Miracle-dz/UniFAR-FTUs huggingface dataset:
Anion binding by pyrrole–pyridine-based macrocyclic polyamides

The synthesis, characterisation and anion-binding properties of new pyrrole–pyridine-based macrocyclic polyamides 7a and 7b are presented. Chloride anion templation in the macrocyclisation reaction has been shown to control [1 + 1] acylation. The anion-binding properties of the receptors have been determined by UV–vis titrations in a DMSO solution and compared with systems with a similar design. The new receptors have been found to display a 10-fold selectivity for hydrogensulphate, dihydrogenphosphate and acetate anions over other anions studied.
Three‐Dimensional Super Lens Composed of Fractal Left‐Handed Materials

Inspired by the fractal concept, a miniaturized and fully printed planar, left‐handed (LH) material with C band (from 4.89 to 5.55 gigahertz) negative permittivity and permeability is proposed, using the transmission‐line (TL) approach. By incorporating four interdigital capacitors in the Sierpinski fractal curves of the second iteration order, the resulting LH‐TL material features large inductance and capacitance values in the equivalent circuit model, and thus facilitates an electrically small dimension and a broadband negative magnetic response. Based on the planar LH‐TL materials and sophisticated theory, a three‐dimensional (3D) free‐space LH‐TL super lens is designed and fabricated using multiple structures. Both numerical and experimental results illustrate that the 3D super lens exhibits excellent impedance matching to free space and significantly improves the imaging resolution. It is shown that the resolution reaches 0.39 λ0 at 5.35 gigahertz, in which the LH‐TL material possesses μ ≈ –μ0 and ε ≈ –ε0 (μ0 and ε0 are free‐space permeability and permittivity).
The action of sulphur monochloride on aromatic primary amines: the Herz reaction

1,3,2-Benzothiazathiolium chlorides are obtained by the action of sulphur monochloride on a primary aromatic amine, its salts, or N-acyl derivatives. Four reactions are involved, (i) the formation of the heterocyclic ring, (ii) the chlorination of the aromatic ring, (iii) replacement of electron-attracting groups in the ortho- and para-positions by chlorine, and (iv) the oxidation of the sulphur atom in position 2. A likely sequence of reactions is considered, and experimental evidence is given in support of each stage.
High-performance indium phosphide nanowires synthesized on amorphous substrates: from formation mechanism to optical and electrical transport measurements

InP NWs have been extensively explored for next-generation electronic and optoelectronic devices due to their excellent carrier mobility, exciton lifetime and wave-guiding characteristics. Typically, those NWs are grown epitaxially on crystalline substrates which could limit their potential applications requiring high growth yield to be printable or transferable on amorphous and flexible substrates. Here, utilizing Au as catalytic seeds, we successfully demonstrate the synthesis of crystalline, stoichiometric and dense InP NWs on amorphous substrates. Notably, the NWs are found to grow via the vapor–liquid–solid (VLS) mechanism with a narrow distribution of diameter (34.6 ± 7.7 nm) uniformly along the entire NW length (>10 μm). Although the grown NWs possess a substantial amount of twin defects, the fabricated NW FETs still exhibit impressive electrical performance with high carrier mobility (∼350 cm2 V−1 s−1) and ION/IOFF ratio (∼106). All these have demonstrated the promising potency of such NWs grown on amorphous substrates for technological applications, as compared to the conventional MBE or MOCVD grown InP NWs.
Effect of sulfur dioxide on the formation mechanism of polychlorinated dibenzodioxin and dibenzofuran in municipal waste combustors

