Studying non-uniform electrodeposition using the wire beam electrode method

Nonuniform electrodeposition is a major concern in almost every practical electrodeposition application. The ablility to control nonuniformity in electrodeposition is the key to successful plating for corrosion resistance, and more especially to meeting the very exacting requirements of electroforming, electrodeposition. This paper presents a novel technique namely the wire beam electrode (WBE) for characterizing and monitoring nonuniform electroplating processes. For the first time, the nonuniform distribution of electroplating currents (NDEC) has been mapped. Preliminary experimental results indicated that electrochemical heterogeneity was the key factor affecting NDEC. The secondary current distribution, rather than the primary current distribution, played a major role in determining the NDEC. This work suggests that the WBE is a practical tool for characterizing and optimizing electrodeposition processes and for verifying the accuracy and completeness of mathematic modelling of electrodeposition processes.

Real solutions number of the switching angles in the selective harmonic eliminated PWM

With the purpose of solving the real solutions number of the nonlinear transcendental equations in the selective harmonic eliminated PWM (SHEPWM) technology, the nonlinear transcendental equations were transformed to a set of polynomial equations with a set of inequality constraints using the multiple-angle formulas, an analytic method based on semi-algebraic systems machine proving algorithm was proposed to classify the real solution number of the switching angles. The complete classifications of the real solution number and the analytic boundary point of the single phase and three phases SHEPWM inverter with switch points of N=3 and the single phase SHEPWM inverter with switch points of N=4 are obtained. The results indicate that the relationship between the modulation ratio and the real solution number can be demonstrated theoretically by this method, which has great implications for the solution procedure of switching angles and the improvement of harmonic elimination effects of the inverter.

Artificial neural network modeling of flow stress in hot rolling

In this study, an artificial neural network model is proposed to predict the flow stress variations during the hot rolling process. Optimization of the proposed neural network with respect to number of neurons within the hidden layer, different training methods and transfer functions of the neural network is performed. The results of the optimal network were compared with those of the conventional analytic method and it is shown that using an optimal neural network the mean calculated error is drastically reduced.

Selecting the individual numerical scale and prioritization method in the analytic hierarchy process : a 2-tuple fuzzy linguistic approach

The validity of the priority vector used in the analytic hierarchy process (AHP) relies on two factors: the selection of a numerical scale and the selection of a prioritization method. The traditional AHP selects only one numerical scale (e.g., the Saaty scale) and one prioritization method (e.g., the eigenvector method) for each particular problem. For this traditional selection approach, there is disagreement on which numerical scale and prioritization method is better in deriving a priority vector. In fact, the best numerical scale and the best prioritization method both rely on the content of the pairwise comparison data provided by the AHP decision makers. By defining a set of concepts regarding the scale function and the linguistic pairwise comparison matrices (LPCMs) of the priority vector and by using LPCMs to unify the format of the input and output of AHP, this paper extends the AHP prioritization process under the 2-tuple fuzzy linguistic model. Based on the extended AHP prioritization process, we present two performance measure criteria to evaluate the effect of the numerical scales and prioritization methods. We also use the performance measure criteria to develop a 2-tuple fuzzy linguistic multicriteria approach to select the best numerical scales and the best prioritization methods for different LPCMs. In this paper, we call this type of selection the individual selection of the numerical scale and prioritization method. We also compare this individual selection with traditional selection by using both random and real data and show better results with individual selection.

Assessment of wilderness quality using the analytic hierarchy process

Wilderness is a unique environmental resource that provides a multitude of use and non-use benefits. The use and management of wilderness depend on the assessment of wilderness quality. Current wilderness assessment in Australia is based on two broad criteria, the remoteness and naturalness of the wilderness, determined using geographic information systems. This paper discusses a complementary assessment method using the Analytic Hierarchy Process (AHP). The AHP can be used to incorporate additional criteria, such as social and cultural criteria, to improve the quality of wilderness assessment. It provides a flexible and compatible method for large-scale wilderness assessments with multiple criteria. The weighting factors for the different criteria can be obtained from expert panels and focus groups.

