Search Results (539)

Maize–soybean intercropping is widely practised to improve land use efficiency, but shading from maize often limits soybean growth and productivity. Melatonin, a plant signaling molecule with antioxidant and growth-regulating properties, has shown potential in mitigating various abiotic stresses, including low light. This study investigated the efficacy of applying foliar melatonin (MT) to enhance shade tolerance and yield performance of soybean under intercropping. Four melatonin concentrations (0, 50, 100, and 150 µM) were applied to soybean grown under mono- and intercropping systems. The results showed that intercropping significantly reduced growth, photosynthetic activity, and yield-related traits. However, the MT application, particularly at 100 µM (MT₁₀₀), effectively mitigated these declines. MT₁₀₀ improved plant height (by up to 32%), leaf area (8%), internode length (up to 41%), grain yield (32%), and biomass dry matter (30%) compared to untreated intercropped plants. It also enhanced SPAD chlorophyll values, photosynthetic rate, stomatal conductance, chlorophyll fluorescence parameters such as Photosystem II efficiency (ɸPSII), maximum PSII quantum yield (Fv/Fm), photochemical quenching (qp), electron transport rate (ETR), Rubisco activity, and soluble protein content. These findings suggest that foliar application of melatonin, especially at 100 µM, can improve shade resilience in soybean by enhancing physiological and biochemical performance, offering a practical strategy for optimizing productivity in intercropping systems. Full article

Spot-welding joints are widely used in modern industries, and their fatigue life is crucial for the safety and reliability of structures. This paper proposes a method for predicting the fatigue life of spot-welding joints by integrating traditional structural stress methods and machine learning algorithms. Systematic fatigue tests were conducted on Q&P980 steel spot-welding joints to investigate the influence of the galvanized layer on fatigue life. It was found that the galvanized layer significantly reduces the fatigue life of spot-welding joints. Further predictions of fatigue life using machine learning algorithms, including Random Forest, Artificial Neural Networks, and Gaussian Process Regression, demonstrated superior prediction accuracy and generalization ability compared to traditional structural stress methods. The Random Forest algorithm achieved an R² value of 0.93, with lower error than traditional methods. This study provides an effective tool for the fatigue life assessment of spot-welding joints and highlights the potential application of machine learning in this field. Full article

To elucidate the photosynthetic physiological mechanisms influencing alfalfa (Medicago sativa L.) yield and quality under varying planting densities, the cultivar ‘Zhongmu No.1’ was used as experimental material. The effects of different row spacing (R1, R2, R3) and seeding rate (S1, S2, S3, S4, S5) combinations on chlorophyll content (ChlM), nitrogen flavonol index (NFI), chlorophyll fluorescence parameters, forage quality, and hay yield were systematically analyzed. Results showed that alfalfa under R1S3 treatment achieved peak values for ChIM, NFI, EE, and hay yield, whereas R1S4 treatment yielded the highest Fv/Fm and CP content. Redundancy analysis further indicated that yield was most strongly associated with ChlM, NFI, Y (II), and qP. Y (II), and qP significantly influenced alfalfa forage quality, exerting negative effects on ADF and NDF, while demonstrating positive effects on CP and EE. In conclusion, narrow row spacing (15 cm) with moderate seeding rates (22.5–30 kg·hm⁻²) optimizes photosynthetic performance while concurrently enhancing both productivity and forage quality in alfalfa cultivated, establishing a theoretical foundation for photosynthetic regulation in high-quality and high-yield alfalfa cultivation. Full article

