attendance software selection problem

Keeping track of employees' time and attendance is difficult and time-consuming task for the companies. Today many companies are performing the digital time and attendance systems that automatically track and process the data to improve their operations and save money. There are many alternatives for the time and attendance systems in the market and appropriate selection among them is not easy in the presence of multiple, usually conflicting, criteria. So this selection may be considered as a Multi Criteria Decision Making (MCDM) problem. In this paper, the new combined decision making approach based on Criteria Importance Through Inter criteria Correlation (CRITIC) and Weighted Aggregated Sum Product Assessment (WASPAS) methods is used for the time and attendance software selection problem of the private hospital. The weights of the criteria are determined by CRITIC method and the alternatives are ranked by WASPAS method for finding the most suitable alternative. The novelty of this paper to the literature is to combine CRITIC and WASPAS methods for the first time.C1 [Tus, Ayseguel; Adali, Esra Aytac] Pamukkale Univ, Dept Business Adm, TR-20070 Denizli, Turkey

Identifying canonical and replicable multi‐scale intrinsic connectivity networks in 100k+ resting‐state fMRI datasets

Despite the known benefits of data-driven approaches, the lack of approaches for identifying functional neuroimaging patterns that capture both individual variations and inter-subject correspondence limits the clinical utility of rsfMRI and its application to single-subject analyses. Here, using rsfMRI data from over 100k individuals across private and public datasets, we identify replicable multi-spatial-scale canonical intrinsic connectivity network (ICN) templates via the use of multi-model-order independent component analysis (ICA). We also study the feasibility of estimating subject-specific ICNs via spatially constrained ICA. The results show that the subject-level ICN estimations vary as a function of the ICN itself, the data length, and the spatial resolution. In general, large-scale ICNs require less data to achieve specific levels of (within- and between-subject) spatial similarity with their templates. Importantly, increasing data length can reduce an ICN's subject-level specificity, suggesting longer scans may not always be desirable. We also find a positive linear relationship between data length and spatial smoothness (possibly due to averaging over intrinsic dynamics), suggesting studies examining optimized data length should consider spatial smoothness. Finally, consistency in spatial similarity between ICNs estimated using the full data and subsets across different data lengths suggests lower within-subject spatial similarity in shorter data is not wholly defined by lower reliability in ICN estimates, but may be an indication of meaningful brain dynamics which average out as data length increases