Developing "Code of Ethics for Medical Professionals, Medical Council of Islamic Republic of Iran"

Background: The medical profession has always been an inspiration for human societies throughout its diverse history. This position and historical authority in the field of ethics has had a different and higher status, in such a way that many of the norms of general ethics and professional ethics, especially principles, such as trust, confidentiality and respect for human dignity, have been developed by medical professionals. Developing guidelines of general and professional ethics is one of the inherent duties of the Medical Council of the Islamic Republic of Iran (IRIMC) as a professional organization. In this regard, the Supreme Council of IRIMC has approved the "Code of Ethics for Medical Professionals"and, in accordance with its legal authority, has annexed it to the disciplinary regulations of IRIMC. Methods: A draft document, the result of extensive literature review, was discussed in 27 expert panel meetings and after receiving and endorsing the stakeholders' point of view, was approved by the IRIMC Supreme Council. Results: The first edition of "Code of Ethics for Medical Professionals, Medical Council of Islamic Republic of Iran"was developed on July 6, 2017 by the Supreme Council of IRIMC. The guideline was set to take effect one year after its enactment. The first edition was revised and completed and final edition was adopted on August 9, 2018 by IRIMC in 13 chapters and 140 articles (original full text is available in the Supplementary file 1). Conclusion: According to the approved decision by the Supreme Council of IRIMC on May 10, 2018, the final edition takes effect as of October 7, 2018. © 2020 The Author(s)

Combined deformation and solidification-driven porosity formation in aluminum alloys

In die-casting processes, the high cooling rates and pressures affect the alloy solidification and deformation behavior, and thereby impact the final mechanical properties of cast components. In this study, isothermal semi-solid compression and subsequent cooling of aluminum die-cast alloy specimens were characterized using fast synchrotron tomography. This enabled the investigation and quantification of gas and shrinkage porosity evolution during deformation and solidification. The analysis of the 4D images (3D plus time) revealed two distinct mechanisms by which porosity formed; (i) deformation-induced growth due to the enrichment of local hydrogen content by the advective hydrogen transport, as well as a pressure drop in the dilatant shear bands, and (ii) diffusion-controlled growth during the solidification. The rates of pore growth were quantified throughout the process, and a Gaussian distribution function was found to represent the variation in the pore growth rate in both regimes. Using a one-dimensional diffusion model for hydrogen pore growth, the hydrogen flux required for driving pore growth during these regimes was estimated, providing a new insight into the role of advective transport associated with the deformation in the mushy region

Evaluation of Antibiotic Resistance in Extended-spectrum Beta-lactamase (ESBL) Genes in the E. coli Isolates of Urinary Infections

BACKGROUND AND OBJECTIVE: Extended-spectrum beta-lactamase (ESBL) enzymes hydrolyze cephalosporins and penicillins. This study aimed to determine the frequency of Escherichia coli strains producing SHV, TEM and CTX-M &beta-lactamase genes and their association by inducing antibiotic resistance. METHODS: In this cross-sectional study, 55 E. coli strains were isolated from urinary samples and cultured on eosin methylene blue (EMB) agar and CHROMagar. After biochemical examinations, antibiotic susceptibility test was performed using the disk-diffusion method according to the guidelines of the Clinical & Laboratory Standards Institute (CLSI). In addition, the presence of blaCTX-M, blaTEM and blaSHV genes was evaluated using specific multiplex polymerase chain reaction (PCR) primers. FINDINGS: In this study, the highest antibiotic resistance was observed against penicillin and erythromycin (96% and 94.5%, respectively), while the highest susceptibility was reported for ciprofloxacin and imipenem (67.2%). Out of 55 samples, 26(47.27%) had the TEM gene, and CTX-M gene was detected in 41 (74.54%) samples. Moreover, TEM and CTX-M genes were simultaneously detected in 32.72% of the samples, while in six samples (10.9%), neither of these genes were present. The SHV gene was not detected in any of the samples. CONCLUSION: According to the results of this study, the production of ESBL was identified in 70% of the investigated E. coli isolates. Therefore, accurate and timely medical care, as well as the use of appropriate antibiotics, is required to prevent the outbreak of ESBL-producing E. coli strains

Geometric Optimization of T-shaped Fin and Inverted Fin Based on Minimum Entropy Generation Objective

