Agena to Docter

This study examined records of 2566 consecutive COVID-19 patients at five Massachusetts hospitals and sought to predict level-of-care requirements based on clinical and laboratory data. Several classification methods were applied and compared against standard pneumonia severity scores. The need for hospitalization, ICU care, and mechanical ventilation were predicted with a validation accuracy of 88%, 87%, and 86%, respectively. Pneumonia severity scores achieve respective accuracies of 73% and 74% for ICU care and ventilation. When predictions are limited to patients with more complex disease, the accuracy of the ICU and ventilation prediction models achieved accuracy of 83% and 82%, respectively. Vital signs, age, BMI, dyspnea, and comorbidities were the most important predictors of hospitalization. Opacities on chest imaging, age, admission vital signs and symptoms, male gender, admission laboratory results, and diabetes were the most important risk factors for ICU admission and mechanical ventilation. The factors identified collectively form a signature of the novel COVID-19 disease.

The purpose of this research is to assess the role of flowproof strategy implementation in improving students’ mathematical proof in similarity and congruence. Proofing similarity and congruence is a prerequisite in developing mathematical skills. The subjects include prospective mathematics teachers, and the study used a qualitative descriptive technique. The implementation of the flow-proof strategy was achieved through four activities, including re-writing existed information, stating what is needed to be proved, using existed rules such as postulate and definition, and writing the results in flowcharts. The findings shows that flow-proof strategy on Geometry construction was implemented appropriately, based on students’ performance in similarity and congruence proofs with percentage of 72% from all research subject.

Validation of the Simplified Motor Score for the Prediction of Brain Injury Outcomes After Trauma

Standalone Virtual Reality (VR) headsets can be used when travelling in cars, trains and planes. However, the constrained spaces around transport seating can leave users with little physical space in which to interact using their hands or controllers, and can increase the risk of invading other passengers' personal space or hitting nearby objects and surfaces. This hinders transport VR users from using most commercial VR applications, which are designed for unobstructed 1-2m 360 ° home spaces. In this paper, we investigated whether three at-a-distance interaction techniques from the literature could be adapted to support common commercial VR movement inputs and so equalise the interaction capabilities of at-home and on-transport users: Linear Gain, Gaze-Supported Remote Hand, and AlphaCursor. First, we analysed commercial VR experiences to identify the most common movement inputs so that we could create gamified tasks based on them. We then investigated how well each technique could support these inputs from a constrained 50x50cm space (representative of an economy plane seat) through a user study (N=16), where participants played all three games with each technique. We measured task performance, unsafe movements (play boundary violations, total arm movement) and subjective experience and compared results to a control 'at-home' condition (with unconstrained movement) to determine how similar performance and experience were. Results showed that Linear Gain was the best technique, with similar performance and user experience to the 'at-home' condition, albeit at the expense of a high number of boundary violations and large arm movements. In contrast, AlphaCursor kept users within bounds and minimised arm movement, but suffered from poorer performance and experience. Based on the results, we provide eight guidelines for the use of, and research into, at-a-distance techniques and constrained spaces.

In this paper, we propose low error approximate absolute difference (LAD_X) hardware. LAD_X hardware has lower maximum and average error, and higher accuracy than the approximate absolute difference (AD) hardware in the literature. It has similar performance with and smaller area than the approximate AD hardware in the literature. The H.264 motion estimation (ME) hardware using LAD_X hardware performs higher quality ME than the H.264 ME hardware using the approximate AD hardware in the literature. It has similar performance with and smaller area than the H.264 ME hardware using the approximate AD hardware in the literature.

An affinity fingerprint is the vector consisting of compound’s affinity or potency against the reference panel of protein targets. Here, we present the QAFFP fingerprint, 440 elements long in silico QSAR-based affinity fingerprint, components of which are predicted by Random Forest regression models trained on bioactivity data from the ChEMBL database. Both real-valued (rv-QAFFP) and binary (b-QAFFP) versions of the QAFFP fingerprint were implemented and their performance in similarity searching, biological activity classification and scaffold hopping was assessed and compared to that of the 1024 bits long Morgan2 fingerprint (the RDKit implementation of the ECFP4 fingerprint). In both similarity searching and biological activity classification, the QAFFP fingerprint yields retrieval rates, measured by AUC (~ 0.65 and ~ 0.70 for similarity searching depending on data sets, and ~ 0.85 for classification) and EF5 (~ 4.67 and ~ 5.82 for similarity searching depending on data sets, and ~ 2.10 for classification), comparable to that of the Morgan2 fingerprint (similarity searching AUC of ~ 0.57 and ~ 0.66, and EF5 of ~ 4.09 and ~ 6.41, depending on data sets, classification AUC of ~ 0.87, and EF5 of ~ 2.16). However, the QAFFP fingerprint outperforms the Morgan2 fingerprint in scaffold hopping as it is able to retrieve 1146 out of existing 1749 scaffolds, while the Morgan2 fingerprint reveals only 864 scaffolds.