The durability and fire resilience of concrete structures are increasingly critical in modern construction, particularly under elevated-temperature exposure. With this context, the current study explores the thermal and microstructural characteristics of geopolymer-based ultra-high-performance concrete (G-UHPC) incorporating quartz powder (QP) and silica fume (SF) after exposure to elevated temperatures. SF was used at 15% and 30% to partially replace the precursor material, while QP was used at 25%, 30%, and 35% as a partial replacement for fine sand. The prepared specimens were exposed to 200 °C, 400 °C, and 800 °C, followed by air cooling. Mechanical strength tests were conducted to evaluate compressive and flexural strengths, as well as failure patterns. Microstructural changes due to thermal exposure were assessed using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS). Among the prepared mixtures, the 30SF35QP mixture exhibited the highest compressive strength (156.0 MPa), followed by the 15SF35QP mix (146.83 MPa). The experimental results demonstrated that G-UHPC underwent varying levels of thermal degradation across the 200–800 °C range yet displayed excellent resistance to thermal spalling. At 200 °C, compressive strength increased due to enhanced geopolymerization, with the control mix showing a 29.8% increase. However, significant strength reductions were observed at 800 °C, where the control mix retained only 30.8% (32.0 MPa) and the 30SF25QP mixture retained 28% (38.0 MPa) of their original strengths. Despite increased porosity and cracking at 800 °C, the 30SF35QP mixture exhibited superior strength retention due to its denser matrix and reduced voids. The EDS results confirmed improved gel stability in the 30% SF mixtures, as evidenced by higher silicon content. These findings suggest that optimizing SF and QP content significantly enhances the fire resistance and structural integrity of G-UHPC, providing practical insights for the design of sustainable, high-performance concrete structures in fire-prone environments. Full article

Path planning and collision avoidance are essential components of an autonomous driving system (ADS), ensuring safe navigation in complex environments shared with other road users. High-quality planning and reliable obstacle avoidance strategies are essential for advancing the SAE autonomy level of autonomous vehicles, which can largely reduce the risk of traffic accidents. In daily driving scenarios, lane changing is a common maneuver used to avoid unexpected obstacles such as parked vehicles or suddenly appearing pedestrians. Notably, lane-changing behavior is also widely regarded as a key evaluation criterion in driver license examinations, highlighting its practical importance in real-world driving. Motivated by this observation, this paper aims to develop an autonomous lane-changing system capable of dynamically avoiding obstacles in multi-lane traffic environments. To achieve this objective, we propose a hierarchical decision-making and control framework in which a Double Deep Q-Network (DDQN) agent operates as the high-level planner to select lane-level maneuvers, while a High-Order Control Lyapunov Function–High-Order Control Barrier Function–based Quadratic Program (HOCLF-HOCBF-QP) serves as the low-level controller to ensure safe and stable trajectory tracking under dynamic constraints. Simulation studies are used to evaluate the planning efficiency and overall collision avoidance performance of the proposed hierarchical control framework. The results demonstrate that the system is capable of autonomously executing appropriate lane-changing maneuvers to avoid multiple obstacles in complex multi-lane traffic environments. In computational cost tests, the low-level controller operates at 100 Hz with an average solve time of 0.66 ms per step, and the high-level policy operates at 5 Hz with an average solve time of 0.60 ms per step. The results demonstrate real-time capability in autonomous driving systems. Full article

Integrating Industry 4.0 technologies into food manufacturing processes transforms traditional quality management practices. This study aims to understand how these technologies are applied across managerial quality functions in the food industry. A systematic literature review was conducted using the Scopus and Web of Science databases, selecting 69 peer-reviewed articles. The analysis identified quality control (QC) and quality assurance (QA) as the most frequently addressed functions. Sensor technology was the most cited, followed by blockchain and artificial intelligence, mainly supporting food safety, process monitoring, and traceability. In contrast, quality design (QD), quality improvement (QI), and quality policy and strategy (QPS) were underrepresented, revealing a gap in strategic and innovation-focused applications. Based on these insights, the Food Quality Management 4.0 (FQM 4.0) framework was developed, mapping the relationship between Industry 4.0 technologies and the five managerial quality functions, with food safety positioned as a transversal dimension. The framework contributes to academia and industry by offering a structured view of technological integration in food quality management and identifying future research and implementation directions. This study highlights the need for broader adoption of advanced technologies to improve transparency, responsiveness, and overall quality performance in the food sector. Full article

