Center Of Data Research Articles

Diagnosing observed extratropical stationary wave changes in boreal winter

Stationary waves are time-averaged zonally asymmetric component of the climatological mean atmospheric circulation, primarily due to the unevenly distributed topography and diabatic heating. Stationary waves are subject to influence from long-term external forcing. In this study, the temporal evolution of the winter (January) Northern Hemisphere stationary waves during 1961–2020 is diagnosed with the fifth generation European Centre for Medium-range Weather Forecasts reanalysis data (ERA5), which shows an overall strengthening in amplitude and an eastward shift in phase. A stationary wave model is used to attribute the stationary wave response to changes in the zonal mean basic state (ΔZM) and the zonally asymmetric diabatic heating forcing (). The pattern of stationary wave changes is well captured by the response to ΔZM alone, whereas the contribution of to the amplitude increases in height and becomes dominant in the stratosphere. is also found to be important in driving stationary wave changes in the North Pacific and Western Europe regions. Furthermore, changes in tropospheric stationary waves are probably a result of internal variability, whereas stratospheric changes are more likely to be driven by external forcing.

Temporal trends in mortality location in patients with anal cancer in the USA: an analysis of the National Center for Health Statistics mortality data

ObjectivesInvestigate trends in where patients died of anal cancer in the USA.MethodsRetrospective cohort study using the US National Center for Health Statistics Wide-Ranging ONline Data for Epidemiologic Research platform from...

Racial and ethnic disparities in early mortality among patients with inborn errors of immunity

Racial and ethnic disparities in life expectancy in the United States have been widely documented. To date, there remains a paucity of similar data in patients with inborn errors of immunity (IEIs). Our aim was to examine racial and ethnic differences in mortality due to an IEI in the United States. We analyzed National Center for Health Statistics national mortality data from 2003 to 2018. We quantified age-adjusted death rate and age-specific death rate as a result of an IEI for each major racial and ethnic group in the United States and examined the association of race and ethnicity with death at a younger age. From 2003 to 2018, IEIs were reported as the underlying or contributing cause of death in 14,970 individuals nationwide. The age-adjusted death rate was highest among Black patients (4.25 per 1,000,000 person years), compared with 2.01, 1.71, 1.50, and 0.92 per 1,000,000 person years for White, American Indian/Alaska Native, Hispanic, and Asian/Pacific Islander patients, respectively. The odds of death before age 65 years were greatest among Black patients (odds ratio [OR]= 5.15 [95% CI= 4.61-5.76]), followed by American Indian/Alaska Native patients (OR= 3.58 [95% CI= 2.30-5.82]), compared with White patients. The odds of death before age 24 years were greater among Hispanic patients than among non-Hispanic patients (OR= 3.60 [95% CI= 3.08-4.18]). Our study highlights racial and ethnic disparities in mortality due to an IEI and the urgent need to further identify and systematically remove barriers in care for historically marginalized patients with IEIs.

Backup Battery Allocation and Workload Migration Against Electrical Load Shedding at Edge

In the 5G era (and the upcoming 6G), Mobile Edge Computing (MEC) has been advocated to serve the massive amount of Internet of Things (IoT) devices by base stations (BSs) and edge data centers (EDCs). Geo-distributed EDCs are generally of much smaller scales as compared to mega DCs and hence of much lower costs, but can have fast response to their users so as to satisfy the demands of real-time applications. As their reliability and availability heavily depend on the electrical power supply, most EDCs are equipped with battery groups as backup power in case of power grid load shedding or outage. In a heterogeneous geo-distributed environment, the QoS of heavily loaded EDCs however can be severely impacted by limited backup power while lightly loaded EDCs may simply waste such precious resources. Moreover, a heavily loaded EDC may suffer from deep discharge of its battery group, which will cause a significant reduction of battery capacity and lifetime. This further aggravates the aforementioned situations should load shedding/outage happen again. In this paper, we carefully analyze the workloads in EDCs and classify them into interactive workloads and batch workloads, respectively. We then develop a novel battery allocation framework with smart workload migration for EDCs, which simultaneously protects interactive workloads from being interrupted and minimizes the waiting time of batch workloads. Our extensive evaluations show that our strategies can optimize all the objectives within a limited overall cost as compared to state-of-the-art practical allocation.

