Search Results (28)

The integration of forestry and agriculture has promoted edible fungi cultivation in forest understory spaces. However, the impact of spent mushroom substrates on forest soils remains unclear. This study explored the use of seafood mushroom spent substrates (SMS) and grass substrates to cultivate Dictyophora indusiata. After cultivation, soil pH stabilized, organic carbon increased by 34.02–62.24%, total nitrogen rose 1.1–1.9-fold, while soil catalase activity increased by 43.78–100.41% and laccase activity surged 3.3–11.2-fold. The 49% Cenchrus fungigraminus and 49% SMS treatment yielded the highest 4-coumaric acid levels in the soil, while all treatments reduced maslinic and pantothenic acid content. SMS as padding material with C. fungigraminus enhanced soil bacterial diversity in the first and following years. Environmental factors and organic acids influenced the recruitment of genus of Latescibacterota, Acidothermus, Rokubacteriales, Candidatus solibacter, and Bacillus, altering organic acid composition. In conclusion, cultivating D. indusiata understory enhanced environmental characteristics, microbial dynamics, and organic acid profiles in forests’ soil in short time. Full article

This study proposes a data-driven optimization framework to enhance emission control performance in diesel engine selective catalytic reduction (SCR) systems under transient operating conditions. A one-dimensional SCR model was constructed in GT-Power, and simulation datasets were generated using experimentally measured inputs from the World Harmonized Transient Cycle (WHTC), with representative emission responses obtained by varying fixed ammonia-to-NOx (A/N) ratios. Building on these datasets, a hybrid prediction model combining Long Short-Term Memory (LSTM) networks and multi-head attention mechanisms was developed to accurately forecast SCR outlet NH₃ leakage and NOx emissions. The model exhibited high predictive accuracy, achieving R² values exceeding 0.977 and low RMSE across training, validation, and test sets. Based on the model predictions, a constrained dynamic multi-objective optimization strategy was implemented to adaptively adjust ammonia dosing, aiming to simultaneously minimize NH₃ leakage and NOx emissions. The optimized NH₃ injection profiles were validated through reapplication in the GT-Power simulation environment. Compared to the baseline fixed-ratio control strategy, the proposed approach reduced NH₃ leakage and NOx emissions by 34.40% and 11.15%, respectively, as determined for the transient segment of the WHTC cycle. These results demonstrate the effectiveness of integrating physics-based simulation, deep learning prediction, and dynamic optimization for improving aftertreatment adaptability and emission compliance in real-world diesel engine applications. All reported values are based on a single simulated WHTC cycle without statistical uncertainty analysis. Full article

Accurate forecasting of photovoltaic (PV) power generation is crucial for optimizing grid operation and ensuring a reliable power supply. However, the inherent volatility and intermittency of solar energy pose significant challenges to grid stability and energy management. This paper proposes a learning model named CECSVB-LSTM, which integrates several advanced techniques: a bidirectional long short-term memory (BILSTM) network, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), variational mode decomposition (VMD), and the Sparrow Search Algorithm (CSSSA) incorporating circle chaos mapping and the Sine Cosine Algorithm. The model first uses CEEMDAN to decompose PV power data into Intrinsic Mode Functions (IMFs), capturing complex nonlinear features. Then, the CSSSA is employed to optimize VMD parameters, particularly the number of modes and the penalty factor, ensuring optimal signal decomposition. Subsequently, BILSTM is used to model time dependencies and predict future PV power output. Empirical tests on a PV dataset from an Australian solar power plant show that the proposed CECSVB-LSTM model significantly outperforms traditional single models and combination models with different decomposition methods, improving R² by more than 7.98% and reducing the root mean square error (RMSE) and mean absolute error (MAE) by at least 60% and 55%, respectively. Full article

To address the inefficiency and high cost of manual potato pickup in segmented harvesting, a dual-disc potato pickup and harvesting device was designed. The device utilizes counter-rotating dual discs to gather and preliminarily lift the potato–soil mixture, and combines it with an elevator chain to achieve potato–soil separation and transportation. Based on Hertz’s collision theory, the impact of disc rotational speed on potato damage was analyzed, establishing a maximum speed limit (≤62.56 r/min). Through kinematic analysis, the disc inclination angle (12–24°) and operational parameters were optimized. Through coupled EDEM-RecurDyn simulations and Box–Behnken experimental design, the optimal parameter combination was determined with the potato loss rate and potato damage rate as evaluation indices: disc rotational speed of 50 r/min, disc inclination angle of 16°, and machine forward speed of 0.6 m/s. Field validation tests revealed that the potato loss rate and potato damage rate were 1.53% and 2.45%, respectively, meeting the requirements of the DB64/T 1795-2021 standard. The research findings demonstrate that this device can efficiently replace manual potato picking, providing a reliable solution for the mechanized harvesting of potatoes. Full article

