A Fair and Efficient Packet Scheduling Scheme for IEEE 802.16 Broadband Wireless Access Systems

This paper proposes a fair and efficient QoS scheduling scheme for IEEE 802.16 BWA systems that satisfies both throughput and delay guarantee to various real and non-real time applications. The proposed QoS scheduling scheme is compared with an existing QoS scheduling scheme proposed in literature in recent past. Simulation results show that the proposed scheduling scheme can provide a tight QoS guarantee in terms of delay, delay violation rate and throughput for all types of traffic as defined in the WiMAX standard, thereby maintaining the fairness and helps to eliminate starvation of lower priority class services. Bandwidth utilization of the system and fairness index of the resources are also encountered to validate the QoS provided by our proposed scheduling scheme

Interaction of Glutathione S-Transferase with Hypericin: A Photophysical Study

The photophysics of hypericin have been studied in its complex with two different isoforms, A1-1 and P1-1, of the protein glutathione S-transferase (GST). One molecule of hypericin binds to each of the two GST subunits. Comparisons are made with our previous results for the hypericin/human serum albumin complex (Photochem. Photobiol. 1999, 69, 633−645). Hypericin binds with high affinity to the GSTs:  0.65 μM for the A1-1 isoform and 0.51 μM for the P1-1 isoform (Biochemistry 2004, 43, 12761−12769). The photophysics and activity of hypericin are strongly modulated by the binding protein. Intramolecular hydrogen-atom transfer is suppressed in both cases. Most importantly, while there is significant singlet oxygen generation from hypericin bound to GST A1-1, binding to GST P1-1 suppresses singlet oxygen generation to almost negligible levels. The data are rationalized in terms of a simple model in which the hypericin photophysics depends entirely upon the decay of the triplet state by two competing processes, quenching by oxygen to yield singlet oxygen and ionization, the latter of these two are proposed to be modulated by A1-1 and P1-1

Enhancement Pattern Mapping for Early Detection of Hepatocellular Carcinoma in Patients with Cirrhosis

BACKGROUND AND AIMS: Limited methods exist to accurately characterize the risk of malignant progression of liver lesions. Enhancement pattern mapping (EPM) measures voxel-based root mean square deviation (RMSD) of parenchyma and the contrast-to-noise (CNR) ratio enhances in malignant lesions. This study investigates the utilization of EPM to differentiate between HCC versus cirrhotic parenchyma with and without benign lesions. METHODS: Patients with cirrhosis undergoing MRI surveillance were studied prospectively. Cases (n=48) were defined as patients with LI-RADS 3 and 4 lesions who developed HCC during surveillance. Controls (n=99) were patients with and without LI-RADS 3 and 4 lesions who did not develop HCC. Manual and automated EPM signals of liver parenchyma between cases and controls were quantitatively validated on an independent patient set using cross validation with manual methods avoiding parenchyma with artifacts or blood vessels. RESULTS: With manual EPM, RMSD of 0.37 was identified as a cutoff for distinguishing lesions that progress to HCC from background parenchyma with and without lesions on pre-diagnostic scans (median time interval 6.8 months) with an area under the curve (AUC) of 0.83 (CI: 0.73-0.94) and a sensitivity, specificity, and accuracy of 0.65, 0.97, and 0.89, respectively. At the time of diagnostic scans, a sensitivity, specificity, and accuracy of 0.79, 0.93, and 0.88 were achieved with manual EPM with an AUC of 0.89 (CI: 0.82-0.96). EPM RMSD signals of background parenchyma that did not progress to HCC in cases and controls were similar (case EPM: 0.22 ± 0.08, control EPM: 0.22 ± 0.09, p=0.8). Automated EPM produced similar quantitative results and performance. CONCLUSION: With manual EPM, a cutoff of 0.37 identifies quantifiable differences between HCC cases and controls approximately six months prior to diagnosis of HCC with an accuracy of 89%

Functional Analysis of Generalized Linear Models Under Nonlinear Constraints With Artificial Intelligence and Machine Learning Applications to the Sciences

This thesis presents multiple fundamental mathematical contributions to Generalized Linear Models (GLMs) ubiquitous to the sciences. The methodologies considered are shown to overcome biased estimates for parameters of interest in the sciences through new mathematical results and their applications in both nonparametric and parametric settings. The results are shown to be uniformly better in comparison to existing widely used methods in the sciences. In extensive simulation studies the methodologies outperform existing Artificial Intelligence (AI) and Machine Learning (ML) methods in the sciences for all around better Model fits, Inference and Prediction (MIP) results without losing interpretability of the parameter estimates. This is because the mathematical construction and their accompanying mathematical foundations ensure that the estimation procedure strongly converges to the parameters of interest. In the first application, I present a parametric version of the methodology (© Elsevier and Journal of Informetrics) titled “Functional analysis of generalized linear models under non-linear constraints with applications to identifying highly-cited papers.” In the second application, I extend this methodology in an entirely nonparametric setting which gives equivalent results to the parametric formulation under various circumstances, but may outperform it as well in others, especially if the underlying Data Generating Process (DGP) is asymmetric. Furthermore, I show that the categorical data models on which the methodologies are applied can be extended to any GLM, continuous or otherwise, while maintaining model interpretability and convergence results. In addition, I present a new prediction performance diagnostic statistic, called Adjusted ROC Statistic (ARS), which allows us to compare whether the prediction performance of various models fitted are statistically different. The nonparametric methodology is then further extended to give a new formulation of the binary regression framework widely used in the sciences. Through extensive simulation studies I show that this version of the methodology is more robust than the previous versions discussed. This general framework is then extended to various AI and ML applications widely used in the sciences. The entirety of the work also has some important consequences for our continued discussion on “statistical significance” vs. “scientific significance.” This includes the need for us to consider the strength of convergence of our methodology in addition to the subtle connections between Topological Spaces and Measure Spaces. Each of which are crucial to ensure almost sure convergence of the parameter estimates through the estimation algorithm presented termed, Latent Adaptive Hierarchical EM Like algorithm or LAHEML. As such, the results present a significantly expanded and more accurate toolset for Mathematicians, Statisticians, Scientists and Decision Makers at all levels for better model fit, inference and prediction outcomes