Exploring the Effect of COVID-19 on Airline Environmental Efficiency Through an Interval Epsilon-based Measure Model

COVID-19 has dealt an unprecedented blow to the aviation industry since 2020. This paper applies the Interval Epsilon-Based Measure (IEBM) model to evaluate the optimal quarterly environmental efficiency of 14 global airlines of passenger and cargo subsystems during 2018-2020. Then, the Time Series Prediction method is applied to forecast the interval data of inputs and outputs from 2021 to 2022 and calculate the quarterly efficiency. Thus, the future development trends of airlines can be predicted. Furthermore, the results accord with reality can verify the credibility and accuracy of the model. Furthermore, the results show that: 1. COVID-19 has hit the passenger subsystem harder, while the freight subsystem has become more efficient; 2. The efficiency of the freight subsystem has inevitably declined in the post-epidemic era; 3. Therefore, the airlines will have a “√” shaped recovery curve in the next few years.

In-Bed Sensorimotor Rehabilitation in Early and Late Subacute Stroke Using a Wearable Elbow Robot: A Pilot Study

Objects: To evaluate the feasibility and effectiveness of in-bed wearable elbow robot training for motor recovery in patients with early and late subacute stroke.Methods: Eleven in-patient stroke survivors (male/female: 7/4, age: 50.7 ± 10.6 years, post-stroke duration: 2.6 ± 1.9 months) received 15 sessions of training over about 4 weeks of hospital stay. During each hourly training, participants received passive stretching and active movement training with motivating games using a wearable elbow rehabilitation robot. Isometric maximum muscle strength (MVC) of elbow flexors and extensors was evaluated using the robot at the beginning and end of each training session. Clinical measures including Fugl-Meyer Assessment of upper extremity (FMA-UE), Motricity Index (MI) for upper extremities, Modified Ashworth Scale (MAS) were measured at baseline, after the 4-week training program, and at a 1-month follow-up. The muscle strength recovery curve over the training period was characterized as a logarithmic learning curve with three parameters (i.e., initial muscle strength, rate of improvement, and number of the training session).Results: At the baseline, participants had moderate to severe upper limb motor impairment {FMA-UE [median (interquartile range)]: 28 (18–45)} and mild spasticity in elbow flexors {MAS [median (interquartile range)]: 0 (0–1)}. After about 4 weeks of training, significant improvements were observed in FMA-UE (p = 0.003) and MI (p = 0.005), and the improvements were sustained at the follow-up. The elbow flexors MVC significantly increased by 1.93 Nm (95% CI: 0.93 to 2.93 Nm, p = 0.017) and the elbow extensor MVC increased by 0.68 Nm (95% CI: 0.05 to 1.98 Nm, p = 0.036). Muscle strength recovery curve showed that patients with severe upper limb motor impairment had a greater improvement rate in elbow flexor strength than those with moderate motor impairment.Conclusion: In-bed wearable elbow robotic rehabilitation is feasible and effective in improving biomechanical and clinical outcomes for early and late subacute stroke in-patients. Results from the pilot study suggested that patients with severe upper limb motor impairment may benefit more from the robot training compared to those with moderate impairment.

HYDRODYNAMIC METHODS FOR THE INVESTIGATION OF WELLS AND RESERVOIR

The analysis of literary sources in this study showed that in order to solve the problem of the pressure recovery curve during hydrodynamic probe of the reservoir in the case of its constant porosity a number of studies were dedicated to the nonstationary solution of the deterministic Darcy equation for an unlimited reservoir or on methods of empirical or semi-empirical modeling for static errors, which is associated with a nontrivial approximation of the asymptotic part of the pressure recovery curve.In this study, a new method for calculating the hydrodynamic parameters of an oil reservoir is proposed, taking into account all the information from the pressure recovery curve. In contrast to the existing methods, the proposed model of pressure versus time adequately describes the entire pressure recovery curve when the well is closed, which makes it possible to fully evaluate the hydrodynamic parameters of the reservoir.

PROCESSING OF OIL WELL PRESSURE RECOVERY CURVES

In the article the development of a mathematical model describing the PRC is studied on the base of pressure recovery curve method.Detailed processing of the pressure recovery curve made it possible to determine the deterioration of reservoir permeability in many wells. Here two methods are considered - stationary (steady conditions of selection) and non- stationary.The article proves that the use of these methods allows to develop a mathematical model to increase the determination of this task.On the base of numerical simulation, the following facts had impact on the results of the pressure recovery curve: well shutdown time, taking into account the initial transition section, taking into account curve change section before well shutdown.The study of variable factors impact on the results is carried out by interval estimation.The mathematical model describing the pressure recovery curve is local and changes its structures. This model can be used in industry conditions.

Modeling recovery curves with application to prostatectomy

Summary In many clinical settings, a patient outcome takes the form of a scalar time series with a recovery curve shape, which is characterized by a sharp drop due to a disruptive event (e.g., surgery) and subsequent monotonic smooth rise towards an asymptotic level not exceeding the pre-event value. We propose a Bayesian model that predicts recovery curves based on information available before the disruptive event. A recovery curve of interest is the quantified sexual function of prostate cancer patients after prostatectomy surgery. We illustrate the utility of our model as a pre-treatment medical decision aid, producing personalized predictions that are both interpretable and accurate. We uncover covariate relationships that agree with and supplement that in existing medical literature.

DETERMINATION OF NEAR WELLBORE ZONE PROPERTIES FROM NON-STATIONARY TEMPERATURE MEASUREMENTS IN THE WELL

Common method of accounting for the deterioration of the filtration properties of the reservoir is the in-troduction of a dimensionless coefficient - skin factor. Traditionally, in order to determine the value of skin factor hydrodynamic research methods (well test) at non-stationary modes are applied (pressure recovery curve, level recovery curve) are used. In recent years, along with the pressure in well logging, bottom hole temperature dynamics is also recorded, that allows to use this data for obtaining the additional information about the reservoir and is a slower process, also about near wellbore zone. In this paper the possibility of de-termining the radius of the zone of permeability deterioration in the near wellbore zone from the non-stationary temperature measurements in well is studied; and also the sensitivity of the inverse problem solution to variation of simulation parameters.