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Addressing real-world challenges in battery diagnostics, particularly under incomplete or inconsistent boundary conditions, has proven difficult with traditional methodologies such as first-principles and atomistic calculations. Despite advances in data assimilation techniques, the overwhelming volume and diversity of data, coupled with the lack of universally accepted models, underscore the limitations of these traditional approaches. Recently, deep learning has emerged as a highly effective tool in overcoming persistent issues in battery diagnostics by adeptly managing expansive design spaces and discerning intricate, multidimensional correlations. This approach resolves challenges previously deemed insurmountable, especially with lost, irregular, or noisy data through the design of specialized network architectures that adhere to physical invariants. However, gaps remain between academic advancements and their practical applications, including challenges in explainability and the computational costs associated with AI-driven solutions. Emerging technologies such as explainable artificial intelligence (XAI), AI for IT operations (AIOps), lifelong machine learning to mitigate catastrophic forgetting, and cloud-based digital twins open new opportunities for intelligent battery life-cycle assessment. In this perspective, we outline these challenges and opportunities, emphasizing the potential of innovative technologies to transform battery diagnostics, as demonstrated by our recent practice and the progress made in the field. This includes promising achievements in both academic and industry field demonstrations in modeling and forecasting the dynamics of multiphysics and multiscale battery systems. These systems feature inhomogeneous cascades of scales, informed by our physical, electrochemical, observational, empirical, and/or mathematical understanding of the battery system. Through data assimilation efforts, meticulous craftsmanship, and elaborate implementations—and by considering the wealth and spatio-temporal heterogeneity of available data—such AI-based intelligent learning philosophies have great potential to achieve better accuracy, faster training, and improved generalization.

Tracking performance and stability of path tracking is crucial for unmanned vehicles' navigational tasks. Researches on vehicle path tracking controllers primarily rely on dynamic models. In contrast, there are less designs and researches that focus on path tracking controller based on kinematic model. This scarcity may stem from the perceived inadequacy in the accuracy of vehicle kinematic models. This research introduces a novel method for modifying the traditional vehicle kinematic model by incorporating a front wheel steering angle modification function to enhance tracking performance of path tracking controllers based on kinematic models. In terms of control strategy selection, the study opted for nonlinear model predictive control with terminal cost. A controller which is founded on the modified model was used on a simulated vehicle on CarSim-Simulink platform to assess the tracking performance. The simulation was designed with a double-shift line reference trajectory and the initial speeds of the simulated vehicle are 5 m/s and 10 m/s, respectively. The results of the simulations indicate that employing a controller based on the modified model could reduce the peak tracking error by 76.45 % and 43.06 %, and the root mean square of the tracking error was reduced by 73.29 % and 36.06 %, respectively.

Objectives: Neuregulin 1 (NRG1) is a risk gene for schizophrenia and involved in neurodevelopment and synaptic plasticity. Polymorphisms in NRG1 may affect psychotic symptoms in schizophrenia. This study investigated the effects of the single nucleotide polymorphism (SNP) rs6982890 on peripheral plasma NRG1 immunoreactivity, clinical symptoms and cognitive functions in schizophrenia patients. Material and methods: We recruited subjects from the Han population of northern China from 2010 to 2022. We first genotyped and analyzed 6 NRG1 SNPS in 1304 patients with schizophrenia and 871 healthy controls. Then, 91 patients with schizophrenia and 40 healthy controls were selected to detect the peripheral plasma NRG1 immunoreactivity by ELISA. Among them, 84 patients were divided into rs6982890 genotypes to analyze the correlation between NRG1 immunoreactivity and clinical symptoms. Results: Rs6982890 allelic frequencies were statistically significant between patients and controls. Baseline peripheral plasma NRG1 immunoreactivity in patients were significantly lower than controls. NRG1 immunoreactivity in patients were significantly increased after 8 weeks of antipsychotic treatment and significantly correlated with clinical symptoms and cognitive function. Genotyping of patients with SNP rs6982890 indicated NRG1 immunoreactivity in CC genotype increased significantly after treatment, while CT genotype had no significant change. Baseline NRG1 immunoreactivity with the CT genotype were significantly higher than CC genotype. Conclusions: NRG1 SNP rs6982890 is significantly associated with schizophrenia in the Han population of northern China, and it may affect the effect of antipsychotic drug treatment by regulating the peripheral plasma NRG1 immunoreactivity.

