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Multi-sensor fusion for unmanned surface vehicles (USVs) is an important issue for autonomous navigation of USVs. In this paper, an improved particle swarm optimization (PSO) is proposed for real-time autonomous navigation of a USV in real maritime environment. To overcome the conventional PSO’s inherent shortcomings, such as easy occurrence of premature convergence and human experience-determined parameters, and to enhance the precision and algorithm robustness of the solution, this work proposes three optimization strategies: linearly descending inertia weight, adaptively controlled acceleration coefficients, and random grouping inversion. Their respective or combinational effects on the effectiveness of path planning are investigated by Monte Carlo simulations for five TSPLIB instances and application tests for the navigation of a self-developed unmanned surface vehicle on the basis of multi-sensor data. Comparative results show that the adaptively controlled acceleration coefficients play a substantial role in reducing the path length and the linearly descending inertia weight help improve the algorithm robustness. Meanwhile, the random grouping inversion optimizes the capacity of local search and maintains the population diversity by stochastically dividing the single swarm into several subgroups. Moreover, the PSO combined with all three strategies shows the best performance with the shortest trajectory and the superior robustness, although retaining solution precision and avoiding being trapped in local optima require more time consumption. The experimental results of our USV demonstrate the effectiveness and efficiency of the proposed method for real-time navigation based on multi-sensor fusion.

Facing the complex coupled process of thermal mass transfer and electrochemical reaction inside fuel cells, the development of a one-dimensional model is an efficient solution to study the influence of mass transfer property parameters on the transfer and reaction process, which can effectively balance the computational efficiency and accuracy. Firstly, a one-dimensional two-phase non-isothermal parametric model is established to capture the performance and state of fuel cell quickly. Then, a sensitivity analysis is performed on various mass transfer parameters of the membrane electrode assembly. Subsequently, a neural network surrogate model and genetic algorithm are combined to optimize the mass transfer property parameters globally. The impact of these parameters on the thermal and mass transfer within the fuel cell is analyzed. The results show that the maximum error between the calculation results of the developed numerical model and the experimental results is 3.87%, and the maximum error between the predicted values of the trained surrogate model and the true values is 0.15%. The mass transfer characteristics of the gas diffusion layer have the most significant impact on the performance of the fuel cell. After optimizing the mass transfer characteristic parameters, the net power density of the fuel cell increased by 5.51%. The combination of the one-dimensional model, the surrogate model, and the genetic algorithm can effectively improve the optimization efficiency.

Recently, issues of climate change, environment abnormality, individual requirements, and national defense have caused extensive attention to the commercial, scientific, and military development of unmanned surface vehicles (USVs). In order to design high-quality routes for a multi-sensor integrated USV, this work improves the conventional particle swarm optimization algorithm by introducing the greedy mechanism and the 2-opt operation, based on a combination strategy. First, a greedy black box is established for particle initialization, overcoming the randomness of the conventional method and excluding a great number of infeasible solutions. Then the greedy selection strategy and 2-opt operation are adopted together for local searches, to maintain population diversity and eliminate path crossovers. In addition, Monte-Carlo simulations of eight instances are conducted to compare the improved algorithm with other existing algorithms. The computation results indicate that the improved algorithm has the superior performance, with the shortest route and satisfactory robustness, although a fraction of computing efficiency becomes sacrificed. Moreover, the effectiveness and reliability of the improved method is also verified by its multi-sensor-based application to a USV model in real marine environments.

Breast cancer is the most common type of cancer in women, and while current treatments can cure the majority of early-stage primary BC cases, recurrence remains a significant challenge. Traditional methods of assessing patient prognosis, such as AJCC, TNM staging, and biochemical markers, are no longer sufficient in the era of precision medicine. Existing tumor models often rely on single selection and simpler algorithms, which can lead to poor effectiveness or overfitting. To address these limitations, this study systematically analyzed RNA-seq high-throughput data and combined 10 machine learning algorithms to construct 117 models. The optimal algorithm combination, StepCox[both] and ridge regression, was identified, and an immune-related gene signature (IRGS) composed of 12 genes was developed. The IRGS demonstrated outstanding predictive performance across multiple datasets and surpassed 10 previously published signatures. GSEA analysis revealed significant enrichment differences in cellular processes, diseases, and immune-related pathways between high- and low-risk recurrence patients. The low recurrence risk group based on IRGS exhibited a stronger immune phenotype and better survival prognosis, which may be associated with higher infiltration of CD4 + and CD8 + T cells. However, high M2 macrophage infiltration suggests potential immune escape in low recurrence risk patients. Combined with immune checkpoint expression levels and TIDE results, it is suggested that low-risk patients may respond positively to immunotherapy. Through drug sensitivity analysis, potential drugs that are more effective for both high- and low-risk groups have been identified. Therefore, the IRGS developed in this study can serve as an adjunct tool for assessing the recurrence risk of breast cancer, potentially enhancing personalized treatment planning, and improving the clinical management of patients with breast cancer.

