Search

Graph convolution subspace clustering has been widely used in the field of hyperspectral image (HSI) unsupervised classification due to its ability to aggregate neighborhood information. However, existing methods focus on using graph convolution techniques to design feature extraction functions, ignoring the mutual optimization of the graph convolution operator and the self-expression coefficient matrix, leading to suboptimal clustering results. In addition, these methods directly construct graphs on raw data, which may be easily affected by noises and then degrade the clustering performance, as the constructed topology is not credible for the training procedure. To address these issues, we propose a novel method called spatial-spectral adaptive graph convolutional subspace clustering (S2AGCSC). We employ the reconstruction coefficient matrix to devise a graph convolutional operator with adjacency matrix, which collaboratively computes both the feature representations and coefficient matrix, and the graph-convolutional operator is updated iteratively and adaptively during training. In addition, we harness a combination of spectral and spatial features to introduce additional view information to help learn more robust features and generate more refined superpixels. Experimental validation on three HSI datasets confirms the efficacy of S2AGCSC.

The existing research on waterfront tour route planning suggests that thermal comfort assessment has a direct impact on people’s outdoor tour experiences. It contributes to understanding the thermal comfort regulation mechanisms of landscape nodes and the scientific planning of tour routes. However, the research in this area is relatively limited and faces complex issues in data processing and analysis. This study takes the West Lake waterfront tour route in Hangzhou, China, as an example. Through empirical research, it validates the feasibility of tour route planning guided by thermal comfort. It employs field surveys, on-site measurements, and data analysis methods primarily based on the Rayman software to explore the relationship between urban outdoor waterfront spaces and microclimates. The study reveals the following findings: (i) Extensive water bodies surrounding an area positively regulate local microclimates. The closer one is to the water and the more water surrounds the area, the stronger the microclimate effect. (ii) Factors such as spatial openness, waterfront orientation, prevailing monsoons, etc. all have certain impacts on the microclimates of the lake and lakeside spaces. (iii) Air temperature has significant variability in its impact on human thermal comfort, with different PET (Physiological Equivalent Temperature) values at each measurement point during different time periods. The study combines the actual human thermal comfort conditions at eight measurement points around the lake with existing tour routes. It proposes two practical tour route optimization designs based on real tour microclimate factors. These designs employ thermal comfort assessment methods to effectively enhance and improve human thermal comfort during tours. This research carries social value and provides new insights for mitigating urban waterfront space microclimates, enhancing tour comfort, and improving landscape environments.

Background: Everolimus is beneficial for patients with hormone receptor-positive and human epidermal growth factor receptor 2-negative (HR+/HER2−) advanced breast cancer (ABC). However, some patients developed drug resistance and the well-established predictor for everolimus efficacy was limited. Objectives: The study was designed to evaluate the efficacy of everolimus in different treatment lines and identify several clinicopathological markers to estimate everolimus efficacy in patients with HR+/HER2− ABC. Design: This was a retrospective and multicenter study. Methods: Between 2014 and 2022, more than 2000 patients with tumors who received everolimus were collected from multiple cancer centers in China (National Cancer Center, Chinese PLA General Hospital, Peking University Cancer Hospital & Institute). A training cohort and two validation cohorts were developed. Results: The training cohort included 338 patients. The median progression-free survival (PFS) for everolimus was 5.6 months, with an objective response rate of 25.1% and a clinical benefit rate of 54.4%. PFS was significantly worse from first-line (1L) to second-line (2L) to third-line (3L), with PFS 1L for 13.5 months, PFS 2L for 6.1 months, and PFS 3L for 4.1 months ( p = 2.9e−6, hazard ratio (HR) = 0.70, 95% confidence interval (CI) = 0.61–0.82). The clinicopathological characteristics, including post-1L everolimus treatment, Ki67 index of more than 40%, more than two metastatic sites at first recurrence, and receiving adjuvant chemotherapy, were independent risk factors for PFS. A predictive model for everolimus efficacy was established using these four factors. In the low-risk group, patients achieved a median PFS of 12.6 months, significantly longer compared to 2.7 months for those in the high-risk group ( p = 2.4e−64, HR = 9.41, 95% CI = 7.05–12.56). The area under the curve was 0.96, 0.95, and 0.94 for 6-month, 1-year, and 3-year PFS, respectively. Internal validation cohort (PFS 18.4 vs 3.1 months, p = 3.6e−11, HR = 3.78, 95% CI = 2.49–5.74) and external validation cohort (PFS 13.5 vs 3.1 months, p = 2.9e−10, HR = 11.53, 95% CI = 4.68–28.37) confirmed its power for estimating clinical benefits of everolimus. Conclusion: A predictive model was successfully established to predict survival outcomes for everolimus in patients with HR+/HER2− ABC, which may provide references for the management of everolimus in Chinese patients with HR+/HER2− ABC.

