A Minimalist Method Toward Severity Assessment and Progression Monitoring of Obstructive Sleep Apnea on the Edge

Artificial Intelligence-enabled applications on edge devices have the potential to revolutionize disease detection and monitoring in future smart health (sHealth) systems. In this study, we investigated a minimalist approach for the severity classification, severity estimation, and progression monitoring of obstructive sleep apnea (OSA) in a home environment using wearables. We used the recursive feature elimination technique to select the best feature set of 70 features from a total of 200 features extracted from polysomnogram. We used a multi-layer perceptron model to investigate the performance of OSA severity classification with all the ranked features to a subset of features available from either Electroencephalography or Heart Rate Variability (HRV) and time duration of SpO2 level. The results indicate that using only computationally inexpensive features from HRV and SpO2, an area under the curve of 0.91 and an accuracy of 83.97% can be achieved for the severity classification of OSA. For estimation of the apnea-hypopnea index, the accuracy of RMSE = 4.6 and R-squared value = 0.71 have been achieved in the test set using only ranked HRV and SpO2 features. The Wilcoxon-signed-rank test indicates a significant change (p < 0.05) in the selected feature values for a progression in the disease over 2.5 years. The method has the potential for integration with edge computing for deployment on everyday wearables. This may facilitate the preliminary severity estimation, monitoring, and management of OSA patients and reduce associated healthcare costs as well as the prevalence of untreated OSA.

Inland waterway network mapping of AIS data for freight transportation planning

Travel demand models (TDMs) with freight forecasts estimate performance metrics for competing infrastructure investments and potential policy changes. Unfortunately, freight TDMs fail to represent non-truck modes with levels of detail adequate for multi-modal infrastructure and policy evaluation. Recent expansions in the availability of maritime movement data, i.e. Automatic Identification System (AIS), make it possible to expand and improve representation of maritime modes within freight TDMs. AIS may be used to track vessel locations as timestamped latitude–longitude points. For estimation, calibration and validation of freight TDMs, this work identifies vessel trips by applying network mapping (map-matching) heuristics to AIS data. The automated methods are evaluated on a 747-mile inland waterway network, with AIS data representing 88% of vessel activity. Inspection of 3820 AIS trajectories was used to train the heuristic parameters including stop time, duration and location. Validation shows 84⋅0% accuracy in detecting stops at ports and 83⋅5% accuracy in identifying trips crossing locks. The resulting map-matched vessel trips may be applied to generate origin–destination matrices, calculate time impedances, etc. The proposed methods are transferable to waterways or maritime port systems, as AIS continues to grow.

Evaluation of initial shockable rhythm as an indicator of short no-flow time in cardiac arrest: a national registry study

BackgroundThe duration from collapse to initiation of cardiopulmonary resuscitation (no-flow time) is one of the most important determinants of outcomes after out-of-hospital cardiac arrest (OHCA). Initial shockable cardiac rhythm (ventricular fibrillation or ventricular tachycardia) is reported to be a marker of short no-flow time; however, there is conflicting evidence regarding the impact of initial shockable cardiac rhythm on treatment decisions. We investigated the association between initial shockable cardiac rhythm and the no-flow time and evaluated whether initial shockable cardiac rhythm can be a marker of short no-flow time in patients with OHCA.MethodsPatients aged 18 years and older experiencing OHCA between 2010 and 2016 were selected from a nationwide population-based Japanese database. The association between the no-flow time duration and initial shockable cardiac rhythm was evaluated. Diagnostic accuracy was evaluated using the sensitivity, specificity and positive predictive value.ResultsA total of 177 634 patients were eligible for the analysis. The median age was 77 years (58.3%, men). Initial shockable cardiac rhythm was recorded in 11.8% of the patients. No-flow time duration was significantly associated with lower probability of initial shockable cardiac rhythm, with an adjusted OR of 0.97 (95% CI 0.96 to 0.97) per additional minute. The sensitivity, specificity and positive predictive value of initial shockable cardiac rhythm to identify a no-flow time of <5 min were 0.12 (95% CI 0.12 to 0.12), 0.88 (95% CI 0.88 to 0.89) and 0.35 (95% CI 0.34 to 0.35), respectively. The positive predictive values were 0.90, 0.95 and 0.99 with no-flow times of 15, 18 and 28 min, respectively.ConclusionsAlthough there was a significant association between initial shockable cardiac rhythm and no-flow time duration, initial shockable cardiac rhythm was not reliable when solely used as a surrogate of a short no-flow time duration after OHCA.