With the rapid expansion of Internet of Things (IoT) and edge computing applications, efficient video transmission under constrained bandwidth and limited computational resources has become increasingly critical. In such environments, perception-based video coding plays a vital role in maintaining acceptable visual quality while minimizing bit rate and processing overhead. Although newer video coding standards have emerged, H.264/AVC remains the dominant compression format in many deployed systems, particularly in commercial CCTV surveillance, due to its compatibility, stability, and widespread hardware support. Motivated by these practical demands, this paper proposes a perception-based video coding algorithm specifically tailored for low-bit-rate H.264/AVC applications. By targeting regions most relevant to the human visual system, the proposed method enhances perceptual quality while optimizing resource usage, making it particularly suitable for embedded systems and bandwidth-limited communication channels. In general, regions containing human faces and those exhibiting significant motion are of primary importance for human perception and should receive higher bit allocation to preserve visual quality. To this end, macroblocks (MBs) containing human faces are detected using the Viola–Jones algorithm, which leverages AdaBoost for feature selection and a cascade of classifiers for fast and accurate detection. This approach is favored over deep learning-based models due to its low computational complexity and real-time capability, making it ideal for latency- and resource-constrained IoT and edge environments. Motion-intensive macroblocks were identified by comparing their motion intensity against the average motion level of preceding reference frames. Based on these criteria, a dynamic quantization parameter (QP) adjustment strategy was applied to assign finer quantization to perceptually important regions of interest (ROIs) in low-bit-rate scenarios. The experimental results show that the proposed method achieves superior subjective visual quality and objective Peak Signal-to-Noise Ratio (PSNR) compared to the standard JM software and other state-of-the-art algorithms under the same bit rate constraints. Moreover, the approach introduces only a marginal increase in computational complexity, highlighting its efficiency. Overall, the proposed algorithm offers an effective balance between visual quality and computational performance, making it well suited for video transmission in bandwidth-constrained, resource-limited IoT and edge computing environments. Full article

As AI-enabled embedded systems such as smart TVs and edge devices demand efficient video processing, Versatile Video Coding (VVC/H.266) becomes essential for bandwidth-constrained Multimedia Internet of Things (M-IoT) applications. However, its block-based coding often introduces compression artifacts. While CNN-based methods effectively reduce these artifacts, maintaining robust performance across varying quantization parameters (QPs) remains challenging. Recent QP-adaptive designs like QA-Filter show promise but are still limited. This paper proposes DRIFT, a QP-adaptive in-loop filtering network for VVC. DRIFT combines a lightweight frequency fusion CNN (LFFCNN) for local enhancement and a Swin Transformer-based global skip connection for capturing long-range dependencies. LFFCNN leverages octave convolution and introduces a novel residual block (FFRB) that integrates multiscale extraction, QP adaptivity, frequency fusion, and spatial-channel attention. A QP estimator (QPE) is further introduced to mitigate double enhancement in inter-coded frames. Experimental results demonstrate that DRIFT achieves BD rate reductions of 6.56% (intra) and 4.83% (inter), with an up to 10.90% gain on the BasketballDrill sequence. Additionally, LFFCNN reduces the model size by 32% while slightly improving the coding performance over QA-Filter. Full article

The garden Nasturtium (Tropaeolum majus L.) is increasingly consumed worldwide due to its culinary appeal and perceived health benefits. However, the chemical markers underlying its functional properties remain insufficiently characterized. Building on evidence from a recent human pilot study confirming both high acceptability and dietary safety, we conducted a comprehensive volatilomic and phytochemical analysis of T. majus flowers and their juice. Headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME/GC-MS) was employed to establish the volatilomic fingerprint of floral tissues and juice. Our analysis revealed a striking dominance of benzyl isothiocyanate and benzonitrile, which together accounted for 88% of the total volatile organic metabolites (VOMs) in the juice, 67% and 21%, respectively. In the floral tissues, benzyl isothiocyanate was even more prevalent, representing 95% of the total volatile profile. Complementary in vitro assays confirmed a substantial total phenolic content and strong antioxidant activity in the flowers. These findings provide a robust chemical rationale for the potential health-promoting attributes of T. majus, while identifying key volatilomic markers that could support future functional and safety claims. In parallel, a benefit–risk assessment framework is discussed in accordance with the European Food Safety Authority (EFSA) guidelines for the Qualified Presumption of Safety (QPS) of edible flowers. Given that both benzyl isothiocyanate and benzonitrile are classified as Cramer Class III substances, a conservative intake threshold of 1.5 μg/kg body weight per day is proposed. To enable quantitative exposure modeling and support the derivation of a tolerable daily intake (TDI), future studies should integrate organic solvent-based extraction methodologies to estimate the total volatile load per gram of floral biomass. This would align risk–benefit assessments with the EFSA’s evolving framework for novel foods and functional ingredients. Full article

