An Engineered Installation Aid Device For Child Restraint System to Mitigate Misuse

This research focuses on the development of a child restraint system installation-aid device (CRSIAD) for the purpose of mitigating child safety seat misuse in terms of installation. A geometric study was performed base on surveying dimensions of currently existing child safety seat products. Material property experiments were conducted to develop an anisotropic wood material model for the CRSIAD in order to virtually evaluate device stress levels. Finite element analysis (FEA) of both the material model and CRSIAD were performed in comparison with lab test data to validate structural performance. The CRSIAD was then fabricated and finalized after multiple design iterations for geometry and components based on in-car testing. User satisfaction survey and professional review by certified CRS installation personnel were completed to ensure the value of CRSIAD as well as provide feedback for future improvements. From the testing results and user feedbacks, the CRSIAD was believed to be an important contribution towards the improvement of child safety in vehicles

Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition

Recurrent Neural Networks (RNNs) are powerful sequence modeling tools. However, when dealing with high dimensional inputs, the training of RNNs becomes computational expensive due to the large number of model parameters. This hinders RNNs from solving many important computer vision tasks, such as Action Recognition in Videos and Image Captioning. To overcome this problem, we propose a compact and flexible structure, namely Block-Term tensor decomposition, which greatly reduces the parameters of RNNs and improves their training efficiency. Compared with alternative low-rank approximations, such as tensor-train RNN (TT-RNN), our method, Block-Term RNN (BT-RNN), is not only more concise (when using the same rank), but also able to attain a better approximation to the original RNNs with much fewer parameters. On three challenging tasks, including Action Recognition in Videos, Image Captioning and Image Generation, BT-RNN outperforms TT-RNN and the standard RNN in terms of both prediction accuracy and convergence rate. Specifically, BT-LSTM utilizes 17,388 times fewer parameters than the standard LSTM to achieve an accuracy improvement over 15.6\% in the Action Recognition task on the UCF11 dataset.Comment: CVPR201

Perceived Stress and Cognitive Functions among Chinese Older Adults: The Moderating Role of Health Status

Objective: The primary purposes of the present study are 1) to investigate the stress-cognition relationship among U.S. Chinese older adults; and 2) to examine the moderating role of health status on the stress-cognition relationship. Method: Data were drawn from the Population Study of Chinese Elderly in Chicago (PINE), which investigated 3,159 Chinese adults over 60 years old living in Chicago. Participants reported health status and completed the Chinese Perceived Stress Scale. Cognitive functions were measured by the East Boston Memory Test, the Digit Span Backwards, the Symbol Digit Modalities Test, and Chinese Mini-Mental State Examination. Results: Controlling for age, sex, education, and income, perceived stress was negatively associated with cognitive functions, whereas health status was positively associated with cognitive functions. In addition, older adults’ health status interacted with stress such that the negative relationships between perceived stress and cognitive functions were more pronounced for those who had poor health than for those who had good health. Conclusion: Findings suggest that physical health is a critical factor moderating the relationship between perceived stress and cognitive functions among U.S. Chinese older adults. Longitudinal research is needed to examine the complex relationships among stress, health, and cognitive functions of U.S. Chinese older adults

NETWORK DEVICE SYSTEM LOGGING SUMMARIZATION BASED ON LOW-RANK ADAPTATION AND CONTRASTIVE LEARNING

Techniques are presented herein that support the automatic generation of refined and summarized text from a system logging (syslog) message sequence. Aspects of the presented techniques employ an abstractive syslog summarization large language model (LLM) that is trained with contrastive learning and then fine-tuned using a Low-Rank Adaptation (LoRA) methodology. Under further aspects of the presented techniques, auxiliary text (such as network incident reports and application incident reports) is added to the prompt of the input of the LLM model to help the model generate a richer syslog summarization

Bayesian Robust Tensor Ring Model for Incomplete Multiway Data

Robust tensor completion (RTC) aims to recover a low-rank tensor from its incomplete observation with outlier corruption. The recently proposed tensor ring (TR) model has demonstrated superiority in solving the RTC problem. However, the existing methods either require a pre-assigned TR rank or aggressively pursue the minimum TR rank, thereby often leading to biased solutions in the presence of noise. In this paper, a Bayesian robust tensor ring decomposition (BRTR) method is proposed to give more accurate solutions to the RTC problem, which can avoid exquisite selection of the TR rank and penalty parameters. A variational Bayesian (VB) algorithm is developed to infer the probability distribution of posteriors. During the learning process, BRTR can prune off slices of core tensor with marginal components, resulting in automatic TR rank detection. Extensive experiments show that BRTR can achieve significantly improved performance than other state-of-the-art methods

Evaluation of Community Health Education Workshops among Chinese Older Adults in Chicago: A Community-Based Participatory Research Approach

Background: Health education is one of the proven ways to improve knowledge and change health attitudes and behaviors. This study is intended to assess the effectiveness of five health workshops in a Chinese community, focusing on depression, elder abuse, nutrition, breast cancer and stroke. Methods: A community-based participatory research approach was implemented to plan and organize the workshops. A total of 236 Chinese community-dwelling older adults participated in different health workshops. Quantitative questionnaires on knowledge, risk factors and outcomes of each health topic were distributed before and after the workshop. Pre and post workshop comparison analyses were conducted to examine the effectiveness of the workshops on knowledge and learning. Results: Overall, the health workshops have significantly improved participants’ understanding throughout the five health themes (P<0.05). Whereas Chinese older adults have limited knowledge on depression, nutrition and stroke, their health knowledge regarding depression and elder abuse were significantly improved after attending the workshops. In addition, health education workshops increased older adults’ understanding of the risk factors and consequences of depression, elder abuse and breast cancer. Conclusion: This study sheds light on the importance of promoting health education, and the complexity and challenges of designing health education for community dwelling Chinese older adults. Significant implications for researchers, community service providers, health service workers and policy makers are discussed

