Showing papers by "Missouri University of Science and Technology published in 2016"
TL;DR: To better understand the impact of Big Data on various marketing activities, enabling firms to better exploit its benefits, a conceptual framework that builds on resource-based theory is proposed.
835 citations
TL;DR: In this article, a metal-rich form of Ni-selenide, nickel subselenides, Ni3Se2 has been investigated as a potential oxygen evolution electrocatalyst under alkaline conditions for the first time.
Abstract: A metal-rich form of Ni-selenide, nickel subselenide, Ni3Se2 has been investigated as a potential oxygen evolution electrocatalyst under alkaline conditions for the first time. The Ni3Se2 phase has a structure similar to the sulfur mineral heazlewoodite, which contains metal–metal bonding. The electrocatalytic activities of Ni3Se2 towards OER were seen to be at par with or even superior to the transition metal oxide based electrocatalyst in terms of onset overpotential for O2 evolution as well as overpotential to reach a current density of 10 mA cm−2 (observed at 290 mV). The electrocatalytic Ni3Se2 films were grown by electrodeposition on conducting substrates and the deposition parameters including the pH of the electrolytic bath, deposition potential, and substrate composition were seen to have some influence on the catalytic activity. So far, Ni3Se2 films deposited on the Au-coated Si substrate was seen to have the lowest overpotential. Annealing of the as-deposited electrocatalytic films in an inert atmosphere, enhanced their catalytic efficiencies by decreasing the overpotential (@10 mA cm−2) as well as increasing the current density. The structure and morphology of these films has been characterized by powder X-ray diffraction, scanning and transmission electron microscopy, Raman, and X-ray photoelectron spectroscopy. Catalytic activities were investigated through detailed electrochemical studies under alkaline conditions, including linear sweep voltammetry, chronoamperometric studies at constant potential, electrochemical surface area determination and calculation of the Tafel slope. The Faradaic efficiency of this catalyst has been estimated by reducing the evolved O2 in a RRDE set-up which also confirmed that the evolved gas was indeed O2. In addition to low overpotentials, these Ni3Se2 electrodeposited films were seen to be exceptionally stable under conditions of continuous O2 evolution for an extended period (42 h).
588 citations
TL;DR: In this paper, the effects of three shaped steel fibers (straight, corrugated, and hooked-end) with different fiber contents by volume on mechanical properties of ultra high performance concrete (UHPC) were investigated.
443 citations
01 May 2016
TL;DR: A series of recent advances in real-time WSANs for industrial control systems are reviewed, with a focus on cyber-physical codesign of wireless control systems that integrate wireless and control designs.
Abstract: With recent adoption of wireless sensor-actuator networks (WSANs) in industrial automation, industrial wireless control systems have emerged as a frontier of cyber-physical systems. Despite their success in industrial monitoring applications, existing WSAN technologies face significant challenges in supporting control systems due to their lack of real-time performance and dynamic wireless conditions in industrial plants. This article reviews a series of recent advances in real-time WSANs for industrial control systems: 1) real-time scheduling algorithms and analyses for WSANs; 2) implementation and experimentation of industrial WSAN protocols; 3) cyber-physical codesign of wireless control systems that integrate wireless and control designs; and 4) a wireless cyber-physical simulator for codesign and evaluation of wireless control systems. This article concludes by highlighting research directions in industrial cyber-physical systems.
317 citations
Journal Article•
TL;DR: This review summarizes the current knowledge of the effects of blue light on the regulation of physiologic functions and the possible effects ofblue light exposure on ocular health and the spectral output of LED-based light sources to minimize the danger that may be associated with blue light exposure.
Abstract: Light-emitting diodes (LEDs) have been used to provide illumination in industrial and commercial environments. LEDs are also used in TVs, computers, smart phones, and tablets. Although the light emitted by most LEDs appears white, LEDs have peak emission in the blue light range (400-490 nm). The accumulating experimental evidence has indicated that exposure to blue light can affect many physiologic functions, and it can be used to treat circadian and sleep dysfunctions. However, blue light can also induce photoreceptor damage. Thus, it is important to consider the spectral output of LED-based light sources to minimize the danger that may be associated with blue light exposure. In this review, we summarize the current knowledge of the effects of blue light on the regulation of physiologic functions and the possible effects of blue light exposure on ocular health.
