Analytical, experimental and numerical study of a graded honeycomb structure under in-plane impact load with low velocity

Given the significance of energy absorption in various industries, light shock absorbers such as honeycomb structure under in-plane and out-of-plane loads have been in the core of attention. The purpose of this research is the analyses of graded honeycomb structure (GHS) behaviour under in-plane impact loading and its optimisation. Primarily, analytical equations for plateau stress and specific energy are represented, taking power hardening model (PHM) and elastic–perfectly plastic model (EPPM) into consideration. For the validation and comparison of acquired analytical equations, the energy absorption of a GHS made of five different aluminium grades is simulated in ABAQUS/CAE. In order to validate the numerical simulation method in ABAQUS, an experimental test has been conducted as the falling a weight with low velocity on a GHS. Numerical results retain an acceptable accordance with experimental ones with a 5.4% occurred error of reaction force. For a structure with a specific kinetic energy, the stress–strain diagram is achieved and compared with the analytical equations obtained. The maximum difference between the numerical and analytical plateau stresses for PHM is 10.58%. However, this value has been measured to be 38.78% for EPPM. In addition, the numerical value of absorbed energy is compared to that of analytical method for two material models. The maximum difference between the numerical and analytical absorbed energies for PHM model is 6.4%, while it retains the value of 48.08% for EPPM. Based on the conducted comparisons, the numerical and analytical results based on PHM are more congruent than EPPM results. Applying sequential quadratic programming method and genetic algorithm, the ratio of structure mass to the absorbed energy is minimised. According to the optimisation results, the structure capacity of absorbing energy increases by 18% compared to the primary model

Primary angiitis of the central nervous system presenting with subacute and fatal course of disease: a case report

BACKGROUND: Primary angiitis of the central nervous system is an idiopathic disorder characterized by vasculitis within the dural confines. The clinical presentation shows a wide variation and the course and the duration of disease are heterogeneous. This rare but treatable disease provides a diagnostic challenge owing to the lack of pathognomonic tests and the necessity of a histological confirmation. CASE PRESENTATION: A 28-year-old patient presenting with headache and fluctuating signs of encephalopathy was treated on the assumption of viral meningoencephalitis. The course of the disease led to his death 10 days after hospital admission. Postmortem examination revealed primary angiitis of the central nervous system. CONCLUSION: Primary angiitis of the central nervous system should always be taken into consideration when suspected infectious inflammation of the central nervous system does not respond to treatment adequately. In order to confirm the diagnosis with the consequence of a modified therapy angiography and combined leptomeningeal and brain biopsy should be considered immediately

Use of Quantitative Pharmacology in the Development of HAE1, a High-Affinity Anti-IgE Monoclonal Antibody

HAE1, a high-affinity anti-IgE monoclonal antibody, is discussed here as a case study in the use of quantitative pharmacology in the development of a second-generation molecule. In vitro, preclinical, and clinical data from the first-generation molecule, omalizumab, were heavily leveraged in the HAE1 program. A preliminary mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model for HAE1 was developed using an existing model for omalizumab, together with in vitro binding data for HAE1 and omalizumab. When phase I data were available, the model was refined by simultaneously modeling PK/PD data from omalizumab studies with the available HAE1 phase I data. The HAE1 clinical program was based on knowledge of the quantitative relationship between a pharmacodynamic biomarker, suppression of free IgE, and clinical response (e.g., lower exacerbation rates) obtained in pivotal studies with omalizumab. A clinical trial simulation platform was developed to predict free IgE levels and clinical responses following attainment of a target free IgE level (≤10 IU/ml). The simulation platform enabled selection of four doses for the phase II dose-ranging trial by two independent methods: dose-response non-linear fitting and linear mixed modeling. Agreement between the two methods provided confidence in the doses selected. Modeling and simulation played a large role in supporting acceleration of the HAE1 program by enabling data-driven decision-making, often based on confirmation of projections and/or learning from incoming new data

Wrist-Worn Wearables Based on Force Myography: On the Significance of User Anthropometry

Background Force myography (FMG) is a non-invasive technology used to track functional movements and hand gestures by sensing volumetric changes in the limbs caused by muscle contraction. Force transmission through tissue implies that differences in tissue mechanics and/or architecture might impact FMG signal acquisition and the accuracy of gesture classifier models. The aim of this study is to identify if and how user anthropometry affects the quality of FMG signal acquisition and the performance of machine learning models trained to classify different hand and wrist gestures based on that data. Methods Wrist and forearm anthropometric measures were collected from a total of 21 volunteers aged between 22 and 82 years old. Participants performed a set of tasks while wearing a custom-designed FMG band. Primary outcome measure was the Spearman’s correlation coefficient (R) between the anthropometric measures and FMG signal quality/ML model performance. Results Results demonstrated moderate (0.3 ≤|R| < 0.67) and strong (0.67 ≤ |R|) relationships for ratio of skinfold thickness to forearm circumference, grip strength and ratio of wrist to forearm circumference. These anthropometric features contributed to 23–30% of the variability in FMG signal acquisition and as much as 50% of the variability in classification accuracy for single gestures. Conclusions Increased grip strength, larger forearm girth, and smaller skinfold-to-forearm circumference ratio improve signal quality and gesture classification accuracy