Preventing the onset of depressive disorders : a meta-analytic review of psychological interventions

OBJECTIVE: A growing number of studies have tested the efficacy of preventive interventions in reducing the incidence of depressive disorders. Until now, no meta-analysis has integrated the results of these studies. METHOD: The authors conducted a meta-analysis. After a comprehensive literature search, 19 studies were identified that met inclusion criteria. The studies had to be randomized controlled studies in which the incidence of depressive disorders (based on diagnostic criteria) in an experimental group could be compared with that of a control group. RESULTS: The mean incidence rate ratio was 0.78, indicating a reduction of the incidence of depressive disorders by 22% in experimental compared with control groups. Heterogeneity was low to moderate (I²=33%). The number needed to treat to prevent one case of depressive disorder was 22. Moderator analyses revealed no systematic differences between target populations or types of prevention (universal, selective, or indicated). The data included indications that prevention based on interpersonal psychotherapy may be more effective than prevention based on cognitive-behavioral therapy. CONCLUSIONS: Prevention of new cases of depressive disorders does seem to be possible. Prevention may become an important way, in addition to treatment, to reduce the enormous public health burden of depression in the coming years.

Analytic prediction of structural stress-strain relations of microstructured metal

Nanostructured and ultra-fine grained metals have higher strength but extremely limited ductility compared to coarse grained metals. However, their ductility can be greatly improved by introducing a specific range of grain sizes in the microstructures. In the paper, multiscale unit cell approach (UCA) is developed and applied to predict the averaged stress-strain relations of the multiscale microstructure metals. The unit cell models are three-phase structured at different scale lengths of 100 nm, 1 μm and 10 μm with different volume fractions and periodic boundary conditions. The contributions of multi-scale microstructures to the macroscopic structural properties of metals are also studied using a analytic approach—two-step mean-field method (TSMF), where three microstructural parameters are introduced and thus mechanical properties such as strength and ductility are presented as a function of these parameters. For verification of these proposed numerical and theoretical algorithms, the structural properties of the pure nickel with three-grain microstructures are studied and the results from FEA and the proposed theory have good agreement.

A new solution method for homogenization of effective properties of electromagnetic honeycombs

In this paper, an analytical model and its new numerical solution using the homogenization method are developed to determine the effective electromagnetic characteristics of honeycombs. Based on the proposed solution method, the electromagnetic properties are obtained by employing the multi-scale homogenization theory and periodical electric (magnetic) potential boundary conditions. Further, the effect of geometry of honeycomb's unit cell on effective electromagnetic properties is investigated with the use of the proposed method. The numerical results are compared with analytic results using the Smith-Scarpa's semi-empirical formula.

Meta-analytic review of neurocognition in bipolar II disorder

Objective: The clinical distinction between bipolar II disorder (BD II) and bipolar I disorder (BD I) is not clear-cut. Cognitive functioning offers the potential to explore objective markers to help delineate this boundary. To examine this issue, we conducted a quantitative review of the cognitive profile of clinically stable patients with BD II in comparison with both patients with BD I and healthy controls.
Method: Meta-analytical methods were used to compare cognitive functioning of BD II disorder with both BD I disorder and healthy controls.
Results: Individuals with BD II were less impaired than those with BD I on verbal memory. There were also small but significant difference in
visual memory and semantic fluency. There were no significant differences in global cognition or in other cognitive domains. Patients with BD II performed poorer than controls in all cognitive domains.
Conclusion: Our findings suggest that with the exception of memory and semantic fluency, cognitive impairment in BD II is as severe as in BD I. Further studies are needed to investigate whether more severe deficits in BD I are related to neurotoxic effects of severe manic episodes on medial temporal structures or neurobiological differences from the onset of the illness.

Cause and effect in studies on unemployment, mental health and suicide: a meta-analytic and conceptual review

Background There are ongoing questions about whether unemployment has causal effects on suicide as this relationship may be confounded by past experiences of mental illness. The present review quantified the effects of adjustment for mental health on the relationship between unemployment and suicide. Findings were used to develop and interpret likely causal models of unemployment, mental health and suicide. Method A random-effects meta-analysis was conducted on five population-based cohort studies where temporal relationships could be clearly ascertained. Results Results of the meta-analysis showed that unemployment was associated with a significantly higher relative risk (RR) of suicide before adjustment for prior mental health [RR 1.58, 95% confidence interval (CI) 1.33–1.83]. After controlling for mental health, the RR of suicide following unemployment was reduced by approximately 37% (RR 1.15, 95% CI 1.00–1.30). Greater exposure to unemployment was associated with higher RR of suicide, and the pooled RR was higher for males than for females. Conclusions Plausible interpretations of likely pathways between unemployment and suicide are complex and difficult to validate given the poor delineation of associations over time and analytic rationale for confounder adjustment evident in the revised literature. Future research would be strengthened by explicit articulation of temporal relationships and causal assumptions. This would be complemented by longitudinal study designs suitable to assess potential confounders, mediators and effect modifiers influencing the relationship between unemployment and suicide.

