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The cross‐cultural universality of people's pursuit of positive self‐esteem is frequently disputed. Most research in this area has contrasted cultures of dignity (Western) and face cultures (East Asian), but less attention has been given to other cultures' views of self‐esteem. In the present work, we examined Pakistan as uniquely influenced by honour culture and South Asian argumentation culture principles, and we contrasted it with Canada (a Western culture of dignity). Across two studies, Pakistanis had less positive self‐esteem discrepancies (i.e., Pakistanis had minimal or no desire for higher self‐esteem) compared to Canadians (who desired much higher self‐esteem than they actually had). Pakistanis also believed less in the agentic benefits of high self‐esteem but more in the communal benefits of high self‐esteem than did Canadians. Differences in each cultures' beliefs about self‐esteem's causal powers partially accounted for the differences in self‐esteem discrepancies. These findings suggest unique conceptualizations of the value of self‐esteem in distinct cultures. [ABSTRACT FROM AUTHOR]

Uncertainty modeling of renewable energy sources, load demand, electricity price, etc. create a high volume of data in smart grids. Accordingly, in this article, a precise forecasting method based on a deep learning concept with microclustering (MC) task is presented. The MC method is structured based on hybrid unsupervised and supervised clustering tasks by K-means and Gaussian support vector machine, respectively. In the proposed method, the input data sequence is clustered by the MC task, and then the forecasting process is employed. By applying the MC, input data in each hour are categorized into different groups, and a distinctive forecasting unit is allocated to each one. In this way, more clusters and forecasting networks are earmarked for the hours with higher fluctuation rates. The bi-directional long short-term memory (B-LSTM), which is one of the newest recurrent artificial neural networks, is proposed as the forecasting unit. The B-LSTM has bidirectional memory—feedforward and feedback loops—that helps us to investigate both previous and future hidden layers data. The optimal number of clusters in each hour is determined based on the Davies–Bouldin index. To evaluate the performance of the proposed method, in this study, three forecasting tasks including the wind speed, load demand, and electricity price are studied in different periods using the Ontario province, Canada, data set. The results are compared with other benchmarking methods to verify the robustness and effectiveness of the proposed method. In fact, the proposed method, which is equipped with the MC technique and B-LSTM networks, significantly promotes the forecasting results, especially in spike points. [ABSTRACT FROM AUTHOR]

Background: Trainees in Otolaryngology–Head and Neck Surgery must gain proficiency in a variety of challenging temporal bone surgical techniques. Traditional teaching has relied on the use of cadavers; however, this method is resource-intensive and does not allow for repeated practice. Virtual reality surgical training is a growing field that is increasingly being adopted in Otolaryngology. CardinalSim is a virtual reality temporal bone surgical simulator that offers a high-quality, inexpensive adjunct to traditional teaching methods. The objective of this study was to establish the face and content validity of CardinalSim through a national study. Methods: Otolaryngologists and resident trainees from across Canada were recruited to evaluate CardinalSim. Ethics approval and informed consent was obtained. A face and content validity questionnaire with questions categorized into 13 domains was distributed to participants following simulator use. Descriptive statistics were used to describe questionnaire results, and either Chi-square or Fishers exact tests were used to compare responses between junior residents, senior residents, and practising surgeons. Results: Sixty-two participants from thirteen different Otolaryngology–Head and Neck Surgery programs were included in the study (32 practicing surgeons; 30 resident trainees). Face validity was achieved for 5 out of 7 domains, while content validity was achieved for 5 out of 6 domains. Significant differences between groups (p-value of < 0.05) were found for one face validity domain (realistic ergonomics, p = 0.002) and two content validity domains (teaching drilling technique, p = 0.011 and overall teaching utility, p = 0.006). The assessment scores, global rating scores, and overall attitudes towards CardinalSim, were universally positive. Open-ended questions identified limitations of the simulator. Conclusion: CardinalSim met acceptable criteria for face and content validity. This temporal bone virtual reality surgical simulation platform may enhance surgical training and be suitable for patient-specific surgical rehearsal for practicing Otolaryngologists. [ABSTRACT FROM AUTHOR]

The article presents the author's views regarding the sustainability of the system of Canada for treating patients needing surgery. The author implies that it has been suggested that the number of surgeons be increased or that nonphysician care providers be recruited to take on some responsibilities so that the efficiency can be improved.