Theoretical Prediction of Chiral 3D Hybrid Organic–Inorganic Perovskites

Hybrid organic–inorganic perovskites (HOIPs), in particular 3D HOIPs, have demonstrated remarkable properties, including ultralong charge‐carrier diffusion lengths, high dielectric constants, low trap densities, tunable absorption and emission wavelengths, strong spin–orbit coupling, and large Rashba splitting. These superior properties have generated intensive research interest in HOIPs for high‐performance optoelectronics and spintronics. Here, 3D hybrid organic–inorganic perovskites that implant chirality through introducing the chiral methylammonium cation are demonstrated. Based on structural optimization, phonon spectra, formation energy, and ab initio molecular dynamics simulations, it is found that the chirality of the chiral cations can be successfully transferred to the framework of 3D HOIPs, and the resulting 3D chiral HOIPs are both kinetically and thermodynamically stable. Combining chirality with the impressive optical, electrical, and spintronic properties of 3D perovskites, 3D chiral perovskites is of great interest in the fields of piezoelectricity, pyroelectricity, ferroelectricity, topological quantum engineering, circularly polarized optoelectronics, and spintronics.     

Estimation of power density in IMPATT using different materials

ABSTRACT

The RF output power dissipated per unit area is calculated using Runge-Kutta method for the high-moderate-moderate-high (n⁺-n-p-p⁺) doping profile of double drift region (DDR)-based impact avalanche transit time (IMPATT) diode by taking different substrate at Ka band. Those substrates are silicon, gallium arsenide, germanium, wurtzite gallium nitride, indium phosphide and 4H-silicon carbide. A comparative study regarding power dissipation ability by the IMPATT using different material is being presented thereby modelling the DDR IMPATT diode in a one-dimensional structure. The IMPATT based on 4H-SiC element has highest power density in the order of 10¹⁰ Wm^?2 and the Si-based counterpart has lowest power density of order 10⁶ Wm^?2 throughout the Ka band. So, 4H-SiC-based IMPATT should be preferable over others for the power density preference based application. This result will be helpful to estimate the power density of the IMPATT for any doping profile and to select the proper element for the optimum design of the IMPATT as far as power density is concerned in the Ka band. Also, we have focused on variation of power density with different junction temperatures and modelled the heat sink with analysis of thermal resistances.     

A model for prediction of dimensional tolerances of laser cut holes in mild steel thin plates

Dimensional variation in laser cutting of sheet metals depends on two factors: the accuracy and repeatability of the positioning system, and the thermal effects of the laser beam on the workpiece material. In this work, a physical model was formulated to estimate the dimensional accuracy of holes produced with laser cutting. The model included the assumption that the layer adjacent to the hole is plastically deformed and contains residual stresses up to the yield strength. The model was used to calculate the size of the hole and cut-out disk of varying radii in steel plates with thicknesses of 3.2 mm and 6.4 mm. The model-predicted data were verified with the experimental data obtained using a 1 kW continuous wave CO₂ laser. Results indicated that there is an excellent correlation between the model and the experimental data especially for smaller diameter holes.     

Distributed Learning for Planning Under Uncertainty Problems with Heterogeneous Teams

This paper considers the problem of multiagent sequential decision making under uncertainty and incomplete knowledge of the state transition model. A distributed learning framework, where each agent learns an individual model and shares the results with the team, is proposed. The challenges associated with this approach include choosing the model representation for each agent and how to effectively share these representations under limited communication. A decentralized extension of the model learning scheme based on the Incremental Feature Dependency Discovery (Dec-iFDD) is presented to address the distributed learning problem. The representation selection problem is solved by leveraging iFDD’s property of adjusting the model complexity based on the observed data. The model sharing problem is addressed by having each agent rank the features of their representation based on the model reduction error and broadcast the most relevant features to their teammates. The algorithm is tested on the multi-agent block building and the persistent search and track missions. The results show that the proposed distributed learning scheme is particularly useful in heterogeneous learning setting, where each agent learns significantly different models. We show through large-scale planning under uncertainty simulations and flight experiments with state-dependent actuator and fuel-burn- rate uncertainty that our planning approach can outperform planners that do not account for heterogeneity between agents.     