The effect of sulfur dioxides on the formation mechanism of polychlorinated dibenzodioxin (PCDD) and polychlorinated dibenzofuran (PCDF) in the postcombustion, downstream region (500-300 [degrees]C) of a municipal waste combustor (MWC) was investigated. Laboratory experiments simulating the flue gases and particle environment of an MWC examined PCDD production under varying conditions. Effects on the concentration of an organic-chlorinating constituent, Cl[sub 2], through both homogeneous reaction with SO[sub 2] and deactivation of a Cl[sub 2]-forming catalyst [Cu(II)] were examined. Experimental results suggest that the reaction of Cu(II) with SO[sub 2] to form CuSO[sub 4] renders that catalyst less active, decreasing PCDD formation. However, this inactivity is not a result of decreased Cl[sub 2] formation, but rather of reduced ability of Cu(II) to promote a second catalytic step of biaryl synthesis. These findings suggest that the apparent lack of PCDD and PCDF in the emissions from coal-fired combustors may be due to the relatively high concentrations of SO[sub 2]. 26 refs., 7 figs.
Analyzing the Effect of the Spectral Interference of Mixed Pixels Using Hyperspectral Imagery

The widespread presence of mixed pixels in remotely sensed images is a pressing challenge for accurate target detection and classification. Linear spectral mixture analysis (LSMA) is commonly used to address this problem by deriving remotely sensed information at the subpixel level. In the implementation of LSMA, the effects of mixed-pixel spectral interference need to be taken into account; mixed spectra would exhibit as a pure spectral characteristic when the abundance of one endmember in a mixed pixel exceeds a specific threshold. However, the thresholds of endmember abundance resulting in mixed-pixel spectral interference remain unclear. Thus, this study designed an experiment to analyze the effect of the spectral interference of mixed pixels and to identify the thresholds causing such interference by spectral similarity measures (spectral angle and spectral distance). Four types of pure endmember spectra (vegetation, high-albedo impervious surface (HIS), low-albedo impervious surface (LIS), soil) and corresponding representative mixed spectra with endmember abundances of 95%–5% at intervals of 5% were collected from Earth Observing-1 Hyperion imagery. Spectral similarity measures among the pure endmember spectra and representative mixed spectra were used to determine the thresholds of endmember abundance that cause spectral interference. The results verified the effect of the spectral interference of mixed pixels. The thresholds of abundance causing mixed-pixel spectral interference in vegetation, HIS, LIS, and soil endmembers were 70%, 75%, 80%, and 70%, respectively. Therefore, when the endmember abundance within mixed pixels exceeds the abovementioned thresholds, these mixed spectra are interfered and would exhibit as a pure spectral characteristic. Accordingly, interfered mixed pixels have to be removed before applying LSMA or other unmixing methods to avoid the effect of spectral interference.
Resilience, Survivability, and Elasticity: A Taxonomy for Change Impact Quantification of Reconfigurable Systems

Context. Modern distributed systems are flexible in moving from one configuration to another during operation in an automated or semi-automated manner, e.g., concerning dynamic CPU allocation and deploying updated versions of system services. Software architects need assurance that the system satisfies agreed quality of service (QoS) despite a change in system configuration. In the literature, under resilience, survivability, and elasticity, there are different change impact quantification approaches that each has different methods for quality metric specification, modeling a change, and impact analysis. However, independent of a particular approach, no taxonomy exists that clarifies a set of general concepts concerning change impact quantification in reconfigurable systems.Objective. We propose a taxonomy by examining existing approaches under the three meta-quality attributes for change impact quantification.Method. We start with works done by heavily cited authors behind resilience, survivability, and elasticity who provided unambiguous and measurable definitions.Result. We classify existing approaches for change impact quantification based on the taxonomy. We demonstrate the application of our taxonomy through an example.Conclusion. The taxonomy provides a unified and structured knowledge across communities that further eases communication and development of new approaches for change impact quantification.
Cold vapour generation and on-line trapping of cadmium species on quartz surface prior to detection by atomic absorption spectrometry

