approach to examine models of illness but are understudied for substance use disorders (SUDs). We created vignettes for multiple common DSM-5 SUDs and assessed SUD illness models among college students. Methods: Seven vignettes in which the protagonist meets DSM-5 diagnostic criteria for SUDs involving tobacco, alcohol, cannabis, Adderall, cocaine, Vicodin, and heroin were pilot tested and randomly assigned to 216 college students who completed measures related to illness recognition, perceived severity, and perceived nature. MANOVAs with Scheffe post-hoc tests were conducted to examine vignette group differences on models of illness. Results: Vignettes met acceptable levels of clarity and plausibility. Participants characterized the protagonist's substance use as a problem, a SUD, or an addiction most frequently with Vicodin, heroin, and cocaine and least frequently with tobacco and cannabis. Participants assigned to the Vicodin, heroin, and cocaine vignettes were the most likely to view the protagonist's situation as serious and life-threatening, whereas those assigned to the cannabis vignette were the least likely. Numerically more participants characterized the pattern of substance use as a problem (91%) or an addiction (90%) than a SUD (76%), while only 15% characterized it as a chronic medical condition. Conclusions: Illness recognition and perceived severity varied across substances and were lowest for cannabis. Few participants conceptualized SUDs as chronic medical conditions. College students may benefit from psychoeducation regarding cannabis use disorder and the chronic medical condition model of SUDs.
Sentiment Analysis is considered as an important research field in text mining, and is significant in recommendation systems and e-learning environments. This research proposes a new methodology of e-learning hybrid Recommendation System Based on Sentiment Analysis (RSBSA) by leveraging tailored Natural Language Processing (NLP) and Convolutional Neural Network (CNN) techniques, to recommend appropriate e-learning materials based on learner’s preferences. Integration is done on fine-grained sentiment analysis models, to classify text reviews of e-content posted on e-learning platform. Two enhanced language models based on ‘Continuous Bag of Word’ and ‘Skip-Gram’ are introduced. Moreover, three resilient language models based on the hybrid language techniques are developed to produce a superior vocabulary representation. These models were trained using various CNN models to predict ratings of resources from online reviews provided by learners. To accomplish this, a customizable dataset ‘ABHR-1’ is used, which is derived from e-content' reviews with corresponding ratings labeled [1-5]. The proposed models are evaluated and tested using ABHR-1 and two public datasets. According to the simulation results, Multiplication-Several-Channels-CNN model outperformed other models with an accuracy of 90.37 % for fine-grained sentiment classification on 5 discrete classes and the empirical results are compared.
Food web changes from the introduction of non‐native species can be complex, with sometimes unexpected or little effect due to food web interactions. Round Goby Neogobius melanostomus became common in samples in Oneida Lake, New York by 2014. Data from a long‐term monitoring program allowed us to document the Round Goby expansion through time. Using shoreline seine, fyke net, bottom trawl, and video surveys we estimated that Round Goby density reached >25,000 fish/ha 6 years after detection and subsequently varied between 4,110 and 26,565 fish/ha. Concurrent benthic invertebrate sampling and long‐term fish diet sampling allowed us to assess the impacts of Round Goby on density of several taxa of benthic invertebrates, and on invertebrate consumption by fish. Significant decreases in density after Round Goby arrival were found for amphipods Amphipoda, caddisflies Trichoptera, snails Gastropoda, and dreissenid mussels Dreissena spp. Densities of invertebrates following Round Goby arrival ranged from 19–48% of densities in the Pre‐Round Goby period. Frequency of occurrence in the diet of Yellow Perch Perca flavescens and White Perch Morone americana was lower after Round Goby became established for amphipods, snails, and in some cases caddisflies and chironomids Chironomidae. The decreased consumption of these invertebrates did not lead to decreases in fish growth; growth increased for some age classes of Yellow Perch and all age classes of White Perch. Despite potential Round Goby predation, burrowing mayfly Hexagenia spp. densities expanded during this time, and likely helped offset reductions in consumption of other invertebrates by Yellow Perch and White Perch, as did consumption of Round Goby. Long‐term monitoring shows that Round Goby decreased the density of several benthic invertebrate species and decreased consumption of these invertebrates by fish, but the effect on growth of Yellow Perch and White Perch was offset by consumption of Round Goby and burrowing mayflies.
