Miracle-dz/UniFAR-FTUs huggingface dataset:
Flame-Retardant Foamed Material Based on Modified Corn Straw Using Two Nitrogenous Layers

Foamed materials based on a biopolymer of crop straws are environmentally friendly, but ignitability limits their application. In this study, two nitrogenous layers were introduced onto corn straw by esterification and grafting for flame-retardant purposes. The inner thin nitrogenous layer consisted of imidazole rings, and the outer thick nitrogenous layer consisted of grafted acrylamide by a free-radical polymerization. The outer nitrogenous layer was simultaneously introduced into the system with a foaming process at 150 °C. Azodiisobutyronitrile acted both as initiator of the polymerization and the main foaming agent, and deionized water acted both as a plasticizing agent and an auxiliary foaming agent, which simplified the process and formula. It was found that cavities of two different sizes were formed. The nonuniformity of the foamed material was ascribed to the heterogeneous foaming precursor consisting of a rigid core and a soft shell. Its excellent flame-retard rating of UL-94 V-0 was ascribed to the two nitrogenous layers, which provides a sufficient nitrogen source for non-combustible gases. A relatively high compression strength of 17.7 MPa was partly due to the fiber of corn straw.
Intelligent generation method of emergency plan for hydraulic engineering based on knowledge graph – take the South-to-North Water Diversion Project as an example

ABSTRACT There are some problems with the traditional emergency plans of hydraulic engineering, such as low digitisation, poor knowledge relevance, insufficient intelligent decision-making, and so on. This paper proposes an intelligent method for generating the hydraulic engineering emergency plan for patrol text based on knowledge graph and machine learning. Firstly, based on the electronic documents of various plans, the knowledge graph of the emergency plan is constructed to realise the high organisation of scattered knowledge, using the skills of knowledge modelling, knowledge extraction, knowledge fusion, and knowledge storage. Then, based on bidirectional encoder representation from transformers (BERT) and bidirectional long-short-term memory with conditional random fields (BiLSTM+CRF), the entity recognition model is constructed to intelligently recognise dangers, projects, parts, and other entities in the patrol text. The Jaccard entity similarity algorithm based on the word2vec model matches the danger entity with the graph danger entities and generates the emergency plan through knowledge retrieval and reasoning. With the performance of the model and the verification of the “Channel Leakage” example, this method has high accuracy in identifying entities (the F1 value is 96.21%) and has high reliability in the generation of emergency plans, which can be applied to the emergency rescue of hydraulic engineering.
The Development of Compatible Tree Biomass Models for Main Species in North-Eastern China

In accordance with weakness of traditional biomass models in which the sum of the above- and below-ground tree components was not equal to the whole tree, compatible biomass equations were developed for the above- and below-ground tree components of 11 kinds of tree species in Heilongjiang Province. The data used to develop biomass models are from 299 trees that were collected from 69 sample plots, and represented a wide range of stand and site conditions in Heilongjiang Province. Based on the total biomass model as restrictions, the compatible tree biomass equations for each component (stems, branches, foliages, and roots) were separately established by considering diameter at breast height (D) as independent variables in the form of simultaneous equations with measure error of independent variables. The evaluation and validation procedures for tree biomass models were performed by using the following statistical criterions: the coefficient of determination (R2), sum square of error (SEE), Mean Bias (MB), and Mean Absolute Bias (MAB). The results showed that although parameter estimation accuracy of compatible biomass equations was not better than traditional biomass models, the compatible biomass equations could effectively overcome the weakness of un-compatible of traditional biomass models.
Cryptic Oral Microbiota: What Is Its Role as Obstructive Sleep Apnea-Related Periodontal Pathogens?

