341. Technological advancements and organizational discrimination : the dual impact of industry 5.0 on migrant workersErhan Aydin, Mushfiqur Rahman, Çağri Bulut, Roberto Biloslavo, 2024, original scientific article Abstract: This study explores the impact of Industry 5.0 on discriminatory behaviors toward migrant
employees within organizations. Through semi-structured qualitative interviews with 15 migrant
workers in the UK, this research identifies key challenges faced by migrant employees amidst the
integration of advanced technologies like AI and robotics in HRM systems. Thematic analysis reveals
that while Industry 5.0 has the potential to mitigate human biases, it can also perpetuate existing
prejudices if not managed effectively. This study highlights two main themes: the experiences of
discrimination and challenges in the context of Industry 5.0, and the role of technology in HRM
systems. The findings indicate that automated HR systems can both reduce and increase biases,
highlighting the importance of inclusive practices and targeted support programs to help migrant
workers adapt to a technologically advanced labor market. This research contributes to the literature
by providing insights into the duality of technological advancements in reducing and reinforcing
workplace discrimination.
Keywords: Industry 5.0, migrant workers, organizational discrimination, human resource management, technological advancements
Published in DiRROS: 30.09.2024; Views: 172; Downloads: 121
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342. Unveiling the solution structure of a DNA duplex with continuous silver-modified Watson-Crick base pairsUroš Javornik, Antonio Pérez-Romero, Carmen López-Chamorro, Rachelle M. Smith, José A. Dobado, Oscar Palacios, Mrinal K. Bera, May Nyman, Janez Plavec, Miguel A. Galindo, 2024, original scientific article Published in DiRROS: 30.09.2024; Views: 187; Downloads: 631
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343. Lessons from accelerating an RBF-FD phase-field model of dendritic growth on GPUsBoštjan Mavrič, Tadej Dobravec, Božidar Šarler, 2024, published scientific conference contribution Abstract: Phase-field modeling of dendritic growth presents the state of the art in the field of solidification modeling and are usually implemented using finite difference models combined with explicit time marching and accelerated by using GPUs. They are a prime candidate for such acceleration, since they require many arithmetic operations on relatively low ammount of data. We present an attempt at porting an existing RBF-FD code optimized for CPU execution to use GPU acceleration while keeping the resulting implementation portable between architectures. We discuss the acceleration achieved, scaling and implementation issues and critically discuss current landscape of GPGPU offerings.
Keywords: meshless methods, phase field, graphical processing unit
Published in DiRROS: 27.09.2024; Views: 216; Downloads: 114
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