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  1. Fulltext Neural Network-Based Prediction Model to Investigate the Influence of Temperature and Moisture on Vibration Characteristics of Skew Laminated Composite Sandwich Plates
    Kallannavar V, Kattimani S, Soudagar MEM, Mujtaba MA, Alshahrani S, Imran M
    Materials (Basel), 2021 Jun 09;14(12).
    PMID: 34207585 DOI: 10.3390/ma14123170
    The present study deals with the development of a prediction model to investigate the impact of temperature and moisture on the vibration response of a skew laminated composite sandwich (LCS) plate using the artificial neural network (ANN) technique. Firstly, a finite element model is generated to incorporate the hygro-elastic and thermo-elastic characteristics of the LCS plate using first-order shear deformation theory (FSDT). Graphite-epoxy composite laminates are used as the face sheets, and DYAD606 viscoelastic material is used as the core material. Non-linear strain-displacement relations are used to generate the initial stiffness matrix in order to represent the stiffness generated from the uniformly varying temperature and moisture concentrations. The mechanical stiffness matrix is derived using linear strain-displacement associations. Then the results obtained from the numerical model are used to train the ANN. About 11,520 data points were collected from the numerical analysis and were used to train the network using the Levenberg-Marquardt algorithm. The developed ANN model is used to study the influence of various process parameters on the frequency response of the system, and the outcomes are compared with the results obtained from the numerical model. Several numerical examples are presented and conferred to comprehend the influence of temperature and moisture on the LCS plates.
  2. An obscured obstacle-Prevalence and nature of micro aggressions amongst healthcare professionals
    Elfaham RH, Allihaydan FS, Baragaa LAA, Elfaham SH, Allihaydan NS, Maqbul MS, et al.
    Semergen, 2024 Mar;50(2):102124.
    PMID: 38043388 DOI: 10.1016/j.semerg.2023.102124
    INTRODUCTION: Microaggressions create negative consequences on the mental health of individuals who experience them, such as feelings of alienation, frustration and low self-esteem. Physicians worldwide are negatively impacted by the detrimental effects of microaggressions and implicit bias. It is imperative to establish the prevalence specificity of the problem hence the aim of this study is to determine the prevalence, nature and determinants of microaggressions amongst healthcare professionals.

    METHOD: The study used an online anonymous survey to collect data including demographics, awareness of the term, experience of microaggression, acts and response. The research findings were analyzed using univariate and multivariate analyses using Chi-square test and binary logistic regression respectively.

    RESULT: A total of 443 participants (40.9% males, 59.1% females) included 403 physicians (91%), 21 dentists (4.7%), 15 nurses (3.4%) and 4 pharmacists (0.9%). More than half of the participants (59.8%) were aware of the term micro-aggression. The percentage was significantly higher among respondents from the western region of Saudi Arabia than the Gulf/Middle Eastern countries. Approximately 38.1% of the participants experienced microaggression and more than half (55.62%) did not report experiencing microaggression. The most common form of microaggression was passive-aggressive behavior (80.5%) followed by invalidation of an opinion (73.4%). Among those who experienced microaggression, (12.9%) reported anger as the most predominant emotional response.

    CONCLUSION: Microaggression is a universal phenomenon. Further research is necessary to determine its prevalence in other countries to establish a comprehensive understanding of its cultural context.

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