Average stresses and force fluctuations in non-cohesive granular materials

A lattice model is presented for investigating the fluctuations in static granular materials under gravitationally induced stress. The model is similar in spirit to the scalar q-model of Coppersmith et al., but ensures balance of all components of forces and torques at each site. The geometric randomness in real granular materials is modeled by choosing random variables at each site, consistent with the assumption of cohesionless grains. Configurations of the model can be generated rapidly, allowing the statistical study of relatively large systems. For a 2D system with rough walls, the model generates configurations consistent with continuum theories for the average stresses (unlike the q-model) without requiring the assumption of a constitutive relation. For a 2D system with periodic boundary conditions, the model generates single-grain force distributions similar to those obtained from the q-model with a singular distribution of q's.Comment: 18 pages, 10 figures. Uses aps,epsfig,graphicx,floats,revte

Differential patterns of PMN-elastase and type III procollagen peptide in knee joint effusions due to acute and chronic sports injuries

In 38 traumatic knee joint effusions the proteolytic enzyme PMN-elastase (PMN-E) and the repair marker procollagen III aminoterminal peptide (PIIINP) were determined. According to the period between trauma and first aspiration of the effusion, the patients were divided into 3 groups. Group I (17 patients; period between trauma and first aspiration not longer than 72 hours) showed high concentrations of PMN-E (up to 5400 ng/ml) and low concentrations of PIIINP (<13 U/ml). Group II (11 patients; aspiration within 4 to 14 days) had mean PMN-E and PIIINP concentrations of 125.6 ng/ml and 52.1 U/ ml, respectively. In group III (10 patients, aspiration after 14 days) mean PMN-E concentration was 123.8 ng/ml and mean PIIINP concentration was 63.4 U/ml. Graphic depiction of PMN-E and PIIINP levels in each individual sample as a function of time between trauma and fluid collection revealed highly increasing PMN-E levels during the first 24 posttraumatic hours, followed by rapidly decreasing levels within 72 hours post trauma, and no change after the 4th posttraumatic day. In contrast, PIIINP increased continuously up to the first posttraumatic week and stayed at high levels up to 90 days (end of the observation period). The differential patterns of PMN-E and PIIINP concentration in knee joint effusions may be useful in estimating the period between trauma and first treatment (aspiration of effusion) and should, therefore, be helpful in detecting degenerative lesions, which seem to be characterized by low PMN-E concomitantly with high PIIINP levels

Prediction of Limit-Bearing Capacity of Footings on Geocell-Reinforced Soils

This paper pertains to the influence of introducing geocell-reinforced granular fill beneath a footing overlying soft soil. Extensive laboratory investigations are available to understand the behavior of geocell reinforcement for footings under various subsoil conditions. Several numerical investigations have been carried out using foundation models to understand the behavior of geosynthetic reinforced granular layers. Existing literature has minimal discussion on analytical modeling of geocell reinforcement. A simple and easy approach to develop a general understanding of the behavior of footings on geocell-reinforced soils would be to consider the contact pressure distribution of the geocell-reinforced bed. Three types of variations of contact pressure distributions, that is, uniform (for short geocell), linear (for intermediate width geocell), and exponential decay (for wide geocell), were considered. The present study investigates the response of strip and circular footings on geocell-reinforced soil. The ultimate bearing capacity of the footings is estimated using Terzaghi's approach based on the general shear failure mechanism. The present analytical model was validated against two independent experimental studies, and the results found to be in reasonable agreement

AI Lung Segmentation and Perfusion Analysis of Dual-Energy CT Can Help to Distinguish COVID-19 Infiltrates from Visually Similar Immunotherapy-Related Pneumonitis Findings and Can Optimize Radiological Workflows

