Miracle-dz/UniFAR-FTUs huggingface dataset:
In Situ Capturing and Absorption of Sulfur Gases Formed during Thermal Treatment of South African Coals

The objective of this study, the first of its kind on these specific South African low-sulfur coals, was to capture H2S and SO2 produced under inert and oxidizing conditions from sulfur compounds present in the coals. The capturing agents were calcium and magnesium oxides formed during the transformation of calcite and dolomite. The effectiveness of two different scrubbing solutions (0.15 M cadmium acetate and 1.1 M potassium hydroxide) for absorption of volatilized H2S and SO2 was also investigated. The bituminous coal (coal A) contained dolomite, calcite, pyrite, and organic sulfur. Lignite (coal B) had a high organic sulfur content and contained gypsum, no or low dolomite and pyrite contents, and no calcite. A third sample (coal C) was prepared by adding 5 wt % potassium carbonate to coal A. Under oxidizing conditions and at elevated temperatures, FeS2 produced Fe2O3, FeO, and SO2. It transformed to FeS and released H2S under inert conditions. Organic sulfur interacted with organically bound calcium and magnesium at 400 °C in an inert atmosphere to form calcium sulfate and oldhamite ((Ca,Mg)S). CaO, produced from calcite or dolomite, reacted with SO2 and O2 at 950 °C to form calcium sulfate. Treatment of lignite at 400–950 °C resulted in 96–98% evolution of sulfur as gases. Hydrogen sulfide formation increased with the increasing thermal treatment temperature under inert conditions for the three coals. Under oxidizing conditions, sulfur dioxide formation decreased with the increasing temperature when heating coals B and C. The lowest ratio (0.01) of H2S to SO2 was achieved during thermal treatment of the blend of coal and potassium carbonate (coal C), implying that almost all of sulfur was retained in the coal C ash/char samples. In situ capturing of sulfur gases by CaO and MgO and by the added K2CO3 in coal C to form calcium/magnesium/potassium sulfates and potassium/calcium/magnesium aluminosilicate glasses during utilization of these and similar coals could reduce the percentage of sulfur volatilized from the coals by 54–100%, thereby potentially decreasing their impact on the environment.
HyScale: Hybrid and Network Scaling of Dockerized Microservices in Cloud Data Centres

When designing modern software, care must be taken to allow for applications to scale based on the demands of its users while still accommodating flexibility in development. Recently, microservices architectures have garnered the attention of many organizations—providing higher levels of scalability, availability, and fault isolation. Many organizations choose to host their microservices architectures in cloud data centres to offset costs. Incidentally, data centres become over-encumbered during peak usage hours and underutilized during off-peak hours. Traditional microservice scaling methods perform either horizontal or vertical scaling exclusively. When used in combination, however, these methods offer complementary benefits and compensate for each other's deficiencies. To leverage the high availability of horizontal scaling and the fine-grained resource control of vertical scaling, we developed two novel hybrid autoscaling algorithms and a dedicated network scaling algorithm and benchmarked them against Google's popular Kubernetes horizontal autoscaling algorithm. Results indicated up to 1.49x speedups in response times for our hybrid algorithms, and 1.69x speedups for our network algorithm under high-burst network loads.
Band Structures of Periodic Porphyrin Nanostructures

Recent progress in the synthesis of π-conjugated porphyrin arrays of different shapes and dimensionalities motivates us to examine the band structures of infinite (periodic) porphyrin nanostructures. We use screened hybrid density functional theory simulations and Wannier function interpolation to obtain accurate band structures of linear chains, 2D nanosheets, and nanotubes made of zinc porphyrins. Porphyrin units are connected by butadiyne (C4) or ethyne (C2) linkers or “fused” (C0), i.e., with no linker. The electronic properties exhibit strong variations with the number of linking carbon atoms (C0/C2/C4). For example, all C0 nanostructures exhibit gapless or metallic band structures, whereas band gaps open for the C2 or C4 structures. The reciprocal space point at which the gaps are observed also show fluctuations with the length of the linkers. We discuss the evolution of the electronic structure of finite porphyrin tubes made of a few stacked six-porphyrin rings toward the behavior of the infinite n.
Molecular Identification of Pectinolytic Streptomyces sp. GHB5 and Optimization of Polygalacturonase Production

Streptomyces sp. GHB5 was isolated from Egyptian soil, optimized for polygalacturonase production and identified as Streptomyces sp. GHB5 by 16S rRNA sequencing and submitted to gene bank data base with accession number LC405930. Submerged fermentation was carried out using Streptomyces sp. GHB5 for polygalacturonase production. Different physico-chemical and nutritional factors were optimized for maximum polygalacturonase production. Results revealed that the maximum polygalacturonase production come to 34.09 U/mg specific activity. The best obtained conditions were achieved using broth medium with working volume of 20 % (v/v), containing 1.5% (w/v) lemon peel and 0.28% (w/v) potassium nitrate with initial medium pH of 7.5, when incubated at 30°C and 180 rpm for 6 days with an inoculum size of 4% (v/v) of 5 days inoculum age.
Supplementary material to "Analysis of functional groups in atmospheric aerosols by infrared
spectroscopy: functional group quantification in US measurement
networks"

