浅析国内外潜孔电钻的结构特点

从一组实测数据出发，在分析潜孔钻机的优越性和存在问题的基础上，对国内外已有的几种潜孔钻机结构上的优缺点进行了分析，并得出了相关的结论     

Studies on the pitting corrosion of zinc in aqueous solutions I. Variations of the potential of the zinc electrode in relation to the concentration of the aggressive anion

The way in which the stationary potential of a zinc electrode previously treated with various passivating and inhibiting solutions changes in the present of the chloride, bromide and iodide ions is studied in solutions of potassium chromate, disodium hydrogen phosphate and sodium tungstanate. It is shown that the potential change as a function of the halogenide concentration follows a S shaped curve. The potentials are established after an induction period the duration of which decreases as the concentration of the aggressive anion increases and/or the concentration of the inhibiting anion decreases. A linear relationship is obtained for the maximum permissible concentration of the aggressive anion at a given concentration of the inhibiting anion. This relationship is theoretically derived from the defect resulting from the specifical adsorption of both ions at the metal surface. While e.g. a temperature increase shifts the potential in pure chromate solutions to more positive potentials, thus improving the protective effect, the permissible concentration of the aggressive anion is simultaneously decreased. This phenomenon suggests that in these conditions the desorption of the inhibiting anions is increased.     

Spurious modes in finite-element methods

This paper describes the problem of spurious modes that appear with finite-element solutions of the vector wave equation. It explains that this problem is caused by inconsistent approximations of the static solutions to the wave equation. Tangential-vector finite elements are described that enforce the tangential continuity of the vector field, but leave the normal component discontinuous. It is shown that tangential elements provide consistent approximations of the static solutions to wave problems, and that spurious modes are not produced by this type of finite element. Applications of the theory presented include problems from microwave and antenna design, and from electromagnetic compatibility     

D.c. properties of ZnO thin films prepared by r.f. magnetron sputtering

In the development of ZnO-based varistors the electrical properties of ZnO/Bi₂O₃ junctions and of the two individual oxides are being investigated. Following our recent work on a.c. conductivity in Al---ZnO---Al sandwich structures we currently report d.c. measurements. The structures were prepared by r.f. magnetron sputtering in an argon/oxygen mixture in the ratio 4:1. Capacitance-voltage data confirm that the Al/ZnO interface does not form a Schottky barrier and measurements of the dependence of capacitance on film thickness indicate that the relative permittivity of the films is approximately 9.7. With increasing voltage the current density changed from an ohmic to a power-law dependence with exponent n≈3. Furthermore measurements of current density as a function of reciprocal temperature showed a linear dependence above about 240 K, with a very low activation energy below this temperature consistent with a hopping process. The higher temperature results may be explained assuming a room-temperature electron concentration n₀ and space-charge-limited conductivity, dominated by traps exponentially distributed with energy E below the conduction band edge according to N = N₀exp(−E/kT_t), where k is Boltzmann's constant. Typical derived values of these parameters are: n₀ = 7.2 × 10¹⁶ m⁻³, N₀ = 1.31 × 10⁴⁵ J⁻¹ m⁻³ and T_t = 623 K. The total trap concentration and the electron mobility were estimated to be 1.13 × 10²⁵ m⁻³ and (5.7−13.1) ×10⁻³m²V⁻¹s⁻¹ respectively.     

冷原子荧光光谱法测定化妆品中汞

结合SYG-Ⅱ型智能冷原子荧光测汞仪的技术结构特点,采用氯化亚锡作还原剂,用冷原子荧光光谱法对化妆品中汞的测定方法进行研究。指出了载气流速、酸浓度和氯化亚锡浓度等因素对检测结果的影响,并对各种影响因素进行优化.研究结果表明:在最佳条件下,化妆品中汞浓度在0～2.0μg.L-1范围内具有良好的线性,相关系数>0.9994。此方法相对标准偏差小于3.52%,检出限为0.02μg.L-1,回收率在(83～107)%之间。     

A hybrid medical decision making system based on principles component analysis, k-NN based weighted pre-processing and adaptive neuro-fuzzy inference system

