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In response to the growing demand for high-performance lithium-ion batteries, this study investigates the crucial role of different carbon sources in enhancing the electrochemical performance of lithium iron phosphate (LiFePO 4 ) cathode materials. Lithium iron phosphate (LiFePO 4 ) suffers from drawbacks, such as low electronic conductivity and low lithium-ion diffusion coefficient, which hinder its industrial development. Carbon is a common surface coating material for LiFePO 4 , and the source, coating method, coating amount, and incorporation method of carbon have a significant impact on the performance of LiFePO 4 materials. In this work, iron phosphate was used as the iron and phosphorus source, and lithium carbonate was used as the lithium source. Glucose, phenolic resin, ascorbic acid, and starch were employed as carbon sources. Ethanol was utilized as a dispersing agent, and ball milling was employed to obtain the LiFePO 4 precursor. Carbon-coated LiFePO 4 cathode materials were synthesized using the carbothermal reduction method, and the effects of different carbon sources on the structure and electrochemical performance of LiFePO 4 materials were systematically investigated. The results showed that, compared to other carbon sources, LiFePO 4 prepared with glucose as the carbon source not only had a higher discharge specific capacity but also better rate cycle performance. Within a voltage range of 2.5–4.2 V, the initial discharge specific capacities at 0.1, 0.5, and 1 C rates were 154.6, 145.6, and 137.6 mAh/g, respectively. After 20 cycles at a 1 C rate, the capacity retention rate was 98.7%, demonstrating excellent electrochemical performance.

Highly effective and highly efficient decontamination of mercury from aqueous media remains a serious task for public health and ecosystem protection. Here we report that this task can be addressed by creating a mercury ‘nano-trap’ as illustrated by functionalizing a high surface area and robust porous organic polymer with a high density of strong mercury chelating groups. The resultant porous organic polymer-based mercury ‘nano-trap’ exhibits a record-high saturation mercury uptake capacity of over 1,000 mg g −1 , and can effectively reduce the mercury(II) concentration from 10 p.p.m. to the extremely low level of smaller than 0.4 p.p.b. well below the acceptable limits in drinking water standards (2 p.p.b.), and can also efficiently remove >99.9% mercury(II) within a few minutes. Our work therefore presents a new benchmark for mercury adsorbent materials and provides a new perspective for removing mercury(II) and also other heavy metal ions from contaminated water for environmental remediation. Decontamination of mercury pollution from fresh water is a serious environmental issue. Here, the authors report a porous organic polymer-based nano-trap, functionalized with mercury chelating groups, capable of efficient and rapid mercury removal from aqueous media.

In recent years, artificial intelligence (AI) has shown great promise in medicine. However, explainability issues make AI applications in clinical usages difficult. Some research has been conducted into explainable artificial intelligence (XAI) to overcome the limitation of the black-box nature of AI methods. Compared with AI techniques such as deep learning, XAI can provide both decision-making and explanations of the model. In this review, we conducted a survey of the recent trends in medical diagnosis and surgical applications using XAI. We have searched articles published between 2019 and 2021 from PubMed, IEEE Xplore, Association for Computing Machinery, and Google Scholar. We included articles which met the selection criteria in the review and then extracted and analyzed relevant information from the studies. Additionally, we provide an experimental showcase on breast cancer diagnosis, and illustrate how XAI can be applied in medical XAI applications. Finally, we summarize the XAI methods utilized in the medical XAI applications, the challenges that the researchers have met, and discuss the future research directions. The survey result indicates that medical XAI is a promising research direction, and this study aims to serve as a reference to medical experts and AI scientists when designing medical XAI applications.

The rapid advancement of digital technology provides core support for the iterative evolution of new business formats across industries. In the field of unmanned driving, the industry is undergoing a critical upgrade from assisted driving technology application to unmanned vehicle operation mode. The identification of challenges and large-scale promotion of this new business model highly depend on the public’s cognitive evaluation and emotional attitude. Thus, a systematic analysis of the public’s focus areas and attitude levels toward the new unmanned driving model holds significant practical value for advancing technology implementation and healthy industrial development. From the cognitive gap perspective, this study selects public comments from the “Apollo Go” platform as samples via text data mining. Combined with the division of public opinion communication life cycle stages, it integrates BERTopic topic modeling and BERT-BiLSTM sentiment analysis models to deconstruct the phased evolutionary characteristics of media and public opinion. It explores the mechanism of social opinion shaping public cognition and reveals the dynamic evolution of public cognitive gap in unmanned driving—from formation and fluctuation to mitigation, i.e., the gap gradually dissolves and transforms into innovative synergy. Ultimately, by integrating Diffusion of Innovations theory, this study theoretically clarifies the resolution mechanism of cognitive gaps and proposes a three-level innovation synergy strategy, thereby providing important academic basis and decision-making references for accelerating the social integration of new unmanned driving business models.