Geriatric Distal Femur Fractures Treated With Distal Femoral Replacement Are Associated With Higher Rates of Readmissions and Complications.

Compare mortality and complications of distal femur fracture repair among elderly patients who receive operative fixation versus distal femur replacement (DFR). Retrospective comparison. Medicare beneficiaries. Patients 65 years of age and older with distal femur fracture identified using Center for Medicare & Medicaid Services data from 2016 to 2019. Operative fixation (open reduction with plating or intramedullary nail) or DFR. Mortality, readmissions, perioperative complications, and 90-day cost were compared between groups using Mahalanobis nearest-neighbor matching to account for differences in age, sex, race, and the Charlson Comorbidity Index. Most patients (90%, 28,251/31,380) received operative fixation. Patients in the fixation group were significantly older (81.1 vs. 80.4 years, P < 0.001), and there were more an open fractures (1.6% vs. 0.5%, P < 0.001). There were no differences in 90-day (difference: 1.2% [-0.5% to 3%], P = 0.16), 6-month (difference: 0.6% [-1.5% to 2.7%], P = 0.59), and 1-year mortality (difference: -3.3% [-2.9 to 2.3], P = 0.80). DFR had greater 90-day (difference: 5.4% [2.8%-8.1%], P < 0.001), 6-month (difference: 6.5% [3.1%-9.9%], P < 0.001), and 1-year readmission (difference: 5.5% [2.2-8.7], P = 0.001). DFR had significantly greater rates of infection, pulmonary embolism, deep vein thrombosis, and device-related complication within 1 year from surgery. DFR ($57,894) was significantly more expensive than operative fixation ($46,016; P < 0.001) during the total 90-day episode. Elderly patients with distal femur fracture have a 22.5% 1-year mortality rate. DFR was associated with significantly greater infection, device-related complication, pulmonary embolism, deep vein thrombosis, cost, and readmission within 90 days, 6 months, and 1 year of surgery. Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.

Leveraging artificial intelligence and mutual authentication to optimize content caching in edge data centers

Available online Edge data centers are designed to meet the stringent QoE requirements of delay-sensitive and computationally intensive services in Content Delivery Network (CDN) and 5G networks. The primary purpose of this paper was to formulate and solve the problem of optimizing many control variables jointly: (i) what contents to store by taking into consideration edge capacity, and (ii) what contents to recommend to each Internet of Everything (IoE) item, based on identity and access management (IAM). In reactive caching policy, we proposed a new Two-Factor Authentication (2FA) scheme founded upon the Elliptic Curve Cryptography (ECC) and one-way hash function for access control. More interestingly, we use Non-negative Matrix Factorization (NMF), Fuzzy C-Means (FCM), Random Forest (RF) and Pearson Correlation (PC) to improve the accuracy and latency of traditional data filtering models. The intelligent recommendation engine we propose is designed to be implemented by cloud for caching and prefetching contents at the edge. The experimental results validate the theoretical guarantees of the proposed solution and its ability to achieve significant performance gains compared to common baseline models.

Validation of the Atmospheric Dispersion Model NAME against Long-Range Tracer Release Experiments

Abstract The Met Office’s atmospheric dispersion model Numerical Atmospheric-Dispersion Modeling Environment (NAME) is validated against controlled tracer release experiments, considering the impact of the driving meteorological data and choices in the parameterization of unresolved motions. The Cross-Appalachian Tracer Experiment (CAPTEX) and Across North America Tracer Experiment (ANATEX) were long-range dispersion experiments in which inert tracers were released and the air concentrations measured across North America in the 1980s. NAME simulations of the experiments have been driven by both reanalysis meteorological data from European Centre for Medium-Range Weather Forecasts (ECMWF) and data from the Advanced Research version of the Weather Research and Forecasting (WRF) Model. NAME predictions of air concentrations are assessed against the experimental measurements, using a ranking method composed of four statistical parameters. Differences in the performance of NAME according to this ranking method are compared when driven by different meteorological sources. The effect of changing parameter values in NAME for the unresolved mesoscale motions parameterization is also considered, in particular, whether the parameter values giving the best performance rank are consistent with values typically used. The performance ranks are compared with analyses in the literature for other particle dispersion models, namely, Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), Stochastic Time-Inverted Lagrangian Transport (STILT), and Flexible Particle (FLEXPART). It is found that NAME performance is comparable to the other dispersion models considered, with the different models responding similarly to differences in driving meteorological data.