The increasing penetration of distributed energy resources (DERs) and power electronic loads challenges the modeling and control of modern distribution networks (DNs). The traditional models often fail to capture the complex aggregate dynamics required for advanced control strategies. This paper proposes a novel framework for DN power regulation based on Neural Ordinary Differential Equations (NODEs) and Model Predictive Control (MPC). NODEs are employed to develop a data-driven, continuous-time dynamic model capturing the aggregate relationship between the voltage at the point of common coupling (PCC) and the network’s power consumption, using only PCC measurements. Building upon this NODE model, an MPC strategy is designed to regulate the DN’s active power by manipulating the PCC voltage. To ensure computational tractability for real-time applications, a local linearization technique is applied to the NODE dynamics within the MPC, transforming the optimization problem into a standard Quadratic Programming (QP) problem that can be solved efficiently. The framework’s efficacy is comprehensively validated through simulations. The NODE model demonstrates high accuracy in predicting the dynamic behavior in a DN against a detailed simulator, with maximum relative errors below 0.35% for active power. The linearized NODE-MPC controller shows effective tracking performance, constraint handling, and computational efficiency, with typical QP solve times below 0.1 s within a 0.1 s control interval. The validation includes offline tests using the NODE model and online co-simulation studies using CloudPSS and Python via Redis. Application scenarios, including Conservation Voltage Reduction (CVR) and supply–demand balancing, further illustrate the practical potential of the proposed approach for enhancing the operation and efficiency of modern distribution networks. Full article

This study proposes a method to detect double compression in H.265/HEVC videos containing B-frames, a scenario underexplored in previous research. The method extracts frame-level encoding features—including frame type, coding unit (CU) size, quantization parameter (QP), and prediction modes—and represents each video as a 28-dimensional feature vector. A bidirectional Long Short-Term Memory (Bi-LSTM) classifier is then trained to model temporal inconsistencies introduced during recompression. To evaluate the method, we created a dataset of 129 HEVC-encoded YUV videos derived from 43 original sequences, covering various bitrate combinations and GOP structures. The proposed method achieved a detection accuracy of 80.06%, outperforming two existing baselines. These results demonstrate the practical applicability of the proposed approach in realistic double compression scenarios. Full article

Under the combined influence of climate change and human activities, the non-stationarity of reservoir runoff has significantly intensified, posing challenges for traditional statistical models to accurately capture its multi-scale abrupt changes. This study focuses on Qianping (QP) Reservoir and systematically integrates climate-driven mechanisms with machine learning approaches to uncover the patterns of runoff evolution and develop high-precision prediction models. The findings offer a novel paradigm for adaptive reservoir operation under non-stationary conditions. In this paper, we employ methods including extreme-point symmetric mode decomposition (ESMD), Bayesian ensemble time series decomposition (BETS), and cross-wavelet transform (XWT) to investigate the variation trends and mutation features of the annual runoff in QP Reservoir. Additionally, four models—ARIMA, LSTM, LSTM-RF, and LSTM-CNN—are utilized for runoff prediction and analysis. The results indicate that: (1) the annual runoff of QP Reservoir exhibits a quasi-8.25-year mid-short-term cycle and a quasi-13.20-year long-term cycle on an annual scale; (2) by using Bayesian estimators based on abrupt change year detection and trend variation algorithms, an abrupt change point with a probability of 79.1% was identified in 1985, with a confidence interval spanning 1984 to 1986; (3) cross-wavelet analysis indicates that the periodic associations between the annual runoff of QP Reservoir and climate-driving factors exhibit spatiotemporal heterogeneity: the AMO, AO, and PNA show multi-scale synergistic interactions; the DMI and ENSO display only phase-specific weak coupling; while solar sunspot activity modulates runoff over long-term cycles; and (4) The NSE of the ARIMA, LSTM, LSTM-RF, and LSTM-CNN models all exceed 0.945, the RMSE is below 0.477 × 10⁹ m³, and the MAE is below 0.297 × 10⁹ m³, Among them, the LSTM-RF model demonstrated the highest accuracy and the most stable predicted fluctuations, indicating that future annual runoff will continue to fluctuate but with a decreasing amplitude. Full article