The waterfront trail is a crucial outdoor activity area for urban residents, but the increasing high temperature in summer has negatively affected the quality of outdoor activities. To address this issue, this article examines the relationship between the spatial form of the waterfront trails and thermal comfort, using the West Lake Hangzhou trails as an example. Seven waterfront trail spaces were selected for the study, and six spatial structure parameters and three climatic parameters were measured on three representative summer days. The Rayman model was then used to calculate the Physiological Equivalent Temperature (PET) value of each sample plot based on five climate parameters, human body parameters, and Sky View Factor (SVF). The results indicate that the primary spatial form factors affecting the microclimate and thermal comfort of the waterfront trails in summer are the average tree height, the average tree spacing, and the ratio of shadow-generating side tree height to the trail width. The average tree height significantly impacts thermal comfort, followed by the tree spacing and the shadow-generating side tree height ratio to the trail width. These parameters primarily affect the thermal comfort of the waterfront trails through air temperature and wind speed. The study’s findings can guide the design of waterfront trail spaces and help create waterfront trails that combine climate comfort and landscape.

Abstract China’s East–West Computing Resources Transmission Project (EWCRT Project) is a unique and innovative path toward developing China’s green digital economy. The EWCRT Project consists of building transregional digital infrastructure, including eight data center hubs and 10 data center clusters across China. Our estimations indicate that the EWCRT Project could result in a reduction of 2125 metric tons (Mt) of CO2 between 2020 and 2050. Combined with other improvement measures, such as an increase in power usage effectiveness (PUE) and a boost in the use of green electricity in these data centers, the project could result in a reduction of 9500 Mt of carbon emissions over the next three decades. Recognizing the heterogeneous makeup of data centers in general, it is crucial to identify diverse strategies that can transform each into “green engines” for the digital economy. Therefore, in this study, we use fuzzy-set qualitative comparative analysis (fsQCA) to characterize the complex conditions of different data center configurations. By doing so, we highlight differentiated strategies for data centers to support the greening of the digital economy.

In the context of water scarcity, understanding the mechanisms influencing and altering agricultural water consumption can offer valuable insights into the scientific management of limited water resources. Using Henan Province as a case study, this research applies the Mann–Kendall test method, the spatial Markov transfer chain model, the optimal parameter geo-detector model, and the Logarithmic Mean Divisia Index (LMDI) decomposition method to investigate the evolution characteristics of agricultural water consumption in Henan Province and its key influencing factors. The findings revealed the following: (1) Agricultural water consumption has shown a significant decline from 1999 to 2022. (2) According to observations, the stability of agricultural water consumption exceeds the spillover effect, and cross-border grade transfer is challenging. Moreover, this phenomenon is influenced by the neighboring regions. (3) The key influencing factors of added agricultural value are the sown area of food crops, total sown area, irrigated area, and average annual air temperature. (4) Among the decomposition effects on agricultural water consumption, the contribution of each decomposition effect to changes in agricultural water consumption and the role of spatial distribution exhibit notable differences. Overall, these findings provide theoretical references for the efficient use of agricultural water resources and sustainable development in the region.

