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Artificial Intelligence Based Meteorological Parameter Forecasting for Optimizing Response of Nuclear Emergency Decision Support System

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原子能科学技术 2024年第10期58卷 2068-2076页

作者： BILAL Ahmed Khan HASEEB ur Rehman QAISAR Nadeem MUHAMMAD Ahmad Naveed Qureshi JAWARIA Ahad MUHAMMAD Naveed Akhtar AMJAD Farooq MASROOR Ahmad Pakistan Institute of engineering and applied sciences Islamabad 44000Pakistan

This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) *** meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support *** of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and ***,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological *** evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South *** performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological *** show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single ***,accuracy is slightly compromised when predicting wind speed *** mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of *** conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.

关键词： prediction of meteorological parameters weather research and forecasting model artificial neural networks nuclear emergency support system

A universal strategy of multi-objective active learning to accelerate the discovery of organic electrode molecules

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Science China Chemistry 2024年第11期67卷 3681-3687页

作者： Jiayi Du Jun Guo Wei Liu Ziwei Li Gang Huang Xinbo Zhang State Key Laboratory of Rare Earth Resource Utilization Changchun Institute of Applied Chemistry Chinese Academy of Sciences school of applied Chemistry and engineering University of Science and Technology of China school of Materials Science and engineering Changchun University of Science and Technology

Organic electrode molecules hold significant potential as the next generation of cathode materials for Li-ion batteries. In this study, we have introduced a multi-objective active learning framework that leverages Bayesian optimization and non-dominated sorting genetic algorithms-Ⅱ. This framework enables the selection of organic molecules characterized by high theoretical energy density and low gap(LUMO-HOMO)(LUMO, lowest unoccupied molecular orbital; HOMO, highest occupied molecular orbital). Remarkably, after only two cycles of active learning, the determination of coefficient can reach 0.962 for theoretical energy density and 0.920 for the gap with a modest dataset of 300 molecules, showcasing superior predictive capabilities. The 2,3,5,6-tetrafluorocyclohexa-2,5-diene-1,4-dione, selected by non-dominated sorting genetic algorithms-Ⅱ, has been successfully applied to Li-ion batteries as cathode materials, demonstrating a high capacity of 288 m Ah g-1and a long cycle life of 1,000 cycles. This outcome underscores the high reliability of our framework. Furthermore, we have also validated the universality and transferability of our framework by applying it to two additional databases, the QM9 and OMEAD. When the training dataset of the model includes at least 500 molecules, the determination of coefficient essentially reaches approximately0.900 for four targets: gap, reduction potential, LUMO, and HOMO. Therefore, the universal framework in our work provides innovative insights applicable to other domains to expedite the screening process for target materials.

关键词： organic electrode molecules Li-ion batteries active learning multi-objective Bayesian optimization

Smart beam via electrically addressing the dye-doped and polymer-stabilized cholesteric liquid crystals

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Chinese Optics Letters 2024年第5期22卷 111-116页

作者：彭程徐春庭胡伟 College of engineering and applied sciences Nanjing UniversityNanjing 210023China

Smart beams play a vital role in modern intelligent vehicles and have recently attracted significant attention.A spatial light modulator with high optical efficiency,low cost,and compact size is crucial for designing smart ***,we mix cholesteric liquid crystals with dichroic black dye and a *** UV polymerization,the sample exhibits a low driving voltage of 26 V,a high transmittance of over 70%,and an On-off ratio over 280,thanks to the joint contribution of both the absorption and the scattering effect.A smart beam device is demonstrated by electrically addressing the dye-doped and polymer-stabilized cholesteric liquid crystal with pixelated *** patterns with arbitrary designs are projected *** switching time reaches several tens of *** strategy brings new designs to intelligent vehicles and may also inspire applications in public information displays,advertising,and even AR/VR displays.

关键词： cholesteric liquid crystal spatial light modulation smart beam dye guest-host effect

Stereoselective recognition of morphine enantiomers by μ-opioid receptor

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National Science Review 2024年第3期11卷 183-191页

作者： Yibo Wang Van A.Ngo Xiaohui Wang Laboratory of Chemical Biology Changchun Institute of Applied ChemistryChinese Academy of Sciences Advanced Computing for Life sciences and engineering Group Science Engagement Section National Center for Computational Sciences Oak Ridge National Lab school of applied Chemistry and engineering University of Science and Technology of China Beijing National Laboratory for Molecular sciences