Travel distance and social vulnerability index: Impact on liver-related mortality among patients with end-stage liver disease.

The reasons for the geographic disparities in liver-related mortality across the US remain ill-defined. We sought to investigate the impact of travel distance to liver transplantation (LT) programs and social vulnerability on county differences in liver-related mortality. Data on LT registrants were obtained from the Scientific Registry of Transplant Recipients Standard Analytic Files (SRTR SAFs) between 2004 and 2019. Liver-related mortality data were obtained from the Center for Disease Control and Prevention's Wide-ranging Online Data for Epidemiologic Research (CDC WONDER) platform. Spatial epidemiological clustering of county-level LT registration and liver-related mortality rates was determined using local Moran's I. Comparison analyses assessed social vulnerability index (SVI) and travel distance within various county clusters. Among 151864 LT waitlist registrants who were diagnosed with liver disease due to hepatitis C virus (HCV) or hepatitis B virus (HBV) (n=68479, 45.1%), alcohol (n=38328, 25.2%), non-alcoholic steatohepatitis (NASH) (n=17485, 11.5%), liver tumors (n=16644, 11.0%), and other diseases (n = 10928, 7.2%), median SVI was 59.3 (IQR, 40.1-83.4). SVI (76.2 vs. 24.3, p<.001) was greater in the highest versus lowest liver-related mortality quartiles. The travel distances to LT centers (143.1 miles vs. 107.2 miles, p<.001) was longer in the lowest versus highest LT registration quartiles. Counties with low LT registration rates and high liver-related mortality rates were associated with long travel distances and high SVI. In contrast, while counties with high LT registration rates and high liver-related mortality rates had comparable SVI, travel distance was relatively shorter. Counties with greater SVIs were associated with higher liver-related mortality, with the highest SVI counties having the highest overall liver-related mortality. Longer travel distances were associated with higher liver-related mortality. These findings highlight the impact of social determinants of health (SDOH) on liver disease outcomes.

Deaths Due to COVID-19 in Patients With Cancer During Different Waves of the Pandemic in the US

With the ongoing relaxation of guidelines to prevent COVID-19 transmission, particularly in hospital settings, medically vulnerable groups, such as patients with cancer, may experience a disparate burden of COVID-19 mortality compared with the general population. To evaluate COVID-19 mortality among US patients with cancer compared with the general US population during different waves of the pandemic. This cross-sectional study used data from the Center for Disease Control and Prevention's Wide-Ranging Online Data for Epidemiologic Research database to examine COVID-19 mortality among US patients with cancer and the general population from March 1, 2020, to May 31, 2022. The number of deaths due to COVID-19 during the 2021 to 2022 winter Omicron surge was compared with deaths during the preceding year's COVID-19 winter surge (when the wild-type SARS-CoV-2 variant was predominant) using mortality ratios. Data were analyzed from July 21 through August 31, 2022. Pandemic wave during which the wild-type variant (December 2020 to February 2021), Delta variant (July 2021 to November 2021), or Omicron variant (December 2021 to February 2022) was predominant. Number of COVID-19 deaths per month. The sample included 34 350 patients with cancer (14 498 females [42.2%] and 19 852 males [57.8%]) and 628 156 members of the general public (276 878 females [44.1%] and 351 278 males [55.9%]) who died from COVID-19 when the wild-type (December 2020-February 2021), Delta (July 2021-November 2021), and winter Omicron (December 2021-February 2022) variants were predominant. Among patients with cancer, the greatest number of COVID-19 deaths per month occurred during the winter Omicron period (n = 5958): at the peak of the winter Omicron period, there were 18% more deaths compared with the peak of the wild-type period. In contrast, among the general public, the greatest number of COVID-19 deaths per month occurred during the wild-type period (n = 105 327), and at the peak of the winter Omicron period, there were 21% fewer COVID-19 deaths compared with the peak of the wild-type period. In subgroup analyses by cancer site, COVID-19 mortality increased the most, by 38%, among patients with lymphoma during the winter Omicron period vs the wild-type period. Findings of this cross-sectional study suggest that patients with cancer had a disparate burden of COVID-19 mortality during the winter Omicron wave compared with the general US population. With the emergence of new, immune-evasive SARS-CoV-2 variants, many of which are anticipated to be resistant to monoclonal antibody treatments, strategies to prevent COVID-19 transmission should remain a high priority.

