Short-term Volume Research Articles

Design of semi-open prosthetic socket with constant force for lower limb.

Prosthetic socket is a key component of the prosthesis for clinical application; its performance directly affects the adaptation of the residual limb to the prosthetic socket. There are long-term and short-term volume fluctuation of the residual limb. The volume fluctuation of the residual limb will lead to the pressure mismatch at the interface of the residual limb and the prosthetic socket, which will cause a series of skin and fit problems. The volume fluctuation of the residual limb is considered a key factor for the successful application of the prosthetic socket; its solution is imminent. The purpose of this paper is to design a prosthetic socket with a constant force unit to solve the problem of volume fluctuation of the residual limb. Constant force extrusion for the residual limb is proposed to solve the problem in this paper. A constant force unit is designed based on the superelasticity of shape memory alloy, and a semi-open prosthetic socket embedded in the constant force unit is designed. The constant force property of prosthetic socket is optimized and verified by finite element analysis. The constant force unit obtains a maximum constant force region of 9.36 mm at 13 mm applied displacement, and the minimum stress amplitude is 0.0264%. The semi-open prosthetic socket may achieve constant pressure at the interface between the residual limb and prosthetic socket within a certain range of the residual limb volume change, and it has a lightweight, good heat dissipation, size adjustability, and constant interface pressure.

Optimization of short-term stock selection based on volume and price using a non-cooperative parallel DEA model

This paper presents a novel approach to portfolio optimization in the field of finance, with a specific focus on short-term yield. Existing literature has mainly utilized fundamental data to predict long-term trends in stock prices, but our proposed methodology utilizes technical indicators based on the theory of chasing up. Furthermore, we address the non-cooperative nature of volume and price fluctuation indicators and introduce non-cooperative theory into the short-term volume and price stock selection scheme for the first time. We propose an optimization of short-term stock selection based on volume and price using a non-cooperative parallel Data Envelopment Analysis (DEA) model, which we apply to Chinese main board listed companies. Our empirical results demonstrate the effectiveness of our model in selecting high-yield stocks in the short term. This paper contributes to the ongoing discussion on portfolio optimization and presents a compelling solution for investors seeking to maximize their financial gains. The proposed methodology can be utilized in practical applications and has significant implications for the financial industry.

Long-term effects of electroconvulsive therapy on brain structure in major depression.

Magnetic resonance imaging (MRI) studies on major depressive disorder (MDD) have predominantly found short-term electroconvulsive therapy (ECT)-related gray matter volume (GMV) increases, but research on the long-term stability of such changes is missing. Our aim was to investigate long-term GMV changes over a 2-year period after ECT administration and their associations with clinical outcome. In this nonrandomized longitudinal study, patients with MDD undergoing ECT (n = 17) are assessed three times by structural MRI: Before ECT (t0), after ECT (t1) and 2 years later (t2). A healthy (n = 21) and MDD non-ECT (n = 33) control group are also measured three times within an equivalent time interval. A 3(group) × 3(time) ANOVA on whole-brain level and correlation analyses with clinical outcome variables is performed. Analyses yield a significant group × time interaction (pFWE < 0.001) resulting from significant volume increases from t0 to t1 and decreases from t1 to t2 in the ECT group, e.g., in limbic areas. There are no effects of time in both control groups. Volume increases from t0 to t1 correlate with immediate and delayed symptom increase, while volume decreases from t1 to t2 correlate with long-term depressive outcome (all p ⩽ 0.049). Volume increases induced by ECT appear to be a transient phenomenon as volume strongly decreased 2 years after ECT. Short-term volume increases are associated with less symptom improvement suggesting that the antidepressant effect of ECT is not due to volume changes. Larger volume decreases are associated with poorer long-term outcome highlighting the interplay between disease progression and structural changes.

