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Abstract Neurotransmitters are key modulators in neuro-immune circuits and have been linked to tumor progression. Medullary thyroid cancer (MTC), an aggressive neuroendocrine tumor, expresses neurotransmitter calcitonin gene-related peptide (CGRP), is insensitive to chemo- and radiotherapies, and the effectiveness of immunotherapies remains unknown. Thus, a comprehensive analysis of the tumor microenvironment would facilitate effective therapies and provide evidence on CGRP’s function outside the nervous system. Here, we compare the single-cell landscape of MTC and papillary thyroid cancer (PTC) and find that expression of CGRP in MTC is associated with dendritic cell (DC) abnormal development characterized by activation of cAMP related pathways and high levels of Kruppel Like Factor 2 (KLF2), correlated with an impaired activity of tumor infiltrating T cells. A CGRP receptor antagonist could offset CGRP detrimental impact on DC development in vitro. Our study provides insights of the MTC immunosuppressive microenvironment, and proposes CGRP receptor as a potential therapeutic target.

Abstract The Fractional Frequency Transmission System (FFTS) operates with low frequency to decrease the electrical length of AC transmission lines, consequently enhancing transmission capacity. The integration of renewable energy sources via FFTS will significantly impact power system planning. This paper presents a Fractional Frequency Transmission Expansion Planning (FF‐TEP) model, which considers the interaction between FFTS and the industrial‐frequency power grid. This model addresses the challenges posed by the integration of offshore wind power through FFTS, and takes several key factors into account, including the active power coupling of Modular Multilevel Matrix Converter (M3C), inverter capacity, and converter transformer capacity. To address the non‐convexity caused by AC power flow, this paper introduces a Genetic‐Particle Swarm Optimization algorithm (G‐PSO) with dynamic penalties tailored to the M3C structure. This approach incorporates both crossover and mutation operators from Genetic Algorithms (GA) into Particle Swarm Optimization (PSO). The penalty function dynamically adapts according to the M3C structure, resulting in improved global optimization while maintaining computational efficiency. The results of two case studies demonstrate that FFTS can significantly enhance transmission capacity while reducing cable charging power. This reduces investment costs and decreases voltage offset issues.

The modular multilevel converter based high voltage direct current (MMC-HVDC) is a dynamic power balancing system. The control system of MMC generally adopts a dual closed-loop vector control strategy based on the traditional instantaneous power model under asymmetric grid state, which has complex control structure and low control accuracy. This paper introduces a flexible instantaneous power model and establishes a general power equation with active power and new reactive power as control objects. Based on this, an improved sliding-mode MMC-HVDC direct power control strategy based on the new instantaneous power model is proposed which combines the flexible instantaneous power model and the improved sliding-mode control method to eliminate the twice grid-frequency ripples in both active and reactive power under asymmetric grid states. Furthermore, it omits the inner-loop controller and power compensation terms while optimizing the control structure. Simulation results show that the proposed method has better dynamic responsiveness, control accuracy and robustness under operating conditions such as asymmetric grid state and parameter perturbation which can better exploit the advantages of the flexible instantaneous power model.

