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Isothermal dispatch models for battery energy storage systems (BESSs) systematically underestimate degradation costs because dispatch-induced Joule heating elevates cell temperature and accelerates ageing through Arrhenius-type kinetics. This paper proposes three integrated contributions. First, a thermal–electrochemical coupling loop embeds a first-order lumped thermal model within the dispatch simulation: cell temperature is updated from I2R heat generation and Newton cooling at each time step, and the resulting temperature trajectory feeds into the Arrhenius stress factors of a semi-empirical degradation model combining Δt-based calendar ageing with Rainflow-based cycle ageing, enabling the optimiser to discover thermally self-regulating strategies. This coupling is critical because, as the results demonstrate, ignoring it leads to systematic underestimation of degradation costs by up to 13%. Second, the resulting four-objective problem (negative profit, thermally coupled degradation cost, SOC deviation, and CVaR imbalance penalty) is solved by a hypervolume-contribution-enhanced NSGA-III (HVC-NSGA-III), which augments reference-point selection with an archive pruned by removing the solution of the smallest individual hypervolume contribution, concentrating Pareto resolution in the knee region. Third, an SOH-adaptive knee-point selection assigns the degradation weight as a monotone function of ageing degree (1−SOH)/(1−SOHEOL), automatically tightening dispatch conservatism as remaining useful life diminishes. Simulations on ENTSO-E data over 96 h show the following: (i) thermal coupling shifts the Pareto front by 8–15% in the degradation dimension with temperature excursions up to 7 K; (ii) HVC-NSGA-III improves hypervolume by 8.7% over standard NSGA-III; (iii) SOH-adaptive selection reduces capacity loss by 27.4% at only 9.1% revenue cost; and (iv) ablation confirms Rainflow (24.8%) and thermal coupling (13.1%) as the two largest contributors.

To address the residual lateral coil misalignment after an unmanned aerial vehicle (UAV) lands on a fixed wireless-charging platform, this study proposes a graph-attention-based knowledge distillation method for embedded self-alignment localization. Four detection-coil voltages form an induced-voltage fingerprint database organized as a multi-scale spatial graph. A graph attention network (GAT) teacher model is trained offline to learn neighborhood correlations in the voltage–position mapping, and its spatial knowledge is distilled into a lightweight Tiny-MLP student model for microcontroller unit (MCU)-based online inference. Experimental results show that the GAT teacher achieves a mean absolute error (MAE) of 0.589 cm, while the distilled Tiny-MLP reduces the MAE of the directly trained Tiny-MLP from 1.548 cm to 1.148 cm (a 25.8% reduction under a fixed seed). In 2000 closed-loop alignment trials with random initial positions, the system achieves an 85.5% success rate under a 0.5 cm threshold, indicating that the method supports low-complexity closed-loop self-alignment for UAV wireless charging.

In response to the significantly reduced efficiency of magnetic coupling wireless charging for unmanned aerial vehicles (UAVs) caused by their high sensitivity to transmitter and receiver coil alignment, as well as landing point errors, a position identification method based on the detection coil-induced voltage fingerprint and embedded neural network regression is proposed. This enables position alignment through a 2D mechanical structure. Firstly, by means of an S–S compensation topology with a bipolar (BP) symmetrical four-detection-coil array deployed at the transmitter, the system effectively suppresses primary direct coupling, ensuring that the position of the receiver coil predominantly determines the detection signals. Secondly, by establishing a voltage fingerprint database during the offline stage and utilizing a multi-layer perceptron–radial basis function (MLP-RBF) regression model, the system achieves high-precision end-to-end positioning and alignment control during the online stage through induced voltage acquisition and data processing. Finally, experiments demonstrate that the proposed method achieves centimeter-level positioning accuracy, with an average error of approximately 1.2 cm and a maximum error of less than 1.8 cm, presenting excellent deployability and engineering applicability.