Coastal areas are prone to thunderstorms. Lightning strikes can damage power facilities and communication systems, thereby leading to serious consequences. The lightning location network achieves lightning location through data fusion from multiple lightning locator nodes and can detect the location and intensity of lightning in real time. It is an important facility for thunderstorm warning and protection in coastal areas. However, when a sensor node in a lightning location network experiences a soft fault, it causes distortion in the lightning location. To achieve fault diagnosis of lightning locator nodes in a multi-node data fusion mode, this study proposes a new lightning location mode: the observer pattern. This paper first analyzes the main factors contributing to the error of the lightning location algorithm under this mode, proposes an observer pattern estimation algorithm (OPE) for lightning location, and defines the proportion of improvement in lightning positioning accuracy (PI) caused by the OPE algorithm. By analyzing the changes in PI in the process of lightning location, this study further proposes a diagnostic algorithm (OPSFD) for soft-fault nodes in a lightning location network. The simulation experiments in the paper demonstrate that the OPE algorithm can effectively improve the positioning accuracy of existing lightning location networks. Therefore, the OPE algorithm is also a low-cost and efficient method for improving the accuracy of existing lightning location networks, and it is suitable for the actual deployment and upgrading of current lightning locators. Meanwhile, the experimental results show that when a soft fault causes the observation error of the node to exceed the normal range, the OPSFD algorithm proposed in this study can effectively diagnose the faulty node.

With rapid progress in DNA synthesis and sequencing, strain engineering starts to be the rate-limiting step in synthetic biology. Here, we report a gRNA-tRNA array for CRISPR-Cas9 (GTR-CRISPR) for multiplexed engineering of Saccharomyces cerevisiae . Using reported gRNAs shown to be effective, this system enables simultaneous disruption of 8 genes with 87% efficiency. We further report an accelerated Lightning GTR-CRISPR that avoids the cloning step in Escherichia coli by directly transforming the Golden Gate reaction mix to yeast. This approach enables disruption of 6 genes in 3 days with 60% efficiency using reported gRNAs and 23% using un-optimized gRNAs. Moreover, we applied the Lightning GTR-CRISPR to simplify yeast lipid networks, resulting in a 30-fold increase in free fatty acid production in 10 days using just two-round deletions of eight previously identified genes. The GTR-CRISPR should be an invaluable addition to the toolbox of synthetic biology and automation. Strain engineering is increasingly the bottleneck step in synthetic biology workflows. Here the authors present GTR-CRISPR for rapid, multiplexed engineering of yeast metabolic pathways.

At present, realizing high-quality automatic welding through online monitoring is a research focus in engineering applications. In this paper, a CNN–LSTM algorithm is proposed, which combines the advantages of convolutional neural networks (CNNs) and long short-term memory networks (LSTMs). The CNN–LSTM algorithm establishes a shallow CNN to extract the primary features of the molten pool image. Then the feature tensor extracted by the CNN is transformed into the feature matrix. Finally, the rows of the feature matrix are fed into the LSTM network for feature fusion. This process realizes the implicit mapping from molten pool images to welding defects. The test results on the self-made molten pool image dataset show that CNN contributes to the overall feasibility of the CNN–LSTM algorithm and LSTM network is the most superior in the feature hybrid stage. The algorithm converges at 300 epochs and the accuracy of defects detection in CO2 welding molten pool is 94%. The processing time of a single image is 0.067 ms, which fully meets the real-time monitoring requirement based on molten pool image. The experimental results on the MNIST and FashionMNIST datasets show that the algorithm is universal and can be used for similar image recognition and classification tasks.

