Temporal change in seismic wave attenuation using highly stable vibration sources

We developed a method to detect attenuation changes during seismic wave propagation excited by precisely controlled artificial seismic sources, namely Accurately Controlled Routinely Operated Signal System (ACROSS), and applied it to monitor the temporal changes for in situ data collected by previous studies. Our method, together with the use of the ACROSS sources, is less susceptible to noise level changes, from which conventional methods such as envelope calculation suffer. The method utilizes the noise level that is independently estimated in the frequency domain and eliminates the influence of the noise from the observed signal. For performance testing, we applied this method to a dataset that was obtained in an experiment at Awaji Island, Japan, from 2000 to 2001. We detected a change in amplitude caused by rainfall, variation in atmospheric temperature, and coseismic ground motions. Among them, coseismic changes are of particular interest because there are limited studies on coseismic attenuation change, in contrast to many studies on coseismic velocity decrease. At the 2000 Western Tottori earthquake (MW = 6.6, epicenter distance of 165 km), a sudden decrease in amplitude of up to 5% was observed. The coseismic amplitude reduction and its anisotropic characteristics, which showed a larger reduction in the direction of the major axis of velocity decrease, were consistent with the opening of fluid-filled cracks, as proposed by previous studies. The $$\Delta {Q}^{-1}$$ Δ Q - 1 corresponding to the amplitude change gives similar values to those reported in previous studies using natural earthquakes. Graphical Abstract

A novel scalable electrode array and system for non-invasively assessing gastric function using flexible electronics

Disorders of gastric function are highly prevalent, but diagnosis often remains symptom-based and inconclusive. Body surface gastric mapping is an emerging diagnostic solution, but current approaches lack scalability and are cumbersome and clinically impractical. We present a novel scalable system for non-invasively mapping gastric electrophysiology in high-resolution (HR) at the body-surface. The system comprises a custom-designed flexible HR sensor array and portable data-logger synchronized to an App, with automated analysis and visualization algorithms. The novel system underwent performance testing then validation in 24 healthy subjects. In all subjects, gastric electrophysiology and meal responses were successfully captured and mapped non-invasively (mean frequency 2.9 ± 0.3 cycles per minute; peak amplitude at mean 60 m postprandially with return to baseline in <4 h). Spatiotemporal mapping showed regular and consistent wave activity of mean direction 182.7°±73 (74.7% antegrade, 7.8% retrograde, 17.5% indeterminate). The presented system is a new diagnostic tool for assessing gastric function that is scalable, validated, and ready for clinical applications, offering several biomarkers that are new to gastroenterology practice.

High-precision nuclear magnetic resonance probe suitable for in situ studies of high-temperature metallic melts

High-temperature nuclear magnetic resonance (NMR) has proven to be very useful for detecting the temperature-induced structural evolution and dynamics in melts. However, the sensitivity and precision of high-temperature NMR probes are limited. Here we report a sensitive and stable high-temperature NMR probe based on laser-heating, suitable for in situ studies of metallic melts, which can work stably at the temperature of up to 2000 K. In our design, a well-designed optical path and the use of a water-cooled copper radio-frequency (RF) coil significantly optimize the signal-to-noise ratio (S/NR) at high temperatures. Additionally, a precise temperature controlling system with an error of less than ±1 K has been designed. After temperature calibration, the temperature measurement error is controlled within ±2 K. As a performance testing, 27Al NMR spectra are measured in Zr-based metallic glass-forming liquid in situ. Results show that the S/NR reaches 45 within 90 s even when the sample's temperature is up to 1500 K and that the isothermal signal drift is better than 0.001 ppm per hour. This high-temperature NMR probe can be used to clarify some highly debated issues about metallic liquids, such as glass transition and liquid-liquid transition.

The Multi Split Ventilator System: Performance Testing of Respiratory Support Shared by Multiple Patients

Ventilator sharing has been proposed as a method of increasing ventilator capacity during instances of critical shortage. We sought to assess the ability of a regulated, shared ventilator system (Multi Split Ventilator System, MSVS) to individualize support to multiple simulated patients using one ventilator. We employed simulated patients of varying size, compliance, minute ventilation requirement, and PEEP requirement. Performance tests were performed to assess the ability of the QSVS, versus control, to achieve individualized respiratory goals to clinically disparate patients sharing a single ventilator following ARDSNet guidelines. Resilience tests measured the effects of simulated adverse events occurring to one patient on another patient sharing a single ventilator. The QSVS met individual oxygenation and ventilation requirements for multiple simulated patients with a tolerance similar to a single ventilator. Abrupt endotracheal tube occlusion or extubation occurring to one patient resulted in modest, clinically tolerable changes in ventilation parameters for the remaining patients. The QSVS is a regulated, shared ventilator system capable of individualizing ventilatory support to clinically dissimilar simulated patients. It is also resilient to common adverse events. The QSVS represents a feasible option to ventilate multiple patients during a severe ventilator shortage.