To address the critical limitations of conventional Grey Wolf Optimization (GWO) in path planning scenarios—including insufficient exploration capability during the initial phase, proneness to local optima entrapment, and inherent deficiency in dynamic obstacle avoidance—this paper proposes a multi-strategy enhanced GWO algorithm. Firstly, the Piecewise chaotic mapping is applied to initialize the Grey Wolf population, enhancing the initial population quality. Secondly, the linear convergence factor is modified to a nonlinear one to balance the algorithm’s global and local search capabilities. Thirdly, Evolutionary Population Dynamics (EPD) is incorporated to enhance the algorithm’s ability to escape local optima, and dynamic weights are used to improve convergence speed and accuracy. Finally, the algorithm is integrated with the Improved Dynamic Window Approach (IDWA) to enhance path smoothness and perform dynamic obstacle avoidance. The proposed algorithm is named PAGWO-IDWA. The results demonstrate that, compared to traditional GWO, PAGWO-IDWA reduces the path length, number of turns, and running time by 9.58%, 33.16%, and 30.31%, respectively. PAGWO-IDWA not only overcomes the limitations of traditional GWO but also enables effective path planning in dynamic environments, generating paths that are both safe and smooth, thus validating the effectiveness of the algorithm. Full article

After wheat harvesting in coastal saline–alkali land, when the straw is returned to the field and the soil is rotary tilled, the lack of reliable discrete element simulation parameter models restricts the optimization and improvement of special tillage and land preparation equipment for saline–alkali land to some extent. In this study, the Hertz–Mindlin with JKR model was used to calibrate the discrete element simulation parameters. Taking the soil-wheat crop residue mixture’s angle of repose as the test index, four groups of parameters that significantly affect the angle of repose and their optimal value ranges were screened out through the Plackett–Burman test and the steepest ascent test. Then, the Box–Behnken test was conducted to obtain the quadratic regression model of the significant parameters and the angle of repose, and the optimal values of the significant parameters were obtained. The optimal parameter combination was used for simulation tests, and the relative errors between the measured values and the simulation test values of the angle of repose and the wheat residue coverage rate were 0.74% and 1.34%. The reliable parameters provide a theoretical basis for the optimization and improvement of the equipment for soil preparation in saline–alkali land. Full article

This study investigated the potential for efficient and resourceful utilization of phosphogypsum (PG) through the preparation of a High-volume Phosphogypsum Cement Stabilized Road Base (HPG-CSSB). The investigation analyzed the unconfined compressive strength (UCS), water stability, strength formation mechanism, microstructure, and pollutant curing mechanism of HPG-CSSB by laser diffraction methods (LD), X-ray diffraction (XRD), fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and inductively coupled plasma-mass spectrometry (ICP-MS). The optimal mix ratio of HPG-CSSB was 4% cement, 1% CA2, 35% PG, and 60% graded crushed stone. The UCS reached 6.6 MPa, 9.3 MPa, and 11.3 MPa at 7, 28, and 60 d, respectively. The alkaline curing agent stimulated cement activity and accelerated the release of Ca²⁺ and SO₄²⁻ from the PG. This formed many C-S-H gels and ettringite (AFt). The curing agent converted Ca²⁺ to C-(A)-S-H gels due to high volcanic ash activity. The diverse hydration products strengthened HPG-CSSB. The HPG-CSSB exhibits favorable water stability, demonstrating a mere 7.6% reduction in strength following 28 d of immersion. The C-S-H gel and AFt generated in the system can carry out ion exchange and adsorption precipitation with F⁻ and PO₄³⁻ in PG, achieving the curing effect of toxic and hazardous substances. HPG-CSSB meets the Class A standard for integrated wastewater discharge. Full article

Bridge crane control systems are complex, multivariable, and nonlinear. However, traditional fuzzy PID control methods rely heavily on expert experience for initial parameter tuning and lack adaptive adjustment for the fuzzy universe. To address these issues, we propose a variable universe fuzzy PID controller based on the improved sparrow search algorithm (ISSA-VUFPID). First, tent chaotic mapping is introduced to initialize the sparrow population, enhancing the algorithm’s global search capability. Second, the positioning strategy of the northern goshawk exploration phase is integrated to improve the search thoroughness of sparrow discoverers within the solution space and to accelerate the optimization process. Last, an adaptive t-distribution perturbation strategy is employed to adjust the positions of sparrow followers, enhancing the algorithm’s optimization ability in the early search phase and focusing on local exploitation in the later phase to improve solution accuracy. The improved algorithm is applied to tune the initial parameters of the PID controller. Additionally, system error and its rate of change are introduced as dynamic parameters into the scaling factor, which is used to achieve adaptive adjustment of the fuzzy universe, thereby enhancing the safety and reliability of the control system. Simulation results demonstrate that the proposed ISSA-VUFPID control method outperforms ISSA-FPID and ISSA-PID control methods. It reduces the trolley’s positioning time and minimizes the load’s maximum swing angle, demonstrating strong adaptability and robustness. This approach greatly enhances the robustness and safety of bridge crane operations. Full article