Medium-duty and heavy-duty vehicles (MHDVs) are major contributors to air pollution and greenhouse gas emissions. California leads in transitioning towards zero-emission MHDVs through a comprehensive approach that includes financial incentive s for zero-emission vehicle (ZEV) purchases, improved infrastructure for battery charging and hydrogen refueling, and extensive public awareness campaigns. The state has enacted Advanced Clean Fleets (ACF) regulations, requiring all MHDVs—including box trucks, vans, work trucks, and tractors—to adopt ZEVs between 2035 and 2042. Consequently, thorough analysis and forecasting of market penetration for various MHDVs in the next decade are essential. This study introduces a purchase probability analysis-based dynamic discrete choice (PPADDC) model specifically designed for vehicle choice modeling in the MHDV sector. The model assesses the probabilities linked to sixteen decision factors that influence truck adoption from 2024 to 2040. Utilizing this model, we project market share and penetration under eight scenarios, reflecting our comprehensive understanding of the California market through observational, empirical, and mathematical perspectives. These scenarios differ in terms of vehicle cost, procurement incentives, and infrastructure development strategies. Our findings provide a strategic framework for achieving the ambitious ZEV targets set by the California Air Resources Board (CARB), illustrating that a combination of technological advancements, policy initiatives, and market incentives can significantly enhance ZEV adoption in the MHDV sector. This study not only confirms the practicality of California ZEV goals but also offers valuable insights for policymakers, industry stakeholders, and researchers working towards a more sustainable transportation future.

To predict the market dynamics of various zero-emission vehicle (ZEV) technologies, this study introduces a dynamic discrete vehicle choice model (VCM) that investigates the probabilities associated with 14 decision factors, applying these to the purchase of ZEVs from 2020 to 2040. Market share and penetration results are presented under eight scenarios, that vary by vehicle costs infrastructure development and incentive strategies. The findings suggest that in the early years, incentives alone may not generate significant market penetration of ZEVs before the infrastructure meets the basic convenience for daily use, especially for fuel cell vehicles (FCVs). However, in later years, incentives play a more important role in the market penetration of ZEVs under well-defined infrastructure networks. By 2040, battery electric vehicles (BEVs) are projected to dominate the market in California. Plug-in hybrid electric vehicles (PHEVs) and FCVs may experience a decline in market share due to improved charging convenience, which benefits the market penetration of BEVs. However, fuel cell plug-in hybrid electric vehicles (FC-PHEVs) could still be beneficial if accessible models are available, considering the limited availability of hydrogen refueling stations. The goal set by the California Air Resources Board (CARB) is achievable, but it requires a sustained combination of measures; no single effort can achieve it. These measures include technological improvements to reduce the cost of ZEVs, a wider range of models available for consumers to choose from based on their desired performance, the establishment of infrastructure (battery chargers and hydrogen dispensers), and attractive incentives aimed at promoting ZEV adoption. The proposed methodology can be adapted for other regions in the United States and globally by carefully examining the inputs for each decision factor at the desired scale.

Ion channels are widely present in cell membranes, serving as crucial pathways for the movement of ions enter and exit cells. Variations in the expression of ion channels are crucial for regulating cellular functions. Among the genes associated with leukemia, certain genes encode ion channels. When these ion channels experience dysfunction or changes in expression, they can impact the physiological functions and signal transduction of hematopoietic cells, thereby regulating leukemia cell proliferation, differentiation, invasion/migration, and apoptosis. This article will provide a comprehensive review of the research progress on the expression and function of various ion channels in leukemia, thoroughly exploring their roles and mechanisms in the onset and progression of the disease, providing new insights and ideas for identifying potential biomarkers and developing new treatment methods for leukemia, thereby promoting innovations in future leukemia diagnosis and therapy.