Abstract In the current era of constant change, both employees and enterprises face the daunting challenge of lifelong learning. To address enterprises’ dissatisfaction with the knowledge provided by traditional universities, a growing number of corporate universities are being established, representing a crucial strategic pathway for enterprises. This study thus adopts the coding methodology of grounded theory to analyze the internal mechanism of a sample of seven corporate universities in China enabling organizations’ lifelong learning. Adopting the knowledge-based view (KBV), this study also identifies how corporate universities empower organizations, establishing a path model whose paths include “generating strategic knowledge”, “sharing business knowledge”, “optimizing governance knowledge” and “transforming cultural knowledge”. Accordingly, this study establishes a theoretical path model and explores how to empower organizations’ lifelong learning in the context of China. Moreover, our qualitative conclusions not only enrich the literature on corporate universities and their complex functions concerning lifelong learning and knowledge management but also have important implications for managers in enterprises and corporate universities.

This paper presents a thorough robust Class-AB power amplifier design and its application in pressure-mode sensor-on-chip nonlinearity correction. Considering its use in piezoresistive sensing applications, a gain-boosting-aided folded cascode structure is utilized to increase the amplifier’s gain by a large amount as well as enhancing the power rejection ability, and a push–pull structure with miller compensation, a floating gate technique, and an adaptive output driving limiting structures are adopted to achieve high-efficiency current driving capability, high stability, and electronic environmental compatibility. This amplifier is applied in a real sensor nonlinearity correction on-chip system. With the help of a self-designed 7-bit + sign DAC and a self-designed two-stage operational amplifier, this system is compatible with nonlinear correction at different signal conditioning output values. It can also drive resistive sensors as small as 300 ohms and as high as tens of thousands of ohms. The designed two-stage operational amplifier utilizes the TSMC 0.18 um process, resulting in a final circuit power consumption of 0.183 mW. The amplifier exhibits a gain greater than 140 dB, a phase margin of 68°, and a unit gain bandwidth exceeding 199.76 kHz. The output voltage range spans from 0 to 4.6 V. The final simulation results indicate that the nonlinear correction system designed in this paper can correct piezoresistive sensors with a nonlinearity of up to ±2.5% under various PVT (Process–Voltage–Temperature) conditions. After calibration by this system, the maximum error in the output voltage is 4 mV, effectively reducing the nonlinearity to 4% of its original value in the worst-case scenario.

In large public facilities, such as airport terminals or open-plan office spaces, the HVAC system typically consumes substantial amounts of energy. However, individuals often gather at specific areas while other zones are occupied by transient or occasional users. To minimize operational energy usage, this paper aims to reduce thermal comfort demands in non-targeted areas. This paper introduces a method for regulating the thermal environment around occupants exclusively in the variable-air-volume (VAV) air conditioning running mode. The investigation utilizes Airpak modeling and experimental verification techniques. Additionally, an analysis of temperature field and velocity field distributions within the room under the “personal space” operation mode is presented. The results suggest that adjusting the numbers of air vents, openings, airflow velocities, and air supply orientations can establish a comfortable thermal environment for inhabitants and reduce the overall ADPI value. The combined air supply mode leads to a 16.7% reduction in power usage compared to traditional full-space operation.

The timely and precise detection of forest fires is critical for halting the spread of wildfires and minimizing ecological and economic damage. However, the large variation in target size and the complexity of the background in UAV remote sensing images increase the difficulty of real-time forest fire detection. To address this challenge, this study proposes a lightweight YOLO model for UAV remote sensing forest fire detection (LUFFD-YOLO) based on attention mechanism and multi-level feature fusion techniques: (1) GhostNetV2 was employed to enhance the conventional convolution in YOLOv8n for decreasing the number of parameters in the model; (2) a plug-and-play enhanced small-object forest fire detection C2f (ESDC2f) structure was proposed to enhance the detection capability for small forest fires; (3) an innovative hierarchical feature-integrated C2f (HFIC2f) structure was proposed to improve the model’s ability to extract information from complex backgrounds and the capability of feature fusion. The LUFFD-YOLO model surpasses the YOLOv8n, achieving a 5.1% enhancement in mAP and a 13% reduction in parameter count and obtaining desirable generalization on different datasets, indicating a good balance between high accuracy and model efficiency. This work would provide significant technical support for real-time forest fire detection using UAV remote-sensing images.