Image-to-image (I2I) translation networks have emerged as promising tools for generating synthetic medical images; however, their clinical reliability and ability to preserve diagnostically relevant features remain underexplored. This study evaluates the performance of state-of-the-art 2D/3D I2I networks for converting ultrasound (US) images to synthetic MRI in prostate cancer (PCa) imaging. The novelty lies in combining radiomics, expert clinical evaluation, and classification performance to comprehensively benchmark these models for potential integration into real-world diagnostic workflows. A dataset of 794 PCa patients was analyzed using ten leading I2I networks to synthesize MRI from US input. Radiomics feature (RF) analysis was performed using Spearman correlation to assess whether high-performing networks (SSIM > 0.85) preserved quantitative imaging biomarkers. A qualitative evaluation by seven experienced physicians assessed the anatomical realism, presence of artifacts, and diagnostic interpretability of synthetic images. Additionally, classification tasks using synthetic images were conducted using two machine learning and one deep learning model to assess the practical diagnostic benefit. Among all networks, 2D-Pix2Pix achieved the highest SSIM (0.855 ± 0.032). RF analysis showed that 76 out of 186 features were preserved post-translation, while the remainder were degraded or lost. Qualitative feedback revealed consistent issues with low-level feature preservation and artifact generation, particularly in lesion-rich regions. These evaluations were conducted to assess whether synthetic MRI retained clinically relevant patterns, supported expert interpretation, and improved diagnostic accuracy. Importantly, classification performance using synthetic MRI significantly exceeded that of US-based input, achieving average accuracy and AUC of ~ 0.93 ± 0.05. Although 2D-Pix2Pix showed the best overall performance in similarity and partial RF preservation, improvements are still required in lesion-level fidelity and artifact suppression. The combination of radiomics, qualitative, and classification analyses offered a holistic view of the current strengths and limitations of I2I models, supporting their potential in clinical applications pending further refinement and validation.

Recognition of spatio-temporal traffic patterns at the network-wide level plays an important role in data-driven intelligent transport systems (ITS) and is a basis for applications such as short-term prediction and scenario-based traffic management. Common practice in the transport literature is to rely on well-known general unsupervised machine-learning methods (e.g., k-means, hierarchical, spectral, DBSCAN) to select the most representative structure and number of day-types based solely on internal evaluation indices. These are easy to calculate but are limited since they only use information in the clustered dataset itself. In addition, the quality of clustering should ideally be demonstrated by external validation criteria, by expert assessment or the performance in its intended application. The main contribution of this paper is to test and compare the common practice of internal validation with external validation criteria represented by the application to short-term prediction, which also serves as a proxy for more general traffic management applications. When compared to external evaluation using short-term prediction, internal evaluation methods have a tendency to underestimate the number of representative day-types needed for the application. Additionally, the paper investigates the impact of using dimensionality reduction. By using just 0.1% of the original dataset dimensions, very similar clustering and prediction performance can be achieved, with up to 20 times lower computational costs, depending on the clustering method. K-means and agglomerative clustering may be the most scalable methods, using up to 60 times fewer computational resources for very similar prediction performance to the p-median clustering.

Faraday shield (FS), a thin-wall metal structure with slits, is used in inductive coupled plasma (ICP) sources to protect the ceramic tube from plasma erosion and eliminate capacitive coupling. Electromagnetic energy is coupled into the driver through the slits, which makes FS an important component of ICP sources. Material and structure, which are critical for FS, should have effects on the distribution of electromagnetic field and temperature. In this contribution, discussions on various materials and structures’ impact on electromagnetic and thermal performance arise. FS made of copper, aluminum, molybdenum and stainless steel, as well as FS of different thickness, slit width, slit numbers and slit shapes, are investigated by numerical simulations referring to the parameters of an actual ICP source. Electromagnetic field, RF power loss, temperature and stress of different FS are compared. The results show that Mo has similar electromagnetic performance with Cu but better thermal performance than Cu for FS. As for structure, RF power transfer efficiency decreases slightly as FS thickens. Appropriate slit width and number are required to allow a sufficient RF coupling and acceptable plasma heat flux to ceramic tube. RF efficiency of z-shaped slotted FS and normally slotted FS is almost the same.