An Image Classification Method Based on Semi-Supervised Classification Learning and Convolutional Neural Networks

This paper aims to propose an improved image classification model to reduce the cost of model construction. Aiming at the problem that network training usually requires the support of a large number of labeled samples, an image classification model based on semi-supervised deep learning is proposed, which uses labeled samples to guide the network to learn unlabeled samples. A convolutional neural network model for simultaneous processing of labeled and unlabeled data is constructed. The tagged data is used to train the Softmax classifier and provide the initial K-means clustering center for the untagged data. The nonsubsampling contourlet layer is used to replace the first convolutional layer of the full convolutional neural network to extract multi-scale depth features, and the nonsubsampling contourlet full convolutional neural network is constructed. The network can extract multi-scale information of the images to be classified, and extract more discriminative deep image features. In addition, the parameters of the nonsubsampled contourlet layers are pre-set and do not require network training. The proposed method has higher classification accuracy than the contrast method on polarimetric SAR images using the nonsubsampled contourlet full convolutional neural network.

The Rising Costs of Alcohol-Associated Liver Disease in the United States.

Alcohol-associated liver disease (ALD) is rising in the United States because of an increase in high-risk drinking, but population-level ALD cost is unknown. Our aim was to project the direct and indirect costs associated with ALD in the US population through 2040. We used a previously validated microsimulation model of alcohol consumption and ALD with model parameters estimated from publicly available data sources, including the National Epidemiologic Survey Alcohol and Related Conditions-III, the Center for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research, the Bureau of Labor Statistics, and published studies informing the impact of alcohol consumption on ALD severity in the United States resident population. The simulated scenario included current and projected ALD-associated costs. From 2022 to 2040, the ALD is projected to cost $880 billion, $355 billion in direct healthcare-related costs, and $525 billion in lost labor and economic consumption. The annual cost of ALD is projected to increase from $31 billion in 2022 to $66 billion (118% increase) in 2040. Although the female population makes up 29% of these costs in 2022, by 2040 on a per annum basis, female costs would be 43% of the total annual expenditure. Increased consumption of alcohol in the US population, especially in females, will cause a steep rise in the economic burden of ALD in the United States. These findings highlight the need for planners and policymakers to plan for the increased impact of liver disease in the United States.

Self-Adaptive Filtering for Ultra-Large-Scale Airborne LiDAR Data in Urban Environments Based on Object Primitive Global Energy Minimization

Filtering from airborne LiDAR datasets in urban area is one important process during the building of digital and smart cities. However, the existing filters encounter poor filtering performance and heavy computational burden when processing large-scale and complicated urban environments. To tackle this issue, a self-adaptive filtering method based on object primitive global energy minimization is proposed in this paper. In this paper, mode points were first acquired for generating the mode graph. The mode points were the cluster centers of the LiDAR data obtained in a mean shift algorithm. The graph constructed with mode points was named “mode graph” in this paper. By defining the energy function based on the mode graph, the filtering process is transformed to iterative global energy minimization. In each iteration, the graph cuts technique was adopted to achieve global energy minimization. Meanwhile, the probability of each point belonging to the ground was updated, which would lead to a new refined ground surface using the points whose probabilities were greater than 0.5. This process was iterated until two successive fitted ground surfaces were determined to be close enough. Four urban samples with different urban environments were adopted for verifying the effectiveness of the filter developed in this paper. Experimental results indicate that the developed filter obtained the best filtering performance. Both the total error and the Kappa coefficient are superior to those of the other three classical filtering methods.

Risk of Severe Maternal Morbidity (SMM) in patients with Placenta Accreta Spectrum (PAS) disorders referred from rural communities to a regional PAS center.

