Suchergebnisse

BACKGROUND: International research collaboration (IRC) is known as one of the important indicators of productivity, efficiency, and validity of universities in the world. In other words, IRC is necessary for the scientific trade-off between researchers in international scientific societies. The study aimed to address the experiences of an academic researcher about factors related to IRC. MATERIALS AND METHODS: The present study was conducted using a qualitative approach and conventional content analysis method. The participants consisted of 19 experienced faculty members and researchers from Isfahan University of Medical Sciences, who were selected based on the purposive and snowball sampling techniques. Data were collected through semi-structured interviews and were analyzed using the content analysis technique. Guba and Lincoln's evaluative criteria, including credibility, confirmability, dependability, and transferability, were applied to evaluate the trustworthiness of the study. RESULTS: According to the research findings, factors of "personal skills," "personality," "professional position," and "scientific activities" under the category of personal factors; "rules and regulations" and "equipment and facilities" under the organizational factors; and "domestic policies" and "foreign policies" were identified under the government factors category. CONCLUSION: Research collaborations are influenced by individual, intra-academic, and extra-academic factors; thus, research policymakers can help further to enhance the quantity and quality of scientific output and promote the university's placing in international rankings through providing conditions that enable international interactions.

In this paper, we propose a cooperative distributed framework to optimize a variety of rate and energy-efficiency (EE) utility functions, such as the minimum-weighted rate or the global EE, for the K-user interference channel. We focus on the single-input multiple-output (SIMO) case, where each user, based solely on local channel state information (CSI) and limited exchange information from other users, optimizes its transmit power and receive beamformer, although the framework can also be extended to the multiple-output multiple-input (MIMO) case. The distributed framework combines an alternating optimization approach with majorization-minimization (MM) techniques, thus ensuring convergence to a stationary point of the centralized cost function. Closed-form power update rules are obtained for some utility functions, thus obtaining very fast convergence algorithms. The receivers treat interference as noise (TIN) and apply the beamformers that maximize the signal-to-interference-plus-noise (SINR). The proposed cooperative distributed algorithms are robust against channel variations and network topology changes and, as our simulation results suggest, they perform close to the centralized solution that requires global CSI. As a benchmark, we also study a non-cooperative distributed framework based on the so-called "signal-to-leakage-plus-noise ratio" (SNLR) that further reduces the overhead of the cooperative version. ; The work of Ignacio Santamaria was supported in part by the Ministerio de Ciencia e Innovación (Gobierno de España) / Agencia Española de Investigación (AEI) / FEDER funds of the European Union (EU) under Grant PID2019-104958RB-C43 (ADELE).