Periodontitis has been commonly linked to periodontopathogens categorized in Socransky’s microbial complexes; however, there is a lack of knowledge regarding “other microorganisms” or “cryptic microorganisms”, which are rarely thought of as significant oral pathogens and have been neither previously categorized nor connected to illnesses in the oral cavity. This study hypothesized that these cryptic microorganisms could contribute to the modulation of oral microbiota present in health or disease (periodontitis and/or obstructive sleep apnea (OSA) patients). For this purpose, the presence and correlation among these cultivable cryptic oral microorganisms were identified, and their possible role in both conditions was determined. Data from oral samples of individuals with or without periodontitis and with or without OSA were obtained from a previous study. Demographic data, clinical oral characteristics, and genera and species of cultivable cryptic oral microorganisms identified by MALDI-TOF were recorded. The data from 75 participants were analyzed to determine the relative frequencies of cultivable cryptic microorganisms’ genera and species, and microbial clusters and correlations tests were performed. According to periodontal condition, dental-biofilm-induced gingivitis in reduced periodontium and stage III periodontitis were found to have the highest diversity of cryptic microorganism species. Based on the experimental condition, these findings showed that there are genera related to disease conditions and others related to healthy conditions, with species that could be related to different chronic diseases being highlighted as periodontitis and OSA comorbidities. The cryptic microorganisms within the oral microbiota of patients with periodontitis and OSA are present as potential pathogens, promoting the development of dysbiotic microbiota and the occurrence of chronic diseases, which have been previously proposed to be common risk factors for periodontitis and OSA. Understanding the function of possible pathogens in the oral microbiota will require more research.
Structural analysis of Ba0.8Sr0.2Ti0.6Zr0.3Mn0.1O3 ceramics

Polycrystalline Ba0.8Sr0. 2Ti0.6Zr0. 3Mn0.1O3 was synthesized by solid-state reaction at 1600°C. The single phase formation of the compound without any impurities was confirmed by the X-ray diffraction technique. The prepared compound crystallized to a cubic structure with a space group of Pm-3m and the refined lattice parameters were a = b = c = 4.0253 Ǻ, α = β = γ = 90°. Rietveld refinement was carried for the powder XRD data using GSAS software and the experimental data peaks were indexed by Powder X software.
Multi‐Event Analysis of Magnetosphere‐Ionosphere Coupling of Nighttime Medium‐Scale Traveling Ionospheric Disturbances From the Ground and the Arase Satellite

Kawai et al. (2021) reported the first ground‐satellite conjugate observation of nighttime medium‐scale traveling ionospheric disturbances (MSTIDs), by analyzing measurements from an airglow imager at Gakona (geographic latitude: 62.39°N, geographic longitude: 214.78°E, magnetic latitude: 63.60°N) and the Arase satellite in the magnetosphere on 3 November 2018. The Arase satellite observed variations in both the polarization electric field and the electron density as the Arase footprint passed through the MSTID structures in the ionosphere. In this study, we investigated whether these electric field and density variations associated with MSTIDs at subauroral latitudes are always observed by Arase in the magnetosphere. We used three airglow imagers installed at Gakona, Athabasca (geographic latitude: 54.60°N, geographic longitude: 246.36°E, magnetic latitude: 61.10°N), and Kapuskasing (geographic latitude: 49.39°N, geographic longitude: 277.81°E, magnetic latitude: 58.70°N) and the Arase satellite. We found eight observations of MSTIDs conjugate with Arase. They indicate that electric field and density variations associated with MSTIDs are not always observed in the magnetosphere. These variations tend to be observed in the magnetosphere during geomagnetically quiet times and when the amplitude of the MSTID is large. We categorized the MSTIDs into those caused by plasma instabilities and gravity waves and found that the electric field and density variations can be observed in the magnetosphere for both types of MSTIDs.
The synergistic effect of Ti and Nb in TiNbC leads to enhanced anode performance for Na-ion batteries - first-principles calculations

