Search Results (54)

This paper investigates perimeter defense strategies for multi-agent systems. Considering the complex scenario with multiple obstacles in the mission environment, a defense strategy based on prioritized path planning is proposed in this paper. The strategy employs a minimum weight matching method to solve the optimal task assignment for interception and determines the task priority based on the relative time window. Meanwhile, the swarm path planning is realized using particle swarm optimization with a designed cost function. Compared with the existing literature, the proposed method can handle large-scale agent-based perimeter defense while accounting for inter-defender collision avoidance and obstacle avoidance. The effectiveness of the strategy is verified through simulation in the mission scenario. Full article

Rice–fish coculture, a traditional integrated agriculture–aquaculture system, has been recognized as a “Globally Important Agricultural Heritage System” due to its ecological and socio-economic benefits. However, the impact of rice–fish coculture on greenhouse gas emissions remains controversial. This study investigated the effects of rice–fish coculture on methane (CH₄) and nitrous oxide (N₂O) emissions in the Qingtian rice–fish system, a 1200-year-old terraced paddy field system in Zhejiang Province, China. A field experiment with two treatments, rice–fish coculture (RF) and rice monoculture (RM), was conducted to examine the relationships between fish activities, water and soil properties, microbial communities, and greenhouse gas fluxes. Results showed that the RF system had significantly higher CH₄ emissions, particularly during the early rice growth stage, compared to the RM system. This increase was attributed to the lower dissolved oxygen levels and higher methanogen abundance in the RF system, likely driven by the grazing, “muddying”, and burrowing activities of fish. In contrast, no significant differences in N₂O emissions were observed between the two systems. Redundancy analysis revealed that water variables contributed more to the variation in greenhouse gas emissions than soil variables. Microbial community analysis indicated that the RF system supported a more diverse microbial community involved in methane cycling processes. These findings provide new insights into the complex interactions between fish activities, environmental factors, and microbial communities in regulating greenhouse gas emissions from rice–fish coculture systems. The results suggest that optimizing water management strategies and exploring the potential of microbial community manipulation could help mitigate greenhouse gas emissions while maintaining the ecological and socio-economic benefits of these traditional integrated agriculture–aquaculture systems. Full article

The design of efficient hydrogen evolution reaction (HER) catalysts to minimize reaction overpotentials plays a pivotal role in advancing water electrolysis and clean energy solutions. Ru-based catalysts, regarded as potential replacements for Pt-based catalysts, face stability challenges during catalytic process. The precise regulation of metal–support interactions effectively prevents Ru nanoparticle degradation while optimizing interfacial electronic properties, enabling the simultaneous enhancement of catalytic activity and stability. Herein, we design an amorphous/crystalline support and employ in situ replacement to develop a Ru-NiP_x-Ni structure. The crystalline Ni phase with ordered atomic arrangement ensures efficient charge transport, while the amorphous phase with unsaturated dangling bonds provides abundant anchoring sites for Ru nanoclusters. This synergistic structure significantly enhances HER performance, which attains overpotentials of 19 mV at 10 mA cm⁻² and 70 mV at 100 mA cm⁻² in 1 m KOH, with sustained operation exceeding 55 h at 100 mA cm⁻². Electrochemical impedance spectroscopy analysis confirms that the Ru-NiP_x-Ni structure not only has a high density of active centers for HER, but also reduces the charge transfer resistance at the electrode–electrolyte interface, which effectively enhances HER kinetics. This study presents new directions for designing high-efficiency HER catalysts. Full article

Background: Roasting conditions significantly influence the sensory profile of Hubei strip-shaped green tea (HSSGT). Methods: This study examined the effects of roast processing on the sensory attributes, color qualities, physicochemical properties, and key aroma compounds of HSSGT. Sensory evaluation, color qualities determination, principal component analysis of physicochemical components (PCA), HS-SPME (headspace solid-phase microextraction) coupled with GC-MS (gas chromatography–mass spectrometry), relative odor activity value (ROAV), gas chromatography–olfactometry (GC-O), and absolute quantification analysis were employed to identify the critical difference in compounds that influence HSSGT desirability. Results: The results indicated that HSSGT roasted at 110 °C for 14 min achieved the highest sensory scores, superior physicochemical qualities, and an enhanced aroma index, which was attributed to shifting the proportion of chestnut to floral volatile compounds. Additionally, sensory-guided ROAV, GC-O, and absolute quantification revealed that linalool, octanal, nonanal, and hexanal were the most significant volatile compounds. The variations in these four critical compounds throughout the roasting process were further elucidated, showing that the ideal roasting conditions heightened floral aromas while diminishing the presence of less desirable green odors. These findings offer technical guidance and theoretical support for producing HSSGT with a more desirable balance of chestnut and floral aroma characteristics. Full article

