Search

Abstract Background This study aimed to analyze the effects of anterior descending mandible (ADM) and free superior mesenteric artery (SMAS) folding flaps on post-parotidectomy facial depression. Methods This retrospective study examined the effects of sex, age, surgical sample size, method, duration, and blood loss on postoperative complications in 65 patients. Results No significant differences involving sex, age, or sample size for surgical resection were observed between the two groups. The incidence of Frey syndrome (P = 0.175) and the locations of facial nerve injuries and tumors were not significantly different between the two groups. However, a statistically significant difference was observed in postoperative facial depression between the groups (P = 0.045, P

Abstract Precast Concrete Sandwich Panel (PCSP) is composed of concrete load-bearing panels, thermal insulation panels, and decorative panels, which are assembled through connectors, integrating load-bearing, thermal insulation, and decorative functions. The connector bears the main shear force between the wall panels, and the shear resistance and insulation performance of the connector largely determine the mechanical stability and insulation effect of the wall panels, which is a key component in PCSPs. The current common practice is to cross assemble stainless steel insulation (SSI) connectors and Glass-Fiber-Reinforced Plastic (GFRP) connectors into PCSPs, which can reduce building energy consumption and save resources while meeting strength and insulation requirements. A large-scale pull-out test on a PCSP with intersecting SSI-GFRP connectors was conducted in this paper. The damage process and damage pattern of PCSP were observed and the shear performance of SSI-GFRP connectors was analyzed. Secondly, a numerical analysis model of the test PCSP was built using ABAQUS finite element software and its validity was verified through the test data. In addition, parameters such as connector diameter, connector number ratio and concrete strength were analyzed for their effect on the shear performance of SSI-GFRP connectors and it was found that connector diameter and connector number ratio had a significant effect. Finally, it is found that there are some differences between the classical theory for calculating the shear performance of SSI-GFRP connectors and the actual results. A theoretical correction factor (ζ) is given to improve the accuracy of the calculation of the classical theory, and its influencing factors and changing rules are investigated.

BackgroundChronic obstructive pulmonary disease (COPD) is a common respiratory disease that often coexists with malnutrition during acute exacerbation (AECOPD) and significantly affects the prognosis. Previous studies have shown that growth differentiation factor 15 (GDF15) levels promote appetite suppression, weight loss, and muscle weakness, and are markedly high in peripheral blood following inflammatory stimulation. However, it is still unknown whether serum GDF15 levels can be used to predict malnutrition in patients with AECOPD.MethodsA total of 142 patients admitted to the Department of Respiratory Medicine at Anshun People’s Hospital between December 2022 and August 2023 were selected for this study. The participants were divided into two groups: malnutrition group (n = 44) and non-malnutrition group (n = 98) based on a body mass index (BMI) < 18.5 kg/m2, according to the Global Leadership Initiative on Malnutrition (GLIM) criteria. Serum GDF15 levels were measured using the enzyme-linked immunosorbent assay (ELISA) and compared between the two groups. Spearman correlation analysis was used to examine the association between serum GDF15 levels, baseline data, and clinical indicators. Binary logistic regression was used to identify the independent risk factors for AECOPD combined with malnutrition. The predictive value of serum GDF15, albumin (ALB), and a combination of these was evaluated to identify malnutrition in patients with AECOPD using a receiver operating characteristic (ROC) curve.ResultsSerum GDF15 levels in patients with malnutrition and AECOPD were significantly higher than those in patients without malnutrition, whereas the serum ALB levels were significantly lower than those in patients without malnutrition (p < 0.001). Moreover, serum GDF15 levels were negatively correlated with BMI (r = −0.562, p < 0.001), mid-arm circumference (r = −0.505, p < 0.001), calf circumference (r = −0.490, p < 0.001), total protein (r = −0.486, p < 0.001), ALB (r = −0.445, p < 0.001), and prognostic nutritional index (r = −0.276, p = 0.001), and positively correlated with C-reactive protein (r = 0.318, p < 0.001), COPD assessment test score (r = 0.286, p = 0.001), modified medical research council classification (r = 0.310, p < 0.001), and global initiative for chronic obstructive pulmonary disease grade (r = 0.177, p = 0.035). Furthermore, serum GDF15 levels were an independent risk factor for malnutrition in patients with AECOPD (OR = 1.010, 95% CI, 1.003∼1.016). The optimal cut-off value of serum GDF15 level was 1,092.885 pg/mL, with a sensitivity of 65.90% and a specificity of 89.80%, while the serum ALB level was 36.15 g/L, with a sensitivity of 86.40% and a specificity of 65.00%, as well as a combined sensitivity of 84.10% and a specificity of 73.90%. Serum GDF15 and serum ALB levels had a good predictive ability (AUC = 0.856, AUC = 0.887), and the ROC revealed a greater combined prediction value for the two (AUC = 0.935).ConclusionSerum GDF15 levels could be used as a potential biomarker in the prediction of malnutrition in patients with AECOPD, offering a guidance for future clinical evaluation of malnutrition.

