Effect of heat loss on the syngas production by fuel-rich combustion in a divergent two-layer burner

The effect of heat loss on the syngas production from partial combustion of fuel-rich in a divergent two-layer burner is numerically studied using two-dimensional model with detailed kinetics GRI-Mech 1.2. Both the radiation and wall heat losses to the surrounding are considered in the computations. It is shown that two types heat losses have different effects on the syngas production. The radiation heat loss has significant effect on the syngas temperature and the syngas temperature is dropped as radiation heat loss is increased, but it has neglected effect on the reforming efficiency and methane conversion efficiency. The wall heat loss has a comprehensive effect on the syngas production. The wall heat loss not only reduces the conversion efficiency, but also significantly decreases the syngas temperature. The effect of wall heat loss becomes weak as the equivalence is increased. The reforming efficiency drops from 0.440 to 0.424 for equivalence ratio of 2 and mixture velocity of 0.17 m/s for the predictions between adiabatic wall and non-adiabatic conditions.     

Preparation and electrical conductivity of KTaO3-based proton conductor

KTaO₃ and KTa_0.9M_0.1O_3-α (M = Ti, Hf, Zr) were prepared by solid state reaction at 1330 °C for 2 h and characterized by x-ray diffraction. The AC impedance technique was used to analyze the sintered solid electrolytes in 1%H₂/Ar and dry air atmosphere. Among KTa_0.9M_0.1O_3-α (M = Ti, Hf, Zr), KTa_0.9Zr_0.1O_3-α displays the highest conductivity in 1%H₂/Ar atmosphere. The carriers transport numbers of solid electrolytes were measured by concentration cell method. The results show KTa_0.9Zr_0.1O_3-α is a pure proton conductor below 525 °C. Stability tests show that KTa_0.9Zr_0.1O_3-α has good chemical stability against CO₂ and H₂O.     

Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases caused by T2DM. Therefore, it becomes inevitable to predict the risks of CKD and CHD in T2DM patients. The current research article presents automated Deep Learning (DL)-based Deep Neural Network (DNN) with Adagrad Optimization Algorithm i.e., DNN-AGOA model to predict CKD and CHD risks in T2DM patients. The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD. This model helps in alarming both T2DM patients and clinicians in advance. At first, the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing. Besides, a Deep Neural Network (DNN) is employed for feature extraction, after which sigmoid function is used for classification. Further, Adagrad optimizer is applied to improve the performance of DNN model. For experimental validation, benchmark medical datasets were used and the results were validated under several dimensions. The proposed model achieved a maximum precision of 93.99%, recall of 94.63%, specificity of 73.34%, accuracy of 92.58%, and F-score of 94.22%. The results attained through experimentation established that the proposed DNN-AGOA model has good prediction capability over other methods.     

Silica-based ceramics toward electromagnetic microwave absorption

Silica-based ceramics have been explored extensively as a class of versatile materials for various applications in architecture, catalysis, energy, machinery, and biomedical engineering. Nevertheless, comprehensive information on silica-based ceramic and electromagnetic microwave (EMW) absorption is scarce, although excellent progress has been made in this field. Here, recent progress in the investigation of silica-based ceramics toward EMW absorption is reviewed. We first introduced the basis of ceramics (characteristics, classification, synthetic methods, potential applications). Subsequently, the silica-based ceramics, including Si-based oxides and alloys, SiOC/SiC/Si₃N₄/SiCN-based composite, Ti₃SiC₂ and composite for EMW absorption were systematically summarized. Notably, the fabrication strategies, absorption properties, and mechanisms of silica-based ceramics are described in detail, with a focus on structure and component design. Lastly, the prospects and ongoing challenges of this field in the future are presented. This review is expected to learn from the past and achieve progress toward the future of silica-based ceramic for EMW absorption.     

Simulations of NBI fast ion loss in the presence of toroidal field ripple on EAST

NBI fast ion losses in the presence of the toroidal field ripple on EAST have been investigated by using the orbit code GYCAVA and the NBI code TGCO. The ripple effect was included in the upgraded version of the GYCAVA code. It is found that loss regions of NBI fast ions are mainly on the low field side near the edge in the presence of ripple. For co-current NBIs, the synergy effect of ripple and Coulomb collision on fast ion losses is dominant, and fast trapped ions located on the low field side are easily lost. The ripple well loss and the ripple stochastic loss of fast ions have been identified from the heat loads of co-current NBI fast ions. The ripple stochastic loss and the collisioninduced loss are much larger than the ripple well loss. Heat loads of lost fast ions are mainly localized on the right side of the radio frequency wave antennas from the inside view toward the first wall. For counter-current NBIs, the first orbit loss due to the magnetic drift is the dominant loss channel. In addition, fast ion loss fraction with ripple and collision for each NBI linearly increases with the effective charge number, which is related to the pitch angle scattering effect.     

