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  1. Hemmati A, Mirghaed SR, Rahmani F, Komasi S
    Malays J Med Sci, 2019 Sep;26(5):74-87.
    PMID: 31728120 DOI: 10.21315/mjms2019.26.5.7
    BACKGROUND: The present study was conducted to determine the differential profile of social anxiety disorder (SAD) and avoidant personality disorder (APD) based on dimensional diagnosis in criterion B of the DSM-5 Alternative Model for Personality Disorders (DSM-5-AMPD) in a college sample.

    METHODS: Samples of this cross-sectional study included 320 (23.08 ± 2.66 years; 57% female) college students in western Iran during February 2015 to December 2017. Liebowitz-social anxiety scale, PID-5, SCID-II, SCID-II-SQ and diagnostic interview for SAD were the tools. The data were analysed using Pearson correlation and multiple linear regression analysis.

    RESULTS: Forty-three and 38 participants met criteria for SAD alone and APD, respectively. Five main domains of PID-5 could explain 29% and 54% of the variance of SAD and APD, respectively. Facets of negative affect, detachment, antagonism, disinhibition, and psychoticism could explain 25% versus 43%, 26% versus 54%, 7% versus 27%, 21% versus 41%, 13% versus 30% of the variance of SAD and APD, respectively.

    CONCLUSION: SAD and APD probably refer to two distinct mental states having prominent anxiety, emotional instability, and interpersonal pattern of avoidance and detachment of challenge. SAD is a simple form of mental disturbances with anxiety in its core features; although, APD is possibly referring to more complicated psychopathology.

  2. Shamshirband S, Joloudari JH, Shirkharkolaie SK, Mojrian S, Rahmani F, Mostafavi S, et al.
    Math Biosci Eng, 2021 Oct 25;18(6):9190-9232.
    PMID: 34814342 DOI: 10.3934/mbe.2021453
    Today's intelligent computing environments, including the Internet of Things (IoT), Cloud Computing (CC), Fog Computing (FC), and Edge Computing (EC), allow many organizations worldwide to optimize their resource allocation regarding the quality of service and energy consumption. Due to the acute conditions of utilizing resources by users and the real-time nature of the data, a comprehensive and integrated computing environment has not yet provided a robust and reliable capability for proper resource allocation. Although traditional resource allocation approaches in a low-capacity hardware resource system are efficient for small-scale resource providers, for a complex system in the conditions of dynamic computing resources and fierce competition in obtaining resources, they cannot develop and adaptively manage the conditions optimally. To optimize the resource allocation with minimal delay, low energy consumption, minimum computational complexity, high scalability, and better resource utilization efficiency, CC/FC/EC/IoT-based computing architectures should be designed intelligently. Therefore, the objective of this research is a comprehensive survey on resource allocation problems using computational intelligence-based evolutionary optimization and mathematical game theory approaches in different computing environments according to the latest scientific research achievements.
  3. Mohamed K, Rodríguez-Román E, Rahmani F, Zhang H, Ivanovska M, Makka SA, et al.
    Infect Control Hosp Epidemiol, 2020 Oct;41(10):1245-1246.
    PMID: 32319878 DOI: 10.1017/ice.2020.162
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