Modified, Reliability-Based Robust Geotechnical Design Method, in Accordance with Eurocode 7

In this paper a modification of the reliability-based robust geotechnical design (RGD) method is proposed. The intention of the proposed modifications is to simplify the method, make it less computationally expensive, and harmonise of the results with Eurocode 7. The complexity of the RGD method mainly stems from the calculation of the design’s robustness measure, which is the feasibility robustness index (ββ). Due to this fact, the replacing of the existing robustness measure with a generalised reliability index (β) is considered. It was demonstrated that β fits into the robustness concept, and is traditionally used as a construction reliability measure, making it intuitive and “user friendly”. It is proposed to conduct a sensitivity analysis using Soboli indices, with the aim of freezing the variables whose contribution to the system response variance is negligible, which will further simplify the method. By changing the robustness measure, the number of the required reliability analyses is significantly decreased. Further reduction is achieved by conducting analyses only for the designs chosen in the scope of the genetic algorithm. The original RGD method is used as an extension of traditional reliability-based design. By applying the proposed modifications, the RGD method can be used as an alternative to the classic and reliability-based design method.

Uncertainty Quantification and Reduction Using Sensitivity Analysis and Hessian Derivatives

We study the use of Hessian interaction terms to quickly identify design variables that reduce variability of system performance. To start we quantify the uncertainty and compute the variance decomposition to determine noise variables that contribute most, all at an initial design. Minimizing the uncertainty is next sought, though probabilistic optimization becomes computationally difficult, whether by including distribution parameters as an objective function or through robust design of experiments. Instead, we consider determining the more easily computed Hessian interaction matrix terms of the variance-contributing noise variables and the variables of any proposed design change. We also relate the Hessian term coefficients to subtractions in Sobol indices and reduction in response variance. Design variable changes that can reduce variability are thereby identified quickly as those with large Hessian terms against noise variables. Furthermore, the Jacobian terms of these design changes can indicate which design variables can shift the mean response, to maintain a desired nominal performance target. Using a combination of easily computed Hessian and Jacobian terms, design changes can be proposed to reduce variability while maintaining a targeted nominal. Lastly, we then recompute the uncertainty and variance decomposition at the more robust design configuration to verify the reduction in variability. This workflow therefore makes use of UQ/SA methods and computes design changes that reduce uncertainty with a minimal 4 runs per design change. An example is shown on a Stirling engine design where the top four variance-contributing tolerances are matched with two design changes identified through Hessian terms, and a new design found with 20% less variance.

Re-thinking the fixed-criterion model of perceptual decision-making

Perceptual decision-making is often conceptualized as the process of comparing an internal decision variable to a categorical boundary, or criterion. How the mind sets such a criterion has been studied from at least two perspectives. First, researchers interested in consciousness have proposed that criterion-crossing determines whether a stimulus is consciously perceived. Second, researchers interested in decision-making have studied how the criterion depends on a range of stimulus and task variables. Both communities have considered the question of how the criterion behaves when sensory information is weak or uncertain. Interestingly, however, they have arrived at different conclusions. Consciousness researchers investigating a phenomenon called "subjective inflation" – a form of metacognitive mismatch in which observers overestimate the quality of their sensory representations in the periphery or at an unattended location – have proposed that the criterion governing subjective visibility is fixed. That is, it does not adjust to changes in sensory uncertainty. Decision-making researchers, on the other hand, have concluded that the criterion does adjust to account for sensory uncertainty, including under inattention. Here, we mathematically demonstrate that previous empirical findings supporting subjective inflation are consistent with either a fixed or a flexible decision criterion. We further show that specific experimental task requirements are necessary to make inferences about the flexibility of the criterion: 1) a clear mapping from decision variable space to stimulus feature space, and 2) a task incentive for observers to adjust their decision criterion as response variance increases. We conclude that the fixed-criterion model of subjective inflation requires re-thinking in light of new evidence from the probabilistic reasoning literature that decision criteria flexibly adjust according to response variance.

