Energy Conservation as Security

"Energy security" is usually defined as the guarantee of a stable and reliable supply of energy at reasonable prices. However, this definition is often misleading because it equates oil supply as the primary focus of a country's energy security considerations. As a developing country with a limited natural resource endowment China does not rely on oil alone. Instead China is one of the few economies in the world that still uses coal as one of its main sources of energy. Therefore, energy security in China is more comprehensive because it must consider the supply of coal, gas, electricity and nuclear energy along with oil imports

Multitask Learning Deep Neural Networks to Combine Revealed and Stated Preference Data

It is an enduring question how to combine revealed preference (RP) and stated preference (SP) data to analyze travel behavior. This study presents a framework of multitask learning deep neural networks (MTLDNNs) for this question, and demonstrates that MTLDNNs are more generic than the traditional nested logit (NL) method, due to its capacity of automatic feature learning and soft constraints. About 1,500 MTLDNN models are designed and applied to the survey data that was collected in Singapore and focused on the RP of four current travel modes and the SP with autonomous vehicles (AV) as the one new travel mode in addition to those in RP. We found that MTLDNNs consistently outperform six benchmark models and particularly the classical NL models by about 5% prediction accuracy in both RP and SP datasets. This performance improvement can be mainly attributed to the soft constraints specific to MTLDNNs, including its innovative architectural design and regularization methods, but not much to the generic capacity of automatic feature learning endowed by a standard feedforward DNN architecture. Besides prediction, MTLDNNs are also interpretable. The empirical results show that AV is mainly the substitute of driving and AV alternative-specific variables are more important than the socio-economic variables in determining AV adoption. Overall, this study introduces a new MTLDNN framework to combine RP and SP, and demonstrates its theoretical flexibility and empirical power for prediction and interpretation. Future studies can design new MTLDNN architectures to reflect the speciality of RP and SP and extend this work to other behavioral analysis

The relationship between metabolic rate and sociability is altered by food-deprivation

Individuals vary in the extent to which they associate with conspecifics, but little is known about the energetic underpinnings of this variation in sociability. Group-living allows individuals to find food more consistently, but within groups, there can be competition for food items. Individuals with an increased metabolic rate could display decreased sociability to reduce competition. Long-term food deprivation (FD) may alter any links between sociability and metabolic rate by affecting motivation to find food. We examined these issues in juvenile qingbo carp Spinibarbus sinensis, to understand how FD and metabolic rate affect sociability. Like many aquatic ectotherms, this species experiences seasonal bouts of FD. Individuals were either: (i) food-deprived for 21 days; or (ii) fed a maintenance ration (control). Fish from each treatment were measured for standard metabolic rate (SMR) and tested for sociability twice: once in the presence of a control stimulus shoal and once with a food-deprived stimulus shoal. Control individuals ventured further from stimulus shoals over a 30-min trial, while food-deprived fish did not change their distance from stimulus shoals as trials progressed. Control fish with a higher SMR were least sociable. Well-fed controls showed decreased sociability when exposed to food-deprived stimulus shoals, but there was evidence of consistency in relative sociability between exposures to different shoal types. Results contrast with previous findings that several days of fasting causes individuals to decrease associations with conspecifics. Prolonged FD may cause individuals to highly prioritize food acquisition, and the decreased vigilance that would accompany continuous foraging may heighten the need for the antipredator benefits of shoaling. Conversely, decreased sociability in well-fed fish with a high SMR probably minimizes intraspecific competition, allowing them to satisfy an increased energetic demand while foraging. Together, these results suggest that FD – a challenge common for many ectothermic species – can affect individual sociability as well as the attractiveness of groups towards conspecifics. In addition, the lack of a link between SMR and sociability in food-deprived fish suggests that, in situations where group membership is linked to fitness, the extent of correlated selection on metabolic traits may be context-dependent

Protein-Protein Interaction Analysis: Expanded Hydrogen/Deuterium Exchange Tandem Mass Spectrometry and Host Cell Protein Characterization