IntroductionFor patients with functional motor disorders of the lower limbs due to brain damage or accidental injury, restoring the ability to stand and walk plays an important role in clinical rehabilitation. Lower limb exoskeleton robots generally require patients to convert themselves to a standing position for use, while being a wearable device with limited movement distance.MethodsThis paper proposes a reconfigurable behavioral assistive robot that integrates the functions of an exoskeleton robot and an assistive standing wheelchair through a novel mechanism. The new mechanism is based on a four-bar linkage, and through simple and stable conformal transformations, the robot can switch between exoskeleton state, sit-to-stand support state, and wheelchair state. This enables the robot to achieve the functions of assisted walking, assisted standing up, supported standing and wheelchair mobility, respectively, thereby meeting the daily activity needs of sit-to-stand transitions and gait training. The configuration transformation module controls seamless switching between different configurations through an industrial computer. Experimental protocols have been developed for wearable testing of robotic prototypes not only for healthy subjects but also for simulated hemiplegic patients.ResultsThe experimental results indicate that the gait tracking effect during robot-assisted walking is satisfactory, and there are no sudden speed changes during the assisted standing up process, providing smooth support to the wearer. Meanwhile, the activation of the main force-generating muscles of the legs and the plantar pressure decreases significantly in healthy subjects and simulated hemiplegic patients wearing the robot for assisted walking and assisted standing-up compared to the situation when the robot is not worn.DiscussionThese experimental findings demonstrate that the reconfigurable behavioral assistive robot prototype of this study is effective, reducing the muscular burden on the wearer during walking and standing up, and provide effective support for the subject's body. The experimental results objectively and comprehensively showcase the effectiveness and potential of the reconfigurable behavioral assistive robot in the realms of behavioral assistance and rehabilitation training.

This paper aims to optimize medical material distribution in closed community logistics networks during sudden outbreaks with a focus on efficient waste collection and reduced consumable distribution time. First, considering the costs of UAV trajectory distribution, impact, threat, and other costs, a forward and reverse scheduling model with time windows for joint multi-distribution center material distribution and waste anti-epidemic materials collection vehicle-UAV is established. Meanwhile, a two-stage metaheuristic algorithm is proposed in this paper. In the first stage of the solution algorithm, we design the multi-strategy guided adaptive differential evolution (MSGA-DE) to plan the multi-UAV cooperative distribution situation in a 3D environment. In the second stage, an improved beluga whale optimization based on hybrid neighborhood search (HNS-IBWO) is combined to solve the vehicle-UAV scheduling and distribution problem. Furthermore, comparing with various cross-validation algorithms, it validates the superiority of MSGA-DE in solving UAV trajectory issues and the convergence speed and accuracy of HNS-IBWO for high-latitude complex optimizations. Finally, a simulation in a closed Shanghai community validates the proposed model. Results demonstrate its effectiveness, especially in terms of convergence, multi-objective search, and global search capabilities when compared to existing algorithms. This offers an efficient solution for vehicle-UAV scheduling in unforeseen epidemic-related closures.

In the context of Industry 4.0 and based on the demand of digital logistics construction of industrial enterprises, this paper integrates the concept of digital twin into Refined Logistics Supply Chain construction. Considering the constraints of multi-distribution center, heterogeneous vehicle performance, distribution cost, quasi-shipment certificate and humanized management, Refined Logistics Supply Chain System (RLSCS) and cross-regional scheduling optimization model of logistics vehicles with multi-distribution center were established. The designed model can minimize the transportation cost, reduce the transportation time, and improve the vehicle load rate. An adaptive elite honey badger target algorithm based on cubic mapping mechanism (IHBA), is designed to solve the model. Further, performance evaluation by optimizing test functions, the convergence performance of IHBA algorithm was demonstrated. Finally, the simulation experiment is carried out according to the actual business data and it is compared to eight other optimistic algorithms. The experimental results show that the proposed algorithm is more effective and more robust, and the related models and algorithms can provide research basis for industrial products digital supply chain system.

