Abstract

In the present study, it was attempted to investigate the cyclic resistance of equi-proportionate silt–sand range pond ash (with 50% fines) at relatively high shear strains using the strain-controlled cyclic triaxial test. The cyclic triaxial tests have been performed considering the effect of relative compaction (97–99%), cyclic shear strain (0.6–1.35%), frequency of loading (0.3–1 Hz) and effective confining pressure (50–100 kPa) on cyclic resistance of pond ash. Dynamic characteristics such as dynamic shear modulus and damping ratio of equi-proportionate silt–sand range pond ash was evaluated for all the parameters considered at high shear strain. The maximum value of the dynamic shear modulus and damping ratio of pond ash observed in this study was 6534.8 kPa and 23.64%, respectively. The dynamic shear modulus and damping ratio of pond ash was decreased from 6534.8 to 5023.87 kPa and 23.64 to 14.17 …

Abstract

We thank the respected discussers for appreciating the relevance of the present work, critically analyzing it, and bringing forth the possible constraints in its applicability. This discussion provides us an opportunity to elaborate the point related to the scope and applicability of original work, especially with focus on the hydrological community that otherwise would possibly remain unclarified. Because there are two discussions of our paper, we have addressed them in two different sections.

Abstract

Rice blast disease is a major threat to rice production worldwide; the causative pathogenic fungus Magnaporthe oryzae induces rice (Oryza sativa) plants to undergo molecular changes that help them to circumvent this fungal attack. Transcriptome studies have demonstrated that many genes are involved in the defense response of rice to M. oryzae, but most of these studies focused on the screening of differentially expressed genes and the studies did not investigate the interactions among genes. We examined the interaction of rice and M. oryzae in a network context. Two near-isogenic lines were profiled at different time-points. Using transcriptome data obtained from an RNASeq analysis, a network based on the relationships among genes was developed through weighted gene co-expression network analysis. The analysis of degree centrality identified numerous hub genes and potential key regulators that control the rice response, providing new insights into the molecular network underlying the resistance of rice to M. oryzae infection. Additionally, a protein-protein interaction network was derived to identify complexes that might physically interact. For example, complexes of OsbHLH148/OsJAZ, OsMYB4 and some components of the phenylpropanoid pathway, as well as MYB/bHLH

Abstract

The Standard Model (SM), when extended with a leptoquark (LQ) and right-handed neutrinos, can have interesting new implications for Higgs physics. We show that sterile neutrinos can induce a boost to the down-type quark Yukawa interactions through a diagonal coupling associated with the quarks and a scalar LQ of electromagnetic charge 1/3. The relative change is moderately larger in the case of the first two generations of quarks, as they have vanishingly small Yukawa couplings in the SM. The enhancement in the couplings would also lead to a non-negligible contribution from the quark fusion process to the production of the 125 GeV Higgs scalar in the SM, though the gluon fusion always dominates. However, this may not be true for a general scalar. As an example, we consider a scenario with a SM-gauge-singlet scalar ϕ where an O (1) coupling between ϕ and the LQ is allowed. The ϕ q q Yukawa …

Abstract

Patterns of DNA methylation are significantly altered in cancers. Interpreting the functional consequences of DNA methylation requires the integration of multiple forms of data. The recent advancement in the next-generation sequencing can help to decode this relationship and in biomarker discovery. In this study, we investigated the methylation patterns of papillary renal cell carcinoma (PRCC) and its relationship with the gene expression using The Cancer Genome Atlas (TCGA) multi-omics data. We found that the promoter and body of tumor suppressor genes, microRNAs and gene clusters and families, including cadherins, protocadherins, claudins and collagens, are hypermethylated in PRCC. Hypomethylated genes in PRCC are associated with the immune function. The gene expression of several novel candidate genes, including interleukin receptor IL17RE and immune checkpoint genes HHLA2, SIRPA and …

Abstract

Information acquisition through crowdsourcing with mobile agents is a popular way to collect data, especially in the context of smart cities where the deployment of dedicated data collectors is expensive and ineffective. It requires trustworthy and effective mechanisms to guarantee that the collected data are accurately acquired and reported. Trustworthy and transparent frameworks can be implemented via smart contracts on blockchain to enable robust information elicitation mechanisms. In this work we develop Orthos, a blockchain-based trustworthy framework for spontaneous location-based information aggregation queries without assuming any prior knowledge about them. We employ game-theoretic mechanisms to incentivize agents to report truthfully and ensure that the information is collected at the desired location while ensuring the privacy of the agents. We identify six necessary characteristics for information aggregation mechanisms to be applicable in spontaneous location-based settings. Orthos implements the function of robust peer truth serum for crowdsourcing (RPTSC), the state-of-the-art information aggregation mechanism using smart contracts. Additionally, as location information is exogenous to RPTSC, we design the Proof-of-Location protocol to ensure that agents gather the data at the desired locations. We examine the performance of Orthos on Rinkeby Ethereum testnet and conduct experiments with live audience.