Currently, MXenes have been identified as promising candidate electrode materials for Na-ion batteries because of their excellent energy storage and electrical conductivity. Among them, dual transition metal MXenes have attracted attention due to their excellent properties as anode materials for Na-ion batteries. In order to explore the reason why dual transition metal MXenes outperform single transition metal MXenes, we systemically investigated and compared the performance of TiNbC-based dual transition metal MXenes (TiNbC, TiNbCF2, and TiNbCO2) with that of Nb2C-based single transition metal MXenes (Nb2C, Nb2CF2, and Nb2CO2) as anode materials for Na-ion batteries based density functional theory calculations. The results showed that TiNbC, TiNbCO2, Nb2C, and Nb2CO2 are promising anode materials for Na-ion batteries due to the low diffusion barrier and high capacity of Na on their surfaces. Compared with the Nb2C-based single transition metal MXenes, the TiNbC-based dual transition metal MXenes have better adsorption performance, diffusion rate, and theoretical storage of Na atoms. The higher adsorption ability of TiNbC and TiNbCO2 to Na atoms was attributed to the synergistic effect of Ti and Nb which increases the interaction between the substrate and Na. This conclusion provides a new insight for the development of other high-performance MXenes-based anode materials for Na-ion batteries.
Targeting Apoptotic Pathway of Cancer Cells with Phytochemicals and Plant-Based Nanomaterials

Apoptosis is the elimination of functionally non-essential, neoplastic, and infected cells via the mitochondrial pathway or death receptor pathway. The process of apoptosis is highly regulated through membrane channels and apoptogenic proteins. Apoptosis maintains cellular balance within the human body through cell cycle progression. Loss of apoptosis control prolongs cancer cell survival and allows the accumulation of mutations that can promote angiogenesis, promote cell proliferation, disrupt differentiation, and increase invasiveness during tumor progression. The apoptotic pathway has been extensively studied as a potential drug target in cancer treatment. However, the off-target activities of drugs and negative implications have been a matter of concern over the years. Phytochemicals (PCs) have been studied for their efficacy in various cancer cell lines individually and synergistically. The development of nanoparticles (NPs) through green synthesis has added a new dimension to the advancement of plant-based nanomaterials for effective cancer treatment. This review provides a detailed insight into the fundamental molecular pathways of programmed cell death and highlights the role of PCs along with the existing drugs and plant-based NPs in treating cancer by targeting its programmed cell death (PCD) network.
SIMULTANEOUS ESTIMATION OF SYSTEM AND INPUT PARAMETERS FROM OUTPUT MEASUREMENTS

System identification of very large structures is of necessity accomplished by analyzing output measurements, as in the case of ambient vibration surveys. Conventional techniques typically identify system parameters by assuming (arguably) that the input is locally Gaussian white, and in so doing, effectively reduce the number of degrees of freedom of the estimation problem to a more tractable number. This paper describes a new approach that has several novel attributes, among them, elimination of the need for the Gaussian white input assumption. The approach involves a filter applied to an identification problem formulated in the frequency domain. The filter simultaneously estimates both system parameters and input excitation characteristics. The estimates we obtain are not guaranteed to be unique (as is true in all other approaches: simultaneous estimation of both system and input possesses too many degrees of freedom to guarantee uniqueness); but we do, nonetheless, identify system parameters and input excitation characteristics that are physically plausible and intuitively reasonable, without making input excitation assumptions. Simulated and laboratory experimental data are used to verify the algorithm and demonstrate its advantages over conventional approaches.
Wastewater-based monitoring of SARS-CoV-2 at UK airports and its potential role in international public health surveillance