AN INTEGRATED MACHINE LEARNING-PROBABILISTIC APPROACH TO PREDICT BEACH VOLUME CHANGE

In the DataBeach study a machine learning model was developed with the aim to improve the efficiency and sustainability of design of soft coastal defense projects. A morphological model based on machine learning was trained and tested to predict beach volume changes with significantly reduced computational time compared to traditional process-based models. The machine learning model was then applied, combined with a ‘penalty function’ for inclusion of morphological feedback, to predict beach volume changes for the study area of approximately 2 km alongshore and on a 10-year project timescale. In order to run many different scenarios for the 10-year prediction, a probabilistic methodology was developed to take into account the uncertainties in this time period. The machine learning-based model provides great benefits for probabilistic simulations, due to the lower computational time, compared to process-based numerical models such as XBeach, and a flexible way in which it can incorporate measurement data. The performance of the tested machine learning models was comparable to that of the short term volume predictions of XBeach. Comparison of 10-year predicted volume changes using the machine learning model and measured beach topography (LiDAR) showed good agreement between measured and predicted volume changes for the dry beach area (when accounting for nourishments), but overestimation in the beach volume change predictions for the intertidal beach. These differences are partially attributed to the poor performance on XBeach for long term, normal wave conditions, which are an important factor in the intertidal area.

Native T2 Predicts Myocardial Inflammation Irrespective of a Patient's Volume Status.

Myocardial inflammation and edema are major pathological features in myocarditis. Myocardial tissue water content and myocardial edema can be quantified via T2 mapping. Thus, cardiac magnetic resonance (CMR) is the noninvasive gold standard for diagnosing myocarditis. Several studies showed an impact of short-term volume changes on T2 relaxation time. Plasma volume status (PVS) is a good surrogate parameter to quantify a patient's volume status, and it is simple to use. The aim of this study was to determine the effect of PVS on the diagnostic value of T2 relaxation time in myocardial inflammation. Between April 2017 and December 2022, patients who were indicated for cardiac CMR were included in our prospective clinical registry. Patients with myocardial inflammation and those with unremarkable findings were analyzed in the present study. A blood sample was drawn, and PVS was calculated. Patients were separated into PVS tertiles to explore a possible nonlinear dose-response relationship. Logistic regression analysis was used to determine whether T2 is an independent predictor of myocardial inflammation. A total of 700 patients (47.43% female) were eligible for analysis. Of these, 551 patients were healthy (78.7%), while 149 (21.3%) showed signs of myocardial inflammation. The T2 relaxation time was elevated in patients with myocardial inflammation (40 ms [IQR 37-42 ms] vs. 38.0 ms [IQR 36-39 ms], p < 0.001). PVS showed no difference between the groups (-12.94 [IQR -18.4--7.28] vs.-12.19 [IQR -18.93--5.87], p = 0.384). T2 showed a clear dose-response relationship with PVS, with increasing T2 values along the PVS tertiles. In spite of this, T2 was found to be an independent marker of myocardial inflammation in logistic regression (OR T2 1.3 [95% CI 1.21-1.39], p < 0.001), even after adjusting for PVS (OR T2 [adj. PVS] 1.31 [95% CI 1.22-1.40], p < 0.001). Despite a dose-response relationship between T2 and the volume status, T2 was found to be an independent indicator of myocardial inflammation.

Influential Factor Analysis and Prediction on Initial Metro Network Ridership in Xi’an, China

To satisfy the adaptability of forecasting the short-term and abrupt volume of the initial metro network, we build the multiple enter linear regression (MELR) model to explore the determinants and forecast the intensity during the twice expansion of the initial metro network in Xi’an. We further compare the prediction of the metro transport capacity between the MELR models with exponential smoothing and autoregressive integrated moving average (ARIMA) models. Results show that the passenger intensity significantly fluctuates with the months and days, and MELR model is more adapted for the short-term prediction of the abrupt volume than the ARIMA model during the new metro line opening and the old line expands, which avoids the drawback of time series models that need a huge database. This study provides a guide for the prediction of initial metro network volume and accurate purchase of the rail vehicles during the metro planning and expends stages.

Short-Term Volume Loading Effects on Estimated Intracranial Pressure in Human Volunteers.