Emerging evidence suggests that microbial dysbiosis plays vital roles in many human cancers. However, knowledge of whether the microbial community in thyroid tumor is related to tumorigenesis remains elusive. In this study, we aimed to explore the microbial community in thyroid tissues and its contribution to papillary thyroid cancer (PTC). In parallel, we performed microbial profiling and transcriptome sequencing in the tumor and adjacent normal tissues of a large cohort of 340 PTC and benign thyroid nodule (BTN) patients. Distinct microbial signatures were identified in PTC, BTN, and their adjacent non-tumor tissues. Intra-thyroid tissue bacteria were verified by means of bacteria staining, fluorescence in situ hybridization, and immunoelectron microscopy. We found that 17 bacterial taxa were differentially abundant in PTC compared with BTN, which included enrichment in PTC of the pathobionts Rhodococcus, Neisseria, Streptococcus, Halomonas, and Devosia, and depletion of the beneficial bacteria Amycolatopsis. These differentially abundant bacteria could differentiate PTC tumor tissues (PTC-T) from BTN tissues (BTN-T) with an area under the curve (AUC) of 81.66%. Microbial network analysis showed increased correlation strengths among the bacterial taxa in PTC-T in comparison with BTN-T. Immune-function-corresponding bacteria (i.e., Erwinia, Bacillus, and Acinetobacter) were found to be enriched in PTC with Hashimoto’s thyroiditis. Moreover, our integrative analysis revealed that the PTC-enriched bacteria had a positive association with key PTC-oncogenic pathway-related genes, including BRAF, KRAS, IRAK4, CTNNB1, PIK3CA, MAP3K7, and EGFR. In conclusion, our results suggest that intratumor bacteria dysbiosis is associated with the thyroid tumorigenesis and oncogenic signaling pathways of PTC.

Abstract Background Negative conversion of nucleic acid was a key factor in deciding discharge or the end of isolation of asymptomatic or mild COVID-19 patients. We aimed to explore the effect of vaccination on the time to negative conversion after Omicron infection. Methods This retrospective cohort study included asymptomatic or mild patients with COVID-19 admitted to Fangcang shelter Hospital from November 10, 2022 to December 2, 2022. The relationship between vaccination status and the time to negative conversion was analyzed by multiple linear regression. Results A total of 2,104 asymptomatic or mild COVID-19 patients were included in the analysis, of whom 1,963 were vaccinated. The mean time to negative conversion of no vaccination, one dose, two doses, and three doses were 12.57 (5.05), 12.18 (3.46), 11.67 (4.86) and 11.22 (4.02) days, respectively (p = 0.002). Compared with no vaccination, two doses (β=-0.88, 95% CI: -1.74, -0.02, p = 0.045), and three doses (β=-1.51, 95% CI: -2.33, -0.70, p

Abstract Background Both dysregulation of mechanistic target of rapamycin (mTOR) signalling and DNA methylation patterns have been shown to be closely associated with tumor progression and serve as promising targets for hepatocellular carcinoma (HCC) therapy. Although their respective roles in HCC have been extensively revealed, the existence of molecular interactions between them remains largely unknown. Methods The association of DNA methylation and mTOR signalling in HCC tissues and cell lines was assessed. A Kaplan‒Meier analysis was applied to estimate the overall survival (OS) and recurrence-free survival (RFS) of HCC patients. The modulation of DNMT1 by mTOR in HCC cell lines was determined. The effect of the drug combination in cell lines and mouse models was examined. Results The results showed that the DNA methylation level was positively associated with the activation of mTOR signalling in HCC tissues and cell lines. Moreover, HCC patients with higher DNA methylation levels and enhanced activation of mTOR signalling exhibited the worst prognosis. Then, we screened methylation-related enzymes and found that the activation of mTOR signalling increased DNMT1 expression and activity. In addition, mTOR enhanced the translational efficiency of DNMT1 in a 4E-BP1-dependent manner, which is based on the pyrimidine rich translational element (PRTE)-containing 5′UTR of DNMT1. Moreover, we demonstrated that the combined inhibition of mTOR and DNMT synergistically inhibited HCC growth in vitro and in vivo. Conclusions In addition to some already identified pro-cancer downstream molecules, the activation of mTOR signalling was found to promote DNA methylation by increasing the translation of DNMT1. Furthermore, combined targeting of mTOR and DNMT1 has been demonstrated to have a more effective tumor suppressive function in HCC.

The effect of β-glucans and their receptor Dectin-1 in tumor development remains controversial. Here the authors show that Dectin-1 signaling promotes the development of colorectal cancer (CRC) by inducing prostaglandin E2 production in myeloid-derived suppressor cells and by suppressing IL-22BP expression, suggesting dectin-1 blockade as a potential therapeutic target for CRC.