Abstract. Peak-fitting (PF) and partial least squares (PLS) regression have been independently developed for estimation of functional groups (FGs) from Fourier Transform Infrared (FTIR) spectra of ambient aerosol collected on Teflon filters. PF is a model that quantifies the functional group composition of the ambient samples by fitting individual Gaussian line shapes to the aerosol spectra. PLS is a data-driven, statistical model calibrated to laboratory standards of relevant compounds and then extrapolated to ambient spectra. In this work, we compare the FG quantification using the most widely used implementations of PF and PLS including their model parameters, and also perform a comparison when the underlying laboratory standards and spectral processing are harmonized. We evaluate the quantification of organic FGs (alcohol COH, carboxylic COOH, alkane CH, carbonyl CO) and ammonium, using external measurements (OC by thermal optical reflectance and ammonium by balance of sulfate and nitrate measured by ion chromatography). Moreover, we explore the sensitivity of FG, OC, OM/OC predictions to a series of possible input parameters available for each algorithm. We evaluate our predictions using 794 samples collected in the Interagency Monitoring of PROtected Visual Environments (IMPROVE) network (USA) in 2011 and 238 laboratory standards from Ruthenburg et al. (https://doi.org/10.1016/j.atmosenv.2013.12.034, 2014). Each model shows different biases. Overall, estimates of OC by FT-IR show high correlation with TOR OC. However, PLS applied to unprocessed (raw spectra) appears to underpredict oxygenated functional groups in rural samples, while other models appear to underestimate aliphatic CH bonds and OC in urban samples. It is possible to adjust model parameters (absorption coefficients for PF and number of latent variables for PLS) within limits consistent with calibration data to reduce these biases, but this analysis reveals that further progress in parameter selection is required. In addition, we find that the influence of scattering and anomalous transmittance of infrared in coarse particle samples can lead to predictions of OC by FTIR which are inconsistent with TOR OC. We also find through several means that most of the quantified carbonyl is likely associated with carboxylic groups rather than ketones or esters. In evaluating state-of-the-art methods for FG abundance by FTIR, we suggest directions for future research.
Antecedents of Tourists Behavioural Intentions, Perspectives of Expectations Confirmation Model: A Study of Select Tourism Cites in South-East Nigeria

Tourism has expanded dramatically in Nigeria over the last six decades, becoming one of the largest and fastest-growing sectors of the Nigerian like in the global economy. The World Travel and Tourism index ranked Nigeria 129th out of 140 countries and Nigeria’s ranking is well below average rankings for sub-Saharan Africa. This study was motivated by the need to explore the antecedents of tourists’ behavioural intentions from the perspectives of the expectations confirmation model. The study extended the model by adding perceived trust to original components. Several studies have been conducted from the perspective of this model in tourism in many countries but none in Nigeria. The study was based on a sample 400 tourists selected from the five states in the South East geopolitical zone of Nigeria out of which 317 respondents returned valid questionnaire. The study population was infinite hence respondents were those seen at the various tourist sites and who agreed to fill the questionnaire. The analysis utilized partial least squares structural equations modelling (PLS-SEM) with the aid of WarpPLS version 7.0. All the hypothesised relationships are statistically significant (Table 6). The 95% confidence intervals straddle no zero in-between for all the hypotheses. The Effect sizes in our analysis range from as high as 0.409 for Confirmation which is the highest to 0.209 for PP and 0.149 for CE. Conf. and PT have 0.094 and 0.084 respectively hence all the IVs in our analysis fall within acceptable range from medium to high effect sizes and are all considered relevant in our model. Implications of the study were also discussed and recommendations for further study were also made.
Supraspinatus Tendon Tears Biomechanical Effects of Acromioplasty on Superior Capsule Reconstruction for Irreparable

http://ajs.sagepub. com/content/early/2015/10/27/0363546515608652 The online version of this article can be found at: DOI: 10.1177/0363546515608652 published online October 27, 2015 Am J Sports Med Teruhisa Mihata, Michelle H. McGarry, Timothy Kahn, Iliya Goldberg, Masashi Neo and Thay Q. Lee Supraspinatus Tendon Tears Biomechanical Effects of Acromioplasty on Superior Capsule Reconstruction for Irreparable
Preparation of sustained-release fragrance based on the cavity structure of β-cyclodextrin and its application in cotton fabric