The primary objective of this study was to assess the risk of Severe Maternal Morbidity (SMM) experienced by patients residing in rural communities when delivered by a multidisciplinary team within a single urban academic center for Placenta Accreta Spectrum (PAS). Subsequently, we aimed to determine a distance dependent relationship between PAS morbidity and distance travelled by patients in rural communities. This was a retrospective cohort study of patients who had PAS histopathologic confirmation and delivery at our institution from 2005-2022. Our objective was to determine the relationship between patient locations (rural vs. urban) and maternal morbidity associated with PAS delivery. Socio-geographic determination of rurality was determined using the National Center for Health Statistics and most recent national census population data. Distance travelled was calculated by patient zip code to our PAS center using global positioning system data. During the study period, 139 patients were managed by cesarean hysterectomy with confirmed PAS histopathology. Of these, ninety-four (67.6%) were from our urban community and forty-five (32.4%) were from surrounding rural communities. The overall SMM incidence was 85% including blood transfusion and 17% without blood transfusions. Patient from rural communities were more likely to experience SMM (28.9% vs. 12.8%, p=0.03) and this was driven by acute renal failure (1.1 vs 11.1%, p=0.01) and disseminated intravascular coagulopathy (1.1 vs 8.8%, p=0.04). SMM did reveal a a distance-dependent relationship with SMM rates of 13.2%, 33.3%, and 43.8% at 50, 100 and 150 miles, respectively (p=0.005). Patients with PAS experience high rates of SMM. Geographic distance to a PAS center appears to significantly impact the overall morbidity a patient experiences. Further research is warranted to address this disparity and optimize patient outcomes for patients in rural communities.

Optimization of Revision Hip Arthroplasty Workflow by Means of Detailed Pre-Surgical Planning Using Computed Tomography Data, Open-Source Software and Three-Dimensional-Printed Models.

In revision hip arthroplasty (RHA), establishing the center of rotation (COR) can be technically challenging due to the acetabular bone destruction that is usually present, particularly in severe cases such as Paprosky type II and III defects. The aim of this study was to demonstrate the use of open-source medical image reconstruction software and low-cost 3D anatomical models in pre-surgical planning of RHA. A total of 10 patients, underwent RHA and were included in the study. Computed tomography (CT) scans were performed for all cases, before surgery and approximately 1 week after the procedure. The reconstruction of CT data, 3D virtual planning of the COR and positioning of acetabular cups, including their inclination and anteversion angles, was carried out using the free open source software platform 3D Slicer. In addition, anatomical models of the pelvis were built on a desktop 3D printer from polylactic acid (PLA). Preoperative and postoperative reconstructed imaging data were compared for each patient, and the position of the acetabular cups as well as the COR were evaluated for each case. Analysis of the pre- and post-op center of rotation position data indicated statistically insignificant differences for the location of the COR on the X-axis (1.5 mm, t = 0.5741, p = 0.5868) with a fairly strong correlation of the results (r = -0.672, p = 0.0982), whilst for the location of the COR in the Y and Z-axes, there was statistical dependence (Y axis, 4.7 mm, t = 3.168 and p = 0.0194; Z axis, 1.9 mm, t = 1.887 and p = 0.1081). A strong correlation for both axes was also observed (Y and Z) (Y-axis, r = 0.9438 and p = 0.0014; Z-axis, r = 0.8829 and p = 0.0084). Analysis of inclination angle values showed a statistically insignificant difference between mean values (3.9 degrees, t = 1.111, p = 0.3092) and a moderate correlation was found between mean values (r = -0.4042, p = 0.3685). Analysis of the anteversion angle showed a statistically insignificant difference between mean values (1.9 degrees, t = 0.8671, p = 0.4192), while a moderate correlation between mean values was found (r = -0.4782, p = 0.2777). Three-dimensional reconstruction software, together with low-cost anatomical models, are very effective tools for pre-surgical planning, which have great potential use in orthopedic surgery, particularly RHA. In up and in- and up and out-type defects, it is essential to establish a new COR and to identify three support points within the revision acetabulum in order to correctly position acetabular cups.

