Search Results (5,821)

Although greenhouse vegetable production is rapidly shifting toward innovative soilless systems, soil-based conventional cultivation still dominates globally. This production system faces growing pressure to transition to sustainable practices. However, introducing biofertilisers into intensive systems often yields inconsistent results. Specifically, their effects on different lettuce traits vary due to complex relationships between genotype, biofertiliser, environmental conditions, and market demands. Single-parameter evaluations fail to balance conflicting criteria, necessitating multi-criteria decision-making (MCDM) methods for selecting optimal choices. This study aims to overcome these inconsistencies through an integrated fuzzy MCDM-based optimisation model. Three lettuce cultivars (‘Carmesi’, ‘Aquino’, and ‘Gaugin’) were grown in an unheated Surčin (Serbia) greenhouse during a 58-day autumn experiment using a complete block design. Four treatments were applied: a control (without fertilisation), effective microorganisms, a Trichoderma-based fertiliser, and their combination. Biofertilisers were applied before transplanting and four times foliarly during the vegetation period via battery sprayer. This defined 12 production models (cultivar–fertiliser pairs), evaluated across 10 criteria: agronomic (core ratio, number of leaves), quality (nitrate content, total antioxidant capacity, total soluble solids, and chlorogenic acid), sensory (overall taste, overall quality), and economic (total variable costs, total income). Four decision-making experts from the Faculty of Agriculture and the ready-to-eat salad industry assessed weighting coefficients using the fuzzy PIPRECIA (PIvot Pairwise RElative Criteria Importance Assessment) method. The fuzzy MARCOS (Measurement Alternatives and Ranking according to COmpromise Solution) method was used to rank the alternatives. To confirm the stability of the obtained ranking with the fuzzy MARCOS method, we performed sensitivity analysis through 20 different scenarios. Applied fuzzy methods identified alternative A11—‘Aquino’ cultivar with combined biofertilisers—as the best-ranked option, followed by A6 and A7. This study validates fuzzy PIPRECIA and fuzzy MARCOS as effective tools for optimising lettuce production models. They support farmers in selecting the most favourable solution based on multiple criteria, aiding the shift from mineral fertilisers to sustainable biofertiliser-based systems in intensive production—especially helpful for producers making this transition. Full article

Precise water quality forecasting is vital for sustainable resource management and public health, especially in semi-arid environments. This study investigates the predictive capabilities of ten Machine Learning (ML) algorithms using a dataset of 308 drinking water samples collected from various districts in Şanlıurfa Province, Türkiye. We evaluated ten predictive models, including Support Vector Regressor (SVR) and Extreme Gradient Boosting (XGBoost), both integrated with dimensionality reduction and hyperparameter optimization. Nineteen physicochemical and microbiological parameters—Temperature, chlorine (Cl⁻), pH, Electrical Conductivity (EC), Total Dissolved Solids (TDS), nitrite (NO₂⁻), nitrate (NO₃⁻), ammonium (NH₄⁺), sulfate (SO₄²⁻), Free Chlorine (Cl₂), calcium (Ca²⁺), magnesium (Mg²⁺), sodium (Na⁺), potassium (K⁺), fluoride (F⁻), trihalomethanes (THMs), Escherichia coli, Enterococci, Total Coliform—were used as input features. The dataset was split into training (75%) and testing (25%) subsets, and model performance was assessed through 10-fold cross-validation and hold-out testing procedures. To improve model generalization and mitigate the effects of class imbalance, we implemented the Adaptive Synthetic Sampling (ADASYN) technique. ML algorithms were evaluated using standard regression metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R²). The LSTM model optimized using Randomized Search outperformed the SVR and XGBoost models, demonstrating the highest accuracy and generalization capability, as evidenced by the superior R² value of 0.999 following ADASYN balancing and the lowest RMSE (1.206). These findings underscore the effectiveness of the LSTM framework in modeling the complex variance of the Weighted Arithmetic Water Quality Index (WAWQI). The findings of this study are expected to support future water quality monitoring strategies, inform policy development, and contribute to sustainable water resource management in arid and semi-arid regions. Full article