Abstract

Demand response is a crucial tool to maintain the stability of the smart grids. With the upcoming research trends in the area of electricity markets, it has become a possibility to design a dynamic pricing system, and consumers are made aware of what they are going to pay. Though the dynamic pricing system (pricing based on the total demand a distributor company is facing) seems to be one possible solution, the current dynamic pricing approaches are either too complex for a consumer to understand or are too naive leading to inefficiencies in the system (either consumer side or distributor side). Due to these limitations, the recent literature is focusing on the approach to provide incentives to the consumers to reduce the electricity, especially in peak hours. For each round, the goal is to select a subset of consumers to whom the distributor should offer incentives so as to minimize the loss which comprises of cost of buying the electricity from the market, uncertainties at consumer end, and cost incurred to the consumers to reduce the electricity which is a private information to the consumers. Due to the uncertainties in the loss function (arising from renewable energy resources as well as consumption needs), traditional auction theory-based incentives face manipulation challenges. Towards this, we propose a novel combinatorial multi-armed bandit (MAB) algorithm, which we refer to as GLS-MAB to learn the uncertainties along with an auction to elicit true costs incurred by the consumers. We prove that our mechanism is regret optimal and is incentive compatible. We further demonstrate efficacy of our algorithms via simulations.

Abstract

The ability of intelligent agents to play games in human-like fashion is popularly considered a benchmark of progress in Artificial Intelligence. In our work, we introduce the first computational model aimed at Pictionary, the popular word-guessing social game. We first introduce Sketch-QA, a guessing task. Styled after Pictionary, Sketch-QA uses incrementally accumulated sketchstroke sequences as visual data. Sketch-QA involves asking a fixed question (“What object is being drawn?”) and gathering open-ended guess-words from human guessers. We analyze the resulting dataset and present many interesting findings therein. Tomimic Pictionary-style guessing, we propose a deep neural model which generates guess-words in response to temporally evolving human-drawn object sketches. Our model even makes human-like mistakes while guessing, thus amplifying the human mimicry factor.We evaluate our model on the large-scale guess-word dataset generated via Sketch-QA task and compare with various baselines. Wealso conduct a Visual Turing Test to obtain human impressions of the guess-words generated by humans and our model. Experimental results demonstrate the promise of our approach for Pictionary and similarly themed games.

Abstract

As the communications among the vehicles, theRoad-Side Units(RSU)and the Edge Servers(ES)take placevia wireless communication and the Internet, an adversary maytake the opportunity to tamper with the data communicatedamong various entities in an Internet of Vehicles (IoV) envi-ronment. Therefore, it demands secure communication amongthe involved entities in an IoV-based Intelligent TransportationSystem (ITS) deployment. In this work, we design a newblockchain-enabled certificate-based authentication scheme forvehicle accident detection and notification in ITS, called BCAS-VADN. In BCAS-VADN, through the authentication process, eachvehicle can securely notify accident related transactions to itsnearby Cluster Head(CH), if an accident is detected on roadseither by its own or neighbor vehicle(s). TheCHthen securelysends the transactions received from the vehicles to itsRSUand subsequently, these transactions are also received secretlyby theESs. TheESis responsible for preparing partial blockcontaining transactions and Merkle tree root, and a digital signa-ture on those information, and then forwarding to its associatedCloud Server(CS)in the Blockchain Center(BC)for completeblock creation, verification and addition of the block using thedesigned consensus process. Due to blockchain technology usage,it is shown that BCAS-VADN is not only secure against variouspotential attacks, but also maintains transparency, immutabilityand decentralization of the information. Furthermore, a compre-hensive comparative analysis reveals that BCAS-VADN achievesbetter security and more functionality attributes, and has lowcommunication and computational overheads as compared toother competitive authentication schemes in IoV. In addition,the practical demonstration using the blockchain technology hasbeen also provided.

Abstract

Agriculture is a vital area for the sustenance ofmankind engulfing manufacturing, security, traceability, andsustainable resource management. With the resources recedingexpeditiously, it is of utmost significance to innovate techniquesthat help in the subsistence of agriculture. The growth of Internetof Things (IoT) and Blockchain technology as two rapidlyemerging fields can ameliorate the state of food chain today. Thispaper provides a rigorous literature review to inspect the state-of-the-art development of the schemes that provide informationsecurity using blockchain technology. After identifying the corerequirements in smart agriculture, a generalized blockchain-based security architecture has been proposed. A detailed costanalysis has been conducted on the studied schemes. A meticu-lous comparative analysis uncovered the drawbacks in existingresearch. Furthermore, detailed analysis of the literature has alsorevealed the security goals towards which the research has beendirected and helped to identify new avenues for future researchusing artificial intelligence.