Search Results (7,030)

This study focuses on the formulation and analysis of problems that are dual to those defined by convex set-valued mappings. Various important classes of optimization problems—such as the classical problems of mathematical and linear programming, as well as extremal problems arising in economic dynamics models—can be reduced to problems of this type. The dual problem proposed in this work is constructed on the basis of the duality theorem connecting the operations of addition and infimal convolution of convex functions, a result that has been previously applied to compact-valued mappings. It appears that, under the so-called nondegeneracy condition, this construction serves as a fundamental approach for deriving duality theorems and establishing both necessary and sufficient optimality conditions. Furthermore, alternative conditions that partially replace the nondegeneracy assumption may also prove valuable for addressing other issues within convex analysis. Full article

Accurate and reliable detection of rice diseases and pests is crucial for ensuring food security. However, traditional deep learning methods often suffer from high rates of missed and false detections when dealing with complex field environments, especially in the presence of tiny disease spots, due to insufficient feature extraction capabilities. To address this issue, this study proposes a high-precision rice disease detection method based on ensemble learning and conducts experiments on a self-built dataset of 12,572 images containing five types of diseases and one type of pest. The ensemble learning model is optimized and constructed through a phased approach: First, using YOLOv8s as the baseline, transfer learning is performed with the agriculture-related dataset PlantDoc. Subsequently, a P2 small-object detection head, an EMA mechanism, and the Focal Loss function are introduced to build an optimized single model, which achieves an mAP_0.5 of 0.899, an absolute improvement of 5.5% compared to the baseline YOLOv8s. Then, three high-performance YOLO object detection models, including the improved model mentioned above, are selected, and the Weighted Box Fusion technique is used to integrate their prediction results to construct the final Ensemble-WBF model. Finally, the AP_0.5 and AR_0.5:0.95 of the model reach 0.922 and 0.648, respectively, with absolute improvements of 2.2% and 3.2% compared to the improved single model, further reducing the false and missed detection rates. The experimental results show that the ensemble learning method proposed in this study can effectively overcome the interference of complex backgrounds, significantly improve the detection accuracy and robustness for tiny and similar diseases, and reduce the missed detection rate, providing an efficient technical solution for the accurate and automated monitoring of rice diseases in real agricultural scenarios. Full article

The vegetation of settlements can be particularly important for ecology and cityscapes and also plays a role in shaping and structuring the fabric of the settlement. However, there are very few settlements where the nature of woody vegetation is a defining characteristic of the settlement image. The vitality and health of the vegetation of a settlement can depend on the extent of development, increasing urbanization and the influencing effects of climate change. We monitored the changes in the vegetation of our study area, Balatonalmádi-Káptalanfüred, Hungary, going back 300 years by analyzing military and historical maps and satellite images, using the NDVI vegetation index of the last 20 years, as well as by field visits, tree examinations based on visual surveys and a plant population survey at 5 sampling points. Our results show that due to the increase in construction, the historical map shows a significant decrease in green space, and the satellite images show a dramatic decrease in the vitality of the remaining green spaces. In addition, field visits have also revealed serious plant health problems, which may lead to a relatively rapid decline of the dominant oak population. The research shows that as the upper canopy level decreases, the second canopy level becomes dominant. In order to preserve the strong, distinctive oak character of the settlement, we make proposals to mitigate the destruction of the current woody vegetation and, in the long term, to replace the stands with climate-resilient species. Full article

Claims are a significant cause of delays and increased costs in international construction projects, yet contract provisions on claims remain inconsistent, narrative, and difficult to apply in practice. This study presents a concise, knowledge-driven checklist for effective claim management in major standard forms, including International Federation of Consulting Engineers (FIDIC), the New Engineering Contract (NEC4), the American Institute of Architects (AIA), and Singapore’s Public Sector Standard Conditions of Contract (PSSCOC). The research mapped 22 entitlement clauses and 12 procedural clauses, then prioritized items through expert interviews and surveys. The final checklist comprises 16 items selected through transparent criteria (mean scores ≥ 4.0 or above group averages) that address critical risk areas. Application to two complex projects demonstrates that a few key clauses, such as those governing variations and timing requirements for requests, supporting documents, and decisions, account for most claim-related risks. Experts indicate that practical periods for submitting claim requests and proofs, and making decisions, are approximately 31, 65, and 61 days, respectively. The proposed checklist converts fragmented contract requirements into an actionable and auditable tool. It enhances clarity, transparency, and fairness in both pre-award reviews and daily project administration, which supports better risk management and minimizes disputes in global construction projects. Full article

