Implications of surface noise for the motional coherence of trapped ions

Electric noise from metallic surfaces is a major obstacle towards quantum applications with trapped ions due to motional heating of the ions. Here, we discuss how the same noise source can also lead to pure dephasing of motional quantum states. The mechanism is particularly relevant at small ion-surface distances, thus imposing a new constraint on trap miniaturization. By means of a free induction decay experiment, we measure the dephasing time of the motion of a single ion trapped 50~$\mu$m above a Cu-Al surface. From the dephasing times we extract the integrated noise below the secular frequency of the ion. We find that none of the most commonly discussed surface noise models for ion traps describes both, the observed heating as well as the measured dephasing, satisfactorily. Thus, our measurements provide a benchmark for future models for the electric noise emitted by metallic surfaces.Comment: (5 pages, 4 figures

Application of Proteomics in Lab Diagnosis

Proteomics is defined as a large-scale study of proteins, in particular their functions and structures. This review was aimed to introduce the application of proteomics in lab diagnosis. Beforehand, we introduce the methods, which were used in proteomics also the advantages and disadvantages of proteomics are challenged. In the end, the necessity of proteomics for understanding the structure, function, and interaction of proteins in different fields of sciences including biomarkers, drug discovery, etc. will be discussed

Identifying Incident Casual Factors to Improve Aviation Transportation Safety: Proposing a Deep Learning Approach

Aviation is a complicated transportation system, and safety is of paramount importance because aircraft failure often involves casualties. Prevention is clearly the best strategy for aviation transportation safety. Learning from past incident data to prevent potential accidents from happening has proved to be a successful approach. To prevent potential safety hazards and make effective prevention plans, aviation safety experts identify primary and contributing factors from incident reports. However, safety experts’ review processes have become prohibitively expensive nowadays. The number of incident reports is increasing rapidly due to the acceleration of advances in information technologies and the growth of the commercial and private aviation transportation industries. Consequently, advanced text mining algorithms should be applied to help aviation safety experts facilitate the process of incident data extraction. This paper focuses on constructing deep-learning-based models to identify causal factors from incident reports. First, we prepare the data sets used for training, validation, and testing with approximately 200,000 qualified incident reports from the Aviation Safety Reporting System (ASRS). Then, we take an open-source natural language model, which is well trained with a large corpus of Wikipedia texts, as the baseline and fine-tune it with the texts in incident reports to make it more suited to our specific research task. Finally, we build and train an attention-based long short-term memory (LSTM) model to identify primary and contributing factors in each incident report. The solution we propose has multilabel capability and is automated and customizable, and it is more accurate and adaptable than traditional machine learning methods in extant research. This novel application of deep learning algorithms to the incident reporting system can efficiently improve aviation safety

Characterization of Iranian grapevine cultivars using microsatellite markers

Sixty-two grapevine (Vitis spp.) accessions from Iran and the USA were characterized at 9 highly polymorphic microsatellite loci using fluorescent primers and a capillary electrophoresis fragment sizing system. The number of alleles observed per locus ranged from 4 to 16 and heterozygosity values ranged from 0.47 to 0.86. Genetic similarity was estimated for each pair of accessions as the proportion of shared alleles. A phenogram constructed from genetic dissimilarity values revealed three clusters, one each for table grapes, wine grapes and rootstocks. The phenogram also revealed three clonal sets (Askari, Bidane and Yaghoti) as well as some synonyms and homonyms among Iranian table grape cultivars.

A multi-scale hybrid approach to the modelling and design of a novel micro-channel cooling structure for the W7X divertor

The second operating phase of the W7X stellarator, with an expanded set of plasma-facing components, includes the test of divertor tiles with a continuous heat load reaching 10 MW/ m2. The divertor tiles are cooled by subcooled water. Here a novel cooling concept, based on a network of parallel arrays of micro-channels (MC) with sub-millimetre dimensions, is investigated on a 0.1 m x 0.1 m tile, realizable by Additive Manufacturing. Detailed CFD simulations of the mock-up are performed to check the cooling uniformity using a multi-scale approach, aiming at limiting the dimension of the computational grid without a major loss of accuracy. First, the detailed hydraulic and thermal characterization on a sub-domain with of a small group of MC is performed. Then, the block of MC is substituted with an equivalent porous strip (PS), calibrating the hydraulic and thermal characteristics of the porous medium. The model is verified on an array of MCs or PSs connected to the same manifolds, showing the capability to reproduce the pressure drop and temperature increase with maximum errors of 1.05% and similar to 20% in nominal conditions, respectively. The numerical model of the entire tile equipped with PSs is then reliably adopted to evaluate the thermal-hydraulic performance of the cooling device