While selective catalytic reduction (SCR) has long been indispensable for nitrogen oxide (NO_x) removal, optimizing its performance remains a significant challenge. This study investigates the combined effects of structural and intake parameters on SCR performance, an aspect often overlooked in previous research. This paper innovatively developed a three-dimensional SCR channel model and employed response surface methodology to conduct an in-depth analysis of multiple key factors. This multidimensional, multi-method approach enables a more comprehensive understanding of SCR system mechanics. Through target optimization, we achieved a simultaneous improvement in three critical indicators: the NO_x conversion rate, pressure drop, and ammonia slip. It is worth noting that the NO_x conversion rate has been optimized from 17.07% to 98.25%, the pressure drop has been increased from 3454.62 Pa to 2558.74 Pa, and the NH₃ slip has been transformed from 122.26 ppm to 17.49 ppm. These results not only advance the theoretical understanding of SCR technology but also provide valuable design insights for practical applications. Our findings pave the way for the development of more efficient and environmentally friendly SCR systems, potentially revolutionizing NO_x control in various industries. Full article

Optimal drip irrigation management in shallow groundwater areas needs to clarify the effects of flow rate and layout designs on the soil moisture, salt distribution, cotton root length density, plant height, leaf area, and yield. In this study, a one-year field experiment was conducted from April to October 2018 in the fifth company of the 16th Regiment in Alar City, Xinjiang, to investigate the effects of various drip flow rates and layout designs of cotton growth. Two drip flow rates (2.8 and 5.6 L·h⁻¹) and two layout designs (one film, two drip tapes, and six rows; one film, three drip tapes, and six rows) were applied to explore the optimal combination, resulting in a total of four treatments that were irrigated three times in the whole growth period. Soil moisture, salt distribution, cotton root length density, plant height, and leaf area were measured. The main results were as follows: (1) Under the same layout designs, the soil moisture content was higher and the soil salinity was lower when the drip flow rate was 5.6 L·h⁻¹, and the cotton root length density, plant height, leaf area, and yield were significantly higher than that of 2.8 L·h⁻¹. (2) Under the same drip flow rate, the soil desalination rate, cotton growth indexes, and yield under the three-tapes treatment were significantly higher than the values of the two-tapes treatment. The actual yield of treatment D was 21.56%, 19.23%, and 11.71% higher than that of treatments A, B, and C, respectively. (3) The crop evapotranspiration of cotton during the two irrigation cycles showed an increasing trend, and the groundwater contribution showed a smaller and then increasing trend. Overall, the combination of three tapes and a drip flow rate of 5.6 L·h⁻¹ had the highest cotton yield and net income, which were 6211.36 kg·hm⁻² and 4820.21 kg·hm⁻² for the theoretical and actual yields. The results of this study can provide a reference for the management of limited irrigation leaching soil salinity and cotton cultivation in shallow groundwater areas. Full article

Short-term power load forecasting plays a key role in daily scheduling and ensuring stable power system operation. The problem of the volatility of the power load sequence and poor prediction accuracy is addressed. In this study, a learning model integrating intelligent optimization algorithms is proposed, which combines an ensemble-learning model based on long short-term memory (LSTM), variational modal decomposition (VMD) and the multi-strategy optimization dung beetle algorithm (MODBO). The aim is to address the shortcomings of the dung beetle optimizer algorithm (DBO) in power load forecasting, such as its time-consuming nature, low accuracy, and ease of falling into local optimum. In this paper, firstly, the dung beetle algorithm is initialized using a lens-imaging reverse-learning strategy to avoid premature convergence of the algorithm. Second, a spiral search strategy is used to update the dynamic positions of the breeding dung beetles to balance the local and global search capabilities. Then, the positions of the foraging dung beetles are updated using an optimal value bootstrapping strategy to avoid falling into a local optimum. Finally, the dynamic-weighting coefficients are used to update the position of the stealing dung beetle to improve the global search ability and convergence of the algorithm. The proposed new algorithm is named MVMO-LSTM. Compared to traditional intelligent algorithms, the four-quarter averages of the RMSE, MAE and R² of MVMO-LSTM are improved by 0.1147–0.7989 KW, 0.09799–0.6937 KW, and 1.00–13.05%, respectively. The experimental results show that the MVMO-LSTM proposed in this paper not only solves the shortcomings of the DBO but also enhances the stability, global optimization capability and information utilization of the model. Full article

