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This study develops an enhanced Secretary Bird Optimization Algorithm (ASBOA) based on the original Secretary Bird Optimization Algorithm (SBOA), aiming to further improve the solution accuracy and convergence speed for wireless sensor network (WSN) deployment and engineering optimization problems. Firstly, a differential collaborative search mechanism is introduced in the exploration phase to reduce the risk of the algorithm falling into local optima. Additionally, an optimal boundary control mechanism is employed to prevent ineffective exploration and enhance convergence speed. Simultaneously, an information retention control mechanism is utilized to update the population. This mechanism ensures that individuals that fail to update have a certain probability of being retained in the next generation population, while guaranteeing that the current global optimal solution remains unchanged, thereby accelerating the algorithm’s convergence. The ASBOA algorithm was evaluated using the CEC2017 and CEC2022 benchmark test functions and compared with other algorithms (such as PSO, GWO, DBO, and CPO). The results show that in the CEC2017 30-dimensional case, ASBOA performed best on 23 out of 30 functions; in the CEC2017 100-dimensional case, ASBOA performed best on 26 out of 30 functions; and in the CEC2022 20-dimensional case, it performed best on 9 out of 12 functions. Furthermore, the convergence curves and boxplot results indicate that ASBOA has faster convergence speed and robustness. Finally, ASBOA was applied to WSN problems and three engineering design problems (three-bar truss, tension/compression spring, and cantilever beam design). In the engineering problems, ASBOA consistently outperformed competing methods, while in the WSN deployment scenario, it achieved a coverage rate of 88.32%, an improvement of 1.12% over the standard SBOA. These results demonstrate that the proposed ASBOA has strong overall performance and significant potential in solving complex optimization problems. Although ASBOA performs well in these problems, its performance in high-dimensional multimodal problems and complex constrained optimization is unstable, and the introduced strategies add some complexity. Additionally, different parameter settings may lead to varying results, and the sensitivity of different problems to these parameters can also differ. It is necessary to adjust the settings according to the specific problem at hand in order to further refine and achieve a more stable version.

Primary thyroid lymphoma (PTL) is a rare malignancy, and this study aimed to develop a prognostic prediction model for PTL using deep learning algorithms while providing interpretable analyses. Machine learning models were employed for mortality risk prediction, with the SHAP framework introduced for feature interpretation, and a DeepSurv model was constructed for comparison with the Cox proportional hazards (Cox-PH) model. Model performance was evaluated using Harrell’s c-index, ROC curves, AUC, calibration curves, and decision curve analysis (DCA). Results revealed that age, ‘B’ symptoms, histological type, and marital status were the most influential factors affecting patient mortality risk, as identified through SHAP analysis, and the DeepSurv model outperformed the Cox model in predicting the test set (consistency indices 0.758 vs. 0.739 and 0.789 vs. 0.779). Additionally, a web application platform was developed based on the DeepSurv model to predict the 5-year survival rate of PTL patients, facilitating the transition from theoretical research to clinical application. This study highlights the potential of deep learning models, particularly DeepSurv, in improving prognostic predictions for PTL and provides a practical tool for guiding clinical treatment decisions. The findings underscore the value of integrating interpretable machine learning frameworks into survival analysis for rare cancers.

Circular RNA (circRNA), a novel class of noncoding RNAs, has been used extensively to complement transcriptome remodeling in the central nervous system, although the genomic coverage provided has rarely been studied in intracerebral hemorrhage (ICH) and is limited and fails to provide a detailed picture of the cerebral transcriptome landscape. Here, we described sequencing-based transcriptome profiling, providing comprehensive analysis of cerebral circRNA, messenger RNA (mRNA) and microRNA (miRNA) expression in ICH rats. In the study, male Sprague–Dawley rats were subjected to ICH, and next-generation sequencing of RNAs isolated from non-hemorrhagic (Sham) and hemorrhagic (ICH) rat brain samples collected 7 (early phase) and 28 (chronic phase) days after insults, was conducted. Bioinformatics analysis was performed to determine miRNA binding sites and gene ontology of circRNAs, target genes of miRNAs, as well as biological functions of mRNAs, altered after ICH. These analyses revealed different expression profiles of circRNAs, mRNAs and miRNAs in day-7 and day-28 ICH groups, respectively, compared with the Sham. In addition, the expression signature of circRNAs was more sensitive to disease progression than that of mRNAs or miRNAs. Further analysis suggested two temporally specific circRNA-miRNA-mRNA networks based on the competitive endogenous RNA theory, which had profound impacts on brain activities after ICH. In summary, these results suggested an important role for circRNAs in the pathogenesis of ICH and in reverse remodeling based on self-protection support, providing deep insights into diverse possibilities for ICH therapy through targeting circRNAs.

