Towards online monitoring of concrete dam displacement subject to time-varying environments: An improved sequential learning approach

Data-based models are widely applied in concrete dam health monitoring. However, most existing models are restricted to offline modeling, which cannot continuously track the displacement behavior with dynamic evolution patterns, especially in time-varying environments. In this paper, sequential learning is introduced to establish an online monitoring model for dam displacement behavior. This approach starts by considering the timeliness difference between old and new data using the forgetting mechanism, and a novel adaptive forgetting extreme learning machine (AF-ELM) is presented. A primary predictor based on AF-ELM is then formulated, which aims to sequentially learn the complex nonlinear relationship between dam displacement and main environmental factors. Considering the chaotic characteristics contained in the residual sequence of the primary predictor, a multi-scale residual-error correction (REC) strategy is devised based on divide-and-conquer scheme. Specifically, time-varying filter-based empirical mode decomposition is adopted to decompose the raw chaotic residual-error series into a set of subseries with more stationarity, which are further aggregated and reconstructed by fuzzy entropy theory and suitable approximation criterion. Finally, the corrected residual sequence is superimposed with the preliminary predictions from AF-ELM to generate the required modeling results. The effectiveness of the proposed model is verified and assessed by taking a real concrete dam as an example and comparing prediction performance with state-of-the-art models. The results show that AF-ELM performs better in displacement prediction compared with benchmark models, and the multi-scale REC can effectively identify the valuable information within the residual sequence. The proposed online monitoring model can more closely track the dynamic variations of displacement data, which provides a fire-new solution for dam behavior prediction and analysis.     

Synthetic data generation: State of the art in health care domain

Recent progress in artificial intelligence and machine learning has led to the growth of research in every aspect of life including the health care domain. However, privacy risks and legislations hinder the availability of patient data to researchers. Synthetic data (SD) has been regarded as a privacy-safe alternative to real data and has lately been employed in many research and academic endeavors. This growing body of research needs to be consolidated for the researchers and practitioners to gain a quick and fruitful comprehension of the state of the art in synthetic data generation in health care. The purpose of this study is to collate and synthesize the current state of synthetic data generation following a narrative review of 70 peer-reviewed studies discussing privacy-preserving synthetic medical data generation techniques. The literature shows the effectiveness of synthetic datasets for different applications in research, academics, and testing according to existing statistical and task-based utility metrics. However, the focus on longitudinal synthetic data seems deficient. Moreover, a unified metric for generic quality assessment of synthetic data is lacking. The results of this review will serve as a quick reference guide for the researchers and practitioners in the healthcare domain to select a suitable synthetic data strategy for their application based on its strengths and weaknesses and pave the path for further research and development in healthcare.     

Human dietary exposure to heavy metals via the consumption of greenshell mussels (Perna canaliculus Gmelin 1791) from the Bay of Islands, northern New Zealand

Cadmium (Cd), mercury (Hg), arsenic (As), lead (Pb) and tin (Sn) concentrations were determined using ICP-MS in soft tissues (wet wt.) from whole greenshell mussels (Perna canaliculus) collected from Urapukapuka-Rawhiti Island, Opua Marina, Waitangi Bridge and Opua Wharf from the Bay of Islands, northern New Zealand (NZ). All samples had metal concentrations well below the Food Standards Australia and New Zealand (FSANZ) maximum limits and were comparable to, or less than, concentrations observed in previous NZ studies. Based on the average values detected in the current study, the concentrations of heavy metals ingested in a ‘typical diet’ containing greenshell mussels are below the provisional tolerable weekly intake (PTWI). However, Māori (indigenous people of New Zealand), Pacific Islanders and Asians consume a far greater quantity of seafood (and therefore heavy metals) than the general public of New Zealand and could potentially consume enough shellfish to exceed the PTWI for Cd (but not for Hg, As, Pb or Sn). Although our results, based on the current PTWIs, indicate no significant health risk to greenshell mussel consumers in this region, PTWIs change over time; concentrations which were thought to be safe are later found to be harmful. Additionally, differences in individual human susceptibilities to various toxins could increase the risk of harm for consumers with low tolerance to heavy metals. We suggest that a survey of the frequency, amount and species consumed by groups whose diet may be largely shellfish-based is required to enable a more comprehensive risk assessment to be made.     

