Search Results (37)

The crystallization kinetics of picromerite play a crucial role in optimizing the fertilizer quality. This study developed a crystallization kinetics model of picromerite. Results show that increasing temperature mainly leads to higher supersaturation, which, in turn, enhances both nucleation and growth rates, with significant improvements in crystal size and uniformity. Higher stirring speed was found to have positive effects on crystal nucleation and growth rate. The decrease in supersaturation leads to the diminution of the driving force for crystallization and the gradual decline in crystallization. The study provides a comprehensive analysis of the relationships between these crystallization conditions and the resultant crystal properties. Full article

Drying chili peppers is a crucial technique for their preservation, as it extends shelf life while minimizing the degradation of high-value bioactive compounds. This study evaluated the impact of modulated solar irradiation on the drying kinetics and quality retention of “Chile de Agua” (Capsicum annuum L.) peppers across three maturity stages (unripe, ripe, and overripe). Two cylindrical solar dryers were employed: a conventional solar dryer (CSD) and a novel Solar Dryer with Dynamic Irradiance Control (SDIC) utilizing Polymer Dispersed Liquid Crystal (PDLC) technology. Drying behavior was analyzed through moisture ratio and drying rate, while quality attributes were assessed via color parameters, capsaicinoid content, and flavonoid profiling using UPLC-PDA-ESI-MS. Results demonstrated that the maturity stage significantly influences drying kinetics; unripe fruits exhibited the fastest dehydration rate, reducing drying time by approximately 14% compared to overripe fruits. Regarding quality, the CSD better preserved color (ΔE of 15.29 for ripe chilies). At the same time, the SDIC system significantly favored the retention of bioactive compounds, maintaining higher concentrations of total capsaicinoids (up to 1700 µg/g DW) and flavonoids such as luteolin (15.9 mg/100 g DW) and quercitrin (11.5 mg/100 g DW), especially in ripe fruits. The findings suggest that optimal processing requires selecting the drying method based on the targeted final use: CSD for color preservation in unripe chilies, or SDIC for maximizing bioactive retention in ripe fruits. Full article

End-to-end (E2E) autonomous driving offers a promising alternative to traditional modular pipelines by mapping raw sensor data directly to vehicle controls, thereby mitigating error propagation. However, prevalent approaches largely rely on dense Bird’s-Eye-View (BEV) feature maps, which incur high computational overhead and necessitate complex post-processing for trajectory generation. To address these limitations, we propose HiPro-AD, a proposal-centric sparse E2E planning framework that fundamentally diverges from dense BEV paradigms. HiPro-AD integrates an efficiency-oriented IM-ResNet-34 encoder with a novel STFormer. This transformer dynamically fuses multi-view spatial features and historical temporal context via a proposal-anchored mechanism, focusing computation strictly on regions relevant to sparse trajectory proposals. Furthermore, trajectory selection is refined by a Pairwise Ranking Scorer, which identifies the optimal plan from diverse candidates based on relative quality. On the NAVSIM benchmark, HiPro-AD achieves a PDMS of 92.6 using only camera input, surpassing prior dense BEV and multimodal methods. On the closed-loop Bench2Drive benchmark, it attains a 37.31% success rate and a driving score of 65.48 with a latency of 67 ms, demonstrating real-time capability. These results validate the efficiency and robustness of our sparse paradigm in complex driving scenarios. Full article

Maize stalk lodging causes substantial yield losses worldwide. Although stalk strength is a genetically determined trait, its molecular mechanisms—particularly the dynamic changes during key developmental stages—remain inadequately characterized due to limitations of single-omics approaches. This study employed an integrated transcriptomic and metabolomic analysis strategy to compare stalk tissues from three maize genotypes with contrasting lodging resistance: the highly resistant inbred line PHB1M, the susceptible inbred line Chang 7-2, and their recombinant inbred line 23NWZ561 (abbreviated as P, C, and Z, respectively). Dynamic sampling of all three genotypes was conducted at both grain-filling and maturity stages, with simultaneous measurement of physiological traits related to stalk strength. Phenotypic analysis revealed that the resistant genotype PHB1M exhibited superior rind penetration strength, cell wall composition (cellulose, hemicellulose, and lignin) content, and vascular bundle development. Multi-omics analysis indicated that the molecular basis of lodging resistance is primarily established during the maturity stage. The transcriptomic and metabolomic profiles of the recombinant inbred line Z shifted from clustering with the susceptible parent C at the grain-filling stage to grouping with the resistant parent P at maturity. Key pathways including phenylpropanoid biosynthesis were significantly enriched specifically at maturity, accompanied by upregulation of related genes (PAL, HCT, CCR) and accumulation of metabolites such as lignin precursors in PHB1M. Integrated analysis identified a core co-expression network within the phenylpropanoid pathway comprising three genes and three metabolites. This study systematically demonstrates that lodging resistance in maize is regulated by transcriptional and metabolic reprogramming during late stalk developmental stages, particularly at maturity, where enhanced activation of the phenylpropanoid biosynthesis pathway plays a central role. These findings provide valuable candidate genes and metabolic markers for breeding lodging-resistant maize varieties. Full article