Against the backdrop of policy-driven transformation in construction industrialization, the EPC general contracting model has emerged as a core pathway for the large-scale development of prefabricated buildings. However, the EPC mode integrates the links of design, procurement, production, and transportation, construction, resulting in a complex coupling correlation among the risk factors of prefabricated construction schedule, which is easy to induce the risk contagion effect and increase the difficulty of risk control of project schedule delay. To address this, this study constructs a hybrid model integrating the “Fuzzy Interpretive Structural Model (FISM)-Coupling Degree Model-Bayesian Network (BN)” to systematically analyze risk contagion mechanisms. Taking an EPC prefabricated building project as an example, FISM is used to reveal the hierarchical structure of risk factors, while the coupling degree model quantifies interaction strengths and maps them into the BN to optimize conditional probability parameters. Through comprehensive hazard analysis, seven key causal risk factors and two critical risk propagation paths are identified. Targeted control measures are designed for the key risk factors, and BN-based simulation is applied to locate critical risk nodes and implement break-chain interventions for the risk paths, resulting in a 23% reduction in the probability of schedule delay. Engineering applications demonstrate that this model can effectively achieve the dynamic identification and blocking of risk paths, providing valuable reference for similar projects and offering informed support for managers in formulating scientific response strategies. Full article

Background: Electroencephalography (EEG) offers millisecond-precision measurement of neural oscillations underlying human cognition and emotion. Despite extensive research, systematic frameworks mapping EEG metrics to psychological constructs remain fragmented. Objective: This interdisciplinary scoping review synthesizes current knowledge linking EEG signatures to affective and cognitive models from a neuroscience perspective. Methods: We examined empirical studies employing diverse EEG methodologies, from traditional spectral analysis to deep learning approaches, across laboratory and naturalistic settings. Results: Affective states manifest through distinct frequency-specific patterns: frontal alpha asymmetry (8–13 Hz) reliably indexes emotional valence with 75–85% classification accuracy, while arousal correlates with widespread beta/gamma power changes. Cognitive processes show characteristic signatures: frontal–midline theta (4–8 Hz) increases linearly with working memory load, alpha suppression marks attentional engagement, and theta/beta ratios provide robust cognitive load indices. Machine learning approaches achieve 85–98% accuracy for subject identification and 70–95% for state classification. However, significant challenges persist: spatial resolution remains limited (2–3 cm), inter-individual variability is substantial (alpha peak frequency: 7–14 Hz range), and overlapping signatures compromise diagnostic specificity across neuropsychiatric conditions. Evidence strongly supports integrated rather than segregated processing, with cross-frequency coupling mechanisms coordinating affective–cognitive interactions. Conclusions: While EEG-based assessment of mental states shows considerable promise for clinical diagnosis, brain–computer interfaces, and adaptive technologies, realizing this potential requires addressing technical limitations, standardizing methodologies, and establishing ethical frameworks for neural data privacy. Progress demands convergent approaches combining technological innovation with theoretical sophistication and ethical consideration. Full article

Background: Polyploidization drives the formation and evolution of angiosperms, profoundly reshaping genomic architecture and function. The 14-3-3 proteins (also known as G-box binding regulators, GRFs) are conserved signaling molecules involved in a range of physiological processes, including developmental signaling and stress responses. Elucidating the evolutionary trajectories of 14-3-3 genes in Brassica napus following allopolyploidization is critical for understanding polyploid crop evolution and developing molecular breeding strategies for improved stress resistance and yield. Results: In this study, forty-eight orthologous 14-3-3 genes were identified in the genome of B. napus, and twenty-two orthologous 14-3-3 genes were found in the genomes of both Brassica rapa and Brassica oleracea. Gene mapping analysis indicated that 14-3-3 genes were broadly distributed across all chromosomes; however, they exhibited significant heterogeneity. Phylogenetic tree construction revealed that 14-3-3 genes can be categorized into two groups: epsilon and non-epsilon genes. Gene structure analysis showed that most non-epsilon genes contain 3-4 exons, while most epsilon genes contain 5-7 exons. Collinearity analysis identified 36 orthologous gene pairs between the A (B. rapa) and C genomes (B. oleracea) but only 28 paralogous gene pairs within the A and C subgenomes of B. napus, indicating that some collinear 14-3-3 genes were lost during allopolyploidization. The Ka/Ks ratios (ratio of non-synonymous to synonymous substitution rate) of the 61 identified duplicated gene pairs were all less than 1, suggesting that these genes underwent purifying selection. Promoter analysis indicated that the average number of cis-acting elements in B. napus 14-3-3 genes was one more than in B. rapa and B. oleracea, implying that allopolyploidization increased the regulatory complexity of 14-3-3 genes. Tissue expression profiling demonstrated that the expression pattern of GRF2 homologs was altered after allopolyploidization. Conclusions: By systematically investigating the copy number, genomic distribution, structure, evolutionary relationships, and expression patterns of 14-3-3 genes in B. napus and its progenitors, this study enhances our understanding of how allopolyploidization promotes gene family evolution. Full article