某硫铁矿场地土壤重金属污染特征及健康风险评估

为研究某硫铁矿场地土壤重金属污染特征,评估场地内重金属污染对人体健康的潜在风险,采用单因子污染指数法和多因子综合污染指数法对场地污染程度进行了评价,利用主成分分析和相关性分析对污染物来源进行了解析,并对超标污染物进行健康风险评估。结果表明：场地内As、Hg、Pb和Cr6+存在不同程度的超标,表层土壤为中度污染,深层土壤为轻度污染;超标污染物As、Pb主要来源于原料硫铁矿,Hg和Cr6+为外来带入型污染;场地内As、Pb、Cr6+、Hg健康风险均超出了可接受风险水平,对场地内企业职工的健康可产生一定威胁。     

浅析机械企业实施质量、环境及职业健康安全管理体系的方法

结合机械企业的现状和发展,分析了机械企业实施质量、环境、职业健康安全管理体系应采取的的方法,只有实现三体系的一体化,并坚持持续改进的原则,一定会对企业的发展产生强大的推动力.     

健康居室环境的营造

阐述了健康与人居环境的关系，从心理健康和身心健康要求的角度，提出营造健康人居环境的观点，并对老年居住环境设施建设提出了几点原则，即尊重自然，尊重人，实现设计与建造设计的美的原则，提出应从住区空间的尺度重新考虑居住空间形态的问题。     

Child Death From Pediatric Illness: Conceptualizing Intervention From a Family/Systems and Public Health Perspective.

Despite the universal and understandable distress associated with the death of a child from pediatric illness, do psychologists help surviving families in ways that match their need for assistance? Many families do not seek psychological help and cope adaptively with their loss. Indications for psychotherapy with bereaved families are based largely on experience with families who engage in treatment and who have greater distress. Other families may not receive sufficient services but are at risk for ongoing difficulties and unlikely to engage in treatment. Suggestions for intervention are made, with attention to the fit of therapeutic approaches with family needs. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

光纤监测技术在土木工程中的应用现状及展望

针对土木工程的特点和对监测技术的要求,总结了近年来国内外光纤监测技术的应用现状和问题所在,阐述了光纤监测技术的发展方向,并对此技术在土木工程领域的应用前景进行了展望.     

循证医学在保健食品再评价中的应用

保健食品随着经济的增长、人们健康状况的改变和对高品质生活的追求而不断发展。我国保健食品在经历了波动性发展后已逐步进入成熟阶段。但由于技术的落后和管理的不完善，已获批准的市售保健食品仍存在很多问题。需要对其进行科学的再评价以保证人们买到质量可靠的产品。循证医学作为一种新的临床实践指导方法得到广泛关注，美国已将其运用到食品卫生领域。运用循证医学方法对保健食品的安全性、功效性和联合使用效果进行再评价可以有效弥补产品上市前评价的不足。通过确定所要评价产品、收集相关实验证据、对资料进行科学的整理分析后对研究对象的安全性和功效性等方面得出可靠结论，并将该结论及时公布，从而起到监督产品质量、指导人们消费的目的。要做到这一点，需要专业机构的建立、指导和监督，以及工作人员职业技能的不断提高，用科学合理的方法进行循证评价研究。     

高效液相色谱法测定保健食品中水飞蓟素的含量

目的建立高效液相色谱法(high performance liquid chromatography,HPLC)测定保健食品中水飞蓟素的含量。方法样品以甲醇为提取剂超声提取30 min,以甲醇-水(55:45,V:V)为流动相,流速为0.8 mL/min,采用色谱柱Agilent TC-C_(18)分离,在288 nm检测波长下经HPLC检测;同时对检测波长、流动相比例、提取剂和提取时间等条件进行优化。结果水飞蓟素浓度在8.152~203.8μg/mL范围内呈良好的线性关系,相关系数为0.9999,平均回收率为100.35%,RSD值为0.95%,方法检出限为11.6 ng/g。结论该方法快速准确,可适用于保健食品中水飞蓟素含量的测定。