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The technology and equipment of this study can improve the technical level of the equipment for peeling and cleaning of Camellia oleifera fruit. Based on the biological characteristics of Camellia oleifera fruit, the size distribution model of Camellia oleifera fruit was established, and the equipment structure and key parameters were determined based on the working principle and design method of the shelling and cleaning mechanism. Based on the analysis of the movement track of the shell stripping and cleaning executive parts, the test scheme is established, and it is optimized that when the crankshaft speed is 240-260r/min and the track speed is 0.4-0.6m/s, the treatment capacity can reach the level of 2000kg/h, and the purification rate is more than 99%. When the vibration frequency of the vibrating motor is set to 50Hz and the horizontal inclination of the separation belt is 50 ° ∼ 55 °, the cleaning effect of the equipment is the best.

In the era of big data, the optimal processing of dynamically better data has become the core concern of academia and industry, and traditional methods are often difficult to meet the high standards of real-time and accuracy due to the complexity, variability, high-speed liquidity, and large scale of data. To this end, this study proposes a dynamic big data processing and optimization model, which improves the particle swarm optimization algorithm (PSO) by introducing adaptive weights and dynamic learning coefficients to improve the global exploration ability and convergence speed, and integrates a hybrid framework of deep reinforcement learning (DRL) (combined with policy gradients such as proximal policy optimization PPO and Q learning) to achieve big data optimization by using its feature extraction and policy adjustment capabilities. Based on real datasets such as 1.2 million pieces of financial transaction data and 8.5 million pieces of social media travel data, the results show that compared with traditional methods (such as traditional PSO, DQN, and independent LSTM-Transformer models), the new model has a 35% increase in data processing speed, a 20% increase in the accuracy of classification tasks (F1 score: 0.92 vs. 0.76 of DQN), a 40% increase in the real-time response ability of dynamic data streams, and a 25% increase in computing resource utilization efficiency. The study elaborates on model architecture innovation, dataset source and scale, benchmarking methods, and key performance indicators (such as processing speed, accuracy, real-time response, and resource efficiency), and provides efficient and scalable solutions for scenarios such as financial risk management and real-time recommendation systems.

Fan, as the most commonly used mechanical equipment, is widely used. In order to solve the problem of fan bearing fault diagnosis, this paper analyzes the main factors affecting fan spindle speed and power generation in operation. The input and output parameters of the performance prediction model are determined. The performance prediction model of wind turbine is established by using generalized regression neural network, and the smoothing factor of GRNN is optimized by comparing the prediction accuracy of the model. Based on this model, the sliding data window method is used to calculate the residual evaluation index of wind turbine speed and power in real time. When the evaluation index continuously exceeds the pre-set threshold, the abnormal state of wind turbine can be judged. In order to obtain wind turbine blades with better aerodynamic performance, a blade aerodynamic performance optimization method based on quantum heredity is proposed. The B é zier curve control point is used as the design variable to represent the continuous chord length and torsion angle distribution of the blade, the blade shape optimization model aiming at the maximum power is established, and the quantum genetic algorithm is used to optimize the chord length and torsion angle of the blade under different constraints. The optimization results of quantum genetic algorithm and classical genetic algorithm are compared and analyzed. Under the same parameters and boundary conditions, the proposed blade aerodynamic optimization method based on quantum genetic optimization is better than the classical genetic optimization method, and can obtain better blade aerodynamic shape and higher wind energy capture efficiency. This method makes up for the shortcomings of traditional fault diagnosis methods, improves the recognition rate of fault types and the accuracy of fault diagnosis, and the diagnosis effect is good.

With the rapid advancement of drone technology and the growing applications in the field of drone engineering, the demand for precise and efficient path planning in complex and dynamic environments has become increasingly important. Traditional algorithms struggle with complex terrain, obstacles, and weather changes, often falling into local optima. This study introduces an Improved Crown Porcupine Optimizer (ICPO) for drone path planning, which enables drones to better avoid obstacles, optimize flight paths, and reduce energy consumption. Inspired by porcupines' defense mechanisms, a visuo-auditory synergy perspective is adopted, improving early convergence by balancing visual and auditory defenses. The study also employs a good point set population initialization strategy to enhance diversity and eliminates the traditional population reduction mechanism. To avoid local optima in later stages, a novel periodic retreat strategy inspired by porcupines' precise defenses is introduced for better position updates. Analysis on the IEEE CEC2022 test set shows that ICPO almost reaches the optimal value, demonstrating robustness and stability. In complex mountainous terrain, ICPO achieved optimal values of 778.1775 and 954.0118; in urban terrain, 366.2789 and 910.1682 and ranked first among the compared algorithms, proving its effectiveness and reliability in drone delivery path planning. Looking ahead, the ICPO will provide greater efficiency and safety for drone path planning in navigating complex environments.

Unmanned on-board mobile base stations (MBSs) can more effectively solve wireless connectivity problems in terrestrial communication networks without fixed infrastructure. The purpose of this article is to minimize the number of MBS required to provide wireless coverage for a set of distributed ground terminals (GTs). Traditional clustering algorithms are no longer applicable because each drone has a different coverage area size and the traditional K-Means clustering algorithm has no limit on the number of heaps that can exceed the maximum coverage area of a single drone, making it impossible for a drone to provide services. In response to this problem, the traditional K-Means clustering algorithm is optimized, and the results of the optimized K-Means clustering algorithm are stacked to ensure that each pile has the corresponding drone capability to serve it.