A quartz trap for on-line preconcentration of Cd species was designed. The cold vapour generation technique was used for the generation of Cd species. The trapping medium was formed by external heating of the inlet arm of a quartz T-tube. The generated analyte species were trapped on a quartz surface heated to the collection temperature, 350 °C, and the collected species were revolatilized when the trap was heated further to revolatilization temperature, 1000 °C, and hydrogen gas was introduced in the trapping medium. Two-level fractional factorial design and central composite design were used to optimize generation conditions in the flow injection mode. The results of the fractional factorial design demonstrated that the factors and their interactions were statistically significant. Three factors, length of reaction coil, carrier HCl concentration and NaBH4 concentration, were considered to be the most significant parameters in the optimization and their optimum values were found to be 30 cm, 0.3 M and 3% (m/v), respectively. Sea-water (BCR), tomato leaves (NIST 1573a) and oyster tissue (NIST 1566b) standard reference materials were analyzed to assess the accuracy of the proposed method. For a collection period of 3.0 min, i.e., 6.0 ml sample volume, the 3σ limit of detection was 1.8 pg ml−1; the enhancement factor for LOD was found to be 90 as compared with FI-HGAAS. The sample throughput rate was 12 h−1.
An I2V communication network for driver assistance in public transport

During the last decade, Advanced Driver Assistant Systems (ADAS) have been implemented on public transport vehicles to ensure the driver collision and lane departure warnings. Due to the high complexity of public roads, these systems are usually based on sensors installed on the vehicle itself, which ensures lines or obstacles recognition, providing warnings directly to the driver. Nevertheless, it is well established that information about the surroundings can be provided directly by the infrastructure. This paper presents the case study of an I2V communication network, whose target is to inform the driver about the number of people waiting at the bus shelter, and about an approaching vehicle in a blind intersection. Detections are guaranteed by cameras installed in strategical points, while communication to the bus is ensured through 4G.
A hybrid Prophet-LSTM Model for Prediction of Air Quality Index

In recent years, air pollution has caused widespread concern in society. This paper takes Nanjing as an example to analyze the changes in its Air Quality Index (AQI) and make predictions, which is of great significance for improving local air quality. Based on the air quality monitoring data of Nanjing from June 2014 to May 2019, a Prophet model is constructed to perform statistical analysis on the data and predict the AQI index from June 2019 to April 2020. At the same time, this paper designs Prophet-SVR hybrid model and Prophet-LSTM hybrid model to optimize the prediction accuracy of Prophet model. The results show that the Prophet-LSTM hybrid model has the best performance, its prediction accuracy is higher than that of the single model, and it has obvious advantages in the prediction of the air quality index.
Design and Implementation Techniques for an MPI-Oriented AMT Runtime

We present the execution model of Virtual Transport (VT) a new, Asynchronous Many-Task (AMT) runtime system that provides unprecedented integration and interoperability with MPI. We have developed VT in conjunction with large production applications to provide a highly incremental, high-value path to AMT adoption in the dominant ecosystem of MPI applications, libraries, and developers. Our aim is that the ‘MPI+X’ model of hybrid parallelism can smoothly extend to become ‘MPI+VT +X’. We illustrate a set of design and implementation techniques that have been useful in building VT. We believe that these ideas and the code embodying them will be useful to others building similar systems, and perhaps provide insight to how MPI might evolve to better support them. We motivate our approach with two applications that are adopting VT and have begun to benefit from increased asynchrony and dynamic load balancing.
Sol–gel transition of methylcellulose in phosphate buffer saline solutions

The effect of phosphate buffer saline (PBS) on the thermoreversible sol–gel transition of methylcellulose has been systematically investigated with turbidimetric, microcalorimetric, and rheological methods. In the presence of PBS, the cloud points in the sol–gel transition and the clear points in the gel–sol transition shift to lower temperatures with increasing PBS content. This salting-out effect is considered to be due to the competition between salts and methylcellulose for water molecules involved in the formation or destruction of cagelike water structures. The microcalorimetric studies prove that with increasing PBS content, the endothermic peak temperature decreases, whereas the enthalpy and entropy changes of the sol–gel transition increase. The rheological studies show that the addition of PBS favors the sol–gel transition, but the gel strength is almost independent of the PBS content, indicating an effect of adding salt equivalent to that of increasing the temperature. The results obtained from the rheological, microcalorimetric, and turbidimetric measurements are consistent. © 2004 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 42: 1849–1860, 2004
Research on the Link Prediction Model of Dynamic Multiplex Social Network Based on Improved Graph Representation Learning