TiO2/MXene-Assisted LDI-MS for Urine Metabolic Profiling in Urinary Disease

Cancer remains an intractable medical problem. Rapid diagnosis and identification of cancer are critical to differentiate it from nonmalignant diseases. High-throughput biofluid metabolic analysis has potential for cancer diagnosis. Nevertheless, the present metabolite analysis method does not meet the demand for high-throughput screening of diseases. Herein, a high-throughput, cost-effective, and noninvasive urine metabolic profiling method based on TiO₂/MXene-assisted laser desorption/ionization mass spectrometry (LDI-MS) is presented for the efficient screening of bladder cancer (BC) and nonmalignant urinary disease. Combined with machine learning, TiO₂/MXene-assisted LDI-MS enables high diagnostic accuracy (96.8%) for the classification of patient groups (including 47 BC and 46 ureteral calculus (UC) patients) from healthy controls (113 cases). In addition, BC patients can also be identified from noncancerous UC individuals with an accuracy of 88.3% in the independent test cohort. Furthermore, metabolite variations between BC and UC individuals are investigated based on relative quantification, and related pathways are also discussed. These results suggest that this method, based on urine metabolic patterns, provides a potential tool for rapidly distinguishing urinary diseases and it may pave the way for precision medicine.     

Cd-substituted NiZnCo ferrite with high dielectric constant and low coercivity for high-frequency electronic devices

This work investigated the effect of replacing Zn²⁺ ions with Cd²⁺ ions on the microstructure and electromagnetic properties of NiZnCo ferrites. The studies show that the Cd²⁺ ions substituted for Zn²⁺ ions at the A sites (tetrahedral sites) of the ferrite lattice. The large ionic radius of the Cd²⁺ ions can cause lattice distortion. Concurrently, the low melting point of CdO can effectively reduce the sintering temperature of NiZnCo ferrite, thereby significantly changing the magnetoelectric properties of NiZnCo ferrite. These changes are mainly manifested as the decrease in the saturation magnetization (Ms) from 66.6 to 58.5 emu/g and the increase in coercivity (Hc) from 31.2 to 34.8 Oe. The dielectric constant increases considerably, dielectric loss tanδ gradually decreases from 4.71 to 0.83 at 10 kHz, and DC resistivity ρ decreases considerably from 8.0 × 10⁴ to 1.61 × 10⁴ Ω m. Therefore, the substitution of Cd²⁺ ions in NiZnCo ferrite provides excellent electrical and magnetic properties, which provide a reference for the development of high-frequency miniaturized electronic equipment.     

Towards artificial intelligence at scale in the chemical industry

In the Industry 4.0 era, the chemical industry is embracing broad adoption of artificial intelligence (AI) and machine learning (ML) methods. This article provides a holistic view of how the industry is transforming digitally towards AI at scale. First, a historical perspective on how the industry used AI to aid humans in better decision-making is shown. Then state-of-the-art AI research addressing industrial needs on reliability and safety, process optimization, supply chain, material discovery, and reaction engineering is highlighted. Finally, a vision of the plant of the future is illustrated with critical components of AI-ready culture, model life cycle management, and renewed role of humans in chemical manufacturing.     

Microstructure,phase, magnetic properties,and electromagnetic wave absorption of graphene oxide – Ni0.7Zn0.3Fe2O4 – Al2O3 aerogel

In this work, an ultralight nanocomposite of graphene oxide aerogels as a matrix and nickel-zinc ferrite (Ni_0.7Zn_0.3Fe₂O₄) nanoparticles as a second phase for the absorption of electromagnetic waves in the frequency of 1–18 GHz were fabricated by the hydrothermal - freeze-drying method. α-Al₂O₃ nanoparticles were used for further impedance matching for applications in electromagnetic wave absorption. XRD, SEM, EDS, and VNA analyses were used to characterize the sample. The effects of the amount of Ni_0.7Zn_0.3Fe₂O₄ (NZF) nanoparticles (GO: NZF volume percent ratio = 5:1 and 2:1) on the absorption of electromagnetic waves were investigated.     

改进RetinaFace的自然场景口罩佩戴检测算法

新型冠状病毒可以通过空气中的飞沫、气溶胶等载体进行传播，在公共场所下正确佩戴口罩可以有效地防止病毒的传播。提出了一种自然场景下人脸口罩佩戴检测方法，对RetinaFace算法进行了改进，增加了人脸口罩佩戴检测任务，优化了损失函数。在特征金字塔网络中引入了一种改进的自注意力机制，增强了特征图的表达能力。建立了包含3 000张图片的数据集，并进行手工标注，用于网络训练。实验结果表明该算法可以有效进行口罩佩戴检测，在自然场景视频中也取得了不错的检测效果。