317 citations
TL;DR: Carbon onions are a relatively new member of the carbon nanomaterials family and are used as electrodes for supercapacitor applications as discussed by the authors, where they provide fast charge/discharge rates resulting in high specific power but present comparatively low specific energy.
Abstract: Carbon onions are a relatively new member of the carbon nanomaterials family. They consist of multiple concentric fullerene-like carbon shells which are highly defective and disordered. Due to their small size of typically below 10 nm, the large external surface area, and high conductivity they are used for supercapacitor applications. As electrode materials, carbon onions provide fast charge/discharge rates resulting in high specific power but present comparatively low specific energy. They improve the performance of activated carbon electrodes as conductive additives and show suitable properties as substrates for redox-active materials. This review provides a critical discussion of the electrochemical properties of different types of carbon onions as electrode materials. It also compares the general advantages and disadvantages of different carbon onion synthesis methods. The physical and chemical properties of carbon onions, in particular nanodiamond-derived carbon onions, are described with emphasis on those parameters especially important for electrochemical energy storage systems, including the structure, conductivity, and porosity. Although the primary focus of current research is on electrode materials for supercapacitors, the use of carbon onions as conductive additives and for redox-active species is also discussed.
317 citations
TL;DR: In this article, a unified model for gas transport in organic nanopores of shale gas reservoirs is presented, accounting for the effects of coupling the real gas effect, stress dependence and an adsorption layer on gas transport.
293 citations
TL;DR: In this article, a family of nonisolated high-voltage-gain dc-dc power electronic converters is proposed, which can be used as multiport converters and draw continuous current from two input sources.
Abstract: A family of nonisolated high-voltage-gain dc–dc power electronic converters is proposed. The suggested topologies can be used as multiport converters and draw continuous current from two input sources. They can also draw continuous current from a single source in an interleaved manner. This versatility makes them appealing in renewable applications such as solar farms. The proposed converters can easily achieve a gain of 20 while benefiting from a continuous input current. Such a converter can individually link a PV panel to a 400-V dc bus. The design and component selection procedures are presented. A 400-W prototype of the proposed converter with $V_{\text{in}} = 20$ and $V_{\text{out}} = 400$ V has been developed to validate the analytical results.
281 citations
TL;DR: This paper considers a time-division duplex system where uplink training is required and an active eavesdropper can attack the training phase to cause pilot contamination at the transmitter, and derives an asymptotic achievable secrecy rate when the number of transmit antennas approaches infinity.
Abstract: In this paper, we investigate secure and reliable transmission strategies for multi-cell multi-user massive multiple-input multiple-output systems with a multi-antenna active eavesdropper. We consider a time-division duplex system where uplink training is required and an active eavesdropper can attack the training phase to cause pilot contamination at the transmitter. This forces the precoder used in the subsequent downlink transmission phase to implicitly beamform toward the eavesdropper, thus increasing its received signal power. Assuming matched filter precoding and artificial noise (AN) generation at the transmitter, we derive an asymptotic achievable secrecy rate when the number of transmit antennas approaches infinity. For the case of a single-antenna active eavesdropper, we obtain a closed-form expression for the optimal power allocation policy for the transmit signal and the AN, and find the minimum transmit power required to ensure reliable secure communication. Furthermore, we show that the transmit antenna correlation diversity of the intended users and the eavesdropper can be exploited in order to improve the secrecy rate. In fact, under certain orthogonality conditions of the channel covariance matrices, the secrecy rate loss introduced by the eavesdropper can be completely mitigated.
272 citations
TL;DR: In this article, the effects of graphite nanoplatelets (GNPs) and carbon nanofibers (CNFs) on mechanical properties of ultra-high-performance concrete (UHPC) are investigated.
Abstract: Effects of graphite nanoplatelets (GNPs) and carbon nanofibers (CNFs) on mechanical properties of ultra-high-performance concrete (UHPC) are investigated. A non-proprietary UHPC mixture composed of 0.5% steel micro fibers, 5% silica fume, and 40% fly ash was used. The content of the nanomaterials ranged from 0 to 0.3% by weight of cementitious materials. The nanomaterials were dispersed using optimized surfactant content and ultra-sonification to ensure uniform dispersion in the UHPC mixture. As the content of nanomaterials is increased from 0 to 0.3%, the tensile strength and energy absorption capacity can be increased by 56% and 187%, respectively; the flexural strength and toughness can be increased by 59% and 276%, respectively. At 0.2% of GNPs, the UHPCs exhibited “strain-hardening” in tension and in flexure.
