Miracle-dz/UniFAR-FTUs huggingface dataset:
GCOAST: Skill assessments of coupling wave and circulation models (NEMO-WAM)

The coupling of models is a commonly used approach when addressing the complex interactions between different components of the Earth system. This study presents the development of a new, high -resolution, coupled ocean and wave model system for the North Sea and the Baltic Sea, which is part of the Geestacht COAstal model SysTem GCOAST. We focus on the nonlinear feedback between strong tidal currents and wind -waves, which can no longer be ignored, in particular in the coastal zone where its role seems to be dominant. The proposed coupling parameterisations account for the feedback between of the upper ocean on the atmospheric circulation by accounting for the effects of the sea level, and ocean temperature and salinity. A focus is given on the newly implemented parameterisations that consider the effect of non-liner contribution and the component transfer of the momentum and energy fluxes from the atmosphere to the ocean thought the waves interface. Sensitivity experiments are performed to estimate the role of different wave-ocean coupling components. The performance of the coupled modelling system is illustrated for the cases of several extreme events. For example, the inclusion of wave coupling changes sea surface temperature, the mixing and ocean circulation and the total sea level leading to better agreement with in -situ and satellite observations. The model comparisons with data from satellite altimeter and in-situ observations showed that the use of the fully coupled system reduces the errors, especially under severe storm conditions.
Conversational Code Analysis: The Future of Secure Coding

The area of software development and secure coding can benefit significantly from advancements in virtual assistants. Research has shown that many coders neglect security in favor of meeting deadlines. This shortcoming leaves systems vulnerable to attackers. While a plethora of tools are available for programmers to scan their code for vulnerabilities, finding the right tool can be challenging. It is therefore imperative to adopt measures to get programmers to utilize code analysis tools that will help them produce more secure code. This chapter looks at the limitations of existing approaches to secure coding and proposes a methodology that allows programmers to scan and fix vulnerabilities in program code by communicating with virtual assistants on their smart devices. With the ubiquitous move towards virtual assistants, it is important to design systems that are more reliant on voice than on standard point-and-click and keyboard-driven approaches. Consequently, we propose MyCodeAnalyzer, a Google Assistant app and code analysis framework, which was designed to interactively scan program code for vulnerabilities and flaws using voice commands during development. We describe the proposed methodology, implement a prototype, test it on a vulnerable project and present our results.
A Survey on Coronavirus Vaccination

Background: Right now many pharmaceutical companies are tried to finalise the vaccines of coronavirus and many are trying In India, vaccination of coronavirus is the world’s largest program Many types of rumours are coming from the side of the public of the whole world about the vaccination The authors are decided to survey in this regard Objective: To investigate immunization trust in locally made coronavirus antibodies and antibody inclination Methods: In December 2020, the authors are surveyed 500 people of the top ten states of India about the acceptance rates and factors influencing acceptance of a coronavirus vaccine For this survey, online platform technology is used to find the response of the participants Results: The results of the survey represent that out of 500 respondents, 53 5% were females 37 6% of people were responded who have an income level of Rs 501-1000 per day 38 8% of those people responded who have an only intermediate qualifi-cation 42 4% of people having the age group 25 to 54 years were responded to the vaccination survey 51 8% of people fully agreed with vaccination but 34% of people suggested if it is coming from the side of the employer, it becomes more successful Conclusion: By survey analysis of this paper, it is concluded that maximum people are trusting and some are not agreed with the vaccination © IJCRR
Adaptive High-Impedance Surface for Prevention of Waveguide’s High-Intensity Wave

