Preliminary Design of Moment-Resisting Frame Buildings for Tolerable Financial Loss

This paper aims to transfer the current performance-based seismic assessment (PBSA) methodology to engineering practice by providing a preliminary design procedure denoted herein as the preliminary performance-based seismic design (PPBSD). PPBSD aims at guiding a conceptual design given the tolerable expected loss and target hazard level. The suggested preliminary design procedure implicitly incorporates the main sources of variability in the seismic performance assessment of building structures including the variability associated with seismic excitation. PPBSD can help stakeholders make informed decisions on how to handle potential seismic risk at the preliminary design level with minimal computational effort. The process for development of design tools required for implementation of PPBSD are described and such development is illustrated for a 4-story reinforced concrete special moment-resisting frame (RC-SMRF) building located in Los Angeles for a 475-year ground motion return period. A design example is offered to demonstrate how PPBSD can be implemented in practice

Cosmic neutrinos at IceCube: θ13\theta_{13}, δ\delta and initial flavor composition

We discuss the prospect of extracting the values of the mixing parameters $\delta$ and $\theta_{13}$ through the detection of cosmic neutrinos in the planned and forthcoming neutrino telescopes. We take the ratio of the muon-track to shower-like events, R, as the realistic quantity that can be measured in the neutrino telescopes. We take into account several sources of uncertainties that enter the analysis. We then examine to what extent the deviation of the initial flavor composition from w_e:w_\mu:w_\tau=1:2:0 can be tested.Comment: 3 pages, 2 figures, Talk given at the TAUP 2009 conference, Rome, Italy; J. Phys. Conf. Series to appea

Self-Mixing Laser Distance-Sensor Enhanced by Multiple Modulation Waveforms

Optical rangefinders based on Self-Mixing Interferometry are widely described in literature, but not yet on the market as commercial instruments. The main reason is that it is relatively easy to propose new elaboration techniques and get results in controlled conditions, while it is very difficult to develop a reliable instrument. In this paper, we propose a laser distance sensor with improved reliability, realized through a wavelength modulation at a different frequency, able to decorrelate single measurement errors and obtain improvement by averages. A dedicated software is implemented to automatically calculate the modulation pre-emphasis, needed to linearize the wavelength modulation. Finally, data selection algorithms allow to overcome signal fading problems due to the speckle effect. A prototype demonstrates the approach with about 0.1 mm accuracy up to 2 m of distance at 200 measurements per second

Toxicity and LC50 determination of phenol and 1-Naphtol in Caspian Kutum and bream fingerlings

In this investigation acute toxicity of phenol and 1-naphthol were determined based on OECD guideline in the laboratory. Experimental fishes were Caspian kutum (Rutilus frisii kutum) and bream (Abramis brama orientalis). Static bioassays were used for acute toxicity tests during the period of 96 hours and all of important physicochemical parameters of water including pH, dissolved oxygen, hardness, temperature and conductivity were monitored continuously and maintained at a constant value. Five treatments were used and three replicates run for each treatment. The 96h LCSO values of phenol and 1-naphthol for Caspian kutum and bream were 21.5928 and 2.1544 mg/lit and 25.1880 and 2.8490 mg/lit, respectively. The Maximum Allowable Concentration (MAC) of phenol in Caspian kutum and bream were 2.1593 and 2.5188 mg/lit, respectively. The MAC value of 1-naphthol in Caspian kutum and bream were 0.2154 and 0.2849 mg/lit, respectively. It is evident from the results of the present study that Caspian kutum is more sensitive comparing to bream and the toxicity of I-naphthol is higher than phenol

Analysis of healthcare service utilization after transport-related injuries by a mixture of hidden Markov models

© 2018 Esmaili et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Background Transport injuries commonly result in significant disease burden, leading to physical disability, mental health deterioration and reduced quality of life. Analyzing the patterns of healthcare service utilization after transport injuries can provide an insight into the health of the affected parties, allow improved health system resource planning, and provide a baseline against which any future system-level interventions can be evaluated. Therefore, this research aims to use time series of service utilization provided by a compensation agency to identify groups of claimants with similar utilization patterns, describe such patterns, and characterize the groups in terms of demographic, accident type and injury type. Methods To achieve this aim, we have proposed an analytical framework that utilizes latent variables to describe the utilization patterns over time and group the claimants into clusters based on their service utilization time series. To perform the clustering without dismissing the temporal dimension of the time series, we have used a well-established statistical approach known as the mixture of hidden Markov models (MHMM). Ensuing the clustering, we have applied multinomial logistic regression to provide a description of the clusters against demographic, injury and accident covariates. Results We have tested our model with data on psychology service utilization from one of the main compensation agencies for transport accidents in Australia, and found that three clear clusters of service utilization can be evinced from the data. These three clusters correspond to claimants who have tended to use the services 1) only briefly after the accident; 2) for an intermediate period of time and in moderate amounts; and 3) for a sustained period of time, and intensely. The size of these clusters is approximately 67%, 27% and 6% of the number of claimants, respectively. The multinomial logistic regression analysis has showed that claimants who were 30 to 60-year-old at the time of accident, were witnesses, and who suffered a soft tissue injury were more likely to be part of the intermediate cluster than the majority cluster. Conversely, claimants who suffered more severe injuries such as a brain head injury or anon-limb fracture injury and who started their service utilization later were more likely to be part of the sustained cluster

