Search

Shahram Yazdani, Maryam Hoseini AbardehDepartment of Medical Education, School of Medical Education, Shahid Beheshti University of Medical Sciences, Tehran, IranCorrespondence: Maryam Hoseini AbardehSchool of Medical Education, Shahid Beheshti University of Medical Sciences, Shahid Chamran Highway, Tehran Velenjak Street, Tehran, IranTel +98 212 243 9982Fax +98 212 243 9784Email hoseini.abardeh@sbmu.ac.irAbstract: Clinical reasoning is a complex cognitive process that is essential to evaluate and manage a patient’s medical problem. The aim of this paper was to provide a critical review of the research literature on clinical reasoning theories and models. To conduct our study, we applied the process of conducting a literature review in four stages in accordance with the approach of Carnwell and Daly. First, we defined the scope of the review as being limited to clinical reasoning theories and models in medical education. In the second stage, we conducted a search based on related words in PubMed, Google Scholar, PsycINFO, ERIC, ScienceDirect and Web of Science databases. In the third stage, we classified the results of the review into three categories, and in the fourth stage, we concluded and informed further studies. Based on the inclusion and exclusion criteria, 31 articles were eligible to be reviewed. Three theories and two models were recognized and classified into three categories. Several theories and models have been proposed in relation to clinical reasoning, but it seems that these theories and models could only explain part of this complex process and not the whole process. Therefore, to fulfill this gap, it may be helpful to build a Meta-model or Meta-theory, which unified all the models, and theories of clinical reasoning.Keywords: clinical reasoning, medical education, review

The retina and primary visual cortex (V1) both exhibit diverse neural populations sensitive to diverse visual features. Yet it remains unclear how neural populations in each area partition stimulus space to span these features. One possibility is that neural populations are organized into discrete groups of neurons, with each group signaling a particular constellation of features. Alternatively, neurons could be continuously distributed across feature-encoding space. To distinguish these possibilities, we presented a battery of visual stimuli to the mouse retina and V1 while measuring neural responses with multi-electrode arrays. Using machine learning approaches, we developed a manifold embedding technique that captures how neural populations partition feature space and how visual responses correlate with physiological and anatomical properties of individual neurons. We show that retinal populations discretely encode features, while V1 populations provide a more continuous representation. Applying the same analysis approach to convolutional neural networks that model visual processing, we demonstrate that they partition features much more similarly to the retina, indicating they are more like big retinas than little brains.

Abstract A distributed multiple‐input multiple‐output (MIMO) dual‐function radar‐communication (D‐MIMO DFRC) system is composed of multiple distributed dual‐function transmitters, multiple radar receivers and multiple communication receivers, which is capable of performing communication and radar tasks simultaneously. In a DFRC system, the goal is on optimising both the sum ‐rate in communication receivers and detection/localisation performance in radar receivers. The secrecy rate is maximised in D‐MIMO DFRC systems by decreasing the eavesdropper data rate as much as possible with a two‐step antenna selection method while maintaining optimal radar performance. In the first step of the proposed method, all transmitter antennas have been classified into groups based on their distance from each other, and each group is called a cluster. Then, a cluster of distributed transmitter antennas is selected based on path fading effects. In the second step of this method, the antenna selection algorithm is performed in the pre‐selected cluster based on channel capacity information utilising QR decomposition. The results show that this antenna selection method, along with low computational complexity and high performance, leads to the maximisation of the secrecy rate. In DFRC systems, it is desirable to minimise the total transmit power while satisfying system requirements to provide low probability of interception (LPI). Finally, after antenna selection, a power allocation strategy is also applied on the selected antennas to optimise the total transmit power and to maximise throughput in communication radar receivers simultaneously, and as a result it leads to provide LPI.

Abstract Amidst the increasing complexity of microgrid optimization, characterized by numerous decision variables and intricate non-linear relationships, there is a pressing need for highly efficient algorithms. This study introduces a tailored Mixed Integer Nonlinear Programming (MINLP) model that optimizes the charging and discharging schedules of electric vehicles (EVs) and energy storage systems (ESS) while incorporating Distributed Flexible AC Transmission System (D-FACTS) devices. To address these challenges, a novel approach based on the Large-Scale Two-Population Algorithm (LSTPA) is proposed. The model's effectiveness was evaluated using a 33-node microgrid, where the proposed method achieved a total purchased energy of 1.2 MWh, a voltage deviation of 0.0357 p.u, and a CPU time of 551 s, outperforming traditional methods like NSGA-II, PSO, and JAYA. Additionally, in a 69-node microgrid, the approach resulted in a total purchased energy of 0.3 MWh and a voltage deviation of 0.0078 p.u. These results demonstrate the superior performance of the proposed method in terms of energy efficiency, voltage stability, and computational time, advancing the efficiency of microgrid management.