Screening potato germplasm for low nitrogen (LN) tolerance is essential for improving nitrogen use efficiency and agricultural sustainability. A set of 156 potato genotypes from diverse sources—including the International Potato Center (CIP), the National Potato Germplasm Repository (CAAS), and varieties and lines bred by the Qinghai Academy of Agriculture and Forestry Sciences—was evaluated under optimal (60 mmol·L⁻¹) and low (3 mmol·L⁻¹) nitrogen conditions using tissue culture. Nine traits related to growth, nitrogen accumulation, and nitrogen use efficiency were measured. Under LN stress, nitrogen physiological efficiency (NPE), uptake efficiency (NUpE), and utilization efficiency (NUE) increased, while most growth-related traits declined. Considerable variation was observed in fresh weight (FW), dry weight (DW), nitrogen accumulation (NA), and NUE, with coefficients of variation ranging from 0.38 to 0.40 under LN and 0.17 to 0.42 under ON. Principal component analysis identified NA and NUpE as the primary contributors to phenotypic variation. Based on comprehensive D-values from cluster analysis, the genotypes were classified into five tolerance groups: Type I—(strong low-nitrogen tolerant (13 accessions); Type II—low-nitrogen tolerant (66 accessions); Type III—moderate low-nitrogen tolerant (36 accessions); Type IV—low-nitrogen sensitive (24 accessions); and Type V—highly low-nitrogen sensitive (17 accessions). Physiological validation revealed two distinct adaptive strategies: a nitrogen conservation strategy (Type I), characterized by high NA and nitrogen content (NC) alongside moderate physiological efficiency, and an efficiency-driven compensation strategy (Types II, IV, and V), marked by low NA and NC but high physiological efficiency. The congruence between multivariate clustering and subsequent physiological measurements confirms that this classification effectively captures genotypic differences in low nitrogen tolerance. Thirteen highly LN-tolerant genotypes—including PIMPERNEL, Favorita, and Spunta—were identified as promising genetic resources for breeding nitrogen-efficient potato cultivars. This tissue culture-based screening method provides a practical tool for evaluating nitrogen tolerance in plants and supports sustainable potato production under limited nitrogen availability. Full article

As product aesthetics increasingly drive consumer preference, quantitative evaluation remains hindered by subjective evaluation biases and the black-box nature of modern artificial intelligence. This study proposes an advanced machine learning framework incorporating sensitivity-aware morphological features for the aesthetic evaluation of industrial products, with automotive design as a representative case. An aesthetic index system and its quantitative formulations are first developed to capture the morphological characteristics of product form. Subjective weights are determined via grey relational analysis (GRA), while objective weights are calculated using the coefficient of variation method (CVM) integrated with the technique for order preference by similarity to an ideal solution (TOPSIS). A game-theoretic weighting approach is then employed to fuse subjective and objective weights, thereby establishing a multi-scale aesthetic evaluation system. Sensitivity analysis is applied to identify six key indicators, forming a high-quality dataset. To enhance prediction performance, a novel model—improved lung performance-based optimization with backpropagation neural network (ILPOBP)—is proposed, where the optimization process leverages a maximin latin hypercube design (MLHD) to enhance exploration efficiency. The ILPOBP model effectively predicts aesthetic ratings based on limited morphological input data. Experimental results demonstrate that the ILPOBP model outperforms baseline models in terms of accuracy and robustness when handling complex aesthetic information, achieving a significantly lower test set mean absolute relative error (MARE = 4.106%). To further enhance model interpretability, Shapley additive explanations (SHAP) are employed to elucidate the internal decision-making mechanisms, offering reverse design insights for product optimization. The proposed framework offers a novel and effective approach for integrating machine learning into the aesthetic assessment of industrial product design. Full article