Assessing the influence of neglected GC-FID variables on the multiple responses using multivariate optimization for the determination of ethanol and acetonitrile in radiopharmaceuticals

Analytical gas chromatography in line with a flame ionization detector (GC-FID) method was developed and validated for direct determination of organic solvents in [18F]fluoro-ethyl-tyrosine ([18F]FET), [18F]fluoromisonidazole ([18F]FMISO) and [18F]fluorothymidine ([18F]FLT). Variables of the splitless time (min) and injection temperature (°C) on the response of analysis time and resolution were optimized with the assistance of a two-level full factorial design and desirability function of Derringer. The proposed procedure was validated following the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) Q2 (R1) guideline. Excellent linearity, R2 > 0.990, indicated that approximately 99% of the response variance could be predicted from ethanol and acetonitrile concentrations ranging from 0.5 to 6.0 mg mL−1 and 0.1 to 0.8 mg mL−1, respectively. The proposed procedure has proved to be selective, sensitive, and accurate (90–110%), with excellent repeatability and precision (RSD < 2%). In the robustness analysis, the findings from the calculated Standardized Effects Values (SE) were insignificant (p > 0.05) and demonstrated that the proposed method was robust for a splitless time of 1.0 ± 0.5 min and an injection temperature of 210 ± 10 °C. The proposed method was also successfully used for the quantitative determination of ethanol and acetonitrile in [18F]FET, [18F]FMISO, and [18F]FLT. Both solvents were well separated (R, 4.1–4.3) within 4.5 min. Therefore, the proposed method is relevant for routine quality control analysis of all 18F-radiopharmaceutical derivatives for the direct determination of ethanol and acetonitrile.

Uncertainty quantification and reduction using Jacobian and Hessian information

Robust design methods have expanded from experimental techniques to include sampling methods, sensitivity analysis and probabilistic optimisation. Such methods typically require many evaluations. We study design and noise variable cross-term second derivatives of a response to quickly identify design variables that reduce response variability. We first compute the response uncertainty and variance decomposition to determine contributing noise variables of an initial design. Then we compute the Hessian second-derivative matrix cross-terms between the variance-contributing noise variables and proposed design change variables. Design variable with large Hessian terms are those that can reduce response variability. We relate the Hessian coefficients to reduction in Sobol indices and response variance change. Next, the first derivative Jacobian terms indicate which design variable can shift the mean to maintain a desired nominal target value. Thereby, design changes can be proposed to reduce variability while maintaining a targeted nominal value. This workflow finds changes that improve robustness with a minimal four runs per design change. We also explore further computation reductions achieved through compounding variables. An example is shown on a Stirling engine where the top four variance-contributing tolerances and design changes identified through 16 Hessian terms generated a design with 20% less variance.

Psychometric evaluation of a newly developed measure of emotionalism after stroke (TEARS-Q)

Objective: To evaluate, psychometrically, a new measure of tearful emotionalism following stroke: Testing Emotionalism After Recent Stroke – Questionnaire (TEARS-Q). Setting: Acute stroke units based in nine Scottish hospitals, in the context of a longitudinal cohort study of post-stroke emotionalism. Subjects: A total of 224 clinically diagnosed stroke survivors recruited between October 1st 2015 and September 30th 2018, within 2 weeks of their stroke. Measures: The measure was the self-report questionnaire TEARS-Q, constructed based on post-stroke tearful emotionalism diagnostic criteria: (i) increased tearfulness, (ii) crying comes on suddenly, with no warning (iii) crying not under usual social control and (iv) crying episodes occur at least once weekly. The reference standard was presence/absence of emotionalism on a diagnostic, semi-structured post-stroke emotionalism interview, administered at the same assessment point. Stroke, mood, cognition and functional outcome measures were also completed by the subjects. Results: A total of 97 subjects were female, with a mean age 65.1 years. 205 subjects had sustained ischaemic stroke. 61 subjects were classified as mild stroke. TEARS-Q was internally consistent (Cronbach’s alpha 0.87). TEARS-Q scores readily discriminated the two groups, with a mean difference of −7.18, 95% CI (−8.07 to −6.29). A cut off score of 2 on TEARS-Q correctly identified 53 of the 61 stroke survivors with tearful emotionalism and 140 of the 156 stroke survivors without tearful emotionalism. One factor accounted for 57% of the item response variance, and all eight TEARS-Q items acceptably discriminated underlying emotionalism. Conclusion: TEARS-Q accurately diagnoses tearful emotionalism after stroke.