Proper interpretation of the thyroid gland functional data is an important issue in diagnosis of thyroid disease. The primary role of the thyroid gland is to help regulation of the body's metabolism. Thyroid hormone produced by thyroid gland provides this. Production of too little thyroid hormone (hypo-thyroidism) or production of too much thyroid hormone (hyper-thyroidism) defines the types of thyroid disease. It is evident that usage of machine learning methods in disease diagnosis has been increasing gradually. In this study, diagnosis of thyroid disease, which is a very common and important disease, was conducted with such a machine learning system. In this study, we have detected on thyroid disease using principles component analysis (PCA), k-nearest neighbor (k-NN) based weighted pre-processing and adaptive neuro-fuzzy inference system (ANFIS). The proposed system has three stages. In the first stage, dimension of thyroid disease dataset that has 5 features is reduced to 2 features using principles component analysis. In the second stage, a new weighting scheme based on k-nearest neighbor (k-NN) method was utilized as a pre-processing step before the main classifier. Then, in the third stage, we have used adaptive neuro-fuzzy inference system to diagnosis of thyroid disease. We took the thyroid disease dataset used in our study from the UCI machine learning database. The obtained classification accuracy of our system was 100% and it was very promising with regard to the other classification applications in literature for this problem.     

Hepatitis disease diagnosis using a new hybrid system based on feature selection (FS) and artificial immune recognition system with fuzzy resource allocation

This paper presents a novel method for diagnosis of hepatitis disease. The proposed method is based on a hybrid method that uses feature selection (FS) and artificial immune recognition system (AIRS) with fuzzy resource allocation mechanism. AIRS has showed an effective performance on several problems such as machine learning benchmark problems and medical classification problems like breast cancer, diabets, liver disorders classification. By hybridizing FS and AIRS with fuzzy resource allocation mechanism, a method is obtained to solve this diagnosis problem via classifying. The robustness of this method with regard to sampling variations is examined using a cross-validation method. We used hepatitis disease dataset which is taken from UCI machine learning repository. We obtained a classification accuracy of 92.59%, which is the highest one reached so far. The classification accuracy was obtained via 10-fold cross validation. The obtained classification accuracy of our system was 92.59% and it was very promising with regard to the other classification applications in literature for this problem. Also, sensitivity, and specificity values for hepatitis disease dataset were obtained as 100 and 85%.     

A novel hybrid method based on artificial immune recognition system (AIRS) with fuzzy weighted pre-processing for thyroid disease diagnosis

Proper interpretation of the thyroid gland functional data is an important issue in the diagnosis of thyroid disease. The primary role of the thyroid gland is to help regulation of the body’s metabolism. Thyroid hormone produced by the thyroid gland provides this. Production of too little thyroid hormone (hypothyroidism) or production of too much thyroid hormone (hyperthyroidism) defines the type of thyroid disease. Artificial immune systems (AISs) is a new but effective branch of artificial intelligence. Among the systems proposed in this field so far, artificial immune recognition system (AIRS), which was proposed by A. Watkins, has shown an effective and intriguing performance on the problems it was applied. This study aims at diagnosing thyroid disease with a new hybrid machine learning method including this classification system. By hybridizing AIRS with a developed Fuzzy weighted pre-processing, a method is obtained to solve this diagnosis problem via classifying. The robustness of this method with regard to sampling variations is examined using a cross-validation method. We used thyroid disease dataset which is taken from UCI machine learning respiratory. We obtained a classification accuracy of 85%, which is the highest one reached so far. The classification accuracy was obtained via a 10-fold cross-validation.     

DESULFURIZATION 0F TURKISH LIGNITES BY CHLORINOLYSIS

ABSTRACT

The reduction and removal of sulfur compounds from Çan K4 (Çanakkale), Keles (Bursa) and Agaçli (Istanbul) lignite samples, obtained from mines close to Istanbul, was studied using chlorine gas Water or mixtures of methyl chloroform-water and carbon tetrachloride-water were used as solvents. Chlorine gas was bubbled through a suspension of lignite sample in solvent for about 1 to 5 hrs. It was observed that removal of sulfur increased with increasing time. Chlorinolysis and the Meyers method were used separately to compare their effects on the reduction of sulfur from Agaçli end Keles lignite samples. Chlorinolysis was found to be more effective than the Meyers method In the removal of organic sulfur and thus the total sulfur.