Designing efficient and cost-effective electrocatalysts is crucial for the large-scale development of sustainable hydrogen energy. Amorphous catalysts hold great promise for application due to their structural flexibility and high exposure of active sites. We report a novel method for the in situ growth of amorphous CoNiRuO_x nanoparticle structures (CoNiRuO_x/NF) on a nickel foam substrate. In 1 m KOH, CoNiRuO_x/NF achieves a current density of 10 mA/cm² with a hydrogen evolution reaction (HER) overpotential of only 43 mV and remains stable for over 100 h at a current density of 100 mA/cm². An alkaline electrolyzer assembled with CoNiRuO_x/NF as the cathode delivers a current density 2.97 times higher than that of an IrO₂||Pt/C electrode pair at the potential of 2 V and exhibits excellent long-term durability exceeding 100 h. Experimental results reveal that the combined replacement and corrosion reactions facilitate the formation of the amorphous CoNiRuO_x structure. This work provides valuable insights for developing efficient and scalable amorphous catalysts. Full article

The total organic carbon (TOC) content in lake sediments is an effective archive indicating past climate changes. However, the resolution of the TOC record has generally been limited by factors such as subsampling intervals, hampering further comprehension of past climate change. Recently, hyperspectral imaging technology has been increasingly employed to scan lake sediment cores, presenting new opportunities to reconstruct high-resolution sequences, but the reconstruction of long-term high-resolution TOC records using hyperspectral imaging and the climate implications have not been well studied. In this study, we scanned sedimentary cores from Wudalianchi Crater Lake in northeast China with a spatial resolution of 400 × 400 μm, utilizing visible and near-infrared (VNIR) hyperspectral imaging technology. Then, a partial least-squares regression (PLSR) model was constructed by comparing eight different preprocessing methods and optimally selecting the best spectral subset combined with a genetic algorithm (GA). Our analysis demonstrates that the PLSR model, constructed using 62 relevant bands selected by the Savitzky–Golay second derivative (D2) preprocessing method and GA, was the most reliable, with the validation set’s R-value reaching a high of 0.91 and RMSE as low as 1.18%. Notably, the spectral range of 656–669 nm showed a strong positive correlation with measured TOC, indicating its sensitivity for TOC estimation. Given this advantage, we reconstructed the TOC records of sediments from the Wudalianchi Crater Lake during the 38–13 ka BP period, which exhibited significant millennial-scale fluctuation events. These corresponded well with the millennial-scale events in pollen and TOC from Lake Sihailongwan, δ¹⁸O records of Greenland ice cores, and δ¹⁸O records from Asian stalagmites. Thus, the combination of hyperspectral imaging and the PLSR model is effective in reconstructing high-resolution TOC changes in lake sediments, which is essential for understanding climate change as well as carbon burial in lakes. Full article

Volatile constituents are critical to the flavor of tea, but the changes in Enshi Yulu tea during the processing have not been clearly understood. Using headspace solid phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME/GC-MS) techniques, we analyze the aroma components of Enshi Yulu tea and changes in them during the processing stages. In total, 242 volatile compounds were identified. From fresh leaves to the shaping process in tea production, there are significant decreases in overall aroma substances, followed by increases after drying. Linalool is the dominant aroma component in Enshi Yulu tea, with a proportion of 12.35%, followed by compounds such as geraniol (7.41%), 2,6-dimethyl-5-heptene (6.93%), phenylmethanol (5.98%), isobutyl acetate (4.16%), hexan-1-ol (3.95%), 2-phenylacetaldehyde (3.80%), and oct-1-ene-3-ol (3.34%). The number of differential volatile components varied by production stage, with 20 up- and 139 down-regulated after steaming, 24 down-regulated after rolling, 60 up- and 51 down-regulated after shaping, and 68 up- and 13 down-regulated after drying. Most variation in expression occurred because of steaming, and the least during the rolling stage. PLS-DA analysis revealed significant differences in aroma components throughout processing and the identification of 100 compounds with higher relative contents, with five distinct change trends. Phenylmethanol, phenylacetaldehyde, (2E)-non-2-enal, oct-1-ene-3-ol, and cis-3-hexenyl hexanoate could exert a profound influence on the overall aroma quality of Enshi Yulu tea during processing. The results offer a scientific foundation and valuable insights for understanding the volatile composition of Enshi Yulu tea and its changes during the processing. Full article

