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This paper proposes a UAV path planning method based on a Multi-strategy Secretary Bird Optimization Algorithm (MSBOA) to address the challenges of navigating complex terrain. First, a pooling mechanism is introduced to enhance population diversity and improve the algorithm’s optimization capabilities, balancing global exploration and local exploitation. Second, a dynamic fitness distance balance technique is incorporated to balance exploration and exploitation, preventing the population from becoming trapped in local optima while improving convergence accuracy. Finally, a greedy selection-based centroid reverse learning approach is used to update the population, enhancing the algorithm’s exploratory performance. To validate the effectiveness of the proposed improved algorithm, the proposed MSBOA is compared with classical and advanced intelligent algorithms by solving the CEC2017 benchmark test functions and a designed UAV environment model. Comparative analysis of simulation results indicates that the proposed MSBOA converges faster and achieves higher accuracy than the traditional SBOA. It effectively handles complex UAV path planning problems, enabling the design of faster, shorter and safer flight paths. This further demonstrates the excellent performance of the multi-strategy SBOA in UAV path planning, highlighting its broad application prospects.

The Editorial Office retracts and removes the article, “Weld Defect Segmentation in X-ray Image with Boundary Label Smoothing” [...]

Weld defect segmentation (WDS) is widely used to detect defects from X-ray images for welds, which is of practical importance for manufacturing in all industries. The key challenge of WDS is that the labeled ground truth of defects is usually not accurate because of the similarities between the candidate defect and noisy background, making it difficult to distinguish some critical defects, such as cracks, from the weld line during the inference stage. In this paper, we propose boundary label smoothing (BLS), which uses Gaussian Blur to soften the labels near object boundaries to provide an appropriate representation of inaccuracy and uncertainty in ground truth labels. We incorporate BLS into dice loss, in combination with focal loss and weighted cross-entropy loss as a hybrid loss, to achieve improved performance on different types of segmentation datasets.

Bipolar disorder (BD) is a common and highly heritable psychiatric disorder, the study of BD genetic characteristics can help with early prevention and individualized treatment. At the same time, BD is a highly heterogeneous polygenic genetic disorder with significant genetic overlap with other psychiatric disorders. In recent years, polygenic risk scores (PRS) derived from genomewide association studies (GWAS) data have been widely used in genetic studies of various complex diseases and can be used to explore the genetic susceptibility of diseases. This review discusses phenotypic associations and genetic correlations with other conditions of BD based on PRS, and provides ideas for genetic studies and prevention of BD. Keywords: bipolar disorder, polygenic risk scores, risk forecast

Large Language Models (LLMs) leverage external tools primarily through generating the API request to enhance task completion efficiency. The accuracy of API request generation significantly determines the capability of LLMs to accomplish tasks. Due to the inherent hallucinations within the LLM, it is difficult to efficiently and accurately generate the correct API request. Current research uses prompt-based feedback to facilitate the LLM-based API request generation. However, existing methods lack factual information and are insufficiently detailed. To address these issues, we propose AutoFeedback, an LLM-based framework for efficient and accurate API request generation, with a Static Scanning Component (SSC) and a Dynamic Analysis Component (DAC). SSC incorporates errors detected in the API requests as pseudo-facts into the feedback, enriching the factual information. DAC retrieves information from API documentation, enhancing the level of detail in feedback. Based on this two components, Autofeedback implementes two feedback loops during the process of generating API requests by the LLM. Extensive experiments demonstrate that it significantly improves accuracy of API request generation and reduces the interaction cost. AutoFeedback achieves an accuracy of 100.00\\% on a real-world API dataset and reduces the cost of interaction with GPT-3.5 Turbo by 23.44\\%, and GPT-4 Turbo by 11.85\\%.

Temporal point process is widely used for sequential data modeling. In this paper, we focus on the problem of modeling sequential event propagation in graph, such as retweeting by social network users, news transmitting between websites, etc. Given a collection of event propagation sequences, conventional point process model consider only the event history, i.e. embed event history into a vector, not the latent graph structure. We propose a Graph Biased Temporal Point Process (GBTPP) leveraging the structural information from graph representation learning, where the direct influence between nodes and indirect influence from event history is modeled respectively. Moreover, the learned node embedding vector is also integrated into the embedded event history as side information. Experiments on a synthetic dataset and two real-world datasets show the efficacy of our model compared to conventional methods and state-of-the-art.

