Thermal Cycling Effects on the Thermoelectric Properties of n-Type In,Ce-Based Skutterudite Compounds

n-Type In-filled CoSb₃ is a known skutterudite compound that has shown promising thermoelectric (TE) properties resulting in high dimensionless figure of merit values at elevated temperatures. Use in various waste heat recovery applications will require survival and operation after exposure to harsh thermal cycling environments. This research focused on uncovering the thermal cycling effects on TE properties of n-type In_0.2Co₄Sb₁₂ and In_0.2Ce_0.15Co₄Sb₁₂ skutterudite compositions as well as quantifying their temperature-dependent structural properties (elastic modulus, shear modulus, and Poisson??s ratio). It was observed that the Seebeck coefficient and resistivity increased only slightly in the double-filled In,Ce skutterudite materials upon thermal cycling. In the In-filled skutterudites the Seebeck coefficient remained approximately the same on thermal cycling, while the electrical resistivity increased significantly after thermal cycling. Results also show that the thermal conductivity marginally decreases in the case of In-filled skutterudites, whereas the reduction is more pronounced in In,Ce-based skutterudite compounds. The possible reason for this kind of reduction can be attributed to grain pinning effects due to formation of nanoinclusions. High-temperature structural property measurements (i.e., Young??s modulus and shear modulus) are also reported. The results show that these structural properties decrease slowly as temperature increases and that the compounds are structurally stable after numerous (up to 200) thermal cycles.     

Metagenomic Insights into Rhizospheric Microbiome Profiling in Lentil Cultivars Unveils Differential Microbial Nitrogen and Phosphorus Metabolism under Rice-Fallow Ecology

The plant rhizosphere interfaces an array of microbiomes related to plant growth and development. Cultivar-specific soil microbial communities with respect to their taxonomic structure and specific function have not been investigated explicitly in improving the adaptation of lentil cultivars under rice-fallow ecology. The present study was carried out to decipher the rhizosphere microbiome assembly of two lentil cultivars under rice-fallow ecology for discerning the diversity of microbial communities and for predicting the function of microbiome genes related to nitrogen (N) and phosphorus (P) cycling processes deploying high-throughput whole (meta) genome sequencing. The metagenome profile of two cultivars detected variable microbiome composition with discrete metabolic activity. Cyanobacteria, Bacteroidetes, Proteobacteria, Gemmatimonadetes, and Thaumarchaeota were abundant phyla in the “Farmer-2” rhizosphere, whereas Actinobacteria, Acidobacteria, Firmicutes, Planctomycetes, Chloroflexi, and some incompletely described procaryotes of the “Candidatus” category were found to be robustly enriched the rhizosphere of “Moitree”. Functional prediction profiles of the microbial metagenomes between two cultivars revealed mostly house keeping genes with general metabolism. Additionally, the rhizosphere of “Moitree” had a high abundance of genes related to denitrification processes. Significant difference was observed regarding P cycling genes between the cultivars. “Moitree” with a profuse root system exhibited better N fixation and translocation ability due to a good “foraging strategy” for improving acquisition of native P under the nutrient depleted rice-fallow ecology. However, “Farmer-2” revealed a better “mining strategy” for enhancing P solubilization and further transportation to sinks. This study warrants comprehensive research for explaining the role of microbiome diversity and cultivar–microbe interactions towards stimulating microbiome-derived soil reactions regarding nutrient availability under rice-fallow ecology.     

High temperature thermopower and electrical resistivity studies in the transparent conducting oxide Sn doped MgIn2O4

Sn doping in an n-type transparent conducting oxide MgIn₂O₄ is carried out and its effect on the high temperature transport properties viz. thermopower and electrical resistivity is studied. A solid solution exists in the composition window Mg_1+xIn_2−2xSn_xO₄ for 0 < x ≤ 0.4. The band gap as well as the transport properties increases with increasing Sn concentration. The high temperature resistivity properties indicate degenerate semiconducting behavior for all the compositions. The highest figure of merit obtained is 0.12 × 10⁻⁴ K⁻¹ for the parent compound at 600 K.     