Targeted next-generation sequencing of dedifferentiated chondrosarcoma in the skull base reveals combined TP53 and PTEN mutations with increased proliferation index, an implication for pathogenesis

Dedifferentiated chondrosarcoma (DDCS) is a rare disease with a dismal prognosis. DDCS consists of two morphologically distinct components: the cartilaginous and noncartilaginous components. Whether the two components originate from the same progenitor cells has been controversial. Recurrent DDCS commonly displays increased proliferation compared with the primary tumor. However, there is no conclusive explanation for this mechanism. In this paper, we present two DDCSs in the sellar region. Patient 1 exclusively exhibited a noncartilaginous component with a TP53 frameshift mutation in the pathological specimens from the first surgery. The tumor recurred after radiation therapy with an exceedingly increased proliferation index. Targeted next-generation sequencing (NGS) revealed the presence of both a TP53 mutation and a PTEN deletion in the cartilaginous and the noncartilaginous components of the recurrent tumor. Fluorescence in situ hybridization and immunostaining confirmed reduced DNA copy number and protein levels of the PTEN gene as a result of the PTEN deletion. Patient 2 exhibited both cartilaginous and noncartilaginous components in the surgical specimens. Targeted NGS of cells from both components showed neither TP53 nor PTEN mutations, making Patient 2 a naïve TP53 and PTEN control for comparison. In conclusion, additional PTEN loss in the background of the TP53 mutation could be the cause of increased proliferation capacity in the recurrent tumor

Big Potential From Silicon-Based Porous Nanomaterials: In Field of Energy Storage and Sensors

Silicon nanoparticles (SiNPs) are the promising materials in the various applications due to their unique properties like large surface area, biocompatibility, stability, excellent optical and electrical properties. Surface, optical and electrical properties are highly dependent on particle size, doping of different materials and so on. Porous structures in silicon nanomaterials not only improve the specific surface area, adsorption, and photoluminescence efficiency but also provide numbers of voids as well as the high surface to volume ratio and enhance the adsorption ability. In this review, we focus on the significance of porous silicon/mesoporous silicon nanoparticles (pSiNPs/mSiNPs) in the applications of energy storage, sensors and bioscience. Silicon as anode material in the lithium-ion batteries (LIBs) faces a huge change in volume during charging/discharging which leads to cracking, electrical contact loss and unstable solid electrolyte interphase. To overcome challenges of Si anode in the LIBs, mSiNPs are the promising candidates with different structures and coating of different materials to enhance electrochemical properties. On the basis of optical properties with tunable wavelength, pSiNPs are catching good results in biosensors and gas sensors. The mSiNPs with different structures and modified surfaces are playing an important role in the detection of biomarkers, drug delivery and diagnosis of cancer and tumors

Growth of carbon nanowalls at atmospheric pressure for one-step gas sensor fabrication

Carbon nanowalls (CNWs), two-dimensional "graphitic" platelets that are typically oriented vertically on a substrate, can exhibit similar properties as graphene. Growth of CNWs reported to date was exclusively carried out at a low pressure. Here, we report on the synthesis of CNWs at atmosphere pressure using "direct current plasma-enhanced chemical vapor deposition" by taking advantage of the high electric field generated in a pin-plate dc glow discharge. CNWs were grown on silicon, stainless steel, and copper substrates without deliberate introduction of catalysts. The as-grown CNW material was mainly mono- and few-layer graphene having patches of O-containing functional groups. However, Raman and X-ray photoelectron spectroscopies confirmed that most of the oxygen groups could be removed by thermal annealing. A gas-sensing device based on such CNWs was fabricated on metal electrodes through direct growth. The sensor responded to relatively low concentrations of NO2 (g) and NH3 (g), thus suggesting high-quality CNWs that are useful for room temperature gas sensors

Hydrogen jet diffusion modeling by using physics-informed graph neural network and sparsely-distributed sensor data

Efficient modeling of jet diffusion during accidental release is critical for operation and maintenance management of hydrogen facilities. Deep learning has proven effective for concentration prediction in gas jet diffusion scenarios. Nonetheless, its reliance on extensive simulations as training data and its potential disregard for physical laws limit its applicability to unseen accidental scenarios. Recently, physics-informed neural networks (PINNs) have emerged to reconstruct spatial information by using data from sparsely-distributed sensors which are easily collected in real-world applications. However, prevailing approaches use the fully-connected neural network as the backbone without considering the spatial dependency of sensor data, which reduces the accuracy of concentration prediction. This study introduces the physics-informed graph deep learning approach (Physic_GNN) for efficient and accurate hydrogen jet diffusion prediction by using sparsely-distributed sensor data. Graph neural network (GNN) is used to model the spatial dependency of such sensor data by using graph nodes at which governing equations describing the physical law of hydrogen jet diffusion are immediately solved. The computed residuals are then applied to constrain the training process. Public experimental data of hydrogen jet is used to compare the accuracy and efficiency between our proposed approach Physic_GNN and state-of-the-art PINN. The results demonstrate our Physic_GNN exhibits higher accuracy and physical consistency of centerline concentration prediction given sparse concentration compared to PINN and more efficient compared to OpenFOAM. The proposed approach enables accurate and robust real-time spatial consequence reconstruction and underlying physical mechanisms analysis by using sparse sensor data