SummaryPurposeAnxiety, depression, and poor quality of life (QOL) were considered important concerns that hindered the rehabilitation of breast cancer survivors. A number of studies have investigated the effects of physical activity, but they have not reached the same conclusions. This review aimed to identify the effects of physical activity on QOL, anxiety, and depression in breast cancer survivors.MethodsPubMed, Embase, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycINFO, SinoMed, CNKI, Vip, and WanFang databases were searched for the time period between January 1, 2012, and April 30, 2022. Studies were included if they were randomized controlled trials of the effects of physical activity on QOL, anxiety, or depression in breast cancer survivors. The tools of the Joanna Briggs Institute were used to assess the quality of the included studies. R software version 4.3.1 was used for meta-analysis.ResultsA total of 26 studies, involving 2105 participants, were included in the systematic review. Among these, 20 studies involving 1228 participants were included in the meta-analysis. Compared with the control group, the results indicated that physical activity can significantly improve QOL(Hedges' g = 0.67; 95% CI 0.41–0.92) and reduce anxiety (Hedges' g = −0.28; 95% CI −0.46 to −0.10) in breast cancer survivors. However, the effect of physical activity on depression (Hedges' g = −0.46; 95% CI −0.99 to 0.06) was not statistically significant.ConclusionsPhysical activity was an effective intervention to improve QOL and reduce anxiety in breast cancer survivors, as well as showed positive trends in depression, although without statistical significance. More well-designed studies are required to clarify the effects of different types of physical activities on the QOL, anxiety, and depression among breast cancer survivors.Registered number on PROSPEROCRD42022363094.https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=363094.

Extensive efforts have been made to harvest energy from water in the form of raindrops , river and ocean waves , tides and others . However, achieving a high density of electrical power generation is challenging. Traditional hydraulic power generation mainly uses electromagnetic generators that are heavy, bulky, and become inefficient with low water supply. An alternative, the water-droplet/solid-based triboelectric nanogenerator, has so far generated peak power densities of less than one watt per square metre, owing to the limitations imposed by interfacial effects-as seen in characterizations of the charge generation and transfer that occur at solid-liquid or liquid-liquid interfaces. Here we develop a device to harvest energy from impinging water droplets by using an architecture that comprises a polytetrafluoroethylene film on an indium tin oxide substrate plus an aluminium electrode. We show that spreading of an impinged water droplet on the device bridges the originally disconnected components into a closed-loop electrical system, transforming the conventional interfacial effect into a bulk effect, and so enhancing the instantaneous power density by several orders of magnitude over equivalent devices that are limited by interfacial effects.

This study investigates the effect of monochromatic light on the body weight (BW), melatonin concentration and its receptors expression levels, intestinal health, and gut microorganisms of Yangzhou geese. Green light (GL) significantly increased BW, melatonin and its receptor expression levels, villus height (VH) and villus height/crypt depth (VH/CD) ratio, superoxide dismutase (SOD), catalase (CAT), and total antioxidant capacity (T-AOC) activities, as well as the abundance of Synergistota and Prevotellaceae_UCG-001, compared with white light (WL). Blue light (BL) significantly increased the mRNA expression of melatonin membrane receptor 1a (Mel1a) and nuclear receptor 1α (RORα), VH and VH/CD ratio, CAT activity, cecal microbes diversity, and decreased malondialdehyde (MDA) levels. Red light (RL) significantly decreased average daily feed intake, reduced the abundances of Synergistota and Prevotellaceae_UCG-001, and increased Mel1a and RORα mRNA expression levels, MDA content, and cecum microbial diversity. Moreover, melatonin levels were significantly higher in the GL and BL groups compared to RL. Furthermore, the mRNA expression levels of Claudin-10, Occludin, and occludens-1 (ZO-1) were significantly upregulated under GL or BL exposures compared to the WL group, whereas RL only enhanced the expression levels of ZO-1. Spearman’s correlation analysis revealed that the relative abundance of Prevotellaceae_UCG-001 exhibited positive correlations with BW, melatonin and its receptors expression, gut health, and antioxidant capacity. Overall, these findings suggested that GL exposure enhanced melatonin synthesis and its receptors expression, modulated intestinal homeostasis and microbial ecology, and ultimately increased goose BW.

Microbial transformation of ginsenosides to increase its pharmaceutical effect is gaining increasing attention in recent years. In this study, Cellulosimicrobium sp. TH-20, which was isolated from soil samples on which ginseng grown, exhibited effective ginsenoside-transforming activity. After protopanaxadiol (PPD)-type ginsenoside (Rb1) and protopanaxatriol (PPT)-type ginsenosides (Re and Rg1) were fed to C . sp . TH20, a total of 12 metabolites, including 6 new intermediate metabolites, were identified. Stepwise deglycosylation and dehydrogenation on the feeding precursors have been observed. The final products were confirmed to be rare ginsenosides Rd, GypXVII, Rg2 and PPT after 96 h transformation with 38–96% yields. The four products showed improved anti-inflammatory activities by using lipopolysaccharide (LPS)-induced murine RAW 264.7 macrophages and the xylene-induced acute inflammatory model of mouse ear edema. The results indicated that they could dramatically attenuate the production of TNF-α more effectively than the precursors. Our study would provide an example of a unique and powerful microbial cell factory for efficiently converting both PPD-type and PPT-type ginsenosides to rare natural products, which extends the drug candidates as novel anti-inflammatory remedies.