To gain a deep understanding of maintenance and repair planning, investigate the weak points of the distribution network, and discover unusual events, it is necessary to trace the shutdowns that occurred in the network. Many incidents happened due to the failure of thermal equipment in schools. On the other hand, the most important task of electricity distribution companies is to provide reliable and stable electricity, which minimal blackouts and standard voltage should accompany. This research uses seasonal time series and artificial neural network approaches to provide models to predict the failure rate of one of the equipment used in two areas covered by the greater Tehran electricity distribution company. These data were extracted weekly from April 2019 to March 2021 from the ENOX incident registration software. For this purpose, after pre-processing the data, the appropriate final model was presented with the help of Minitab and MATLAB software. Also, average air temperature, rainfall, and wind speed were selected as input variables for the neural network. The mean square error has been used to evaluate the proposed models’ error rate. The results show that the time series models performed better than the multi-layer perceptron neural network in predicting the failure rate of the target equipment and can be used to predict future periods.

In light of the metaverse’s vast expansion, it’s a crucial intellectual platform that’s transforming the video game industry and spurring creative innovation and technological advancement. Considering the distinctive niche that Taiwan occupies within the realm of the video game industry, this study uses a total of 11 video game companies in Taiwan as samples. The study spans a period of 16 years, from 2007 to 2022, and utilizes the random effect regression model for analysis. The study results illustrate that intellectual capital efficiency exerts varying contributions to the creation of value across different corporate value indicators within the video game industry. Among the factors, HCE, SCE, and CEE demonstrate the highest explanatory power for ROE, reaching up to 82.23%. Following this, they account for 73.57% of the variance in market share, but only a meager 13.67% for Tobin’s Q. This study is the empirical evidence that different methods of measuring intellectual capital and various definitions of value creation in an industry may lead to divergent results and managerial implications in intellectual capital research. Hence, it is worthwhile for subsequent studies to continue clarifying and delving deeper into these aspects.

After the pandemic (COVID-19), there is a dire need to gain a competitive advantage for tourism organizations which can be accomplished by implementing new technologies to facilitate sustainable healthier services. Given that, the study aims to shed light on the importance of digital leadership to improve sustainable business performance considering the parallel mediation of digital technology and digital technology support in the tourism sector of Pakistan. The sample population consists of technology-based tourism organizations in Pakistan. Cochran’s formula was chosen for sampling, in which 37 organizations with 792 employees were selected for data through a random sampling technique. The collected data were analyzed through structural equation modeling, and findings reveal that digital leadership positively influences sustainable business performance. Furthermore, the mediating role of technological leadership support and digital technologies partially mediates the association between digital leadership and sustainable performance.

The rapid progress of information technology has made public online participation in policy formulation an inevitable product of modern government reshaping and reconstruction. However, compared with developed countries, citizens’ online participation in policy formulation in China started relatively late. Thus, in order to explore an effective and efficient method for Chinese citizens’ participation in policy formulation, this research made a brief review of the experiences from the typical developed country of United States of America at first, followed by some other developed countries such as Singapore, South Korea, and Japan in Asia with similar situations. Still, combined with the current situation of the China itself into consideration, this research further proposes targeted recommendations. It is expected that the findings in this research could provide some references for the Chinese government to form more effective and efficient theoretical frameworks targeted at the future development trends of the Chinese society and accordingly, to improve the construction of democracy in China.

To save patients’ lives, it is important to go for an early diagnosis of intracranial hemorrhage (ICH). For diagnosing ICH, the widely used method is non-contrast computed tomography (NCCT). It has fast acquisition and availability in medical emergency facilities. To predict hematoma progression and mortality, it is important to estimate the volume of intracranial hemorrhage. Radiologists can manually delineate the ICH region to estimate the hematoma volume. This process takes time and undergoes inter-rater variability. In this research paper, we develop and discuss a fine segmentation model and a coarse model for intracranial hemorrhage segmentations. Basically, two different models are discussed for intracranial hemorrhage segmentation. We trained a 2DDensNet in the first model for coarse segmentation and cascaded the coarse segmentation mask output in the fine segmentation model along with input training samples. A nnUNet model is trained in the second fine stage and will use the segmentation labels of the coarse model with true labels for intracranial hemorrhage segmentation. An optimal performance for intracranial hemorrhage segmentation solution is obtained.