Axial flux magnetic-geared double-rotor machines (AMGDRMs) are characterized as a potential core component of the power splitting system in hybrid electric vehicles (HEVs). Conventional AMGDRMs suffer from low mechanical strength due to high axial magnetic force. In this paper, an interior-modulating-rotor axial flux magnetic-geared double-rotor machine (IMR-AMGDRM) is proposed to solve this problem. An analytical model of the IMR-AMGDRM is proposed to predict the magnetic field distribution and electromagnetic performances. Since analytical model is based on 2-D geometry, a 2-D approximation of IMR-AMGDRM which has similar output performance to 3-D finite element analysis (FEA) is presented based on the permanent magnet (PM) magnetic energy equivalent principle. Utilizing subdomain method, the magnetic field distribution and electromagnetic performances are calculated. Finally, the accuracy of the proposed analytical model is validated by 3-D FEA.Index Terms—Analytical model, axial flux, magnetic-geared machine, double-rotor machine.I. IntroductionAxial flux magnetic-geared double-rotor machines (AMGDRMs), which enable high torque density, have been considered a promising candidate for power splitting in hybrid electric vehicles (HEVs) [1]. However, conventional AMGDRMs suffer from low mechanical strength due to high axial magnetic force. To enhance mechanical strength, an interior-modulating-rotor axial flux magnetic-geared double-rotor machine (IMR-AMGDRM) which can reduce axial magnetic force dramatically is proposed in this paper. Fig. 1 (a) and (b) illustrate the configuration of the proposed IMR-AMGDRM.As axial flux type machine, IMR-AMGDRMs have complex 3-D magnetic circuits. 3-D finite element analysis (FEA) can offer accurate results by taking into account the nonlinear factors such as magnetic saturation and flux leakage. But the major drawback of 3-D FEA is serious time cost. Analytical model which is much lower time-consuming provides an alternative for electromagnetic performance prediction in the initial design procedure.In this paper, an analytical model of the IMR-AMGDRM is proposed. The geometry of analytical model is derived based on PM magnetic energy equivalent principle to guarantee similar output performance to 3-D FEA. The solution to analytical model is calculated by subdomain method. To evaluate the accuracy of the proposed analytical model, the results computed by analytical model and that of 3-D FEA are compared.II. Configuration of IMR-AMGDRMIn conventional configuration, the inner and outer rotors are the permanent magnet (PM) rotor and the modulating rotor, respectively. Since the modulating rotor is closer to the PM rotor than stator, the modulating rotor suffers from high axial magnetic force. Furthermore, the modulating rotor is hollow and thus the low mechanical strength of modulating rotor becomes the major drawback of conventional configuration [1]. To solve this problem, the IMR-AMGDRM which switches positions of the PM rotor and the modulating rotor is proposed. In interior-modulating-rotor configuration, as shown in Fig. 1 (a) and (b), the PM rotor is between the modulating rotor and stator, i.e. the inner and outer rotors are the modulating rotor and the PM rotor, respectively. Owing to that the PM rotor suffers from reverse axial magnetic forces from both sides simultaneously, the total axial magnetic force is dramatically reduced. Hence, although the outer rotor is hollow in both configurations, the axial magnetic force of interior-modulating-rotor configuration is significantly decreased, leading to higher mechanical strength and reliability.III.Geometry of 2-D ApproximationGenerally, the unrolled section of axial flux machine at mean radius is used as 2-D approximation. However, few references are found to evaluate the electromagnetic performance equivalence of such approximation. To make the electromagnetic performance of 3-D model and 2-D approximation equal, the amount of magnetic energy of the PM should remain unchanged after approximation, i.e. PM magnetic energy equivalent principle [2]. A 2-D approximation of IMR-AMGDRM which has identical output performance is presented by using PM magnetic energy equivalent principle.IV. Analytical Calculation of IMR-AMGDRMThe proposed analytical model is illustrated in Fig. 1 (c). It can be seen that the model consists of four subdomains. Region i, I, II and III represent the ith slot of the modulating rotor, the inner air gap, the PM rotor and the outer air gap, respectively. Utilizing subdomain method, the magnetic field distributions in the inner and outer air gaps are obtained by the solution of Laplace’s and Poisson’s equations. Then, the electromagnetic performances such as no-load back EMF, torque and axial force can be derived from the calculated magnetic field distributions.Comparisons of computation accuracy between analytical model and 3-D FEA is shown in Fig. 2. As can be observed, the results of analytical model are well consistent with that of 3-D FEA, validating the proposed method. **

Fifty three children aged 6 years old who had weighed less than 1251 g at birth without cerebral palsy and receiving mainstream education, were entered into a controlled study of motor skills. The index and control children were matched by age, sex, and school. The index group were considered by their teachers to have similar academic performances to their index group were considered by their teachers to have similar academic performances to their controls, although two index cases were receiving remedial teaching. On the test of motor impairment extremely low birthweight children had significantly more motor difficulties than controls. In addition, the index group had more minor neurological signs, lower intelligence quotients, and more adverse behavioural traits. The higher motor impairment scores among index children were independent of differences in intelligence quotient between the two groups. There was no association between impairment score and the presence or degree of periventricular haemorrhage or periventricular leucomalacia on neonatal cerebral ultrasound. Children with Apgar scores at five minutes of less than 7 had significantly higher impairment scores compared with those whose scores were 7 or more. Three perinatal factors (Apgar score at five minutes, neonatal septicaemia, and abnormal movements) explained 32% of the variance in impairment score at the age of 6 years. In children who do not have cerebral palsy perinatal factors may still be important in the development of motor skills. The presence of subtle neuromotor impairments at 6 years of age has implications for schooling that need further evaluation.