254 citations
TL;DR: In this article, a simple and effective double-side pullout testing method was adopted to characterize the interfacial bond properties, which include pullout load-slip relationship, bond strength, and pullout energy, of steel fiber-matrix in ultra-high strength cement-based material (UHSC) with 0-25% silica fume by the mass of binder.
Abstract: The use of silica fume can significantly enhance mechanical properties of concrete given its beneficial filling and pozzolanic effects. In this study, a simple and effective double-side pullout testing method was adopted to characterize the interfacial bond properties, which include pullout load-slip relationship, bond strength, and pullout energy, of steel fiber-matrix in ultra-high strength cement-based material (UHSC) with 0–25% silica fume by the mass of binder. The effects of silica fume content on flowability, heat of hydration, compressive and flexural strengths, hydration products, and pore structure of matrix at different curing time were evaluated as well. Backscatter scanning electron microscopy (BSEM) and micro-hardness measurement were used to examine the quality of interfacial transition zone (ITZ) around the fiber. In terms of the results, the optimal silica fume content could be in the range of 15%–25%. UHSC mixtures with these dosages of silica fume showed significant improvement in pullout behavior. Its bond strength and pullout energy at 28 d could increase by 170% and 250% compared to the reference samples without any silica fume. The microstructural observation verified the findings on the macro-properties development. Formation of more and higher strength of hydration products and refinement of ITZ around the fiber ensured higher micro-hardness, and thus improved the bond to fiber.
TL;DR: The design and realization of ultrathin plasmonic metasurface holograms made of subwavelength nanoslits for reconstructing both two- and three-dimensional full-color holographic images and will advance various holographic applications.
Abstract: Holography is one of the most attractive approaches for reconstructing optical images, due to its capability of recording both the amplitude and phase information on light scattered from objects. Recently, optical metasurfaces for manipulating the wavefront of light with well-controlled amplitude, phase, and polarization have been utilized to reproduce computer-generated holograms. However, the currently available metasurface holograms have only been designed to achieve limited colors and record either amplitude or phase information. This fact significantly limits the performance of metasurface holograms to reconstruct full-color images with low noise and high quality. Here, we report the design and realization of ultrathin plasmonic metasurface holograms made of subwavelength nanoslits for reconstructing both two- and three-dimensional full-color holographic images. The wavelength-multiplexed metasurface holograms with both amplitude and phase modulations at subwavelength scale can faithfully produce not...
TL;DR: This paper presents a novel approximation-based event-triggered control of multi-input multi-output uncertain nonlinear continuous-time systems in affine form that is approximated using a linearly parameterized neural network in the context of event-based sampling.
Abstract: This paper presents a novel approximation-based event-triggered control of multi-input multi-output uncertain nonlinear continuous-time systems in affine form. The controller is approximated using a linearly parameterized neural network (NN) in the context of event-based sampling. After revisiting the NN approximation property in the context of event-based sampling, an event-triggered condition is proposed using the Lyapunov technique to reduce the network resource utilization and to generate the required number of events for the NN approximation. In addition, a novel weight update law for aperiodic tuning of the NN weights at triggered instants is proposed to relax the knowledge of complete system dynamics and to reduce the computation when compared with the traditional NN-based control. Nonetheless, a nonzero positive lower bound for the inter-event times is guaranteed to avoid the accumulation of events or Zeno behavior. For analyzing the stability, the event-triggered system is modeled as a nonlinear impulsive dynamical system and the Lyapunov technique is used to show local ultimate boundedness of all signals. Furthermore, in order to overcome the unnecessary triggered events when the system states are inside the ultimate bound, a dead-zone operator is used to reset the event-trigger errors to zero. Finally, the analytical design is substantiated with numerical results.
01 Dec 2016
TL;DR: This study proposes a new and robust machine learning model based on a convolutional neural network (CNN) to automatically classify single cells in thin blood smears on standard microscope slides as either infected or uninfected.