Transient thermal finite-element analysis of fused filament fabrication process

Purpose The thermal behavior at the interfaces (of the deposited strands) during fused filament fabrication (FFF) technique strongly influences bond formation and it is a time- and temperature-dependent process. The processing parameters affect the thermal behavior at the interfaces and the purpose of the paper is to simulate using temperature-dependent (nonlinear) thermal properties rather than constant properties. Design/methodology/approach Nonlinear temperature-dependent thermal properties are used to simulate the FFF process in a simulation software. The finite-element model is first established by comparing the simulation results with that of analytical and experimental results of acrylonitrile butadiene styrene and polylactic acid. Strand temperature and time duration to reach critical sintering temperature for the bond formation are estimated for one of the deposition sequences. Findings Temperatures are estimated at an interface and are then compared with the experimental results, which shows a close match. The results of the average time duration (time to reach the critical sintering temperature) of strands with the defined deposition sequences show that the first interface has the highest average time duration. Varying processing parameters show that higher temperatures of the extruder and envelope along with higher extruder diameter and lower convective heat transfer coefficient will have more time available for bonding between the strands. Originality/value A novel numerical model is developed using temperature-dependent (nonlinear) thermal properties to simulate FFF processes. The model estimates the temperature evolution at the strand interfaces. It helps to evaluate the time duration to reach critical sintering temperature (temperature above which the bond formation occurs) as it cools from extrusion temperature.

Random Forest Based Power Sustainability and Cost Optimization in Smart Grid

Presently power control and management play a vigorous role in information technology and power management. Instead of non-renewable power manufacturing, renewable power manufacturing is preferred by every organization for controlling resource consumption, price reduction and efficient power management. Smart grid efficiently satisfies these requirements with the integration of machine learning algorithms. Machine learning algorithms are used in a smart grid for power requirement prediction, power distribution, failure identification etc. The proposed Random Forest-based smart grid system classifies the power grid into different zones like high and low power utilization. The power zones are divided into number of sub-zones and map to random forest branches. The sub-zone and branch mapping process used to identify the quantity of power utilized and the non-utilized in a zone. The non-utilized power quantity and location of power availabilities are identified and distributed the required quantity of power to the requester in a minimal response time and price. The priority power scheduling algorithm collect request from consumer and send the request to producer based on priority. The producer analysed the requester existing power utilization quantity and availability of power for scheduling the power distribution to the requester based on priority. The proposed Random Forest based sustainability and price optimization technique in smart grid experimental results are compared to existing machine learning techniques like SVM, KNN and NB. The proposed random forest-based identification technique identifies the exact location of the power availability, which takes minimal processing time and quick responses to the requestor. Additionally, the smart meter based smart grid technique identifies the faults in short time duration than the conventional energy management technique is also proven in the experimental results.

Green Synthesis of Stable Spherical Monodisperse Silver Nanoparticles Using a Cell-Free Extract of Trichoderma reesei

In the current study, a green method for the preparation of silver nanoparticles (AgNPs) is presented as an alternative to conventional chemical and physical approaches. A biomass of Trichoderma reesei (T. reesei) fungus was used as a green and renewable source of reductase enzymes and metabolites, which are capable of transforming Ag+ ions into AgNPs with a small size (mainly 2–6 nm) and narrow size distribution (2–25 nm). Moreover, extracellular biosynthesis was carried out with a cell-free water extract (CFE) of T. reesei, which allows for facile monitoring of the bioreduction process using UV–Vis spectroscopy and investigation of the effect of experimental conditions on the transformation of Ag+ ions into AgNPs, as well as the simple isolation of as-prepared AgNPs for the study of their size, morphology and antibacterial properties. In continuation to our previous results about the influence of media on T. reesei cultivation, the amount of biomass used for CFE preparation and the concentration of Ag+ ion solution, herein, we present the impact of temperature (4, 20, 30 and 40 °C), agitation and time duration on the biosynthesis of AgNPs and their properties. A high stability of AgNPs in aqueous colloids was observed and attributed to the capping effect of the biomolecules as shown by the zeta potential (−49.0/−51.4 mV) and confirmed by the hydrodynamic size of 190.8/116.8 nm of AgNPs.

Critical Factors Responsible for Time Overruns in Nigeria Building Construction Industry

Time overruns are major problems facing the Nigerian construction industry. It’s of high concern to those who are involved in the construction industry. This study was carried out to identify the major causes of time overruns in the Nigerian building construction industry, by means of a literature review and a questionnaire survey. A total of twenty (20) time overrun causative factors were obtained from the literature. The questionnaire survey was distributed to randomly selected respondents from a combination of clients, consultants, contractors, site engineers, project managers and sub-contractors. In all, one hundred and forty-one (141) questionnaires were distributed to randomly selected respondents (clients, consultants, contractors, site-engineers, project-managers and sub-contractors), one hundred and thirty-two (132) questionnaires were returned out of which three (3) questionnaires were found incomplete and invalid. Only one hundred and twenty-nine (129) questionnaires were found consistent and valid for use in this research. Relative Importance Index (RII) and Severity Index were used to carry out a ranking analysis. Based on the data received, the five (5) most severe factors influencing project handling overtime in Nigeria construction industries are Inaccurate evaluation of projects time/duration (91.9%), Risk and uncertainty associated with projects (91.6%), Complexity of works (87.6%), Weak regulation and control (86.8%) and Lack of financial power with severity (86.3%).