Abstract Medical images often require segmenting into different regions in the first analysis stage. Relevant features are selected to differentiate various regions from each other, and the images are segmented into meaningful (anatomically significant) regions based on these features. The purpose of this study is to present a model for segmenting and identifying the local tumor formation in MR images of the human brain. The proposed system operates in an unsupervised manner to minimize the intervention of expert users and to achieve an acceptable speed in the tumor classification process. The proposed method includes several steps of preprocessing for different brain image classify that Perform the normalization task. These preprocessing steps lead to more accurate results in high-resolution images and ultimately improve the accuracy and sensitivity of tumor separation from brain tissue. The output of this stage is applied to a self-encoding neural network for image zoning. By nature of self-encoding networks, leads to reduce the dimensionality of tumor pixels from the surrounding healthy environment, which significantly helps remove regions incorrectly extracted as tumors. Finally, by extracting features from the previous stage's output through Otsu thresholding, the surrounding area and type of tumor are also extracted. The proposed method was trained and tested using the BRATS2020 database and evaluated by various performance metrics. The results based on the Dice Similarity Coefficient (DSC) show an accuracy of 97% for the entire MR image and improved detection accuracy compared to other methods, as well as a reduction in the cost of the diagnostic process.

Objective(s): Caring for COVID-19 patients was a new and challenging experience for nurses. Knowing the effect of psychological, emotional, and spiritual care on physiological responses, nurses provided holistic care. Methods: This qualitative study was conducted with the conventional content analysis approach in one of the referral hospitals for COVID-19 in Iran. Data were collected using in-depth, semi-structured individual interviews. Data was analyzed using the Lundman and Granheim qualitative content analysis and the Lincoln and Goba criteria. Results: In all 17 nurses were interviewed. The mean age (SD) of participants was 34.41 ± 7.49 years. The findings indicated a "butterfly-like care" with four main categories [caring with all one's heart, resilience, self-sacrifice, and conscious presence in danger). Conclusion: During the COVID-19 pandemic, nurses, being aware of all the health and social consequences and overcoming fear and doubt, volunteered to take care of patients and save lives, and despite understanding the risk of being infected, they lovingly surrounded the patients. Considering the central role of nurses in the treatment team, special attention should be given to the maintenance and strengthening of nurses in the planning of the development of healthcare systems in the face of the upcoming epidemics.