To address the challenges of difficult quantitative design and potential coil mismatch in auxiliary coils within wireless power transfer systems, a data-driven parameter optimization method based on multi-objective particle swarm optimization (MOPSO) was proposed. First, based on the inductor–capacitor–capacitor series (LCC-S) compensation topology, a mechanism-based analysis was conducted, establishing coil side length A and number of turns N as core optimization variables. Subsequently, a collaborative optimization framework integrating “parametric simulation–surrogate modeling–active learning” was established. An offline fingerprint database was constructed via finite element simulation, and a high-accuracy surrogate model was developed using a kernel ridge regression ensemble approach. Active learning strategies were employed to adaptively augment data points and mitigate uncertainty. Finally, the multi-objective particle swarm optimization (MOPSO) algorithm was applied to identify the Pareto-optimal solution set. Experimental results reveal that the optimized auxiliary coil parameters achieved positioning errors below 8 mm at all test points. The maximum positioning error was significantly reduced by approximately 80% compared to the traditional empirical approach, providing a useful parameter-selection reference for high-precision wireless charging alignment systems under the investigated static operating conditions.

This paper proposes a high-misalignment tolerant and auto-detuned wireless charging system (WCS), which has constant current/voltage (CC/CV) outputs and a lightweight receiver for the unmanned aerial vehicle (UAV). First, the system structure and operating principle are analyzed. The auto-detuned series–series (SS) compensation topology that improves the anti-misalignment capability of the system is realized by a magnetic flux-controlled inductor (MFCI). Then, the excellent y-axis anti-misalignment capability of the modified DD coupler (MDDC) is optimized to ensure the desired misalignment range. The transformer, which is the core component in the MFCI, is designed to provide a suitable control margin for CC/CV charging. Finally, simulation and experimental results verify the proposed method.

This paper proposes a high-misalignment tolerant and auto-detuned wireless charging system (WCS), which has constant current/voltage (CC/CV) outputs and a lightweight receiver for the unmanned aerial vehicle (UAV). First, the system structure and operating principle are analyzed. The auto-detuned series–series (SS) compensation topology that improves the anti-misalignment capability of the system is realized by a magnetic flux-controlled inductor (MFCI). Then, the excellent y -axis anti-misalignment capability of the modified DD coupler (MDDC) is optimized to ensure the desired misalignment range. The transformer, which is the core component in the MFCI, is designed to provide a suitable control margin for CC/CV charging. Finally, simulation and experimental results verify the proposed method.

As is known to all，Traffic congestion in Beijing city is Increasingly serious.In order to thoroughly analyze the congestion causes of Beijing`s intersection, this paper uses VISSIM to simulate the traffic phenomenon, and in-depth analysis of traffic flow characteristics. Through the simulation of observation, this paper gets the simulation output parameters and basic characteristics of the intersection, and in-depth researches lead to the intersection of the critical causes of congestion, and put forward the corresponding improving countermeasures.