Abstract Background and aim Preoperative evaluation of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) is important for surgical strategy determination. We aimed to develop and establish a preoperative predictive model for MVI status based on DNA methylation markers. Methods A total of 35 HCC tissues and the matched peritumoral normal liver tissues as well as 35 corresponding HCC patients’ plasma samples and 24 healthy plasma samples were used for genome-wide methylation sequencing and subsequent methylation haplotype block (MHB) analysis. Predictive models were constructed based on selected MHB markers and 3-cross validation was used. Results We grouped 35 HCC patients into 2 categories, including the MVI− group with 17 tissue and plasma samples, and MVI + group with 18 tissue and plasma samples. We identified a tissue DNA methylation signature with an AUC of 98.0% and a circulating free DNA (cfDNA) methylation signature with an AUC of 96.0% for HCC detection. Furthermore, we established a tissue DNA methylation signature for MVI status prediction, and achieved an AUC of 85.9%. Based on the MVI status predicted by the DNA methylation signature, the recurrence-free survival (RFS) and overall survival (OS) were significantly better in the predicted MVI− group than that in the predicted MVI + group. Conclusions In this study, we identified a cfDNA methylation signature for HCC detection and a tissue DNA methylation signature for MVI status prediction with high accuracy.

Despite the availability of booster doses against SARS-CoV-2, variants of concern are capable of escaping immune responses established by vaccination or natural infection. In this work, the authors detail the immune responses elicited in a cohort of healthcare workers, receiving a fourth dose of inactivated vaccine.

Abstract Background Vaccination is important in influenza prevention but the immune response wanes with age. The circadian nature of the immune system suggests that adjusting the time of vaccination may provide an opportunity to improve immunogenicity. Our previous cluster trial in Birmingham suggested differences between morning and afternoon vaccination for some strains in the influenza vaccine in older adults. Whether this effect is also seen in a younger age group with less likelihood of compromised immunity is unknown. We therefore conducted an individual-based randomized controlled trial in Guangzhou to test the hypothesis that influenza vaccination in the morning induces a stronger immune response in older adults than afternoon vaccination. We included adults in middle age to determine if the effect was also seen in younger age groups. Results Of the 418 participants randomised, 389 (93.1%, 191 middle-aged adults aged 50–60 years and 198 older adults aged 65–75 years) were followed up. Overall, there was no significant difference between the antibody titers (geometric mean /95% CI) after morning vs afternoon vaccination (A/H1N1: 39.9 (32.4, 49.1) vs. 33.0 (26.7, 40.7), p = 0.178; A/H3N2: 92.2 (82.8, 102.7) vs. 82.0 (73.8, 91.2), p = 0.091; B: 15.8 (13.9, 17.9) vs. 14.4 (12.8, 16.3), p = 0.092), respectively. However, in pre-specified subgroup analyses, post-vaccination titers for morning versus afternoon vaccination in the 65–75 years subgroup were (A/H1N1): 49.5 (36.7, 66.6) vs. 32.9 (24.7, 43.9), p = 0.050; (A/H3N2): 93.5 (80.6, 108.5) vs. 73.1 (62.9, 84.9), p = 0.021; (B): 16.6 (13.8, 20.1) vs. 14.4 (12.3, 17.0), p = 0.095, respectively. Among females, antibody titers for morning versus afternoon vaccination were (A/H1N1): 46.9 (35.6, 61.8) vs. 31.1 (23.8, 40.7), p = 0.030; (A/H3N2): 96.0 (83.5, 110.3) vs. 84.7 (74.4, 96.5), p = 0.176; (B): 14.8 (12.7, 17.3) vs. 13.0 (11.3, 14.9), p = 0.061, respectively. In the 50–60 years old subgroup and males, there were no significant differences between morning and afternoon vaccination. Conclusions Morning vaccination may enhance the immunogenicity to influenza vaccine in adults aged over 65 and women. An intervention to modify vaccination programs to vaccinate older individuals in the morning is simple, cost free and feasible in most health systems.