【Background】 Combined use of surface water and groundwater for irrigation is a common practice in the lower reaches of the Yellow River Basin to control groundwater table at a desirable depth. In this paper, we investigate the optimal timing and quantity of irrigation using groundwater, based on crop water consumption and crop yields in the region. 【Method】 The analysis was based on modelling. A dissipative hydrological model was used to simulate water balance influenced by irrigation using different combinations of surface water and groundwater. The results were combined with the Jensen crop water production function to analyze the variation in the yields of winter wheat and summer corn with irrigation. 【Result】 ① Significant water deficits were identified in early April, mid- to late-May, early June, late July, and early August, which negatively impacted crop yields. Supplementary irrigation using groundwater in mid-October did not have a noticeable effect on crop yield, and the difference in crop yields between irrigations using surface and groundwater and irrigation using only surface water was minimal. ② The relative yield of winter wheat during the jointing stage and the relative yield of summer maize were sensitive to supplementary irrigation using groundwater, underscoring the importance of timing in irrigation using groundwater. ③ Supplementary irrigation of 40 mm using groundwater during the jointing stage of winter wheat notably increased its grain yield, with the optimal ratio of surface water to groundwater for irrigation being 5.25∶1. Irrigating 60 mm with groundwater for summer maize during the heading stage also boosted yield, with the optimal ratio of surface water to groundwater for irrigation being 4.4∶1. 【Conclusion】 Optimizing the timing and quantity of groundwater for irrigation is critical for improving crop yields in the lower reaches of the Yellow River basin. In our experimental study, irrigating 40 mm of groundwater for winter wheat during the jointing stage and 60 mm for summer maize during the heading stage can significantly improve their yields.

Knowledge of protein-protein interactions and their binding sites is indispensable for in-depth understanding of the networks in living cells. With the avalanche of protein sequences generated in the postgenomic age, it is critical to develop computational methods for identifying in a timely fashion the protein-protein binding sites (PPBSs) based on the sequence information alone because the information obtained by this way can be used for both biomedical research and drug development. To address such a challenge, we have proposed a new predictor, called iPPBS-Opt, in which we have used: (1) the K-Nearest Neighbors Cleaning (KNNC) and Inserting Hypothetical Training Samples (IHTS) treatments to optimize the training dataset; (2) the ensemble voting approach to select the most relevant features; and (3) the stationary wavelet transform to formulate the statistical samples. Cross-validation tests by targeting the experiment-confirmed results have demonstrated that the new predictor is very promising, implying that the aforementioned practices are indeed very effective. Particularly, the approach of using the wavelets to express protein/peptide sequences might be the key in grasping the problem’s essence, fully consistent with the findings that many important biological functions of proteins can be elucidated with their low-frequency internal motions. To maximize the convenience of most experimental scientists, we have provided a step-by-step guide on how to use the predictor’s web server (http://www.jci-bioinfo.cn/iPPBS-Opt) to get the desired results without the need to go through the complicated mathematical equations involved.

Accurate drought assessments are critical for mitigating the deleterious impacts of water scarcity on communities across the world. In many regions, deficits in soil moisture represent a key driver of drought conditions. However, relationships between soil moisture and widely used drought indicators have not been thoroughly evaluated. In addition, there has not been an in‐depth assessment of the accuracy of operational soil moisture models used for drought monitoring. Here, we used 2,405 observed time series of soil moisture from 637 long‐term monitoring stations across the conterminous United States to test the ability of meteorological drought indices and soil moisture models to accurately characterize soil moisture drought. The optimal timescales for meteorological drought indices varied substantially by depth, but were ∼30 days for depth averaged conditions; progressively longer timescales (∼10–80 days) represent progressively deeper soil moisture (2–36 in.). However, soil moisture models (including Short‐term Prediction Research and Transition Center, Soil Moisture Active Passive L4, and Topofire) significantly outperformed the meteorological drought indices for predicting standardized soil moisture anomalies and drought conditions. Additionally, soil moisture models represent near instantaneous conditions, implicitly aggregating antecedent data thereby eliminating the need for timescales, providing a more effective and convenient method for soil moisture drought monitoring. We conclude that soil moisture models provide a straightforward and favorable alternative to meteorological drought indices that better characterize soil moisture drought. Key Points Optimal drought index timescales for soil moisture are relatively short (less than 100 days) and increase with increasing soil depth The Short‐term Prediction Research and Transition Center, Topofire and Soil Moisture Active Passive L4 models are more accurate than timescale optimized drought indices for soil moisture anomaly prediction Drought monitoring should favor the use of soil moisture models over meteorological drought indices for characterizing soil moisture drought

Current research on electricity‐gas–integrated energy systems (EG‐IESs) often overlooks power quality issues prevalent in power systems. Voltage sags, critical and frequent power quality disturbance, significantly affect the EG‐IES due to sensitive coupling devices. To minimize economic losses from voltage sags in the EG‐IES, this study introduces an optimal configuration methodology for EG‐IES coupling devices, considering fault propagation within both electrical and gas subsystems. Initially, the impact of voltage sags on the bidirectional interaction of the EG‐IES is analyzed, with a focus on the influence of coupling devices. Subsequently, tolerance characteristic curves for compressors and gas turbines are presented, and a system economic loss model, based on the tolerance curves of coupling devices, is developed. An objective function is then formulated to minimize economic losses, incorporating a coupling device cost model, and solved using an enhanced particle swarm optimization algorithm to determine the optimal configuration of coupling devices. The efficacy and applicability of the proposed method are validated using an EG‐IES model comprising the IEEE 14‐bus system and an 11‐node gas network. The results indicate that the proposed optimal configuration method for EG‐IES coupling devices, implemented during the planning phase, effectively reduces losses caused by voltage sags in the EG‐IES while accounting for equipment installation costs.