A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning

• A spectral machine learning approach is proposed for predicting mixed antibiotic. • Pretreatment is far simpler than traditional detection methods. • Performance of the model is compared in different influencing factors. • Spectral machine learning is promising in the detection of complex substances. Antibiotics are widely used in medicine and animal husbandry. However, due to the resistance of antibiotics to degradation, large amounts of antibiotics enter the environment, posing a potential risk to the ecosystem and public health. Therefore, the detection of antibiotics in the environment is necessary. Nevertheless, conventional detection methods usually involve complex pretreatment techniques and expensive instrumentation, which impose considerable time and economic costs. In this paper, we proposed a method for the fast detection of mixed antibiotics based on simplified pretreatment using spectral machine learning. With the help of a modified spectrometer, a large number of characteristic images were generated to map antibiotic information. The relationship between characteristic images and antibiotic concentrations was established by machine learning model. The coefficient of determination and root mean squared error were used to evaluate the prediction performance of the machine learning model. The results show that a well-trained machine learning model can accurately predict multiple antibiotic concentrations simultaneously with almost no pretreatment. The results from this study have some referential value for promoting the development of environmental detection technologies and digital environmental management strategies.     

Uncertainty propagation in vegetation distribution models based on ensemble classifiers

Ensemble learning techniques are increasingly applied for species and vegetation distribution modelling, often resulting in more accurate predictions. At the same time, uncertainty assessment of distribution models is gaining attention. In this study, Random Forests, an ensemble learning technique, is selected for vegetation distribution modelling based on environmental variables. The impact of two important sources of uncertainty, that is the uncertainty on spatial interpolation of environmental variables and the uncertainty on species clustering into vegetation types, is quantified based on sequential Gaussian simulation and pseudo-randomization tests, respectively. An empirical assessment of the uncertainty propagation to the distribution modelling results indicated a gradual decrease in performance with increasing input uncertainty. The test set error ranged from 30.83% to 52.63% and from 30.83% to 83.62%, when the uncertainty ranges on spatial interpolation and on vegetation clustering, respectively, were fully covered. Shannon’s entropy, which is proposed as a measure for uncertainty of ensemble predictions, revealed a similar increasing trend in prediction uncertainty. The implications of these results in an empirical distribution modelling framework are further discussed with respect to monitoring setup, spatial interpolation and species clustering.     

Monitoring and adaptive management: resolving social and organisational issues to improve information sharing in natural resource management

Adaptive, or 'learning by doing', approaches are often advocated as a means of providing increased understanding within natural resource management. However, a number of organisational and social issues need to be resolved if these approaches are to be used successfully. A case study in the South Island high country of New Zealand is used to review what is needed to support an ongoing community-based monitoring and adaptive management programme. First, the case study is described, paying attention to the social context of the resource management problem. The results of a workshop that explored this problem are then outlined, along with a proposed information flow suggested by participants. Requirements for future steps to resolve these problems (such as information protocols and a multi-stakeholder information system) are discussed. Finally, some broad lessons are drawn from this exercise that could help others developing similar approaches.     

Finding Hope in the Millennium Ecosystem Assessment

Abstract:  Over the past quarter century, a new scientific activity has emerged: collective assessments by large numbers of scientists from different disciplines combining their expertise to better understand human interrelations with nature and to inform policy. The Millennium Ecosystem Assessment exceeded all such assessments before it in both the breadth of its coverage and the depth of its analysis of socioecological system dynamics. The findings are not encouraging. Nearly all ecosystems are being degraded and will continue to be degraded for decades to come even if policy changes are initiated now. For scientists participating in the assessment, the MA had another disconcerting aspect. It clearly shows that our fragmented, disciplinary knowledges cannot simply be combined to form an understanding of a whole complex system. Counterbalancing the despair of the findings and scientific difficulties of aggregating specialized knowledges, the MA demonstrated the potential of a deliberative democratic approach to grappling with complex problems .     

Public Nutrition in Emergencies: An Overview of Debates, Dilemmas and Decision-making

This paper introduces and discusses the main themes and issues arising from the workshop 'International Public Nutrition in Emergencies: The Potential for Improving Practice'.
Good co-ordination within the nutrition sector of the international humanitarian response system has led to a range of achievements in recent years. Major constraints to improving programme impact remain, however, including misconceptions about the scope of nutrition among the wider humanitarian system, which tends to give it a narrow focus on malnutrition and feeding people. In contrast to this limited view, the Public Nutrition approach brings a more broad-based emphasis to assessing and responding to nutritional problems in emergencies, and takes into account the wider social, economic and political causes of malnutrition.
Six case study presentations illustrated the various components of a Public Nutrition approach, including in-depth assessment, analysis and tailoring programmes accordingly. Additional presentations considered the nature of vulnerability, the concept of Public Nutrition, the responsibilities for addressing nutritional problems and some of the operational tools and frameworks in current use.
Participants agreed on the necessity of raising levels of awareness and understanding among all actors in the humanitarian sphere about the impact of their actions on nutrition. Strategies for achieving this included developing better multi-sectoral working relationships and also strengthening relationships with donors and key decision-makers in the humanitarian system. Other related strategies included institutional learning, training and capacity building, particularly in relation to institutions based in developing countries and building upon initiatives such as the Sphere Project, which has successfully brought together the various actors within the humanitarian system in order to improve quality of response.     

