Performance enhancement of large scale networks with heterogeneous traffic.

Finally, these findings are applied towards improving the performance of the Differentiated Services architecture by developing a new Refined Assured Forwarding framework where heterogeneous traffic flows share the same aggregate class. The new framework requires minimal modification to the existing Diffserv routers. The efficiency of the new architecture in enhancing the performance of Diffserv is demonstrated by simulation results under different traffic scenarios.This dissertation builds on the notion that segregating traffic with disparate characteristics into separate channels generally results in a better performance. Through a quantitative analysis, it precisely defines the number of classes and the allocation of traffic into these classes that will lead to optimal performance from a latency standpoint. Additionally, it weakens the most generally used assumption of exponential or geometric distribution of traffic service time in the integration versus segregation studies to date by including self-similarity in network traffic.The dissertation also develops a pricing model based on resource usage in a system with segregated channels. Based on analytical results, this dissertation proposes a scheme whereby a service provider can develop compensatory and fair prices for customers with varying QoS requirements under a wide variety of ambient traffic scenarios.This dissertation provides novel techniques for improving the Quality of Service by enhancing the performance of queue management in large scale packet switched networks with a high volume of traffic. Networks combine traffic from multiple sources which have disparate characteristics. Multiplexing such heterogeneous traffic usually results in adverse effects on the overall performance of the network

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Precise Control over the Individual DMD Micromirror for Volumetric Three-Dimensional Display Applications

Digital light processing (DLP) uses a digital micromirror device (DMD) to control light, effectively acting as an array of optical switching elements. DMDs have been widely used as high-speed spatial light modulators for projection applications. This paper proposes a software tool that converts sketches drawn on the screen of a personal computer (PC) into mirror-copy representations across the DMD micromirror array such that corresponding images of the sketches are projected onto a screen. The software tool continuously monitors the PC screen for any new rendering and updates the projected identical, mirror-copy over the DMD surface. Furthermore, the development enables a user to render a three-dimensional (3D) volumetric image by simply drawing slices of the 3D image over a previously specified number of application windows. Continuous projection of these slices formsa3D image under complete control over the projection speed-up tol3,300 frames/sec. The software tool also provides zooming and scrolling features that allow a user to access individual pixels, or micromirrors, on the DMD surface. The software tool described in this paper successfully demonstrates its applicability for the process of volumetric 3D display by way of data acquisition from a PC screen and the subsequent creation and display of 3D image slices, which can later be assembled into a volumetric image

Monitoring of Location Parameters with a Measurement Error under the Bayesian Approach Using Ranked-Based Sampling Designs with Applications in Industrial Engineering

To detect sustainable changes in the production processes, memory-type control charts are frequently utilized. This study is conducted to assess the performance of the Bayesian adaptive exponentially weighted moving average (AEWMA) control chart using ranked set sampling schemes following two different loss functions in the presence of a measurement error for posterior and posterior predictive distributions using conjugate priors. This study is based on the covariate model and multiple measurement methods in the presence of a measurement error (ME). The performance of the proposed Bayesian-AEWMA control chart with ME has been evaluated through the average run length and the standard deviation of the run length. Finally, a real-life application in semiconductor manufacturing was conducted to evaluate the effectiveness of the proposed Bayesian-AEWMA control chart with a measurement error based on different ranked set sampling schemes. The results demonstrate that the proposed control chart, in the presence of a measurement error, performed well in detecting out-of-control signals compared to the existing control chart. However, the median ranked set sampling scheme (MRSS) proved to be better than the other two schemes in the presence of a measurement error

U-Shaped Low-Complexity Type-2 Fuzzy LSTM Neural Network for Speech Enhancement

Speech enhancement (SE) aims to improve the intelligibility and perceptual quality of speech contaminated by noise signals through spectral or temporal changes. Deep learning models achieve speech enhancement and estimate the magnitude spectrum. This paper proposes a novel and computationally efficient deep learning model to enhance noisy speech. The model pre-processes the noisy speech magnitude by redistributing energy from high-energy voiced segments to low-energy unvoiced segments using an adaptive power law transformation while maintaining the total energy of the speech signals constant. A U-shaped fuzzy long short-term memory (UFLSTM) estimates the magnitude of a time-frequency (T-F) mask by using the pre-processed data. Residual connections to the similar-shaped layers are added to avoid gradient decay. Attention process is adopted by modifying the forget gate of UFLSTM. To make a causal speech enhancement system, the processing does not include any future audio frames. We compare the proposed speech enhancement to other deep learning models in different noisy environments with signal-to-noise ratios of 0 dB, 5 dB, and 10 dB. The experiments show that the proposed SE system outscores the competing deep learning models and considerably improves speech intelligibility and quality. In terms of STOI and PESQ, the LibriSpeech database improves results by (0.211) 21.1% and (0.95) 36.39%, respectively, over noisy speech in seen noisy conditions, and by (0.199) 19.9% and (0.94) 35.69% over noisy speech in unseen noisy conditions. Further, the cross-corpus analysis shows that proposed SE system performs better when trained with the DNS dataset as compared to the LibriSpeech, VoiceBank, and TIMIT datasets

Enhancement of Antibacterial Properties, Surface Morphology and In Vitro Bioactivity of Hydroxyapatite-Zinc Oxide Nanocomposite Coating by Electrophoretic Deposition Technique

To develop medical-grade stainless-steel 316L implants that are biocompatible, non-toxic and antibacterial, such implants need to be coated with biomaterials to meet the current demanding properties of biomedical materials. Hydroxyapatite (HA) is commonly used as a bone implant coating due to its excellent biocompatible properties. Zinc oxide (ZnO) nanoparticles are added to HA to increase its antibacterial and cohesion properties. The specimens were made of a stainless-steel grade 316 substrate coated with HA-ZnO using the electrophoretic deposition technique (EPD), and were subsequently characterized using scanning electron microscopy (SEM), energy dispersive X-ray (EDX), stylus profilometry, electrochemical corrosion testing and Fourier transform infrared (FTIR) spectroscopy. Additionally, cross-hatch tests, cell viability assays, antibacterial assessment and in vitro activity tests in simulated body fluid (SBF) were performed. The results showed that the HA-ZnO coating was uniform and resistant to corrosion in an acceptable range. FTIR confirmed the presence of HA-ZnO compositions, and the in vitro response and adhesion were in accordance with standard requirements for biomedical materials. Cell viability confirmed the viability of cells in an acceptable range (>70%). In addition, the antibacterial activity of ZnO was confirmed on Staphylococcus aureus. Thus, the HA-ZnO samples are recommended for biomedical applications