Biosensors are powerful tools for detecting, real-time imaging, and quantifying molecules, but rapidly constructing diverse genetically encoded biosensors remains challenging. Here, we report a method to rapidly convert enzymes into genetically encoded circularly permuted fluorescent protein-based indicators to detect organic acids (GECFINDER). ANL superfamily enzymes undergo hinge-mediated ligand-coupling domain movement during catalysis. We introduce a circularly permuted fluorescent protein into enzymes hinges, converting ligand-induced conformational changes into significant fluorescence signal changes. We obtain 11 GECFINDERs for detecting phenylalanine, glutamic acid and other acids. GECFINDER-Phe3 and GECFINDER-Glu can efficiently and accurately quantify target molecules in biological samples in vitro. This method simplifies amino acid quantification without requiring complex equipment, potentially serving as point-of-care testing tools for clinical applications in low-resource environments. We also develop a GECFINDER-enabled droplet-based microfluidic high-throughput screening method for obtaining high-yield industrial strains. Our method provides a foundation for using enzymes as untapped blueprint resources for biosensor design, creation, and application. Biosensors have a wide number of potential applications, but rapidly constructing genetically encoded biosensors remains challenging. Here, authors report a method for rapidly converting ANL superfamily enzymes into biosensors for organic acids, based on their conformational changes upon binding.

FinFET devices and Silicon-On-Insulator (SOI) devices are two mainstream technical routes after the planar MOSFET reached the limit for scaling. The SOI FinFET devices combine the benefits of FinFET and SOI devices, which can be further boosted by SiGe channels. In this work, we develop an optimizing strategy of the Ge fraction in SiGe Channels of SGOI FinFET devices. The simulation results of ring oscillator (RO) circuits and SRAM cells reveal that altering the Ge fraction can improve the performance and power of different circuits for different applications.

Fusarium wilt, caused by Fusarium oxysporum, is one of the most notorious diseases of cash crops. The use of microbial fungicides is an effective measure for controlling Fusarium wilt, and the genus Bacillus is an important resource for the development of microbial fungicides. Fusaric acid (FA) produced by F. oxysporum can inhibit the growth of Bacillus, thus affecting the control efficacy of microbial fungicides. Therefore, screening FA-tolerant biocontrol Bacillus may help to improve the biocontrol effect on Fusarium wilt. In this study, a method for screening biocontrol agents against Fusarium wilt was established based on tolerance to FA and antagonism against F. oxysporum. Three promising biocontrol bacteria, named B31, F68, and 30833, were obtained to successfully control tomato, watermelon, and cucumber Fusarium wilt. Strains B31, F68, and 30833 were identified as B. velezensis by phylogenetic analysis of the 16S rDNA, gyrB, rpoB, and rpoC gene sequences. Coculture assays revealed that strains B31, F68, and 30833 showed increased tolerance to F. oxysporum and its metabolites compared with B. velezensis strain FZB42. Further experiments confirmed that 10 µg/mL FA completely inhibited the growth of strain FZB42, while strains B31, F68, and 30833 maintained normal growth at 20 µg/mL FA and partial growth at 40 µg/mL FA. Compared with strain FZB42, strains B31, F68, and 30833 exhibited significantly greater tolerance to FA.