Green synthesis of silver nanoparticles-based nanofluids and investigation of their antimicrobial activities

We have proposed a facile green technique for synthesizing silver nanoparticles-based nanofluids at high temperature and pressure using low molecular weight lactulose solution, which is playing the role of a reducing as well as stabilizing agent. The particle/crystallite sizes, morphology, crystallinity of the nanoparticles are characterized using spectroscopic, microscopic, and diffraction techniques. Since the properties of nanofluids are attractive for technological applications, the investigation of their thermal and electrical conductivities is also immensely important. The material shows a significant enhancement of both thermal and electrical conductivities in comparison to the base fluid due to high surface area, enhanced Brownian motion and layering at the liquid–solid interface of the nanofluids. Moreover, these nanofluids offer excellent antimicrobial activities to different gram class bacteria.     

A Signature Analysis Technique for the Identification of Failing Vectors with Application to Scan-BIST*

We present a new technique for uniquely identifying a single failing vector in an interval of test vectors. This technique is applicable to combinational circuits and for scan-BIST in sequential circuits with multiple scan chains. The proposed method relies on the linearity properties of the MISR and on the use of two test sequences, which are both applied to the circuit under test. The second test sequence is derived from the first in a straightforward manner and the same test pattern source is used for both test sequences. If an interval contains only a single failing vector, the algebraic analysis is guaranteed to identify it. We also show analytically that if an interval contains two failing vectors, the probability that this case is interpreted as one failing vector is very low. We present experimental results for the ISCAS benchmark circuits to demonstrate the use of the proposed method for identifying failing test vectors.     

Strategy construction for parity games with imperfect information

We consider two-player parity games with imperfect information in which strategies rely on observations that provide imperfect information about the history of a play. To solve such games, i.e., to determine the winning regions of players and corresponding winning strategies, one can use the subset construction to build an equivalent perfect-information game. Recently, an algorithm that avoids the inefficient subset construction has been proposed. The algorithm performs a fixed-point computation in a lattice of antichains, thus maintaining a succinct representation of state sets. However, this representation does not allow to recover winning strategies.In this paper, we build on the antichain approach to develop an algorithm for constructing the winning strategies in parity games of imperfect information. One major obstacle in adapting the classical procedure is that the complementation of attractor sets would break the invariant of downward-closedness on which the antichain representation relies. We overcome this difficulty by decomposing problem instances recursively into games with a combination of reachability, safety, and simpler parity conditions. We also report on an experimental implementation of our algorithm; to our knowledge, this is the first implementation of a procedure for solving imperfect-information parity games on graphs.     

Automated competitive analysis of real-time scheduling with graph games

This paper is devoted to automatic competitive analysis of real-time scheduling algorithms for firm-deadline tasksets, where only completed tasks contribute some utility to the system. Given such a taskset \({\mathcal {T}}\), the competitive ratio of an on-line scheduling algorithm \({\mathcal {A}}\) for \({\mathcal {T}}\) is the worst-case utility ratio of \({\mathcal {A}}\) over the utility achieved by a clairvoyant algorithm. We leverage the theory of quantitative graph games to address the competitive analysis and competitive synthesis problems. For the competitive analysis case, given any taskset \({\mathcal {T}}\) and any finite-memory on-line scheduling algorithm \({\mathcal {A}}\), we show that the competitive ratio of \({\mathcal {A}}\) in \({\mathcal {T}}\) can be computed in polynomial time in the size of the state space of \({\mathcal {A}}\). Our approach is flexible as it also provides ways to model meaningful constraints on the released task sequences that determine the competitive ratio. We provide an experimental study of many well-known on-line scheduling algorithms, which demonstrates the feasibility of our competitive analysis approach that effectively replaces human ingenuity (required for finding worst-case scenarios) by computing power. For the competitive synthesis case, we are just given a taskset \({\mathcal {T}}\), and the goal is to automatically synthesize an optimal on-line scheduling algorithm \({\mathcal {A}}\), i.e., one that guarantees the largest competitive ratio possible for \({\mathcal {T}}\). We show how the competitive synthesis problem can be reduced to a two-player graph game with partial information, and establish that the computational complexity of solving this game is Np-complete. The competitive synthesis problem is hence in Np in the size of the state space of the non-deterministic labeled transition system encoding the taskset. Overall, the proposed framework assists in the selection of suitable scheduling algorithms for a given taskset, which is in fact the most common situation in real-time systems design.     

Reduction of stochastic parity to stochastic mean-payoff games

A stochastic graph game is played by two players on a game graph with probabilistic transitions. We consider stochastic graph games with ω-regular winning conditions specified as parity objectives, and mean-payoff (or limit-average) objectives. These games lie in NP ∩ coNP. We present a polynomial-time Turing reduction of stochastic parity games to stochastic mean-payoff games.