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Abstract The Traveling Salesman Problem (TSP) is a classic problem in combinatorial optimization, aiming to find the shortest path that traverses all cities and eventually returns to the starting point. The ant colony optimization algorithm has achieved significant results, but when the number of cities increases, the ant colony algorithm is prone to fall into local optimal solutions, making it difficult to obtain the global optimal path. To overcome this limitation, this paper proposes an innovative hybrid ant colony algorithm. Our main motivation is to introduce other optimization strategies to improve the global search ability and convergence speed of the ant colony algorithm in solving TSP problems. We first incorporate the iterative solution of the sparrow search algorithm (SSA) into the ant colony algorithm to provide a better initial pheromone distribution. Second, we improve the pheromone update method to enhance the algorithm’s diversity during the search process and reduce the risk of falling into local optima. Finally, we define a dynamic pheromone evaporation factor to adjust the pheromone evaporation rate according to real-time changes in the search process. Through simulation tests on large-scale TSP problems and practical applications, we find that the hybrid ant colony algorithm outperforms the ant colony algorithm in both accuracy and running time. In Eg.2, the average accuracy of ISSA-ACO is improved by 12%, and the average running time is reduced by 45.6%. This study not only provides a new and effective method for solving large-scale TSP problems but also provides valuable references and insights for the application of ant colony algorithms in solving other complex optimization problems. At the same time, our research further verifies the effectiveness of improving heuristic algorithms by fusing different optimization strategies, providing new ideas and directions for future algorithm design and optimization.

Abstract Background As of September 2023, more than 1,000 cases of monkeypox (mpox) have been reported in China. Based on the available evidence, men who have sex with men (MSM) are at high risk for mpox infection. This study aimed to analyses the self-reported infection status, knowledge, attitude and influencing factors of monkeypox among MSM in Jiaxing City, China. Methods A web-based cross-sectional survey was conducted in September 2023 to gather data on participants’ socio-demographic profiles, mpox-related knowledge, sexual behavior characteristics, and other potentially related information to mpox knowledge. Multivariate regression modeling was employed to analyze the factors influencing the level of mpox-related knowledge. Results A total of 562 MSM were recruited; 4.3% self-reported being HIV-positive, 83.3% of respondents had heard of mpox, and 2.3% of them reported having suspected symptoms. 89.7% of respondents were willing to be vaccinated against mpox, but only 24.8% had a high level of knowledge about mpox. The main factors influencing knowledge of mpox were education level, household registration, homosexual anal intercourse in the past 6 months, and taking the HIV pre-exposure prophylaxis (PrEP). Conclusions Knowledge of mpox among MSM living in the Jiaxing area needs to be enhanced, but willingness to get vaccinated is high. Educational level, household location, sexual behavior and PrEP use have important effects on knowledge of mpox. Individuals exhibiting symptoms indicative of suspected mpox had a diminutive consultation frequency, and it is imperative to augment screening efforts for mpox symptoms within specific demographic groups to prevent the underreporting of mpox cases.

BackgroundMineral deficiencies, such as iron (Fe), zinc (Zn), and selenium (Se), play crucial roles in inflammation and immune responses and are linked to chronic inflammatory skin diseases. This study used genome-wide association study (GWAS) data and Mendelian randomization (MR) to investigate the genetic causality among serum levels of five minerals (Fe, Cu, Zn, Se, Ca), three iron metabolism indicators (TSAT, TIBC, ferritin), and three chronic inflammatory skin diseases [psoriasis (PS), atopic dermatitis (AD), acne vulgaris (AV)].MethodsTwo-sample MR analyses using the “TwoSample MR” package in R were conducted with aggregate outcome data from the FinnGen database. The inverse-variance-weighted (IVW) method was applied to assess causal relationships between mineral levels and disease outcomes. Robustness was examined via heterogeneity and pleiotropy tests.ResultsIVW analysis showed significant association between blood transferrin saturation (TSAT) and PS (p = 0.004, OR = 1.18). Serum Zn and Se levels showed inverse correlation with AD (p = 0.039, OR = 0.92). However, due to limited SNPs, robustness was reduced.ConclusionTSAT is genetically linked to PS, highlighting iron homeostasis in disease development. Zn and Se intake may reduce AD risk.