In recent years, the path planning of robot has been a hot research direction, and multirobot formation has practical application prospect in our life. This article proposes a hybrid path planning algorithm applied to robot formation. The improved Rapidly Exploring Random Trees algorithm PQ-RRT∗ with new distance evaluation function is used as a global planning algorithm to generate the initial global path. The determined parent nodes and child nodes are used as the starting points and target points of the local planning algorithm, respectively. The dynamic window approach is used as the local planning algorithm to avoid dynamic obstacles. At the same time, the algorithm restricts the movement of robots inside the formation to avoid internal collisions. The local optimal path is selected by the evaluation function containing the possibility of formation collision. Therefore, multiple mobile robots can quickly and safely reach the global target point in a complex environment with dynamic and static obstacles through the hybrid path planning algorithm. Numerical simulations are given to verify the effectiveness and superiority of the proposed hybrid path planning algorithm.

Examining the role of resting heart rate (RHR) in hypertension risk could improve our understanding of its pathogenesis. However, most relevant studies have been conducted in developed countries such as the United States and Brazil, with no evidence for the older Chinese population. Therefore, this study aimed to investigate the association between RHR and the risk of developing hypertension in an older Chinese population. A total of 3836 participants from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) were included in this prospective study. Using Cox proportional hazard models, the association between RHR and the risk of incident hypertension was evaluated, with results expressed as hazard ratios (HRs) and 95% confidence intervals (CIs). Multiplicative interaction effects were analyzed for RHR with age, sex, and regular exercise, and subgroup analyses of the association were also conducted based on these factors. Sensitivity analyses were conducted to evaluate the robustness of the results. During a 4.86‐year follow‐up, 1449 incident cases of hypertension occurred. Every 10 beats per min increase in RHR was associated with a 6% higher risk of incident hypertension (HR = 1.06, 95% CI: 1.01–1.12). Subgroup analyses demonstrated significant associations in women, participants aged ≥80, and those who did not exercise regularly. The sensitivity analyses confirmed the consistency of these results. These findings indicate that a faster RHR is associated with a higher risk of incident hypertension in the older Chinese population, suggesting its potential use in identifying older individuals at greater risk of incident hypertension.

Hepatocellular death is a sensitive parameter for detecting acute liver injury (ALI) of toxic, viral, metabolic, and autoimmune origin. Ferroptosis has recently been implicated in carbon tetrachloride (CCl 4 )-induced ALI. However, the underpinning mechanism and mechanistic basis remain elusive. In this study, bicyclol, a proprietary hepatoprotectant in China, and ferroptosis-specific inhibitor ferrostatin-1 (Fer-1) were administered in CCl 4 -injured mice. A panel of ferroptosis-related markers, including mitochondria morphology, reactive oxygen species production, protein adducts in response to lipid peroxidation, and key modulators of ferroptotic process, was determined in vivo. Erastin-treated L-O2 hepatocytes were transfected with glutathione peroxidase 4 (GPx4) or nuclear factor erythroid 2-related factor 2 (Nrf2) siRNA to delineate the pathway of bicyclol against ferroptosis in vitro. As a result, CCl 4 led to iron accumulation, excessive reactive oxygen species production, enhanced lipid peroxidation, and characteristic morphological changes in mitochondria, along with a decrease in GPx4 and xCT protein levels in ALI mice liver, all of which were generally observed in ferroptosis. The use of Fer-1 further corroborated that ferroptosis is responsible for liver damage. Bicyclol exerted its hepatoprotection by preventing the aforesaid ferroptotic process. Furthermore, bicyclol alleviated erastin-induced cellular inviability, destruction, and lipid peroxidation in vitro. Knockdown of GPx4 diminished these protective activities against perturbations associated with ferroptosis in L-O2 hepatocytes. Additionally, Nrf2 silencing drastically reduced GPx4 levels, and further impeded the medicinal effects of bicyclol. In summary, positively regulating Nrf2-GPx4 axis by bicyclol can prevent ferroptosis in CCl 4 -induced ALI in mice.