A partially mixed-methods case study in a Fortune 50 technology company was conducted to delineate the interaction between organisational culture (OC) types (competitive, bureaucratic and clan) and intra-organisational knowledge sharing (KS). This study provided empirical evidence that show differences in KS horizontally (peer-to-peer) and vertically (between direct-report and manager) within an organisation. By focussing on “socialization” adopted from the organisational knowledge creation theory, the iceberg theory and the competing values framework, we addressed an unexamined area within the body of knowledge. Survey data of 82 employees and interview data of 23 employees were analysed. Multivariate analysis of covariance (MANCOVA) was used to analyse the quantitative survey data. The qualitative interview data were analysed through content analysis. A triangulation design was then followed to merge the data through an equivalent status ([Formula: see text]) interpretation to derive meta-inferences. MANCOVA displayed a statistically significant interaction between OC and KS via socialisation. The triangulated results showed that OC types distinctly impacted KS via socialisation with differences between seeking, contributing and the direction of knowledge flow (vertical and horizontal). The empirical evidence shows that organisations must consider the direction of knowledge flow (vertical or horizontal) when enforcing cultural values to drive KS via socialisation. Similarly, researchers should not ignore the directional knowledge sharing paradigm, nor the organisational knowledge creation theory, when examining intra-organisational KS.
A high-efficiency crystalline silicon-based solar cell in the visible and near-infrared regions is introduced in this paper. A textured TiO2 layer grown on top of the active silicon layer and a back reflector with gratings are used to enhance the solar cell performance. The given structure is simulated using the finite difference time domain (FDTD) method to determine the solar cell’s performance. The simulation toolbox calculates the short circuit current density by solving Maxwell’s equation, and the open-circuit voltage will be calculated numerically according to the material parameters. Hence, each simulation process calculates the fill factor and power conversion efficiency numerically. The optimization of the crystalline silicon active layer thickness and the dimensions of the back reflector grating are given in this work. The grating period structure of the Al back reflector is covered with a graphene layer to improve the absorption of the solar cell, where the periodicity, height, and width of the gratings are optimized. Furthermore, the optimum height of the textured TiO2 layer is simulated to produce the maximum efficiency using light absorption and short circuit current density. In addition, plasmonic nanoparticles are distributed on the textured surface to enhance the light absorption, with different radii, with radius 50, 75, 100, and 125 nm. The absorbed light energy for different nanoparticle materials, Au, Ag, Al, and Cu, are simulated and compared to determine the best performance. The obtained short circuit current density is 61.9 ma/cm2, open-circuit voltage is 0.6 V, fill factor is 0.83, and the power conversion efficiency is 30.6%. The proposed crystalline silicon solar cell improves the short circuit current density by almost 89% and the power conversion efficiency by almost 34%.
A low-cost Si-based optical nano-sensor that monitors traditional water pollutants is introduced in this paper. The introduced sensor works in the near-infrared region, 900 nm to 2500 nm spectral range. The proposed structure consists of a Si layer with an optimized thickness of 300 nm on the top of the Al layer acting as a back reflector. On the top of the Si layer, the water pollutants are modeled as nanoparticle materials of different sizes. The finite difference time domain method is utilized to optimize the thicknesses of the Si layer by analyzing the optical light absorption considering different Si layer thicknesses and different pollutant nanoparticles’ sizes. Different interpolation techniques, including polynomials with various degrees and locally weighted smoothing quadratic regression, are used to find the best fitting model representing the simulated data points with goodness of fit analysis. Three features are proposed to identify the water pollutant with its size, peak absorption wavelength, relative amplitude, and a full width at half maximum. The device’s performance in detecting six different pollutants, silver, aluminum, copper, chromium, selenium, and ammonia, is evaluated. Sensitivity, a figure of merit, and a quality factor are used to evaluate the proposed sensor. The obtained maximum sensitivity is 11,300 nm/RIU, FOM of 740, and quality factor of 670.
