It is believed that finding the pattern and nature of WPV is the first step to develop suitable strategies to deal with the issue. Establishing WPV management teams and enacting appropriate laws can improve workplace safety for nurses and patients' care quality.

Adversarial audio attacks can be considered as a small perturbation unperceptive to human ears that is intentionally added to an audio signal and causes a machine learning model to make mistakes. This poses a security concern about the safety of machine learning models since the adversarial attacks can fool such models toward the wrong predictions. In this paper we first review some strong adversarial attacks that may affect both audio signals and their 2D representations and evaluate the resiliency of deep learning models and support vector machines (SVM) trained on 2D audio representations such as short time Fourier transform, discrete wavelet transform (DWT) and cross recurrent plot against several state-of-the-art adversarial attacks. Next, we propose a novel approach based on pre-processed DWT representation of audio signals and SVM to secure audio systems against adversarial attacks. The proposed architecture has several preprocessing modules for generating and enhancing spectrograms including dimension reduction and smoothing. We extract features from small patches of the spectrograms using the speeded up robust feature (SURF) algorithm which are further used to transform into cluster distance distribution using the K-Means++ algorithm. Finally, SURF-generated vectors are encoded by this codebook and the resulting codewords are used for training a SVM. All these steps yield to a novel approach for audio classification that provides a good tradeoff between accuracy and resilience. Experimental results on three environmental sound datasets show the competitive performance of the proposed approach compared to the deep neural networks both in terms of accuracy and robustness against strong adversarial attacks.

Background: Nurse-physician communication in the healthcare setting is an important subject that requires international attention because of its relationship with nurses' job satisfaction, turnover, patient safety, and above all, the quality of care. The importance of conducting studies on communication in different cultures and contexts in order to increase nurses' knowledge regarding nurse-physician communication cannot be overemphasized. Aim: The purpose of this study was to explore the perspectives and experiences of Iranian nurses regarding nurse-physician communication. Methods: A qualitative study, using the content analysis approach, was conducted. Semistructured interviews were held with 22 female nurses with a Bachelor's degree who were working in two teaching hospitals in an urban area of Iran. Results: During the data analysis, three main themes emerged: "no independence in decision-making", "lack of acknowledgment of nurses' capabilities", and "unequal support by the healthcare system". Conclusion:Healthcare team members and administrators should listen to nurses' perspectives and try to address the problematic areas of nurse-physician communication if they are improving the quality of nursing care that is expected.

In this paper we propose a novel environmental sound classification approach incorporating unsupervised feature learning via the spherical K-Means++ algorithm and a new architecture for high-level data augmentation. The audio signal is transformed into a 2D representation using a discrete wavelet transform (DWT). The DWT spectrograms are then augmented by a novel architecture for cycle-consistent generative adversarial network. This high-level augmentation bootstraps generated spectrograms in both intra-and inter-class manners by translating structural features from sample to sample. A codebook is built by coding the DWT spectrograms with the speeded-up robust feature detector and the K-Means++ algorithm. The Random forest is the final learning algorithm which learns the environmental sound classification task from the code vectors. Experimental results in four benchmarking environmental sound datasets (ESC-10, ESC-50, UrbanSound8k, and DCASE-2017) have shown that the proposed classification approach outperforms most of the state-of-the-art classifiers, including convolutional neural networks such as AlexNet and GoogLeNet, improving the classification rate between 3.51% and 14.34%, depending on the dataset.