No abstract

Due to rapid growth in Virtual Reality (VR) technology, the industry of VR is expected to grow around $26.89 billion by 2022. However, with its extensive growth and immersive inclusion in human life, health-related issues are reported including, but not limited to nauseated feeling, vomiting, dizziness and cold sweats. These issues introduce a well-known side effect termed as motion sickness in VR users. Consequently, motion sickness limits the VR community in the full adaptation of this immersive technology. Since there is no lack of literature investigating motion sickness caused by VR, yet researches on the effect of VR on human's physiology is still in its infancy. This study presents novel findings, by comparing different factors such as gender, motion sickness experience, 3D games experience and VR experience. Furthermore, it reports the impact of concerning factors in a within-subjects design (46 participants participated in an experiment) under different virtual environment genres. The key findings of this article report that there is a significant difference in the amount of motion sickness when shifting from pleasant to the horror genre of the environment and having a strong dependence on gender. Moreover, the type of virtual environment is an essential factor that has a notable effect on the user's blood pressure, blood sugar and heart rate. However, past experiences with motion sickness and 3D games show no significant impact on the user's level of motion sickness.

Global software development (GSD) practice has been increasingly emerging in the recent few decades in the field of business and software industry. On the one hand, many software development organizations get the benefits of GSD, including but not limited to reduced cost, cheap labor, round the clock working and skilled professionals. On the other hand, these organizations have to face several challenges because of GSD. These challenges pose serious threats to the stability of the GSD projects. Communication between distributed team members is one of the most crucial challenges in GSD. Therefore, the current study aims to identify the communication risk in GSD and also evaluate the impact of these communication risks in GSD environment. A Systematic Literature Review (SLR) has been performed to identify all the communication-related issues in GSD. After that, a conceptual framework has been proposed for evaluating the impact of those issues on communication risk in GSD. An empirical evaluation has been performed on data collected from the software organizations of Pakistan working in GSD based environment. The finding of our study demonstrates that geographical distance, socio-temporal distance, socio-culture distance, team member's attitude, team issues, organizational & architectural issue and customer issue have a significant direct impact on communication risk in GSD. The study also shows that there is a significant correlation between findings of SLR and empirical investigation (r = 0.460, P = 0.005). Further, we believe that the results of our research can help to tackle the issues related to communication in GSD. Therefore, it will help to improve the performance of the development activities of GSD organizations.

Advocates of software engineering and software project management stated in the literature that creeping of scope is one of the most common causes for the failure of software projects. Also, advocates believed that it could occur in almost every software project, which leads to compromise in quality, delayed schedules, increase cost and decreased customer satisfaction. However, the lack of empirical evidence demands a comprehensive investigation to identify the factors of scope creep and to propose a conceptual framework to empirically evaluate the impact of scope creep on software project success. To determine the scope creep factors in this study, two exploratory methods, i.e. a Systematic Literature Review (SLR) and interview from experts are performed. Following the analysis of these methods, a conceptual framework is proposed. To empirically evaluate the proposed conceptual framework, data is collected through a survey method. Next, the collected data is analyzed through Partial Least Squares' Structural Equation Modelling (PLS-SEM). From the results, it is evident that the identified factors of scope creep are negatively associated with software project success. The results of empirical evaluation also second the findings of SLR. The outcome of the study may help the practitioners to understand the dynamics of factors, which undermine scope creep in software SMEs and to assist them in the development of effective control and mitigation strategies, therefore, to increase the project success rate. INDEX TERMS Scope management, scope creep, requirement creep, software project success, partial least squares structural equation modeling.

Glioblastoma (GBM) is the most high-risk and grievous tumour in the brain that causes the death of more than 50% of the patients within one to 2 years after diagnosis. Accurate detection and prognosis of this disease are critical to provide essential guidelines for treatment planning. This study proposed using a deep learning-based network for the GBM segmentation and radiomic features for the patient's overall survival (OS) time prediction. The segmentation model used in this study was a modified U-Net-based deep 3D multi-level dilated convolutional neural network. It uses multiple kernels of altered sizes to capture contextual information at different levels. The proposed scheme for OS time prediction overcomes the problem of information loss caused by the derivation of features in a single view due to the variation in the neighbouring pixels of the tumorous region. The selected features were based on texture, shape, and volume. These features were computed from the segmented components of tumour in axial, coronal, and sagittal views of magnetic resonance imaging slices. The proposed models were trained and evaluated on the BraTS 2019 dataset. Experimental results of OS time prediction on the validation data have showed an accuracy of 48.3%, with the mean squared error of 92 599.598. On the validation data, the segmentation model achieved a mean dice similarity coefficient of 0.75, 0.89, and 0.80 for enhancing tumour, whole tumour, and tumour core, respectively. Future work is warranted to improve the overall performance of OS time prediction based on the findings in this study.