Direction of arrival (DOA) is one of the essential topics in array signal processing that has many applications in communications, smart antennas, seismology, acoustics, radars, and many more. As the applications of DOA estimation are broadened, the challenges in implementing a DOA algorithm arise. Different environments require different modifications to the existing methods. This paper reviews the DOA algorithms in the literature. It evaluates and compares the performance of the three well known algorithms, including MUSIC, ESPRIT, and Eigenvalue Decomposition (EVD), with and without using adaptive directional time–frequency distributions (ADTFD) at the preprocessing stage. We simulated a case with four sources and three receivers. The sources were well separated. Signals were received at each sensor with an SNR value of −5 dB, 0 dB, 5 dB, and 10 dB. The angles of the sources were 15, 30, 45, and 60 degrees. The simulation results show that the ADTFD algorithm significantly improved the performance of MUSIC, while it did not provide similar results for the ESPRIT and EVD methods. As expected, the computation time of the algorithms was increased by implementing the ADTFD algorithm as a preprocessing step.
Little is known about the impact that disaster volunteerism has on nurses. It is important to hear the experiences of those who return again to better understand the reasons that call them back. Using grounded theory methodology, 20 nurses who responded to more than one disaster event participated in semistructured interviews. Capacity for the art of nursing, confidence in performing the role, fostering the team among the chaos, and humanistic symbiosis emerged, leading to a core category, facilitating self-transcendence, guided by Reed’s middle-range theory. With repeat deployments come enhanced personal rewards that provide meaningful opportunities for self-transcendence.
Machine and deep learning techniques are two branches of artificial intelligence that have proven very efficient in solving advanced human problems. The automotive industry is currently using this technology to support drivers with advanced driver assistance systems. These systems can assist various functions for proper driving and estimate drivers’ capability of stable driving behavior and road safety. Many studies have proved that the driver’s emotions are the significant factors that manage the driver’s behavior, leading to severe vehicle collisions. Therefore, continuous monitoring of drivers’ emotions can help predict their behavior to avoid accidents. A novel hybrid network architecture using a deep neural network and support vector machine has been developed to predict between six and seven driver’s emotions in different poses, occlusions, and illumination conditions to achieve this goal. To determine the emotions, a fusion of Gabor and LBP features has been utilized to find the features and been classified using a support vector machine classifier combined with a convolutional neural network. Our proposed model achieved better performance accuracy of 84.41%, 95.05%, 98.57%, and 98.64% for FER 2013, CK+, KDEF, and KMU-FED datasets, respectively.
As VLSI technology is shifting from microelectronics to nanoelectronics era, bi-stable rotaxane emerges as a promising candidate for molecular electronics. A typical voltage-driven rotaxane consists of a cyclobis-(paraquat-pp-phenylene) macrocycle encircling a dumbbell shape molecular chain and moving between two stations on the chain: tetrathiafulvalene (TTF) and 1,5-dioxynaphthalene (DNP). As a molecular switch, the macrocycle can move reversibly between two stations along its axis with appropriate driving voltage, resulting in two stable molecular conformational states with distinct high and low resistance. This makes it a well-suited candidate to represent binary states (“0” and “1”) for digital electronics. In this work, we performed molecular simulation to investigate the switching mechanism of rotaxane molecule. We used distance and angle variables to characterize the movement of the macrocycle along the chain, and compared the switching behavior of rotaxane in water, ethanol, dimethyl ether and vacuum. The results show that the solvent environment plays an important role in the switching characteristics of rotaxane molecule. The switching of rotaxane is stable, controllable, reversible and repeatable. We also looked into potential failure mechanism of the rotaxane, which could shed light on the fault model, testing and reliability enhancement of rotaxane based molecular electronics. Our simulation results support that rotaxane molecules possess potential to be used for molecular memory and logic applications.