To investigate the imaging findings of macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) on CT and MRI, and examine their diagnostic performance and prognostic significance. We retrospectively enrolled 220 consecutive patients who underwent hepatic resection between June 2009 and December 2013 for single treatment-naïve HCC, who have preoperative CT and gadoxetic acid-enhanced MRI. Independent reviews of histopathology and imaging were performed by two reviewers. Previously reported imaging findings, LI-RADS category, and CT attenuation of MTM-HCC were investigated. The diagnostic performance of the MTM-HCC diagnostic criteria was compared across imaging modalities. MTM-HCC was associated with ≥ 50% arterial phase hypovascular component, intratumoral artery, arterial phase peritumoral enhancement, and non-smooth tumor margin on CT and MRI (p < .05). Arterial phase hypovascular components were less commonly observed on MRI subtraction images than on CT or MRI, while non-rim arterial phase hyperenhancement and LR-5 were more commonly observed on MRI subtraction images than on MRI (p < .05). MTM-HCC showed lower tumor attenuation in the CT arterial phase (p = .01). Rhee's criteria, defined as ≥ 50% hypovascular component and ≥ 2 ancillary findings (intratumoral artery, arterial phase peritumoral enhancement, and non-smooth tumor margin), showed similar diagnostic performance for MRI (sensitivity, 41%; specificity, 97%) and CT (sensitivity, 31%; specificity, 94%). Rhee's criteria on CT were independent prognostic factors for overall survival. The MRI diagnostic criteria for MTM-HCC are applicable on CT, showing similar diagnostic performance and prognostic significance. For MTM-HCC, arterial phase subtraction images can aid in the HCC diagnosis by depicting subtle arterial hypervascularity. • MTM-HCC on CT demonstrated previously described MRI findings, including arterial phase hypovascular component, intratumoral artery, arterial phase peritumoral enhancement, and necrosis. • The MRI diagnostic criteria for MTM-HCC were also applicable to CT, showing comparable diagnostic performance and prognostic significance. • On arterial phase subtraction imaging, MTM-HCC more frequently demonstrated non-rim enhancement and LR-5 and less frequently LR-M than MRI arterial phase, which may aid in the diagnosis of HCC.

The frictional performance of a bearing is of significant interest in any mechanical system where there are lubricated surfaces under load and in relative motion. Surface topography plays a major role in determining the coefficient of friction for the bearing because the size of the fluid film and topography are of a comparable order. The problem of optimising topography for such a system is complicated by the separation in scales between the size of the lubricated domain and that of the topography, which is of at least one order of magnitude or more smaller. This paper introduces a multiscale method for optimising the small scale topography for improved frictional performance of the large scale bearing. The approach fully couples the elastohydrodynamic lubrication at both scales between pressure generated in the lubricant and deformation of the bounding surfaces. Homogenised small scale data is used to inform the large scale model and is represented using Moving Least Squares metamodels calibrated by cross validation. An optimal topography for a minimum coefficient of friction for the bearing is identified and comparisons made of local minima in the response, where very different topographies with similar frictional performance are observed. Comparisons of the optimal topography with the smooth surface model demonstrated the complexity of capturing the non-linear effect of topography and the necessity of the multiscale method in capturing this. Deviations from the smooth surface model were quantified by the metamodel coefficients and showed how topographies with a similar frictional performance have very different characteristics.

Caches often employ write-back instead of write-through, since write-back avoids unnecessary transfers for multiple writes to the same block. For several reasons, however, it is undesirable that a significant number of cache lines will be marked "dirty". Energy-efficient cache organizations, for example, often apply techniques that resize, reconfigure, or turn off (parts of) the cache. In such cache organizations, dirty lines have to be written back before the cache is reconfigured. The delay imposed by these write-backs or the required additional logic and buffers can significantly reduce the attained energy savings. A cache organization called the clean/dirty cache (CD-cache) is proposed that combines the properties of write-back and write-through. It avoids unnecessary transfers for recurring writes, while restricting the number of dirty lines to a hard limit. Detailed experimental results show that the CD-cache reduces the number of dirty lines significantly, while achieving similar or better performance. We also use the CD-cache to implement cache decay. Experimental results show that the CD-cache attains similar or higher performance than a normal decay cache, while using a significantly less complex design.

Correlation and validity of imputed PaO2/FiO2 and SpO2/FiO2 in patients with invasive mechanical ventilation at 2600 m above sea level