In the natural and social systems of the real world, various network can be seen everywhere. The world where people live can be seen as a combination of network with different dimensions. Link prediction formalizes the interaction behavior between people. Traditional link prediction methods mainly study the user behavior of static social network. This article studied the dynamic graph representation learning so as to put forward an improved link prediction model in dynamic social network. Besides, the interactions in the real world can be multiple, links at different moments may have different meanings. The proposed model firstly solved the problem of link prediction on multiple kinds of edges. The whole embedding of each node is separated into two parts, basic embedding and edge embedding. Then the proposed model selected time slices for dynamic social network to get the graph embeddings in different snapshots. What’s more, the <inline-formula> <tex-math notation="LaTeX">$t+1$ </tex-math></inline-formula> time step embedding vector was used to validate <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> time step prediction effect and the proposed model performed better than traditional graph representation learning methods.
Pair Trading with an Ontology of SEC Financial Reports

Pair trading is a market-neutral strategy which is based on the use of standard, well-known statistical tests applied to time series of price to identify suitable pairs of stock. This article studies the potential benefits of using additional qualitative information for this type of trade. Here we use an ontology to represent and structure information extracted from financial SEC reports in a way that is optimised for search. These mandatory reports are originally published as XML using a schema that has varied over the years. The XML format itself is not easy to query, e.g. projections to fields or their composition are hard to find even when using an XML store. Our ontology-based approach provides uniformity of representation, which is further enhanced by the strife to use common vocabulary wherever possible. The ontology is then used to identify links between companies, by finding common senior employees or major shareholders. This is also potentially useful information to identify suitable pairs of stock. We show that the ontology increases the probability of selecting cointegrated pairs of stock from the data, with no negative effect on the survival time of such pairs when compared to random ones.
Comparison study between conventional machine learning and distributed multi-task learning models

Applying machine learning in IoT devices is a challenge due to various reasons, such as the tremendous amount of data generated from IoT, the limitation of IoT devices' resources, and the non-IID nature of IoT data. On the other hand, transferring the generated IoT data to the cloud to train machine learning models consumes a lot of Bandwidth. Applying the distributed learning aspect in IoT large-scale deployments solves such issues, by employing edge computing devices as local cloud models in each location. This solution enhances the network overhead and helps in obtaining general models. However, this comes at the expense of the accuracy of the generated models. This paper provides a comparison study between applying a conventional machine learning model with a distributed multi-task learning model and discusses the factors that affect the distributed multi-task learning model.
Fault detection of tank-system using ChangeFinder and SVM

Tank-system is used in the process industry and needs to detect fault early to increase productivity. In recent years, research on fault detection by processing real-time sensing data has been actively conducted. The early fault detection method makes the system dependable. In this paper, a fault detection method using Support Vector Machine (SVM) combined with ChangeFinder, both are machine learning methods, is proposed, and the effectiveness is confirmed by simulation and experiment.
The effect of doping on the hardness of silicon carbide single crystal and its first principles calculations

The hardness values of 4H-SiC silicon with different nitrogen and vanadium doping concentrations is measured by nanoinderter. It is found that the hardness value is positively correlated with the nitrogen doping concentration ranges from 3.50×1017 to 8.02×1018cm-3, and high vanadium doping concentration will reduce the hardness value of the silicon carbide. Base on the first principles, the elastic constants of Vasp software are simulated and calculated, and the bulk modulus and shear modulus of non-doped, nitrogen-doped and vanadium-doped 4H-SiC are estimated by Voigt-Reuss-Hill. The calculation results show that the shear modulus of vanadium-doped silicon carbide and nitrogen-doped silicon carbide are lower than that of non-doped silicon carbide.
Improving the Security of Microservice Systems by Detecting and Tolerating Intrusions