Abstract Background Humanistic education is an important part of nursing education. Final-year nursing students’ perceptions of nursing humanistic education are under-investigated. This study aimed to examined final-year nursing students’ perceptions of nursing humanistic education in both school and hospital. Methods This was a cross-sectional descriptive study conducted from May to June 2022 among 107 final-year nursing students with a self-designed questionnaire. Results Final-year nursing students recognized the importance of humanistic education, scoring above 4.0 on a 1–5 scale, while their initiative to enhance humanistic qualities was relatively low. Students’ satisfaction with the number of humanities courses offered was only 3.7 ± 0.862. Moreover, 62.6% of students believed there was a need to enhance humanistic environmental development including corridor culture. The “monotonous teaching format” (63.6%) and “teaching methods” (64.5%) have emerged as focal points that students identified as needing attention and improvement. Conclusions In the future, nursing humanistic education can be enhanced by increasing the proportion of humanities, improving teaching methods, stimulating students’ learning motivation, and strengthening the construction of humanistic environment.

BackgroundBranched gold and silver nanoparticles coated with polydopamine (Au-Ag-PDA) demonstrate high photothermal conversion efficiency. Utilizing umbilical cord mesenchymal stem cell membranes (MSCM) as an effective drug delivery system, our preliminary studies investigated the suppression of sebum secretion in sebaceous glands using MSCM-coated Au-Ag-PDA nano-particles (Au-Ag-PDA@MSCM) combined with 808 nm laser irradiation, showing potential for dermatological applications in acne treatment.MethodsThis study employs proteomic analysis, complemented by subsequent techniques such as Western blotting (WB), small interfering RNA (siRNA), and transmission electron microscopy, to further investigate the differential mechanisms by which Au-Ag-PDA and Au-Ag-PDA@MSCM-mediated photothermal therapy (PTT) suppress sebum secretion.ResultsOur proteomic analysis indicated mitochondrial respiratory chain damage in sebaceous gland tissues post-PTT, with further validation revealing ferroptosis in sebaceous cells and tissues. Acyl-CoA Synthetase Long-Chain Family Member 4 (Acsl4) has been identified as a critical target, with Au-Ag-PDA@MSCM demonstrating enhanced ferroptotic effects.ConclusionThese findings significantly advance our understanding of how PTT mediated by Au-Ag-PDA@MSCM nanoparticles reduces sebum secretion and underscore the pivotal role of MSCM in inducing ferroptosis in sebaceous glands, thus providing a robust theoretical foundation for employing PTT via specific molecular pathways in acne treatment.

Plant health is closely related to microbial communities present in specific host increasingly challenged by pathogen attack. Soil-borne diseases cause an imbalance of the already colonized plant rhizosphere microbial community, resulting in restricting agricultural production. In this study, tobacco black shank (TBS), caused by Phytophthora nicotianae was used as a disease model to explore the impact of rhizosphere microorganisms on tobacco health and assessed the use of introduced microbes to mitigate the spread of disease in an environment-friendly way. Here, field investigation confirmed that the higher pathogen load in the field was directly proportional to the higher disease index. We isolated 223 strains of bacteria from the tobacco rhizosphere soil in Yuxi City, Yunnan, China and confirmed eight candidate strains with potential antagonism against P. nicotianae. The strains were further identified as Bacillus based on morphology, physiological and biochemical characteristics, and molecular analysis. Under field conditions, sterilized and non-sterilized rhizospheric soil showed significant differences in terms of pathogen growth. Our results indicated potential bacteria in the tobacco rhizosphere have a protective effect against TBS. Notably, eight potential Bacillus strains and simple synthetic microbial consortium as the antagonistic group could reduce the number of the pathogen in soil effectively and resist the TBS in the greenhouse. P. nicotianae and rhizosphere antagonists co-affect tobacco health, and proved that potential microbes could effectively reduce a load of P. nicotianae and control plant diseases in an ecological friendly way for plant health and protection.