Negotiating academic conflict in discussion sections of doctoral dissertations

This is the author accepted manuscript. The final version is available from John Benjamins Publishing Company via the DOI in this recordThis study explores how doctoral students negotiated academic conflict (AC) in discussion section of their dissertations and what engagement resources they utilized to convey academic conflict. To this end, discussion chapters of 30 doctoral dissertations in Applied Linguistics (15 samples by each writer group) were analyzed using Huston’s (1991) academic conflict framework and Martin and White’s (2005) engagement system of Appraisal Theory. The functional analysis constituted discovering components of academic conflict and engagement resources in the discussions. We found that components of academic conflict determined engagement values used to convey them. The linguistic background of the authors was less of an issue in resolving conflicts. The two writer groups managed academic conflict and related engagement resources more or less similarly in different components of academic conflict. They mainly expressed their novel contribution readily and identified the flaws of previous research; however, both writer groups showed little tendency to explain controversial points. The findings have pedagogical implications for academic writing courses highlighting the importance of developing awareness of AC and resolving the conflicts

Gene Diversity of Trichomonas vaginalis Isolates

Background: Trichomonas vaginalis is protozoan parasite responsible for trichomoniasis and is more common in high-risk behavior group such as prostitute individuals. Interest in trichomoni­asis is due to increase one's susceptibility to viruses such as herpes, human papillomavirus and HIV. The aim of this study was to find genotypic differences between the isolates.Methods: Forty isolates from prisoners' women in Tehran province were used in this study. The random amplified polymorphic DNA (RAPD) technique was used to determine genetic differ­ences among isolates and was correlated with patient's records. By each primer the banding pat­tern size of each isolates was scored (bp), genetic differences were studied, and the genealogical tree was constructed by using NTSYS software program and UPGMA method.Results: The least number of bands were seen by using primer OPD8 and the most by using OPD3. Results showed no significant difference in isolates from different geographical areas in Iran. By using primer OPD1 specific amplified fragment with length 1300 base pair were found in only 8 isolates. All these isolates were belonged to addicted women; however, six belonged to asymptomatic patients and two to symptomatic ones.Conclusion: There was not much genetic diversity in T vaginalis isolates from three different geo­graphical areas

Investigating Risk Factors and Predicting Complications in Deep Brain Stimulation Surgery with Machine Learning Algorithms

© 2019 Elsevier Inc. Background: Deep brain stimulation (DBS) surgery is an option for patients experiencing medically resistant neurologic symptoms. DBS complications are rare; finding significant predictors requires a large number of surgeries. Machine learning algorithms may be used to effectively predict these outcomes. The aims of this study were to 1) investigate preoperative clinical risk factors and 2) build machine learning models to predict adverse outcomes. Methods: This multicenter registry collected clinical and demographic characteristics of patients undergoing DBS surgery (n = 501) and tabulated occurrence of complications. Logistic regression was used to evaluate risk factors. Supervised learning algorithms were trained and validated on 70% and 30%, respectively, of both oversampled and original registry data. Performance was evaluated using area under the receiver operating characteristics curve (AUC), sensitivity, specificity, and accuracy. Results: Logistic regression showed that the risk of complication was related to the operating institution in which the surgery was performed (odds ratio [OR] = 0.44, confidence interval [CI] = 0.25–0.78), body mass index (OR = 0.94, CI = 0.89–0.99), and diabetes (OR = 2.33, CI = 1.18–4.60). Patients with diabetes were almost 3× more likely to return to the operating room (OR = 2.78, CI = 1.31–5.88). Patients with a history of smoking were 4× more likely to experience postoperative infection (OR = 4.20, CI = 1.21–14.61). Supervised learning algorithms demonstrated high discrimination performance when predicting any complication (AUC = 0.86), a complication within 12 months (AUC = 0.91), return to the operating room (AUC = 0.88), and infection (AUC = 0.97). Age, body mass index, procedure side, gender, and a diagnosis of Parkinson disease were influential features. Conclusions: Multiple significant complication risk factors were identified, and supervised learning algorithms effectively predicted adverse outcomes in DBS surgery