The requirement for motion control of robotic arms in industrial settings is a dynamic field. This study examines the principles and derivation of the kinematics of the robotic arm based on the D-H parameter model. Additionally, the introduction of the seventh joint is proposed as a faster solution for solving the inverse kinematics of the robotic arm. Swarm intelligence optimization for path planning is currently advancing, and our proposed improved algorithm, the Golden Eagle search algorithm, enhances the traditional Golden Eagle search algorithm Jining by integrating a stochastic gradient descent strategy and Cauchy mutation strategy. We compare our IGEO algorithm with various other algorithms, and the findings demonstrate that the robotic arm can adeptly circumnavigate obstacles while walking seamlessly through environments with multiple obstacles. The IGEO algorithm is adept at navigating paths obstructed by multiple obstacles. It improves the accuracy by 15.38% as compared to the conventional algorithm and also improves it a lot as compared to other optimisation algorithms by up to 29.88%. It provides a solution to the path planning problem of robotic arms with excellent robustness and accuracy in finding the shortest collision-free path.

A variable pressure differential fuzzy control method is proposed based on the online identification method for key parameters and the fuzzy subset inference fuzzy control method of the chilled water system network model. Firstly, a phase plane fuzzy identification method is proposed for the most unfavorable thermal loop. The study focuses on analyzing the trend of room temperature deviation and deviation change in different quadrants in the phase plane. Furthermore, we establish a chilled water pipe network model that recalculates flow variation in both the main pipe and each branch pipe section to eliminate the most unfavorable thermal loop. Finally, the test platform for the fan coil variable flow air conditioning water system was designed and constructed to meet the requirements of energy-saving regulation. Additionally, the network monitoring system for the test platform was completed. The calibration and debugging results demonstrate that the monitoring error is within ±5.0%, ensuring precise control of room temperature at the end of the branch within ±0.5 °C. Results demonstrate that our novel method exhibits superior stability in room temperature control compared to traditional linear variable pressure differential set point controls while achieving energy saving ranging from 4.7% to 6.5%.

Due to the unique distance and angles involved in satellite remote sensing, ships appear with a small pixel area in images, leading to insufficient feature representation. This results in suboptimal performance in ship detection, including potential misses and false detections. Moreover, the complexity of backgrounds in remote sensing images of ships and the clustering of vessels also adversely affect the accuracy of ship detection. Therefore, this paper proposes an optimized model named SSMA-YOLO, based on YOLOv8n. First, this paper introduces a newly designed SSC2f structure that incorporates spatial and channel convolution (SCConv) and spatial group-wise enhancement (SGE) attention mechanisms. This design reduces spatial and channel redundancies within the neural network, enhancing detection accuracy while simultaneously reducing the model’s parameter count. Second, the newly designed MC2f structure employs the multidimensional collaborative attention (MCA) mechanism to efficiently model spatial and channel features, enhancing recognition efficiency in complex backgrounds. Additionally, the asymptotic feature pyramid network (AFPN) structure was designed for progressively fusing multi-level features from the backbone layers, overcoming challenges posed by multi-scale variations. Experiments of the ships dataset show that the proposed model achieved a 4.4% increase in mAP compared to the state-of-the-art single-stage target detection YOLOv8n model while also reducing the number of parameters by 23%.

Water deficit and soil salinization are the primary abiotic stress factors hindering maize growth. To assess the effect of water and salt stress on xylem embolism in maize and investigate the relationship between drought resistance and xylem vulnerability, a greenhouse experiment was designed using two maize cultivars, Zhengdan 958 (drought-resistant) and Denghai 605 (drought-sensitive). Four treatments were included: control (CK), water deficit (WD), salt stress (SS), and combined water and salt stress (WS). Various hydraulic characteristic indicators, such as stem xylem water potential, leaf xylem water potential, the specific hydraulic conductivity (Ks) and percentage loss of conductivity (PLC), were analyzed. Specific hydraulic conductivity curves and vulnerability curves were constructed, and the hydraulic safety margin (HSM) of the xylem was determined based on stomatal conductance (Gs). The results indicated that the hydraulic conductivity and embolism resistance of maize xylem were not correlated. Compared to Denghai 605, Zhengdan 958 had lower maximum specific hydraulic conductivity Ksmax and P50 values (xylem water potential at 50% PLC) in all treatments, indicating lower water transport capacity but stronger resistance to embolism. Under single-cultivar conditions, salt stress had a greater inhibitory effect on Ksmax and HSM in maize xylem compared to water deficit; thus, more severe embolism was found under salt stress. Under different treatment conditions, Zhengdan 958 had a larger HSM than Denghai 605, showing a wider water transport safety range and overall superior water transport security. To summarize, water and salt stress inhibited the water transport efficiency of the xylem in maize stems, and stronger drought-resistant cultivars showed greater resistance to embolism and larger hydraulic safety margins.