Abstract: Malaria is a major global health threat. The standard way of diagnosing malaria is by visually examining blood smears for parasite-infected red blood cells under the microscope by qualified technicians. This method is inefficient and the diagnosis depends on the experience and the knowledge of the person doing the examination. Automatic image recognition technologies based on machine learning have been applied to malaria blood smears for diagnosis before. However, the practical performance has not been sufficient so far. This study proposes a new and robust machine learning model based on a convolutional neural network (CNN) to automatically classify single cells in thin blood smears on standard microscope slides as either infected or uninfected. In a ten-fold cross-validation based on 27,578 single cell images, the average accuracy of our new 16-layer CNN model is 97.37%. A transfer learning model only achieves 91.99% on the same images. The CNN model shows superiority over the transfer learning model in all performance indicators such as sensitivity (96.99% vs 89.00%), specificity (97.75% vs 94.98%), precision (97.73% vs 95.12%), F1 score (97.36% vs 90.24%), and Matthews correlation coefficient (94.75% vs 85.25%).
Proceedings Article•
04 May 2016
TL;DR: The melanoma diagnosis in dermoscopic images was addressed by the Dermoscope Image Analysis Benchmark Challenge (DIAB) as discussed by the authors, which was divided into sub-challenges for each task involved in image analysis, including lesion segmentation, feature detection within a lesion and classification of melanoma.
Abstract: In this article, we describe the design and implementation of a publicly accessible dermatology image analysis benchmark challenge. The goal of the challenge is to sup- port research and development of algorithms for automated diagnosis of melanoma, a lethal form of skin cancer, from dermoscopic images. The challenge was divided into sub-challenges for each task involved in image analysis, including lesion segmentation, dermoscopic feature detection within a lesion, and classification of melanoma. Training data included 900 images. A separate test dataset of 379 images was provided to measure resultant performance of systems developed with the training data. Ground truth for both training and test sets was generated by a panel of dermoscopic experts. In total, there were 79 submissions from a group of 38 participants, making this the largest standardized and comparative study for melanoma diagnosis in dermoscopic images to date. While the official challenge duration and ranking of participants has concluded, the datasets remain available for further research and development.
TL;DR: 3D-printed monoliths with zeolite loadings as high as 90 wt % exhibit adsorption uptake that is comparable to that of powder sorbents, and show reasonably good mechanical stability that can eventually prevent attrition and dusting issues commonly encountered in traditional pellets and beads packing systems.
Abstract: Structured adsorbents, especially in the form of monolithic contactors, offer an excellent gas–solid contacting strategy for the development of practical and scalable CO2 capture technologies. In this study, the fabrication of three-dimensional (3D)-printed 13X and 5A zeolite monoliths with novel structures and their use in CO2 removal from air are reported. The physical and structural properties of these printed monoliths are evaluated and compared with their powder counterparts. Our results indicate that 3D-printed monoliths with zeolite loadings as high as 90 wt % exhibit adsorption uptake that is comparable to that of powder sorbents. The adsorption capacities of 5A and 13X monoliths were found to be 1.59 and 1.60 mmol/g, respectively, using 5000 ppm (0.5%) CO2 in nitrogen at room temperature. The dynamic CO2/N2 breakthrough experiments show relatively fast dynamics for monolithic structures. In addition, the printed zeolite monoliths show reasonably good mechanical stability that can eventually preve...
TL;DR: Past studies are reviewed to clearly differentiate between direct- and indirect-phytovolatilization and the plant physiology driving phytovol atilization in different ecosystems and future research needs are presented that could better quantify phytomolecular fluxes at field scale.
Abstract: Plants can interact with a variety of organic compounds, and thereby affect the fate and transport of many environmental contaminants. Volatile organic compounds may be volatilized from stems or leaves (direct phytovolatilization) or from soil due to plant root activities (indirect phytovolatilization). Fluxes of contaminants volatilizing from plants are important across scales ranging from local contaminant spills to global fluxes of methane emanating from ecosystems biochemically reducing organic carbon. In this article past studies are reviewed to clearly differentiate between direct- and indirect-phytovolatilization and we discuss the plant physiology driving phytovolatilization in different ecosystems. Current measurement techniques are also described, including common difficulties in experimental design. We also discuss reports of phytovolatilization in the literature, finding that compounds with low octanol-air partitioning coefficients are more likely to be phytovolatilized (log KOA < 5). Reports ...
