Energetic Bio-Activation of Some Organic Molecules and Their Antioxidant Activity in the Pulp of the Moroccan Argan Tree «Argania spinosa L.»

Argania spinosa L. Skeels is an emblematic tree in Morocco, known worldwide for its medicinal and nutritional value. Its fruits contain kernels used to prepare an edible oil, the leaves are used to feed livestock, and its wood is used as fuel. If the oil acquires high importance, the other components of the fruit of the argan are undervalued. Our objective is to invest the waste of the argan industry. Particularly, our study aimed to assess the effect of thermal activation of argan pulp on its therapeutic value, its phenolic profile and its functional and physicochemical properties. After heat treatment, the HPLC analysis for the average total phenolic content varied from 2% to 37%, depending on temperature. The antioxidant activity was increased with heat treatment. Higher values of antioxidant activity, polyphenol and pigment content were recorded at 70 °C. Functional properties analysis indicated that water solubility index and water absorption capacity were significantly affected by heat stress. Physicochemical analysis showed that moisture content, titratable acidity and soluble solids were affected.     

Optimal Energy-Storage Configuration for Microgrids Based on SOH Estimation and Deep Q-Network

Energy storage is an important adjustment method to improve the economy and reliability of a power system. Due to the complexity of the coupling relationship of elements such as the power source, load, and energy storage in the microgrid, there are problems of insufficient performance in terms of economic operation and efficient dispatching. In view of this, this paper proposes an energy storage configuration optimization model based on reinforcement learning and battery state of health assessment. Firstly, a quantitative assessment of battery health life loss based on deep learning was performed. Secondly, on the basis of considering comprehensive energy complementarity, a two-layer optimal configuration model was designed to optimize the capacity configuration and dispatch operation. Finally, the feasibility of the proposed method in microgrid energy storage planning and operation was verified by experimentation. By integrating reinforcement learning and traditional optimization methods, the proposed method did not rely on the accurate prediction of the power supply and load and can make decisions based only on the real-time information of the microgrid. In this paper, the advantages and disadvantages of the proposed method and existing methods were analyzed, and the results show that the proposed method can effectively improve the performance of dynamic planning for energy storage in microgrids.     

New Chemotypes for the Inhibition of (p)ppGpp Synthesis in the Quest for New Antimicrobial Compounds

Antimicrobial resistance (AMR) poses a serious threat to our society from both the medical and economic point of view, while the antibiotic discovery pipeline has been dwindling over the last decades. Targeting non-essential bacterial pathways, such as those leading to antibiotic persistence, a bacterial bet-hedging strategy, will lead to new molecular entities displaying low selective pressure, thereby reducing the insurgence of AMR. Here, we describe a way to target (p)ppGpp (guanosine tetra- or penta-phosphate) signaling, a non-essential pathway involved in the formation of persisters, with a structure-based approach. A superfamily of enzymes called RSH (RelA/SpoT Homolog) regulates the intracellular levels of this alarmone. We virtually screened several fragment libraries against the (p)ppGpp synthetase domain of our RSH chosen model Rel_Seq, selected three main chemotypes, and measured their interaction with Rel_Seq by thermal shift assay and STD-NMR. Most of the tested fragments are selective for the synthetase domain, allowing us to select the aminobenzoic acid scaffold as a hit for lead development.     

Exploration of the Crystal Structure and Thermal and Spectroscopic Properties of Monoclinic Praseodymium Sulfate Pr2(SO4)3

Praseodymium sulfate was obtained by the precipitation method and the crystal structure was determined by Rietveld analysis. Pr₂(SO₄)₃ is crystallized in the monoclinic structure, space group C2/c, with cell parameters a = 21.6052 (4), b = 6.7237 (1) and c = 6.9777 (1) Å, β = 107.9148 (7)°, Z = 4, V = 964.48 (3) ų (T = 150 °C). The thermal expansion of Pr₂(SO₄)₃ is strongly anisotropic. As was obtained by XRD measurements, all cell parameters are increased on heating. However, due to a strong increase of the monoclinic angle β, there is a direction of negative thermal expansion. In the argon atmosphere, Pr₂(SO₄)₃ is stable in the temperature range of T = 30–870 °C. The kinetics of the thermal decomposition process of praseodymium sulfate octahydrate Pr₂(SO₄)₃·8H₂O was studied as well. The vibrational properties of Pr₂(SO₄)₃ were examined by Raman and Fourier-transform infrared absorption spectroscopy methods. The band gap structure of Pr₂(SO₄)₃ was evaluated by ab initio calculations, and it was found that the valence band top is dominated by the p electrons of oxygen ions, while the conduction band bottom is formed by the d electrons of Pr³⁺ ions. The exact position of ZPL is determined via PL and PLE spectra at 77 K to be at 481 nm, and that enabled a correct assignment of luminescent bands. The maximum luminescent band in Pr₂(SO₄)₃ belongs to the ³P₀ → ³F₂ transition at 640 nm.     

