GC/MS ANALYSIS OF COAL TAR COMPOSITION PRODUCED FROM COAL PYROLYSIS

Coal tar is a significant product generated from coal pyrolysis. A detailed analytical study on its composition and chemical structure will be of great advantage to its further processing and utilization. Using a combined method of planigraphy-gas chromatograph/mass spectroscopy (GC/MS), this work presents a composition analysis on the coal tar generated in the experiment. The analysis gives a satisfactory result, which offers a referable theoretical foundation for the further processing and utilization of coal tar. KEY WORDS: Coking-coals, Coal pyrolysis, Coal tar, GC/MS Bull. Chem. Soc. Ethiop. 2007, 21(2), 229-240

Application of Quantum Machine Learning in a Higgs Physics Study at the CEPC

Machine learning has blossomed in recent decades and has become essential in many fields. It significantly solved some problems in particle physics -- particle reconstruction, event classification, etc. However, it is now time to break the limitation of conventional machine learning with quantum computing. A support-vector machine algorithm with a quantum kernel estimator (QSVM-Kernel) leverages high-dimensional quantum state space to identify a signal from backgrounds. In this study, we have pioneered employing this quantum machine learning algorithm to study the $e^{+}e^{-} \rightarrow ZH$ process at the Circular Electron-Positron Collider (CEPC), a proposed Higgs factory to study electroweak symmetry breaking of particle physics. Using 6 qubits on quantum computer simulators, we optimised the QSVM-Kernel algorithm and obtained a classification performance similar to the classical support-vector machine algorithm. Furthermore, we have validated the QSVM-Kernel algorithm using 6-qubits on quantum computer hardware from both IBM and Origin Quantum: the classification performances of both are approaching noiseless quantum computer simulators. In addition, the Origin Quantum hardware results are similar to the IBM Quantum hardware within the uncertainties in our study. Our study shows that state-of-the-art quantum computing technologies could be utilised by particle physics, a branch of fundamental science that relies on big experimental data

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

<b>GC/MS analysis of coal tar composition produced from coal pyrolysis</b>

Coal tar is a significant product generated from coal pyrolysis. A detailed analytical study on its composition and chemical structure will be of great advantage to its further processing and utilization. Using a combined method of planigraphy-gas chromatograph/mass spectroscopy (GC/MS), this work presents a composition analysis on the coal tar generated in the experiment. The analysis gives a satisfactory result, which offers a referable theoretical foundation for the further processing and utilization of coal tar

Boosting the Performance of LLIE Methods via Unsupervised Weight Map Generation Network

Over the past decade, significant advancements have been made in low-light image enhancement (LLIE) methods due to the robust capabilities of deep learning in non-linear mapping, feature extraction, and representation. However, the pursuit of a universally superior method that consistently outperforms others across diverse scenarios remains challenging. This challenge primarily arises from the inherent data bias in deep learning-based approaches, stemming from disparities in image statistical distributions between training and testing datasets. To tackle this problem, we propose an unsupervised weight map generation network aimed at effectively integrating pre-enhanced images generated from carefully selected complementary LLIE methods. Our ultimate goal is to enhance the overall enhancement performance by leveraging these pre-enhanced images, therewith culminating the enhancement workflow in a dual-stage execution paradigm. To be more specific, in the preprocessing stage, we initially employ two distinct LLIE methods, namely Night and PairLIE, chosen specifically for their complementary enhancement characteristics, to process the given input low-light image. The resultant outputs, termed pre-enhanced images, serve as dual target images for fusion in the subsequent image fusion stage. Subsequently, at the fusion stage, we utilize an unsupervised UNet architecture to determine the optimal pixel-level weight maps for merging the pre-enhanced images. This process is adeptly directed by a specially formulated loss function in conjunction with the no-reference image quality algorithm, namely the naturalness image quality evaluator (NIQE). Finally, based on a mixed weighting mechanism that combines generated pixel-level local weights with image-level global empirical weights, the pre-enhanced images are fused to produce the final enhanced image. Our experimental findings demonstrate exceptional performance across a range of datasets, surpassing various state-of-the-art methods, including two pre-enhancement methods, involved in the comparison. This outstanding performance is attributed to the harmonious integration of diverse LLIE methods, which yields robust and high-quality enhancement outcomes across various scenarios. Furthermore, our approach exhibits scalability and adaptability, ensuring compatibility with future advancements in enhancement technologies while maintaining superior performance in this rapidly evolving field