It is well established that air travel plays a key role in the global spread of many enteric and respiratory diseases, including COVID-19. Even with travel restrictions (e.g. mask wearing, negative COVID-19 test prior to departure), SARS-CoV-2 may be transmitted by asymptomatic or pre-symptomatic individuals carrying the virus. Due to the limitation of current clinical surveillance approaches, complementary methods need to be developed to allow estimation of the frequency of SARS-CoV-2 entry across international borders. Wastewater-based epidemiology (WBE) represents one such approach, allowing the unbiased sampling of SARS-CoV-2 carriage by passenger cohorts entering via airports. In this study, we monitored sewage in samples from terminals (n = 150) and aircraft (n = 32) at three major international airports in the UK for 1–3 weeks in March 2022. As the raw samples were more turbid than typical municipal wastewater, we used beef extract treatment followed by polyethylene glycol (PEG) precipitation to concentrate viruses, followed by reverse transcription quantitative PCR (RT-qPCR) for the detection of SARS-CoV-2 and a faecal indicator virus, crAssphage. All samples taken from sewers at the arrival terminals of Heathrow and Bristol airports, and 85% of samples taken from sites at Edinburgh airport, were positive for SARS-CoV-2. This suggests a high COVID-19 prevalence among passengers and/or airport staff members. Samples derived from aircraft also showed 93% SARS-CoV-2 positivity. No difference in viral prevalence was found before and after COVID-19 travel restrictions were lifted. Our results suggest that WBE is a useful tool for monitoring the global transfer rate of human pathogens and other disease-causing agents across international borders and should form part of wider international efforts to monitor and contain the spread of future disease outbreaks.
Time-domain analysis of magnetic-coated wire antennas

The interaction of transient electromagnetic pulses (EMP) with perfect electric conductor thin wires with a thin magnetic coating, or mixed (dielectric and magnetic) coating, is studied in the time domain. The numerical procedure is based on the solution of the thin-wire electric field integral equation (EFIE) by the method of moments (MM), using a marching-on-in-time procedure. Numerical results are compared with measurements and with other authors' data. >
Simultaneous analysis of driver behaviour and road condition for driver distraction detection

The design of intelligent driver assistance systems is of increasing importance for the vehicle-producing industry and road-safety solutions. This article starts with a review of road-situation monitoring and driver's behaviour analysis. This article also discusses lane tracking using vision (or other) sensors, and the strength or weakness of different methods of driver behaviour analysis (e.g. iris or pupil status monitoring, and EEG spectrum analysis). This article focuses then on image analysis techniques and develops a multi-faceted approach in order to analyse driver's face and eye status via implementing a real-time AdaBoost cascade classifier with Haar-like features. The proposed method is tested in a research vehicle for driver distraction detection using a binocular camera. The developed algorithm is robust in detecting different types of driver distraction such as drowsiness, fatigue, drunk driving or the performance of secondary tasks.
A User-Centered Security Approach to Create an IoT based Multi-Layered Fog-Cloud Architecture for Data Optimization in Raised Bed Farming

Cloud Computing and Internet of Things (IoT) are popularly intertwined to create a network of smart systems, especially in the field of Agriculture. As agricultural industries rise, augmented use of heterogeneous communication and sensing on the field would be needed. For all this data to be transferred and processed on the cloud efficiently, low latency rates, consistently high bandwidths, minimal congestion, etc. would be needed, which, is still a complication with the current cloud computing models. This paper proposes a multi-layered fog architecture that detects outliers in the data received from the sensing environment based on three categories: Classification, Isolation, and Clustering and then aggregates it before sending it to the cloud. The architecture works closely in a user-centered design approach that connects the farmers and analysts to the fog allowing them to create an automated agricultural system. With the help of Fog, processing abilities are brought closer to the data source which reduces the load on cloud resources, thereby making the overall system a lot more efficient and secured. This paper also presents a prototype of the interface that can be used to monitor and control IoT devices on the field as well as define fuzzy rules for the agricultural system.
Numerical comparison of hybridized discontinuous Galerkin and finite volume methods for incompressible flow

In this study, a hybridizable discontinuous Galerkin method is presented for solving the incompressible Navier–Stokes equation. In our formulation, the convective part is linearized using a Picard iteration, for which there exists a necessary criterion for convergence. We show that our novel hybridized implementation can be used as an alternative method for solving a range of problems in the field of incompressible fluid dynamics. We demonstrate this by comparing the performance of our method with standard finite volume solvers, specifically the well‐established finite volume method of second order in space, such as the icoFoam and simpleFoam of the OpenFOAM package for three typical fluid problems. These are the Taylor–Green vortex, the 180‐degree fence case and the DFG benchmark. Our careful comparison yields convincing evidence for the use of hybridizable discontinuous Galerkin method as a competitive alternative because of their high accuracy and better stability properties. Copyright © 2014 John Wiley & Sons, Ltd.
! ! -Base and inﬁnite-dimensional compact sets in locally convex spaces