Chinese herbal medicine is rich in active ingredients that can promote growth and enhance immune function. In this study, Lycium barbarum, Panax ginseng, Astragalus membranaceus and Phragmitis rhizoma were crushed and mixed to prepare a compound Chinese herbal medicine. The basic feed of Megalobrama amblycephala was supplemented with 0 (control group), 1% (T1), 2% (T2) and 4% (T3) of this compound medicine. After raising for 90 days, in the T1 and T2 experimental groups, the length and width of intestinal villi and the activities of amylase, trypsin and lipase in the intestine were significantly higher than those in the control group. The weight gain rate and specific growth rates were highest and the feed coefficient was lowest in the T2 experimental group. In the control group, a large number of dilated hepatic sinusoids were detected, while this number significantly decreased in the T1 experimental group and they were not detected at all in the T2 and T3 experimental groups. The spleen and liver body indices were highest in the T2 experimental group. In all experimental groups, the Lys content and the activities of T-SOD, CAT, ACP, AKP and GSH-PX in serum were significantly higher than those of the control group. The expression of IgM, C3, TNF-ɑ and IL-1β in the head kidney; C3, TNF-ɑ and IL-1β in the spleen; C3 and IL-1β in the gills; IgM, C3 and IL-1β in liver; and IL-1β in the intestine was highest in the T2 experimental group. After challenge with Aeromonas hydrophila, the cumulative mortality rate of M. amblycephala was lowest in the T2 experimental group. The results of this study indicated that this compound Chinese herbal medicine could significantly enhance immunity, increase the activity of intestinal digestion-related enzymes and promote the growth of M. amblycephala. The appropriate addition amount of this compound Chinese herbal medicine in the basic feed of M. amblycephala was 2%. Full article

Sheep farming length of stay in the feedlot directly influences system profitability, mainly due to the high cost of feed. Thus, the use of predictive models based on body measurements is an important tool to define the optimal slaughter point and the ideal feedlot period. Thus, the aim was to evaluate predictive models of meat cuts and tissue carcasses concerning weight at slaughter (WS), loin eye area (LEA), and subcutaneous fat thickness (SFT) obtained by ultrasound of the lumbar region of lambs. The WS and ultrasound measurements were obtained from a pre-slaughter collection of 45 lambs, divided into five groups, each weighing 15, 20, 25, 30, or 35 kg, with nine replications per group. Three regression models were evaluated: WS, LEA, and SFT (independent variables) and the cuts yield or tissue composition (dependent variable). Increasing WS resulted in greater carcass weight and commercial cuts. Above 15 kg body weight, bone weight showed little or no increase (allometric coefficient = 0.06), whereas muscle and fat tissues increased steadily, with allometric coefficients of 0.25 and 0.12, respectively. The commercial cuts showed a high and significant correlation with WS and LEA. The muscle and bone proportion of the leg had a significant (p < 0.10) correlation with SFT. For the weight of commercial cuts estimates, the inclusion of LEA and/or SFT with WS did not improve the coefficient of determination but made the predictions equivalent to the measured values. There were high determination coefficients when WS was only used to predict muscle, fat, and bone weight, but it was not efficient in predicting the muscle/fat and muscle/bone ratios and the percentage of tissues. The WS was the variable that best explained the weight and tissue content. The inclusion of LEA and/or SFT made little improvement to the predictive models. Full article

With the widespread adoption of container technology, its shared kernel architecture has made abnormal file access behavior a key precursor to container escape and lateral attacks, necessitating precise and efficient runtime detection mechanisms. However, existing monitoring methods typically suffer from issues such as insufficient granularity in data collection, limited path semantic modeling capabilities, and low anomaly detection accuracy. To address these challenges, this paper proposes an eBPF-based method for detecting abnormal file access in containers. A lightweight kernel-level monitoring mechanism is constructed to capture access behavior in real time at the system call level, effectively enhancing both the granularity of data collection and the completeness of context. At the feature modeling layer, a multimodal path semantic representation method is designed, combining risk-layer rules and semantic vectorization strategies to enhance the hierarchical expression of path structures and improve context modeling ability. In the detection layer, an attention-enhanced autoencoder model is introduced, achieving high-precision identification of abnormal access behavior and low false-positive monitoring under unsupervised conditions through a path segment attention mechanism and weighted reconstruction loss function. Experiments in real container environments show that the proposed method achieves a recall rate of $82.0\%$, a false-positive rate of $0.79\%$, and a Matthews correlation coefficient of 0.852, significantly outperforming mainstream unsupervised detection methods such as Isolation Forest, One-Class SVM, and Local Outlier Factor. These results verify the advantages of the proposed method in terms of detection accuracy, real-time performance, and system friendliness, providing an efficient and feasible solution for enhancing the detection of unknown attacks in container runtimes. Full article