Accurate, scalable mapping of coral reef habitats is essential for monitoring ecosystem health and detecting change over time. In this study, we introduce a novel mathematically based nonlinear spectral unmixing method for benthic habitat classification, which provides sub-pixel estimates of benthic composition, capturing the mixed benthic composition within individual pixels. We compare its performance against two machine learning approaches: semi-supervised K-Means clustering and AdaBoost decision trees. All models were applied to high-resolution PlanetScope satellite imagery and ICESat-2-derived terrain metrics. Models were trained using a ground truth dataset constructed from benthic photoquadrats collected at Heron Reef, Australia, with additional input features including band ratios, standardized band differences, and derived ICESat-2 metrics such as rugosity and slope. While AdaBoost achieved the highest overall accuracy ($93.3\%$) and benefited most from ICESat-2 features, K-Means performed less well ($85.9\%$) and declined when these metrics were included. The spectral unmixing method uniquely captured sub-pixel habitat abundance, offering a more nuanced and ecologically realistic view of reef composition despite lower discrete classification accuracy ($64.8\%$). These findings highlight nonlinear spectral unmixing as a promising approach for fine-scale, transferable coral reef habitat mapping, especially in complex or heterogeneous reef environments. Full article

Autonomous berthing of unmanned surface vehicles (USVs) requires high-precision positioning and accurate detection of navigable region in complex port environments. This paper presents an integrated LiDAR-based approach to address these challenges. A high-precision 3D point cloud map of the berth is first constructed by fusing LiDAR data with real-time kinematic (RTK) measurements. USV pose is then estimated by matching real-time LiDAR scans to the prior map, achieving robust, RTK-independent localization. For safe navigation, a novel navigable region detection algorithm is proposed, which combines point cloud projection, inner-boundary extraction, and target clustering. This method accurately identifies quay walls and obstacles, generating reliable navigable areas and ensuring collision-free berthing. Field experiments conducted in Ling Shui Port, Dalian, China, validate the proposed approach. Results show that the map-based positioning reduces absolute trajectory error (ATE) by 55.29% and relative trajectory error (RTE) by 38.71% compared to scan matching, while the navigable region detection algorithm provides precise and stable navigable regions. These outcomes demonstrate the effectiveness and practical applicability of the proposed method for autonomous USV berthing. Full article

Erosion-corrosion is a predominant failure mechanism in the petrochemical, energy, and offshore engineering sectors, causing substantial economic losses and posing significant threats to equipment safety and personnel well-being. To address this critical issue, the present study employs a systematic approach to examine the current status and estimate the future trends in erosion-corrosion research. By utilizing bibliometric techniques, the study constructs a comprehensive knowledge map to analyze the chronological progress, research institutions, journal distribution, collaborative networks, research hotspots and cutting-edge trends in this field. The bibliometric analysis reveals that research hotspots are primarily focused on the erosion-corrosion mechanism, equipment, materials, coating structure reinforcement, and new process of anticorrosion strategies. These findings suggest an interdisciplinary integration trend and the emergence of intelligent prevention and control methods. By elucidating the evolution and future direction of erosion-corrosion research, this study offers valuable insights for advancing academic progress and technological innovation in this area. Full article

Vegetation fires are among the most common natural disasters, posing significant threats to people and the natural environment worldwide. Density-based clustering methods can be used to identify geospatial clustering patterns of fire points. It further helps reveal the spatial distribution characteristics of wildfires, which are crucial for regional-specific fire mapping, prediction, mitigation, and protection. DBSCAN (density-based spatial clustering of applications with noise) is widely used for clustering spatial objects. It needs two user-determined threshold values: the local radius and the minimum number of neighboring points for core points, which require user expertise and background information. This work proposes a dual-population genetic optimization to determine threshold values of DBSCAN for clustering vegetation fire points in western China. By constructing randomly generated threshold populations, optimized threshold values are obtained through crossover, mutation, and inter-population exchange, measured by multiple clustering metrics. Focusing on vegetation wildfires in western China during 2016–2022, the results reveal that vegetation wildfires can be divided into eight regions, each exhibiting distinct spatiotemporal patterns and geographic contexts. Full article