Path planning and obstacle avoidance are fundamental problems in unmanned ground vehicle path planning. Aiming at the limitations of Deep Reinforcement Learning (DRL) algorithms in unmanned ground vehicle path planning, such as low sampling rate, insufficient exploration, and unstable training, this paper proposes an improved algorithm called Dual Priority Experience and Ornstein–Uhlenbeck Soft Actor-Critic (DPEOU-SAC) based on Ornstein–Uhlenbeck (OU noise) and double-factor prioritized sampling experience replay (DPE) with the introduction of expert experience, which is used to help the agent achieve faster and better path planning and obstacle avoidance. Firstly, OU noise enhances the agent’s action selection quality through temporal correlation, thereby improving the agent’s detection performance in complex unknown environments. Meanwhile, the experience replay is based on double-factor preferential sampling, which has better sample continuity and sample utilization. Then, the introduced expert experience can help the agent to find the optimal path with faster training speed and avoid falling into a local optimum, thus achieving stable training. Finally, the proposed DPEOU-SAC algorithm is tested against other deep reinforcement learning algorithms in four different simulation environments. The experimental results show that the convergence speed of DPEOU-SAC is 88.99% higher than the traditional SAC algorithm, and the shortest path length of DPEOU-SAC is 27.24, which is shorter than that of SAC. Full article

To address the anti-swing issue of the payload in bridge cranes, Proportional–Integral–Derivative (PID) control is a commonly used method. However, parameter tuning of the PID controller relies on empirical knowledge and often leads to system overshoot. This paper proposes an Improved Sparrow Search Algorithm (ISSA) to optimize the gains of PID controllers, alleviating adverse effects on payload oscillation and trolley positioning during the operation of overhead cranes. First, tent map chaos mapping is introduced to initialize the sparrow population, enhancing the algorithm’s global search capability. Then, by integrating sine and cosine concepts along with nonlinear learning factors, the updating mechanism of discoverer positions is dynamically adjusted, expediting the solving process. Finally, the Lévy flight strategy is employed to update follower positions, thereby enhancing the algorithm’s local escape capability. Additionally, a fitness function containing overshoot penalties is proposed to address overshoot issues. Simulation results indicate that the overshoot rates of all algorithms remain less than 3%. Moreover, compared with the Sparrow Search Algorithm (SSA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Whale optimization Algorithm (WOA), the optimized PID control system with the ISSA algorithm exhibits superior control performance and possesses certain robustness and adaptability. Full article

Improving the positioning accuracy and stability of a single BDS/INS sensor system in agricultural machinery is important for expanding the application scenarios of agricultural machinery. This paper proposes a navigation and positioning model based on an improved bee-colony-algorithm-optimized BP network (the IABC-BP model). The main aspect of this work involves introducing adaptive coefficients and speed adjustment coefficients that obey Gaussian distribution to ensure the balance between the rate of convergence, group flexibility, and searchability in the search process. The implicit adaptive layer formula of the BP network is proposed, and the BDS/INS navigation and positioning model for agricultural machinery is established using the IABC algorithm and the Kalman filter. Simulation tests and analyses of real-world application scenarios were conducted on the model, and the results showed that, compared with the original model, the performance of the model improved by 90.65%, 84.11%, and 25.96%, indicating that the proposed model has high accuracy and effectiveness. In the information fusion and compensation correction mode, the algorithm processes errors such as longitude and latitude within the target range and can achieve reliable navigation and positioning accuracy in a short period. At the same time, the model has good stability and generalization ability, and can be applied to other navigation scenarios in the future to expand its application scope. Full article

Correctly anticipating PV electricity production may lessen stochastic fluctuations and incentivize energy consumption. To address the intermittent and unpredictable nature of photovoltaic power generation, this article presents an ensemble learning model (MVMD-CLES) based on the whale optimization algorithm (WOA), variational mode decomposition (VMD), convolutional neural network (CNN), long and short-term memory (LSTM), and extreme learning machine (ELM) stacking. Given the variances in the spatiotemporal distribution of photovoltaic data and meteorological features, a multi-branch character extraction iterative mixture learning model is proposed: we apply the MWOA algorithm to find the optimal decomposition times and VMD penalty factor, and then divide the PV power sequences into sub-modes with different frequencies using a two-layer algorithmic structure to reconstruct the obtained power components. The primary learner is CNN–BiLSTM, which is utilized to understand the temporal and spatial correlation of PV power from information about the weather and the output of photovoltaic cells, and the LSTM learns the periodicity and proximity correlation of the power data and obtains the corresponding component predictions. The second level is the secondary learner—the output of the first layer is learned again using the ELM to attenuate noise and achieve short-term prediction. In different case studies, regardless of weather changes, the proposed method is provided with the best group of consistency and constancy, with an average RMSE improvement of 12.08–39.14% over a single-step forecast compared to other models, the average forecast RMSE increased by 5.71–9.47% for the first two steps. Full article