Derrida's Deconstruction and Environmental Graphics: Examination and Analysis Case Study of the Mental Image of Tourists in Isfahan

Background and Aim: Derrida's Deconstruction has key elements in the schema that affect current realms of environmental graphics. The challenges in current urban life require that graphic science gets involved, at some level, in the architecture and the environment. Therefore, the purpose of the current study was to examine the effects of using key elements of environmental graphics based on Derrida's Deconstruction in the schemata of tourists in Isfahan. Methods: library archives and field surveys were employed for data collection in this study. As such, the visual, emotional evidence and documents in the library archives were used to evaluate the criteria involved. Considering the nature of the study, the statistical population consisted of two groups of citizens of Isfahan. The first group includes 18 experts, and the second includes 213 domestic tourists in Isfahan in 2019. Findings: The results of the study indicated that there is a strong and significant relationship between the variables of balance, continuity in the environment, creating unity in the environment, diversity in the environment, texture and light. Furthermore, the findings revealed that the variable of light is extremely strong in correlation, the variable of emphasis in the environment has a very weak correlation, and the variable of proportionality is negatively correlated. Also, based on the beta coefficient obtained from regression, the greatest impact pertained to the components of light, diversity, texture, balance, continuity, unity in the environment, proportionality and emphasis. Conclusion: This study showed a significant relationship between the schemata of tourists and context, diversity, light, unity in the environment, proportionality, continuity, balance and emphasis, among which the variable of light was determined to have the greatest effect

The Effect of Assertiveness Program on Clinical Competence of Intensive Care Units Nurses; A Randomized Clinical Trial

Introduction: Clinical competence of nurses is an effective factor in ensuring the quality of care in a competitive modern world. Given the vital role of nurses and the weakness of existing assertiveness, this concept as a communication style can play an important role in improving performance and improving the quality of care in tense stressful care settings. The purpose of this study was to determine the effect of the assertiveness program on the clinical competence of nurses in intensive care units. Method: In this clinical trial, 70 nurses of ShahreKord in 2018 were randomly allocated into two groups experimental and control. The experimental group was trained in 6 sessions of ninety minutes, while the control group did not receive the training. Data was collected before, immediately and 3 months after intervention by demographic and CIRN Clinical Competency Questionnaire and analyzed using SPSS 17 with independent t-test, ANOVA with repeated measurement and Chi-square. Results: There was no significant difference between the mean scores of clinical competency between the two groups before intervention, but this difference was significant in the immediately and 3 months after intervention based on independent t-test (P<0.05). ANOVA test with repeated measurement showed a significant difference in the process of changes in mean scores in the three stages of measurement (before, immediately and three months after the intervention) (P<0.001). Conclusion: Assertiveness education leads to increasing the clinical competence of nurses in intensive care units. Therefore, it is suggested to be used in nursing education courses

Feature Interpretation Using Generative Adversarial Networks (FIGAN): A Framework for Visualizing a CNN&#x2019;s Learned Features

Convolutional neural networks (CNNs) are increasingly being explored and used for a variety of classification tasks in medical imaging, but current methods for post hoc explainability are limited. Most commonly used methods highlight portions of the input image that contribute to classification. While this provides a form of spatial localization relevant for focal disease processes, it may not be sufficient for co-localized or diffuse disease processes such as pulmonary edema or fibrosis. For the latter, new methods are required to isolate diffuse texture features employed by the CNN where localization alone is ambiguous. We therefore propose a novel strategy for eliciting explainability, called Feature Interpretation using Generative Adversarial Networks (FIGAN), which provides visualization of features used by a CNN for classification or regression. FIGAN uses a conditional generative adversarial network to synthesize images that span the range of a CNN&#x2019;s principal embedded features. We apply FIGAN to two previously developed CNNs and show that the resulting feature interpretations can clarify ambiguities within attention areas highlighted by existing explainability methods. In addition, we perform a series of experiments to study the effect of auxiliary segmentations, training sample size, and image resolution on FIGAN&#x2019;s ability to provide consistent and interpretable synthetic images

Optimizing Micropropagation of Apple (Malus × Domestica Borkh) and in Vitro Root Induction By Piriformospora indica