Objective N 6 -methyladenosine (m 6 A) mRNA modification triggers malignant behaviors of tumor cells and thereby drives malignant progression in gastric cancer (GC). However, data regarding the prognostic values of m 6 A RNA methylation-related long non-coding RNAs (lncRNAs) in GC are very limited in the literature. We aimed to investigate the prognostic potential of m 6 A-related lncRNAs in predicting prognosis and monitoring immunotherapy efficacy in GC patients. Methods Transcriptome and clinical data were obtained from GC biopsies from Cancer Genome Atlas (TCGA). M 6 A-related lncRNAs associated with GC were identified by constructing a co-expression network, and the gene pairs differentially expressed in GC were selected using univariate analysis. We constructed a risk model based on prognosis-related lncRNA pairs selected using the LASSO algorithm and quantified the best cutoff by comparing the area under the curve (AUC) for risk stratification. A risk model with the optimal discrimination between high- and low-risk GC patients was established. Its feasibility for overall survival prediction and discrimination of clinicopathological features, tumor-infiltrating immune cells, and biomarkers of immune checkpoint inhibitors between high- and low-risk groups were assessed. Results Finally, we identified 11 m 6 A-related lncRNA pairs associated with GC prognosis based on transcriptome analysis of 375 GC specimens and 32 normal tissues. A risk model was constructed with an AUC of 0.8790. We stratified GC patients into high- and low-risk groups at a cutoff of 1.442. As expected, patients in the low-risk group had longer overall survival versus the high-risk group. Infiltration of cancer-associated fibroblasts, endothelial cells, macrophages, particularly M2 macrophages, and monocytes was more severe in high-risk patients than low-risk individuals, who exhibited high CD4 + Th1 cell infiltration in GC. Altered expressions of immune-related genes were observed in both groups. PD-1 and LAG3 expressions were found higher in low-risk patients than high-risk patients. Immunotherapy, either single or combined use of PD-1 or CTLA4 inhibitors, had better efficacy in low-risk patients than high-risk patients. Conclusion The new risk model based on a 11-m 6 A-related lncRNA signature can serve as an independent predictor for GC prognosis prediction and may aid in the development of personalized immunotherapy strategies for patients.

Cities play a vital role in social development, which contribute to more than 70% of global carbon emission. Low-carbon city construction and decarbonization of the energy sector are the critical strategies to cope with the increasingly serious climate change problems, and low-carbon technologies have attracted extensive attention. However, the potential of such technologies to reduce carbon emissions is constrained by various factors, such as space, operational environment, and safety concerns. As an essential territorial natural resource, underground space can provide large-scale and stable space support for existing low-carbon technologies. Integrating underground space and low-carbon technologies could be a promising approach towards carbon neutrality, and hence, warrants further exploration. First, a comprehensive review of the existing low-carbon technologies including the technical bottlenecks is presented. Second, the features of underground space and its low carbon potential are summarized. Moreover, a framework for the underground space based integrated energy system is proposed, including system configuration, operational mechanisms, and the resulting benefits. Finally, the research prospect and key challenges required to be settled are highlighted.

Background Fusarium oxysporum is a prevalent fungal pathogen that diminishes soybean yield through seedling disease and root rot. Preventing Fusarium oxysporum root rot (FORR) damage entails on the identification of resistance genes and developing resistant cultivars. Therefore, conducting fine mapping and marker development for FORR resistance genes is of great significance for fostering the cultivation of resistant varieties. In this study, 350 soybean germplasm accessions, mainly from Northeast China, underwent genotyping using the SoySNP50K Illumina BeadChip, which includes 52,041 single nucleotide polymorphisms (SNPs). Their resistance to FORR was assessed in a greenhouse. Genome-wide association studies utilizing the general linear model, mixed linear model, compressed mixed linear model, and settlement of MLM under progressively exclusive relationship models were conducted to identify marker-trait associations while effectively controlling for population structure. Results The results demonstrated that these models effectively managed population structure. Eight SNP loci significantly associated with FORR resistance in soybean were detected, primarily located on Chromosome 6. Notably, there was a strong linkage disequilibrium between the large-effect SNPs ss715595462 and ss715595463, contributing substantially to phenotypic variation. Within the genetic interval encompassing these loci, 28 genes were present, with one gene Glyma.06G088400 encoding a protein kinase family protein containing a leucine-rich repeat domain identified as a potential candidate gene in the reference genome of Williams82. Additionally, quantitative real-time reverse transcription polymerase chain reaction analysis evaluated the gene expression levels between highly resistant and susceptible accessions, focusing on primary root tissues collected at different time points after F. oxysporum inoculation. Among the examined genes, only this gene emerged as the strongest candidate associated with FORR resistance. Conclusions The identification of this candidate gene Glyma.06G088400 improves our understanding of soybean resistance to FORR and the markers strongly linked to resistance can be beneficial for molecular marker-assisted selection in breeding resistant soybean accessions against F. oxysporum .