Hierarchical Joint Graph Learning and Multivariate Time Series Forecasting

Multivariate time series is prevalent in many scientific and industrial domains. Modeling multivariate signals is challenging due to their long-range temporal dependencies and intricate interactions--both direct and indirect. To confront these complexities, we introduce a method of representing multivariate signals as nodes in a graph with edges indicating interdependency between them. Specifically, we leverage graph neural networks (GNN) and attention mechanisms to efficiently learn the underlying relationships within the time series data. Moreover, we suggest employing hierarchical signal decompositions running over the graphs to capture multiple spatial dependencies. The effectiveness of our proposed model is evaluated across various real-world benchmark datasets designed for long-term forecasting tasks. The results consistently showcase the superiority of our model, achieving an average 23\% reduction in mean squared error (MSE) compared to existing models.Comment: Temporal Graph Learning Workshop @ NeurIPS 2023, New Orleans, United State

Spherical Sb Core/Nb2O5-C Double-Shell Structured Composite as an Anode Material for Li Secondary Batteries

Antimony (Sb)-based materials are considered to be attractive for use in Li secondary battery anodes because of their high capacity. However, their huge volume change during Li insertion-extraction cycling limits their cycle performance. The Sb-active material can be combined with intercalation-based active materials to address these issues. In this study, spherical Sb core/Nb2O5 shell structured composite materials were synthesized through a simple solvothermal process and a carbon coating was simultaneously added during heat treatment using a naphthalene precursor. The resulting double-shelled materials were characterized with X-ray diffraction, Raman spectroscopy, X-ray photoelectron spectroscopy, and electron microscopy. The electrochemical test results showed that a reversible capacity of more than 450 mAh g−1 was retained after 100 cycles. This improved performance is ascribed to the double-shelled structure. The large volume change of the nano-sized Sb core material was alleviated by the double-shelled structure, which consisted of crystalline orthorhombic Nb2O5 and amorphous carbon. The shell materials also aided rapid charge transport

Practical Operation Strategies for Energy Storage System under Uncertainty

Recent advances in battery technologies have reduced the financial burden of using the energy storage system (ESS) for customers. Peak cut, one of the benefits of using ESS, can be achieved through proper charging/discharging scheduling of ESS. However, peak cut is sensitive to load-forecasting error, and even a small forecasting error may result in the failure of peak cut. In this paper, we propose a two-phase approach of day-ahead optimization and real-time control for minimizing the total cost that comes from time-of-use (TOU), peak load, and battery degradation. In day-ahead optimization, we propose to use an internalized pricing to manage peak load in addition to the cost from TOU. The proposed method can be implemented by using dynamic programming, which also has an advantage of accommodating the state-dependent battery degradation cost. Then in real-time control, we propose a concept of marginal power to alleviate the performance loss incurred from load-forecasting error and mimic the offline optimal battery scheduling by learning from load-forecasting error. By exploiting the marginal power, real-time ESS charging/discharging power gets close to the offline optimal battery scheduling. Case studies show that under load-forecasting uncertainty, the peak power using the proposed method is only 22.4% higher than the offline optimal peak power, while the day-ahead optimization has 76.8% higher peak power than the offline optimal power. In terms of profit, the proposed method achieves 77.0% of the offline optimal profit while the day-ahead method only earns 19.6% of the offline optimal profit, which shows the substantial improvement of the proposed method

Perceived Neighborhood Environment Associated with Sarcopenia in Urban-Dwelling Older Adults: The Korean Frailty and Aging Cohort Study (KFACS)

Sarcopenia is associated with adverse health outcomes among older individuals. However, little is known about its association with neighborhood environmental factors. We explored the relationship between sarcopenia and perceived neighborhood environmental factors among community-dwelling older adults aged 70–84 years. We analyzed 1778 participants (mean age of 75.9 ± 3.8 years; 54.0% women) who lived in urban areas and underwent dual-energy X-ray absorptiometry from the Korean Frailty and Aging Cohort Study. Sarcopenia was defined according to the Asian Working Group for Sarcopenia 2019 definition. Perceived neighborhood environmental factors were assessed using the Environmental Module of the International Physical Activity Questionnaire (IPAQ-E). In the multivariate analysis, compared to the fifth quintile of the IPAQ-E score, the odds ratios (ORs) and 95% confidence intervals (CIs) for sarcopenia in the first, second, third, and fourth quintiles were 2.13 (1.40–3.24), 1.72 (1.12–2.64), 1.75 (1.15–2.66), and 1.62 (1.06–2.47), respectively. These neighborhood environmental characteristics were linked with an increased likelihood of sarcopenia: no public transportation access (OR = 2.04; 95% CI = 1.19–3.48), poor recreational facilities access (OR = 1.39; 95% CI = 1.01–1.90), absence of destination (OR = 1.53; 95% CI = 1.06–2.20), many hill hazards (OR = 1.36; 95% CI = 1.03–1.78), and lack of traffic safety (OR = 1.35; 95% CI = 1.02–1.78). Thus, better neighborhood environmental strategies may help prevent sarcopenia among urban-dwelling older adults

Poly(ether ether ketone)-Induced Surface Modification of Polyethylene Separators for Li-Ion Batteries

With the global effort to reduce fossil fuels and to use eco-friendly energy, interest in Li-ion batteries (LIBs) is rapidly increasing. In the LIB system, the separator is an important component for determining the rate performance and safety of cells. Although polyolefin separators are commercially used in LIBs, they still suffer from inferior electrolyte wettability and low thermal stability issues. Here, we introduce a chemical surface modification for polyethylene (PE) separators using a poly(ether ether ketone) (PEEK) coating. The separators were pretreated in a tannic acid solution to enforce the adhesion of the coated layers. Then, PEEK was coated onto the PE surface by a doctor blading method. The separators were examined by infrared spectroscopy, and the surface properties were characterized by electrolyte uptake and contact angle measurements. The treated surface was hydrophilic, and the ionic conductivity of the cell with the modified separator was significantly improved. As a result, the corresponding rate performance was significantly improved. The surface modification strategy proposed here can be applied to polyolefin-based separators as well

Superior metal storage behavior of Zn-containing porous carbon nanostructures for Na and Li metal batteries

Na and Li metals are ideal anodes for high-energy rechargeable batteries. However, their poor coulombic efficiency and the associated safety issues due to dendritic metal deposition and large volume changes during metallic plating/stripping are the major causes that persistently hinder their practical energy-storage applications. Herein, Zn-containing carbon nanostructures derived from typical zeolitic imidazolate framework-8 (ZIF-8) are proposed as advanced metal anode materials for both Na and Li metal batteries. The three-dimensional interconnected porous surfaces confine metallic Na and Li within the nanostructures, filling the carbon scaffold, and covering the electrode surface to form stable solid electrolyte interphases. Based on the metal nucleation and growth results obtained through various electrochemical tests, Zn incorporation and pore morphology have been revealed to be the key factors that regulate the metal nucleation and metallic plating/stripping during cycling. Thus, the incorporation of Zn in the closed pores of a carbon nanostructure improves the cycle performance, even in carbonate electrolytes, and produces stable Na and Li metallic anodes for next-generation rechargeable batteries