Abstract This paper proposes an algorithm for missile manoeuvring based on a hierarchical proximal policy optimization (PPO) reinforcement learning algorithm, which enables a missile to guide to a target and evade an interceptor at the same time. Based on the idea of task hierarchy, the agent has a two-layer structure, in which low-level agents control basic actions and are controlled by a high-level agent. The low level has two agents called a guidance agent and an evasion agent, which are trained in simple scenarios and embedded in the high-level agent. The high level has a policy selector agent, which chooses one of the low-level agents to activate at each decision moment. The reward functions for each agent are different, considering the guidance accuracy, flight time, and energy consumption metrics, as well as a field-of-view constraint. Simulation shows that the PPO algorithm without a hierarchical structure cannot complete the task, while the hierarchical PPO algorithm has a 100% success rate on a test dataset. The agent shows good adaptability and strong robustness to the second-order lag of autopilot and measurement noises. Compared with a traditional guidance law, the reinforcement learning guidance law has satisfactory guidance accuracy and significant advantages in average time and average energy consumption.

UAV route planning is the key issue for application of UAV in real-world scenarios. Compared with the traditional route planning methods, although the intelligent optimization algorithm has stronger applicability and optimization performance, it also has the problem of poor convergence accuracy and easy to fall into local optimization. Therefore, an intelligent route planning method for UAV based on chaotic random opposition-based learning and cauchy mutation improved Moth-flame optimization algorithm (OLTC-MFO) is proposed. First, the terrain environment is constructed by digital elevation map, and the threat model is established to realize the equivalent three-dimensional (3D) environment. Then, the opposite population is introduced to increase the diversity of solutions and improve the search speed of the algorithm. Then, the Logistic-Tent chaos map is introduced to realize random perturbation of flame position, which improves the global search capability of the algorithm. Finally, the probability operator and Cauchy mutation operator are introduced, which makes the algorithm not only accept the current solution with a certain probability, but also jump out of the current sub-optimal solution, thus enhancing the global search capability of the algorithm. The simulation results show that when the number of iterations is 1000, the length of route planning based on OLTC-MFO algorithm is 45.3716km shorter than MFO algorithm, and the convergence result of this method is stable and more accurate, which achieves the purpose of assisting UAV combat decision-making.

Unmanned aerial vehicle (UAV) swarm cooperative decision-making has attracted increasing attentions because of its low-cost, reusable, and distributed characteristics. However, existing non-learning-based methods rely on small-scale, known scenarios, and cannot solve complex multi-agent cooperation problem in large-scale, uncertain scenarios. This paper proposes a hierarchical multi-agent reinforcement learning (HMARL) method to solve the heterogeneous UAV swarm cooperative decision-making problem for the typical suppression of enemy air defense (SEAD) mission, which is decoupled into two sub-problems, i.e., the higher-level target allocation (TA) sub-problem and the lower-level cooperative attacking (CA) sub-problem. A HMARL agent model, consisting of a multi-agent deep Q network (MADQN) based TA agent and multiple independent asynchronous proximal policy optimization (IAPPO) based CA agents, is established. MADQN-TA agent can dynamically adjust the TA schemes according to the relative position. To encourage exploration and promote learning efficiency, the Metropolis criterion and inter-agent information exchange techniques are introduced. IAPPO-CA agent adopts independent learning paradigm, which can easily scale with the number of agents. Comparative simulation results validate the effectiveness, robustness, and scalability of the proposed method.