This article presents an adaptive high-impedance surface (A-HIS) that can be applied in the prevention of high-intensity wave propagating in <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>-band waveguide. Once the embedded diodes loaded on A-HIS are switched from OFF- to ON-states by the high-intensity incident wave, the boundary condition of the waveguide’s bottom wall would adaptively change from a perfect electric conductor (PEC) to a perfect magnetic conductor (PMC), and the propagation would be prevented. To demonstrate this nonlinear characteristic, eigenmodes of the periodic unit-cells in two states of diodes were calculated, and the dispersive performance was analyzed. The bandwidth was further derived through the bandgaps between its intrinsic modes. Finally, a prototype with <inline-formula> <tex-math notation="LaTeX">$5\times 10$ </tex-math></inline-formula> elements was designed, fabricated, and measured in a WR90 waveguide. The results indicate that, on condition of incident wave with relatively low power intensity, signals can propagate through the A-HIS with a low insertion loss by no more than 1 dB; in the case of relatively high power intensity, this loaded A-HIS can attenuate the incident wave by more than 10 dB and absorb more than 80% power within its operation bandwidth.
Generalising Multilingual Concept-to-Text NLG with Language Agnostic Delexicalisation

Concept-to-text Natural Language Generation is the task of expressing an input meaning representation in natural language. Previous approaches in this task have been able to generalise to rare or unseen instances by relying on a delexicalisation of the input. However, this often requires that the input appears verbatim in the output text. This poses challenges in multilingual settings, where the task expands to generate the output text in multiple languages given the same input. In this paper, we explore the application of multilingual models in concept-to-text and propose Language Agnostic Delexicalisation, a novel delexicalisation method that uses multilingual pretrained embeddings, and employs a character-level post-editing model to inflect words in their correct form during relexicalisation. Our experiments across five datasets and five languages show that multilingual models outperform monolingual models in concept-to-text and that our framework outperforms previous approaches, especially in low resource conditions.
Fabrication of Metal Sulfide Ag2S via Zone Melting Method and Evaluation of Its Thermoelectric Property

Metal sulfide Ag2S is an attractive semiconductor due to its excellent physical and chemical property that enable it with wide applications in fields of catalysis, sensing, optoelectronics in past years. In present work, Ø18 mm× 50 mm Ag2S ingot was successfully prepared using zone melting method and its potential thermoelectric (TE) behavior was investigated. Ag2S has standard monoclinic P21/c space group (α-Ag2S phase) below 450 K and will transfer to cubic structure (β-Ag2S phase) over this temperature. Ag2S is a n-type semiconductor as the Seebeck coefficient S is always negative due to the Ag interstitial ions in the material that can provide additional electrons. The S value is about -1200 μV·K near room temperature and will decline to -680 μV·K at 440 K and finally decreases to ~-100 μV·K at β-Ag2S state. The electrical conductivity σ of α-Ag2S is almost zero. However, the value sharply jumps to ~40000.5 S·m as the material just changes to β-Ag2S and then gradually deceases to 33256.2 S·m 1 at 650 K. Hall measurement demonstrates the carrier concentration nH of Ag2S is suddenly increased from the level of ~10 cm to ~10 cm during phase transition. The total thermal conductivity κ of α-Ag2S is ~0.20 W·m ·K and the value is ~0.45 W·m·K to β-Ag2S. Ultimately, a maximum ZT= 0.57 is achieved around 580 K that means Ag2S might be a promising middle-temperature TE material.
PillarSegNet: Pillar-based Semantic Grid Map Estimation using Sparse LiDAR Data

Semantic understanding of the surrounding environment is essential for automated vehicles. The recent publication of the SemanticKITTI dataset stimulates the research on semantic segmentation of LiDAR point clouds in urban scenarios. While most existing approaches predict sparse pointwise semantic classes for the sparse input LiDAR scan, we propose PillarSegNet to be able to output a dense semantic grid map. In contrast to a previously proposed grid map method, PillarSegNet uses PointNet to learn features directly from the 3D point cloud and then conducts 2D semantic segmentation in the top view. To train and evaluate our approach, we use both sparse and dense ground truth, where the dense ground truth is obtained from multiple superimposed scans. Experimental results on the SemanticKITTI dataset show that PillarSegNet achieves a performance gain of about 10% mIoU over the state-of-the-art grid map method.
Comprehensive Study: How the Context Information of Different Granularity Affects Dialogue State Tracking?