DETECTION OF KNEE OSTEOARTHRITIS BASED ON CENTER OF PRESSURE DATA AND THE BAT ALGORITHM

The high rate of knee osteoarthritis has raised the need for accurate diagnostic methods. In this study, we propose a precise detection method using the center of pressure data obtained from the patients. The introduced automatic detection pipeline is based on the two modern algorithms of grey wolf and BAT. The extracted statistical features and the obtained data from healthy individuals and patients are processed with the grey wolf binary algorithm. The results are fed into the binary bat algorithm to select important features and increase the pipeline accuracy. Then the groups are classified using a four-layer neural network. We show that the proposed method with a simple four-layer neural network offers fantastic accuracy in high-speed processing large data and classifies the high-dimensional knee osteoarthritis center of pressure data with appropriate precision, recall, specificity, and F1 values. The proposed method has direct applications in knee osteoarthritis diagnostics in clinics.

Relationship between Lidar-Derived Canopy Densities and the Scattering Phase Center of High-Resolution TanDEM-X Data

The estimation of forestry parameters is essential to understanding the three-dimensional structure of forests. In this respect, the potential of X-band synthetic aperture radar (SAR) has been recognized for years. Many studies have been conducted on deriving tree heights with SAR data, but few have paid attention to the effects of the canopy structure. Canopy density plays an important role since it provides information about the vertical distribution of dominant scatterers in the forest. In this study, the position of the scattering phase center (SPC) of interferometric X-band SAR data is investigated with regard to the densest vegetation layer in a deciduous and coniferous forest in Germany by applying a canopy density index from high-resolution airborne laser scanning data. Two different methods defining the densest layer are introduced and compared with the position of the TanDEM-X SPC. The results indicate that the position of the SPC often coincides with the densest layer, with mean differences ranging from −1.6 m to +0.7 m in the deciduous forest and +1.9 m in the coniferous forest. Regarding relative tree heights, the SAR signal on average penetrates up to 15% (3.4 m) of the average tree height in the coniferous forest. In the deciduous forest, the difference increases to 18% (6.2 m) during summer and 24% (8.2 m) during winter. These findings highlight the importance of considering not only tree height but also canopy density when delineating SAR-based forest heights. The vertical structure of the canopy influences the position of the SPC, and incorporating canopy density can improve the accuracy of SAR-derived forest height estimations.

Data requirements and availabilities for material passports: A digitally enabled framework for improving the circularity of existing buildings

Passports for circularity, e.g., digital product passports and material passports (MPs), have gained recognition as essential policy instruments for the Circular Economy goals of the European Union. Despite the growing number of approaches, there is a lack of knowledge about the data requirements and availabilities to create MPs for existing buildings. By deploying a mixed-method research design, this study identified the potential users and their data needs within the context of European social housing organisations. Three rounds of validation interviews with a total of 38 participants were conducted to create a data template for an MP covering maintenance, renovation, and demolition stages. This data template was then tested in a case study from the Netherlands to determine critical data gaps in creating MPs, including, but not limited to the composition of materials, presence of toxic or hazardous contents, condition assessment, and reuse and recycling potential of a product. Finally, an MP framework is proposed to address these data gaps by utilising the capabilities of enabling digital technologies (e.g., artificial intelligence and scanning systems) and supportive knowledge of human actors. This framework supports further research and innovation in data provision in creating MPs to narrow, slow, close, and regenerate the loops.

Effects of a proprioceptive focal stimulation (Equistasi®) on reducing the biomechanical risk factors associated with ACL injury in female footballers.