To address issues such as low visual SLAM (Simultaneous Localization and Mapping) positioning accuracy and poor map construction robustness caused by light variations, foliage occlusion, and texture repetition in unstructured orchard environments, this paper proposes an orchard robot navigation method based on an improved RTAB-Map algorithm. By integrating ORB-SLAM3 as the visual odometry module within the RTAB-Map framework, the system achieves significantly improved accuracy and stability in pose estimation. During the post-processing stage of map generation, a height filtering strategy is proposed to effectively filter out low-hanging branch point clouds, thereby generating raster maps that better meet navigation requirements. The navigation layer integrates the ROS (Robot Operating System) Navigation framework, employing the A* algorithm for global path planning while incorporating the TEB (Timed Elastic Band) algorithm to achieve real-time local obstacle avoidance and dynamic adjustment. Experimental results demonstrate that the improved system exhibits higher mapping consistency in simulated orchard environments, with the odometry’s absolute trajectory error reduced by approximately 45.5%. The robot can reliably plan paths and traverse areas with low-hanging branches. This study provides a solution for autonomous navigation in agricultural settings that balances precision with practicality. Full article

Psychological safety (PS), knowledge management (KM), and organizational learning (OL) are increasingly recognized as critical foundations for resilient, adaptive, and innovative organizations. However, the connections among these constructs remain fragmented in the literature, making bibliometric mapping an essential step to consolidate knowledge in this domain. This study analyzes the relationships between PS, KM, and OL, identifying thematic patterns and theoretical contributions that support the integration of these constructs into organizational cultures. Drawing from empirical literature indexed in Web of Science (WoS) (2000–2025), we applied the SPIDER framework and PRISMA methodology to identify and evaluate 103 peer-reviewed articles. Using VOSviewer (version 1.6.20) and data mining techniques, we generated bibliometric networks and thematic clusters that offer a comprehensive view of the conceptual landscape. Findings reveal that PS acts as a key enabler of knowledge sharing and OL, particularly in inclusive environments with leadership support and tolerance for error. An inductively developed conceptual model illustrates how trust-driven cultures can enhance knowledge flows and reduce dysfunctional behaviors such as knowledge hiding. By mapping these intersections, the study consolidates fragmented literature and demonstrates how PS, KM, and OL contribute to sustainable learning cultures while also highlighting promising avenues for future research on collective learning and organizational resilience. Full article

More than 60% of earth excavation operations have been accomplished by various excavators. However, complex working loads always cause the fracture failure of excavator sticks because of insufficient strength. For prolonging the service life of excavator stick, a structural optimization design method based on the improved mayfly optimization algorithm (TTL-MA) is proposed to improve the stiffness of excavator stick. Firstly, by using the central composite design (CCD) method, 161 sets of simulation samples are obtained with eight selected structural design parameters of excavator stick. Then, relying on the simulation samples, an agent model between the excavator stick’s structural design parameters and the structural quality objectives, deformation, first-order minimum intrinsic frequency, and stress is constructed by using a Backpropagation neural network (BPNN). Finally, to further enhance the optimization search capability of the Mayfly Algorithm (MA), three improvement strategies were incorporated: Tent chaotic mapping for mayfly population initialization, adaptive t-distribution perturbation for velocity updating, and Lévy flight strategy for enhanced position updating. The results show that under the three constraints of the maximum equivalent von Mises stress σ_max ≤ 150 MPa, maximum deformation δ_max ≤ 2.5 mm, and the first-order minimum intrinsic frequency H_min ≥ 55 Hz, the optimized excavator stick reduces the mass and maximum stress by 7.9% and 11.9%, respectively. The improved mayfly optimization algorithm has strong optimization ability for the optimization design of excavator stick structure, which can provide a reference for similar complex engineering machinery structure optimization problems. Full article

After intensive artificial selection, the development of celestial eyes in goldfish involves the eyeballs protuberating and turning upwards. Thus, the celestial eye goldfish is an excellent model for both evolutionary and human ocular disease studies. Here, two mapping populations of goldfish with segregating eye phenotypes in the offspring were constructed. Through whole-genome sequencing and RNA-seq for eyeball samples, a premature stop codon in Exon 38 of the LRP2 gene was identified as the top candidate mutation for the celestial eye in goldfish. Fatty acid metabolism and epidermal cells, especially keratocyte-related functions, were inhibited in the eyeballs of celestial eye goldfish, while inflammatory reactions and extracellular matrix secretions were stimulated. These results suggest the dysfunction of the cornea in the celestial eye goldfish, and the same for the retina, which could be the results of the truncated LRP2 protein. In addition, the same gene, LRP2, is in charge of similar phenotypes (celestial eye and telescope eye) in goldfish, but these phenotypes have no shared mutations. In conclusion, the candidate mutation for the celestial eye in goldfish was identified by this study for the first time, and parallel evolutions of similar phenotypes at the molecular level under artificial selection were observed. These findings provide insights into the developmental and evolutionary processes of morphological changes in the eyes of goldfish. Full article