Growth and New Directions: CALA Academic Resources and Repository System

The Chinese American Librarians Association’s Academic Resources & Repository System (CALASYS) was established in 2013 and has been growing gradually ever since. To seek sustainable and greater growth in the future, the CALASYS 2019-2020 Committee reviewed previous efforts and explored new potentials in the repository’s content development, interface and functionality improvement and community engagement. This presentation will cover several issues that the Committee has addressed since its forming: developing new content for CALASYS such as a new top-level collection called ”Chinese Culture Heritage & Chinese Studies” and its children collections including the CALA Best Book Award Collection; starting or resuming testing on several Omeka plugins whose implementation would enhance the system’s functionality and performance significantly, such as Exhibit Builder, User Profile, Search by Metadata, CSS Editor and Geolocation; exploring other Omeka instances’ interfaces and improving the CALASYS’ appearance and presentation. This poster will also cover the continuing development of the CALA Archives, CALA Chapter Collections and CALA Member Scholarly Achievements collection, metadata editing and enhancement, statistics and usage of the repository, as well as involving students and CALA members in working with the repository. To develop an organization’s institutional repository is a long-term task, this presentation will conclude with a discussion of the lessons learned and strategies and tips on working with the repository for the committee members and the community

Opening CALASYS to All Members

Since the Chinese American Librarians Association’s Academic Resources and Repository System (CALASYS, https://ir.cala-web.org/) was initiated in 2013, its collections have grown gradually by way of the Committee’s curation and entries with occasional help from LIS students. In order to resolve the bottleneck problems, promote CALASYS and expand its content, the 2020-2021 CALASYS Committee has strongly pursued the idea of opening CALASYS to all of the CALA members. The Committee began to implement the author self-contribution plug-in in the CALASYS’ Omeka platform in 2020. This poster will focus on the implementation of the self-contribution plug-in. It will cover the main steps and tasks of the implementation, including making metadata contribution templates, selecting copyright options, establishing contributor verification, testing workflow and developing end-user guide and back-end management documentations. It will also address the Committee’s work on creating training materials on workflow and metadata and plans on providing training sessions online to the CALA community. It will include the CALASYS’ history, its main features, collections, and usage statistics as well. By opening CALASYS to all members, it is hoped that it will better achieve the CALA’s strategic plan of 2020-2025, “Make CALA’s impact on local, state, national, and international levels.” Meanwhile, the bottleneck problems will be resolved and CALASYS will continue to grow at a faster pace in a more inclusive direction. The accompanying video is also available at: https://youtu.be/q9g4SXsnuO0

Lifecycle Cost Optimization for Electric Bus Systems With Different Charging Methods: Collaborative Optimization of Infrastructure Procurement and Fleet Scheduling

Battery electric buses (BEBs) have been regarded as effective options for sustainable mobility while their promotion is highly affected by the total cost associated with their entire life cycle from the perspective of urban transit agencies. In this research, we develop a collaborative optimization model for the lifecycle cost of BEB system, considering both overnight and opportunity charging methods. This model aims to jointly optimize the initial capital cost and use-phase operating cost by synchronously planning the infrastructure procurement and fleet scheduling. In particular, several practical factors, such as charging pattern effect, battery downsizing benefits, and time-of-use dynamic electricity price, are considered to improve the applicability of the model. A hybrid heuristic based on the tabu search and immune genetic algorithm is customized to effectively solve the model that is reformulated as the bi-level optimization problem. A numerical case study is presented to demonstrate the model and solution method. The results indicate that the proposed optimization model can help to reduce the lifecycle cost by 7.77% and 6.64% for overnight and opportunity charging systems, respectively, compared to the conventional management strategy. Additionally, a series of simulations for sensitivity analysis are conducted to further evaluate the key parameters and compare their respective life cycle performance. The policy implications for BEB promotion are also discussed

Spatiotemporal Extremes of Temperature and Precipitation During 1960–2015 in the Yangtze River Basin (China) and Impacts on Vegetation Dynamics

