Neural network modeling of hydrological systems: A review of implementation techniques

This paper presents a review of procedural steps and implementation techniques used in the development of artificial intelligence models, generally referred to as artificial neural networks (ANNs), within the water resources domain. It focusses on identifying different areas wherein ANNs have found application thereby elucidating its advantages and disadvantages as well as various challenges encountered in its use. Results from this review provide useful insights into how the performance of ANNs can be improved and potential areas of application that are yet to be explored in hydrological modeling. Recommendations for Resource Managers

• Development of integrated and hybrid artificial intelligent tools is critical to achieving improved forecasts in hydrological modeling studies.

• Further research into comprehending the internal mechanisms of neural networks is required to obtain a practical meaning of each network component deployed to solve real‐world problems.

• More robust optimization techniques and tools like differential evolution, particle swarm optimization and deep neural nets, are yet to be fully explored in the water resources analysis, and should be given more attention to enhance neural networks aptitude for modeling complex and nonlinear hydrological processes.

     

高中化学创新实验课程的设计与实施*

将学生所学的化学理论知识与生活实际和学科最前沿的研究热点相结合,开发并实施了 “氢氧化镁纳米材料的制备及其在处理重金属废水中的应用” 创新实验课程。该课程通过文献调研、方案设计、实验操作、结果与讨论以及论文撰写等环节,激发学生的创新潜能,系统培养学生的创新综合素质和责任意识,这有助于更好地达成化学学科核心素养。     

金色梦想，一触即发——化学专业校外实践教学基地建设与思考

高等教育要适应世界潮流和时代变化,本科教育须进一步提高实践教学的比重,加强理论教学与实践教学相结合,完善协同育人机制。结合学科发展和企业发展需求,中山大学化学学院与金发科技股份有限公司(简称金发科技)共同建设大学生实践教学基地。本文以中山大学-金发科技本科实践教学基地为例,探讨大学生校外实践基地建设过程中遇到的问题以及实践基地的建设内容与成果体会。     

LaPO_4∶Ce,Tb纳米荧光粉末的合成及其在指纹无损显现中的应用

采用溶剂热法在水和乙二醇的混合溶剂中合成出了高质量的LaPO_4∶Ce,Tb纳米荧光粉末。然后通过透射电子显微镜(TEM)、粉末X射线衍射(XRD)、荧光光谱(FS)等表征手段分别对稀土纳米荧光粉末的微观形貌、晶体类型、荧光性能进行了表征,合成纳米荧光粉末的形貌为单分散的纳米棒,平均长度为700nm、平均直径为20nm,其晶体结构为单斜LaPO_4晶型,并且在254nm紫外光照射下能够产生较强的绿色荧光。最后将合成的纳米荧光粉末应用于光滑客体表面汗潜指纹的无损显现中,并详细考察了指纹显现的对比度、灵敏度、选择性、背景干扰等指标。实验结果表明,使用LaPO_4∶Ce,Tb纳米荧光粉末显现的指纹在254nm紫外光的激发下能够产生明亮的绿色荧光,指纹乳突纹线部位连贯清晰、细微特征反映明显,指纹与客体之间的对比反差强烈、客体产生的背景干扰较小,因此该显现方法具有较高的对比度、灵敏度和选择性。本显现方法具有操作方法简单、显现效果优良、适用范围广泛等优点。本研究的重要创新之处在于,经LaPO_4∶Ce,Tb纳米荧光粉末显现后的指纹还可以进行后续的DNA提取及检测,这是传统的指纹显现粉末所不能及的。本研究为指纹物证和生物物证这两大物证的同时利用提供了有益参考。     

非经典碳正离子简介

非经典碳正离子作为一种特殊的反应中间体,具有重要的理论和实际研究的意义。本文以碳正离子理论的发展为主线,以历史上非经典碳正离子(特别是降冰片基正离子)之争为中心,简要综述了Winstein、Brown和Olah三人的观点,并结合最新进展进行评述。此外,还对除降冰片基正离子之外的非经典碳正离子进行了适当的讨论。     

早期干预对极低出生体重早产儿智能发育的影响研究

目的 探讨早期干预对极低出生体重早产儿智能发育的影响,为极低出生体重早产儿的早期干预提供依据。方法 239 例极低出生体重早产儿为早产随访组,采用鲍秀兰早期教育资料制定的干预计划,进行系统干预,并与326 例失访的早产儿童以及与随访组相似的326 例正常儿童进行对照分析。采用Gesell 儿童发育量表等标准化工具进行发育评估。结果 早产随访组Gesell 儿童发育量表5 项均分、总发育商（developmental quotient,DQ）总分与早产失访组相比差异均有统计学差异（均P＜0.05）。结论 早期干预能显著促进极低出生体重早产儿的智能发育,提高患儿的生活质量。     

电化学生物传感器在环境监测中的应用及发展前景

简要介绍了电化学生物传感器的工作原理,重点论述了电化学生物传感器在环境监测领域的应用及其研究进展,主要包括水环境污染物和大气污染物的监测,以及农药残留的监测等.同时,对电化学生物传感器的发展方向及前景进行了展望.     