Sustainable development is the theme of world economic development in the 21st century. As a key part of sustainable development, sustainable land use (SLU) encompasses economic development and environmentally friendly and social progress. In recent decades, China has formulated many environmental regulatory policies to achieve sustainable development and "carbon peaking and carbon neutrality (double-carbon)" goals, among which the carbon emission trading scheme (CETS) is the most representative and provides valuable research. In this paper, we aimed to reflect the spatio-temporal evolution of SLU in China under the influence of environmental regulatory policies through an indicator measurement strategy based on the DID estimation method. The study conclusions are as follows: (1) The CETS can effectively improve SLU from the perspectives of economic development and environmentally friendly progress, and the impact has primarily been in the pilot areas. And, its effectiveness is closely linked to local locational factors. (2) With respect to the dimension of economic development, the CETS has not changed the provincial distribution patterns of SLU; rather, it continues to remain "high to low, east to west". However, regarding the environmentally friendly progress dimension, the CETS has significantly changed the provincial distribution patterns of SLU, which are characterized by spatial agglomeration with urban agglomerations such as the Pearl River Delta (PRD) and the Yangtze River Delta (YRD) as the core. (3) The screening results of the SLU indicators based on economic development showed that the CETS primarily improved the innovation capacities of pilot regions, and the impacts on economic levels were relatively small. Similarly, the screening results of the SLU indicators based on environmentally friendly progress showed that the CETS had primarily acted on reducing pollution emission intensity and strengthening greening construction, revealing only short-term effects on improving energy use efficiency. Based on the above, this paper explored the meaning and role of the CETS in more detail, with a view to providing insight into the implementation and formulation of environmental regulation policies.

A system with multiple cooperating unmanned aerial vehicles (multi-UAVs) can use its advantages to accomplish complicated tasks. Recent developments in deep reinforcement learning (DRL) offer good prospects for decision-making for multi-UAV systems. However, the safety and training efficiencies of DRL still need to be improved before practical use. This study presents a transfer-safe soft actor-critic (TSSAC) for multi-UAV decision-making. Decision-making by each UAV is modeled with a constrained Markov decision process (CMDP), in which safety is constrained to maximize the return. The soft actor-critic-Lagrangian (SAC-Lagrangian) algorithm is combined with a modified Lagrangian multiplier in the CMDP model. Moreover, parameter-based transfer learning is used to enable cooperative and efficient training of the tasks to the multi-UAVs. Simulation experiments indicate that the proposed method can improve the safety and training efficiencies and allow the UAVs to adapt to a dynamic scenario.

Abstract Aims/Introduction To compare the association of hypertension plus hyperuricemia with four insulin resistance surrogates, including glucose and triglycerides (TyG index), TyG index with body mass index (TyG‐BMI), the ratio of triglycerides divided by high‐density lipoprotein cholesterol (TG/HDL‐C) and metabolic score for insulin resistance (METS‐IR). Materials and Methods Data from a cross‐sectional epidemiological study enrolling a representative population sample aged ≥65 years were used to calculate the four indexes. The association with hypertension plus hyperuricemia and insulin resistance surrogates was examined with multivariate logistic regression and receiver operating characteristic. Results A total of 4,352 participants were included, including 93 (2.1%) patients with hyperuricemia alone, 2,875 (66.1%) with hypertension alone and 587 (13.5%) with hypertension plus hyperuricemia. Mutivariate logistic regression showed that TyG index, TyG‐BMI, TG/HDL‐C and METS‐IR were all significantly correlated with hyperuricemia, hypertension and hypertension plus hyperuricemia. Compared with the lowest quartile, the odds ratios (OR) of the highest quartile of the four indicators for hypertension plus hyperuricemia were TyG index: OR 6.39 (95% confidence interval [CI] 4.17–9.78); TyG‐BMI: OR 8.54 (95% CI 5.58–13.09); TG/HDL‐C: OR 7.21 (95% CI 4.72–11.01); METS‐IR: OR 9.30 (95% CI 6.00–14.43), respectively. TyG‐BMI and METS‐IR had moderate discriminative abilities for hypertension plus hyperuricemia and the AUC values were 0.72 (95% CI 0.70–0.74) and 0.73 (95% CI 0.70–0.75). Conclusions The present study suggested that TyG index, TyG‐BMI, TG/HDL‐C and METS‐IR had a significant correlation with hypertension plus hyperuricemia, and TyG‐BMI and METS‐IR had discriminative abilities for hypertension plus hyperuricemia.