Summary: Background: Lymph node metastasis (LNM) assessment in patients with papillary thyroid carcinoma (PTC) is of great value. This study aimed to develop a deep learning model applied to intraoperative frozen section for prediction of LNM in PTC patients. Methods: We established a deep-learning model (ThyNet-LNM) with the multiple-instance learning framework to predict LNM using whole slide images (WSIs) from intraoperative frozen sections of PTC. Data for the development and validation of ThyNet-LNM were retrospectively derived from four hospitals from January 2018 to December 2021. The ThyNet-LNM was trained using 1987 WSIs from 1120 patients obtained at the First Affiliated Hospital of Sun Yat-sen University. The ThyNet-LNM was then validated in the independent internal test set (479 WSIs from 280 patients) as well as three external test sets (1335 WSIs from 692 patients). The performance of ThyNet-LNM was further compared with preoperative ultrasound and computed tomography (CT). Findings: The area under the receiver operating characteristic curves (AUCs) of ThyNet-LNM were 0.80 (95% CI 0.74–0.84), 0.81 (95% CI 0.77–0.86), 0.76 (95% CI 0.68–0.83), and 0.81 (95% CI 0.75–0.85) in internal test set and three external test sets, respectively. The AUCs of ThyNet-LNM were significantly higher than those of ultrasound and CT or their combination in all four test sets (all P

Abstract Aims With prevalence of hepatocellular carcinoma (HCC) in low-risk population (LRP), establishing a non-invasive diagnostic strategy becomes increasingly urgent to spare unnecessary biopsies in this population. The purposes of this study were to find characterisics of HCC and to establish a proper non-invasive method to diagnose HCC in LRP. Methods A total of 681 patients in LRP (defined as the population without cirrhosis, chronic HBV infection or HCC history) were collected from 2 institutions. The images of computed tomography (CT) and magnetic resonance imaging (MRI) were manually analysed. We divided the patients into the training cohort (n = 324) and the internal validating cohort (n = 139) by admission time in the first institution. The cohort in the second institution was viewed as the external validation (n = 218). A multivariate logistic regression model incorporating both imaging and clinical independent risk predictors was developed. C-statistics was used to evaluate the diagnostic performance. Results Besides the major imaging features of HCC (non-rim enhancement, washout and enhancing capsule), tumor necrosis or severe ischemia (TNSI) on imaging and two clinical characteristics (gender and alpha fetoprotein) were also independently associated with HCC diagnosis (all P

Emerging SARS-CoV-2 variants with multiple mutations raise concerns on vaccine effectiveness. Here, Kang et al. report that a quadrivalent mosaic nanoparticle vaccine displaying spike proteins from the SARS-CoV-2 prototype and three different VOCs confer protection against SARS-CoV-2 variants in mice.

Abstract SARS-CoV-2 inactivated vaccines have shown remarkable efficacy in clinical trials, especially in reducing severe illness and casualty. However, the waning of humoral immunity over time has raised concern over the durability of immune memory following vaccination. Thus, we conducted a nonrandomized trial among the healthcare workers (HCWs) to investigate the long-term sustainability of SARS-CoV-2-specific B cells and T cells stimulated by inactivated vaccines and the potential need for a third booster dose. Although neutralizing antibodies elicited by the standard two-dose vaccination schedule dropped from a peak of 29.3 arbitrary units (AU)/mL to 8.8 AU/mL 5 months after the second vaccination, spike-specific memory B and T cells were still detectable, forming the basis for a quick recall response. As expected, the faded humoral immune response was vigorously elevated to 63.6 AU/mL by 7.2 folds 1 week after the third dose along with abundant spike-specific circulating follicular helper T cells in parallel. Meanwhile, spike-specific CD4+ and CD8+ T cells were also robustly elevated by 5.9 and 2.7 folds respectively. Robust expansion of memory pools by the third dose potentiated greater durability of protective immune responses. Another key finding in this trial was that HCWs with low serological response to two doses were not truly “non-responders” but fully equipped with immune memory that could be quickly recalled by a third dose even 5 months after the second vaccination. Collectively, these data provide insights into the generation of long-term immunological memory by the inactivated vaccine, which could be rapidly recalled and further boosted by a third dose.