Quantitative analysis of leaf cell microstructure is crucial for deciphering agronomic traits in Chinese cabbage, including photosynthetic efficiency, stress tolerance, and yield potential. Traditional manual observation methods are inefficient and highly subjective, failing to meet the demands of large-scale breeding for high-throughput, reproducible microscopic phenotyping. To transition breeding practices from experience-driven to data-driven, there is an urgent need to establish automated, standardized systems for acquiring cell-scale phenotypes. Therefore, this study proposes an automated instance segmentation and phenotyping analysis framework for multi-tissue cells in Chinese cabbage leaf cross-sections. This framework systematically optimizes Mask R-CNN by introducing an attention mechanism to enhance cellular feature responses in complex backgrounds. It employs weighted multi-scale feature fusion to process densely distributed small-scale cells and integrates a refined boundary optimization module to improve recognition accuracy in adherent and blurred regions. On a microscopic image dataset spanning multiple varieties, this method achieves high-precision predictions in instance segmentation tasks. Based on the predicted cell masks, an interactive phenotyping analysis tool was further developed to automatically extract standardized single-cell morphological parameters, including area, perimeter, and Feret’s diameter. The measured parameters exhibit high consistency with manual annotations (correlation coefficients (r) all exceed 0.97). This framework enables high-throughput, standardized phenotypic analysis at the cellular level of leaf cross-sections, providing a reliable method for the digital and automated interpretation of crop microscopic traits. This technical solution not only supports the systematic integration of microscopic phenotypes in Chinese cabbage breeding but also offers a scalable solution for cellular-scale phenotypic research in other crops. Full article

A model and tracking control method for a double-steering wheeled climbing robot (DSWCR) are presented in this article. The dynamic model of the DSWCR system is established using the Lagrange equation, considering the effects of slipping, variations in gravity and the friction coefficient, and wall/wheel interaction forces. During wall motion, the DSWCR is subject to uncertainties introduced from both the state and model. To address the tracking problem of the DSWCR under state and model uncertainties, an adaptive dynamic programming (ADP) controller based on zero-sum theory is proposed. The stability of the DSWCR tracking system and the convergence of the weights in a neural network are demonstrated. Finally, simulations and a prototype experiment are conducted to verify the optimality and robustness of the proposed control method. Full article

Perovskite solar cells (PSCs) are approaching large-scale deployment, yet short lifetimes and end-of-life risks make circular strategies essential. Here we propose a time-resolved Sustainability Benefit Ratio (SBR), a dimensionless indicator that aggregates (i) physically accounted greenhouse-gas emissions/avoidance and (ii) monetized life-cycle costs converted to CO₂-equivalent via an economic carbon-intensity coefficient (CC), enabling a unified assessment of environmental performance and economic burdens over time. This work highlights design for remanufacturing as a key enabler of circular PSC deployment. Using an industrially relevant carbon-based PSC architecture designed for remanufacturing, we simulate multi-cycle operation under periodic remanufacturing and repeated new manufacturing, and derive an analytic steady-state limit, SBR_ss. Remanufacturing markedly increases long-run circular value relative to renewal replacement under realistic lifetimes, while conventional payback economic indicators diverge in timing, motivating an explicit bridge between environmental payback and economic feasibility. We therefore introduce a circular value weighting factor β applied only to CC-converted terms, where β = 0 recovers a purely physical CO₂-based benefit-to-burden ratio, and β-sweeps transparently represent stakeholder-dependent emphasis on valuation-weighted burdens/credits. Finally, feasibility-constrained design maps and Bayesian optimization demonstrate that SBR_ss can serve as a practical objective function to efficiently explore economically viable remanufacturing specifications and identify dominant design levers governing circular value. Full article

