Towards real-time interest point detection and description for mobile and robotic devices

Convolutional Neural Networks (CNNs) have been successfully adopted by state-of-the-art feature point detection and description networks for the past number of years. The focus of these systems has been predominately on the accuracy of the system, rather than on its efficiency or ability to be implemented in real-time on embedded robotic devices. This paper demonstrates how techniques, developed for other CNN use cases, can be integrated into interest point detection and description systems to compress their network size and reduce the computational complexity; this reduces the barrier to their uptake in computationally challenged environments. This paper documents the integration of these techniques into the popular Reliable Detector and Descriptor (R2D2) network. Along with the integration details, a comprehensive Key Performance Indicator (KPI) framework is developed to test all aspects of the networks. As a result, this paper presents a lightweight variant of the R2D2 network that significantly reduces parameters and computational complexity while crucially maintaining an acceptable level of accuracy. Consequently, this new compressed network is more appropriate for use in real world systems and advances the efforts to implement such CNN based system for mobile devices

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

Biological activity of Boswellia serrata Roxb. oleo gum resin essential oil: effects of extraction by supercritical carbon dioxide and traditional methods

The findings of this study suggests that chemical composition, essential oil yield, antioxidant and antimicrobial activity of Boswellia serrata oleo gum resin essential oils extracted by hydro distillation, steam distillation and supercritical fluid carbon dioxide methods vary greatly from each other. The optimum essential oil yield was obtained using hydro distillation method (8.18 ± 0.15 %). The essential oils isolated through different extraction methods contained remarkable amounts of total phenolics and total flavonoids. Essential oil isolated through supercritical fluid carbon dioxide extraction exhibited better antioxidant activity with highest free radical scavenging potential (96.16 ± 1.57 %), inhibition of linoleic acid oxidation (94.18 ± 1.47 %) and hydrogen peroxide free radical scavenging potential (68.25 ± 1.02 %). Moreover, the antimicrobial activity of essential oils was performed through well diffusion, resazurin microtiter plate and micro dilution broth assay assays. The essential oil isolated through steam distillation method revealed highest antimicrobial activity with maximum inhibition zone (24.21 ± 0.34 to12.08 ± 0.30 mm) and least MIC values (35.18 ± 0.77 to 281.46 ± 7.03 µg/mL). The comparison of chemical composition of essential oils isolated at different extraction methods have shown that the concentration of α-thujene, camphene, β-pinene, myrcene, limonene, m-cymene and cis-verbenol was higher in steam distilled essential oil as compared to hydro and supercritical fluid carbon dioxide extracted essential oils. These compounds may be responsible for the higher antimicrobial activity of Boswellia serrata oleo gum resin steam distilled essential oil

Antioxidant, antimicrobial, antitumor, and cytotoxic activities of an important medicinal plant (Euphorbia royleana) from Pakistan

The aim of present study was to evaluate antioxidant, antimicrobial, and antitumor activities of methanol, hexane, and aqueous extracts of fresh Euphorbia royleana. Total phenolic and flavonoid contents were estimated as gallic acid and querectin equivalents, respectively. Antioxidant activity was assessed by scavenging of free 2,2′- diphenyl-1-picrylhydrazyl radicals and reduction of ferric ions, and it was observed that inhibition values increase linearly with increase in concentration of extract. The results of ferric reducing antioxidant power assay showed that hexane extract has maximum ferric reducing power (12.70 ± 0.49 mg gallic acid equivalents/g of plant extract). Maximum phenolic (47.47 ± 0.71 μg gallic acid equivalents/mg of plant extract) and flavonoid (63.68 ± 0.43 μg querectin equivalents/mg of plant extract) contents were also found in the hexane extract. Furthermore, we examined antimicrobial activity of the three extracts (methanol, hexane, aqueous) against a panel of microorganisms (Escherichia coli, Bacillus subtillis, Pasteurella multocida, Aspergillus niger, and Fusarium solani) by disc-diffusion assay, and found the hexane extract to be the best antimicrobial agent. Hexane extract was also observed as to be most effective in a potato disc assay. As hexane extract showed potent activity in all the investigated assays, it was targeted for cytotoxic assessment. Maximum cytotoxicity (61.66%) by hexane extract was found at 800 μg/mL. It is concluded that investigated extracts have potential for isolation of antioxidant and antimicrobial compounds for the pharmaceutical industry

A review of applications of artificial intelligence in cardiorespiratory rehabilitation

Implementations of artificial intelligence and machine learning are becoming commonplace in multiple application domains. This is in part due to advancements in computing hardware that have helped outsource the computation of resource-intensive mathematics related to artificial intelligence and machine learning to the chips of multi-core and parallel computing architectures. Partly it is due to the widespread appeal of machine learning as a suite of handy tools to fix practical issues. Many fields have become beneficiaries of artificial intelligence and machine learning and cardiorespiratory rehabilitation is no exception.The aim of this paper is to review the current state of the art of the applications of artificial intelligence and machine learning in cardiorespiratory rehabilitation. We have taken a multidimensional view to addressing the needs and utility of artificial intelligence and machine learning in cardiorespiratory rehabilitation. We start with the most primitive applications of machine learning reported in existing literature in making medical devices for analyzing heartbeats and respiratory functions. We then discuss more recent approaches including deep learning to analyze performance or suggest alternative choices for food or exercise. Applications and utility of most recent feats such as explainable artificial intelligence are also discussed and conclusions around the current state of the art and possible future directions are proposed

Image velocimetry and statistical analysis of a mesh-coupled axial blade distributor for mass transfer in a swirling bed

A mesh-coupled axial blade distributor was tested for fluidization of particles in a swirling fluidized bed. The bed velocity was estimated experimentally using a high-speed imaging device and MATLAB supported particle image velocimetry (PIV). The bed velocity was also predicted statistically with a response surface analysis method. During statistical analysis, the confidence interval of bed velocity remained between 0.49485 and 0.49998. The bed velocity was measured about 0.49741m/s and 0.538m/s through experimental and statistical methods, respectively. The experimental and statistical analysis revealed similar bed weights and superficial velocities with a slight difference of 6.4◦ in blade angles