BACKGROUND: Short-term fluid loading is used as part of post-spaceflight medical procedures and clinical treatment in hospitals. Hypervolemia with hemodilution induced by rapid fluid infusion reportedly impaired dynamic cerebral autoregulation. However, the effects on intracranial pressure (ICP) remain unknown. Therefore, we estimated ICP noninvasively (nICP) to examine whether rapid fluid infusion would raise ICP.METHODS: Twelve healthy male volunteers underwent two discrete normal saline (NS) infusions (15 and 30 ml · kg-1 stages, NS-15 and NS-30, respectively) at a rate of 100 ml · min-1. The cerebral blood flow (CBF) velocity (CBFv) waveform from the middle cerebral artery obtained by transcranial Doppler ultrasonography was recorded, as was the arterial blood pressure (ABP) waveform at the radial artery obtained by tonometry. We then used these waveforms to calculate nICP, cerebral artery compliance, and the pulsatility index (PI) in an intracranial hydraulic model.RESULTS: nICP increased significantly in both infusion stages from preinfusion (preinfusion: 7.6 ± 3.4 mmHg; NS-15: 10.9 ± 3.3 mmHg; NS-30: 11.7 ± 4.2 mmHg). No significant changes were observed in cerebral artery compliance or PI. Although ABP did not change in any stage, CBFv increased significantly (preinfusion: 67 ± 10 cm · s-1; NS-15: 72 ± 12 cm · s-1; NS-30: 73 ± 12 cm · s-1).DISCUSSION: Hypervolemia with hemodilution induced by rapid fluid infusion caused increases in nICP and CBFv. No changes were observed in cerebral artery compliance or PI related to cerebrovascular impedance. These findings suggest that rapid fluid infusion may raise ICP with increased CBF.Kurazumi T, Ogawa Y, Takko C, Kato T, Konishi T, Iwasaki K. Short-term volume loading effects on estimated intracranial pressure in human volunteers. Aerosp Med Hum Perform. 2022; 93(4):347-353.

Influence of aluminum sulfate on properties and hydration of magnesium potassium phosphate cements

Aluminum sulfate shows a positive effect on preventing efflorescence of magnesium potassium phosphate (MKP) cements. Its influence on the MKP cement properties and hydration is explored in this study. Compared to a reference cement (Mg/PO4 molar ratio = 4, w/c ratio = 0.25), aluminum sulfate retards setting, increases compressive strength, and has little influence on the short-term volume stability. Aluminum sulfate slows down the hydration kinetics, decreases the pH values, accelerates the depletion of KH2PO4, and thus mitigates the formation of efflorescence. It also modifies the hydrate assemblage. In addition to K-struvite, aluminum sulfate leads to the formations of arcanite and aluminum-containing hydrates such as amorphous aluminum phosphate, possibly phosphate containing LDH (layered double hydroxide) and minyulite-like phases. The formation of amorphous MgHPO4 and minor amounts of bobierrite and brucite as observed in the reference paste, are suppressed in the presence of aluminum.

High-intensity interval training improves fat oxidation during submaximal exercise in active young men

This study aimed to examine the effects of four-weeks high-intensity interval training (HIIT) on fat oxidation responses during submaximal exercise in active young men. For this purpose, 20 active young men (who participated in the exercise three times per week) were divided into two groups, including a training group (age: 19.3±0.48 years, V̇O2peak 2.9±0.35 l/min, n=10) and a control group (age: 19.7±0.67 years, V̇O2peak 2.7±0.26 l/min, n=10). The training group performed high-intensity interval training for three sessions per week. Specifically, each session included 8-11 intensive cycling efforts comprising of 60 s duration. A 75 s low pedalling rate (30 W) was used as an active recovery between the intervals. Furthermore, a V̇O2peak test was performed prior to, at the end of two weeks and after the training period. Also, a 60 min constant cycling protocol was performed at ~60% V̇O2peak, in addition to the V̇O2peak test, before and after the training protocol. To assess plasma free fatty acids and glucose, blood samples were taken during a 60-min aerobic exercise prior to and following the training period. An increase (17.8%) in V̇O2peak was observed for the HIIT group after the training period compared to the control group (P&lt;0.05). The HIIT group performed the 60 min sub-maximal exercise test at a lower percentage of V̇O2peak, and decreases in the respiratory exchange ratio were greater in the HIIT group than in the control group (P&lt;0.05). Compared to the pre-test values and control group results, the HIIT group used less carbohydrate and more lipid oxidation during submaximal exercise (P&lt;0.05). The present study’s results indicate that short-term low volume HIIT can increase aerobic capacity and fat oxidation during submaximal exercise.