Rapid determination of catecholamines in urine samples by nonaqueous microchip electrophoresis with LIF detection

A method was developed for the rapid separation of catecholamines by nonaqueous microchip electrophoresis (NAMCE) with LIF detection, A homemade pump‐free negative pressure sampling device was used for rapid bias‐free sampling in NAMCE, the injection time was 0.5 s and the electrophoresis separation conditions were optimized. Under the optimized conditions, the samples were separated completely in <1 min. The average migration times of the epinephrine (E), dopamine (DA), and norepinephrine (NE) were 34.26, 43.81, and 50.07 s, with an RSD of 1.05, 1.26, and 0.89% (n = 7), respectively. The linearity of the method ranged from 0.0125 to 2.0 mg/L for E and 0.025～4.0 mg/L for DA and NE, with correlation coefficients ranging between 0.9978 and 0.9986. The detection limits of E, DA, and NE were 2.5, 5.0, and 5.0 μg/L, respectively. The recoveries of E, DA, and NE in spiked urine samples were between 86 and 103%, with RSDs of 4.5～6.8% (n = 5). The proposed NAMCE with LIF detection combined with a pump‐free negative pressure sampling device is a simple, inexpensive, energy efficient, miniaturized system that can be successfully applied for the determination of catecholamines in urine samples.     

Development of a sensitive and reliable high performance liquid chromatography method with fluorescence detection for high-throughput analysis of multi-class mycotoxins in Coix seed

As an edible and medicinal plant, Coix seed is readily contaminated by more than one group of mycotoxins resulting in potential risk to human health. A reliable and sensitive method has been developed to determine seven mycotoxins (aflatoxins B₁, B₂, G₁, G₂, zearalenone, α-zearalenol, and β-zearalenol) simultaneously in 10 batches of Coix seed marketed in China. The method is based on a rapid ultrasound-assisted solid–liquid extraction (USLE) using methanol/water (80/20) followed by immunoaffinity column (IAC) clean-up, on-line photochemical derivatization (PCD), and high performance liquid chromatography coupled with fluorescence detection (HPLC-FLD). Careful optimization of extraction, clean-up, separation and detection conditions was accomplished to increase sample throughput and to attain rapid separation and sensitive detection. Method validation was performed by analyzing samples spiked at three different concentrations for the seven mycotoxins. Recoveries were from 73.5% to 107.3%, with relative standard deviations (RSDs) lower than 7.7%. The intra- and inter-day precisions, expressed as RSDs, were lower than 4% for all studied analytes. Limits of detection and quantification ranged from 0.01 to 50.2 μg kg⁻¹, and from 0.04 to 125.5 μg kg⁻¹, respectively, which were below the tolerance levels for mycotoxins set by the European Union. Samples that tested positive were further analyzed by HPLC tandem electrospray ionization mass spectrometry for confirmatory purposes. This is the first application of USLE-IAC-HPLC-PCD-FLD for detecting the occurrence of multi-class mycotoxins in Coix seed.     

Grand challenges in quantum‐classical modeling of molecule–surface interactions

A detailed understanding of the adsorption of small molecules or macromolecules to a materials surface is of importance, for example, in the context of material and biomaterial research. Classical atomistic simulations in principle provide microscopic insight in the complex entropic and enthalpic interplay at the interface. However, an application of classical atomistic simulation techniques to such interface systems is a nontrivial problem, mostly because commonly used force fields cannot be straightforwardly applied, as they are usually developed to reproduce bulk properties of either solids or liquids but not the interfacial region between two phases. Therefore, a dual‐scale modeling approach has often been the method of choice in the past, in which the classical force field is parameterized such that quantum chemical information on near‐surface conformations and adsorption energies is reproduced by the classical force field. We will discuss in this review the current state‐of‐the‐art of quantum‐classical modeling of molecule–surface interactions and outline the major challenges in this field. In this context, we will, among other things, lay emphasis on discussing ways to obtain representable force fields and propose systematic and system‐independent strategies to optimize the quantum‐classical fitting procedure. © 2013 Wiley Periodicals, Inc.