Aircraft icing due to severe cold and local factors increases the risk of flight delays and safety issues. Therefore, this study focuses on optimizing de-icing allocation and adapting to dynamic flight schedules at medium to large airports. Moreover, it aims to establish a centralized de-icing methodology employing unmanned de-icing vehicles to achieve the dual objectives of minimizing flight delay times and enhancing airport de-icing efficiency. To achieve these goals, a mixed-integer bi-level programming model is formulated, where the upper-level planning guides the allocation of de-icing positions and the lower-level planning addresses the collaborative scheduling of the multiple unmanned de-icing vehicles. In addition, a two-stage algorithm is introduced, encompassing a Mixed Variable Neighborhood Search Genetic Algorithm (MVNS-GA) as well as a Multi-Strategy Enhanced Heuristic Greedy Algorithm (MSEH-GA). Both algorithms are rigorously assessed through horizontal comparisons. This demonstrates the effectiveness and competitiveness of these algorithms. Finally, a model simulation is conducted at a major northwestern hub airport in China, providing empirical evidence of the proposed approach’s efficiency. The results show that research offers a practical solution for optimizing the use of multiple unmanned de-icing vehicles in aircraft de-icing tasks at medium to large airports. Therefore, delays are mitigated, and de-icing operations are improved.

The myriapod fauna of China is still poorly known and very little attention has been paid to the study of Lithobiomorpha, with only more 100 species/subspecies hitherto known from the country, amongst which are only three species of Validifemur. Here we are describing a new species from northwest China.A new lithobiid species, Validifemur radispinipes sp. n., is described and illustrated from Wolong Mountain Park, Jingyuan County, Guyuan City, Ningxia Hui Autonomous Region, northwest China. The new species is compared with V. pedodontus Ma, Song & Zhu, 2007 from Shaanxi Province, China. Type specimens are deposited in the Institute of Myriapodology, School of Life Sciences, Hengshui University, Hengshui, China.

The traditional optimal allocation of water resources mostly focuses on the allocation of different water supply sources to various types of water users in life, production, and ecology, and cannot reflect the differences in time and space of a certain type of water users. Therefore, this paper constructs a refined model for the optimal allocation of water resources, and further explores the optimal allocation of water resources with multiple water sources, multiple users, and multiple objectives. The main steps of the model construction are: (1) Regional gridding. The planting structure map and land use type map of the same resolution in the study area are selected and superimposed on them, the application attributes of each cell can be directly obtained. (2) Water demand gridding. On the basis of gridding in the study area, according to the application attributes of each cell, the quota method is used to predict the water demand of each cell. Then the water demand forecast result for the entire research area is obtained. (3) Water supply gridding. It is possible to reflect the types of water supply sources that a certain or a certain type of cell can accept, as well as the types of water supply sources that can be supplied by a certain type of water supply sources, by determining the types of water supply sources in the research area. The supply ranges that various water supply sources can reach, followed by the completion of the water supply grid. (4) According to the results of water demand gridding and water supply gridding, the water demand of each cell and the corresponding water supply source and water supply topological relationship are established, and the objective function is to minimize water shortage and maximize economic benefits to build a refined model for the optimal allocation of water resources.(5) The particle swarm algorithm is introduced to solve it, and the Pareto optimal solutions is obtained to determine the optimal water distribution for each cell. (6) Taking Guantao County, China as an example, the model was applied. In terms of minimum water shortage, the total water demand in Guantao County is predicted to be 118.8719 million m3 in the planning year (P = 50 %), while the total water supply is 125.2818 million m3. The water supply can meet the demand for water. In terms of financial advantages, the planned level of water Annual economic benefits will amount to 4.0865 million yuan.