Post-harvest food loss significantly threatens global food security, and solar drying offers a sustainable preservation solution. The effectiveness of solar drying depends on consumer acceptance, in which color is a critical quality attribute. This study investigated how solar irradiance and temperature affect color degradation during the drying of pineapple (Ananas comosus), orange (Citrus × sinensis), and beet (Beta vulgaris L.). Experiments conducted in Zacatecas, Mexico, compared a Solar Dryer with Dynamic Irradiation Control (SDIC), which limited irradiance to 700 W/m², against an uncontrolled Cylindrical Solar Dryer (CSD). The results indicate that the controlled SDIC environment promotes gradual and uniform color preservation by minimizing rapid thermal stress. In contrast, the fluctuating high irradiance and temperature of the CSD caused faster, less uniform color changes. Statistical analyses confirmed that both irradiance and temperature significantly impacted color parameters (p < 0.05). The SDIC method reduced the total color change (ΔE) by 30–47% in pineapple and beet compared to the CSD. Regression models identified temperature as the primary driver of redness (a*) degradation, while irradiance was strongly correlated with changes in yellowness (b*). This research highlights the necessity of optimizing solar drying conditions to enhance the quality of dried produce. By improving visual appeal, this optimized green technology can help reduce food waste and support the transition to more sustainable fod processing systems. This controlled approach reduced the total color change (ΔE) by 30–47% in pineapple and beet compared to the CSD, demonstrating its significant potential for quality preservation. Full article

Cybersickness poses a significant challenge to the widespread adoption of virtual reality (VR), as it impairs user experience and operational performance. This study proposes a physiological modeling approach to objectively assess cybersickness severity during VR experience. An interactive VR experiment was conducted, inducing varying levels of cybersickness through VR navigation tasks under different field-of-view and graphic quality settings. Physiological signals (i.e., electrodermal activity (EDA) and electrocardiogram (ECG)) were continuously recorded and extracted to build multiple machine learning regression models for cybersickness prediction. The results showed that EDA-based models consistently outperformed ECG-based models across all algorithms, with the Ensemble Learning model achieving the highest predictive accuracy (R² = 0.98). In contrast, ECG-based models yielded limited predictive capability (R² = 0.53). Combining ECG with EDA features showed little improvement in model accuracy, suggesting a limited complementary role of ECG features. SHAP-based feature importance analysis revealed that EDA features (e.g., mean, maximum, and variance of skin conductance) were the most effective features in cybersickness prediction, which captured both tonic arousal and phasic autonomic responses during the cybersickness process. ECG features such as SDNN and HRMAD contributed modestly, offering physiological interpretability despite being less effective in cybersickness prediction. The findings demonstrate the feasibility of using low-burden physiological signals for accurate and interpretable prediction of cybersickness severity. The proposed approach supports the development of lightweight, real-time monitoring systems for VR applications, offering practical advantages in terms of simplicity, adaptability, and deployment potential. Full article

