The purpose of this research is to investigate the moderating role of perceived organizational support in the relationship between emotional intelligence and job performance. Data were gathered through self-administered questionnaire from a sample of 316 employees working in banks located in Islamabad. As hypothesized, job performance significantly associated with emotional intelligence and moderating effect of perceived organizational support was also substantiated. The data were analysed statistically using IBM SPSS Statistics 20 to find out correlation and regression analysis between study variables, reliability of research instrument, strength of relationship between independent and dependent variables, moderating effect perceived organizational support in the relationship between emotional intelligence and job performance was also substantiated. Findings suggest that emotional intelligence have positive impact on employee's job performance, and perceived organizational support moderate the relation between emotional intelligence and job performance such that the relationship between emotional intelligence and job performance more stronger/positive when perceived organizational support is high. Detailed data analysis, discussion and conclusion with limitations and future research directions are also discussed.

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Early detection of lung cancer is an effective way to improve the survival rate of patients. It is a critical step to have accurate detection of lung nodules in computed tomography (CT) images for the diagnosis of lung cancer. However, due to the heterogeneity of the lung nodules and the complexity of the surrounding environment, it is a challenge to develop a robust nodule detection method. Numerous efforts have been made to develop an efficient Computer-aided detection (CADe) systems, albeit none compliance with the routine workflow of radiologists which limits the adaptability of such CADe system. To overcome this deficiency, in this study we propose a lung nodule detection scheme which fully incorporates the clinic workflow of radiologists. Particularly, we exploit Bi-Directional Maximum intensity projection (MIP) images of various thicknesses (i.e., 3, 5 and 10mm) along with 3D patch of CT scan, consisting of 10 adjacent slices to feed into self distillation based Multi-Encoders Network (MEDS-Net). The propose architecture first condense 3D patch input to three channels by using dense block which consists of dense units which effectively examines the nodule presence from 2D axial slices. This condensed information, along with the forward and backward MIP images is fed to three different encoders to learn the most meaningful representation which is forwarded into decoded block at various levels. At decoder block, we employ self distillation mechanism by connecting the distillation block which contains five lung nodule detectors. It helps to expedite the convergence and improves the learning ability of proposed architecture. Finally, the propose scheme reduces the false positives by complementing the main detector with auxiliary detectors. The proposed scheme has been rigorously evaluated on 888 scans of LUNA16 dataset and obtained a CPM score of 93.6%. The results demonstrate that incorporating of bi-direction MIP images enables MEDS-Net to effectively distinguish nodules from surroundings which helps to achieve the sensitivity of . false positives per scans with the sensitivity of 91.5% and 92.8% with the false positive rate of 0.25 and 0.5 per scan, respectively.

We highlight two mechanisms of limited attention for expert information intermediaries, i.e., analysts, and the effects of such limited attention on the market price discovery process. We approach analysts' limited attention from the perspective of day-to-day arrival of information and processing of tasks. We examine the attention-limiting role of competing tasks (number of earnings announcements and forecasts for portfolio firms) and distracting events (number of earnings announcements for non-portfolio firms) in analysts' forecast accuracy and the effects of such, on the subsequent price discovery process. Our results show that competing tasks worsen analysts' forecast accuracy, and competing task induced limited attention delays the market price adjustment process. On the other hand, distracting events can improve analysts' forecast accuracy and accelerate market price adjustments when such events relate to analysts' portfolio firms through industry memberships.

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