With the advance of data technologies, gig platforms have applied data and algorithms to their management and put more stringent requirements on gig workers through algorithmic management. Gig workers might perform destructive deviant behavior when coping with algorithmic management. It is meaningful to examine how the algorithmic management applied to gig platforms could lead to gig workers’ destructive deviant behavior. Based on the challenge–hindrance framework, we developed a research model and validated it with survey data collected from 423 food delivery riders. We employed multi-level linear regression analysis in data analysis and found that perceived algorithmic management was appraised as both a hindrance and a challenge. As a hindrance, it elicits working/family deviant behavior; as a challenge, it helps reduce working/family deviant behavior. Regulatory focus (a prevention focus vs. a promotion focus) moderates the effect of perceived algorithmic management on stress appraisals (hindrance appraisals vs. challenge appraisals). This study explains algorithmic management’s impact on gig workers’ destructive deviant behavior through the appraisal of algorithmic management as both a challenge and a hindrance. It also provides practical advice to gig platforms, gig workers and policymakers on how to balance the challenge and hindrance roles of algorithmic management in gig work.

Thyroid hormone sensitivity has emerged as a critical factor in various diseases. However, its relationship with ischemic stroke-associated pneumonia (iSAP) in euthyroid patients with ischemic stroke remains poorly understood. This study aims to elucidate the association between thyroid hormone sensitivity indices and iSAP risk. A total of 1,767 euthyroid patients with ischemic stroke were enrolled and categorized into the iSAP group (n = 376) and the non-iSAP group (n = 1,391). Univariate and multivariate logistic regression analyses were performed to assess the association between thyroid hormone sensitivity indices-including free triiodothyronine (FT3), free thyroxine (FT4), FT3/FT4 ratio, thyroid-stimulating hormone index (TSHI), Thyrotroph T4 Resistance Index (TT4RI), and thyroid feedback quantile-based indices (TFQI-FT3, TFQI-FT4)-and iSAP risk. The predictive performance of these indices was evaluated using receiver operating characteristic (ROC) curve analysis. Compared with the non-iSAP group, patients with iSAP were older and exhibited a higher prevalence of atrial fibrillation (AF) and chronic obstructive pulmonary disease (COPD), along with greater stroke severity (higher NIHSS scores). Univariate analysis demonstrated that higher FT3 levels (OR = 0.85, 95% CI: 0.80-0.91, p < 0.0001) and a higher FT3/FT4 ratio (OR = 0.80, 95% CI: 0.69-0.92, p = 0.0018) were statistically associated with lower odds of developing iSAP. After adjusting for confounders, multivariate analysis revealed that a higher FT3/FT4 ratio remained inversely associated with iSAP occurrence (Q3 vs. Q1: OR = 0.40, 95% CI: 0.26-0.62, p < 0.0001; Q4 vs. Q1: OR = 0.31, 95% CI: 0.19-0.48, p < 0.0001). Additionally, elevated TFQI-FT3 levels showed a significant inverse association with iSAP occurrence (OR = 0.35, 95% CI: 0.18-0.67, p = 0.0017). ROC analysis demonstrated that the FT3/FT4 ratio and the Age, Atrial fibrillation, Dysphagia, Sex, and Stroke Severity(A2DS2)score exhibited moderate predictive accuracy for iSAP, with area under the curve (AUC) values of 0.711 and 0.763, respectively. In euthyroid patients with ischemic stroke, a lower FT3/FT4 ratio and reduced TFQI-FT3 levels were linked to higher odds of iSAP. These exploratory findings suggest that thyroid hormone sensitivity indices, particularly the FT3/FT4 ratio, may serve as potential predictive markers and warrant validation in prospective studies.

Endometrial cancers are complex ecosystems composed of cells with distinct phenotypes, genotypes, and epigenetic states. Current models do not adequately reflect oncogenic origin and pathological progression in patients. Here we use single-cell RNA sequencing to profile cells from normal endometrium, atypical endometrial hyperplasia, and endometrioid endometrial cancer (EEC), which altogether represent the step-by-step development of endometrial cancer. We find that EEC originates from endometrial epithelial cells but not stromal cells, and unciliated glandular epithelium is the source of EEC. We also identify LCN2 + /SAA1/2 + cells as a featured subpopulation of endometrial tumorigenesis. Finally, the stromal niche and immune environment changes during EEC progression are described. This study elucidates the evolution of cell populations in EEC development at single-cell resolution, which would provide a direction to facilitate EEC research and diagnosis. Many aspects of the tumourigenesis of endometrioid endometrial cancers (EEC) are still poorly understood. Here, the authors use single-cell transcriptomics to characterise EEC tumours and their microenvironment across different stages, and reveal potential cells of origin for EEC.