Exchange Rate Fluctuations in Pakistan, Causes and Remedies

Exchange rate plays a crucial part in the development of the country and it highlights the prosperity of the country's economy. This study takes various set of determinants, which affect the instability of rate of exchange in the country. This study investigated the relationship of interest rate, inflation, Forex, trade balance and inflow of net foreign capital with exchange rate. The study analyzed the data for the period 1972 to 2014 for Pakistan. Multi-level statistical estimation techniques, VECM, Johnson co-integration, impulse response, variance decomposition and granger causality are applied. The results demonstrated long relationship of interest rate, trade balance, foreign exchange reserve, net foreign capital inflow and exchange rate. The study also confirmed the positive effect of these variables on exchange rate. The study can be especially significant for the government, to make appropriate action to better deal with exchange rate volatility. Keywords: VECM, Granger Causality, Instability of Exchange Rate, Pakistan.

ANALISIS BEBAN GEMPA TERHADAP KEKUATAN STRUKTUR BANGUNAN MULTI DEGRRE OF FREEDOME

S t r u c t u r al a n aly sis is pla n n e d p h a s e s o f a b uildin g , e s p e cially t h e hig h b uildin g s . I n t h e a n aly sis o f t h e s t r u c t u r e r e q uir e d t o f a cilit a t e t h e m o d elin g c alc ula tio n s r e fle c t a c t u al c o n ditio n s in t h e field , b o t h in s t r u c t u r e a n d in t h e lo a d e d . B e c a u s e alm o s t all p a r t s o f I n d o n e sia , in clu din g t h e e a r t h q u a k e - pro ne areas is a challenge for civil engineering planning in order to design earthquake resistant buildings. Indonesia has many experienced tremendous earthquake W ritin g t his p r o p o s al is in t e n d e d t o b e a ble t o k n o w t h e s t r e n g t h a n d structure of the response S trength multi degree of freedome, on soft ground, located in Tangerang when worn earthquake lateral loads, analysis is done with the help of the SAP program in 2000 ver.15, to get the style - the style such as: the base shear force, lateral force level, ro lling moment and lateral deviation. D y n a mic a n aly sis w a s c o n d u c t e d u sin g t h e r e s p o n s e s p e c t r u m a n aly sis , mass modeling performed with a lump mass models, the sum of the response variance is reviewed with some combinations, such as: CQC, SRSs and ABSSUM.

Mapping-Based Hierarchical Sensitivity Analysis for Multilevel Systems With Multidimensional Correlations

Hierarchical sensitivity analysis (HSA) of multilevel systems is to assess the effect of system’s input uncertainties on the variations of system’s performance through integrating the sensitivity indices of subsystems. However, it is difficult to deal with the engineering systems with complicated correlations among various variables across levels by using the existing hierarchical sensitivity analysis method based on variance decomposition. To overcome this limitation, a mapping-based hierarchical sensitivity analysis method is proposed to obtain sensitivity indices of multilevel systems with multidimensional correlations. For subsystems with dependent variables, a mapping-based sensitivity analysis, consisting of vine copula theory, Rosenblatt transformation, and polynomial chaos expansion (PCE) technique, is provided for obtaining the marginal sensitivity indices. The marginal sensitivity indices can allow us to distinguish between the mutual depend contribution and the independent contribution of an input to the response variance. Then, extended aggregation formulations for local variables and shared variables are developed to integrate the sensitivity indices of subsystems at each level so as to estimate the global effect of inputs on the response. Finally, this paper presents a computational framework that combines related techniques step by step. The effectiveness of the proposed mapping-based hierarchical sensitivity analysis (MHSA) method is verified by a mathematical example and a multiscale composite material.

Evaluation of training on the programs to optimize antimicrobial use in medical residents of the province of Las Palmas

Background. Antibiotic resistance is a threat to global public health. This situation makes essential to establish programs to optimize antimicrobial use (PROA). Training needs are identified in the PROA of resident physicians and the results of the analysis of the associations between study variables and training in the rational and prudent use of antibiotics are presented in this analysis. Methods. Cross-sectional and analytical study through a self-administered questionnaire to a group of 506 medical residents of the province of Las Palmas. The association between resident’s characteristics and PROA training was calculated through logistic regression. Results. The associations between response variance and speciality were observed in most of the core component analysis (opportunity p=0.003, training p=0.007, motivation p=0.055 and hand hygiene p=0.044), followed by variance according to sex (capacity p=0.028, theoretical knowledge p=0.013, hand hygiene p=0.002). Very few differences were associated with age (capacity p=0,051 and hand hygiene p=0,054) or the year of expertise (hand hygiene p=0,032). Conclusions. The main training needs of resident physicians include one health, motivation, training, hand hygiene and information. The type of speciality followed by sex are the most important determinants on antibiotic use and resistance for resident physicians