To address high uncertainty, dynamic evolution, and limited information in emergency decision-making for major sudden disasters, this paper proposes a sliding-window game-theoretic method with four reference points for emergency response selection. Firstly, interval-valued T-spherical fuzzy sets are adopted to capture decision-makers’ uncertain and hesitant evaluations in interval form. Subsequently, a four-reference-point framework, including the external, internal, average development speed, and ideal proximity reference points, is established to reflect stage-dependent psychological baselines. Furthermore, criterion weights are updated by a sliding-window game-theoretic combination weighting scheme that integrates entropy, anti-entropy, criteria importance through intercriteria correlation, and the coefficient of variation, and performs rolling updates across stages. Prospect values are then computed relative to the four reference points and aggregated to rank alternatives at each stage. Finally, a case study of the 2024 Huludao extreme rainfall event applies the proposed method to evaluate four candidate schemes across six criteria over three decision stages. Results show that rescue cost has the highest weight in all stages, while the importance of rescue speed decreases and social impact increases as the response progresses. The proposed method identifies a comprehensive flood relief scheme led by the People’s Liberation Army and the People’s Armed Police Force as the best option in all stages, because it achieves the highest comprehensive prospect values among all alternatives. Comparative analyses indicate more consistent identification of the optimal scheme than existing approaches, supporting sustainable and resource-efficient disaster management. Full article

Amid frequent external shocks and deepening market linkages, the information-transmission structure of financial markets is more prone to phase-specific abrupt changes, creating a need for real-time monitoring methods. This study develops an online framework to track financial information-flow networks and to provide early warnings of structural changes under exogenous shocks. Methodologically, information-flow networks are constructed from return spillovers using the Diebold–Yilmaz framework. An Exponential Random Graph Model is then employed to quantify how exogenous variables affect edge formation. Statistical process control methods, namely the Multivariate Cumulative Sum (MCUSUM) and Multivariate Exponentially Weighted Moving Average (MEWMA), are introduced to online monitoring of exogenous-effect coefficients. The simulation study uses simulated data to assess whether the two charts are properly calibrated and sensitive to alarms. The empirical study uses Shanghai Stock Exchange (SSE) 180 constituent stocks and exogenous variables—7-day Fixing Repo Rate (FR007), M2 growth rate (M2), the China Economic Policy Uncertainty Index (CEPU), and the Global Economic Policy Uncertainty Index (GEPU) over 2011–2025. The results indicate that both charts achieve the target in-control average run length, and detection accelerates with shock magnitude; FR007 is generally negative, M2 is positive, and uncertainty measures vary strongly over time; monitoring reveals shock clustering and long-term drift, implying both shock amplification and structural drift in the information-flow network. Practically, the framework provides an implementable warning tool for tracking shock amplification, supporting timely risk management. Full article

Autonomous aerial refueling is a key technology for enhancing the endurance of unmanned aerial vehicles; however, the wingtip vortices generated by the tanker create a strong three-dimensional wake-vortex flow field, whose downwash and lateral airflow can impose significant rolling moments on the follower Unmanned Aerial Vehicle (UAV), posing a serious threat to flight safety. To address this issue, this study proposes an integrated framework that combines wake-vortex risk-field modeling with optimal path planning. The classical Hallock–Burnham (HB) model is first employed to predict vortex descent and lateral transport, while a two-phase model is used to characterize the temporal decay of vortex circulation. The predicted vortex parameters are then coupled with the UAV’s aerodynamic characteristics, and the rolling-moment coefficient (RMC) is introduced as a risk metric to compute its spatiotemporal distribution in three dimensions, thereby transforming the invisible wake-vortex disturbance into a visualizable and quantifiable dynamic three-dimensional risk map. On this basis, a wake-vortex-aware path-planning algorithm based on particle swarm optimization (PSO) is developed, incorporating adaptive weighting and elitist mutation strategies. A multi-objective cost function considering path length, safety, and smoothness is further constructed to search for an optimal safe path under wake-vortex influence. Simulation results indicate that, compared with the classical A* and Rapidly-Exploring Random Tree (RRT) algorithms, the proposed method reduces cumulative risk exposure by approximately 90% and 75%, respectively, while limiting the increase in path length to about 8% (significantly lower than the increases of 40% for A* and 44% for RRT). In addition, the maximum turning angle is constrained within 10°, and the computation time remains around 0.052 s, satisfying real-time requirements. These results demonstrate that the proposed method can generate safe, efficient, and dynamically feasible paths for UAV aerial refueling and provide a valuable reference for wake-vortex avoidance in similar aerospace missions. Full article

