Improving social performance through innovative small green businesses: knowledge sharing and green entrepreneurial intention as antecedents

Small businesses are thought to be largely responsible for environmental pollution despite the fact that businesses of all shapes and sizes contribute to this issue. This research explores how important factors such as knowledge sharing (KS) and green entrepreneurial intention (GEI) might help small businesses in Saudi Arabia develop and implement green innovation (GI). It also seeks to determine whether GI is a mediating variable that explains the connection between GEI, KS, and social performance (SP). Accordingly, an online survey was used to collect responses from 284 small entrepreneurs in Saudi Arabia engaged in various types of business activities. The study used partial least squares structural equation modelling for data analysis and hypothesis testing. The results show that GI considerably influences SP while also having a significant link with both GEI and KS. Further, the study reveals that the relationship between GEI, KS, and SP is mediated by GI. The study offers a plethora of suggestions to various stakeholders generally and to Saudi authorities specifically

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Effects of Ball Milling Time on the Fire Performance Red Mud Intumescent Coating

Considerable efforts have been made by several researchers worldwide to mass utilize bauxite waste (red-mud) in various applications. The examples of the red mud utilization around the world are building/construction materials such as bricks, stabilized blocks and steel coatings . In this study, the surface properties of the red mud treated by varying duration of ball milling process were investigated for their heat shielding and applicability in intumescent coating. Firstly, the red mud were treated with ball milling process to further discourage agglomeration of the particles. The red mud were subsequently mixed with other ingredients such as ammonium polyphate, boric acid, melamine and expandable graphite. The coating were applied on the steel plate and went fire and furnace test. During the fire test, the temperature of steel plate was recorded for 60 minutes at an interval of 1 minute

Effects of Ball Milling Time on the Fire Performance Red Mud Intumescent Coating

Considerable efforts have been made by several researchers worldwide to mass utilize bauxite waste (red-mud) in various applications. The examples of the red mud utilization around the world are building/construction materials such as bricks, stabilized blocks and steel coatings . In this study, the surface properties of the red mud treated by varying duration of ball milling process were investigated for their heat shielding and applicability in intumescent coating. Firstly, the red mud were treated with ball milling process to further discourage agglomeration of the particles. The red mud were subsequently mixed with other ingredients such as ammonium polyphate, boric acid, melamine and expandable graphite. The coating were applied on the steel plate and went fire and furnace test. During the fire test, the temperature of steel plate was recorded for 60 minutes at an interval of 1 minute

Do Uncertainty and Financial Development Influence the FDI Inflow of a Developing Nation? A Time Series ARDL Approach

The study focuses on investigating the long-term and the short-term effect of uncertainty, and financial development on the FDI inflow of Pakistan during the period 2001–2019. To achieve the objective of this study, we obtained the data from World Development Indicators (WDI) and the European policy uncertainty index’s websites. The dependent variable was FDI inflow. Experimental variables of the study are uncertainty and financial development. The stationarity testing revealed that FDI and Economic Policy Uncertainty (EUP) have weak significance and FD has no significance. However, by taking the first difference, all the variables become highly significant. Similarly, it is further indicated that the optimal lag level is four. Additionally, the bound test confirmed that a long-term relationship (co-integration) existed between the variables of the study. The ARDL estimations conclude that uncertainty and financial development have long-run as well as short-run effects on FDI inflow for Pakistan during the period of study. The uncertainty plays a strong part in decreasing the FDI inflow, whereas financial development plays a strong part in enhancing the FDI inflow in Pakistan during the period of study

Do Uncertainty and Financial Development Influence the FDI Inflow of a Developing Nation? A Time Series ARDL Approach

The study focuses on investigating the long-term and the short-term effect of uncertainty, and financial development on the FDI inflow of Pakistan during the period 2001–2019. To achieve the objective of this study, we obtained the data from World Development Indicators (WDI) and the European policy uncertainty index’s websites. The dependent variable was FDI inflow. Experimental variables of the study are uncertainty and financial development. The stationarity testing revealed that FDI and Economic Policy Uncertainty (EUP) have weak significance and FD has no significance. However, by taking the first difference, all the variables become highly significant. Similarly, it is further indicated that the optimal lag level is four. Additionally, the bound test confirmed that a long-term relationship (co-integration) existed between the variables of the study. The ARDL estimations conclude that uncertainty and financial development have long-run as well as short-run effects on FDI inflow for Pakistan during the period of study. The uncertainty plays a strong part in decreasing the FDI inflow, whereas financial development plays a strong part in enhancing the FDI inflow in Pakistan during the period of study

Deep Learning in High Voltage Engineering: A Literature Review

Condition monitoring of high voltage apparatus is of much importance for the maintenance of electric power systems. Whether it is detecting faults or partial discharges that take place in high voltage equipment, or detecting contamination and degradation of outdoor insulators, deep learning which is a branch of machine learning has been extensively investigated. Instead of using hand-crafted manual features as an input for the traditional machine learning algorithms, deep learning algorithms use raw data as the input where the feature extraction stage is integrated in the learning stage, resulting in a more automated process. This is the main advantage of using deep learning instead of traditional machine learning techniques. This paper presents a review of the recent literature on the application of deep learning techniques in monitoring high voltage apparatus such as GIS, transformers, cables, rotating machines, and outdoor insulators