The Effect Of Hot Spots On The Performance Of Mesh--Based Networks

Direct network performance is affected by different design parameters which include number of virtual channels, number of ports, routing algorithm, switching technique, deadlock handling technique, packet size, and buffer size. Another factor that affects network performance is the traffic pattern. In this thesis, we study the effect of hotspot traffic on system performance. Specifically, we study the effect of hotspot factor, hotspot number, and hot spot location on the performance of mesh-based networks. Simulations are run on two network topologies, both the mesh and torus. We pay more attention to meshes because they are widely used in commercial machines. Comparisons between oblivious wormhole switching and chaotic packet switching are reported. Overall packet switching proved to be more efficient in terms of throughput when compared to wormhole switching. In the case of uniform random traffic, it is shown that the differences between chaotic and oblivious routing are indistinguishable. Networks with low number of hotspots show better performance. As the number of hotspots increases network latency tends to increase. It is shown that when the hotspot factor increases, performance of packet switching is better than that of wormhole switching. It is also shown that the location of hotspots affects network performance particularly with the oblivious routers since their achieved latencies proved to be more vulnerable to changes in the hotspot location. It is also shown that the smaller the size of the network the earlier network saturation occurs. Further, it is shown that the chaos router’s adaptivity is useful in this case. Finally, for tori, performance is not greatly affected by hotspot presence. This is mostly due to the symmetric nature of tori

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

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Amperometric Bioelectronic Tongue for glucose determination

An amperometric Bioelectronic Tongue is reported for glucose determination that contains eight sensor electrodes constructed using different metal electrodes (Pt, Au), oxidoreductase enzymes (glucose oxidase, ascorbate oxidase, uricase), and membrane coatings (Nafion, chitosan). The response to varying concentrations of glucose, ascorbic acid, uric acid, and acetaminophen was tested for two models, concentration determination by current density measurements at individual electrodes and concentration determination by a linear regression model for the entire electrode array. The reduced chi-squared for the full array model was found to be about one order of magnitude lower than that for the individual-electrode model. Discrimination of glucose from chemical interference by the other three species is accomplished through a combination of enzyme catalysis, metal electrocatalysis, and membrane surface charge. The benefit of incorporating enzyme electrodes into the sensor array is illustrated by the lower correlation coefficients between different enzyme electrodes relative to non-enzyme coated electrodes. This approach can be more generally applied to detection of other substrates of oxidoreductase enzymes

Synthesis and Structure–Activity Relationships of 1-Aryl-β-carbolines as Affinity Probes for the 5-Hydroxytryptamine Receptor

Simple β-carbolines have been shown to bind several protein receptors that are important for signaling in the central nervous system. Herein, we expand our previous efforts into the synthesis and biological activity of these heterocycles by studying their neuropharmacological activity. A diverse set of 1-aryl-β-carbolines has been synthesized via Suzuki cross-coupling from a common triflate precursor and several substituted aryl boronic acids. The resulting 26-member library was subjected to primary screening at 10 μM for activity against 40 protein receptors implicated in brain signaling. The Ki was subsequently determined for several lead structures. The most potent activity, as low as 100 nM, was found against the 5-hydroxytryptamine subtype-2 family of receptors. In-depth structure–activity relationships for these synthetic β-carbolines have been developed, which point to the importance of a 3-indolyl substituent attached to the 1-position of the β-carboline and a 6-methoxy substituent on the β-carboline core

