In this study, three novel multi-directional energy-absorbing honeycombs were designed to meet the requirements in the crash of uncertain directions, which are named as bow-shaped honeycomb (BSHC), staggered honeycomb (SGHC) and corrugated honeycomb (CGHC). These innovative designs can significantly narrow the huge gap of the energy absorption capacity between the in-plane and out-of-plane directions of traditional honeycombs. Compression tests were conducted in three orthogonal directions. The BSHC is found to have the smallest densification strain but the highest plateau stress in each direction. The SGHC can only balance the energy absorption between out-of-plane and in-plane-x directions. The CGHC demonstrates a better densification strain and the highest multi-directional energy absorption coefficient. The detailed and equivalent finite element models of CGHC were further established and validated, and both exhibited high accuracy. Finally, a honeycomb anti-climber, with only about half length of the traditional guided honeycomb anti-climber, was designed and equipped with metro vehicles. Simulations were conducted under eccentric collision scenario. The results demonstrated that the CGHC anti-climber was capable of orderly deformation in the axial direction (out-of-plane direction) while effectively resisting the vertical (in-plane-y direction) force during collision. The energy absorption capacity of CGHC anti-climber was significantly enhanced as compared to the HEHC anti-climber under eccentric collision scenario.

The increased number of accidents involving UAVs striking people has caused great societal concern. Therefore, it is necessary to study the threat potential and severity of ground collision accidents of UAVs. This study analyzed the head and neck injury risk resulting from the impact of medium-mass UAVs. First, the finite element (FE) model of the M200-Hybrid III 50th dummy was established, in which the M200 itself had been validated against drop tests, and then the M200-Hybrid III 50th model was validated based on the experimental data of the M200 impacting on the dummy head. High consistency between the simulations and the experiments was observed. Second, simulations were conducted to analyze the head and neck injury severity at different impact speeds, angles, and locations of the M200. As the impact speed increases, HIC15 increases exponentially and N_ij increases linearly. It is found that the critical speed range causing injury for both vertical and horizontal impacts is 7–9 m/s. As the impact angle increases, HIC15 varies in the form of the sum of an inverse proportional function and a linear function, and N_ij increases in the form of the sum of power and quadratic function. In addition, the battery-first and the top of mainframe impacts lead to the highest probability of head and neck injury among different impact locations, while the landing gear-first is the lowest. Finally, a biomechanical model (THUMS) was incorporated to develop the M200-THUMS model to study the tissue-level injury of the head and neck under different impact conditions. The simulation results show that neck is prone to ligament injury under vertical impact, while the skull is more likely to be fractured under horizontal impact.

提出了一种吸能更加优秀的新型夹心八边形蜂窝.首先,建立了可快速预测夹心八边形蜂窝轴向压缩平台应力的理论模型,并对八边形和内嵌四边形蜂窝边长的变化对平台应力以及相对密度的影响进行了预测.然后,通过六边形蜂窝轴向压缩试验和仿真对比,验证了蜂窝建模和仿真方法的正确性;在建模方法和模型验证的基础之上,建立了新型夹心八边形蜂窝的有限元模型,分析了其变形模式以及边长参数对蜂窝吸能能力的影响,验证了理论模型的正确性.此外,进行了夹心八边形蜂窝和六边形、正方形蜂窝吸能能力的对比分析,结果表明新设计的夹心八边形蜂窝具有一定的吸能优势.最后,进行了直升机驾驶舱简化模型和夹心八边形蜂窝的耦合跌落仿真,定性分析了夹心八边形蜂窝的吸能能力.发现该蜂窝相较六边形蜂窝更适用于吸能能力要求高的场合,本文研究结果可以为新型蜂窝缓冲结构的设计提供依据.

Early warning prediction of traumatic hemorrhagic shock (THS) can greatly reduce patient mortality and morbidity. We aimed to develop and validate models with different stepped feature sets to predict THS in advance. From the PLA General Hospital Emergency Rescue Database and Medical Information Mart for Intensive Care III, we identified 604 and 1,614 patients, respectively. Two popular machine learning algorithms (i.e., extreme gradient boosting [XGBoost] and logistic regression) were applied. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the performance of the models. By analyzing the feature importance based on XGBoost, we found that features in vital signs (VS), routine blood (RB), and blood gas analysis (BG) were the most relevant to THS (0.292, 0.249, and 0.225, respectively). Thus, the stepped relationships existing in them were revealed. Furthermore, the three stepped feature sets (i.e., VS, VS + RB, and VS + RB + sBG) were passed to the two machine learning algorithms to predict THS in the subsequent T hours (where T = 3, 2, 1, or 0.5), respectively. Results showed that the XGBoost model performance was significantly better than the logistic regression. The model using vital signs alone achieved good performance at the half-hour time window (AUROC = 0.935), and the performance was increased when laboratory results were added, especially when the time window was 1 h (AUROC = 0.950 and 0.968, respectively). These good-performing interpretable models demonstrated acceptable generalization ability in external validation, which could flexibly and rollingly predict THS T hours (where T = 0.5, 1) prior to clinical recognition. A prospective study is necessary to determine the clinical utility of the proposed THS prediction models.

