Occupational Safety and Health in Finland

In Finland, occupational safety is the responsibility of the employer, while the occupational safety and health laws are enforced by the Labour Inspection Service, an organization of the state. The Labour Inspection is divided in 11 administrative districts, and it employs 360 professionals. They are mandated to carry out site visits without prearranged appointments to inspect safe work situations, working hours, construction safety, or any aspect of accident risks. The inspectors are also mandated to verify the existence of sufficient occupational health services as prescribed by the Occupational Health Services Act for all employees. The occupational health services are typically provided by enterprise-owned medical departments, by mutually owned health care centers, by private practitioners, or by municipal health care centers. The latter are required by law to provide all services as prescribed by the legislation to anyone who comes to the facility. This situation is prevalent in the countryside, where there are very few private caregiving centers. Declaring occupational accidents and disease cases is mandatory, and the Inspection districts examine all accidents to establish causes and consequences, and to initiate prosecution in case of criminal negligence. Labour Inspection Districts are also notified of the new occupational disease cases as they are declared to insurance companies. Insurance for occupational disease, accidents, and death is an obligation of the employer, although they can choose the insurance company. The medical confidentiality between the workers and their occupational health care providers is very strict. Official statistics are maintained by the state Statistics Finland organization, and they may be used, for example, for research purposes by scientific institutes like the Finnish Institute of Occupational Health. Construction industry accounts for 25% of all fatal accidents (120 cases per 1 million working hours), followed by mechanical, wood, metal, machinery, and pulp and paper industry (10% each of fatal accidents) with rates ranging from 100–160 cases per 1 million working hours. There are some 5,000 occupational disease cases per year (rate 22/10,000 employed). The major disease categories include repetitive strain injuries (1,300 cases), respiratory allergies (600 cases), occupational skin diseases (1,000 cases), and 900 cases of noise-caused hearing loss. In 1998, 589 cases of asbestos-related diseases were reported.     

一株受二噁英类化合物诱导表达绿色荧光蛋白的胃癌细胞系的建立

将人工合成的5个相连的XRE(Xeobiotic response element)和minimal CMV(Human cytomegalovirus)启动子,插入载体pEGFP-1,构建得到载体pXRE5-EGFP.以pXRE5-EGFP转染人胃癌细胞SGC-7901,经过筛选得到一株受2,3,7,8-四氯代二苯并二英(2,3,7,8-tetrachlorodibenzo-p-dioxin,TCDD)诱导表达绿色荧光蛋白的细胞系XRE5-SGC-7901.将XRE5-SGC-7901细胞株暴露于不同浓度TCDD(0、250、500pg·mL-1)中,发现TCDD暴露组XRE5-SGC-7901细胞中绿色荧光蛋白的表达量显著升高,且与TCDD暴露浓度呈正相关.新构建的细胞株具有二英类化合物诱导表达绿色荧光蛋白的功能.     

干扰烟草GA 20-氧化酶siRNA植物表达载体的构建及矮化烟草的产生

根据烟草(Nicotiana tabacum)GA 20-氧化酶基因序列,设计2对分别含有特定酶切位点的特异引物,以烟草基因组DNA为模板,扩增目的基因(约250 bp)片段.将正、反向目的片段分别插入中间载体的内含子两侧.再经BamH I和Sac I双酶切回收约700 bp的目的片段,插入到双元载体质粒p2355中,成功构建了含GA 20-氧化酶基因片段的反向重复序列植物表达载体p23700,其转录产物能形成发夹RNA(hpRNA),产生小分子干扰RNA,干扰目的基因的表达.将p23700质粒导入根癌农杆菌EHA105中并转化烟草叶片细胞,经选择分化培养,获得表型矮化的转基因烟草.图4参14     

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。     

支持向量机用于芳烃类化合物对芳烃受体亲和性QSAR研究

尝试将支持向量机(SVM)应用于3种典型芳烃类环境毒物(PCDD,PCDF和PCB)定量构效关系研究,通过对芳烃受体亲和性考察,结果发现该组样本的生物活性在一定程度上与分子电性距离矢量具有非线性联系.SVM对内部和外部样本都具良好稳定性能和预测能力:所得模型拟合、交叉检验、外部预测复相关系数及均方根误差分别为R2cum=0.922、Q2cum=0.825、Q2ext=0.834和RMSext=0.531将其与文献报道及多元线性回归、偏最小二乘、人工神经网络进行比较,结果表明对小样本、非线性问题SVM具较强拓展性及泛化能力,故在环境毒物评价和控制中具有广阔应用前景.     

基于灰色GM(1,1)-马尔科夫模型的职业病预测研究

为准确预测量化我国职业病的发病趋势,在灰色GM(1,1)模型的基础上结合马尔科夫过程构建灰色GM(1,1)-马尔科夫预测模型,探讨灰色GM(1,1)-马尔科夫模型在职业病预测领域的应用。通过平均相对误差、后验差比值、小误差概率3个指标对该组合模型的预测精度进行评估。结果表明:10维灰色GM(1,1)-马尔科夫模型与原始数列的拟合程度较高,预测精度等级为一级（好）,该组合模型的预测精度优于单一的灰色GM(1,1)预测模型;在遵循新陈代谢原理的情况下,我国职业病发病呈现上升态势,2015—2018年的职业病发病例数依次为31 196,36 284,37 724,39 147例。     

基于SVR的含缺陷管道剩余强度研究

针对采用标准预测含缺陷管道剩余强度误差较大这一问题,在Matlab中建立基于SVR的含缺陷管道剩余强度预测模型,并基于60组含缺陷管道爆破试验数据进行训练测试,以验证模型的实际性能.结果表明:SVR模型预测测试集结果的最小相对误差为0.55％,最大相对误差为10.35％,平均相对误差为2.63％,预测结果的R2高达0....     

基于SVM的矿井通风阻力系数影响因素分析与预测

矿井通风阻力系数是通风安全最重要的基础参数之一,为了实现矿井通风阻力系数简单准确地预测,提出了利用支持向量机（SVM）来预测矿井通风阻力系数的方法。通过分析影响因子与矿井通风阻力系数的相关性关系,并利用MATLAB逐步建立单影响因素与矿井通风阻力系数、多影响因素与矿井通风阻力系数之间的SVM预测模型,对比分析各预测模型的相对误差,确定最佳矿井通风阻力系数预测模型,即当输入模型影响因素为巷道断面积、周长和支护方式时,预测相对误差小于10%的样本数占测试样本的76%,相对误差小于20%的样本数占测试样本的90%。结果表明:该预测方法在矿井通风阻力系数预测中是可行的,并具较高的准确性。     

基于MDE和分形优化SVM的周期来压预测

为了解决周期来压的预测问题,首先对已知支架周期来压荷载曲线使用多重差异进化算法(MDE)进行拟合,将每重拟合形成的单一正弦曲线与上次差余曲线(Ei)再作差余曲线(Ei+1)。将这些Ei图通过分形几何的盒子法计算维度和相关系数(r)。将每条Ei的维度、r和支架相对距离(L)作为输入值,对应的Ei的周期Ti、缩放系数Si和纵移系数Di作为目标值,使用支持向量机(SVM)进行训练。通过对维度和r规律的研究得到拟设置支架处荷载各Ei的维度和r,带入训练后的SVM模拟得到Ei的Ti、Si和Di,进而得到Ei的表达式。将上述Ei求和即为所求拟设置支架处的周期来压荷载。实例分析说明,该种方法预测结果可以大体反映支架周期来压的基本形式和变化规律。