Based on the theory of strategic alliances and social networks, this article empirically studies the relationship between partnership, information sharing, and sustainable performance through a questionnaire survey of Chinese sports equipment manufacturers. The findings show that partnerships have a positive impact on sustainable performance; that information sharing plays a role in mediating the relationships between trust, cooperation, and sustainable performance; and that government support can positively impact the effect of partnerships on sustainable performance. Through empirical research, this article proves the mechanism of the impact of partnership on alliance performance, further expands the theoretical basis for enterprises’ establishment of strategic alliances, and has important enlightening significance for enterprises within alliances aiming to rationally use the networks inside and outside their alliances to obtain knowledge and resources and improve their sustainable performance.

This study investigates the associations among member ability, member relationships, knowledge sharing, and innovation performance in eSports industry knowledge alliance. A survey strategy and purposive sampling were applied, and the analysis was conducted on a sample of 311 senior managers from the China eSports Association. The hypotheses were tested using SPSS 24.0 software and AMOS 24.0 software. This study shows that member ability and member relationships have both a direct and indirect effect on innovation performance. Firstly, member ability, member relationships, and member knowledge sharing significantly impact the innovation performance of eSports industry knowledge alliances. Secondly, member knowledge sharing plays a mediating role in the effect of member ability and membership relationship on innovation performance. This pioneering article explores the interaction mechanisms between member ability, member relationships, and innovation performance in eSports industry knowledge alliance. The research results are conducive to the development of the eSports industry toward deep integration and sustainable development and provide a reference for similar knowledge-intensive enterprise alliance behaviors.

Rapid and precise air operation mission planning is a key technology in unmanned aerial vehicles (UAVs) autonomous combat in battles. In this paper, an end-to-end UAV intelligent mission planning method based on deep reinforcement learning (DRL) is proposed to solve the shortcomings of the traditional intelligent optimization algorithm, such as relying on simple, static, low-dimensional scenarios, and poor scalability. Specifically, the suppression of enemy air defense (SEAD) mission planning is described as a sequential decision-making problem and formalized as a Markov decision process (MDP). Then, the SEAD intelligent planning model based on the proximal policy optimization (PPO) algorithm is established and a general intelligent planning architecture is proposed. Furthermore, three policy training tricks, i.e., domain randomization, maximizing policy entropy, and underlying network parameter sharing, are introduced to improve the learning performance and generalizability of PPO. Experiments results show that the model in this work is efficient and stable, and can be adapted to the unknown continuous high-dimensional environment. It can be concluded that the UAV intelligent mission planning model based on DRL has powerful intelligent planning performance, and provides a new idea for researching UAV autonomy.