A novel aggregate gene selection method for microarray data classification

This paper introduces a novel method for gene selection based on a modification of analytic hierarchy process (AHP). The modified AHP (MAHP) is able to deal with quantitative factors that are statistics of five individual gene ranking methods: two-sample t-test, entropy test, receiver operating characteristic curve, Wilcoxon test, and signal to noise ratio. The most prominent discriminant genes serve as inputs to a range of classifiers including linear discriminant analysis, k-nearest neighbors, probabilistic neural network, support vector machine, and multilayer perceptron. Gene subsets selected by MAHP are compared with those of four competing approaches: information gain, symmetrical uncertainty, Bhattacharyya distance and ReliefF. Four benchmark microarray datasets: diffuse large B-cell lymphoma, leukemia cancer, prostate and colon are utilized for experiments. As the number of samples in microarray data datasets are limited, the leave one out cross validation strategy is applied rather than the traditional cross validation. Experimental results demonstrate the significant dominance of the proposed MAHP against the competing methods in terms of both accuracy and stability. With a benefit of inexpensive computational cost, MAHP is useful for cancer diagnosis using DNA gene expression profiles in the real clinical practice.

A factor analytical method to interactive effects dynamic panel models with or without unit root

In a recent study, Bai (Fixed-Effects Dynamic Panel Models, A Factor Analytical Method. Econometrica 81, 285-314, 2013a) proposes a new factor analytic (FA) method to the estimation of dynamic panel data models, which has the unique and very useful property that it is completely bias-free. However, while certainly appealing, it is restricted to fixed effects models without a unit root. In many situations of practical relevance this is a rather restrictive consideration. The purpose of the current study is therefore to extend the FA approach to cover models with multiple interactive effects and a possible unit root.

Letters, green cards, telephone calls and postcards: systematic and meta-analytic review of brief contact interventions for reducing self-harm, suicide attempts and suicide

BACKGROUND: There is growing interest in brief contact interventions for self-harm and suicide attempt. AIMS: To synthesise the evidence regarding the effectiveness of brief contact interventions for reducing self-harm, suicide attempt and suicide. METHOD: A systematic review and random-effects meta-analyses were conducted of randomised controlled trials using brief contact interventions (telephone contacts; emergency or crisis cards; and postcard or letter contacts). Several sensitivity analyses were conducted to examine study quality and subgroup effects. RESULTS: We found 14 eligible studies overall, of which 12 were amenable to meta-analyses. For any subsequent episode of self-harm or suicide attempt, there was a non-significant reduction in the overall pooled odds ratio (OR) of 0.87 (95% CI 0.74-1.04, P = 0119) for intervention compared with control. The number of repetitions per person was significantly reduced in intervention v. control (incidence rate ratio IRR = 066, 95% CI 0.54-0.80, P<0001). There was no significant reduction in the odds of suicide in intervention compared with control (OR = 0.58, 95% CI 0.24-1.38). CONCLUSIONS: A non-significant positive effect on repeated self-harm, suicide attempt and suicide and a significant effect on the number of episodes of repeated self-harm or suicide attempts per person (based on only three studies) means that brief contact interventions cannot yet be recommended for widespread clinical implementation. We recommend further assessment of possible benefits in well-designed trials in clinical populations.

Consensus building in a local context for the AHP-GDM with the individual numerical scale and prioritization method

The methodology for selecting the individual numerical scale and prioritization method has recently been presented and justified in the analytic hierarchy process (AHP). In this study, we further propose a novel AHP-group decision making (GDM) model in a local context (a unique criterion), based on the individual selection of the numerical scale and prioritization method. The resolution framework of the AHP-GDM with the individual numerical scale and prioritization method is first proposed. Then, based on linguistic Euclidean distance (LED) and linguistic minimum violations (LMV), the novel consensus measure is defined so that the consensus degree among decision makers who use different numerical scales and prioritization methods can be analyzed. Next, a consensus reaching model is proposed to help decision makers improve the consensus degree. In this consensus reaching model, the LED-based and LMV-based consensus rules are proposed and used. Finally, a new individual consistency index and its properties are proposed for the use of the individual numerical scale and prioritization method in the AHP-GDM. Simulation experiments and numerical examples are presented to demonstrate the validity of the proposed model.