Evaluation of volunteer programs in non-profit organizations dedicated to urban river protection in the U.S. and China: The Huron River Watershed Council and The Protect Environment Together Association

The Huron River Watershed Council (HWRC) in Ann Arbor, Michigan, and The Protect the Environment Together Association (PETA) in Beijing are two environmental nonprofit organizations in the USA and China that focus on environmental protection of urban rivers. Volunteers provide important support for these two organizations. Understanding the factors that bring volunteer satisfaction and the motivation of volunteers to keep participating is an important evaluation item for these and other nonprofit organizations. This study used 5-point Likert scale electronic surveys to assess critical factors that bring volunteer satisfaction and semi-structured interviews were used to understand the evolution of volunteers’ motivations to continue participating and to solicit suggestions to improve volunteer satisfaction. Volunteer responses from the two organizations were compared to identify the major similarities and differences in the two organizations' experiences with volunteers. Results show that protecting the environment is the main factor that brings satisfaction to volunteers, which is consistent with previous studies. Being able to provide help to the organization is another important factor. A good impression left by the first volunteering experience increases the chances that volunteers continue participating, and their motivation and source of satisfaction is enriched and enhanced with the number of times they participate. The closer social relationships with others and being valued are the main enriched aspects. HRWC is a well-established and run organization and been affirmed by their volunteers. They could maintain the current level of organization or potentially strengthen cooperation with college student clubs and developing more diverse marketing methods to expand volunteer engagement by youth. PETA is a newer organization, to raise the enthusiasm and efficiency of volunteers, they might consider increasing communication and interaction among volunteers, between volunteers and PETA personnel, and between volunteers and service objects (schools).Master of ScienceSchool for Environment and SustainabilityUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/163667/1/Li_Jiangyun_Thesis.pd

Unmet healthcare needs predict frailty onset in the middle-aged and older population in China: A prospective cohort analysis

ObjectivesOlder populations have a relatively high prevalence of unmet healthcare needs, which can result in poor health status. Moreover, in the coming century, frailty is expected to become one of the most serious global public health challenges. However, there is a lack of clear evidence proving an association between unmet healthcare needs and frailty. This study aimed to assess whether unmet healthcare needs predict the onset of frailty in China.MethodsThe association between frailty and unmet healthcare needs was explored by analyzing data from the China Health and Retirement Longitudinal Study (CHARLS) using random-effects logistic regression and Cox regression with time-varying exposure.ResultsAt baseline, 7,719 respondents were included in the analysis. Random-effects logistic regression shows that unmet outpatient healthcare needs were associated with increased risk of both contemporaneous (adjusted OR [aOR], 1.17; 95% CI, 1.02–1.35) and lagged (aOR, 1.24; 95% CI, 1.05–1.45) frailty, as were unmet inpatient needs (contemporaneous: aOR, 1.28; 95% CI, 1.00–1.64; lagged: aOR, 1.55; 95% CI, 1.17–2.06). For respondents not classified as frail at baseline (n = 5,392), Cox regression with time-varying exposure shows significant associations of both unmet outpatient needs (adjusted HR, 1.23; 95% CI, 1.05–1.44) and unmet inpatient needs (adjusted HR, 1.48; 95% CI, 1.11–1.99) with increased risk of developing frailty.ConclusionsReducing unmet healthcare needs would be a valuable intervention to decrease frailty risk and promote healthy aging in middle-aged and older populations. It is urgent and essential that the equity and accessibility of the medical insurance and health delivery systems be strengthened

Attention guided global enhancement and local refinement network for semantic segmentation

The encoder-decoder architecture is widely used as a lightweight semantic segmentation network. However, it struggles with a limited performance compared to a well-designed Dilated-FCN model for two major problems. First, commonly used upsampling methods in the decoder such as interpolation and deconvolution suffer from a local receptive field, unable to encode global contexts. Second, low-level features may bring noises to the network decoder through skip connections for the inadequacy of semantic concepts in early encoder layers. To tackle these challenges, a Global Enhancement Method is proposed to aggregate global information from high-level feature maps and adaptively distribute them to different decoder layers, alleviating the shortage of global contexts in the upsampling process. Besides, a Local Refinement Module is developed by utilizing the decoder features as the semantic guidance to refine the noisy encoder features before the fusion of these two (the decoder features and the encoder features). Then, the two methods are integrated into a Context Fusion Block, and based on that, a novel Attention guided Global enhancement and Local refinement Network (AGLN) is elaborately designed. Extensive experiments on PASCAL Context, ADE20K, and PASCAL VOC 2012 datasets have demonstrated the effectiveness of the proposed approach. In particular, with a vanilla ResNet-101 backbone, AGLN achieves the state-of-the-art result (56.23% mean IoU) on the PASCAL Context dataset. The code is available at https://github.com/zhasen1996/AGLN.Comment: 12 pages, 6 figure

Experimental investigation of the flame retardant and form-stable composite phase change materials for a power battery thermal management system