To address the issue of excessive chemical fertilizer use in agricultural production, this study conducted a pot experiment with four treatments: CK (no fertilization), T1 (the application of potassium magnesium sulfate fertilizer), T2 (the application of slow-release fertilizer equal to T1), and T3 (the application of slow-release fertilizer with the same fertility as T1). The effects of these treatments on garlic seedling yield, growth quality, chlorophyll content, photosynthetic characteristics, and the soil environment were investigated to evaluate the feasibility of replacing conventional fertilizers with slow-release formulations. The results showed that compared with CK, all three fertilized treatments (T1, T2, and T3) significantly increased the plant heights and stem diameters of the garlic sprouts (p < 0.05). Plant height increased by 14.85%, 17.81%, and 27.75%, while stem diameter increased by 9.36%, 8.83%, and 13.96%, respectively. Additionally, the chlorophyll content increased by 4.34%, 7.22%, and 8.05% across T1, T2, and T3, respectively. Among the treatments, T3 exhibited the best overall growth performance. Compared with those in the CK group, the contents of soluble sugars, soluble proteins, free amino acids, vitamin C, and allicin increased by 64.74%, 112.17%, 126.82%, 36.15%, and 45.43%, respectively. Furthermore, soil organic matter, available potassium, magnesium, and phosphorus increased by 109.02%, 886.25%, 91.65%, and 103.14%, respectively. The principal component analysis indicated that soil pH and exchangeable magnesium were representative indicators reflecting the differences in the soil’s chemical properties under different fertilization treatments. Compared with the CK group, the metal contents in the T1 group slightly increased, while those in T2 and T3 generally decreased, suggesting that the application of slow-release fertilizer exerts a certain remediation effect on soils contaminated with heavy metals. This may be attributed to the chemical precipitation and ion exchange capacities of phosphogypsum, as well as the high adsorption and cation exchange capacity of bentonite, which help reduce the leaching of soil metal ions. In summary, slow-release fertilizers not only promote garlic sprout growth but also enhance soil quality by regulating its chemical properties. Full article

Underground brine as a liquid mineral resource available for development and utilization has attracted widespread attention. However, how the mining process affects the hydrochemical characteristics and evolution of underground brine has yet to be fully understood. Herein, 207 underground brine samples were collected from the Luobei mining area of the Lop Nur region during pre-exploitation (2006), exploitation (2019), and late exploitation (2023) to explore the dynamic change characteristics and evolution mechanisms of the underground brine hydrochemistry using the combination of statistical analysis, spatial interpolation, correlation analysis, and ion ratio analysis. The results indicated that Na⁺ and Cl⁻ were the dominant ionic components in the brine, and their concentrations remained relatively stable throughout the mining process. However, the content of Mg²⁺ increased gradually during the mining process (increased by 45.08% in the middle stage and 3.09% in the later stage). The elevation in Mg²⁺ concentration during the mining process could be attributed to the dissolution of Mg-bearing minerals, reverse cation exchange, and mixed recharge. This research furnishes a scientific foundation for a more in-depth comprehension of the disturbance mechanism of brine-mining activities on the groundwater chemical system in the mining area and for the sustainable exploitation of brine resources. Full article

The quality effects of spectral irradiance conditions during papaya (Carica papaya L.) drying were investigated using three different dryers: a solar dryer with dynamic irradiance control (SDIC), a cylindrical solar dryer (CSD), and a solar simulator dryer (SSD). This study builds upon previous PDLC film applications in solar drying by specifically examining its impact on phytochemical preservation and color degradation, addressing gaps in spectral-specific effects on food quality parameters. The drying conditions were as follows: a temperature of 50 °C for each method, 700 w/m² for both SDIC and solar simulator dryers (SSD), and full solar irradiance for the cylindrical solar dryer (CSD). The cylindrical solar dryer exhibited 210 min of drying time due to higher solar irradiance than SDIC (300 min), while SSD lasted 180 min. Drying rates were highest for CSD (0.056 g H₂O/g d.m. min⁻¹), followed by SDIC (0.027 g H₂O/g d.m. min⁻¹). Color analysis revealed that CSD resulted in the most significant color degradation, followed by SSD and SDIC. This was attributed to the varying spectral composition of radiation in each method. The CSD, with a full solar spectrum, including higher UV and visible radiation, induced more pronounced color changes than SDIC, which received lower intensity radiation in these ranges. Chemical analyses showed that SSD samples had the highest antioxidant activity (1432.91 µmol TE/g dw by ABTS) and phenolic content (58.92 mg GAE/100 g), suggesting simulated conditions may better preserve certain phytochemicals. SDIC maintained better carotenoid-related color parameters while showing intermediate antioxidant levels (1084.09 µmol TE/g dw). These results demonstrate that irradiance control significantly impacts drying efficiency and quality parameters. Full article

Fault prediction of hydropower station is crucial for the stable operation of generator set equipment, but the traditional method struggles to deal with data with an imbalanced distribution and untrustworthiness. This paper proposes a fault detection method based on a convolutional neural network (CNNs) and long short-term memory network (LSTM) with a generative adversarial network (GAN). Firstly, a reliability mechanism based on principal component analysis (PCA) is designed to solve the problem of data bias caused by multiple monitoring devices. Then, the CNN-LSTM network is used to predict time series data, and the GAN is used to expand fault data samples to solve the problem of an unbalanced data distribution. Meanwhile, a multi-scale feature extraction network with time–frequency information is designed to improve the accuracy of fault detection. Finally, a dynamic multi-task training algorithm is proposed to ensure the convergence and training efficiency of the deep models. Experimental results show that compared with RNN, GRU, SVM, and threshold detection algorithms, the proposed fault prediction method improves the accuracy performance by $5.5\%$, $4.8\%$, $7.8\%$, and $9.3\%$, with at least a $160\%$ improvement in the fault recall rate. Full article