A decomposition-based forecasting method with transfer learning for railway short-term passenger flow in holidays

The forecasting of the railway short-term volume plays an essential role in the railway company, which is the basement of the ticket assignment, the line planning, and the passenger station management, and so on. However, the short-term flow forecasting tends to yield poor performance in holidays in China and leads to a low service-of-level for the passengers. In order to improve the accuracy of the short-term passenger flow volume forecasting of high-speed railway, and overcome the challenges in holidays, transfer learning with a time series decomposition is managed. The proposed decomposition-based forecasting method with transfer learning is based on a boosting method which is an instance-based transfer. Firstly, the time series is decomposed into linear time series and nonlinear time series. Using the SARIMA model to predict linear time series, the nonlinear time series is acquired and transformed as feature-label samples with a feature selection to the transfer learning. To limit the negative transfer, two sample filtering methods are proposed by the computing of similarity of time series. Finally, the samples in the source domain are learned by a TrAdaboost model which is embedded with Random Forest adjusting the weights of training samples to reduce the negative transfer. The 24 stations of the Jinghu High-speed railway are tested in our experiments. Our transfer learning model outperforms the baselines. The results also show the effectiveness of feature selection and sample filtering methods. It is proved that this method can be applied to the short-term passenger flow prediction of high-speed railway efficiently, and it is beneficial to improve the efficiency of resource allocation and the service level of high-speed railway transportation.

Is the Spatial-Temporal Dependence Model Reliable for the Short-Term Freight Volume Forecast of Inland Ports? A Case Study of the Yangtze River, China

The purpose of this study is to investigate whether spatial-temporal dependence models can improve the prediction performance of short-term freight volume forecasts in inland ports. To evaluate the effectiveness of spatial-temporal dependence forecasting, the basic time series forecasting models for use in our comparison were first built based on an autoregression integrated moving average model (ARIMA), a back-propagation neural network (BPNN), and support vector regression (SVR). Subsequently, combining a gradient boosting decision tree (GBDT) with SVR, an SVR-GBDT model for spatial-temporal dependence forecast was constructed. The SVR model was only used to build a spatial-temporal dependence forecasting model, which does not distinguish spatial and temporal information but instead takes them as data features. Taking inland ports in the Yangtze River as an example, the results indicated that the ports’ weekly freight volumes had a higher autocorrelation with the previous 1–3 weeks, and the Pearson correlation values of the ports’ weekly cargo volume were mainly located in the interval (0.2–0.5). In addition, the weekly freight volumes of the inland ports were higher depending on their past data, and the spatial-temporal dependence model improved the performance of the weekly freight volume forecasts for the inland river. This study may help to (1) reveal the significance of spatial correlation factors in ports’ short-term freight volume predictions, (2) develop prediction models for inland ports, and (3) improve the planning and operation of port entities.

Temporal mixture ensemble models for probabilistic forecasting of intraday cryptocurrency volume

We study the problem of the intraday short-term volume forecasting in cryptocurrency multi-markets. The predictions are built by using transaction and order book data from different markets where the exchange takes place. Methodologically, we propose a temporal mixture ensemble, capable of adaptively exploiting, for the forecasting, different sources of data and providing a volume point estimate, as well as its uncertainty. We provide evidence of the clear outperformance of our model with respect to econometric models. Moreover our model performs slightly better than Gradient Boosting Machine while having a much clearer interpretability of the results. Finally, we show that the above results are robust also when restricting the prediction analysis to each volume quartile.