Dialogue state tracking (DST) plays a key role in task-oriented dialogue systems to monitor the user’s goal. In general, there are two strategies to track a dialogue state: predicting it from scratch and updating it from previous state. The scratch-based strategy obtains each slot value by inquiring all the dialogue history, and the previous-based strategy relies on the current turn dialogue to update the previous dialogue state. However, it is hard for the scratch-based strategy to correctly track short-dependency dialogue state because of noise; meanwhile, the previous-based strategy is not very useful for long-dependency dialogue state tracking. Obviously, it plays different roles for the context information of different granularity to track different kinds of dialogue states. Thus, in this paper, we will study and discuss how the context information of different granularity affects dialogue state tracking. First, we explore how greatly different granularities affect dialogue state tracking. Then, we further discuss how to combine multiple granularities for dialogue state tracking. Finally, we apply the findings about context granularity to few-shot learning scenario. Besides, we have publicly released all codes.
Effects of rhamnolipids on the cell surface characteristics of Sphingomonas sp. GY2B and the biodegradation of phenanthrene

A number of biosurfactants have been applied in the bioremediation of contaminated environment; however, their impact on bioremediation is controversial. The present study was focused on the effects of biosurfactants on the biodegradation capability and the interaction of biosurfactants with bacterial cells. The impacts of the widely used biosurfactant rhamnolipids on a typical phenanthrene-degrading Sphingomonas sp. GY2B were examined. It was observed that the removal percentages of phenanthrene decreased when rhamnolipids were added in higher than 1 critical micelle concentration (CMC); however, no difference in the removal percentages was observed after 24 h treatment when the rhamnolipids were added in lower than 1 CMC. This may be due to the fact that higher concentrations (above 1 CMC) of rhamnolipids cause the sequestration of PAHs into surfactant micelles. This phenomenon was further confirmed by the results of FTIR analysis. SEM images and the zeta potential test further proved that the addition of rhamnolipids decreased the cell surface hydrophobicity (CSH) and reduced the amount of filamentary materials. The ratio of saturated fatty acids to unsaturated fatty acids (sat/unsat) of cells showed a decreasing trend from 38.50 to 26.10 in the absence of rhamnolipids. However, no obvious difference was observed with 1 CMC of rhamnolipids (35.34 and 33.77 at 24 h and 48 h, respectively), indicating that rhamnolipids may not enhance the membrane fluidity and transmembrane transport of phenanthrene. Overall, the addition of rhamnolipids in a concentration above its CMC could cause a lag period in the biodegradation of phenanthrene. Therefore, it is necessary to determine the optimal dosage of the biosurfactants to enhance the surfactant-amended remediation applications involving the biodegradation of PAHs.
Aerospace Sliding Mode Control Toolbox: Relative Degree Approach with Resource Prospector Lander and Launch Vehicle Case Studies

Conventional Sliding mode control and observation techniques are widely used in aerospace applications, including aircrafts, UAVs, launch vehicles, missile interceptors, and hypersonic missiles. This work is dedicated to creating a MATLAB-based sliding mode controller design and simulation software toolbox that aims to support aerospace vehicle applications. An architecture of the aerospace sliding mode control toolbox (SMC Aero) using the relative degree approach is proposed. The SMC Aero libraries include 1st order sliding mode control (1-SMC), second order sliding mode control (2-SMC), higher order sliding mode (HOSM) control (either fixed gain or adaptive), as well as higher order sliding mode differentiators. The efficacy of the SMC Aero toolbox is confirmed in two case studies: controlling and simulating resource prospector lander (RPL) soft landing on the Moon and launch vehicle (LV) attitude control in ascent mode.
Stronger Privacy for Federated Collaborative Filtering With Implicit Feedback