Microservice architectures adoption is growing expeditiously in market size and adoption, including in business-critical systems. This is due to agility in development and deployment further increased by containers and their characteristics. Ensuring security is still a major concern due to challenges faced such as resource separation and isolation, as improper access to one service might compromise complete systems. This doctoral work intends to advance the security of microservice systems through research and improvement of methodologies for detection, tolerance and mitigation of security intrusions, while overcoming challenges related to multi-tenancy, heterogeneity, dynamicity of systems and environments. Our preliminary research shows that host-based IDSes are applicable in container environments. This will be extended to dynamic scenarios, serving as a steppingstone to research intrusion tolerance techniques suited to these environments. These methodologies will be demonstrated in realistic microservice systems: complex, dynamic, scalable and elastic.
Unconstrained Face Identification using Ensembles trained on Clustered Data

Open-set face recognition describes a scenario where unknown subjects, unseen during training stage, appear on test time. Not only it requires methods that accurately identify individuals of interest, but also demands approaches that effectively deal with unfamiliar faces. This work details a scalable open-set face identification approach to galleries composed of hundreds and thousands of subjects. It is composed of clustering and ensemble of binary learning algorithms that estimates when query face samples belong to the face gallery and then retrieves their correct identity. The approach selects the most suitable gallery subjects and use the ensemble to improve prediction performance. We carry out experiments on well-known LFW and YTF benchmarks. Results show that competitive performance can be achieved even when targeting scalability.
OFDM based bidirectional multi-relay SWIPT strategy for 6G IoT networks

6G IoT networks aim for providing significantly higher data rates and extremely lower latency. However, due to the increasingly scarce spectrum bands and ever-growing massive number IoT devices (IoDs) deployed, 6G IoT networks face two critical challenges, i.e., energy limitation and severe signal attenuation. Simultaneous wireless information and power transfer (SWIPT) and cooperative relaying provide effective ways to address these two challenges. In this paper, we investigate the energy self-sustainability (ESS) of 6G IoT network and propose an OFDM based bidirectional multi-relay SWIPT strategy for 6G IoT networks. In the proposed strategy, the transmission process is equally divided into two phases. Specifically, in phase1 two source nodes transmit their signals to relay nodes which will then use different subcarrier sets to decode information and harvest energy, respectively. In phase2 relay nodes forward signals to corresponding destination nodes with the harvested energy. We maximize the weighted sum transmission rate by optimizing subcarriers and power allocation. Our proposed strategy achieves larger weighted sum transmission rate comparing with the benchmark scheme.
Viricidal Potential of Plasmonic and Metal Oxide Nanostructures: A Review

Viral diseases resulting to global pandemics occurring through the years have extremely affected the human population and global economies. With the rise of novel pathogenic viruses and resistance of the known ones to conventional drugs and medications, exploration of new materials with antiviral properties has become an expanding interest notjust in the field of biomedicine but also in materials science, particularly in nanotechnology. The unique physicochemical properties of nanostructures derived from gold and silver, and metal oxides have attracted considerable attention as novel antiviral nanomaterials. In this paper, we review the antiviral potential of plasmonic gold and silver, and metal oxide nanostructures against known human pathogenic viruses such as herpes, hepatitis, dengue, influenza, measles, human immunodeficiency virus, transmissible gastroenteritis virus and other viruses. Experimental investigations revealed the promising potential of both plasmonic and metal oxide nanostructures as antiviral agents. The mechanisms of antiviral action were found to vary for different nanomaterials and its target virus while some mechanisms remained unclear. The promising yet undeterministic antiviral potential of these nanomaterials paved a more interesting platform for further research and biomedical exploration.