(1) To explore the potential impact of an AI dual-energy CT (DECT) prototype on decision making and workflows by investigating its capabilities to differentiate COVID-19 from immunotherapy-related pneumonitis. (2) Methods: From 3 April 2020 to 12 February 2021, DECT from biometrically matching patients with COVID-19, pneumonitis, and inconspicuous findings were selected from our clinical routine. Three blinded readers independently scored each pulmonary lobe analogous to CO-RADS. Inter-rater agreement was determined with an intraclass correlation coefficient (ICC). Averaged perfusion metrics per lobe (iodine uptake in mg, volume without vessels in ml, iodine concentration in mg/mL) were extracted using manual segmentation and an AI DECT prototype. A generalized linear mixed model was used to investigate metric validity and potential distinctions at equal CO-RADS scores. Multinomial regression measured the contribution “Reader”, “CO-RADS score”, and “perfusion metrics” to diagnosis. The time to diagnosis was measured for manual vs. AI segmentation. (3) Results: We included 105 patients (62 ± 13 years, mean BMI 27 ± 2). There were no significant differences between manually and AI-extracted perfusion metrics (p = 0.999). Regardless of the CO-RADS score, iodine uptake and concentration per lobe were significantly higher in COVID-19 than in pneumonitis (p p < 0.001). (4) Conclusions: The investigated AI prototype positively impacts decision making and workflows by extracting perfusion metrics that differentiate COVID-19 from visually similar pneumonitis significantly faster than radiologists

Assessing the Accuracy of an Artificial Intelligence-Based Segmentation Algorithm for the Thoracic Aorta in Computed Tomography Applications

The aim was to evaluate the accuracy of a prototypical artificial intelligence-based algorithm for automated segmentation and diameter measurement of the thoracic aorta (TA) using CT. One hundred twenty-two patients who underwent dual-source CT were retrospectively included. Ninety-three of these patients had been administered intravenous iodinated contrast. Images were evaluated using the prototypical algorithm, which segments the TA and determines the corresponding diameters at predefined anatomical locations based on the American Heart Association guidelines. The reference standard was established by two radiologists individually in a blinded, randomized fashion. Equivalency was tested and inter-reader agreement was assessed using intra-class correlation (ICC). In total, 99.2% of the parameters measured by the prototype were assessable. In nine patients, the prototype failed to determine one diameter along the vessel. Measurements along the TA did not differ between the algorithm and readers (p &gt; 0.05), establishing equivalence. Inter-reader agreement between the algorithm and readers (ICC &ge; 0.961; 95% CI: 0.940&ndash;0.974), and between the readers was excellent (ICC &ge; 0.879; 95% CI: 0.818&ndash;0.92). The evaluated prototypical AI-based algorithm accurately measured TA diameters at each region of interest independent of the use of either contrast utilization or pathology. This indicates that the prototypical algorithm has substantial potential as a valuable tool in the rapid clinical evaluation of aortic pathology

Structured Digital Self-Assessment of Patient Anamnesis Prior to Computed Tomography: Performance Evaluation and Added Value

The aim of this study was to evaluate the performance of a tablet-based, digitized structured self-assessment (DSSA) of patient anamnesis (PA) prior to computed tomography (CT). Of the 317 patients consecutively referred for CT, the majority (n = 294) was able to complete the tablet-based questionnaire, which consisted of 67 items covering social anamnesis, lifestyle factors (e.g., tobacco abuse), medical history (e.g., kidney diseases), current symptoms, and the usability of the system. Patients were able to mark unclear questions for a subsequent discussion with the radiologist. Critical issues for the CT examination were structured and automatically highlighted as 'red flags' (RFs) in order to improve patient interaction. RFs and marked questions were highly prevalent (69.5% and 26%). Missing creatinine values (33.3%), kidney diseases (14.4%), thyroid diseases (10.6%), metformin (5.5%), claustrophobia (4.1%), allergic reactions to contrast agents (2.4%), and pathological TSH values (2.0%) were highlighted most frequently as RFs. Patient feedback regarding the comprehensibility of the questionnaire and the tablet usability was mainly positive (90.9%; 86.2%). With advanced age, however, patients provided more negative feedback for both (p = 0.007; p = 0.039). The time effort was less than 20 min for 85.1% of patients, and faster patients were significantly younger (p = 0.046). Overall, the DSSA of PA prior to CT shows a high success rate and is well accepted by most patients. RFs and marked questions were common and helped to focus patients' interactions and reporting towards decisive aspects

Status of sustainable strawberry production within Europe

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