The story of Amir ‌Arslan Namdar is one of the famous folk stories of Persian literature from the Qajar era and one of the most famous romance stories that marks the transition from the short story to the novel. Amir Arslan was written in the middle of the Qajar era and includes some features of traditional and ancient Persian stories, as well as some features of novels. This feature is also evident in the illustrations of many lithographed manuscripts of the book. This article aims to explore the cultural identity and features of this story using a cultural studies approach. It analyzes the hidden and obvious cultural and identity structures of the story by comparing the text and image, while also exploring the hidden cultural roots in this story. The article introduces and studies these cultural aspects, specifically focusing on the illustrations of the lithographed manuscript from 1317 AH.IntroductionThe indicators of cultural studies include the importance of culture in the study of social structure, anthropological and sociological studies, studies focused on popular culture, the discussion of subcultures, their analysis, and their relationship with the dominant culture, as well as the cultivation of the relationship between cultural forms and the sociological approach to culture.One of the significant sources for the social-cultural history of Iran is the folk stories that were created and discussed according to the social, cultural, political, and economic conditions of their time, revealing the rituals and customs of each period. The political, economic, social, and cultural situation of society has a direct effect on folk literature.The story of Amir Arslan is one of the popular and valuable stories of the Qajar era, providing a complete reflection of Persian literature in the late 13th century Hijri. This article compares the text and image, examining the content of the story from the perspective of cultural identity and the visual representations within it. It introduces the visual and content aspects of the lithographed manuscript of Amir Arslan Namdar (dated 1317 AH) available in the collection of manuscripts at Iran's National Library. The article aims to uncover the identity and cultural structures of the text and image through a cultural studies approach.Literature ReviewAmong the researches carried out on the story of Amir Arslan and the study in the field of literature of the Qajar period, there is a book From Story to Novel written by Hadi Yavari, which was published in 1390 by Sokhan publishing, and the author in this book deals with various aspects of the story. The most detailed research on Amir Arslan was done by Mohammad Jafar Mahjoub, which was published in three issues and in the form of a series of articles in Sokhan journal in 1339 and 1340. Also, William L. Hanaway (1991), Gholamhossein Yousefi (1357) and Christophe Balay (1377) have published articles on the subject of this story, which mostly investigated and studied its literary aspects.MethodologyThe research method in this article is descriptive-analytical with a historical approach, and the information was collected from library data.ConclusionThe results indicate that throughout their history, Iranians rediscovered their real identity and identity elements in the face of different challenges, including Iskander's attack and Hellenistic culture, the Arab attack and the arrival of Islam, the invasion of Turk and Mongol peoples, and exposure to the West. The Firang style can be considered as one layer of identity in Qajar culture, gradually formed and stabilized in response to Western influence. Archaism can be seen as a result of Iran's encounter with Western civilization, serving as a means to preserve a culture that faced European invasion.Naser al-Din Shah, along with other Qajar kings, considered himself fascinated and related to the Sassanid ancestors, emphasizing the golden and proud era of ancient Iran in the history of the country. This principle of antiquity spread throughout Qajar culture and particularly influenced Qajar-era art. In an attempt to overcome decline and backwardness, intellectuals turned to the golden period of Iran's history, adding another layer of identity to Qajar culture, referring to ancient Iran.Furthermore, the legacy of Islam and Shia thoughts, inherited from previous eras, particularly the Safavid era, served as a means of legitimacy among the people for the Qajars. References to Islam, the Muslim religion, and Islamic characters can be found in folk stories. The story presents Islam with respect, aiming to be pleasant and acceptable to the general population and gain their favor.The research results also indicate that due to the existence of multiple identities in Qajar culture and society, multiple identity layers can be identified in the literature and art of this era. In the story of Amir Arslan Namdar, three layers of identity can be observed: the identity layer of Firang style at the first level, followed by the traditional Iranian-Islamic identity layer at the second level, and the identity layer of archaism at the lower or weaker level of literary and visual identities.According to the cultural studies approach, it was found that the diverse culture of the people in the Qajar period encompassed these layers of identity. In addition to them, the article discusses the passivity of women in society, the prominent role of spells, magic, etc., and the interest of the population in these areas.

Abstract Background Improving the quality of clinical training is synonymous with accomplishing nursing education goals and improving the quality of nursing care. This study aimed to improve the quality of clinical training in Maternal and Neonatal Health (MNH) in nursing students. Methods This action research was conducted in two cycles from June 2017 to June 2018. The study setting was the School of Nursing and Midwifery of Lorestan University of Medical Sciences in southwest Iran. The participants were nursing students, faculty members, clinical trainers, educational managers, and teaching personnel. In the first cycle, semi-structured interviews were held with stakeholders to identify clinical problems and improvement strategies. Based on the results of these sessions, the changes needed to improve the quality of clinical training were planned and implemented. The weaknesses and strengths of the implemented changes were then identified through group discussions with the stakeholders. In the second cycle, a second plan was carried out to correct the weaknesses of the changes planned in the first cycle, and the modifications were implemented and evaluated. Results The main three categories extracted included an unsystematic curriculum and inadequate monitoring, inadequate resources and facilities, and the student’s lack of motivation. The measures taken for improvement included holding communication workshops, developing and internalizing logbooks, reducing the number of students in clinical training groups, using modern clinical training methods, and changing clinical evaluation methods. Conclusion Improving communication skills among the students, trainers, and hospital personnel and using modern clinical training methods, such as conceptual maps, triple jumps, and clinical skill centers, are the best strategies for improving clinical training in MNH nursing students.

The present study aimed to investigate the effects of the EASY Minds program on working memory and selective attention of a group of Iranian students with maths learning disabilities. The study had a quasi-experimental research design with pre-test, post-test, and follow-up tests. Forty students with maths learning disabilities were randomly assigned to either experimental or control groups. The findings showed that the program improved working memory and selective attention of the experimental group, and this effect lasted for a two-month follow-up. It can be concluded that the EASY Minds program can be used as an interventional technique to improve working memory and selective attention of students with maths learning disabilities.