Given the widespread influence of U-Net and FPN network architectures on infrared small target detection tasks on existing models, these structures frequently incorporate a significant number of downsampling operations, thereby rendering the preservation of small target information and contextual interaction both challenging and computation-consuming. To tackle these challenges, we introduce a parallel connected lateral chain network (PCLC-Net), an innovative architecture in the domain of infrared small target detection, that preserves large-scale feature maps while minimizing downsampling operations. The PCLC-Net preserves large-scale feature maps to prevent small target information loss, integrates causal-based retention gates (CBR Gates) within each chain for improved feature selection and fusion, and leverages the attention-based network-wide feature map aggregation (AN-FMA) output module to ensure that all feature maps abundant with small target information contribute effectively to the model’s output. The experimental results reveal the PCLC-Net, with minimal nodes and just a single downsampling, achieves near state-of-the-art performance using just 0.16M parameters (40% of the current smallest model), yielding an $IoU$ of 80.8%, $P_d$ of 95.1%, and $F_a$ of $28.6\times {10}^{-6}$ on the BIT-SIRST dataset. Full article

The industrial sector accounts for approximately 65% of global energy consumption, with projections indicating a steady annual increase of 1.2% in energy demand. In the context of growing concerns about climate change and the need for sustainable energy solutions, solar thermal energy has emerged as a promising technology for reducing reliance on fossil fuels. With its ability to provide high-efficiency heat for industrial processes at temperatures ranging from 150 °C to over 500 °C, solar thermal power generation offers significant potential for decarbonizing energy-intensive industries. This review provides a comprehensive analysis of various solar thermal technologies, including parabolic troughs, solar towers, and linear Fresnel reflectors, comparing their effectiveness across different industrial applications such as process heating, desalination, and combined heat and power (CHP) systems. For instance, parabolic trough systems have demonstrated optimal performance in high-temperature applications, achieving efficiency levels up to 80% for steam generation, while solar towers are particularly suitable for large-scale, high-temperature operations, reaching temperatures above 1000 °C. The paper also evaluates the economic feasibility of these technologies, showing that solar thermal systems can achieve a levelized cost of energy (LCOE) of USD 60–100 per MWh, making them competitive with conventional energy sources in many regions. However, challenges such as high initial investment, intermittency of solar resource, and integration into existing industrial infrastructure remain significant barriers. This review not only discusses the technical principles and economic aspects of solar thermal power generation but also outlines specific recommendations for enhancing the scalability and industrial applicability of these technologies in the near future. Full article

The application of slow-release fertilizers is essential for improving fertilizer utilization efficiency and promoting sustainable agricultural development. Unlike traditional single organic polymer-coated or inorganic-coated fertilizers, this study utilized biodegradable modified polyvinyl alcohol (PVA) as a binder and cheap, readily available phosphogypsum–bentonite as an inorganic coating material to develop a novel slow-release potassium magnesium sulfate fertilizer (SRPMSF). This study initially examined the influence of SA dosage on PVA properties. XRD, FTIR, TGA, and water resistance analyses revealed that sodium alginate exhibits good compatibility with polyvinyl alcohol, enhancing its heat and water resistance. Ultimately, PVA–SA-2 (1.2% sodium alginate) was chosen as the optimal binder for SRPMSF production. Furthermore, this study investigated the impact of bentonite on the physical and slow-release properties of the SRPMSF by varying the phosphogypsum-to-bentonite ratio. This experiment included five treatment methods: the treatments consist of SRPMSF-1 (0 g bentonite), SRPMSF-2 (phosphogypsum/bentonite ratio of 4:1), SRPMSF-3 (3:2), SRPMSF-4 (2:3), and SRPMSF-5 (1:4). A control group (PMSF) was also included. The results indicated that, as the bentonite content increased, both the particle size and compressive strength of the coated slow-release fertilizer increased, with the SRPMSF particle sizes ranging from 3.00 to 4.50 mm. The compressive strength of the SRPMSF ranged from 20.85 to 43.78 N, meeting the requirements for industrial production. The soil column leaching method was employed to assess the nutrient release rate of the fertilizers. The experimental results indicated that, compared to the PMSF, the SRPMSF effectively regulated nutrient release. Pot experiments demonstrated that the SRPMSF significantly enhanced garlic seedling growth compared to the PMSF. In conclusion, a new type of slow-release fertilizer with good slow-release performance is prepared in this paper, which can improve the utilization rate of fertilizer and reduce the economic loss and is conducive to the sustainable development of agriculture. Full article

