New Insights on Avoiding the Causes of Projects Delays: A Framework

In this paper, we study and analyze the reasons behind the delay of some projects. Our aim is shed light on how one reduces the effect of the different obstacles and improves achievement either in quality or time. Different challenges play the main reasons in the stalled project including planning, designing, construction, and weak leaders or poor administrative decisions. We focus on developing a framework that helps in improving the management of the projects and finishing the projects on time. This includes developing a contingency plan and coordination mechanism between internal and external stakeholders. Following the steps of the methodology will help project managers to avoid project delay and solve the problems of the staled ones. Finally, we represent some managerial insights and recommendations that related parties should follow with the projects

Mediastinal plasmacytoma with multiple myeloma presenting as a diagnostic dilemma

Plasmacytoma are extramedullary accumulations of plasma cells. Most extramedullary Plasmacytomas are associated with the upper respiratory tract. The mediastinum is rarely involved. We report a rare case of mediastinal plasmacytoma with multiple myeloma. The patient is 66 year old woman presented with bone pains and mediastinal mass on CT scan and MRI. The preliminary diagnosis of occult lung cancer with mediastinal involvement, and widespread skeletal metastasis was made, although lymphoproliferative disorder along with germ cell tumor was also kept in differentials

Modeling Camera Effects to Improve Visual Learning from Synthetic Data

Recent work has focused on generating synthetic imagery to increase the size and variability of training data for learning visual tasks in urban scenes. This includes increasing the occurrence of occlusions or varying environmental and weather effects. However, few have addressed modeling variation in the sensor domain. Sensor effects can degrade real images, limiting generalizability of network performance on visual tasks trained on synthetic data and tested in real environments. This paper proposes an efficient, automatic, physically-based augmentation pipeline to vary sensor effects --chromatic aberration, blur, exposure, noise, and color cast-- for synthetic imagery. In particular, this paper illustrates that augmenting synthetic training datasets with the proposed pipeline reduces the domain gap between synthetic and real domains for the task of object detection in urban driving scenes

Predicting an observer's task using multi-fixation pattern analysis

Since Yarbus's seminal work in 1965, vision scientists have argued that people's eye movement patterns differ depending upon their task. This suggests that we may be able to infer a person's task (or mental state) from their eye movements alone. Recently, this was attempted by Greene et al. [2012] in a Yarbus-like replication study; however, they were unable to successfully predict the task given to their observer. We reanalyze their data, and show that by using more powerful algorithms it is possible to predict the observer's task. We also used our algorithms to infer the image being viewed by an observer and their identity. More generally, we show how off-the-shelf algorithms from machine learning can be used to make inferences from an observer's eye movements, using an approach we call Multi-Fixation Pattern Analysis (MFPA)

Crystal structures of pure 3-(4-bromo-2-chlorophenyl)-1-(pyridin-4-yl) benzo [4, 5] imidazo [1, 2-d][1, 2, 4] triazin-4 (3H)-one and contaminated with 3-(4-bromophenyl)-1 …

The side product of the cyclocondensation reaction between ethyl benzimidazole-2-carboxylate and the nitrile imine of the corresponding hydrazonyl chloride, C20H11BrClN5O, crystallized in two crystal forms. Form (1) is a co-crystal of the target compound (without any chlorine substituent) and a side product containing a Cl atom in position 2 of the bromophenyl group, C20H12BrN5O·0.143C20H11BrClN5O. (2) contains the pure side product. The slightly different conformation of the ring systems leads to a different packing of (1) and (2), but both crystal structures are dominated by π–π interactions

Co3O4 Nanocrystals on Graphene as a Synergistic Catalyst for Oxygen Reduction Reaction

Catalysts for oxygen reduction and evolution reactions are at the heart of key renewable energy technologies including fuel cells and water splitting. Despite tremendous efforts, developing oxygen electrode catalysts with high activity at low costs remains a grand challenge. Here, we report a hybrid material of Co3O4 nanocrystals grown on reduced graphene oxide (GO) as a high-performance bi-functional catalyst for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). While Co3O4 or graphene oxide alone has little catalytic activity, their hybrid exhibits an unexpected, surprisingly high ORR activity that is further enhanced by nitrogen-doping of graphene. The Co3O4/N-doped graphene hybrid exhibits similar catalytic activity but superior stability to Pt in alkaline solutions. The same hybrid is also highly active for OER, making it a high performance non-precious metal based bi-catalyst for both ORR and OER. The unusual catalytic activity arises from synergetic chemical coupling effects between Co3O4 and graphene.Comment: published in Nature Material

Predicting an observer's task using multi-fixation pattern analysis

Since Yarbus's seminal work in 1965, vision scientists have argued that people's eye movement patterns differ depending upon their task. This suggests that we may be able to infer a person's task (or mental state) from their eye movements alone. Recently, this was attempted by Greene et al. [2012] in a Yarbus-like replication study; however, they were unable to successfully predict the task given to their observer. We reanalyze their data, and show that by using more powerful algorithms it is possible to predict the observer's task. We also used our algorithms to infer the image being viewed by an observer and their identity. More generally, we show how off-the-shelf algorithms from machine learning can be used to make inferences from an observer's eye movements, using an approach we call Multi-Fixation Pattern Analysis (MFPA)