基于改进VGG网络的单体热电池X光图像无损检测方法研究

为解决单体热电池生产中出现的安装错误、人工检测耗时耗力的问题,提出一个结合迁移学习和卷积 神 经网络(convolutional neural network,CNN) 的单体热电池缺陷检测模型。首先,对数据集图像进行裁剪、加噪等预处理,以VGG16(visual geometry group 16) 网络作为 模型的骨干架构,在瓶颈层后增添选择性核(selective kernel,SK) 卷积;然后,增添全局平均池化(global average pooling,GAP) 层, 增加Dropout层及添加 L2 正则化等微调操作,得到单体热电池缺陷检测模型Q-VGGNet;最后,在大型公开数据集ImageNet上进 行预训练学习,将获得的权重参数迁移到单体热电池图像识别模型Q-VGGNet上。测试实验表明:6种 网络模型对数据集缺陷图像的总体识别准确率分别达到了98.39%、94.44%、97.27%、96.34%、93.71%、 95.61%,Q-VGGNet网 络模型 对合格图像和 漏装负极、极耳断裂、漏装集流片3种缺陷图像 识别准确率 分别达到了99.6%,95.9%,99.6%和98.4%。检测结果表明:该方法能够更准确、快速地检测热电池缺陷, 拥有良好的缺陷诊断能力,较传统方法提高近3%,为人工检测单体热电池缺陷提供了良好的解决途径。     

热暴露对SiC纤维增强Ti基复合材料基体织构的影响

本文利用电子背散射衍射（EBSD）技术,研究了高温热暴露对SiC纤维增强Ti-6A1—4V复合材料基体织构的影响。结果表明,相对于制备态复合材料较为集中的晶粒取向,900℃10h热暴露后的复合材料其晶粒生长的方向既有基面方向,又有棱面方向,同时还存在锥面方向。随着热暴露时间的延长,900℃75h热暴露的复合材料基面方向的生长方式与900℃10h热暴露的复合材料情况恰好相反。另外,无论是基面方向还是棱面方向,随着热暴露时间的延长,晶粒生长的取向均有向单一取向演化的趋势,且锥面方向上的生长会逐渐消失。     

Positron Emission Tomography Radiopharmaceuticals in Differentiated Thyroid Cancer

Differentiated thyroid cancer (DTC), arising from thyroid follicular epithelial cells, is the most common type of thyroid cancer. Despite the well-known utilization of radioiodine treatment in DTC, i.e., iodine-131, radioiodine imaging in DTC is typically performed with iodine-123 and iodine-131, with the current hybrid scanner performing single photon emission tomography/computed tomography (SPECT/CT). Positron emission tomography/computed tomography (PET/CT) provides superior visualization and quantification of functions at the molecular level; thus, lesion assessment can be improved compared to that of SPECT/CT. Various types of cancer, including radioiodine-refractory DTC, can be detected by 2-[¹⁸F]fluoro-2-deoxy-D-glucose ([¹⁸F]FDG), the most well-known and widely used PET radiopharmaceutical. Several other PET radiopharmaceuticals have been developed, although some are limited in availability despite their potential clinical utilizations. This article aims to summarize PET radiopharmaceuticals in DTC, focusing on molecular pathways and applications.     

注碳外延硅的荧光特性研究

研究了注碳外延硅经氢气退火及电化学腐蚀处理后的荧光特性。经能量为50keV,剂量为2×1016cm-2的碳离子注入后的外延单晶硅片,在氢气氛下高温退火及电化学腐蚀处理。荧光谱仪分析表明电化学腐蚀是蓝光发射的前提,并且不同的电化学腐蚀条件对发光强度和峰位影响极大。不同的激发光波长亦可影响发光谱的峰位。     

抗震高可靠电子枪的设计

电视跟踪、电视制导是导弹武器系统的重要组成部份,摄象器件又是其中的关键器件。本文着重从抗震、高可靠方面介绍摄象器件电子枪的结构和性能设计,该电子枪应用于重点军事工程,经多次试验和实际使用,性能稳定,达到了高可靠的要求,是目前国内唯一的抗震高可靠电子枪。     

电子的介电常数研究

提出了电子介电常数的计算模型,从不同的角度得到了电子的非相对论散射截面,进一步得到了电子的非相对论性介电常数,发现电子是一色散介质,介电常数是波粒二象性的统一;得到了导体的等效介电常数,验证了所得电子介电常数的有效性;填补了此项研究的空白。