Background: Despite the widespread implementation of treat-to-target strategies and modern disease-modifying antirheumatic drugs, a substantial proportion of patients with rheumatoid arthritis (RA) continue to report unmet needs (UNs), defined as a mismatch between patient expectations and symptom burden on the one hand and outcomes achieved with current care on the other. Patient–physician discordance in global assessments may reflect multidimensional influences, including pain mechanisms, psychosocial factors, functional impairment, and communication gaps, extending beyond inflammatory disease activity. Methods: In this cross-sectional study, 133 patients with RA and 57 healthy controls were included. UNs were operationalized as the signed difference between patient global assessment and physician global assessment (ΔPGA–PhGA). Clinical variables, patient-reported outcomes, and Short Form-36 (SF-36) domains were incorporated into two regularized partial correlation network models estimated using the extended Bayesian information criterion graphical least absolute shrinkage and selection operator (EBICglasso). Node centrality indices (strength, signed strength, betweenness, and closeness) were calculated. Network stability was evaluated using 2000 bootstrap resamples and correlation stability (CS) coefficients. Results: In the clinical network, pain intensity demonstrated the highest strength centrality and the strongest direct association with UNs. In contrast, Disease Activity Score in 28 joints with C-reactive protein (DAS28-CRP) showed no direct association with UNs after accounting for shared variance. In the SF-36-based quality-of-life network, UNs exhibited inverse associations, particularly with perceived health change and role–emotional functioning. Stability analyses indicated acceptable to good robustness (clinical network: CS = 0.59 for edge weights and 0.44 for strength; SF-36 network: CS = 0.59), supporting the reliability of the estimated network structures. Conclusions: UNs in RA are not solely determined by inflammatory disease activity but are embedded within interconnected clinical and psychosocial domains. Pain occupies a structurally central position in the clinical network, whereas perceived health change and emotional role limitations characterize the quality-of-life context of UNs. These findings underscore the importance of multidimensional and patient-centered assessment strategies in RA management. Full article

Supplemental lighting is essential for overcoming low-light stress and enabling overwintering tomato production in greenhouses. This study investigated the effects of LED supplemental lighting with different spectral qualities in the upper and lower canopy on the fruit weight and quality of tomatoes. Six treatments were established: upper-red/lower-blue (R_UB_L), full red (R), full blue (B), upper-blue/lower-red (B_UR_L), red–blue mixture (RB), and a non-lit control (CK). The results demonstrated that: (1) All supplemental lighting treatments increased tomato fruit weight. During the early overwintering stage (October–December), the highest fruit weight was observed under the RB treatment, representing an increase of 22.62–24.02% compared to CK at the same truss positions. The light gain coefficient (LGC) under RB treatment reached up to 4.41 times that of other treatments. During the later phase (January–February), the B_UR_L treatment achieved the highest LGC, reaching 1.28 to 5.30 times that of other treatments, and it increased the fruit weight by 48.2–72.88% compared to CK. (2) Regarding fruit quality, R and B_UR_L promoted lycopene accumulation the most, followed by RB treatment. Additionally, lycopene was found positively correlated with key color parameters (a, a*/b*, CCI, and C). (3) Compared to CK, all supplemental lighting treatments increased the soluble sugar content in tomato fruits (ranging 5.36~95.35%), with the highest sugar–acid ratios typically observed under R or B_UR_L treatments. The RB treatment yielded the highest VC levels during the later overwintering stage, exceeding the control by 29.97–39.65%. In summary, for overwintering greenhouse tomato production, application of the RB treatment during the early phase (October to December) and transition to the B_UR_L treatment in the late phase (January to February) could be considered. This phased strategy may help achieve synergistic improvements in yield, fruit coloration, and quality. Full article