In recent years, significant progress has been made in the multi-target tracking (MTT) of unmanned aerial vehicle (UAV) swarms. Most existing MTT approaches rely on the ideal assumption of a pre-set target trajectory. However, in practice, the trajectory of a moving target cannot be known by the UAV in advance, which poses a great challenge for realizing real-time tracking. Meanwhile, state-of-the-art multi-agent value-based methods have achieved significant progress for cooperative tasks. In contrast, multi-agent actor-critic (MAAC) methods face high variance and credit assignment issues. To address the aforementioned issues, this paper proposes a learning-based factored multi-agent soft actor-critic (FMASAC) scheme under the maximum entropy framework, where the UAV swarm is able to learn cooperative MTT in an unknown environment. This method introduces the idea of value decomposition into the MAAC setting to reduce the variance in policy updates and learn efficient credit assignment. Moreover, to further increase the detection tracking coverage of a UAV swarm, a spatial entropy reward (SER), inspired by the spatial entropy concept, is proposed in this scheme. Experiments demonstrated that the FMASAC can significantly improve the cooperative MTT performance of a UAV swarm, and it outperforms existing baselines in terms of the mean reward and tracking success rates. Additionally, the proposed scheme scales more successfully as the number of UAVs and targets increases.

Aiming at the problems of difficult handling of three-dimensional flight conflicts and unfair distribution of resolution costs, we propose a multi-aircraft conflict resolution method based on the network cooperative game. Firstly, we establish a flight conflict network model with aircraft as nodes and the conflict relationship between node pairs as edges. After that, we propose a comprehensive network index that can evaluate the effect of resolution strategy. Based on the concept of “nucleolus solution”, we establish a conflict network alliance with all nodes as participants, and balance the interests of all participants through the resolution cost function. In order to improve the timeliness of the method, we propose two optimization methods: adjusting high-priority nodes and customizing the initial resolution scheme. Finally, we combine the NSGA-II algorithm to solve the optimal conflict resolution scheme. The simulation results show that our method can adjust 10 aircraft in 15.17 s and resolve 12 flight conflicts in a complex conflict scenario containing 40 aircraft; our method reduces the resolution cost by more than 22.1% on average compared with the method without considering the resolution cost. The method ensures both the conflict resolution capability and the reduction in resolution cost.

Objective: Chronic kidney disease (CKD) and cardiovascular disease (CVD) have a high morbidity and mortality among the elderly. Low levels of high-density lipoprotein cholesterol (HDL-C), a traditional risk marker for CVD, are common in CKD patients. Little is known about the association of low HDL-C with renal dysfunction in the community dwelling population.Methods: This was a population-based cross-sectional study included 4,753 participants enrolled in a prospective study, the Shanghai Elderly Cardiovascular Health (SHECH) study. Estimated glomerular filtration rate (eGFR), calculated by the Chinese Modification of Diet in Renal Disease (C-MDRD equation), was used to assess renal dysfunction. Associations between renal dysfunction and low HDL-C were evaluated using multiple logistic regression models and restricted cubic splines.Results: Of 4,649 individuals who met inclusion criteria, 620 (13.34%) had low HDL-C at

Breakage in maize kernels and vertical pressure in grains lead to the uneven distribution of grain bulk density, which easily causes undesired problems in terms of grain storage. The objective of this study was, therefore, to determine the compression and heat production of the whole kernel (WK) and half kernel (HK) under two different loadings, i.e., 50 and 150 kPa, in maize bulk. An easy-to-use element testing system was developed by modification of an oedometer, and an empirical–analytical–numerical method was established to evaluate fungal heat production, considering kernel breakage and vertical pressure. Based on the experimental results, it was found that breakage induced larger compression; the compression of HK was 62% and 58% higher than that of WK at 50 kPa and 150 kPa, respectively. The creep model of the Hooke spring–Kelvin model in series can be used to accurately describe the creep behavior of maize bulk. Fungi and aerobic plate counting (APC) were affected significantly by the breakage and vertical pressure. APC in HK was 19 and 15 times that of WK under 150 and 50 kPa, respectively. The heat released by the development of fungi was found to be directly related to the APC results. The average temperatures of WK and HK under 150 and 50 kPa were 11.1%, 9.7%, 7.9%, and 7.6% higher than the room temperature, respectively. A numerical method was established to simulate the temperature increase due to fungi development. Based on the numerical results, heat production (Q) by fungi was estimated, and the results showed that the Q in HK was 1.29 and 1.32 times that of WK on average under 150 and 50 kPa. Additionally, the heat production results agreed very well with the APC results.