Stereospecific recognition of chiral molecules plays a crucial role in biological systems. The μ-opioid receptor（MOR） exhibits binding affinity towards（-）-morphine, a well-established gold standard in pain management, while it shows minimal binding affinity for the（+）-morphine enantiomer, resulting in a lack of analgesic activity. Understanding how MOR stereoselectively recognizes morphine enantiomers has remained a puzzle in neuroscience and pharmacology for over half-a-century due to the lack of direct observation techniques. To unravel this mystery, we constructed the binding and unbinding processes of morphine enantiomers with MOR via molecular dynamics simulations to investigate the thermodynamics and kinetics governing MOR's stereoselective recognition of morphine enantiomers. Our findings reveal that the binding of（-）-morphine stabilizes MOR in its activated state, exhibiting a deep energy well and a prolonged residence time. In contrast,（+）-morphine fails to sustain the activation state of ***, the results suggest that specific residues, namely D1142.50and D1473.32, are deprotonated in the active state of MOR bound to（-）-morphine. This work highlights that the selectivity in molecular recognition goes beyond binding affinities, extending into the realm of residence time.

关键词： stereoselective recognition thermodynamics and kinetics residence time morphine enantiomers μ-opioid receptor

Modularized viromimetic polymer nanoparticle vaccines(VPNVaxs) to elicit durable and effective humoral immune responses

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National Science Review 2024年第3期11卷 200-214页

作者： Zichao Huang Xinyu Zhuang Liping Liu Jiayu Zhao Sheng Ma Xinghui Si Zhenyi Zhu Fan Wu Ningyi Jin Mingyao Tian Wantong Song Xuesi Chen Key Laboratory of Polymer Ecomaterials Changchun Institute of Applied ChemistryChinese Academy of Sciences school of applied Chemistry and engineering University of Science and Technology of China Changchun Veterinary Research Institute Chinese Academy of Agricultural Sciences Jilin Biomedical Polymers engineering Laboratory State Key Laboratory of Polymer Physics and Chemistry Changchun Institute of Applied Chemistry Chinese Academy of Sciences

Virus-like particle(VLP) vaccines had shown great potential during the COVID-19 pandemic, and was thought to be the next generation of antiviral vaccine technology due to viromimetic structures. However,the time-consuming and complicated processes in establishing a current recombinant-protein–based VLP vaccine has limited its quick launch to the out-bursting pandemic. To simplify and optimize VLP vaccine design, we herein report a kind of viromimetic polymer nanoparticle vaccine(VPNVax), with subunit receptor-binding domain(RBD) proteins conjugated to the surface of polyethylene glycol-b-polylactic acid(PEG-b-PLA) nanoparticles for vaccination against SARS-CoV-2. The preparation of VPNVax based on synthetic polymer particle and chemical post-conjugation makes it possible to rapidly replace the antigens and construct matched vaccines at the emergence of different viruses. Using this modular preparation system, we identified that VPNVax with surface protein coverage of 20%–25% had the best immunostimulatory activity, which could keep high levels of specific antibody titers over 5 months and induce virus neutralizing activity when combined with an aluminum adjuvant. Moreover, the polymer nano-vectors could be armed with more immune-adjuvant functions by loading immunostimulant agents or chemical chirality design. This VPNVax platform provides a novel kind of rapidly producing and efficient vaccine against different variants of SARS-CoV-2 as well as other viral pandemics.

关键词： vaccine polymer nanomaterials SARS-CoV-2

Mass transfer in heap leaching of weathered crust elution deposited rare earth ore with Al_(2)(SO_(4))_(3) solution

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Journal of Rare Earths 2024年第6期42卷 1175-1181,I0007页

作者： Jingqun Yin Dedong Zeng Jun Tian Yaqing Weng Institute of applied Chemistry Jiangxi Academy of SciencesNanchang330096China school of Chemistry and Chemical engineering Nanchang UniversityNanchang330031China

In order to increase efficiency and avoid NH^(4+)-N pollution in the leaching process of weathered crust elution-deposited rare earth ores,the mass transfer in heap leaching with Al_(2)(SO_(4))_(3) solution was simulated with column elution and experimentally *** results indicate that the leaching yield is also up to 99%for leaching with aluminum sulfate instead of ammonium *** optimal flow rate is 60 mL/h,the height equivalent to a theoretical plate(HETP)is 1.29 mm *** peak value position of RE^(3+)is 60 mL/h,Fe^(3+)is 100 mL/h,however,Al^(3+)is 150 mL/h,and the heap leaching process has kinetic separation effect.