Acute kidney injury is a common critical disease with a high mortality. The large number of indicators in AKI patients makes it difficult for clinicians to quickly and accurately determine the patient’s condition. This study used machine learning methods to filter key indicators and use key indicator data to achieve advance prediction of AKI so that a small number of indicators could be measured to reliably predict AKI and provide auxiliary decision support for clinical staff. Sequential forward selection based on feature importance calculated by XGBoost was used to screen out 17 key indicators. Three machine learning algorithms were used to make predictions, namely, logistic regression (LR), decision tree, and XGBoost. To verify the validity of the method, data were extracted from the MIMIC III database and the eICU-CRD database for 1,009 and 1,327 AKI patients, respectively. The MIMIC III database was used for internal validation, and the eICU-CRD database was used for external validation. For all three machine learning algorithms, the prediction performance from using only the key indicator dataset was very close to that from using the full dataset. The XGBoost algorithm performed the best, and LR was the next best. The decision tree performed the worst. The key indicator screening method proposed in this study can achieve a good predictive performance while streamlining the number of indicators.

Honeycomb is widely used in the collision field due to its excellent energy-absorption characteristics. However, for the traditional hexagon honeycomb, there is a huge gap between the in-plane and out-of-plane energy absorption capacity, which is not suitable for the scenarios with uncertain collision direction such as helicopter crashworthiness. In this study, an equivalent model for the bow-shaped honeycomb with multi-directional load-carrying and energy-absorption capabilities is established. The verification results show that the model has high accuracy. In addition, a cockpit finite element model is established and the honeycomb equivalent model was incorporated. Finally, a 15-degree drop simulation was carried out on the cockpit model, and the results are compared with those from the helicopter coupled with traditional hexagon honeycomb. It is found that the multi- directional load-carrying honeycomb has higher energy absorption capacity, which can provide better protection capability for pilots.

A high-performing interpretable model is proposed to predict the risk of deterioration in coronavirus disease 2019 (COVID-19) patients. The model was developed using a cohort of 3028 patients diagnosed with COVID-19 and exhibiting common clinical symptoms that were internally verified (AUC 0.8517, 95% CI 0.8433, 0.8601). A total of 15 high risk factors for deterioration and their approximate warning ranges were identified. This included prothrombin time (PT), prothrombin activity, lactate dehydrogenase, international normalized ratio, heart rate, body-mass index (BMI), D-dimer, creatine kinase, hematocrit, urine specific gravity, magnesium, globulin, activated partial thromboplastin time, lymphocyte count (L%), and platelet count. Four of these indicators (PT, heart rate, BMI, HCT) and comorbidities were selected for a streamlined combination of indicators to produce faster results. The resulting model showed good predictive performance (AUC 0.7941 95% CI 0.7926, 0.8151). A website for quick pre-screening online was also developed as part of the study.

An aluminum honeycomb is widely used in the field of impact cushioning because of its excellent performance. In order to solve the problem of large difference between the in-plane and out-of-plane load-carrying capacities of traditional honeycombs, three new configurations of honeycombs were proposed as follows: bow-shaped, staggered and folded configurations. The finite element models for these new honeycombs were established, and their deformation modes and load-carrying capacities were analyzed. The results show that under the same relative density, compared with the traditional hexagonal honeycombs, the three new configurations can reduce the difference of load-carrying capacity in in-plane and out of plane directions. The average in-plane/out of plane (I/O) ratio of loading-carrying capacity of the bow-shaped honeycombs in two coplanar directions increased by 21.3 times. For the staggered honeycomb, the load-carrying capacity of each in-plane direction is of great difference, in which the I/O ratio of the excellent direction is increased by 42 times due to its special structure. For the folded honeycomb, the I/O ratio is increased by 21.3 times on average. The research results can provide a new idea and reference for the design of honeycomb structure under multi-directional impact load.

Yong Liu 1 Jiaming Wang Yi Tang Rong Su Qianwen Yang 1The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China, 510150; 2The University of Melbourne, Melbourne, Australia, 3052 Abstract: In December 2019, a new coronavirus pneumonia began to break out globally. COVID-19 pandemic challenges the health systems worldwide and influences the treatments for other diseases. The incidence rate of breast cancer ranks first among all malignant tumors among women. During the pandemic, medical workers should strictly monitor the condition of patients and strengthen the management and prevention measures to make sure patients can be operated safely. This article will discuss the arrangements and management of surgical treatment for patients with breast cancer.