Dynamics of phenol synergistic biodegradation by Chloroperoxidase and bacterial strains

Chloroperoxidase (CPO) can catalyze phenol reacting peroxide reaction generating with H2O2 to catechol,which can reduce the inhibitory effect of phenol degradating generating bacterial strains.At the meantime,it can accelerate the rate of phenol's biodegradation.The results show that 10 U/L of CPO has 67.85% conversion rate of 300 mg/L phenol within 2 h with an appropriate amount of H2O2.While the degradation rate of phenol degradation under synergy of strain and CPO is up to 70.72%,increased by 62.2% comparing with a single strain degradation rate (8.52%).Supplementary catechol in the reaction system can further verified that the intermediate products can be good for forming the co-substrate effect in bacteria,thereby improving the phenol degradation efficiency of biological bacterial cells.Biodegradation dynamics analysis shows that the maximum specific degradation rate of the CPO and strain synergistic.The maximum specific degradation rate qmax=0.000195 h-1,the matrix saturation constant Ks=1.0501 mg/L,and the substrate inhibition constant KI=5.1272 mg/L when phenol concentration in the range of 100~1 200 mg/L

Super High Dosing with a Novel Buttiauxella Phytase Continuously Improves Growth Performance, Nutrient Digestibility, and Mineral Status of Weaned Pigs

This study was conducted to evaluate the efficacy of a novel Buttiauxella phytase to pigs fed P-deficient, corn-soybean meal diets. One hundred and twenty crossbred piglets (9.53 +/- 0.84 kg) were allocated to one of five treatments which consisted of four low P diets (0.61 % Ca and 0.46 % total P) supplemented with 0, 500, 1,000, or 20,000 FTU/kg phytase as well as a positive control diet (0.77 % Ca and 0.62 % total P). Each treatment had six replicated pens with four pigs per pen. Pigs were fed the experimental diets for 28 days. Phytase supplementation linearly improved (P < 0.05) average daily gain (ADG), feed conversion ratio (FCR), and apparent total tract digestibility (ATTD) of dry matter, gross energy, crude protein, Ca, and P in weaned pigs. Super high dosing with phytase (20,000 FTU/kg) further increased (P < 0.05) ADG compared with 500 FTU/kg phytase inclusion group, as well as ATTD of Ca and P. Metacarpal bone characteristics and several trace mineral concentration in bone, plasma, or organ tissues were linearly (P < 0.05) improved at increasing dose of phytase. Super high dosing with phytase (20,000 FTU/kg) supplementation improved (P < 0.05) Mn and Zn concentration in bone compared to normal dose of phytase supplementation (500 or 1,000 FTU/kg). In conclusion, supplementation of 500 FTU of Buttiauxella phytase/kg and above effectively hydrolyzed phytate in a low-P corn-soybean diet for pigs. In addition, a super high dosing with phytase (20,000 FTU/kg) improved macro- or micro mineral availability and growth performance

Reconstruction of Surface Kinematics From Sea Surface Height Using Neural Networks

Abstract The Surface Water and Ocean Topography (SWOT) satellite is expected to observe sea surface height (SSH) down to scales approaching ∼15 km, revealing submesoscale patterns that have never before been observed on global scales. Features at these soon‐to‐be‐observed scales, however, are expected to be significantly influenced by internal gravity waves, fronts, and other ageostrophic processes, presenting a serious challenge for estimating surface velocities from SWOT observations. Here we show that a data‐driven approach can be used to estimate the surface flow, particularly the kinematic signatures of smaller scale flows, from SSH observations, and that it performs significantly better than using the geostrophic relationship. We use a Convolutional Neural Network (CNN) trained on submesoscale‐permitting high‐resolution simulations to test the possibility of reconstructing surface vorticity, strain, and divergence from snapshots of SSH. By evaluating success using pointwise accuracy and vorticity‐strain‐divergence joint distributions, we show that the CNN works well when inertial gravity wave amplitudes are relatively weak. When the wave amplitudes are strong, reconstructions of vorticity and strain are less accurate; however, we find that the CNN naturally filters the wave‐divergence, making divergence a surprisingly reliable field to reconstruct. We also show that when applied to realistic simulations, a CNN model pretrained with simpler simulation data performs well, indicating a possible path forward for estimating real flow statistics with limited observations