The present study reported the formation of fragrance/β-cyclodextrin (β-CD) inclusion complexes aimed to promote thermal stability and controlled release. Then, the fragrance/β-CD inclusion complexes were applied in cotton fabrics through impregnation to obtain aromatic cotton fabrics. The surface morphology and structures of the fragrance/β-CD inclusion complexes were examined via scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy; these analysis results confirmed the formation of inclusion complexes. The stability tests were performed by thermogravimetric analysis and the results confirmed these inclusion complexes had good thermal stability. The surface morphology of the cotton fabrics impregnated by fragrance/β-CD inclusion complexes were tested by SEM. Slow-release experiments of the aromatic cotton fabrics performed by gas chromatography-mass spectrometry and an electronic nose proved that the cotton fabrics impregnated by fragrance/β-CD inclusion complexes had excellent slow-release properties. Moreover, the continuous aroma release time of the cotton fabrics impregnated by fragrance/β-CD inclusion complexes reached up to 80 days.
The effect of soil fertility and harvesting stage on maize seed quality under rain-fed conditions

Abstract Management practices during seed development are crucial for boosting seed quality and establishment. This study determined the interactive effect of soil fertility and maturity stage on maize seed quality. The cultivar SC701 were harvested at milk, dent, and physiological maturity stages and dried to <12% moisture content. Field trials were split plots replicated four times. Seed quality was evaluated using standard germination test, vigor indices and electrical conductivity test. Highly significant interactions (p < .001) occurred for most of the seedling germination and vigor parameters measured. The interactions had strong impact on the germination characteristics and vigor of seeds at dent and physiological maturity. Seeds at dent stage under optimal fertilization gave higher seed quality. Optimal fertilizer application and harvesting at physiological maturity leads to maximum acquisition of seed vigor. However, in the presence of adverse weather conditions seeds from late dent stage under optimally fertilized conditions still give high seed quality.
Naver Labs Europe’s Systems for the Document-Level Generation and Translation Task at WNGT 2019

Recently, neural models led to significant improvements in both machine translation (MT) and natural language generation tasks (NLG). However, generation of long descriptive summaries conditioned on structured data remains an open challenge. Likewise, MT that goes beyond sentence-level context is still an open issue (e.g., document-level MT or MT with metadata). To address these challenges, we propose to leverage data from both tasks and do transfer learning between MT, NLG, and MT with source-side metadata (MT+NLG). First, we train document-based MT systems with large amounts of parallel data. Then, we adapt these models to pure NLG and MT+NLG tasks by fine-tuning with smaller amounts of domain-specific data. This end-to-end NLG approach, without data selection and planning, outperforms the previous state of the art on the Rotowire NLG task. We participated to the “Document Generation and Translation” task at WNGT 2019, and ranked first in all tracks.
Flash X-ray diffraction imaging in 3D: a proposed analysis pipeline

Modern Flash X-ray diffraction Imaging (FXI) acquires diffraction signals from single biomolecules at a high repetition rate from X-ray Free Electron Lasers (XFELs), easily obtaining millions of 2D diffraction patterns from a single experiment. Due to the stochastic nature of FXI experiments and the massive volumes of data, retrieving 3D electron densities from raw 2D diffraction patterns is a challenging and time-consuming task. We propose a semi-automatic data analysis pipeline for FXI experiments, which includes four steps: hit-finding and preliminary filtering, pattern classification, 3D Fourier reconstruction, and post-analysis. We also include a recently developed bootstrap methodology in the post-analysis step for uncertainty analysis and quality control. To achieve the best possible resolution, we further suggest using background subtraction, signal windowing, and convex optimization techniques when retrieving the Fourier phases in the post-analysis step. As an application example, we quantified the 3D electron structure of the PR772 virus using the proposed data analysis pipeline. The retrieved structure was above the detector edge resolution and clearly showed the pseudo-icosahedral capsid of the PR772.
CATS: Customizable Abstractive Topic-based Summarization

Neural sequence-to-sequence models are the state-of-the-art approach used in abstractive summarization of textual documents, useful for producing condensed versions of source text narratives without being restricted to using only words from the original text. Despite the advances in abstractive summarization, custom generation of summaries (e.g., towards a user’s preference) remains unexplored. In this article, we present CATS, an abstractive neural summarization model that summarizes content in a sequence-to-sequence fashion while also introducing a new mechanism to control the underlying latent topic distribution of the produced summaries. We empirically illustrate the efficacy of our model in producing customized summaries and present findings that facilitate the design of such systems. We use the well-known CNN/DailyMail dataset to evaluate our model. Furthermore, we present a transfer-learning method and demonstrate the effectiveness of our approach in a low resource setting, i.e., abstractive summarization of meetings minutes, where combining the main available meetings’ transcripts datasets, AMI and International Computer Science Institute(ICSI), results in merely a few hundred training documents.
Proprioceptive Neuromuscular Facilitation Kinesio Taping Improves Range of Motion of Ankle Dorsiflexion and Balance Ability in Chronic Stroke Patients