Temperature Analysis of Flaring (AR11283) and non-Flaring (AR12194) Coronal Loops

Here, we study the temperature structure of flaring and non-flaring coronal loops, using extracted loops from images taken in six extreme ultraviolet (EUV) channels recorded by Atmospheric Imaging Assembly (AIA)/ Solar Dynamic Observatory (SDO). We use data for loops of X2.1-class-flaring active region (AR11283) during 22:10UT till 23:00UT, on 2011, September 6; and non-flaring active region (AR12194) during 08:00:00UT till 09:00:00UT on 2014, October 26. By using spatially-synthesized Gaussian DEM forward-fitting method, we calculate the peak temperatures for each strip of the loops. We apply the Lomb-Scargle method to compute the oscillations periods for the temperature series of each strip. The periods of the temperature oscillations for the flaring loops are ranged from 7 min to 28.4 min. These temperature oscillations show very close behavior to the slow-mode oscillation. We observe that the temperature oscillations in the flaring loops are started at least around 10 minutes before the transverse oscillations and continue for a long time duration even after the transverse oscillations are ended. The temperature amplitudes are increased at the flaring time (during 20 min) in the flaring loops. The periods of the temperatures obtained for the non-flaring loops are ranged from 8.5 min to 30 min,but their significances are less (below 0.5) in comparison with the flaring ones (near to one). Hence the detected temperature periods for the non-flaring loops' strips are less probable in comparison with the flaring ones, and maybe they are just fluctuations. Based on our confined observations, it seems that the flaring loops' periods show more diversity and their temperatures have wider ranges of variation than the non-flaring ones. More accurate commentary in this respect requires more extensive statistical research and broader observations.

Spatio-temporal Variability of Lightning Climatology and Its Association With Thunderstorm Indices Over India

Lightning is an electrical discharge - a'spark' or 'flash' as charged regions in the atmosphere instantly balance themselves through this discharge. It is a beautiful and deadly naturally occurring phenomenon. In June 2020, more than a hundred people died in the state Bihar of India only in three days’ span due to lightning events. In this work, Lightning Imaging Sensor (LIS) information from the Tropical Rainfall Measuring Mission (TRMM) satellite with a very high spatial resolution of 0.1 X 0.1 degree has been utilized to create the climatology of India for 16 years from 1998 to 2013. Diurnal, monthly, and seasonal variations in the occurrence of lightning flash rate density have also been analyzed. TRMM satellite low-resolution monthly time series (LRMTS) with 2.5-degree resolution datasets have been used for lightning trend analysis. The diurnal lightning event mainly occurs in the afternoon/evening (1400-1900 Hrs) time duration around 0.001 flashes/km2/hr. The highest lightning occurred in May (0.04 flashes/km2/day) and the least in December (0.005 flashes/km2/day). The distribution of lightning flash counts by season over India landmass is mainly in pre-monsoon (MAM) ranges from 0.248 – 0.491 flashes/km2/day, and monsoon (JJA) ranges from 0.284 – 0.451 flashes/km2/day and decreases afterward. Spatially, the distribution of lightning flashes mainly at North-Eastern region along with Bangladesh, Bihar, Jharkhand, Orissa, and Jammu & Kashmir region. The CAPE and K Index have positively correlated with the flash rate density seasonally but CAPE is more significantly correlated. This study also focused on finding of lightning hotspots region of India district wise and Rajouri district in Jammu and Kashmir got the highest lightning with 121 flashes/km2/yr.

A Comparative Study of LAMP and PCR in Relation to Time and Cost

Background: The Loop-mediated isothermal amplification (LAMP) represents a very sensitive, easy to use, and less time consuming diagnostic method. Aims: The aim was to establish a simple, cost-effective, molecular technique. Materials and methods: An analytical study was conducted using two hundred acute serum samples using two different molecular techniques; qPCR and LAMP to standardize a costeffective and less time-consuming technique. Results: The cost of in-house LAMP reagents was one-ninth of the cost of commercial qPCR. Consume cost was 23 times less than qPCR besides, lab setup cost was 92 times less than qPCR. More importantly, LAMP requires 5-6 times less time duration than qPCR. Conclusion: Due to its simple short-time operation with low cost, it would be a prevalent molecular technique globally, particularly in Bangladesh. J Shaheed Suhrawardy Med Coll 2020; 12(2): 72-75