مقدمه: خستگی ذهنی عبارت است از خسته شدن جسم یا روح که می تواند بر اثر استرس، کار زیاد، مصرف زیاد دارو و یا بیماری جسمی یا روحی ایجاد شود. خستگی از عوامل کاهنده بهره وری و نیز بروز حوادث است. بنابراین، اندازه گیری آن اهمیت دارد. این مطالعه با هدف تعیین میزان همخوانی اندازه گیری خستگی ذهنی بر اساس نتایج مقیاس خوداظهاری VAS و دستگاه فلیکرفیوژن انجام گرفته است.مواد و روش ها: یک مطالعه مقطعی روی 30 نفر از دانشجویان انجام شد. پس از آنکه شرکت کنندگان وظایف فکری را (حل تمرینات ریاضی و پاسخ دهی به پرسشنامه MMPI انجام دادند، خستگی ذهنی آنها با استفاده از مقیاس خوداظهاری VAS و دستگاه فلیکرفیوژن اندازه گیری و نتایج آن به وسیله آزمون تی زوجی و ضریب همبستگی اسپیرمن (سطح معنی داری کمتر از0.05 ) در نرم افزار SPSS V11.5 آنالیز شد.یافته ها: نتایج اندازه گیری با هر دو روش مقیاس خوداظهاری VAS و دستگاه فلیکرفیوژن نشان داد، خستگی ذهنی پس از انجام هر یک از وظایف فکری نام برده افزایش معنی دار می یابد. اما نتایج دو روش با یکدیگر همخوانی ندارند.نتیجه گیری: با توجه به اینکه مقیاس خوداظهاری VAS یک روش ذهنی (سابجکتیو) است، به نظر می رسد عدم همخوانی دو روش، بیشتر مربوط به ناکارآمدی این مقیاس باشد. بنابراین پیشنهاد می شود بررسی مجدد با روش های دقیق تر مانند الکتروآنسفالوگرام صورت گیرد.

Automatic topic detection seems unavoidable in social media analysis due to big text data which their users generate. Clustering-based methods are one of the most important and up-to-date categories in topic detection. The goal of this research is to have a wide study on this category. Therefore, this paper aims to study the main components of clustering-based-topic-detection, which are embedding methods, distance metrics, and clustering algorithms. Transfer learning and consequently pretrained language models and word embeddings have been considered in recent years. Regarding the importance of embedding methods, the efficiency of five new embedding methods, from earlier to recent ones, are compared in this paper. To conduct our study, two commonly used distance metrics, in addition to five important clustering algorithms in the field of topic detection, are implemented by the authors. As COVID-19 has turned into a hot trending topic on social networks in recent years, a dataset including one-month tweets collected with COVID-19-related hashtags is used for this study. More than 7500 experiments are performed to determine tunable parameters. Then all combinations of embedding methods, distance metrics and clustering algorithms (50 combinations) are evaluated using Silhouette metric. Results show that T5 strongly outperforms other embedding methods, cosine distance is weakly better than other distance metrics, and DBSCAN is superior to other clustering algorithms.

Evaluating the cutting rate (CR) of stones is important in the cost estimation and the planning of the stone processing plants. This research used regression models to estimate the stones’ CR based on their physico-mechanical characteristics. Stone processing factories in Mahallat City (Markazi province, Iran) were visited, and the CR of diamond circular saws was recorded on six different travertine stones. Next, the stone block samples were collected from the quarries for laboratory tests. Stones’ porosity (n), uniaxial compressive strength (UCS), and Schmidt hammer hardness (SH) were determined in the laboratory as their physico-mechanical characteristics. Correlation relationships of CR with physico-mechanical characteristics were evaluated using simple and multiple regression analyses, and estimator models were developed. Results showed that multiple regression models are more reliable than simple regression for estimating the stones’ CR. The validity of the developed multiple regression models was verified with the published data of one researcher. The findings indicated that these models are accurate enough for estimating the CR of stones. Consequently, the multiple regression models provide practical advantages for estimating the CR and save time and cost during the planning and design of the stone processing factories.