Lithium-ion batteries (LIBs) are widely used in electrical vehicles (EVs), but safety issues with LIBs still occur frequently. State of charge (SOC) and state of health (SOH) are two crucial parameters for describing the state of LIBs. However, due to inconsistencies that may occur among hundreds to thousands of battery cells connected in series and parallel in the battery pack, these parameters can be difficult to estimate accurately. To address this problem, this paper proposes a joint SOC and SOH estimation method based on the nonlinear state space reconstruction (NSSR) and long short-term memory (LSTM) neural network. An experiment testbed was set up to measure the SOC and SOH of battery packs under different criteria and configurations, and thousands of charging/discharging cycles were recorded. The joint estimation algorithms were validated using testbed data, and the errors for SOC and SOH estimation were found to be within 2.5% and 1.3%, respectively, which is smaller than the errors obtained using traditional Ah-Integral and LSTM-only algorithms.

Coffee is one of the most popular beverages in the world. Annual coffee consumption continues to increase, but at the same time, it generates a large amount of spent coffee grounds from the brewing process that give rise to environmental problems. An appropriate solution to manage these spent coffee grounds (SCGs) becomes crucial. Our project aims at discussing the feasibility of utilizing SCGs to synthesize polylactic acid (PLA) as a recycling application for SCGs. Producing PLA from SCGs offers the opportunity to contribute to the circular economy and sustainability due to the significant volume of coffee consumption. This approach reduces waste, provides environmental benefits, and promotes the use of renewable resources. Through repurposing SCGs into PLA, we can close the loop, minimize environmental impact, and create a more sustainable alternative to fossil-fuel-based plastics. This paper first discusses the properties and potential recycling applications of spent coffee grounds. The production of PLA from lactic acid and the potential processes for converting SCGs to lactic acid are then briefly discussed. From our review, it is feasible to utilize SCGs as the primary source for lactic acid production via bacteria fermentation and, further, to produce PLA via ring-opening polymerization. Possible ways to improve the yield and a corresponding cost analysis are also discussed.

Bridges, especially cable-stayed bridges, play an important role in modern transportation systems. The safety status of bridge cables, as an important component of cable-stayed bridges, determines the health status of the entire bridge. As a non-destructive real-time detection technology, acoustic emission has the advantages of high detection efficiency and low cost. This paper focuses on the issue that a large amount of data are generated during the process of health monitoring of bridge cables. A novel acoustic emission signal segmentation algorithm is proposed with the aim to facilitate the extraction of acoustic emission signal characteristics. The proposed algorithm can save data storage space efficiently. Moreover, it can be adapted to different working conditions according to the adjustment of parameters in order to accurately screen out the target acoustic emission signal. Through the acoustic emission signal acquisition experiments of three bridges, the characteristics of the noise signal in the acquisition process are extracted. A comprehensive analysis of the signal in the time domain, frequency domain and time-frequency domain is carried out. The noise signal filtering parameter thresholds are proposed according to the analysis results.

With the booming development of bridge construction, bridge operation and maintenance have always been major issues to ensure the safety of the community. Affected by the long-term service of bridges and natural factors, the safety and durability of cables can be threatened. Cables are critical stress-bearing elements of large bridges such as cable-stayed bridges. Realizing the health monitoring of bridge cables is the key to ensuring the normal operation of bridges. Acoustic emission (AE) is a dynamic nondestructive testing method that is increasingly used in the local monitoring of bridge cables. In this paper, a testbed is described for generating the acoustic emission signals for signal identification testing with machine learning (ML) models. Owing to the limited number of measured signals being available, an algorithm is proposed to simulate acoustic emission signals for model training. A multi-angle feature extraction method is proposed to extract the acoustic emission signals and construct a comprehensive feature vector to characterize the acoustic emission signals. Seven ML models are trained with the simulated acoustic emission signals. Long short-term memory (LSTM) has been specially applied for deep learning demonstration which requires a large amount of training data. As all machine learning models (including LSTM) provide desired performance, it shows that the proposed approach of simulating acoustic emission signals can be effective.

Urban thermal environments, characterized by the interplay between indoor and outdoor conditions, pose growing challenges in high-density coastal cities. This study proposes a multi-scale, integrative framework that couples a satellite-derived land surface temperature (LST) analysis with microscale building performance simulations to holistically evaluate the high-density urban thermal environment in subtropical climates. The results reveal that compact, high-density morphologies reduce outdoor heat stress (UTCI) through self-shading but lead to significantly higher cooling loads, energy use intensity (EUI), and poorer daylight autonomy (DA) due to restricted ventilation and limited sky exposure. In contrast, more open, vegetation-rich forms improve ventilation and reduce indoor energy demand, yet exhibit higher UTCI values in exposed areas and increased lighting energy use in poorly oriented spaces. This study also proposes actionable design strategies, including optimal building spacing (≥15 m), façade orientation (30–60° offset from west), SVF regulation (0.4–0.6), and the integration of vertical greenery to balance solar access, ventilation, and shading. These findings offer evidence-based guidance for embedding morphological performance metrics into planning policies and building design codes. This work advances the integration of outdoor and indoor performance evaluation and supports climate-adaptive urban form design through quantitative, policy-relevant insights.

In recent years, lithium-ion batteries (LIBs) have gained immense popularity and widespread adoption in various applications. Due to the voltage and capacity requirements of these applications, a single LIB is often inadequate. To meet these demands, LIBs are interconnected in series and parallel configurations to achieve higher voltage and capacity levels. To improve performance and prolong the lifespan of battery packs, battery equalization methods are employed to ensure balanced state-of-charge (SOC) and voltage levels among individual battery cells. Passive battery equalization methods are commonly utilized in industrial applications due to their simplicity and cost-effectiveness. The implementation of complex techniques to enhance performance may not be suitable for passive equalization methods, as it would introduce complications and increase implementation costs. This paper introduces two innovative equalization methods: the rank-sum ratio (RSR) based method and the piecewise function based (PFSO) method. These methods leverage real-time internal resistance measurement devices to enhance the equalization process. To evaluate their effectiveness, the simulation model of battery module is developed based on the Thévenin-based equivalent circuit, utilizing data obtained from hybrid pulse power characteristic (HPPC) tests. Simulation results demonstrate that the proposed methods outperform other passive equalization methods, including the SOC-based and voltage-based methods. In a setting with a 16-cell battery module possessing a total capacity of 51.2V and 104Ah, the RSR and PFSO methods can equalize the battery module, adjusting the capacity from 98.97Ah to 102.56Ah and 101.12Ah, respectively, as opposed to the 100.90Ah and 100.09Ah achieved by the SOC-based and voltage-based methods.