Self-Organizing Map (SOM) neural networks can project complex, high-dimensional data onto a two-dimensional plane for data visualization, enabling an intuitive understanding of the distribution and symmetric structures of such data, thereby facilitating the clustering and anomaly detection of complex high-dimensional data. However, this algorithm is sensitive to the initial weight matrix and suffers from insufficient feature extraction. To address these issues, this paper proposes an improved SOM based on virtual winning neurons (virtual-winner SOMs, vwSOMs). In this method, the principal component analysis (PCA) is utilized to generate the initial weight matrix, allowing the weights to better capture the main features of the data and thereby enhance clustering performance. Subsequently, when new input sample data are mapped to the output layer, multiple neurons with a high similarity in the weight matrix are selected to calculate a virtual winning neuron, which is then used to update the weight matrix to comprehensively represent the input data features within a minimal error range, thus improving the algorithm’s robustness. Multiple datasets were used to analyze the clustering performance of vwSOM. On the Iris dataset, the S is 0.5262, the F1 value is 0.93, the ACC value is 0.9412, and the VA is 0.0012, and the experimental result with the Wine dataset shows that the S is 0.5255, the F1 value is 0.93, the ACC value is 0.9401, and the VA is 0.0014. Finally, to further demonstrate the performance of the algorithm, we use the more complex Waveform dataset; the S is 0.5101, the F1 value is 0.88, the ACC value is 0.8931, and the VA is 0.0033. All the experimental results show that the proposed algorithm can significantly improve clustering accuracy and have better stability, and its algorithm complexity can meet the requirements for real-time data processing. Full article

Many important plants’ agronomic traits, such as crop yield, stress tolerance, and other traits, are controlled by multiple genes and exhibit complex inheritance patterns. Traditional breeding methods often encounter difficulties in dealing with these traits due to their complexity. However, genomic selection (GS), which utilizes high-density molecular markers across the entire genome to facilitate selection in breeding programs, excels in capturing the genetic variation associated with these traits. This enables more accurate and efficient selection in breeding. The traditional crop genome selection model, based on statistical methods or machine learning models, often treats samples as independent entities while neglecting the abundance latent relational information among them. Consequently, this limitation hampers their predictive performance. In this study, we proposed a novel crop genome selection model based on hypergraph attention networks for genomic prediction (HGATGS). This model incorporates dynamic hyperedges that are designed based on sample similarity to validate the efficacy of high-order relationships between samples for phenotypic prediction. By introducing an attention mechanism, it assigns weights to different hyperedges and nodes, thereby enhancing the ability to capture kinship relationships among samples. Additionally, residual connections are incorporated between hypergraph convolutional layers to further improve model stability and performance. The model was validated on datasets for multiple crops, including wheat, corn, and rice. The results showed that HGATGS significantly outperformed traditional statistical methods and machine learning models on the Wheat 599, Rice 299, and G2F 2017 datasets. On Wheat 599, HGATGS achieved a correlation coefficient of 0.54, a 14.9% improvement over methods like R-BLUP and BayesA (0.47). On Rice 299, HGATGS reached 0.45, a 66.7% increase compared to other models like R-BLUP and SVR (0.27). On G2F 2017, HGATGS attained 0.88, slightly surpassing other models like R-BLUP and BayesA (0.87). We conducted ablation experiments to compare the model’s performance across three datasets, and found that the model integrating hypergraph attention and residual connections performed optimally. Subsequent comparisons of the model’s prediction performance with dynamically selected different k values revealed optimal performance when K = (3,4). The model’s prediction performance was also compared across different single nucleotide polymorphisms (SNPs) and sample sizes in various datasets, with HGATGS consistently outperforming the comparison models. Finally, visualizations of the constructed hypergraph structures showed that certain nodes have high connection densities with hyperedges. These nodes often represent varieties or genotypes with significant impacts on traits. During feature aggregation, these high-connectivity nodes contribute significantly to the prediction results and demonstrate better prediction performance across multiple traits in multiple crops. This demonstrates that the method of constructing hypergraphs through correlation relationships for prediction is highly effective. Full article