With the development of artificial intelligence, more and more companies have begun to get involved in the field of computer vision. At present, the theoretical research on intelligent accounting system at home and abroad has been quite mature. The optimization and upgrade of accounting algorithms through computer vision will effectively reduce the operating costs of group companies. With the reengineering of corporate financial processes and the optimized configuration brought by information technology, the management model innovation brought by new technology will be the use of intelligence The focus of attention of group companies in the accounting system structure model. Computer vision has been a hot spot in the current intelligent research. Some scholars have combined intelligent accounting with computer vision. Based on the analysis of financial business processes, the processes that have the characteristics of repetitive operations and rule determination in corporate shared services are passed through computers. Visual technology to improve efficiency. The experimental results of this article show that the intelligent accounting system structure and intelligent accounting algorithm based on computer vision have increased the financial accounting efficiency of enterprises by 16.8%, and the lack of compound talents that improve the combination of enterprise finance and information technology and Low level of intelligence, etc. Based on optimization goals and principles, the performance evaluation, forecasting and tax management process of the operation management module of the intelligent accounting center are optimized. Through the mutual coupling of optimization modules and computer vision intelligent technology.
This work introduces a high-efficiency organic solar cell with grating nanostructure in both hole and electron transport layers and plasmonic gold nanoparticles (Au NPs) distributed on the zinc oxide (ZnO) layer. The periods of the grating structure in both hole and electro transport layers were optimized using Lumerical finite difference time domain (FDTD) solution software. The optimum AuNP radius distributed on the ZnO layer was also simulated and analyzed before studying the effect of changing the temperature on the solar cell performance, fill factor, and power conversion efficiency. In addition, optical and electrical models were used to calculate the short circuit current density, fill factor, and overall efficiency of the produced polymer solar cell nanostructure. The maximum obtained short circuit current density and efficiency of the solar cell were 18.11 mA/cm2 and 9.46%, respectively, which gives a high light absorption in the visible region. Furthermore, the effect of light polarization for incident light angles from θ = 0° to 70° with step angle 10° on the electrical and optical parameters were also studied. Finally, optical power, electric field, and magnetic field distribution inside the nanostructure are also illustrated.
Monitoring drivers’ emotions is the key aspect of designing advanced driver assistance systems (ADAS) in intelligent vehicles. To ensure safety and track the possibility of vehicles’ road accidents, emotional monitoring will play a key role in justifying the mental status of the driver while driving the vehicle. However, the pose variations, illumination conditions, and occlusions are the factors that affect the detection of driver emotions from proper monitoring. To overcome these challenges, two novel approaches using machine learning methods and deep neural networks are proposed to monitor various drivers’ expressions in different pose variations, illuminations, and occlusions. We obtained the remarkable accuracy of 93.41%, 83.68%, 98.47%, and 98.18% for CK+, FER 2013, KDEF, and KMU-FED datasets, respectively, for the first approach and improved accuracy of 96.15%, 84.58%, 99.18%, and 99.09% for CK+, FER 2013, KDEF, and KMU-FED datasets respectively in the second approach, compared to the existing state-of-the-art methods.
As the human population of the Lake Ontario basin continues to grow, targeted research and monitoring activities to inform adaptive management are increasingly important for protecting the Lake Ontario ecosystem. As the most downstream of the Great Lakes, the Lake Ontario ecosystem is under pressure from a wide range of stressors including chemical contaminants and invasive species. This special issue highlights the broad network of binational research and monitoring efforts by federal, state, and provincial agencies and academic partners that took place during the 2018 Cooperative Science and Monitoring Initiative (CSMI) field year for Lake Ontario. The research and monitoring by creative and collaborative teams assembled under the umbrella of CSMI 2018 includes projects that investigated a wide variety of factors impacting the lake ecosystem, ranging from physics to chemistry and biology. This issue also provides examples of data sharing/synthesis and modeling tools that promote the use of these extensive datasets to explore ecosystem management options. The research and monitoring outcomes from CSMI 2018 provide managers with current information on the Lake Ontario ecosystem to inform decision making and guide restoration and protection efforts.
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