Abstract SARS-CoV-2 variants have evolved a variety of critical mutations, leading to antigenicity changes and immune escape. The recent emerging SARS-CoV-2 Omicron variant attracted global attention due to its significant resistance to current antibody therapies and vaccines. Here, we profiled the mutations of Omicron and other various circulating SARS-CoV-2 variants in parallel by computational interface analysis and in vitro experimental assays. We identified critical mutations that lead to antigenicity changes and diminished neutralization efficiency of a panel of 14 antibodies due to diverse molecular mechanisms influencing the antigen-antibody interaction. Our study identified that Omicron exhibited extraordinary potency in immune escape compared to the other variants of concern, and explores the application of computational interface analysis in SARS-CoV-2 mutation surveillance and demonstrates its potential for the early identification of concerning variants, providing preliminary guidance for neutralizing antibody therapy.

Electrochemical CO2 reduction (ECR) holds great potential to alleviate the greenhouse effect and our dependence on fossil fuels by integrating renewable energy for the electrosynthesis of high-value fuels from CO2. However, the high thermodynamic energy barrier, sluggish reaction kinetics, inadequate CO2 conversion rate, poor selectivity for the target product, and rapid electrocatalyst degradation severely limit its further industrial-scale application. Although numerous strategies have been proposed to enhance ECR performances from various perspectives, scattered studies fail to comprehensively elucidate the underlying effect-performance relationships toward ECR. Thus, this review presents a comparative summary and a deep discussion with respect to the effects strongly-correlated with ECR, including intrinsic effects of materials caused by various sizes, shapes, compositions, defects, interfaces, and ligands; structure-induced effects derived from diverse confinements, strains, and fields; electrolyte effects introduced by different solutes, solvents, cations, and anions; and environment effects induced by distinct ionomers, pressures, temperatures, gas impurities, and flow rates, with an emphasis on elaborating how these effects shape ECR electrocatalytic activities and selectivity and the underlying mechanisms. In addition, the challenges and prospects behind different effects resulting from various factors are suggested to inspire more attention towards high-throughput theoretical calculations and in situ/operando techniques to unlock the essence of enhanced ECR performance and realize its ultimate application. [ABSTRACT FROM AUTHOR]

Abstract Background Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection causes coronavirus disease 2019 (COVID‐19), which is still devastating economies and communities globally. The increasing infections of variants of concern (VOCs) in vaccinated population have raised concerns about the effectiveness of current vaccines. Patients with autoimmune diseases (PAD) under immunosuppressant treatments are facing higher risk of infection and potentially lower immune responses to SARS‐CoV‐2 vaccination. Methods Blood samples were collected from PAD or healthy controls (HC) who finished two or three doses of inactivated vaccines. Spike peptides derived from wild‐type strain, delta, omicron BA.1 were utilised to evaluate T cell responses and their cross‐recognition of delta and omicron in HC and PAD by flow cytometry and ex vivo IFNγ‐ELISpot. Results We found that inactivated vaccine‐induced spike‐specific memory T cells were long‐lasting in both PAD and HC. These spike‐specific T cells were highly conserved and cross‐recognized delta and omicron. Moreover, a third inactivated vaccine expanded spike‐specific T cells that responded to delta and omicron spike peptides substantially in both PAD and HC. Importantly, the polyfunctionality of spike‐specific memory T cells was preserved in terms of cytokine and cytotoxic responses. Although the extent of T cell responses was lower in PAD after two‐dose, T cell responses were boosted to a greater magnitude in PAD by the third dose, bringing comparable spike‐specific T cell immunity after the third dose. Conclusion Inactivated vaccine‐induced spike‐specific T cells remain largely intact against delta and omicron variants. This study expands our understanding of inactivated vaccine‐induced T cell responses in PAD and HC, which could have important indications for vaccination strategy.