Football presents a high rate of lower limb injuries and high incidence of Anterior Cruciate Ligament (ACL) rupture, especially in women. Due to this there is the need to optimize current prevention programs. This study aims to verify the possibility to reduce the biomechanical risk factors associated with ACL injury, through the application of proprioceptive stimulation by means of the Equistasi® device. Ten elite female footballers were enrolled and received the device for 4 weeks (5 days/week, 1h/day). Athletes were assessed directly on-field at four time points: T0 and T1 (evaluation without and with the device), T2 (after 2 weeks), T4 (after 4 weeks) while performing two different tasks: Romberg Test, and four sidestep cutting maneuvers bilaterally. Seven video cameras synchronized with a plantar pressure system were used, thirty double colored tapes were applied on anatomical landmarks, and three dimensional coordinates reconstructed. Vertical ground reaction forces and center of pressure data were extracted from the plantar pressure insoles. Hip, knee, and ankle flexion-extension angles and moments were computed as well as abd-adduction joint torques. From the Romberg Test both center of pressure descriptive variables and frequency analysis parameters were extracted. Each variable was compared among the different time frames, T1, T2 and T4, through Friedman Test for non-parametric repeated measures (p<0.05); Wilcoxon Signed Rank Test was used for comparing variables between T0 and T1 (p<0.05) and across the different time frames as follows: T1-T2, T2-T4 and T1-T4. Statistically significant differences in both posturographic and biomechanical variables between the assessment at T0 and T1 were detected. Reduced hip and knee abduction torques were revealed in association with reduced both ground reaction forces and ankle dorsiflexion torque from T1 up to T4. The proprioceptive stimuli showed to have the potential to improve cutting biomechanics mainly with respect to the ligament and quadriceps dominance theories. Results of the present study, even if preliminary and on a small sample size, could be considered promising towards the inclusion of proprioceptive training in injury prevention programs.

Fine-Grained Online Energy Management of Edge Data Centers Using Per-Core Power Gating and Dynamic Voltage and Frequency Scaling

It is important to minimize the energy consumption of large-scale, geographically distributed edge data centers (EDCs). While modern processing units (PUs) have energy-saving features like Dynamic Voltage and Frequency Scaling (DVFS) and Per-Core Power Gating (PCPG), optimization is still complex and requires a holistic approach. This paper presents a new decentralized, three-timescale, online optimization approach that enables multicore micro data centers (MDCs) to optimize their per-PU power states, per-enabled-PU voltage-frequency levels and offloading schedules at three different timescales. The key idea is that we employ multi-timescale Lyapunov optimization to decouple the energy minimization between workload scheduling and result delivery at a small timescale and PU configuration at large timescales. Another important aspect is that we apply the primal decomposition to decouple the PU configuration between a per-enabled-PU voltage-frequency level at an intermediate timescale and a per-PU power state at a large timescale. Experiments demonstrate that the proposed approach improves energy efficiency significantly by up to 4.5 times in our considered lightly loaded situations where DVFS alone does not work effectively, compared to existing benchmarks.

Free-breathing high isotropic resolution quantitative susceptibility mapping (QSM) of liver using 3D multi-echo UTE cones acquisition and respiratory motion-resolved image reconstruction.

To enable free-breathing and high isotropic resolution liver quantitative susceptibility mapping (QSM) using 3D multi-echo UTE cones acquisition and respiratory motion-resolved image reconstruction. Using 3D multi-echo UTE cones MRI, a respiratory motion was estimated from the k-space center of the imaging data. After sorting the k-space data with estimated motion, respiratory motion state-resolved reconstruction was performed for multi-echo data followed by nonlinear least-squares fitting for proton density fat fraction (PDFF), , and fat-corrected B0 field maps. PDFF and B0 field maps were subsequently used for QSM reconstruction. The proposed method was compared with motion-averaged (gridding) reconstruction and conventional 3D multi-echo Cartesian MRI in moving gadolinium phantom and in vivo studies. Region of interest (ROI)-based linear regression analysis was performed on these methods to investigate correlations between gadolinium concentration and QSM in the phantom study and between and QSM in in vivo study. Cones with motion-resolved reconstruction showed sharper image quality compared to motion-averaged reconstruction with a substantial reduction of motion artifacts in both moving phantom and in vivo studies. For ROI-based linear regression analysis of the phantom study, susceptibility values from cones with motion-resolved reconstruction ( = 0.31 0.05, = 0.999) and Cartesian without motion ( = 0.32 0.04, = 1.000) showed linear relationships with gadolinium concentrations and showed good agreement with each other. For in vivo, motion-resolved reconstruction showed higher goodness of fit ( = 0.00261 0.524, = 0.977) compared to motion-averaged reconstruction ( = 0.0021 0.572, = 0.723) in ROI-based linear regression analysis between and QSM. Feasibility of free-breathing liver QSM was demonstrated with motion-resolved 3D multi-echo UTE cones MRI, achieving high isotropic resolution currently unachievable in conventional Cartesian MRI.