With increasing dual pressure from global large energy consumption and environmental protection, multiple integrated energy systems (IESs) can provide more effective ways to achieve better energy utilization performance. However, in actual circumstances, many challenges have been brought to coupling multiple energy sources along with the uncertainty of each generated power to achieve efficient operation of IESs. To resolve this problem, this article reviews primary research on integrated energy optimization and scheduling technology to give constructive guidance in power systems. Firstly, the conceptual composition and classification of IESs are presented. Secondly, the coupling relationship between multiple energy sources based on mathematical expression is studied deeply. Thirdly, the scheduling of IESs with different types and regions is classified, analyzed, and summarized for clarification. Fourthly, on this basis, potential solutions for applications of key optimization technologies involved in the scheduling process in IESs can be found systematically. Finally, the future development trends to optimize scheduling integrated energy systems is explored and prospected in depth. Full article

This paper considers a class of distributed online aggregative optimization problems over an undirected and connected network. It takes into account an unknown dynamic environment and some aggregation functions, which is different from the problem formulation of the existing approach, making the aggregative optimization problem more challenging. A distributed online optimization algorithm is designed for the considered problem via the mirror descent algorithm and the distributed average tracking method. In particular, the dynamic environment and the gradient are estimated by the averaged tracking methods, and then an online optimization algorithm is designed via a dynamic mirror descent method. It is shown that the dynamic regret is bounded in the order of $\mathcal{O}\left(\sqrt{T}\right)$. Finally, the effectiveness of the designed algorithm is verified by some simulations of cooperative control of a multi-robot system. Full article

Hypertension is a major risk factor for many serious diseases. With the aging population and lifestyle changes, the incidence of hypertension continues to rise, imposing a significant medical cost burden on patients and severely affecting their quality of life. Early intervention can greatly reduce the prevalence of hypertension. Research on hypertension early warning models based on electronic health records (EHRs) is an important and effective method for achieving early hypertension warning. However, limited by the scarcity and imbalance of multivisit records, and the nonstationary characteristics of hypertension features, it is difficult to predict the probability of hypertension prevalence in a patient effectively. Therefore, this study proposes an online hypertension monitoring model (HRP-OG) based on reinforcement learning and generative feature replay. It transforms the hypertension prediction problem into a sequential decision problem, achieving risk prediction of hypertension for patients using multivisit records. Sensors embedded in medical devices and wearables continuously capture real-time physiological data such as blood pressure, heart rate, and activity levels, which are integrated into the EHR. The fit between the samples generated by the generator and the real visit data is evaluated using maximum likelihood estimation, which can reduce the adversarial discrepancy between the feature space of hypertension and incoming incremental data, and the model is updated online based on real-time data using generative feature replay. The incorporation of sensor data ensures that the model adapts dynamically to changes in the condition of patients, facilitating timely interventions. In this study, the publicly available MIMIC-III data are used for validation, and the experimental results demonstrate that compared to existing advanced methods, HRP-OG can effectively improve the accuracy of hypertension risk prediction for few-shot multivisit record in nonstationary environments. Full article

Chlorphenamine maleate is a prohibited additive found in herbal teas and health foods. Excessive intake of this substance can result in adverse health effects. In this study, two novel haptens, PEM and bepotastine (PB1), mimicking chlorphenamine maleate structure were designed and synthesized based on molecular simulation for developing two corresponding polyclonal antibodies (PEM-Ab and PB1-Ab), respectively. Afterward, an indirect competitive enzyme-linked immunosorbent assay (ic-ELISA) was developed to quickly and accurately detect chlorphenamine maleate in herbal teas using PB1-Ab, which has a high sensitivity and specificity. For chlorphenamine maleate, the half-maximal inhibitory concentration (IC₅₀) and limit of detection (LOD) of PB1-Ab under ideal circumstances were found to be 1.18 µg/L and 0.07 µg/L, respectively. Besides, an environmentally friendly sample pre-treatment strategy was employed that allowed easy and effective elimination of complex matrices. The ic-ELISA method observed the average recovery rate from 87.7% to 94.0% with the variance coefficient (CV) ranging from 2.2% to 9.4%. Additionally, the identification of 25 commercially available herbal teas using liquid chromatography-tandem mass spectrometry (LC-MS/MS) further confirmed the validity of our detection. The results of the two methods are consistent. Overall, the proposed ic-ELISA could be an ultrasensitive and reliable method for chlorphenamine maleate adulterated in foods or exposure to the environment. Full article