TL;DR: In this paper, a battery operation cost model is proposed which considers a battery as an equivalent fuel-run generator to enable it to be incorporated into a unit commitment problem, and a probabilistic constrained approach is used to incorporate the uncertainties of the renewable sources and load demands into the unit commitment and economic dispatch problems.
Abstract: Integration of renewable energy resources in microgrids has been increasing in recent decades. Due to the randomness in renewable resources such as solar and wind, the power generated can deviate from forecasted values. This variation may cause increased operating costs for committing costly reserve units or penalty costs for shedding load. In addition, it is often desired to charge/discharge and coordinate the energy storage units in an efficient and economical way. To address these problems, a novel battery operation cost model is proposed which considers a battery as an equivalent fuel-run generator to enable it to be incorporated into a unit commitment problem. A probabilistic constrained approach is used to incorporate the uncertainties of the renewable sources and load demands into the unit commitment (UC) and economic dispatch problems.
TL;DR: In this paper, the effects of nano-CaCO 3 and nano-SiO 2 contents on flowability, heat of hydration, mechanical properties, phase change, and pore structure of ultra-high strength concrete (UHSC) were investigated.
Abstract: Nanomaterials have attracted much interest in cement-based materials during the past decade. In this study, the effects of different nano-CaCO 3 and nano-SiO 2 contents on flowability, heat of hydration, mechanical properties, phase change, and pore structure of ultra-high strength concrete (UHSC) were investigated. The dosages of nano-CaCO 3 were 0, 1.6%, 3.2%, 4.8%, and 6.4%, by the mass of cementitious materials, while the dosages of nano-SiO 2 were 0, 0.5%, 1.0%, 1.5%, and 2%. The results indicated that both nano-CaCO 3 and nano-SiO 2 decreased the flowability and increased the heat of hydration with the increase of their contents. The optimal dosages to enhance compressive and flexural strengths were 1.6%–4.8% for the nano-CaCO 3 and 0.5%–1.5% for the nano-SiO 2 . Although compressive and flexural strengths were comparable for the two nanomaterials after 28 d, their strength development tendencies with age were different. UHSC mixtures with nano-SiO 2 showed continuous and sharp increase in strength with age up to 7 d, while those with nano-CaCO 3 showed almost constant strength between 3 and 7 d, but sharp increase thereafter. Thermal gravimetry (TG) analysis demonstrated that the calcium hydroxide (CH) content in UHSC samples decreased significantly with the increase of nano-SiO 2 content, but remained almost constant for those with nano-CaCO 3 . Mercury intrusion porosimetry (MIP) results showed that both porosity and critical pore size decreased with the increase of hydration time as well as the increase of nanoparticles content to an optimal threshold, beyond which porosity decreased. The difference between them was that nano-CaCO 3 mainly reacted with C 3 A to form carboaluminates, while nano-SiO 2 reacted with Ca(OH) 2 to form C S H. Both nano-CaCO 3 and nano-SiO 2 demonstrated nucleation and filling effects and resulted in less porous and more homogeneous structure.
TL;DR: In this article, a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches, is presented, and an agenda of open research challenges in incentivizing users in participatory learning is discussed.
Abstract: Participatory sensing is a powerful paradigm that takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users’ willingness to submit up-to-date and accurate information, it is paramount to effectively incentivize users’ active and reliable participation. In this article, we survey existing literature on incentive mechanisms for participatory sensing systems. In particular, we present a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, we discuss an agenda of open research challenges in incentivizing users in participatory sensing.
TL;DR: The Co7Se8 bifunctional catalyst enabled water electrolysis in alkaline solution at a cell voltage of 1.6 V and shows unprecedented catalytic performance especially with the patterned growth of catalyst rods and tubes.