With the wide application of deep learning, power inspection technology has made great progress. However, substation inspection videos often present challenges such as complex backgrounds, uneven lighting distribution, variations in the appearance of power equipment targets, and occlusions, which increase the difficulty of object segmentation and tracking, thereby adversely affecting the accuracy and reliability of power equipment condition monitoring. In this paper, a pixel-level equalized memory matching network (PEMMN) for power intelligent inspection segmentation and tracking is proposed. Firstly, an equalized memory matching network is designed to collect historical information about the target using a memory bank, in which a pixel-level equalized matching method is used to ensure that the reference frame information can be transferred to the current frame reliably, guiding the segmentation tracker to focus on the most informative region in the current frame. Then, to prevent memory explosion and the accumulation of segmentation template errors, a mask quality evaluation module is introduced to obtain the confidence level of the current segmentation result so as to selectively store the frames with high segmentation quality to ensure the reliability of the memory update. Finally, the synthetic feature map generated by the PEMMN and the mask quality assessment strategy are unified into the segmentation tracking framework to achieve accurate segmentation and robust tracking. Experimental results show that the method performs excellently on real substation inspection scenarios and three generalized datasets and has high practical value.

In modern energy systems, substations are the core of electricity transmission and distribution. However, similar appearance and small size pose significant challenges for automatic identification of electrical devices. To address these issues, we collect and annotate the substation rotated device dataset (SRDD). Further, feature fusion and feature refinement network (F3RNet) are constructed based on the classic structure pattern of backbone-neck-head. Considering the similar appearance of electrical devices, the deconvolution fusion module (DFM) is designed to enhance the expression of feature information. The balanced feature pyramid (BFP) is embedded to aggregate the global feature. The feature refinement is constructed to adjust the original feature maps by considering the feature alignment between the anchors and devices. It can generate more accurate feature vectors. To address the problem of sample imbalance between electrical devices, the gradient harmonized mechanism (GHM) loss is utilized to adjust the weight of each sample. The ablation experiments are conducted on the SRDD dataset. F3RNet achieves the best detection performance compared with classical object detection networks. Also, it is verified that the features from global feature maps can effectively recognize the similar and small devices.

Abstract In order to solve the problem that the conventional tracker is not adapted to the abrupt motion, a tracking algorithm based on the improved salp swarm algorithm (ISSA) was proposed. Visual tracking is considered to be a process of locating the optimal position through the interaction between leaders and followers in successive images. Firstly, the adaptive mechanism of leader and follower is introduced into the original salp swarm algorithm (SSA) to balance the exploitation and exploration of the algorithm. This method can improve the accuracy and effect of tracking. Secondly, the golden‐sine algorithm was used to update the position of followers, considering that the SSA had a single spatial search mode for followers and was easy to fall into the local optimum. By comparing with 19 classical tracking algorithms, qualitative and quantitative analysis is carried out to verify the tracking effect of the proposed method. A large number of experimental results show that the algorithm proposed here has good performance in visual tracking, especially for mutation motion tracking.

Extensive occurrence of rice sheath blight has been observed in China in recent years due to agricultural practices and climatic conditions, posing a serious threat to rice production. Assessing habitat suitability for rice sheath blight at a regional scale can provide important information for disease forecasting. In this context, the present study aims to propose a regional-scale habitat suitability evaluation method for rice sheath blight in Yangzhou city using multisource data, including remote sensing data, meteorological data, and disease survey data. By combining the epidemiological characteristics of the crop disease and the Relief-F algorithm, some habitat variables from key stages were selected. The maximum entropy (Maxent) and logistic regression models were adopted and compared in constructing the disease habitat suitability assessment model. The results from the Relief-F algorithm showed that some remote sensing variables in specific temporal phases are particularly crucial for evaluating disease habitat suitability, including the MODIS products of LAI (4–20 August), FPAR (9–25 June), NDVI (12–20 August), and LST (11–27 July). Based on these remote sensing variables and meteorological features, the Maxent model yielded better accuracy than the logistic regression model, with an area under the curve (AUC) value of 0.90, overall accuracy (OA) of 0.75, and a true skill statistics (TSS) value of 0.76. Indeed, the results of the habitat suitability assessment models were consistent with the actual distribution of the disease in the study area, suggesting promising predictive capability. Therefore, it is feasible to utilize remotely sensed and meteorological variables for assessing disease habitat suitability at a regional scale. The proposed method is expected to facilitate prevention and control practices for rice sheath blight disease.