Recommender systems are commonly trained on centrally-collected user interaction data like views or clicks. This practice however raises serious privacy concerns regarding the recommender’s collection and handling of potentially sensitive data. Several privacy-aware recommender systems have been proposed in recent literature, but comparatively little attention has been given to systems at the intersection of implicit feedback and privacy. To address this shortcoming, we propose a practical federated recommender system for implicit data under user-level local differential privacy (LDP). The privacy-utility trade-off is controlled by parameters ϵ and k, regulating the per-update privacy budget and the number of ϵ-LDP gradient updates sent by each user, respectively. To further protect the user’s privacy, we introduce a proxy network to reduce the fingerprinting surface by anonymizing and shuffling the reports before forwarding them to the recommender. We empirically demonstrate the effectiveness of our framework on the MovieLens dataset, achieving up to Hit Ratio with K=10 (HR@10) 0.68 on 50,000 users with 5,000 items. Even on the full dataset, we show that it is possible to achieve reasonable utility with HR@10>0.5 without compromising user privacy.
RAPID HIGH-PERFORMANCE THIN LAYER CHROMATOGRAPHIC QUANTITATIVE ESTIMATION OF CAFFEINE IN VARIOUS FOODS AND BEVERAGES

Caffeine is an alkaloid and well-known CNS stimulant usually added in most foods and beverages. Caffeine had drawn more attention in many nutritional products due to its stimulant effect on CNS, so that the product to which caffeine is added becomes more popular in the market. The present work was carried out to extract and estimate the amount of caffeine in the various food and beverages by chromatography technique using HighPerformance Thin Layer Chromatography. Procured marketed products like confectioneries, foods and beverages are subjected for extraction and estimated the caffeine content by HPTLC method by using mobile phase Ethyl acetate: Methanol (9: 1) and quantified. Monster and Tzinga both energy drinks showed the content within the label claim (23.96 and 25.43% respectively). However other products though they did not have a label claim, showed significant caffeine content, in that dark chocolate showed the highest 11.40%, whereas diet coke did not show any caffeine content. The consumable food products like biscuits, energy drinks, and other beverages with added caffeine should have quantitative labeling on the wrapper to help the consumer to know about the caffeine. But label claim of caffeine is missing on some products, HPTLC method helps to estimate the caffeine content from the marketed products.
Taxonomy of Polar Subspaces of Multi-Qubit Symplectic Polar Spaces of Small Rank

We study certain physically-relevant subgeometries of binary symplectic polar spaces W(2N−1,2) of small rank N, when the points of these spaces canonically encode N-qubit observables. Key characteristics of a subspace of such a space W(2N−1,2) are: the number of its negative lines, the distribution of types of observables, the character of the geometric hyperplane the subspace shares with the distinguished (non-singular) quadric of W(2N−1,2) and the structure of its Veldkamp space. In particular, we classify and count polar subspaces of W(2N−1,2) whose rank is N−1. W(3,2) features three negative lines of the same type and its W(1,2)’s are of five different types. W(5,2) is endowed with 90 negative lines of two types and its W(3,2)’s split into 13 types. A total of 279 out of 480 W(3,2)’s with three negative lines are composite, i.e., they all originate from the two-qubit W(3,2). Given a three-qubit W(3,2) and any of its geometric hyperplanes, there are three other W(3,2)’s possessing the same hyperplane. The same holds if a geometric hyperplane is replaced by a ‘planar’ tricentric triad. A hyperbolic quadric of W(5,2) is found to host particular sets of seven W(3,2)’s, each of them being uniquely tied to a Conwell heptad with respect to the quadric. There is also a particular type of W(3,2)’s, a representative of which features a point each line through which is negative. Finally, W(7,2) is found to possess 1908 negative lines of five types and its W(5,2)’s fall into as many as 29 types. A total of 1524 out of 1560 W(5,2)’s with 90 negative lines originate from the three-qubit W(5,2). Remarkably, the difference in the number of negative lines for any two distinct types of four-qubit W(5,2)’s is a multiple of four.
Exploiting Path Information for Anchor Based Graph Neural Network