Recently, extreme climate variation has been studied in different parts of the world, and the present study aims to study the impacts of climate extremes on vegetation. In this study, we analyzed the spatiotemporal variations of temperature and precipitation extremes during 1960–2015 in the Yangtze River Basin (YRB) using the Mann-Kendall (MK) test with Sen’s slope estimator and kriging interpolation method based on daily precipitation (P), maximum temperature (Tmax), and minimum temperature (Tmin). We also analyzed the vegetation dynamics in the YRB during 1982–2015 using Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) datasets and investigated the relationship between temperature and precipitation extremes and NDVI using Pearson correlation coefficients. The results showed a pronounced increase in the annual mean maximum temperature (Tnav) and mean minimum temperature (Txav) at the rate of 0.23 °C/10 years and 0.15 °C/10 years, respectively, during 1960–2015. In addition, the occurrence of warm days and warm nights shows increasing trends at the rate of 1.36 days/10 years and 1.70 days/10 years, respectively, while cold days and cold nights decreased at the rate of 1.09 days/10 years and 2.69 days/10 years, respectively, during 1960–2015. The precipitation extremes, such as very wet days (R95, the 95th percentile of daily precipitation events), very wet day precipitation (R95p, the number of days with rainfall above R95), rainstorm (R50, the number of days with rainfall above 50 mm), and maximum 1-day precipitation (RX1day), all show pronounced increasing trends during 1960–2015. In general, annual mean NDVI over the whole YRB increased at the rate of 0.01/10 years during 1982–2015, with an increasing transition around 1994. Spatially, annual mean NDVI increased in the northern, eastern, and parts of southwestern YRB, while it decreased in the YRD and parts of southern YRB during 1982–2015. The correlation coefficients showed that annual mean NDVI was closely correlated with temperature extremes during 1982–2015 and 1995–2015, but no significant correlation with precipitation extremes was observed. However, the decrease in NDVI was correlated with increasing R95p and R95 during 1982–1994

Scaling of global input–output networks

Examining scaling patterns of networks can help understand how structural features relate to the behavior of the networks. Input–output networks consist of industries as nodes and inter-industrial exchanges of products as links. Previous studies consider limited measures for node strengths and link weights, and also ignore the impact of dataset choice. We consider a comprehensive set of indicators in this study that are important in economic analysis, and also examine the impact of dataset choice, by studying input–output networks in individual countries and the entire world. Results show that Burr, Log-Logistic, Log-normal, and Weibull distributions can better describe scaling patterns of global input–output networks. We also find that dataset choice has limited impacts on the observed scaling patterns. Our findings can help examine the quality of economic statistics, estimate missing data in economic statistics, and identify key nodes and links in input–output networks to support economic policymaking

Income-based greenhouse gas emissions of nations

Accounting for greenhouse gas (GHG) emissions of nations is essential to understanding their importance to global climate change and help inform the policymaking on global GHG mitigation. Previous studies have made efforts to evaluate direct GHG emissions of nations (a.k.a. production-based accounting method) and GHG emissions caused by the final consumption of nations (a.k.a. consumption-based accounting method), but overlooked downstream GHG emissions enabled by primary inputs of individual nations and sectors (a.k.a. income-based accounting method). Here we show that the income-based accounting method reveals new GHG emission profiles for nations and sectors. The rapid development of mining industries drives income-based GHG emissions of resource-exporting nations (e.g., Australia, Canada, and Russia) during 1995–2009. Moreover, the rapid development of sectors producing basic materials and providing financial intermediation services drives income-based GHG emissions of developing nations (e.g., China, Indonesia, India, and Brazil) during this period. The income-based accounting can support supply side policy decisions and provide additional information for determining GHG emission quotas based on cumulative emissions of nations and designing policies for shared responsibilities

Scale, distribution and variations of global greenhouse gas emissions driven by U.S. households

