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Providing quality of service (QoS) in wireless communication networks has become an important consideration for supporting variety of applications. IEEE 802.16 based WiMAX is the most promising technology for broadband wireless access with best QoS features for tripe play (voice, video and data) service users. Unlike wired networks, QoS support is difficult in wireless networks due to variable and unpredictable nature of wireless channels. In transmission of voice and video main issue involves allocation of available resources among the users to meet QoS criteria such as delay, jitter and throughput requirements to maximize goodput, to minimize power consumption while keeping feasible algorithm flexibility and ensuring system scalability. WiMAX assures guaranteed QoS by including several mechanisms at the MAC layer such as admission control and scheduling. Packet scheduling is a process of resolving contention for bandwidth which determines allocation of bandwidth among users and their transmission order. Various approaches for classification of scheduling algorithms in WiMAX have appeared in literature as homogeneous, hybrid and opportunistic scheduling algorithms. The paper consolidates the parameters and performance metrics that need to be considered in developing a scheduler. The paper surveys recently proposed scheduling algorithms, their shortcomings, assumptions, suitability and improvement issues associated with these uplink scheduling algorithms.

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Parrondo's gave a paradox, where two losing games together yields winning game when played alternatively, called as Parrondo game (a switching strategy). Rani first time studied superior iterates (i.e. superior orbit) in discrete dynamics and proves that it increases the solution domain of dynamical system. In this paper, we have studied and analyzed the stability of modified and extended logistic map for describing the dynamics of multi-scaled population (sexual reproduction) in superior orbit. We have shown that the stability of above maps is extended, and also we found some examples of "chaos 1 + chaos 2 = order" or "undesirable 1 + undesirable 2 = desirable" dynamic behavior in superior orbit.

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Pulmonary fibrosis is a severe chronic lung disease that causes irreversible scarring in the tissues of the lungs, which results in the loss of lung capacity. The Forced Vital Capacity (FVC) of the patient is an interesting measure to investigate this disease to have the prognosis of the disease. This paper proposes a deep learning-based FVC-Net architecture to predict the progression of the disease from the patient’s computed tomography (CT) scan and the patient’s metadata. The input to the model combines the image score generated based on the degree of honeycombing for a patient identified based on segmented lung images and the metadata. This input is then fed to a 3-layer net to obtain the final output. The performance of the proposed FVC-Net model is compared with various contemporary state-of-the-art deep learning-based models, which are available on a cohort from the pulmonary fibrosis progression dataset. The model showcased significant improvement in the performance over other models for modified Laplace Log-Likelihood (−6.64). Finally, the paper concludes with some prospects to be explored in the proposed study.