Abstract: Electrodeposited Co7Se8 nanostructures exhibiting flake-like morphology show bifunctional catalytic activity for oxygen evolution and hydrogen evolution reaction (OER and HER, respectively) in alkaline medium with long-term durability (>12 h) and high Faradaic efficiency (99.62%). In addition to low Tafel slope (32.6 mV per decade), the Co7Se8 OER electrocatalyst also exhibited very low overpotential to achieve 10 mA cm–2 (0.26 V) which is lower than other transition metal chalcogenide based OER electrocatalysts reported in the literature and significantly lower than the state-of-the-art precious metal oxides. A low Tafel slope (59.1 mV per decade) was also obtained for the HER catalytic activity in alkaline electrolyte. The OER catalytic activity could be further improved by creating arrays of 3-dimensional rod-like and tubular structures of Co7Se8 through confined electrodeposition on lithographically patterned nanoelectrodes. Such arrays of patterned nanostructures produced exceptionally high mass acti...
TL;DR: Electrochemical tests show that the new electrodes outperform conventional slurry processed electrodes, which is due to different binder distribution, and bonding tests of the dry-deposited particles onto the current collector show thatthe bonding strength is greater than slurry-cast electrodes.
Abstract: Lithium ion battery electrodes were manufactured using a new, completely dry powder painting process. The solvents used for conventional slurry-cast electrodes have been completely removed. Thermal activation time has been greatly reduced due to the time and resource demanding solvent evaporation process needed with slurry-cast electrode manufacturing being replaced by a hot rolling process. It has been found that thermal activation time to induce mechanical bonding of the thermoplastic polymer to the remaining active electrode particles is only a few seconds. Removing the solvent and drying process allows large-scale Li-ion battery production to be more economically viable in markets such as automotive energy storage systems. By understanding the surface energies of various powders which govern the powder mixing and binder distribution, bonding tests of the dry-deposited particles onto the current collector show that the bonding strength is greater than slurry-cast electrodes, 148.8 kPa as compared to 84.3 kPa. Electrochemical tests show that the new electrodes outperform conventional slurry processed electrodes, which is due to different binder distribution.
TL;DR: In this paper, a Rapid Single Particle ICP-MS (SP-ICP-MS) method was developed to characterize and quantify titanium-containing, titanium dioxide, silver, and gold nanoparticles in surface water and treated drinking water.
TL;DR: A new metal-organic framework, Fe-BTTri, is found to be highly selective in the adsorption of CO over a variety of other gas molecules, making it extremely effective, for example, in the removal of trace CO from mixtures with H2, N2, and CH4.
Abstract: A new metal–organic framework, Fe-BTTri (Fe3[(Fe4Cl)3(BTTri)8]2·18CH3OH, H3BTTri =1,3,5-tris(1H-1,2,3-triazol-5-yl)benzene)), is found to be highly selective in the adsorption of CO over a variety of other gas molecules, making it extremely effective, for example, in the removal of trace CO from mixtures with H2, N2, and CH4. This framework not only displays significant CO adsorption capacity at very low pressures (1.45 mmol/g at just 100 μbar), but, importantly, also exhibits readily reversible CO binding. Fe-BTTri utilizes a unique spin state change mechanism to bind CO in which the coordinatively unsaturated, high-spin FeII centers of the framework convert to octahedral, low-spin FeII centers upon CO coordination. Desorption of CO converts the FeII sites back to a high-spin ground state, enabling the facile regeneration and recyclability of the material. This spin state change is supported by characterization via infrared spectroscopy, single crystal X-ray analysis, Mossbauer spectroscopy, and magnetic...
01 Aug 2016
TL;DR: Additive manufacturing (AM) technology provides new opportunities to automatically and flexibly fabricate parts with complicated shapes and architectures that could not be produced by conventional manufacturing processes, thus enabling unprecedented design flexibilities and application opportunities as mentioned in this paper.
Abstract: Additive Manufacturing (AM) technology provides new opportunities to automatically and flexibly fabricate parts with complicated shapes and architectures that could not be produced by conventional manufacturing processes, thus enabling unprecedented design flexibilities and application opportunities. The lattice structure possesses many superior properties to solid material and conventional structures. It is able to integrate more than one function into a physical part, which makes it attractive to a wide range of applications. With AM technology the lattice structure can be fabricated by adding material layer-by-layer directly from a Computer-Aided Design (CAD) model, rather than the conventional processes with complicated procedures. AM lattice structures have been intensively studied for more than ten years with significant progress having been made. This paper reviews and discusses AM processes, design methods and considerations, mechanical behavior, and applications for lattice structures enabled by this emerging technology.