Insufficient denitrification and limited phosphorus uptake hinder nitrogen and phosphorus removal in constructed wetlands (CWs). Sponge iron is a promising material for the removal of phosphorus and nitrogen because of its strong reducing power, high electronegativity, and inexpensive cost. The influence of factors including initial solution pH, dosage, and the Fe/C ratio was investigated. A vertical flow CW with sponge iron (CW-I) was established, and a traditional gravel bed (CW-G) was used as a control group. The kinetic analysis demonstrated that for both nitrogen and phosphorus, pseudo-second-order kinetics were superior. The theoretical adsorption capacities of sponge iron for nitrate (${\mathrm{N}\mathrm{O}}_3^{-}$-N) and phosphate (${\mathrm{P}\mathrm{O}}_4^{3-}$-P) were 1294.5 mg/kg and 583.6 mg/kg, respectively. Under different hydraulic retention times (HRT), CW-I had better total nitrogen (TN) and total phosphorus (TP) removal efficiencies (6.08–15.18% and 5.00–20.67%, respectively) than CW-G. The enhancing effect of sponge iron on nitrogen and phosphorus removal was best when HRT was 48 h. The increase in HRT improved not only the nitrogen and phosphorus removal effects of CWs but also the reduction capacity of iron and the phosphorus removal effect. The main mechanisms of synergistic nitrogen and phosphorus removal were chemical reduction, ion exchange, electrostatic adsorption, and precipitation formation. Full article

The centimetre-level accuracy of Ultra-wideband (UWB) has attracted significant attention in indoor positioning. However, the precision of UWB positioning is severely compromised by non-line-of-sight (NLOS) conditions that arise from complex indoor environments. On the other hand, odometry is widely applicable to wheeled robots due to its reliable short-term accuracy and high sampling frequency, but it suffers from long-term drift. This paper proposes a tightly coupled fusion method with a Dynamic Unscented Kalman Filter (DUKF), which utilises odometry to identify and mitigate NLOS effects on UWB measurements. Horizontal Dilution of Precision (HDOP) was introduced to assess the impact of geometric distribution between robots and UWB anchors on UWB positioning accuracy. By dynamically adjusting UKF parameters based on NLOS condition, HDOP values, and robot motion status, the proposed method achieves excellent UWB positioning results in a severe NLOS environment, which enables UWB positioning even when only one line-of-sight (LOS) UWB anchor is available. Experimental results under severe NLOS conditions demonstrate that the proposed system achieves a Root Mean Square Error (RMSE) of approximately 7.5 cm. Full article

For 5G base stations equipped with multiple energy sources, such as energy storage systems (ESSs) and photovoltaic (PV) power generation, energy management is crucial, directly influencing the operational cost. Hence, aiming at increasing the utilization rate of PV power generation and improving the lifetime of the battery, thereby reducing the operating cost of the base station, a hierarchical energy management strategy based on the improved dung beetle optimization (IDBO) algorithm is proposed in this paper. The first control layer provides bus voltage control to each power module. In the second control layer, a dynamic balance control strategy calculates the power of the ESSs using the proportional–integral (PI) controller and distributes power based on the state of charge (SOC) and virtual resistance. The third control layer uses the IDBO algorithm to solve the DC microgrid’s optimization model in order to achieve the minimum daily operational cost goal. Simulation results demonstrate that the proposed IDBO algorithm reduces the daily cost in both scenarios by about 14.64% and 9.49% compared to the baseline method. Finally, the feasibility and effectiveness of the proposed hierarchical energy management strategy are verified through experimental results. Full article

Existing point-to-point registration methods often suffer from inaccuracies caused by erroneous matches and noisy correspondences, leading to significant decreases in registration accuracy and efficiency. To address these challenges, this paper presents a new coarse registration method based on a geometric constraint and a matrix evaluation. Compared to traditional registration methods that require a minimum of three correspondences to complete the registration, the proposed method only requires two correspondences to generate a transformation matrix. Additionally, by using geometric constraints to select out high-quality correspondences and evaluating the matrix, we greatly increase the likelihood of finding the optimal result. In the proposed method, we first employ a combination of descriptors and keypoint detection techniques to generate initial correspondences. Next, we utilize the nearest neighbor similarity ratio (NNSR) to select high-quality correspondences. Subsequently, we evaluate the quality of these correspondences using rigidity constraints and salient points’ distance constraints, favoring higher-scoring correspondences. For each selected correspondence pair, we compute the rotation and translation matrix based on their centroids and local reference frames. With the transformation matrices of the source and target point clouds known, we deduce the transformation matrix of the source point cloud in reverse. To identify the best-transformed point cloud, we propose an evaluation method based on the overlap ratio and inliers points. Through parameter experiments, we investigate the performance of the proposed method under various parameter settings. By conducting comparative experiments, we verified that the proposed method’s geometric constraints, evaluation methods, and transformation matrix computation consistently outperformed other methods in terms of root mean square error (RMSE) values. Additionally, we validated that our chosen combination for generating initial correspondences outperforms other descriptor and keypoint detection combinations in terms of the registration result accuracy. Furthermore, we compared our method with several feature-matching registration methods, and the results demonstrate the superior accuracy of our approach. Ultimately, by testing the proposed method on various types of point cloud datasets, we convincingly established its effectiveness. Based on the evaluation and selection of correspondences and the registration result’s quality, our proposed method offers a solution with fewer iterations and higher accuracy. Full article