Accreditation of nursing clinical services: Development of an appraisal tool

Aim: This study aimed to determine comprehensive and applicable indicators for assessing the quality of nursing clinical services. Design: Methodological research. Methods: The checklist was designed in three phases (conceptualization, item generation and item reduction). In the first phase, a qualitative study using conventional content analysis was performed to clarify the concept of accreditation of clinical nursing services. In the second phase, using the views of experts was obtained in phase 1 and then by a review of the literature, related items were extracted, and item pool was formed. In the last phase, validity and reliability of the checklist were examined. Result: Based on three phases (Conceptualization, Item Generation and Item Reduction), the accreditation indicators of clinical nursing services were extracted in three dimensions including structure, process and outcome at two levels of organizational (including structural and outcome indicators) and individual performance appraisal (process indicators) in 19 main categories. © 2020 The Authors. Nursing Open published by John Wiley & Sons Ltd

The productivity and its barriers in public hospitals: Case study of Iran

Background: Due to the increasing health care costs, the issue of productivity in hospitals must be taken into great consideration in order to provide, preserve and promote public health services. Thus, increasing the level of productivity must become the main aim of any hospital. Objective of this study is to determine the total factor productivity and its components over the period under the study. Methods: In this cross sectional study, total factor productivity changes of hospitals affiliated to Tehran University of Medical Sciences were measured according to Malmquist index over the period 2009-2014. To estimate total productivity changes using Data Envelopment Analysis method, inputoriented and variable return to scale assumptions were applied and Deap2.1 software was used. Results: The mean value of total productivity changes was 1.013. It means that during the study period the productivity experienced a 1.3 decrease. Technological efficiency changes have the greatest influence on productivity decrease than the other factors. Scale efficiency, managerial efficiency and technical efficiency changes were ranked. Conclusion: Lack of knowledge of hospital personnel on proper application of technology in patient treatment is the main factor leading to productivity decrease resulting from technological changes in the studied hospitals. Therefore, holding courses for personnel in order to teach them the proper use of technology in diagnosis and patient care can be helpful

Doxorubicin Removal from Water using Acid-treated Activated Carbon, Multi-walled Carbon Nanotubes, and Montmorillonite

Medical wastewater is a significant contributor to environmental pollution, posing severe risks to both human health and the environment. To resolve this challenge, the removal of anti-cancer drugs from medical wastewater has been considered. This study investigated the removal of doxorubicin, an effective anti-cancer drug, from an aqueous solution using three types of adsorbents: activated carbon, multi-walled carbon nanotube, and montmorillonite. Our findings revealed that carbon nanotubes exhibited superior performance in doxorubicin removal from water compared to the other two adsorbents. Specifically, the maximum adsorption capacity of doxorubicin with an initial concentration of 50 mg L–1 on the carbon nanotube reached 500 mg g–1. In addition, surface modification of the adsorbents with acid resulted in a 15 % and 41 % increase in adsorption capacity, and an 85 % and 67 % reduction in equilibrium time for carbon nanotube and montmorillonite, respectively. The increasing pH proved to enhance the adsorption efficiency of carbon nanotubes and activated carbon, with the best performance achieved at solution pH of 10 and 8 for MWCNTs and AC, respectively

Psychometric features of the persian version of self-efficacy tool for patients with hypertension

Background: Hypertension is one of the causes of mortality that can be prevented. Self-efficacy with regard to patients� performance predicts their abilities to change high-risk behaviors. Positive self-efficacy in patients with hypertension predicts compliance, adherence to medications, diet and exercise regimens, and behavioral self-management. Objectives: This study aimed to examine the psychometric features of self-efficacy questionnaire in patients with hypertension. Patients and Methods: In this cross-sectional study, 260 patients with hypertension were selected by multistage cluster sampling in Tehran�s public places to complete the Persian version of hypertension self-efficacy questionnaire. Then, face validity, content, and structure of the questionnaire were evaluated. To determine the reliability of the questionnaire, test-retest method with a two-week interval and Cronbach�s alpha coefficient were used. All data analyses were performed using the SPSS statistical software, version 18.0. Results: According to the results of Content Validity Ratio (CVR), three items were eliminated. The results of exploratory and confirmatory analyses identified three factors, including diet regimen, disease management, and adherence to treatment. The goodness of fit of the three-factor self-efficacy model in patients with hypertension was confirmed based on standard indices (RMSEA = 0.082, NNFI = 0.90, CFI = 0.91, IFI = 0.91, and X2/df = 328.35). Besides, internal consistency of diet regimen, disease management, and adherence to treatment based on Cronbach�s alpha was 0.849, 0.471, and 0.572, respectively. Conclusions: The three-factor structure of the self-efficacy questionnaire showed appropriate validity and reliability in patients with hypertension. Thus, this tool can help caregivers and health service providers assess self-efficacy of hypertensive patients and plan and implement educational and clinical interventions. © 2018, Iranian Cardiovascular Research Journal. All rights reserved