Simultaneous Localization and Mapping (SLAM) is the core technology of intelligent substation inspection robot. Because of lightweight computation, Rao-Blackwellized Particle Filter (RBPF) is widely used in two-dimensional SLAM. However, it suffers from poor positioning accuracy, low robustness and rapid cumulative errors despite recent improvement. This paper presents a lidar SLAM system based on RBPF and graph optimization that can adapt to unstructured operating environment of substation. Firstly, the diversity of particles is increased by rebuilding the resample algorithm to improve the robustness of the system, and high-quality poses are estimated in submaps. Secondly, the multi-submap system is established to construct odometry constraints (one pose corresponds to two submaps). Furthermore, loop detector is an important part of optimization algorithm, and the branch-bound method is used to reduce computation burden and accelerate the loop detection. Finally, global poses of robot are optimized by the whole odometry and loop constraints in real time. Experiment results show that the proposed method is more accurate than other methods, and can maintain and produce high-precision positioning and mapping in complex substation operation and maintenance environment. It provides a new idea for intelligent substation inspection and positioning method.

Epithelial-mesenchymal transdifferentiation of alveolar type Ⅱ epithelial cells is a vital source of pulmonary myofibroblasts, and myofibroblasts formation is recognized as an important phase in the pathological process of silicosis. miR-30c-5p has been determined to be relevant in the activation of the epithelial-mesenchymal transition (EMT) in numerous disease processes. However, elucidating the role played by miR-30c-5p in the silicosis-associated EMT process remains a great challenge. In this work, based on the establishment of mouse silicosis and A549 cells EMT models, miR-30c-5p was interfered with in vivo and in vitro models to reveal its effects on EMT and autophagy. Moreover, metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), connective tissue growth factor (CTGF), autophagy-related gene 5 (ATG5), and autophagy were further interfered with in the A549 cells models to uncover the possible molecular mechanism through which miR-30c-5p inhibits silicosis associated EMT. The results demonstrated the targeted binding of miR-30c-5p to CTGF, ATG5, and MALAT1, and showed that miR-30c-5p could prevent EMT in lung epithelial cells by acting on CTGF and ATG5-associated autophagy, thereby inhibiting the silicosis fibrosis process. Furthermore, we also found that lncRNA MALAT1 might competitively absorb miR-30c-5p and affect the EMT of lung epithelial cells. In a word, interfering with miR-30c-5p and its related molecules (MALAT1, CTGF, and ATG5-associated autophagy) may provide a reference point for the application of silicosis intervention-related targets.

Silicosis, a disease characterized by diffuse fibrosis of the lung tissue, is caused by long-term inhalation of free silica (SiO2) dust in the occupational environment and is currently the most serious occupational diseases of pneumoconiosis. Several studies have suggested that alveolar type Ⅱ epithelial cells (AEC Ⅱ) undergo epithelial-mesenchymal transition (EMT) as one of the crucial components of silicosis in lung fibroblasts. A2aR can play a critical regulatory role in fibrosis-related diseases by modulating the Wnt/β-catenin pathway, but its function in the EMT process of silicosis has not been explained. In this study, an EMT model of A549 cells was established. The results revealed that A2aR expression is reduced in the EMT model. Furthermore, activation of A2aR or suppression of the Wnt/β-catenin pathway reversed the EMT process, while the opposite result was obtained by inhibiting A2aR. In addition, activation of A2aR in a mouse silicosis model inhibited the Wnt/β-catenin pathway and ameliorated the extent of silica-induced lung fibrosis in mice. To sum up, we uncovered that A2aR inhibits fibrosis and the EMT process in silicosis by regulating the Wnt/β-catenin pathway. Our study can provide an experimental basis for elucidating the role of A2aR in the development of silicosis and offer new ideas for further exploration of interventions for silicosis.