© 2020 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/.An efficient battery thermal management system (BTMS) will undoubtedlypromote the performance and lifespan of battery packs. In this study, a novelflame-retarded composite PCMs composed by paraffin (PA), expanded graphite (EG), ammonium polyphosphate (APP), red phosphorus (RP) and epoxy resin (ER) has been proposed for battery module. The thermophysical and flame retardant properties are investigated at both macro and micro levels. The results show that the proposed composite PCMs with an APP/RP ratio of 23/10 exhibit the optimum flame retardant performance. Besides, the APP/RP-based composite PCMs for 18650 ternary battery module has also been researched comparing with air cooled and PCM with pure PA modes. The experimental results indicated that the fire retardant PCMs shown significant cooling and temperature balancing advantages for battery module, leading to a 44.7% and 30.1% reduction rate of the peak temperature and the maintenance of the maximum temperature difference within 1.36°C at a 3 C discharge rate for 25°C. Even at 45°C, the temperature uniformity can still be controlled within 5°C. Thus, this research indicates the composite PCM had good flame retardant and form stable properties, it would be utilized in BTMS, energy storage and other fields.Peer reviewe

Med-Tuning: Exploring Parameter-Efficient Transfer Learning for Medical Volumetric Segmentation

Deep learning based medical volumetric segmentation methods either train the model from scratch or follow the standard "pre-training then finetuning" paradigm. Although finetuning a well pre-trained model on downstream tasks can harness its representation power, the standard full finetuning is costly in terms of computation and memory footprint. In this paper, we present the first study on parameter-efficient transfer learning for medical volumetric segmentation and propose a novel framework named Med-Tuning based on intra-stage feature enhancement and inter-stage feature interaction. Given a large-scale pre-trained model on 2D natural images, our method can exploit both the multi-scale spatial feature representations and temporal correlations along image slices, which are crucial for accurate medical volumetric segmentation. Extensive experiments on three benchmark datasets (including CT and MRI) show that our method can achieve better results than previous state-of-the-art parameter-efficient transfer learning methods and full finetuning for the segmentation task, with much less tuned parameter costs. Compared to full finetuning, our method reduces the finetuned model parameters by up to 4x, with even better segmentation performance

Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation

Recent works have shown that the computational efficiency of 3D medical image (e.g. CT and MRI) segmentation can be impressively improved by dynamic inference based on slice-wise complexity. As a pioneering work, a dynamic architecture network for medical volumetric segmentation (i.e. Med-DANet) has achieved a favorable accuracy and efficiency trade-off by dynamically selecting a suitable 2D candidate model from the pre-defined model bank for different slices. However, the issues of incomplete data analysis, high training costs, and the two-stage pipeline in Med-DANet require further improvement. To this end, this paper further explores a unified formulation of the dynamic inference framework from the perspective of both the data itself and the model structure. For each slice of the input volume, our proposed method dynamically selects an important foreground region for segmentation based on the policy generated by our Decision Network and Crop Position Network. Besides, we propose to insert a stage-wise quantization selector to the employed segmentation model (e.g. U-Net) for dynamic architecture adapting. Extensive experiments on BraTS 2019 and 2020 show that our method achieves comparable or better performance than previous state-of-the-art methods with much less model complexity. Compared with previous methods Med-DANet and TransBTS with dynamic and static architecture respectively, our framework improves the model efficiency by up to nearly 4.1 and 17.3 times with comparable segmentation results on BraTS 2019.Comment: Accepted by WACV 202

Structural Optimization and Thermal Management with PCM-Honeycomb Combination for Photovoltaic-Battery Integrated System

© 2022 Xinxi Li et al. This is an open access article distributed under the Creative Commons Attribution License, https://creativecommons.org/licenses/by/4.0/Power lithium–ion batteries retired from the electric vehicles (EVs) are confronting many problems such as environment pollution and energy dissipation. Traditional photovoltaic (PV) battery systems are exhibiting many issues such as being bulky and expensive, high working temperature, and short service span. In order to address these problems, in this study, a novel PV–battery device integrating PV controllers and battery module into an independent device is proposed. Phase change material (PCM) as the energy storage material has been utilized in battery module, and the aluminum honeycomb is combined with PCM to improve the heat conductivity under natural convection conditions. Three types of PV battery systems including the general PV–battery integrated system (G–PBIS), honeycomb PV–battery integrated system (H–PBIS), and honeycomb–paraffin PV–battery integrated system (HP–PBIS) have been investigated in detail. The results reveal that the maximum temperature of the HP–PBIS coupling with the double–layer 10×165×75 mm3 PCM was reduced to 53.72°C, exhibiting an optimum cooling effect among various PV battery systems. Thus, it can be concluded that the aluminum honeycomb provides the structural reliability and good thermal conductivity, and the PCM surrounding battery module can control the temperature rising and balance the temperature uniformly. Besides, the optimum PV–battery integrated system performs a promising future in energy storage fields.Peer reviewedFinal Published versio