This study aimed to determine the effect of a proprioceptive neuromuscular facilitation (PNF) pattern Kinesio taping (KT) application on the ankle dorsiflexion range of motion (DF-ROM) and balance ability in patients with chronic stroke. This crossover study included 18 patients with stroke. The subjects were randomly assigned to three interventions: barefoot, ankle KT (A-KT), and PNF-KT. The A-KT was applied to the gastrocnemius and tibialis anterior (TA) muscles, and subtalar eversion. The PNF-KT was applied on the extensor hallucis, extensor digitorum, and TA muscles. DR-ROM was measured using the iSen™, a wearable sensor. Balance ability was assessed based on static balance, measured by the Biodex Balance System (BBS), and dynamic balance, measured by the timed up and go (TUG) test and dynamic gait index (DGI). Compared with the barefoot and A-KT interventions, PNF-KT showed significant improvements in the ankle DF-ROM and BBS scores, TUG, and DGI. PNF-KT, for functional muscle synergy, improved the ankle DF-ROM and balance ability in patients with chronic stroke. Therefore, the application of PNF-KT may be a feasible therapeutic method for improving ankle movement and balance in patients with chronic stroke. Additional research is recommended to identify the long-term effects of the PNF-KT.
Weather impact on macroscopic traffic stream variables prediction using recurrent learning approach

Abstract Accurate prediction of the macroscopic traffic stream variables is essential for traffic operation and management in an intelligent transportation system. Adverse weather conditions like fog, rainfall, and snowfall affect the driver’s visibility, vehicle mobility, and road capacity. The rainfall effect on traffic is not directly proportional to the distance between the weather station and the road because of terrain feature constraints. The prolonged rainfall weakens the drainage system, affects soil absorption capability, which causes waterlogging. The weather event has a spatiotemporal correlation with traffic stream variables, as waterlogging on the road due to rainfall affects the traffic on adjacent roads. The spatiotemporal and prolonged impact of rainfall is not studied in the literature. In this research, we examine whether the inclusion of the rainfall variable improves the traffic stream variables prediction of a deep learning model or not. We use the RNN and LSTM models to capture the spatiotemporal correlation between traffic and rainfall data using past and current traffic and weather information. To capture the prolonged impact of rainfall more extended past sequence of rainfall data than traffic data is used in this study. The roads prone to waterlogging are more affected due to rainfall compared to freeways. Thus we examine the effect of rain on traffic stream variables prediction for different types of roads. The test experiments show that the inclusion of weather data improves the prediction accuracy of the model. The LSTM outperforms other models to capture the spatiotemporal relationship between the rainfall and traffic stream variables.
Parameter Reduction and Optimisation for Point Cloud and Occupancy Mapping Algorithms

Occupancy mapping is widely used to generate volumetric 3D environment models from point clouds, informing a robotic platform which parts of the environment are free and which are not. The selection of the parameters that govern the point cloud generation algorithms and mapping algorithms affects the process and the quality of the final map. Although previous studies have been reported in the literature on optimising major parameter configurations, research in the process to identify optimal parameter sets to achieve best occupancy mapping performance remains limited. The current work aims to fill this gap with a two-step principled methodology that first identifies the most significant parameters by conducting Neighbourhood Component Analysis on all parameters and then optimise those using grid search with the area under the Receiver Operating Characteristic curve. This study is conducted on 20 data sets with specially designed targets, providing precise ground truths for evaluation purposes. The methodology is tested on OctoMap with point clouds created by applying StereoSGBM on the images from a stereo camera. A clear indication can be seen that mapping parameters are more important than point cloud generation parameters. Moreover, up to 15% improvement in mapping performance can be achieved over default parameters.
Combustion, Performance, and Emission Study of a Research Diesel Engine Fueled with Palm Oil Biodiesel and Its Additive

More requirements for diesel fuel and a huge rise in air pollution, owing to the surplus usage of diesel fuel, give us a prospect to discover environmentally friendly and clean fuels for the existing unmodified diesel engines. The present work is aimed to analyze the combustion, performance, and emission characteristics of a neat palm oil biodiesel (POBD)-fueled diesel engine with silver oxide (Ag2O) as a additive in various mass fractions (5 and 10 ppm), and the results are compared to conventional diesel. Experiments were conducted in a natural aspirated, two-cylinder diesel engine at a constant speed and compression ratio of 2400 rpm and 17:1, respectively. Ag2O was added with neat POBD using an ultrasonicator. The experimental investigation on the diesel engine reveals that the addition of Ag2O nanoparticles to palm oil mill effluent (POME) resulted in enhancement in ignition characteristics because of the enhanced surface area/volume ratio. In addition, the addition of Ag2O nanoparticles to POME resu.