Since energy management is one of the most critical concerns, it is essential to determine the engineering construct irreversibility. Determination of entropy generation as a basis for evaluation of the irreversibility of heat transfer processes has become a significant method to reflect the heat transfer quality. The current study is dedicated to geometric optimization of T-shaped fins and inverted fins (cavities) using the constructal method to reach the minimum entropy generation as the optimization objective. The temperature distribution is determined according to a 1D analytical model and a 2D numerical model for a T-shaped fin and a T-shaped cavity, respectively. Furthermore, a comparison is made between the present optimal designs relying on entropy generation minimization (EGM) and the optimal designs presented in the literature and based on thermal conductance maximization (TCM) for the fin and hotspot temperature minimization (HTM) for the cavity. While the two optimization approaches have the same constraints, the results reveal that the optimal designs mainly have significant dependence on the type of optimization objective. However, it is shown that the T-shaped fin optimized via EGM produces 24% less entropy than the design optimized by TCM, with the thermal conductance lower only by 4%. On the other hand, the EGM-based optimal cavity generates entropy by about 8% less than that of the HTM-based optimal cavity, with an approximately 19% rise in the hotspot temperature. Considering both objectives for a more comprehensive comparison, the current article introduces a multi-objective optimization for fin and cavity

Latexify Math: Mathematical Formula Markup Revision to Assist Collaborative Editing in Math Q&A Sites

Collaborative editing questions and answers plays an important role in quality control of Mathematics StackExchange which is a math Q&A Site. Our study of post edits in Mathematics Stack Exchange shows that there is a large number of math-related edits about latexifying formulas, revising LaTeX and converting the blurred math formula screenshots to LaTeX sequence. Despite its importance, manually editing one math-related post especially those with complex mathematical formulas is time-consuming and error-prone even for experienced users. To assist post owners and editors to do this editing, we have developed an edit-assistance tool, MathLatexEdit for formula latexification, LaTeX revision and screenshot transcription. We formulate this formula editing task as a translation problem, in which an original post is translated to a revised post. MathLatexEdit implements a deep learning based approach including two encoder-decoder models for textual and visual LaTeX edit recommendation with math-specific inference. The two models are trained on large-scale historical original-edited post pairs and synthesized screenshot-formula pairs. Our evaluation of MathLatexEdit not only demonstrates the accuracy of our model, but also the usefulness of MathLatexEdit in editing real-world posts which are accepted in Mathematics Stack Exchange

Modelling human-centric aspects of end-users with iStar

Modelling human-centric aspects of end-users with iSta

Human Values Violations in Stack Overflow: An Exploratory Study

Human Values Violations in Stack Overflow: An Exploratory Stud

Fifth International Workshop on Human Factors in Modeling / Modeling of Human Factors (HuFaMo'21)

Studying human factors and experiences in modeling helps to gain knowledge on the process and use of modeling in practice to optimize system outcomes and user well-being. Moreover, to better support the human aspects of stakeholders and end-users of the software, incorporating the human aspects into software engineering processes from the early modeling and design stages is essential. Considering human factors in software design involves not only 1) studying in detail the interactions between users (humans), but also 2) studying the human factors of the end-users of the system. The HuFaMo workshop was established in 2015 to promote this form of research by creating a venue to discuss and disseminate these topics. The HuFaMo Workshop originally aimed at studying human factors for software systems (implemented or not) allowing humans to model. Hosted within the MODELS conference, the first four editions of the workshop benefited from a high-quality audience, allowing significant progress on this issue. For this fifth edition, we have widened the scope of the workshop to consider the modeling of human factors during the design of the software as well. This helps to study all types of relationships that modeling and human factors can have and their impact on processes, products, and end-users as well as others that might be affected by the system

Automated identification of security discussions in microservices systems: Industrial surveys and experiments

Lack of awareness and knowledge of microservices-specific security challenges and solutions often leads to ill-informed security decisions in microservices system development. We claim that identifying and leveraging security discussions scattered in existing microservices systems can partially close this gap. We define security discussion as “a paragraph from developer discussions that includes design decisions, challenges, or solutions relating to security”. We first surveyed 67 practitioners and found that securing microservices systems is a unique challenge and that having access to security discussions is useful for making security decisions. The survey also confirms the usefulness of potential tools that can automatically identify such security discussions. We developed fifteen machine/deep learning models to automatically identify security discussions. We applied these models on a manually constructed dataset consisting of 4,813 security discussions and 12,464 non-security discussions. We found that all the models can effectively identify security discussions: an average precision of 84.86%, recall of 72.80%, F1-score of 77.89%, AUC of 83.75% and G-mean 82.77%. DeepM1, a deep learning model, performs the best, achieving above 84% in all metrics and significantly outperforms three baselines. Finally, the practitioners’ feedback collected from a validation survey reveals that security discussions identified by DeepM1 have promising applications in practice