Object detection in low-light environments is often hampered by unfavorable factors such as low brightness, low contrast, and noise, which lead to issues like missed detections and false positives. To address these challenges, this paper proposes a low-light object detection algorithm named Dark-YOLO, which dynamically extracts features. First, an adaptive image enhancement module is introduced to restore image information and enrich feature details. Second, the spatial feature pyramid module is improved by incorporating cross-overlapping average pooling and max pooling to extract salient features while retaining global and local information. Then, a dynamic feature extraction module is designed, which combines partial convolution with a parameter-free attention mechanism, allowing the model to flexibly capture critical and effective information from the image. Finally, a dimension reciprocal attention module is introduced to ensure the model can comprehensively consider various features within the image. Experimental results show that the proposed model achieves an mAP@50 of 71.3% and an mAP@50-95 of 44.2% on the real-world low-light dataset ExDark, demonstrating that Dark-YOLO effectively detects objects under low-light conditions. Furthermore, facial recognition in dark environments is a particularly challenging task. Dark-YOLO demonstrates outstanding performance on the DarkFace dataset, achieving an mAP@50 of 49.1% and an mAP@50-95 of 21.9%, further validating its effectiveness for face detection under complex low-light conditions.

Esophageal cancer (EC) is the sixth leading cause of cancer-related deaths worldwide. Western medicine has played a leading role in its treatment, but its prognosis remains unsatisfactory. Therefore, the development of effective therapies is important. Traditional Chinese medicine (TCM) has been practiced for thousands of years, and involves taking measures before diseases occur, deteriorate, and recur. Interestingly, there is growing evidence that TCM can improve the therapeutic effects in reversing precancerous lesions, inhibiting the recurrence and metastasis of EC. In this article, we review traditional Chinese herbs and formulas that have preventive and therapeutic effects on EC, summarize the application and research status of TCM in patients with EC, and discuss its shortcomings and prospects in the context of translational, evidence-based, and precision medicine.

The characterization of bacterial strains with efficient root colonization ability and the mechanisms responsible for their efficient colonization is critical for the identification and application of beneficial bacteria. In this study, we found that Burkholderia strain B23 exhibited a strong niche differentiation between the rhizosphere and rhizoplane (a niche with more abundant easy‐to‐use nutrients but stronger selective pressures compared with the tightly adjacent rhizosphere) when inoculated into the field‐grown citrus trees. Full‐length 16S rDNA amplicon analysis demonstrated that the relative abundance of B23 in the rhizoplane microbiome at 3, 5, and 9 days post‐inoculation (dpi) was always higher than that at 1 dpi, whereas its relative abundance in the rhizosphere microbiome was decreased continuously, as demonstrated by a 3.18‐fold decrease at 9 dpi compared to 1 dpi. Time‐series comparative expression profiling of B23 between the rhizoplane and rhizosphere was performed at representative time points (1, 3, and 9 dpi) through metatranscriptomic analysis, and the results demonstrated that multiple genes involved in the uptake and utilization of easy‐to‐use carbohydrates and amino acids and those involved in metabolism, energy production, replication, and translation were upregulated in the rhizoplane compared with the rhizosphere at 1 dpi and 3 dpi. Several genes involved in resistance to plant‐ and microbial competitor‐derived stresses exhibited higher expression activities in the rhizoplane compared with the rhizosphere. Furthermore, gene loci responsible for the biosynthesis of the key antifungal and antibacterial metabolites occidiofungin and ornibactin were induced, and their expression levels remained relatively stable from 3 dpi to 9 dpi in the rhizoplane but not in the rhizosphere. Collectively, our findings provide novel lights into the mechanisms underlying the root colonization of the inoculated bacterial strains and serve as a basis for the identification of strains with efficient colonization ability, thus contributing to the development of beneficial bacteria applications. Burkholderia strain B23 exhibited a strong niche differentiation between the rhizosphere and rhizoplane when inoculated into the field‐grown citrus trees. Time‐series comparative expression profiling of B23 between the rhizoplane and rhizosphere was performed at representative time points (1, 3, and 9 dpi) through metatranscriptomic analysis, and the results demonstrated that multiple genes involved in the uptake and utilization of easy‐to‐use carbohydrates and amino acids and those involved in metabolism, energy production, replication, and translation were upregulated in the rhizoplane compared with the rhizosphere at 1 dpi and 3 dpi. The gene loci responsible for the biosynthesis of the key antifungal and antibacterial metabolites occidiofungin and ornibactin were induced and their expression levels remained relatively stable from 3 dpi to 9 dpi in the rhizoplane but not in the rhizosphere.