Intelligent Diagnosis and Classification of Keratitis

A corneal ulcer is an open sore that forms on the cornea; it is usually caused by an infection or injury and can result in ocular morbidity. Early detection and discrimination between different ulcer diseases reduces the chances of visual disability. Traditional clinical methods that use slit-lamp images can be tiresome, expensive, and time-consuming. Instead, this paper proposes a deep learning approach to diagnose corneal ulcers, enabling better, improved treatment. This paper suggests two modes to classify corneal images using manual and automatic deep learning feature extraction. Different dimensionality reduction techniques are utilized to uncover the most significant features that give the best results. Experimental results show that manual and automatic feature extraction techniques succeeded in discriminating ulcers from a general grading perspective, with ~93% accuracy using the 30 most significant features extracted using various dimensionality reduction techniques. On the other hand, automatic deep learning feature extraction discriminated severity grading with a higher accuracy than type grading regardless of the number of features used. To the best of our knowledge, this is the first report to ever attempt to distinguish corneal ulcers based on their grade grading, type grading, ulcer shape, and distribution. Identifying corneal ulcers at an early stage is a preventive measure that reduces aggravation and helps track the efficacy of adapted medical treatment, improving the general public health in remote, underserved areas

Automated Detection of Corneal Ulcer Using Combination Image Processing and Deep Learning

A corneal ulcers are one of the most common eye diseases. They come from various infections, such as bacteria, viruses, or parasites. They may lead to ocular morbidity and visual disability. Therefore, early detection can reduce the probability of reaching the visually impaired. One of the most common techniques exploited for corneal ulcer screening is slit-lamp images. This paper proposes two highly accurate automated systems to localize the corneal ulcer region. The designed approaches are image processing techniques with Hough transform and deep learning approaches. The two methods are validated and tested on the publicly available SUSTech-SYSU database. The accuracy is evaluated and compared between both systems. Both systems achieve an accuracy of more than 90%. However, the deep learning approach is more accurate than the traditional image processing techniques. It reaches 98.9% accuracy and Dice similarity 99.3%. However, the first method does not require parameters to optimize an explicit training model. The two approaches can perform well in the medical field. Moreover, the first model has more leverage than the deep learning model because the last one needs a large training dataset to build reliable software in clinics. Both proposed methods help physicians in corneal ulcer level assessment and improve treatment efficiency

LiverNet: Diagnosis of Liver Tumors in Human CT Images

Liver cancer contributes to the increasing mortality rate in the world. Therefore, early detection may lead to a decrease in morbidity and increase the chance of survival rate. This research offers a computer-aided diagnosis system, which uses computed tomography scans to categorize hepatic tumors as benign or malignant. The 3D segmented liver from the LiTS17 dataset is passed through a Convolutional Neural Network (CNN) to detect and classify the existing tumors as benign or malignant. In this work, we propose a novel light CNN with eight layers and just one conventional layer to classify the segmented liver. This proposed model is utilized in two different tracks; the first track uses deep learning classification and achieves a 95.6% accuracy. Meanwhile, the second track uses the automatically extracted features together with a Support Vector Machine (SVM) classifier and achieves 100% accuracy. The proposed network is light, fast, reliable, and accurate. It can be exploited by an oncological specialist, which will make the diagnosis a simple task. Furthermore, the proposed network achieves high accuracy without the curation of images, which will reduce time and cost

LiverNet: Diagnosis of Liver Tumors in Human CT Images

Liver cancer contributes to the increasing mortality rate in the world. Therefore, early detection may lead to a decrease in morbidity and increase the chance of survival rate. This research offers a computer-aided diagnosis system, which uses computed tomography scans to categorize hepatic tumors as benign or malignant. The 3D segmented liver from the LiTS17 dataset is passed through a Convolutional Neural Network (CNN) to detect and classify the existing tumors as benign or malignant. In this work, we propose a novel light CNN with eight layers and just one conventional layer to classify the segmented liver. This proposed model is utilized in two different tracks; the first track uses deep learning classification and achieves a 95.6% accuracy. Meanwhile, the second track uses the automatically extracted features together with a Support Vector Machine (SVM) classifier and achieves 100% accuracy. The proposed network is light, fast, reliable, and accurate. It can be exploited by an oncological specialist, which will make the diagnosis a simple task. Furthermore, the proposed network achieves high accuracy without the curation of images, which will reduce time and cost