Social media in undergraduate medical education: A systematic review.

INTRODUCTION: There are over 3.81 billion worldwide active social media (SoMe) users. SoMe are ubiquitous in medical education, with roles across undergraduate programmes, including professionalism, blended learning, well being and mentoring. Previous systematic reviews took place before recent explosions in SoMe popularity and revealed a paucity of high-quality empirical studies assessing its effectiveness in medical education. This review aimed to synthesise evidence regarding SoMe interventions in undergraduate medical education, to identify features associated with positive and negative outcomes. METHODS: Authors searched 31 key terms through seven databases, in addition to references, citation and hand searching, between 16 June and 16 July 2020. Studies describing SoMe interventions and research on exposure to existing SoMe were included. Title, abstract and full paper screening were undertaken independently by two reviewers. Included papers were assessed for methodological quality using the Medical Education Research Study Quality Instrument (MERSQI) and/or the Standards for Reporting Qualitative Research (SRQR) instrument. Extracted data were synthesised using narrative synthesis. RESULTS: 112 studies from 26 countries met inclusion criteria. Methodological quality of included studies had not significantly improved since 2013. Engagement and satisfaction with SoMe platforms in medical education are described. Students felt SoMe flattened hierarchies and improved communication with educators. SoMe use was associated with improvement in objective knowledge assessment scores and self-reported clinical and professional performance, however evidence for long term knowledge retention was limited. SoMe use was occasionally linked to adverse impacts upon mental and physical health. Professionalism was heavily investigated and considered important, though generally negative correlations between SoMe use and medical professionalism may exist. CONCLUSIONS: Social media is enjoyable for students who may improve short term knowledge retention and can aid communication between learners and educators. However, higher-quality study is required to identify longer-term impact upon knowledge and skills, provide clarification on professionalism standards and protect against harms

A Sensorless Intelligent System to Detect Dust on PV Panels for Optimized Cleaning Units

Deployment of photovoltaic (PV) systems has recently been encouraged for large-scale and small-scale businesses in order to meet the global green energy targets. However, one of the most significant hurdles that limits the spread of PV applications is the dust accumulated on the PV panels’ surfaces, especially in desert regions. Numerous studies sought the use of cameras, sensors, power datasets, and other detection elements to detect the dust on PV panels; however, these methods pose more maintenance, accuracy, and economic challenges. Therefore, this paper proposes an intelligent system to detect the dust level on the PV panels to optimally operate the attached dust cleaning units (DCUs). Unlike previous strategies, this study utilizes the expanded knowledge and collected data for solar irradiation and PV-generated power, along with the forecasted ambient temperature. An expert artificial intelligence (AI) computational system, adopted with the MATLAB platform, is utilized for a high level of data prediction and processing. The AI was used in this study in order to estimate the unprovided information, emulate the provided measurements, and accommodate more input/output data. The feasibility of the proposed system is investigated using actual field data during all possible weather conditions

A Sensorless Intelligent System to Detect Dust on PV Panels for Optimized Cleaning Units

Deployment of photovoltaic (PV) systems has recently been encouraged for large-scale and small-scale businesses in order to meet the global green energy targets. However, one of the most significant hurdles that limits the spread of PV applications is the dust accumulated on the PV panels’ surfaces, especially in desert regions. Numerous studies sought the use of cameras, sensors, power datasets, and other detection elements to detect the dust on PV panels; however, these methods pose more maintenance, accuracy, and economic challenges. Therefore, this paper proposes an intelligent system to detect the dust level on the PV panels to optimally operate the attached dust cleaning units (DCUs). Unlike previous strategies, this study utilizes the expanded knowledge and collected data for solar irradiation and PV-generated power, along with the forecasted ambient temperature. An expert artificial intelligence (AI) computational system, adopted with the MATLAB platform, is utilized for a high level of data prediction and processing. The AI was used in this study in order to estimate the unprovided information, emulate the provided measurements, and accommodate more input/output data. The feasibility of the proposed system is investigated using actual field data during all possible weather conditions.</jats:p

Performance Assessment User Interface to Enhance the Utilization of Grid-Connected Residential PV Systems

The share of renewable energy resources in modern electrical power networks is increasing in order to meet environmental and technical targets. Consequently, energy researchers and power providers have been focusing on optimizing the integration of renewable energy into existing power grids. One of the most significant growing applications of renewable energy resources is residential photovoltaic (PV) systems; therefore, this paper discusses a new methodology to enhance the utilization of small-scale and medium-scale PV systems. For this purpose, this study proposes a user-friendly interface to help novice users optimally design their own PV projects with the highest possible utilization of the installed panels. Unlike the commercially available design tools, the proposed interface in this paper provides a higher degree-of-freedom computational process, as well as the option of improving the generated power quality, while maintaining the simplicity of the required tools and inputs. The proposed methodology mainly relies on a deep mathematical analysis considering different generation and consumption aspects, such as the load profile, time of usage, ambient temperature, PV system specifications and location. Furthermore, the mechanism of integrating a small portion of Energy Storage Systems (ESSs), to improve the quality of the extracted power, is also discussed in this study. The user interface provides the ability to estimate optimal ESS usage versus the estimated price when energy is urgently required. The case study was conducted in Riyadh, Saudi Arabia, and the results showed an essential improvement in the efficiency, solar fraction and power quality of the studied PV project, which can be extended to other home and distributed generation (DG) scales