The senescence of adipose stem cells (ASCs) impairs healthy adipose tissue remodeling, causing metabolic maladaptation to energy surplus. The intrinsic molecular pathways and potential therapy targets for ASC senescence are largely unclear. Here, we showed that visceral ASCs were prone to senescence that was caused by reactive oxygen species (ROS) overload, especially mitochondrial ROS. These senescent ASCs failed to sustain efficient glucose influx, pentose phosphate pathway (PPP) and redox homeostasis. We showed that CD90 silence restricted the glucose uptake by ASCs and thus disrupted their PPP and anti-oxidant system, resulting in ASC senescence. Notably, fibroblast growth factor 21 (FGF21) treatment significantly reduced the senescent phenotypes of ASCs by augmenting CD90 protein via glycosylation, which promoted glucose influx via the AKT-GLUT4 axis and therefore mitigated ROS overload. For diet-induced obese mice, chronic administration of low-dose FGF21 relieved their visceral white adipose tissue (VAT) dysfunction and systemic metabolic disorders. In particular, VAT homeostasis was restored in FGF21-treated obese mice, where ASC repertoire was markedly recovered, accompanied by CD90 elevation and anti-senescent phenotypes in these ASCs. Collectively, we reveal a molecular mechanism of ASC senescence by which CD90 downregulation interferes glucose influx into PPP and redox homeostasis. And we propose a FGF21-based strategy for healthy VAT remodeling, which targets CD90 glycosylation to correct ASC senescence and therefore combat obesity-related metabolic dysfunction.

Nowadays, evaporatively cooled office buildings commonly observed in dry hot areas in summer of China. However, few dedicated studies to record the local residents' thermal comfort and adaptability in these buildings. The contribution of adaptive comfort theory on thermal perception still remains unclear for optimizing office building design parameters. Hence, to deeper probe the adaptive thermal comfort of the related indoor environment, a field study of office buildings during summer considering evaporative cooling air conditioned (ECA) and naturally ventilated (NV) mode was conducted in Turpan, China. Based on 931 valid datasets collected from questionnaires, we found that the neutral temperature (Tn) of 28.4 °C in ECA group, 0.6 °C lower than NV group (29.0 °C). A lower air temperature (Ta) and higher humidity (RH)/air-velocity (Va) were expected in two modes, and Va has a stronger influence than RH on mean thermal sensation votes (MTSV). Meanwhile, occupants can adapt to current indoor environment through physiological, psychological and behavioral adjustments, while the clothing regulation had limited effect on MTSV unless the outdoor temperature exceeds 38 °C. Whether in ECA or NV mode, the predicted mean votes (PMV) model overestimated actual thermal sensation when operative temperature (Top) beyond 28 °C. Adaptive models were also proved varied from that in current standards, which indicated that they were not suitable for evaluating the studied buildings in Turpan. Above findings could suggest us a better understanding of the occupants' thermal adaptability, thereby providing the reference of design parameters revision and passive strategies for local newly/renovated buildings.

A considerable chunk of population in China live in rural areas. To create a suitable living environment in winter, many of them resort to additional solar house to improve the indoor thermal environment. Most of the previous studies focus on how the additional solar house changes the environment, but few try to optimize the size of that house. Therefore, this paper carries out field measurement and numerical simulation to evaluate the indoor thermal environment of a traditional building of Danihe Village, an important residential settlement in Xi’an, northwestern China’s Shaanxi Province, and explores the most suitable size of the additional solar house for improving the indoor thermal environment of the building in winter. The results show that the width of the additional solar house should be controlled within the range of 600–2400 mm, which provides the residents indoor with the best thermal environment. The research results offer a guide for improving indoor thermal environment and reducing indoor energy cost in future.

Summary: Adipose-derived stem cells (ASCs) drive healthy visceral adipose tissue (VAT) expansion via adipocyte hyperplasia. Obesity induces ASC senescence that causes VAT dysfunction and metabolic disorders. It is challenging to restrain this process by biological intervention, as mechanisms of controlling VAT ASC senescence remain unclear. We demonstrate that a population of CX3CR1hi macrophages is maintained in mouse VAT during short-term energy surplus, which sustains ASCs by restraining their senescence, driving adaptive VAT expansion and metabolic health. Long-term overnutrition induces diminishment of CX3CR1hi macrophages in mouse VAT accompanied by ASC senescence and exhaustion, while transferring CX3CR1hi macrophages restores ASC reservoir and triggers VAT beiging to alleviate the metabolic maladaptation. Mechanistically, visceral ASCs attract macrophages via MCP-1 and shape their CX3CR1hi phenotype via exosomes; these macrophages relieve ASC senescence by promoting the arginase1-eIF5A hypusination axis. These findings identify VAT CX3CR1hi macrophages as ASC supporters and unravel their therapeutic potential for metabolic maladaptation to obesity.