关键词： Raree arths Mass transfer Process optimization Heapleaching Weathered crust elution-deposited rare earth ore

Model-free method for LQ mean-field social control problems with one-dimensional state space

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Control Theory and Technology 2024年第3期22卷 479-486页

作者： Zhenhui Xu Tielong Shen school of engineering Tokyo Institute of TechnologyTokyo 152-8550Japan Department of engineering and applied sciences Sophia UniversityTokyo 102-8554Japan

This paper presents a novel model-free method to solve linear quadratic(LQ)mean-field control problems with one-dimensional state space and multiplicative *** focus is on the infinite horizon LQ setting,where the conditions for solution either stabilization or optimization can be formulated as two algebraic Riccati equations(AREs).The proposed approach leverages the integral reinforcement learning technique to iteratively solve the drift-coefficient-dependent stochastic ARE(SARE)and other indefinite ARE,without requiring knowledge of the system dynamics.A numerical example is given to demonstrate the effectiveness of the proposed algorithm.

关键词： Mean-field control Social optima Infinite horizon Reinforcement learning

Preparation of Chemically Recyclable Poly(ether-alt-ester) by the Ring Opening Polymerization of Cyclic Monomers Synthesized by Coupling Glycolide and Epoxides

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Chinese Journal of Polymer Science 2024年第2期42卷 168-175,I0006页

作者： Feng Ren Zhuang-Zhuang Liang Ming-Xin Niu Chen-Yang Hu Xuan Pang Key Laboratory of Polymer Ecomaterials Changchun Institute of Applied ChemistryChinese Academy of SciencesChangchun130022China school of applied Chemistry and engineering University of Science and Technology of ChinaHefei230026China

Polyester and polyether are two key oxygenated polymers, and completely alternative sequence of poly(ester-alt-ether) could efficiently combine the advantages(including flexibility, degradability, etc.) of both segments. Currently, despite their copolymers could be synthesized from one-pot mixture of cyclic esters and epoxides, perfectly alternative microstructure is very challenging to realize and typically restricted to certain monomer pairs. Moving forward, synthesizing poly(ester-alt-ether) from commercially available and largescale monomers would be a significant advance. For example, successfully commercialized poly(glycolic acid)(PGA), which is not easily soluble in polymers due to its high crystallinity and is brittle and difficult to control the degradation cycle, would encounter a new paradigm if engineered into poly(ester-altether). In this work, starting from the design of monomer with hybrid structures, we successfully synthesized a series of 1,4-dioxan-2-one containing different substituents based on glycolide(GA) and epoxides using commercially available Salen-Cr(III) and PPNCl catalytic *** new monomers underwent ring-opening polymerization(ROP) to form a series of poly(ester-alt-ether) with perfectly alternating glycolic acid and propylene glycol repeat units under catalytic system of thiourea/base. The poly(ester-alt-ether) have significantly lower glass-transition temperature than PGA. Additionally, the poly(ester-alt-ether) can be chemically recovered to monomer using Sn(Oct)2 or 1,8-diazabicyclo[5.4.0]undecane-7-ene(DBU) as a catalyst in solution, thus establishing a closed-loop life cycle. From monomers derived from GA and epoxides, this work furnishes a novel strategy for the synthesis of poly(ester-alt-ether) with chemical recyclability.

关键词： Polyester Polyether Alternative copolymer Ring-opening polymerization

Polyoxometalates: metallodrug agents for combating amyloid aggregation

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National Science Review 2024年第7期11卷 279-291页

作者： Mengmeng Ma Zhenqi Liu Huisi Zhao Haochen Zhang Jinsong Ren Xiaogang Qu Laboratory of Chemical Biology and State Key Laboratory of Rare Earth Resource Utilization Changchun Institute of Applied ChemistryChinese Academy of Sciences school of applied Chemistry and engineering University of Science and Technology of China

Alzheimer's disease(AD) is a devastating neurodegenerative disease that affects ～50 million people globally. The accumulation of amyloid-β(Aβ) plaques, a predominant pathological feature of AD, plays a crucial role in AD pathogenesis. In this respect, Aβ has been regarded as a highly promising therapeutic target for AD treatment. Polyoxometalates(POMs) are a novel class of metallodrugs being developed as modulators of Aβ aggregation, owing to their negative charge, polarity, and three-dimensional *** traditional discrete inorganic complexes, POMs contain tens to hundreds of metal atoms,showcasing remarkable tunability and diversity in nuclearities, sizes, and shapes. The easily adjustable and structurally variable nature of POMs allows for their favorable interactions with Aβ. This mini-review presents a balanced overview of recent progress in using POMs to mitigate amyloidosis. Clear correlations between anti-amyloid activities and structural features of POMs are also elaborated in detail. Finally, we discuss the current challenges and future prospects of POMs in combating AD.

关键词： amyloid inhibition metallodrug polyoxometalate amyloid-β Alzheimer's disease