The U.S. household consumption, a key engine for the global economy, has significant carbon footprints across the world. Understanding how the U.S. household consumption on specific goods or services drives global greenhouse gas (GHG) emissions is important to guide consumption-side strategies for climate mitigation. Here we examined global GHG emissions driven by the U.S. household consumption from 1995 to 2014 using an environmentally extended multi-regional input-output model and detailed U.S. consumer expenditure survey data. The results show that the annual carbon footprint of the U.S. households ranged from 17.7 to 20.6 tCO2eq/capita with an expanding proportion occurring overseas. Housing and transportation contributed 53–66% of the domestic carbon footprint. Overseas carbon footprint shows an overall increasing trajectory, from 16.4% of the total carbon footprint in 1995 to the peak of 20.4% in 2006. These findings provide valuable insights on the scale, distribution, and variations of the global GHG emissions driven by the U.S. household consumption for developing consumption-side strategies in the U.S. for climate mitigation.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/150690/1/Scale, distribution and variations of global greenhouse gas emissions driven by U.S. households.pdfDescription of Scale, distribution and variations of global greenhouse gas emissions driven by U.S. households.pdf : Main articl

Optimization and evaluation of multi-bed adsorbent tube method in collection of volatile organic compounds

The feasibility of using adsorbent tubes to collect volatile organic compounds (VOCs) has been demonstrated since the 1990's and standardized as Compendium Method TO-17 by the U.S. Environmental Protection Agency (U.S EPA). This paper investigates sampling and analytical variables on concentrations of 57 ozone (O-3) precursors (C-2-C-12 aliphatic and aromatic VOCs) specified for the Photochemical Assessment Monitoring Station (PAMS). Laboratory and field tests examined multi-bed adsorbent tubes containing a sorbate combination of Tenax TA, Carbograph 1 TD, and Carboxen 1003. Analyte stabilities were influenced by both collection tube temperature and ambient O-3 concentrations. Analytes degraded during storage, while blank levels were elevated by passive adsorption. Adsorbent tube storage under cold temperatures (- 10 degrees C) in a preservation container filled with solid silica gel and anhydrous calcium sulfate (CaSO4) ensured sample integrity. A high efficiency (> 99%) O-3 scrubber (i.e., copper coil tube filled with saturated potassium iodide [KM removed O-3 (i.e., < 200 ppbv) from the air stream with a sampling capacity of 30 h. Water vapor scrubbers interfered with VOC measurements. The optimal thermal desorption-gas chromatography/mass spectrometry (TD-GC/MS) desorption time of 8 min was found at 330 degrees C. Good linearity (R-2 > 0.995) was achieved for individual analyte calibrations (with the exception of acetylene) for mixing ratios of 0.08-1.96 ppbv. The method detection limits (MDLs) were below 0.055 ppbv for a 3 L sample volume. Replicate analyses showed relative standard deviations (RSDs) of < 10%, with the majority of the analytes within < 5%

Deep Learning Approach for Large-Scale, Real-Time Quantification of Green Fluorescent Protein-Labeled Biological Samples in Microreactors

Absolute quantification of biological samples entails determining expression levels in precise numerical copies, offering enhanced accuracy and superior performance for rare templates. However, existing methodologies suffer from significant limitations: flow cytometers are both costly and intricate, while fluorescence imaging relying on software tools or manual counting is time-consuming and prone to inaccuracies. In this study, we have devised a comprehensive deep-learning-enabled pipeline that enables the automated segmentation and classification of GFP (green fluorescent protein)-labeled microreactors, facilitating real-time absolute quantification. Our findings demonstrate the efficacy of this technique in accurately predicting the sizes and occupancy status of microreactors using standard laboratory fluorescence microscopes, thereby providing precise measurements of template concentrations. Notably, our approach exhibits an analysis speed of quantifying over 2,000 microreactors (across 10 images) within remarkably 2.5 seconds, and a dynamic range spanning from 56.52 to 1569.43 copies per micron-liter. Furthermore, our Deep-dGFP algorithm showcases remarkable generalization capabilities, as it can be directly applied to various GFP-labeling scenarios, including droplet-based, microwell-based, and agarose-based biological applications. To the best of our knowledge, this represents the first successful implementation of an all-in-one image analysis algorithm in droplet digital PCR (polymerase chain reaction), microwell digital PCR, droplet single-cell sequencing, agarose digital PCR, and bacterial quantification, without necessitating any transfer learning steps, modifications, or retraining procedures. We firmly believe that our Deep-dGFP technique will be readily embraced by biomedical laboratories and holds potential for further development in related clinical applications.Comment: 23 pages, 6 figures, 1 tabl