With the acceleration of urbanization in China, urban problems have become increasingly serious, and have led to the concept of the Park City. This concept involves a consideration of the comprehensive value of green space in order to give full play to the maximum benefit of that green space and, thus, realize the sustainable development of the city. Therefore, an exploration of the optimal scale of urban green space will have theoretical and practical significance for the construction of the Park City and the rational allocation of urban land resources. Taking Xi'an as an example, the present paper comprehensively considers the socioeconomic and ecological benefits of urban green space, and calculates its optimal scale. The results indicate that: (i) in terms of the socioeconomic benefits alone, the per capita amount of green area in Xi’an needs to be larger than 11.08 m2, and there is no theoretical optimal size; (ii) in terms of ecological benefits alone, the per capita amount of green area in Xi’an needs to be greater than 22.98 m2; (iii) in terms of the combined socioeconomic and ecological benefits, the per capita amount of green area of Xi’an needs to be greater than 24.89 m2; (iv) the optimal scale for any city is more siginficantly impacted by the ecological effects than by the socioeconomic effects; (v) to satisfy the development and construction needs of various cities, with their individual characteristics, the optimal urban green space scale should be explored according to the target city’s developmental stage and land use situation. The methods and results of the present study are intended to provide a reference for the future construction of Park Cities in China, as well as the planning of green space systems in other cities globally, and to open new prospects for future research.

N6-methyladenosine (m6A) has an important epitranscriptomic modification that controls cancer self-renewal and cell fate. The addition of m6A to mRNA is a reversible modification. The deposition of m6A is encoded by a methyltransferase complex involving three homologous factors, jargonized as “writers,” “erasers,” and “readers.” However, their roles in pancreatic adenocarcinoma (PAAD) are underexploited. With the use of The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases, we provided an mRNA signature that may improve the prognostic prediction of PAAD patients based on the genetic status of m6A regulators. PAAD patients with genetic alteration of m6A regulators had worse disease-free and overall survival. After comparing PAAD groups with/without genetic alteration of m6A regulators, we identified 196 differentially expressed genes (DEGs). Then, we generated a 16-mRNA signature score system through least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Multivariate cox regression analysis demonstrated that a high-risk score significantly correlates with poor prognosis. Moreover, time-dependent receiver operating characteristic (ROC) curves revealed it was effective in predicting the overall survival in both training and validation sets. PAH, ZPLD1, PPFIA3, and TNNT1 from our signature also exhibited an independent prognostic value. Collectively, these findings can improve the understanding of m6A modifications in PAAD and potentially guide therapies in PAAD patients.

Pancreatic ductal adenocarcinoma (PDAC) is among the most lethal cancers due to frequently late diagnosis and futile treatment. It is a crucial necessity to determine the mechanisms of PDAC. Y-box Binding Protein 1 (YBX1), a highly conserved transcription factor, has been previously reported to play a role in various hallmarks of cancer. We show here that YBX1 is significantly overexpressed in PDAC and correlates with poor prognosis and reduced survival. In PDAC cell lines, YBX1 regulated cell-cycle progression, proliferation, and the expression of glycogen synthase kinase 3 beta (GSK3B) and cell-cycle-related proteins cyclin D1 and E1. Dual-luciferase reporter and chromatin immunoprecipitation (ChIP) assays established that YBX1 binds to the promoter of GSK3B, suggesting that YBX1 promotes pancreatic cancer cell growth through induction of GSK3B expression. These findings offer important insights into the mechanisms underlying pathologic proliferation in PDAC.

Summary: Despite great progress in battery safety modeling, accurately predicting the evolution of multiphysics systems is extremely challenging. The question on how to ensure safety of billions of automotive batteries during their lifetime remains unanswered. In this study, we overcome the challenge by developing machine learning techniques based on the recorded data that are uploaded to the cloud. Using charging voltage and temperature curves from early cycles that are yet to exhibit symptoms of battery failure, we apply data-driven models to both predict and classify the sample data by health condition based on the observational, empirical, physical, and statistical understanding of the multiscale systems. The best well-integrated machine learning models achieve a verified classification accuracy of 96.3% (exhibiting an increase of 20.4% from initial model) and an average misclassification test error of 7.7%. Our findings highlight the need for cloud-based artificial intelligence technology tailored to robustly and accurately predict battery failure in real-world applications.