The inoculation of plant species with mycorrhiza fungus Piriformospora indica results in enhancement of growth, increase in yield, and induction of resistance to biotic and abiotic diseases through improvement of the root system. The aim of the present study was to optimize in vitro propagation protocol for three indigenous apples (Malus × domestica) cultivars (ꞌGolbaharꞌ, ꞌSharbatiꞌ, ꞌSoltani Shabestariꞌ) and one commercial cultivar (ꞌGolden Deliciousꞌ). Furthermore, the efficiency of P. indica at rooting stage was investigated on three cultivars (ꞌSharbatiꞌ, ꞌSoltani Shabestariꞌ, ꞌGolden Deliciousꞌ). Establishment and proliferation stages were optimized by collecting explants at different seasons and comparing different culture media respectively. Rooting optimization included six treatments containing different concentrations of auxins in the presence or absence of P. indica. Results showed that at the establishment stage, a maximum percent of survival was observed in explants collected in spring. At the proliferation stage, different media had a divergent effects on distinct cultivars. Although all cultivars reacted favourable to micropropagation in MS (Murashige & Skoog 1962) basal medium, the presence or absence of cytokinin 2ip (N6-(2-Isopentenyl) adenine) in the culture media showed significant and incremental improvements in growth indices. In all cultivars highest rooting percent, root length, root thickness, and the number of roots/explant was observed in MS media containing auxins for three weeks followed by a treatment of MS medium containing P. indica for another three weeks. Plantlets treated with P. indica, grow stronger and healthier at the acclimation stage compared to the ones that excluded P. indica

Coupled Hidden Markov Model-Based Method for Apnea Bradycardia Detection.

International audienceIn this paper, we present a novel framework for the coupled hidden Markov model (CHMM), based on the forward and backward recursions and conditional probabilities, given a multidimensional observation. In the proposed framework, the interdependencies of states networks are modeled with Markovian-like transition laws that influence the evolution of hidden states in all channels. Moreover, an offline inference approach by maximum likelihood estimation is proposed for the learning procedure of model parameters. To evaluate its performance, we first apply the CHMM model to classify and detect disturbances using synthetic data generated by the FitzHugh-Nagumo model. The average sensitivity and specificity of the classification are above 93.98% and 95.38% and those of the detection reach 94.49% and 99.34%, respectively. The method is also evaluated using a clinical database composed of annotated physiological signal recordings of neonates suffering from apnea-bradycardia. Different combinations of beat-to-beat features extracted from electrocardiographic signals constitute the multidimensional observations for which the proposed CHMM model is applied, to detect each apnea bradycardia episode. The proposed approach is finally compared to other previously proposed HMM-based detection methods. Our CHMM provides the best performance on this clinical database, presenting an average sensitivity of 95.74% and specificity of 91.88% while it reduces the detection delay by -0.59 s

Deep Learning Based Staging of Bone Lesions From Computed Tomography Scans

In this study, we formulated an efficient deep learning-based classification strategy for characterizing metastatic bone lesions using computed tomography scans (CTs) of prostate cancer patients. For this purpose, 2,880 annotated bone lesions from CT scans of 114 patients diagnosed with prostate cancer were used for training, validation, and final evaluation. These annotations were in the form of lesion full segmentation, lesion type and labels of either benign or malignant. In this work, we present our approach in developing the state-of-the-art model to classify bone lesions as benign or malignant, where (1) we introduce a valuable dataset to address a clinically important problem, (2) we increase the reliability of our model by patient-level stratification of our dataset following lesion-aware distribution at each of the training, validation, and test splits, (3) we explore the impact of lesion texture, morphology, size, location, and volumetric information on the classification performance, (4) we investigate the functionality of lesion classification using different algorithms including lesion-based average 2D ResNet-50, lesion-based average 2D ResNeXt-50, 3D ResNet-18, 3D ResNet-50, as well as the ensemble of 2D ResNet-50 and 3D ResNet-18. For this purpose, we employed a train/validation/test split equal to 75&#x0025;/12&#x0025;/13&#x0025; with several data augmentation methods applied to the training dataset to avoid overfitting and to increase reliability. We achieved an accuracy of 92.2&#x0025; for correct classification of benign vs. malignant bone lesions in the test set using an ensemble of lesion-based average 2D ResNet-50 and 3D ResNet-18 with texture, volumetric information, and morphology having the greatest discriminative power respectively. To the best of our knowledge, this is the highest ever achieved lesion-level accuracy having a very comprehensive data set for such a clinically important problem. This level of classification performance in the early stages of metastasis development bodes well for clinical translation of this strategy