A locally convex space (lcs) E is said to have an ω ω -base if E has a neighborhood base { U α : α ∈ ω ω } at zero such that U β ⊆ U α for all α ≤ β . The class of lcs with an ω ω -base is large, among others contains all ( LM ) -spaces (hence ( LF ) -spaces), strong duals of distinguished Fréchet lcs (hence spaces of distributions D (cid:5) (cid:5)) ). A remarkable result of Cascales-Orihuela states that every compact set in an lcs with an ω ω -base is metrizable. Our main result shows that every uncountable-dimensional lcs with an ω ω -base contains an inﬁnite-dimensional metrizable compact subset. On the other hand, the countable-dimensional vector space ϕ endowed with the ﬁnest locally convex topology has an ω ω -base but contains no inﬁnite-dimensional compact subsets. It turns out that ϕ is a unique inﬁnite-dimensional locally convex space which is a k R -space containing no inﬁnite-dimensional compact subsets. Applications to spaces C p ( X ) are provided.
Binding, Phases, and Locality

The central issue addressed here is syntactic locality, and the main proposal is that movement and anaphoric relations are governed by a unified concept of locality. The specific phenomena to be investigated are (i) infinitive constructions, in particular, Accusativus cum Infinitivo (AcI) complements, (ii) the German Possessor Dative Construction (PDC), with a dative nominal playing the role of both possessor and affectee, and (iii) binding, the conditions under which reflexive and nonreflexive pronouns may occur. The focus is mainly on binding and how to account for instances of noncomplementarity, but also on the PDC, which can be analyzed as possessor raising. Ultimately, it will become clear that the unifying principle of locality must be the phase, and that phasehood determines the transparency/opacity of phrases (CP, vP, DP, and PP) for both movement and anaphoric relations.
HAM QUALITY EVALUATION ASSISTED BY GC-IMS

In recent years, Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) has been successfully employed in food science as a control technique for the prevention of fraud according to food and labeling regulations. In this work, we propose the use of GC-IMS technique to assess the quality of Iberian ham with regard to the Iberian Pig’s diet (either nourished with feed or with acorns). For this purpose, we have acquired a dataset composed of 53 samples of Iberian ham from different food providers using a commercial GC-IMS (FlavourSpec, from G.A.S. Dortmund, Germany). Intensive signal pre-processing for GC-IMS was applied to the raw data. This dataset was employed to create 4 Partial Least Squares Discriminant Analysis (PLSDA) models corresponding to different train/test partitions of the dataset. Nearly perfect classification rates (above 91 %) were obtained for each partition of the dataset, denoting the high power of GC-IMS to characterize food samples.
Analysis of longitudinal data with non‐ignorable non‐monotone missing values

A full likelihood method is proposed to analyse continuous longitudinal data with non‐ignorable (informative) missing values and non‐monotone patterns. The problem arose in a breast cancer clinical trial where repeated assessments of quality of life were collected: patients rated their coping ability during and after treatment. We allow the missingness probabilities to depend on unobserved responses, and we use a multivariate normal model for the outcomes. A first‐order Markov dependence structure for the responses is a natural choice and facilitates the construction of the likelihood; estimates are obtained via the Nelder–Mead simplex algorithm. Computations are difficult and become intractable with more than three or four assessments. Applying the method to the quality‐of‐life data results in easily interpretable estimates, confirms the suspicion that the data are non‐ignorably missing and highlights the likely bias of standard methods. Although treatment comparisons are not affected here, the methods are useful for obtaining unbiased means and estimating trends over time.