基于链式多重插补的WOA-ELM煤与瓦斯突出预测模型

为了提高缺失数据下煤与瓦斯突出预测准确率,提出1种基于链式支持向量机多重插补(MICE＿SVM)的鲸鱼优化算法(WOA)-极限学习机(ELM)预测模型,以淮南朱集矿区为例,选取5个煤与瓦斯突出影响指标作为模型特征,采用提出的MICE＿SVM算法插补突出事故数据中缺失值,利用WOA优选ELM输入层权值及隐含层阈值,构建煤与瓦斯突出预测模型,将插补后数据用于WOA-ELM模型的训练与测试,并与其他模型的预测效果对比。研究结果表明：MICE＿SVM插补前、后的有突出数据预测准确率分别为83.02%,90.41%,MICE＿SVM显著提高了有突出预测准确率,对无突出和整体的预测准确率提高不明显;数据插补后WOA优化ELM对无突出、有突出和整体的预测准确率分别为97.94%,96.25%,96.48%,较优化前分别提高了5.79%,5.84%,5.55%,数据插补后WOA-ELM为最佳预测模型。     

胚胎期和哺乳期全氟辛烷磺酸(PFOS)暴露致大鼠学习记忆能力下降

为了探讨全氟辛烷磺酸(PFOS)的发育神经毒性,寻找PFOS发育神经毒性作用的敏感期,利用水迷宫和组织病理切片技术,研究了胚胎期和哺乳期暴露于PFOS后新生大鼠发育情况、学习记忆能力、抓力以及海马组织病理学改变。结果显示：PFOS导致仔鼠发育迟缓,睁眼期延迟。仔鼠出生后体重与对照组相比出现显著性降低。同一PFOS暴露浓度下,胚胎期暴露组体重低于哺乳期暴露组,抓力差异不显著。水迷宫实验结果显示,TT15(胚胎期和哺乳期均暴露于15 mg·L^-1 PFOS)和TC15(仅胚胎期暴露于15 mg·L^-1 PFOS)暴露组仔鼠逃避潜伏期显著高于对照组,且TC15暴露组仔鼠逃避潜伏期显著性高于CT15(仅哺乳期暴露于15 mg·L^-1 PFOS)暴露组。空间探索实验中,TT15暴露组仔鼠在目标象限的游泳时间显著性低于对照组,其他组无显著性差异。组织病理切片结果显示暴露组海马组织细胞数量减少,出现细胞凋亡现象。结果表明,PFOS造成仔鼠的发育延迟以及学习记忆能力下降的关键作用时期可能是胚胎期。     

基于机器学习分类算法解析EIS数据的有机涂层性能评价方法

目的 基于机器学习分类算法快速评估有机涂层的防腐性能。方法 通过实验室加速试验模拟涂层真实的退化过程,并根据测得的电化学数据,分析不同退化阶段的等效电路元件参数。随后,采用随机抽样方法获取大量数据,用于机器学习模型训练。通过对比支持向量机(SVM)、k最近邻(k-NN)和随机森林(RF)3种不同的机器学习算法,以及多种输入特征集训练的涂层性能分类器模型的准确率,分析最适合用于涂层性能快速评估的机器学习算法和电化学特征。结果 根据不同输入特征训练的k-NN和RF模型均表现出良好的预测效果,而SVM模型的预测效果相对较差。根据不同频率范围训练的分类器模型中,在低频区表现最佳,而在高频区表现较差。结论 基于阻抗虚部、虚部+实部和阻抗模值3种输入特征训练的RF分类器模型的预测效果最准确。不同频率区间内,低频区的阻抗特征更能准确表征涂层性能。     

基于SA-ELM的爆破震动速度预测

针对爆破震动速度与其影响因素之间的复杂非线性,结合模拟退火算法(SA)的全局寻优性,提出了一种新的SA-ELM算法.以矿山周边建筑物爆破震动实测数据作为训练样本,选取总药量、最大段药量、测点与爆破点距离、地面震动特性、建筑物震动特性等8个影响因素作为输入变量,建立了爆破震动速度预测的SA-ELM模型.模型训练值和预测值与实测值的均方误差(MSE)分别为0.20和3.26,平均相对误差控制在5％以内,显示出该模型具有良好的训练精度和泛化能力.对比传统ELM模型,SA-ELM模型不但提高了精度和泛化能力,而且降低了隐层节点数变化对训练结果的影响,提高了模型的稳定性.     

基于CDIO的高职市场营销专业教学模式创新与实践研究

在市场营销专业教学中,通过借鉴CDIO教学理念,建立三系统、四模块教学体系,制定基于项目的"做中学"的教学内容,采用行为导向的六步教学法引导学生主动学习,通过多元化方式进行评价,加强机制建设与教学团队建设。