A comparison of lipids from liver and hepatoma subcellular membranes

Subcellular fractions of nuclei, mitochondria, endoplasmic reticulum, plasma membrane and cytosol were prepared from liver and hepatoma 72288CTC. Marker enzyme activities, biochemical compositions and electron microscopy were used to establish purity. Hepatoma NADH: cytochrome C reductase and 5′-nucleotidase exhibited abnormal subcellular distributions. The lipids from the subcellular fractions were examined in detail. Mitochondria and plasma membranes were characterized by elevated percentages of diphosphatidylglycrerol and sphingomyelin, respectively, in both tissues. All hepatoma subcellular fractions contained dramatically elevated levels of sphingomyelin and cholesterol, two components that form preferential strong complexes in vitro. The fatty acid composition of hepatoma sphingomyelin differed markedlg from liver and, unlike liver, did not exhibit organelle specific compositions. Some hepatoma lipid classes contained reduced percentages of palmitate while others contained higher levels. Hepatoma phosphatidylcholine and phosphatidylethanolamine from organelles contained lower percentages of long chain polyunsaturated fatty acids than liver. Generally, unique fatty acid profiles exhibited by individual phospholipid classes of liver subcellular fractions were absent or much reduced in the hepatoma. The ratios of oleate to vaccenate were near one for most of the phospholipid classes of most liver fractions, but all hepatoma classes, with few exceptions, contained a much higher percentage of oleate in all subcellular fractions. The hypothesis is proposed that the origin of some acyl moieties for the biosynthesis of various hepatome lipid classes differs from liver sources. The possible changes in acyl pools, sources and compartments for complex lipid biosynthesis could result in change in the quantities of molecular species that could contribute to the abnormal properties of the hepatoma membranes.     

Identification and resolution of conflicts during ontological integration using rules

Abstract: Integration of ontologies of information sources and consumers is an important phase in achieving web‐based interoperability. The present work describes an approach for identifying certain semantic conflicts while integrating ontologies of heterogeneous information sources. This paper is focused on the identification of homonymy and synonymy between elements in ontologies. In the present work the concepts of homonymy and synonymy are synonymous to naming conflicts and entity identifier conflicts, respectively, and partial synonymy is synonymous to schema isomorphism conflicts. The concept of the mask of interoperability is introduced for the identification of synonymy. The mask of interoperability is expressed in a declarative way as a set of rules, which can then be used for resolution of conflicts during integration of ontologies. As proof of concept, ontologies are implemented using the XML‐based ontology language Ontology Web Language (OWL), and the rules are implemented using the emerging rule language Semantic Web Rule Language (SWRL). This representation in OWL and SWRL allows the ontology to be executable, flexibly extendable and platform‐independent. The OWL facts and SWRL rules are used by the Jess and Bossam reasoning engine to identify semantic homonymy and synonymy.     

Structural and electrical properties of mixed oxides of manganese and vanadium: A new semiconductor oxide thermistor material

Binary oxides of manganese and vanadium have been synthesized by solid state sintering, in which the mass ratio of the individual components Mn₂O₃ and VO₂ have been varied from 90:10 to 5:95. The bulk ceramic samples were characterized by X-ray diffraction and scanning electron microscopy with energy dispersive X-ray analysis. The initial compositions either rich in Mn₂O₃ or in equi-proportion by mass with VO₂ yield β-Mn₂V₂O₇ or a new crystalline form of Mn₂V₂O₇, with unit cell parameters: a = 7.73091 Å, b = 6.640788 Å, c = 6.70779 Å α = γ = 90° and β = 98.7086° which is designated as γ-Mn₂V₂O₇. The compositions, richer in VO₂ produce MnV₂O₆ co-existing with V₂O₅ the proportion of which increases with increase in VO₂. The surface microanalysis shows a spherical-granular morphology in Mn₂V₂O₇ structure and plate/rod-like structures co-existing with granular morphology in case of MnV₂O₆ together with V₂O₅. The electrical parameters of the negative temperature coefficient thermistors were determined. Depending on the constituent oxide composition, the NTC thermistors showed room temperature resistivity in the range of 6.52 × 10² to 6.1 × 10⁶ Ω-cm. The thermistor constant and activation energy are in the range of 0.12–0.458 eV and 1393–4801 K, respectively.     

Collusion set detection using graph clustering

Many mal-practices in stock market trading—e.g., circular trading and price manipulation—use the modus operandi of collusion. Informally, a set of traders is a candidate collusion set when they have “heavy trading” among themselves, as compared to their trading with others. We formalize the problem of detection of collusion sets, if any, in the given trading database. We show that naïve approaches are inefficient for real-life situations. We adapt and apply two well-known graph clustering algorithms for this problem. We also propose a new graph clustering algorithm, specifically tailored for detecting collusion sets. A novel feature of our approach is the use of Dempster–Schafer theory of evidence to combine the candidate collusion sets detected by individual algorithms. Treating individual experiments as evidence, this approach allows us to quantify the confidence (or belief) in the candidate collusion sets. We present detailed simulation experiments to demonstrate effectiveness of the proposed algorithms.     

Fuel cells for non-automotive uses: Status and prospects

Fuel cells are in varying stages of commercialization for both automotive and non-automotive applications. The fuel cell industry has made substantial progress but still needs to reduce costs and improve performance to compete successfully with established technologies. In just 5 years, costs have been reduced by a factor of two while improving efficiency and durability. Based on interviews with fuel cell manufacturers in the U.S., Japan and the EU and information from published sources, a model of non-automotive fuel cell markets is constructed and used to estimate the impacts of government policies and to project the potential evolution of the industry to 2025. The model includes the effects of learning-by-doing, scale economies and exogenous technological progress on component and system costs, estimates customer choices between fuel cell and competing established technologies, and attempts to measure the impacts of government policies. With continued policy support it appears likely that the industry can become self-sustaining within the next decade.