Learning node representation that incorporating information from graph structure beneﬁts wide range of tasks on graph. Majority of existing graph neural networks (GNNs) have limited power in capturing position information for a given node. The idea of positioning nodes with selected anchors has been exploit, yet mainly rely on explicit labeling of distance information. Here we propose Graph Inference Representation (GIR), an anchor based GNN encoding path information related to anchors for each node. Abilities to get position-aware embedding are theoretically and experimentally investigated on GIRs and its core variants. Further, the complementary characteristic of GIRs and typical GNNs embeddings are demonstrated. We show that GIRs get outperformed results on position-aware scenario, and could improve GNNs results by fuse GIRs embedding.
Research and Application of Smart Contract Based on Ethereum Blockchain

With the rapid development of blockchain technology and the increasing demand for partial decentralization of the Internet, the application of underlying technology based on blockchain has been widely concerned. Along with decentralized objects, the programmable financial system represented by Ethereum has been gotten more and more attention. However, smart contract sacrifices its security to improve decentralization. So Ethereum has the fatal problem with a large number of users, and negligence of users in coding contract threatens the entire Ethereum network. Therefore, this paper aims to research and expand applications of smart contract usage in Ethereum blockchain. We start from basic concepts to define structure of Ethereum, and then discuss security issues on smart contract. In the end, an optimized smart contract application of auction is implemented, which is useful for further consolidating and understanding of smart contract in practice.
gComm: An environment for investigating generalization in Grounded Language Acquisition

gComm is a step towards developing a robust platform to foster research in grounded language acquisition in a more challenging and realistic setting. It comprises a 2-d grid environment with a set of agents (a stationary speaker and a mobile listener connected via a communication channel) exposed to a continuous array of tasks in a partially observable setting. The key to solving these tasks lies in agents developing linguistic abilities and utilizing them for efficiently exploring the environment. The speaker and listener have access to information provided in different modalities, i.e. the speaker's input is a natural language instruction that contains the target and task specifications and the listener's input is its grid-view. Each must rely on the other to complete the assigned task, however, the only way they can achieve the same, is to develop and use some form of communication. gComm provides several tools for studying different forms of communication and assessing their generalization.
Improved Shelf Life of Brown Rice by Heat and Microwave Treatment

Widespread marketing and consumption of brown rice is limited by its short shelf life, attributed to the susceptibility of the lipids in the bran to rapid lipolytic (lipase) reactions which cause rancidity or spoilage. Findings from this study established optimum heat and microwave treatment conditions that could effectively inactivate lipase enzyme to consequently stabilize brown rice against rancidity, without affecting its antioxidant activity for up to 8 months of storage at room temperature. Improvement in shelf life of brown rice through these optimum treatment conditions was confirmed through monitoring the changes in physicochemical properties (free fatty acids, phenolics content and antioxidant activity), and raw and cooked sensory attributes and acceptability during storage. These treatment technologies for improving the shelf life of brown rice can now be adapted to pilot-scale and subsequently to commercial-scale applications.
A Two-Dimension Force Model Between High-Temperature Superconducting Bulk YBaCuO and Halbach-Type Permanent Magnet Guideway

With the merits of self-levitation, self-stabilization, zero magnetic drag, environment-friendliness, and low energy consumption, high-temperature superconducting (HTS) maglev is regarded as one of the main technology modes for the future rail transit. For the researches on the lateral-vertical coupled dynamics of the HTS maglev, there is still a lack of lateral-vertical coupled force model that can characterize the macroscopic force between high-temperature superconductor and permanent magnet guideway (PMG). In this article, a scanning experiment of levitation force and guidance force in a selected area on the vertical plane was carried out, and the law of levitation force and guidance force changing with coordinates was studied as well. Moreover, the finite element method was used to calculate the magnetic field distribution of the Halbach-type PMG, meanwhile, the Lorentz force formula was utilized to analyze the influence law of PMG's magnetic field on the levitation force and guidance force. On the basis of this research, a 2-D force model that neglects the hysteretic between high-temperature superconductor and PMG was established. By comparing the simulation results of the model with the measured data of the experiment, it is found that both of them are in good agreement, which verifies the reliability of the proposed model. The 2-D force model is very helpful to the future research and calculation of the dynamics of the HTS maglev.