Clostridium novyi is a common pathogen in domestic animals and humans, and alpha-toxin is the main cause of its pathogenesis. Because it is a fastidious organism, obtaining alpha-toxin is expensive. Therefore, we proposed an in silico study to synthesize epitopes in cultures of Escherichia coli BL21 pLysS (DE3). First, we used a stirred-tank bioreactor, developing a dry mass yield (DMY) of 0.77 g/L in batch cultures and 1.03 g/L in fed-batch cultures, without acetic acid production. With scale-up using a system without mechanical agitation, there was a higher DMY (1.20 g/L) with 0.56 mmol/mL of alpha-toxin epitope 1 (DE3/Ep1) and 0.61 mmol/mL of alpha-toxin epitope 2 (DE3/Ep2), with a similar profile for O2 consumption, glucose, and no acetic acid production. The kinetic parameters µ(h⁻¹), YX/S, YP/S, QP, and QX did not differ significantly; however, the kinetic data were superior. Our results suggest that in silico tools allow epitope selection and bioprocess standardization. This system provides cost savings and technological advances for the veterinary vaccine industry. Full article

In this study, the functions of the photosynthetic machinery were evaluated using chlorophyll a fluorescence technique (PAM and JIP test) in pea plants (Pisum sativum L. cv Borec) and its LHC II oligomerization variants (mutants Costata 2/133 and Coeruleovireus 2/16). The oligomeric forms of LHCII increased in the following order: Costata 2/133 < Borec wt < Coeruleovireus 2/16. Data revealed that the mutant with higher LHCII oligomerization (Coeruleovireus 2/16) at low light intensity (LL, 150 µmol photons/m²·s) exhibited the following: (i) decreased energy dissipation and increased electron transport efficiency; (ii) higher reaction center density; (iii) increased amounts of the open reaction centers (qp) and their excitation efficiency (Φexc); and (iv) influenced the reoxidation of Q_A⁻, alleviating its interaction with plastoquinone. These effects enhanced photosynthetic performance related to PSII photochemistry (PI_ABS) and overall photosynthetic efficiency (PItotal). High light intensity (HL, 500 µmol photons/m²·s) caused a reduction in open reaction centers (qp), excitation efficiency (Φexc), photochemical energy conversion of PSII (Φ_PSII), maximum efficiency of PSII photochemistry in light (Fv′/Fm′), and linear electron transport via PSII, with more pronounced effects observed in membranes with a lower degree of LHCII oligomerization (Costata 2/133). This study provides novel experimental evidence for the pivotal role of the LHCII structural organization in determining the efficiency of light-dependent reactions of photosynthesis. Full article

The use of Stymjod and Tytanit supports natural resistance mechanisms and improves the condition of Phleum pratense, which is in line with sustainable agriculture and integrated production. The aim of the research was to determine the effect of Stymjod and Tytanit and of the number of their sprays per growth cycle on chlorophyll fluorescence and the content of chlorophyll pigments in Phleum pratense L. leaf blades. The following research factors were used: Factor I—treatment: Control; Tytanit—0.04%; Stymjod—2.5%. Factor II—number of sprays: L1—plants sprayed one time; L2—plants sprayed two times. The use of the Tytanit regulator in the cultivation of Phleum pratense L. contributed to an increase in chlorophyll fluorescence parameters, i.e., qN and qP. In addition, an increase in chlorophyll a and b content was noted. The application of the Stymjod stimulator increased ΔF/Fm′. For the vast majority of chlorophyll a fluorescence parameters, higher values were noted when plants were sprayed two times per growth cycle, i.e., Fv/Fm, ΔF/Fm′, qN, chlorophyll pigments. Full article