Performance of Waste Insulating Mineral Oil-Based Biodiesel in a Direct-Injection CI Engine

Mineral oil has been used as an insulating fluid in the power industry. However, surplus waste oil poses serious environmental threats because of disposal concerns. Waste to biofuel is an excellent way to deal with waste material from various sources. In this study, the trans-esterification method was utilised to convert the waste-insulating mineral oil into a quality bio-fuel. Waste-insulating transformer oil was converted to biodiesel, and it was tested according to ASTM standards. Four different blends of waste-insulating biodiesel with diesel in 25 per cent (WIOBD25), 50 per cent (WIOBD50), 75 per cent (WIOBD75), and 100 per cent fractions (WIOBD100), were used for performance testing in a direct injection compression ignition (DICI) engine. The combustion parameters such as BSFC, EGT, and BTE were evaluated with varying crank angles and constant engine speed. The waste-insulating biodiesel performance results are then compared with diesel fuel. BSFC increased as the biofuel mixture in diesel was raised, and the brake thermal efficiency (BTE) was significantly reduced compared to diesel for all WIOBD diesel mixtures. Due to the combustion process, a high pressure and heat release rate (HRR) were noticed inside the cylinder with the waste-insulating oil-derived biodiesel samples. WIOBD biodiesel blends produced lower levels of hydrocarbon, carbon monoxide, and smoke emissions than diesel fuel, but greater levels of nitrogen oxides (NOx) were produced than diesel fuel. In addition to lower emissions combined with improved engine performance, the WIOBD25 fuel blend has been found to be experimentally optimal for practical application. As a result, the test findings indicated that WIOBD biodiesel might be used as a substitute for conventional diesel fuel.

Statistical analysis for explosives detection system test and evaluation

The verification of trace explosives detection systems is often constrained to small sample sets, so it is important to support the significance of the results with statistical analysis. As binary measurements, the trials are assessed using binomial statistics. A method is described based on the probability confidence interval and expressed in terms of the upper confidence interval bound that reports the probability of successful detection and its level of statistical confidence. These parameters provide useful measures of the system’s performance. The propriety of combining statistics for similar tests—for example in trace detection trials of an explosive on multiple surfaces—is examined by statistical tests. The use of normal statistics is commonly applied to binary testing, but the confidence intervals are known to behave poorly in many circumstances, including small sample numbers. The improvement of the normal approximation with increasing sample number is shown not to be substantial for the typical numbers used in this type of explosives detection system testing, and binary statistics are preferred. The methods and techniques described here for testing trace detection can be applied as well to performance testing of explosives detection systems in general.

Comparison Decision Tree and Logistic Regression Machine Learning Classification Algorithms to determine Covid-19

Many people today are unsure whether they have COVID-19. The frequent fever, dry cough, and sore throat are all signs and symptoms of COVID-19. If a person has signs or symptoms of coronavirus disease 2019 (COVID-19), he/she should see the doctor or go to a clinic as soon as possible. As a result, it's vital to learn and comprehend the fundamental differences. COVID-19 can cause a wide range of symptoms. The experiments were carried out using two Machine Learning Classification Algorithms, namely Decision Tree (DT) and Logistic Regression (LR). Both algorithms were written and analyzed using the Python program in Jupyter Notebook 6.4.5. From the results obtained in the experiments of covid symptoms dataset, on average, the DT model has obtained the best cross-validation average and the testing performance average compared to the LR machine learning models. For cross-validation results, the DT model has achieved an accuracy of 98.0%. For performance testing, the DT model has achieved an accuracy of 98.0%. The LR has obtained the second-best result on the average of cross-validation performance and the testing results. For cross-validation results, the LR model has achieved an accuracy of 96.0%. For performance testing, the LR model has achieved an accuracy of 97.0%. Consequently, the DT for the COVID-19 symptoms dataset is outperforming the LR for cross-validation and testing results.