Construction of short-term tourist volume prediction model based on Improved PSO algorithm

Construction of short-term tourist volume prediction model based on Improved PSO algorithm

High Hearing Memory Of Schoolchildren And Students And Factors Affecting It

Non-regular mental and physical activity of schoolchildren and students, excessive use of mobile phones, harmful habits (smoking and snuff), iodine deficiency, poor diet, lack of sleep are the main reasons for memory loss decrease is observed. In our experiments, long-term memory volume was up to 37% and short-term memory volume was up to 51% in 6th grade students (13-year-olds); In 8th grade students (15-year-olds), long-term memory was up to 83% and short-term memory was up to 61%; In 10th grade students (17-year-olds), long-term memory volume was up to 83% and short-term memory volume was up to 57%; In first-year students (19-year-olds), we found that long-term memory decreased by 68% and short-term memory by 49%. Decreased memory, in turn, has a negative impact on the mastery performance of pupils and students.

Creating Short-Term Volume Flexibility in Healthcare Capacity Management.

It is well-known that unpredictable variations in supply and demand of capacity in healthcare systems create the need for flexibility. The main tools used to create short-term volume flexibility in the healthcare system include overtime, temporary staff from internal calling lists, moving staff across units, internal staffing pools, external staffing agencies, queuing patients, and purchasing care from external providers. We study the creation of short-term volume flexibility in healthcare systems to manage short-term capacity losses and demand fluctuations. A questionnaire was developed and distributed among healthcare managers in the Region Västra Götaland healthcare system. Respondents were asked to what extent they used each tool to create short-term flexibility in capacity. Data were analyzed using multiple regression analysis. Several significant tendencies were found, including that acute units use overtime and internal staffing pools to a larger extent, and queuing patients and external providers to a lesser extent than planned units. The prerequisites and required managerial approaches used to efficiently manage aggregate capacity in the system differ substantially between different parts of the system. These differences must be addressed when, for example, capacity pools are considered. These results serve as a stepping stone towards a more thorough understanding of efficient capacity management in healthcare systems.

COVID-19: Recovery Models for Radiology Departments.

The coronavirus disease 2019 (COVID-19) pandemic has greatly affected demand for imaging services, with marked reductions in demand for elective imaging and image-guided interventional procedures. To guide radiology planning and recovery from this unprecedented impact, three recovery models were developed to predict imaging volume over the course of the COVID-19 pandemic: (1) a long-term volume model with three scenarios based on prior disease outbreaks and other historical analogues, to aid in long-term planning when the pandemic was just beginning; (2) a short-term volume model based on the supply-demand approach, leveraging increasingly available COVID-19 data points to predict examination volume on a week-to-week basis; and (3) a next-wave model to estimate the impact from future COVID-19 surges. The authors present these models as techniques that can be used at any stage in an unpredictable pandemic timeline.

CT-based quantification of short-term tissue shrinkage following hepatic microwave ablation in an in vivo porcine liver model.

Microwave ablation (MWA) is a minimally invasive treatment option for solid tumors and belongs to the local ablative therapeutic techniques, based on thermal tissue coagulation. So far there are mainly ex vivo studies that describe tissue shrinkage during MWA. To characterize short-term volume changes of the ablated zone following hepatic MWA in an in vivo porcine liver model using contrast-enhanced computer tomography (CECT). We performed multiple hepatic MWA with constant energy parameters in healthy, narcotized and laparotomized domestic pigs. The volumes of the ablated areas were calculated from venous phase CT scans, immediately after the ablation and in short-term courses of up to 2 h after MWA. In total, 19 thermally ablated areas in 10 porcine livers could be analyzed (n = 6 with two volume measurements during the measurement period and n = 13 with three measurements). Both groups showed a statistically significant but heterogeneous volume reduction of up to 12% (median 6%) of the ablated zones in CECT scans during the measurement period (P < 0.001 [n = 13] and P = 0.042 [n = 6]). However, the dimension and dynamics of volume changes were heterogenous both absolutely and relatively. We observed a significant short-term volume reduction of ablated liver tissue in vivo. This volume shrinkage must be considered in clinical practice for technically successful tumor treatment by MWA and therefore it should be further investigated in in vivo studies.