Hepatocellular carcinoma (HCC) is the leading cause of cancer-related death worldwide. Local ablation, such as radiofrequency ablation, microwave ablation, cryoablation and irreversible electroporation, etc., are well established in elimination and control of HCC. However, high recurrence rate after local ablation remains the biggest challenge for HCC management. Novel and effective therapeutic strategies to improve long-term survival are urgently needed. Accumulating studies have reported the role of ablation in modulating the tumor signaling pathway and the immune microenvironment to both eliminate residual/metastatic tumor and promote tumor progression. Ablation has been shown to elicit tumor-specific immune responses by inducing massive cell death and releasing tumor antigen. Immunotherapies that unleash the immune system have the potential to enhance the anti-tumor immunity induced by ablation. Multiple combinatory strategies have been explored in preclinical and clinical studies. In this review, we comprehensively summarize the latest progress on different mechanisms underlying the effects of ablation on tumor cells and tumor microenvironment. We further analyze the clinical trials testing the combination of ablation and immunotherapies, and discuss the possible role of immunomodulation to boost the anti-tumor effects of ablation and prevent HCC recurrence.

Abstract The evidence of the impact of traditional statistical (TS) and artificial intelligence (AI) tool interventions in clinical practice was limited. This study aimed to investigate the clinical impact and quality of randomized controlled trials (RCTs) involving interventions evaluating TS, machine learning (ML), and deep learning (DL) prediction tools. A systematic review on PubMed was conducted to identify RCTs involving TS/ML/DL tool interventions in the past decade. A total of 65 RCTs from 26,082 records were included. A majority of them had model development studies and generally good performance was achieved. The function of TS and ML tools in the RCTs mainly included assistive treatment decisions, assistive diagnosis, and risk stratification, but DL trials were only conducted for assistive diagnosis. Nearly two-fifths of the trial interventions showed no clinical benefit compared to standard care. Though DL and ML interventions achieved higher rates of positive results than TS in the RCTs, in trials with low risk of bias (17/65) the advantage of DL to TS was reduced while the advantage of ML to TS disappeared. The current applications of DL were not yet fully spread performed in medicine. It is predictable that DL will integrate more complex clinical problems than ML and TS tools in the future. Therefore, rigorous studies are required before the clinical application of these tools.

Background: The past decade has witnessed a dramatic increase in the number of patients with inflammatory bowel disease (IBD) in China. The nationwide burden of hospitalization remains unclear, however. We aimed to address this gap by conducting analysis using a nationwide database. Methods: Population-based hospitalization rates from 2013 to 2018 were calculated by extrapolating the number of patients in the database to the national level. Surgical rates, annual hospital charges, and length of stay were also used for quantification of hospitalization burden. The Poisson regression analysis and the Cochran–Armitage trend test were conducted to analyze temporal trends as expressed as annual percentage of change (APC) with 95% confidential intervals (CIs). Results: From 2013 to 2018, the hospitalization rates for Crohn’s disease (CD) and ulcerative colitis (UC) in China increased from 2.20 (95% CI = 2.17–2.22) to 3.62 (3.59–3.65) per 100,000 inhabitants ( p