TL;DR: An event-triggered near optimal control of uncertain nonlinear discrete-time systems with Lyapunov technique used in conjunction with the event-trigger condition to guarantee the ultimate boundedness of the closed-loop system is presented.
Abstract: This paper presents an event-triggered near optimal control of uncertain nonlinear discrete-time systems. Event-driven neurodynamic programming (NDP) is utilized to design the control policy. A neural network (NN)-based identifier, with event-based state and input vectors, is utilized to learn the system dynamics. An actor–critic framework is used to learn the cost function and the optimal control input. The NN weights of the identifier, the critic, and the actor NNs are tuned aperiodically once every triggered instant. An adaptive event-trigger condition to decide the trigger instants is derived. Thus, a suitable number of events are generated to ensure a desired accuracy of approximation. A near optimal performance is achieved without using value and/or policy iterations. A detailed analysis of nontrivial inter-event times with an explicit formula to show the reduction in computation is also derived. The Lyapunov technique is used in conjunction with the event-trigger condition to guarantee the ultimate boundedness of the closed-loop system. The simulation results are included to verify the performance of the controller. The net result is the development of event-driven NDP.
TL;DR: Tuning the vascular barrier leakiness through ND surface group functionalization could provide an alternative strategy for the cancer nanomedicine to traverse across the vascular Barrier.
Abstract: Cancer nanomedicine vehicles are required to cross the vascular barrier to reach the tumor site in order to ensure the successful delivery of their therapeutic load. Here, nanodiamond (ND) variants were shown to induce surface dependent vascular barrier leakiness. The ND-induced leakiness was found to be mediated by the increase in intracellular reactive oxygen species (ROS) and Ca2+. These then in turn triggered the loss in endothelial cell–endothelial cell connections of the vascular barrier and also triggered their quasi-stable cytoskeletal remodelling. This ND driven increase in leakiness allowed more doxorubicin drug to penetrate through the vascular barrier to reach the cancer cells. This increase in the doxorubicin penetration subsequently led to an increase in the cancer killing effect. Overall, tuning the vascular barrier leakiness through ND surface group functionalization could provide an alternative strategy for the cancer nanomedicine to traverse across the vascular barrier.
TL;DR: In this paper, trasmall iron phosphide nanoparticles have been reported as an efficient electrocatalyst for the oxygen evolution reaction under alkaline conditions with a low overpotential and Tafel slope.
Abstract: Ultrasmall iron phosphide nanoparticles have been reported as an efficient electrocatalyst for the oxygen evolution reaction under alkaline conditions with a low overpotential and Tafel slope. Mixing the FeP nanoparticles with reduced graphene oxide further reduces the overpotential to 260 mV at 10 mA cm−2, which is one of the lowest values reported for OER electrocatalysts.
TL;DR: A detailed trading model that provides a more effective and intelligent way for recognizing trading signals and assisting investors with trading decisions by utilizing a system that adapts both the inputs and the prediction model based on the desired output is led.
Abstract: The system provides an automated and adaptive model selection process.The system predicts the stock price direction, rather than the forecasted level.Particle swarm optimization is used to reduce computation time.Denoising is used to deal with stock market volatility. Predicting the direction and movement of stock index prices is difficult, often leading to excessive trading, transaction costs, and missed opportunities. Often traders need a systematic method to not only spot trading opportunities, but to also provide a consistent approach, thereby minimizing trading errors and costs. While mechanical trading systems exist, they are usually designed for a specific stock, stock index, or other financial asset, and are often highly dependent on preselected inputs and model parameters that are expected to continue providing trading information well after the initial training or back-tested model development period. The following research leads to a detailed trading model that provides a more effective and intelligent way for recognizing trading signals and assisting investors with trading decisions by utilizing a system that adapts both the inputs and the prediction model based on the desired output. To illustrate the adaptive approach, multiple inputs and modeling techniques are utilized, including neural networks, particle swarm optimization, and denoising. Simulations with stock indexes illustrate how traders can generate higher returns using the developed adaptive decision support system model. The benefits of adding adaptive and intelligent decision making to forecasts are also discussed.