Elucidating protein structures, dynamics, and interactions is critical for understanding their roles in health and disease. In the biopharmaceutical industry, bioanalytical science allows, e.g., insights into how these complex molecules interact, and enables their characterization to assure safety and consistency of biotherapeutic manufacturing. Mass spectrometry (MS)-based methods, such as hydrogen/deuterium exchange (HDX) MS, are powerful tools for structural proteomics. This dissertation presents analytical strategies for incorporating gas-phase fragmentation for improved structural resolution in HDX MS workflows in both positive and negative ion mode, and for improving host cell protein characterization in biopharmaceuticals. HDX is typically performed in positive ion mode. In Chapter 2, the feasibility of several negative ion mode tandem mass spectrometry (MS/MS) techniques for HDX coupling is explored. Regio-selectively deuterated peptide anions were fragmented by negative-ion collision induced dissociation (nCID), negative-ion free radical initiated peptide sequencing (nFRIPS), electron detachment dissociation (EDD), and negative-ion electron capture dissociation (niECD) to determine the extent of H/D scrambling in each MS/MS technique. nCID induces extensive H/D scrambling involving histidine C-2 and Cβ-hydrogen atoms for histidine-containing peptides. nFRIPS proceeds with complete hydrogen scrambling but without histidine participation, whereas EDD and niECD demonstrate moderate scrambling with slightly lower levels in niECD. However, improved ionization efficiency, ion transmission, and fragmentation efficiency under HDX quenching conditions are needed for routine application of niECD in HDX MS workflows. Due to the acidic HDX quenching conditions, the acid protease pepsin is employed for protein digestion. However, resulting peptic peptides may not contain basic residues and therefore carry less average charge than typical tryptic peptides. In Chapter 3, supercharging strategies are explored for combination with electron capture/transfer dissociation (ECD/ETD)-based HDX MS/MS in positive ion mode. These MS/MS techniques require analytes to carry at least two positive charges. The supercharging reagent m-NBA was found to enhance the average charge state for a variety of pepsin-derived peptides, thus increasing ECD/ETD fragmentation efficiency and peptide sequence coverage in bottom-up HDX liquid chromatography (LC)/MS workflows. Retention time shifts in the presence of m-NBA were avoided by injecting m-NBA through a mixing tee following the analytical column. During these experiments, b-type ions were observed in ECD spectra of supercharged peptides. Such fragment ions are atypical in ECD but have been found at increased levels for peptides containing few or no basic residues. In Chapter 4, we find that such peptides also show abundant hydrogen atom loss from the charge-reduced radical species. We show that, upon ECD of supercharged peptides, the number and abundance of b ions increases with increasing charge state; b ions are prevalent in ECD spectra when the number of protons is higher than the number of basic sites. Under the same conditions, significantly less abundant b ions were seen in ETD than ECD. The observed difference between ECD and ETD is likely due to different internal energy prior to dissociation. We propose that b ions should be considered in ExD database searches for supercharged peptides in HDX MS/MS, and that ETD may be superior to ECD for minimizing deuterium scrambling in such experiments. In Chapter 5, an offline hydrophilic interaction chromatography (HILIC) sample preparation method for improving detection of residual host cell proteins (HCPs) in biotherapeutic proteins by LC MS/MS is described. This method enriches HCPs while depleting high abundance biotherapeutic proteins, enabling detection of previously unobserved HCPs in drug substances.PHDChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/150000/1/qywang_1.pd

Fairness-enhancing deep learning for ride-hailing demand prediction

Short-term demand forecasting for on-demand ride-hailing services is one of the fundamental issues in intelligent transportation systems. However, previous travel demand forecasting research predominantly focused on improving prediction accuracy, ignoring fairness issues such as systematic underestimations of travel demand in disadvantaged neighborhoods. This study investigates how to measure, evaluate, and enhance prediction fairness between disadvantaged and privileged communities in spatial-temporal demand forecasting of ride-hailing services. A two-pronged approach is taken to reduce the demand prediction bias. First, we develop a novel deep learning model architecture, named socially aware neural network (SA-Net), to integrate the socio-demographics and ridership information for fair demand prediction through an innovative socially-aware convolution operation. Second, we propose a bias-mitigation regularization method to mitigate the mean percentage prediction error gap between different groups. The experimental results, validated on the real-world Chicago Transportation Network Company (TNC) data, show that the de-biasing SA-Net can achieve better predictive performance in both prediction accuracy and fairness. Specifically, the SA-Net improves prediction accuracy for both the disadvantaged and privileged groups compared with the state-of-the-art models. When coupled with the bias mitigation regularization method, the de-biasing SA-Net effectively bridges the mean percentage prediction error gap between the disadvantaged and privileged groups, and also protects the disadvantaged regions against systematic underestimation of TNC demand. Our proposed de-biasing method can be adopted in many existing short-term travel demand estimation models, and can be utilized for various other spatial-temporal prediction tasks such as crime incidents predictions

A secure cross-domain interaction scheme for blockchain-based intelligent transportation systems

Si, H., Li, W., Wang, Q., Cao, H., Bação, F., & Sun, C. (2023). A secure cross-domain interaction scheme for blockchain-based intelligent transportation systems. PeerJ Computer Science, (November 2023), 1-36. https://doi.org/10.7717/peerj-cs.1678, https://doi.org/10.7717/peerj-cs.1678/supp-1, https://doi.org/10.7717/peerj-cs.1678/supp-2---This work was supported by the Henan Province Key Science-technology Research Project under Grant No. 232102520006 and 232102210122, the Key Research Project of Henan Provincial Higher Education Institution under Grant No. 23A520005, and the Henan Province Major Public Welfare Projects under Grant No. 201300210300. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.In the intelligent transportation system (ITS), secure and efficient data communication among vehicles, road testing equipment, computing nodes, and transportation agencies is important for building a smart city-integrated transportation system. However, the traditional centralized processing approach may face threats in terms of data leakage and trust. The use of distributed, tamper-proof blockchain technology can improve the decentralized storage and security of data in the ITS network. However, the cross-trust domain devices, terminals, and transportation agencies in the heterogeneous blockchain network of the ITS still face great challenges in trusted data communication and interoperability. In this article, we propose a heterogeneous cross-chain interaction mechanism based on relay nodes and identity encryption to solve the problem of data cross-domain interaction between devices and agencies in the ITS. First, we propose the ITS cross-chain communication framework and improve the cross-chain interaction model. The relay nodes are interconnected through libP2P to form a relay node chain, which is used for cross-chain information verification and transmission. Secondly, we propose a relay node secure access scheme based on identity-based encryption to provide reliable identity authentication for relay nodes. Finally, we build a standard cross-chain communication protocol and cross-chain transaction lifecycle for this mechanism. We use Hyperledger Fabric and FISCO BCOS blockchain to design and implement this solution, and verify the feasibility of this cross-chain interaction mechanism. The experimental results show that the mechanism can achieve a stable data cross-chain read throughput of 2,000 transactions per second, which can meet the requirements of secure and efficient cross-chain communication and interaction among heterogeneous blockchains in the ITS, and has high application value.publishersversionpublishe