The rapid development of natural language processing technology and improvements in computer performance in recent years have resulted in the wide-scale development and adoption of human–machine dialogue systems. In this study, the Icc_dialogue model is proposed to enhance the semantic awareness of moods for emotional interactive robots. Equipped with a voice interaction module, emotion calculation is conducted based on model responses, and rules for calculating users’ degree of interest are formulated. By evaluating the degree of interest, the system can determine whether it should transition to a new topic to maintain the user’s interest. This model can also address issues such as overly purposeful responses and rigid emotional expressions in generated replies. Simultaneously, this study explores topic continuation after answering a question, the construction of dialogue rounds, keyword counting, and the creation of a target text similarity matrix for each text in the dialogue dataset. The matrix is normalized, weights are assigned, and the final text score is calculated. In the text with the highest score, the content of dialogue continuation is determined by calculating a subsequent sentence with the highest similarity. This resolves the issue in which the conversational bot fails to continue dialogue on a topic after answering a question, instead waiting for the user to voluntarily provide more information, resulting in topic interruption. As described in the experimental section, both automatic and manual evaluations were conducted to validate the significant improvement in the mood semantic awareness model’s performance in terms of dialogue quality and user experience.

In recent years, the development of image super-resolution (SR) has explored the capabilities of convolutional neural networks (CNNs). The current research tends to use deeper CNNs to improve performance. However, blindly increasing the depth of the network does not effectively enhance its performance. Moreover, as the network depth increases, more issues arise during the training process, requiring additional training techniques. In this paper, we propose a lightweight image super-resolution reconstruction algorithm (SISR-RFDM) based on the residual feature distillation mechanism (RFDM). Building upon residual blocks, we introduce spatial attention (SA) modules to provide more informative cues for recovering high-frequency details such as image edges and textures. Additionally, the output of each residual block is utilized as hierarchical features for global feature fusion (GFF), enhancing inter-layer information flow and feature reuse. Finally, all these features are fed into the reconstruction module to restore high-quality images. Experimental results demonstrate that our proposed algorithm outperforms other comparative algorithms in terms of both subjective visual effects and objective evaluation quality. The peak signal-to-noise ratio (PSNR) is improved by 0.23 dB, and the structural similarity index (SSIM) reaches 0.9607.

Previous studies on tunnel fires have primarily focused on tunnels with flat ceilings and lacked studies on tunnels with beams. The present study is predicated on a reduced-scale tunnel model with a beam structure. Through meticulous analysis of the effects of factors such as longitudinal ventilation velocity and beam dimensions, the study unveils the distribution pattern of ceiling temperatures under the longitudinal smoke exhaust mode. The findings suggest that the presence of beams can induce turbulence in the longitudinal ventilation airflow. It has been demonstrated that the magnitude of this phenomenon is directly proportional to the spacing of the beams. This results in fluctuations in the ceiling temperature rise close to the combustion zone. The smoke storage capacity of the open cavities formed between adjacent beams is significantly affected by the beam height, thereby influencing the overall temperature rise beneath the ceiling. The greater the beam height, the higher the overall ceiling temperature rise near the combustion zone, but the lower the ceiling temperature rise downstream of the fire source. A prediction model for the longitudinal decay of ceiling temperature downstream of the fire source in tunnels with beams has been obtained. This model is related to the dimensionless beam dimension.

The Mapeng pluton in the central Taihang Mountains hosts significant gold mineralization; however, the magmatic processes controlling its emplacement, crystallization, and potential role in ore genesis remain debated. Previous petrological and geochemical studies have identified three internal lithofacies zones and suggested magma mixing. However, it remains uncertain whether these zones formed through in situ fractional crystallization or multiple intrusive pulses, and how magmatic dynamics contributed to gold enrichment. To address these questions, we applied quantitative crystal size distribution (CSD) analysis to constrain the intrusion history and evaluate its implications for mineralization. The CSD curves of quartz in the Mapeng granite are typically concave, with characteristic lengths (CLs) ranging from 0.78 to 1.43 mm, slopes from −1.29 to −0.70, and intercepts from −2.10 to 0.95. These variations indicate strong fluctuations in crystal growth and nucleation rates, suggesting a major influence of magma mixing. For plagioclase, the CL values range from 0.56 to 2.50 mm, slopes from −4.40 to −1.33, and intercepts from −1.21 to 3.48, further supporting the idea of multistage magma injection and crystal coarsening. Regarding crystal spatial distribution and alignment, the crystal aggregation degree (R value) ranges from 0.79 to 1.14, and the alignment factor (AF value) ranges from 0.01 to 0.19. These values suggest that the crystals tend to aggregate spatially, with their alignment degree being extremely weak, which indicates rapid magma flow disturbed by mixing processes. Notably, the R value and AF value show a negative correlation (R2 > 0.6) in the central facies and a positive correlation in the transitional facies, revealing differences in crystal accumulation mechanisms among different lithofacies zones. By synthesizing the covariance of CSD parameters and texture indices, this study infers that the Mapeng pluton experienced multiple batches of magma injection during its emplacement and consolidation. These injection events accelerated crystal dissolution and regrowth, thereby promoting crystal coarsening and textural reorganization. This study provides new quantitative mineral–textural evidence.