Volumetric changes of intracranial metastases during the course of fractionated stereotactic radiosurgery and significance of adaptive planning.

Fractionated Gamma Knife surgery (FGKS) has recently been used to treat large brain metastases. However, little is known about specific volume changes of lesions during the course of treatment. The authors investigated short-term volume changes of metastatic lesions during FGKS. The authors analyzed 33 patients with 40 lesions who underwent FGKS for intracranial metastases of non-small-cell lung cancer (NSCLC; 25 patients with 32 lesions) and breast cancer (8 patients with 8 lesions). FGKS was performed in 3-5 fractions. Baseline MRI was performed before the first fraction. MRI was repeated after 1 or 2 fractions. Adaptive planning was executed based on new images. The median prescription dose was 8 Gy (range 6-10 Gy) with a 50% isodose line. On follow-up MRI, 18 of 40 lesions (45.0%) showed decreased tumor volumes (TVs). A significant difference was observed between baseline (median 15.8 cm3) and follow-up (median 14.2 cm3) volumes (p < 0.001). A conformity index was significantly decreased when it was assumed that adaptive planning was not implemented, from baseline (mean 0.96) to follow-up (mean 0.90, p < 0.001). The average reduction rate was 1.5% per day. The median follow-up duration was 29.5 weeks (range 9-94 weeks). During the follow-up period, local recurrence occurred in 5 lesions. The TV showed changes with a high dose of radiation during the course of FGKS. Volumetric change caused a significant difference in the clinical parameters. It is expected that adaptive planning would be helpful in the case of radiosensitive tumors such as NSCLCs or breast cancer to ensure an adequate dose to the target area and reduce unnecessary exposure of normal tissue to radiation.

Short-Term Shipping Freight Volume Prediction Based on Temporal-Spatial Features

Short-Term Shipping Freight Volume Prediction Based on Temporal-Spatial Features

Effect of acute volume overload on echocardiographic indices of right ventricular function and dyssynchrony assessed by use of speckle tracking echocardiography in healthy dogs.

OBJECTIVE To determine the relationship between acute volume overload and echocardiographic indices of right ventricular (RV) function and dyssynchrony in healthy dogs. ANIMALS 7 healthy Beagles. PROCEDURES Right heart catheterization and echocardiography were performed in 7 healthy anesthetized Beagles at baseline and after induction of volume overload. Volume overload was induced by IV infusion of lactated Ringer solution (150 mL/kg/h for 90 minutes). Echocardiographic indices of RV function, including peak velocity of systolic tricuspid annular motion, tricuspid annulus plane systolic excursion, fractional area change, RV Tei index, RV longitudinal strain (RVLS), and systolic RV longitudinal strain rate (RVLSR), were obtained by use of speckle tracking echocardiography (STE). In addition, SD of the systolic shortening time of the right ventricle for the 6 segments (RV-SD6) was determined with STE. RESULTS Volume overload significantly increased the RV end-diastolic pressure, compared with the baseline value. Echocardiographic indices of RV function, except for septal RVLSR, were significantly enhanced by volume overload. In contrast, RV-SD6 did not change with volume overload. Although echocardiographic indices of RV function, except for septal RVLSR, were correlated with RV end-diastolic pressure, RV-SD6 was not correlated. CONCLUSIONS AND CLINICAL RELEVANCE Echocardiographic indices of RV function, including RVLS and RVLSR, were affected by acute short-term volume overload. Therefore, results for assessment of RV function by use of STE in dogs with clinical conditions associated with right-sided chronic volume overload, such as tricuspid and pulmonic regurgitation, should be interpreted with caution.