Large deviations sum-queue optimality of a radial sum-rate monotone opportunistic scheduler

A centralized wireless system is considered that is serving a fixed set of users with time varying channel capacities. An opportunistic scheduling rule in this context selects a user (or users) to serve based on the current channel state and user queues. Unless the user traffic is symmetric and/or the underlying capacity region a polymatroid, little is known concerning how performance optimal schedulers should tradeoff "maximizing current service rate" (being opportunistic) versus "balancing unequal queues" (enhancing user-diversity to enable future high service rate opportunities). By contrast with currently proposed opportunistic schedulers, e.g., MaxWeight and Exp Rule, a radial sum-rate monotone (RSM) scheduler de-emphasizes queue-balancing in favor of greedily maximizing the system service rate as the queue-lengths are scaled up linearly. In this paper it is shown that an RSM opportunistic scheduler, p-Log Rule, is not only throughput-optimal, but also maximizes the asymptotic exponential decay rate of the sum-queue distribution for a two-queue system. The result complements existing optimality results for opportunistic scheduling and point to RSM schedulers as a good design choice given the need for robustness in wireless systems with both heterogeneity and high degree of uncertainty.Comment: Revised version. Major changes include addition of details/intermediate steps in various proofs, a summary of technical steps in Table 1, and correction of typos

Configurable data acquisition for cloud-centric IoT

© 2018 ACM. In the era of smart devices, the reliance of intelligence is the abundance of data. This data is heterogeneous in nature and generated by many small-scale sensory devices. With their taskspecific nature, these sensory devices are energy efficient resources with limited computing and storage power. The effective utilization of these devices requires collaborative execution with remote storage and computing power. Internet of Things defines one such cluster formation of these devices which are single-task specific in themselves but achieves higher goals when executed in a collective environment. Data acquisition from these devices is accumulated at scalable resources like cloud where intelligent processes constitute the foundation of smart environments. However, the current implementations of data acquisition for cloud-centric IoT are driven by the APIs of the device manufacturers, resulting in platforms which are less dynamic and configurable for data acquisition. In this paper, we propose our cloud-centric methodology with the focus on configurable data acquisition from IoT. Our methodology decouples communication of data from the strategical usage of the sensory device. Thus, making it more dynamic and applicable for evolutionary smart environments

Evaluation of Gas Potential in Early Cretaceous Shale Lower Indus Basin, Pakistan

The shale gas potential of the early Cretaceous Talhar shale member of the lower Goru Formation in lower Indus basin of Sanghar district, Sindh province, has been evaluated using 2D seismic and well logs data. Graphical and empirical techniques were applied to calculate various geochemical parameters for the evaluation of shale plays. Total Organic Carbon (TOC) is calculated by Passey's (∆LogR) Overlay technique and its values are about 2.44 wt.%. Vitrinite Reflectance (Ro) is estimated by graphical and empirical relation and the value lies between 0.95-1.0 which implies that the Talhar shale member is in peak oil and initial gas generation phase. Log-derived Maturity Index (LMI) is calculated by NPHI, RHOB or density log and uranium logs. The average value is about 0.55 which reflects that the formation is probably in the initial maturation phase. Kerogen Volume (Vk) is estimated by empirical relation using RHOB log as input and it is about 13 wt. % which reflects that the formation has enough potential of oil/gas expulsion. The results are compared and validated with a study of the adjacent field of the same basin. This study reveals that Talhar member of lower Goru Formation could be the future probable potential unconventional reservoir for exploration in lower Indus basin of Pakistan

Polyester Usage in Manufacturing of Electrical and Mechanical Products and Assemblies

This chapter focuses on the processes in which polyester is usually used for the manufacturing of mechanical components and assemblies. Various methods of manufacturing these products are mentioned in this chapter. These methods include wet layup method, filament winding, pultrusion, vacuum bagging and autoclave curing, resin transfer molding (RTM) and vacuum-assisted resin transfer molding (VARTM). Various production levels and properties can be achieved by polyester resin using abovementioned processes. Each process has its own benefits and disadvantages, which are discussed in this chapter. Furthermore, the use of polyester in making electrical insulation is also discussed in the chapter. Advantages and disadvantages of each impregnation technique are also explained

Left atrial volumes and associated stroke subtypes.

Background: Cardio embolism and cerebrovascular atherosclerosis are two major mechanisms of stroke. Studies investigating associations between advanced echocardiographic parameters and stroke mechanisms are limited. Methods: This study is a standardized review of 633 patients admitted to the stroke service of a tertiary care hospital following a standardized stroke investigation and management pathway. Stroke subtypes were characterized using the Causative Classification System, using the hospitals online radiologic archival system with CCS certified stroke investigators. Patients with two mechanisms were excluded. Results: Patients with cardioembolic stroke had a higher proportion of atrial fibrillation (p \u3c 0.001), acute myocardial infarction (p \u3c 0.001) and ischemic heart disease (p \u3c 0.001). On electrocardiogram (ECG) and transthoracic Echo (TTE), patients with cardioembolic stroke had a greater atrial fibrillation (p \u3c .00), left ventricular thrombus (p \u3c .00), left ventricular ejection fraction \u3c30% (p \u3e\u3c .00) and global hypokinesia (p \u3c .00) Patients with cardioembolic stroke had higher mean left atrial volume indices (LAVi) (p \u3c 0.001), mean left ventricular mass indices (LVMi) (p \u3c 0.05) and mean left atrial diameters (LAD) (p \u3c 0.05). At LAVi of 29–33 ml/m2 , the risk of atherothrombotic stroke increased. The risk of cardioembolic stroke increased with LAVi of 34 ml/m2 and above. Conclusion: Left atrial volume indices may be linked to specific stroke phenotype. At mild increases in left atrial dimensions, the risks of atherosclerotic stroke are high, and probably reflect hypertension as the unifying mechanism. Further increases in left atrial dimensions shifts the risk towards cardioembolic stroke

Biotechnology: a powerful tool for the removal of cadmium from aquatic systems

The prime objective of the present research work was to evaluate the efficiency of bio-machine for the removal of Cadmium (Cd) from aquatic systems. Aspergillus niger fungus was used as bio-machine to remove Cd from aquatic systems. Twenty three different strains (IIB-1 to IIB-23) were isolated from industrial effluents and the Langmuir and Freundlich models were applied to the best Cadmium removal strain IIB-23 in order to obtain the adsorption parameters. Different parameters such as pH, temperatur e, contact time, initial metal concentratio, and biomass dosage on the biosorption of Cd were studied. The percent removal of Cd initially increased with an increase in pH ranging from 5.5-6.5 and then decreased by increasing pH from 7.0-7.5. An optimized pH used for Cd removal from aquatic systems was found to be 6.5. Additionally, an optimum amount of biomass was 1.33 g for the maximum removal of Cd from the aqueous solutions with initial metal concentration of 75 mg/L. The results obtained thus indicated that Langmuir model is the best suited for the removal of Cd from aquatic systems

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care