Plant health is of utmost importance for optimal agricultural production and sustainability. Unfortunately, biotic and abiotic factors put a major constraint on crop safety and productivity. Plant diseases caused by oomycetes inflict serious damage to various crops. Moreover, the injudicious use of chemical pesticides poses threats related to pesticide resistance development in pathogens and environmental pollution. Biocontrol offers an effective solution for disease control; however, research on biocontrol of oomycete-related diseases is scarce. Thus, this study undertakes the screening of biocontrol resources for the effective management of oomycete-related plant diseases. In this regard, 86 isolates of Trichoderma spp. were assessed against Phytophthora nicotianae, P. capsici, Pythium vexans, P. ultimum, and P. dissotocum through dual culture assay. Furthermore, the antagonistic effect of selected isolates was studied against tobacco black shank disease and damping-off of cucumber seedlings in the greenhouse. The relative control effect of the three antagonistic Trichoderma strains AR-4, Tv-1, and ST4-1 on tobacco black shank was more than 60%, which was not significantly different from 6.88 gl−1 fluopicolide–propamocarb. Whereas, the relative control effect of Trichoderma AR-4 and ST4-1 on damping-off of cucumber seedlings was 80.33% and 82.67%, respectively, which were significantly higher than Trichoderma Tv-1 (35.49%) and fluopicolide–propamocarb (47.82%). According to the morphological and molecular characterization, the fungal strains AR-4, Tv-1, and ST4-1 were identified as Trichoderma koningiopsis, T. asperellum, and T. gamsii, respectively. In conclusion, the strains exhibited a strong antagonistic effect against oomycete pathogens and can be integrated into disease management strategies.

As for the challenges of heavy oil gathering and transportation in China's oil fields at high water-cut stage, the cyclone field with low shear strength was constructed, the cylindrical cyclone separator was used for oil-water pre-separation, and the numerical simulation method was adopted for research. The Mixture model and RNG k-ε model were used to numerically simulate the cyclonic separation characteristics of heavy oil, so as to investigate the effects of dispersed phase particle size, oil viscosity, density, split ratio and inlet flow rate on the oil content at the outlet of underflow and pressure drop at the outlet of overflow. The results show that the separation characteristics of heavy oil in cylindrical cyclone differ from those of other oils. The oil content at the outlet of underflow is significantly affected by the particle size of dispersed phase, but the pressure drop is rarely affected. The effects of oil density and viscosity on the oil content at the outlet are quite different, and there should be an optimal separation viscosity zone (750-830 mPa·s) within the research range of the selected viscosity. Oil content at the outlet of underflow decreases with the increase of inlet flow rate. Split ratio has little effect on pressure drop, and the simulation results are consistent with the existing experimental results.

Osteosarcoma (OS) is the most common primary bone tumor in children and young people. Traditional surgical excision combined with chemotherapy presents many limitations, such as resistance and systemic side effects of chemotherapy drugs, postoperative recurrence, and bone defects. Given these limitations, novel therapeutic modalities for OS treatment using nanometer-sized platform-based chemotherapeutic delivery have emerged as a promising alternative therapy. This form of therapy offers multiple advantages, such as accurate delivery of the drug to the tumor site and repair of limited bone defects after tumor resection. In this review, we briefly summarize nanoplatforms, including liposomes, polymeric nanoparticles, inorganic nanoparticles, nanomicelles, dendrimers, nanocapsules, and exosomes. The essential shortcomings involved in these nanoplatforms, such as poor stability, immunogenicity, insufficient circulation, and drug leakage are also discussed, and related solutions are briefly proposed. Finally, the application prospects of nanoplatforms in the treatment of OS are discussed.