The ability to control multiphase flows is essential for applications such as microvalves, chemical analyses, microreactors, and multiphase separators. Furthermore, more specific controls, including the positional navigation control of fluids under steady-state pressures, will improve the development of these applications. Here, we present a fundamentally new photothermally induced liquid gating system that allows light-controlled contactless fluid transport and gas/liquid separations at designated locations, with seconds response times, under constant pressures. Experiments and theoretical calculations demonstrate the stability of our system and its novel regulation mechanism, which is based on a photothermally induced liquid-reconfigurable gate with a change in the surface/interfacial tension and Marangoni flow redistribution of the gating liquid at the illuminated location. This regulation mechanism with positional navigation properties requires neither mechanical parts nor complex accessories and can further enable the miniaturization and integration of various engineering processes. Our application demonstrations confirm the potential of this system in fields of smart valves, multiphase separations, multiphase microreactors, and beyond.

A light-responsive and corrosion resistant gas valve with non-thermal effective liquid gating positional flow control under a constant pressure by incorporating dynamic gating liquid with light-responsiveness of solid porous substrate.

Unmanned Aerial Vehicles (UAVs) are increasingly utilized for urban patrol and defense owing to their low cost, high mobility, and rapid deployment. This paper proposes a multi-UAV mission planning model that takes into account mission execution rates, flight energy consumption costs, and impact costs. A kinematics and dynamics model of a quadcopter UAV is established, and the UAV’s flight state is analyzed. Due to the difficulties in addressing 3D UAV kinematic constraints and poor uniformity using traditional optimization algorithms, a lightning search algorithm (LSA) based on multi-layer nesting and random walk strategies (MNRW-LSA) is proposed. The convergence performance of the MNRW-LSA algorithm is demonstrated by comparing it with several other algorithms, such as the Golden Jackal Optimization (GJO), Hunter–Prey Optimization (HPO), Pelican Optimization Algorithm (POA), Reptile Search Algorithm (RSA), and the Golden Eagle Optimization (GEO) using optimization test functions, Friedman and Nemenyi tests. Additionally, a greedy strategy is added to the Rapidly-Exploring Random Tree (RRT) algorithm to initialize the trajectories for simulation experiments using a 3D city model. The results indicate that the proposed algorithm can enhance global convergence and robustness, shorten convergence time, improve UAV execution coverage, and reduce energy consumption. Compared with other algorithms, such as Particle Swarm Optimization (PSO), Simulated Annealing (SA), and LSA, the proposed method has greater advantages in addressing multi-UAV trajectory planning problems.

In order to solve the problem where the amount of screw fertilizer distributor can only be adjusted by rotating speed and poor fertilization uniformity at low rotational speeds, a blocking wheel-type screw fertilizer distributor was designed. Single factor and L9(34) orthogonal simulation tests based on EDEM software were carried out to optimize the distributor variables at a speed of 20 r/min. The bench verification test was built under the same conditions as the simulation tests to verify the results of the simulation. Finally, the bench performance tests were carried out to evaluate distributor performance. The results of simulation tests revealed that the minimum coefficient of variation of fertilization uniformity (CVFU) was 19.27%, with the structural parameter combination of the inner diameter (17 mm), pitch (45 mm), outlet distance (40 mm), and number of screw heads (1). The verification test results showed that the changing trend and values of the CVFU were almost the same as the simulation tests. The results of the performance test revealed that when the opening width of the blocking wheel was 10–30 mm and the rotation speed was 20–60 r/min, the amount of fertilizer per lap (FAPL) was in the range of 27.74–38.15 g/r; the maximum CVFU and the coefficient of variation of fertilization stability (CVFS) were 29.43% and 2.18%, respectively, which met the requirements of the industry standard. This research provides a good reference for optimizing the screw fertilizer distribution and for researchers in the field of precision fertilization.

This paper concerns with a class of double phase problem without Ambrosetti-Rabinowitz-type growth condition. Under reasonable hypotheses, we establish the existence of infinitely many solutions by using the variant fountain theorems due to Zou [41]. [ABSTRACT FROM AUTHOR]