Abstract Background Targeted therapy and immunotherapy put forward higher demands for accurate lung cancer classification, as well as benign versus malignant disease discrimination. Digital whole slide images (WSIs) witnessed the transition from traditional histopathology to computational approaches, arousing a hype of deep learning methods for histopathological analysis. We aimed at exploring the potential of deep learning models in the identification of lung cancer subtypes and cancer mimics from WSIs. Methods We initially obtained 741 WSIs from the First Affiliated Hospital of Sun Yat-sen University (SYSUFH) for the deep learning model development, optimization, and verification. Additional 318 WSIs from SYSUFH, 212 from Shenzhen People’s Hospital, and 422 from The Cancer Genome Atlas were further collected for multi-centre verification. EfficientNet-B5- and ResNet-50-based deep learning methods were developed and compared using the metrics of recall, precision, F1-score, and areas under the curve (AUCs). A threshold-based tumour-first aggregation approach was proposed and implemented for the label inferencing of WSIs with complex tissue components. Four pathologists of different levels from SYSUFH reviewed all the testing slides blindly, and the diagnosing results were used for quantitative comparisons with the best performing deep learning model. Results We developed the first deep learning-based six-type classifier for histopathological WSI classification of lung adenocarcinoma, lung squamous cell carcinoma, small cell lung carcinoma, pulmonary tuberculosis, organizing pneumonia, and normal lung. The EfficientNet-B5-based model outperformed ResNet-50 and was selected as the backbone in the classifier. Tested on 1067 slides from four cohorts of different medical centres, AUCs of 0.970, 0.918, 0.963, and 0.978 were achieved, respectively. The classifier achieved high consistence to the ground truth and attending pathologists with high intraclass correlation coefficients over 0.873. Conclusions Multi-cohort testing demonstrated our six-type classifier achieved consistent and comparable performance to experienced pathologists and gained advantages over other existing computational methods. The visualization of prediction heatmap improved the model interpretability intuitively. The classifier with the threshold-based tumour-first label inferencing method exhibited excellent accuracy and feasibility in classifying lung cancers and confused nonneoplastic tissues, indicating that deep learning can resolve complex multi-class tissue classification that conforms to real-world histopathological scenarios.

In a Magnetically Coupled Resonant Wireless Power Transfer (MCR-WPT) system, the magnetic coupling coil is one of the key factors that determines the system's output power, transmission efficiency, anti-offset capability, and so on. This article proposes a coil parameter optimization method for a wireless power transfer system based on crowding distance division and adaptive genetic operators. Through optimizing the design of decision variables, such as the numbers of transmitting and receiving coil turns, the spacings between transmitting and receiving coil turns, the inner radii of the transmitting and receiving coils, and the vertical distance of the coil, the best transmission performance can be achieved. This study improves the NSGA-II algorithm through proposing a genetic operator algorithm for average crowding and high crowding populations based on adaptive operators, as well as a genetic operator algorithm for low crowding populations based on information entropy. These improved algorithms avoid problems inherent to traditional genetic operators such as fixed genetic proportions, do not easily cause the algorithm to fall into a local optimal solution, and show better convergence in the ZDT1–ZDT3 test functions. The optimization design method in this article is not only independent of commercial software such as ANSYS Maxwell 2021 R1, but can also significantly improve the calculation speed compared with traditional simulation software. [ABSTRACT FROM AUTHOR]

Electrochemical CO2 reduction reaction (CO2RR) holds a great potential for converting CO2 into valuable carbon‐based chemicals and fuels. A promising strategy for enhancing CO2RR performance is the deliberate structural design of electrocatalysts, which can maximize the utilization of inherent structural advantages. In this work, SnO2 nanocubes (NCs) and nanorods (NRs) are synthesized using a surface energy‐driven growth orientation method, where the stable (110) facet and the highly energetic (001) facet constitute the SnO2 nanostructures. Leveraging the inherent structural merits of different facets on SnO2, theoretical calculations reveal that the (001) facet plays a primary role in inhibiting hydrogen evolution reaction (HER), while both (110) and (001) facets are highly favorable for CO2‐to‐formate conversion under the external bias. As a result, SnO2 NCs with a higher facet ratio of (001)/(110) achieve nearly 100% selectivity for the formation of carbonaceous products during CO2RR. More importantly, a maximum partial current density of about 1 A cm−2 with a formate Faradaic efficiency (FE) of over 90% is achieved in a flow cell, distinguishing it from most of the reported Sn‐based electrocatalysts. These results highlight the strategic advantages of leveraging the inherent structure of nanomaterials for efficient CO2RR. [ABSTRACT FROM AUTHOR]