Deep learning has brought revolutionary progress to computer vision, so intelligent inspection equipment based on computer vision has developed rapidly. However, due to the large number of existing deep features, it is difficult to deploy it on mobile devices to achieve real-time tracking speed. This paper presents a target-aware deep feature compression for power intelligent inspection tracking. First, a negative balance loss function is designed to mine channel features suitable for the current inspection scene by shrinking the contribution of pure background negative samples and enhancing the impact of difficult negative samples. Based on this, the deep feature compression model is combined with Siamese tracking framework to achieve real-time and robust tracking. Finally, we evaluate the proposed method on real application scenarios and general data to prove the practicability of the proposed method.

Rust of transmission line fittings is a major hidden risk to transmission safety. Since the fittings located at high altitude are inconvenient to detect and maintain, machine vision techniques have been introduced to realize the intelligent rust detection with the help of unmanned aerial vehicles (UAV). Due to the small size of fittings and disturbance of complex environmental background, however, there are often cases of missing detection and false detection. To improve the detection reliability and robustness, this paper proposes a new robust Faster R-CNN model with feature enhancement mechanism for the rust detection of transmission line fitting. Different from current methods that improve feature representation in front end, this paper adopts an idea of back-end feature enhancement. First, the residual network ResNet-101 is introduced as the backbone network to extract rich discriminative information from the UAV images. Second, a new feature enhancement mechanism is added after the region of interest (ROI) pooling layer. Through calculating the similarity between each region proposal and the others, the feature weights of the region proposals containing target object can be enhanced via the overlaying of the object’s representation. The weight of the disturbance terms can then be relatively reduced. Empirical evaluation is conducted on some real-world UAV monitoring images. The comparative results demonstrate the effectiveness of the proposed model in terms of detection precision and recall rate, with the average precision of rust detection 97.07%, indicating that the proposed method can provide an reliable and robust solution for the rust detection.

To improve the motion distortion caused by LiDAR data at low and medium frame rates when moving, this paper proposes an improved algorithm for scanning matching of estimated velocity that combines an IMU and odometer. First, the information of the IMU and the odometer is fused, and the pose of the LiDAR is obtained using the linear interpolation method. The ICP method is used to scan and match the LiDAR data. The data fused by the IMU and the odometer provide the optimal initial value for the ICP. The estimated speed of the LiDAR is introduced as the termination condition of the ICP method iteration to realize the compensation of the LiDAR data. The experimental comparative analysis shows that the algorithm is better than the ICP algorithm and the VICP algorithm in matching accuracy.

The Australian native plant finger lemon (Citrus australasica) is well-known for its distinct flavor and high economic and medicinal value. 'YaoJi' is a typical purple variety of finger lemon, but its genome has not been analyzed yet. In this study, we used three-generation nanopore sequencing technology to sequence and assemble the genome of the finger lemon and combined it with the transcriptome analysis of different varieties with different colors of rinds at the same developmental period to investigate the mechanism of the differences in the color of the rinds of finger lemons. According to the results, the finger lemon genome was built and has nine chromosomes and a size of 314.63 Mb (Contig N50, 6.60 Mb; Scaffold N50, 32.81 Mb). By using homologous protein comparison and gene annotation, a total of 21,154 protein-coding genes and 164.7 Mb (52.35%) of repetitive sequences have been identified. The phylogenetic evolutionary tree shows that 10 Mya years ago finger lemon diverged from sweet orange (Citrus sinensis (L.) Osbeck) and Cremantine red orange (Citrus x clementina). Presumably it's a whole genome duplication (WGD) event according to the synonymous substitution rates (KS). Transcriptome analysis showed that the differences in the rind color of finger lemon were associated with higher expression of PAL, 4CL, and ANS genes in flavonoid biosynthesis and VDE and PSY genes in carotenoid synthesis. Transcription factors regulating flavonoid biosynthesis, such as bHLH, MYB, WD40, and NAC were also identified, which provides insights into the origin and evolution of finger lemon species and functional gene mining. [ABSTRACT FROM AUTHOR]