Search Results (27,108)

Based on the panel data of daily meteorological stations and winter wheat yield in Henan Province from 2000 to 2023, this study comprehensively used the Mann–Kendall trend test, wavelet coherence analysis (WTC), and other methods to reveal the temporal and spatial evolution of extreme precipitation and its multi-scale stress mechanism on grain yield. The results showed the following: (1) Extreme precipitation showed the characteristics of ‘frequent fluctuation-gentle trend-strong spatial heterogeneity’, and the maximum daily precipitation in spring (RX1DAY) showed a significant uplift. The increase in rainstorm events (R95p/R99p) in the southern region during the summer is particularly prominent; at the same time, the number of consecutive drought days (CDDs > 15 d) in the middle of autumn was significantly prolonged. It was also found that 2010 is a significant mutation node. Since then, the synergistic effect of ‘increasing drought days–increasing rainstorm frequency’ has begun to appear, and the short-period coherence of super-strong precipitation (R99p) has risen to more than 0.8. (2) The spatial pattern of winter wheat in Henan is characterized by the three-level differentiation of ‘stable core area, sensitive transition zone and shrinking suburban area’, and the stability of winter wheat has improved but there are still local risks. (3) There is a multi-scale stress mechanism of extreme precipitation on winter wheat yield. The long-period (4–8 years) drought and flood events drive the system risk through a 1–2-year lag effect (short-period (0.5–2 years) medium rainstorm intensity directly impacted the production system). This study proposes a ‘sub-scale governance’ strategy, using a 1–2-year lag window to establish a rainstorm warning mechanism, and optimizing drainage facilities for high-risk areas of floods in the south to improve the climate resilience of the agricultural system against the background of climate change. Full article

Prostate cancer (PC) remains a leading cause of malignancy in men worldwide, with current diagnostic methods such as prostate-specific antigen (PSA) testing and tissue biopsies facing limitations in specificity, invasiveness, and ability to capture tumor heterogeneity. Liquid biopsy, especially analysis of circulating tumor DNA (ctDNA), has emerged as a transformative tool for non-invasive detection, real-time monitoring, and treatment selection for PC. This review examines the role of ctDNA in both localized and metastatic PCs, focusing on its utility in early detection, risk stratification, therapy selection, and post-treatment monitoring. In localized PC, ctDNA-based biomarkers, including ctDNA fraction, methylation patterns, fragmentation profiles, and mutations, demonstrate promise in improving diagnostic accuracy and predicting disease recurrence. For metastatic PC, ctDNA analysis provides insights into tumor burden, genomic alterations, and resistance mechanisms, enabling immediate assessment of treatment response and guiding therapeutic decisions. Despite challenges such as the low ctDNA abundance in early-stage disease and the need for standardized protocols, advances in sequencing technologies and multimodal approaches enhance the clinical applicability of ctDNA. Integrating ctDNA with imaging and traditional biomarkers offers a pathway to precision oncology, ultimately improving outcomes. This review underscores the potential of ctDNA to redefine PC management while addressing current limitations and future directions for research and clinical implementation. Full article

Due to the complex spatial structure, dense tree distribution, and narrow passages in orchard environments, traditional path planning algorithms often struggle with large path deviations, frequent turning, and reduced navigational safety. In order to overcome these challenges, this paper proposes a hybrid path planning algorithm based on improved D* Lite for narrow forest orchard environments. The proposed approach enhances path feasibility and improves the robustness of the navigation system. The algorithm begins by constructing a 2D grid map reflecting the orchard layout and inflates the tree regions to create safety buffers for reliable path planning. For global path planning, an enhanced D* Lite algorithm is used with a cost function that jointly considers centerline proximity, turning angle smoothness, and directional consistency. This guides the path to remain close to the orchard row centerline, improving structural adaptability and path rationality. Narrow passages along the initial path are detected, and local replanning is performed using a Hybrid A* algorithm that accounts for the kinematic constraints of a differential tracked robot. This generates curvature-continuous and directionally stable segments that replace the original narrow-path portions. Finally, a gradient descent method is applied to smooth the overall path, improving trajectory continuity and execution stability. Field experiments in representative orchard environments demonstrate that the proposed hybrid algorithm significantly outperforms traditional D* Lite and KD* Lite-B methods in terms of path accuracy and navigational safety. The average deviation from the centerline is only 0.06 m, representing reductions of 75.55% and 38.27% compared to traditional D* Lite and KD* Lite-B, respectively, thereby enabling high-precision centerline tracking. Moreover, the number of hazardous nodes, defined as path points near obstacles, was reduced to five, marking decreases of 92.86% and 68.75%, respectively, and substantially enhancing navigation safety. These results confirm the method’s strong applicability in complex, constrained orchard environments and its potential as a foundation for efficient, safe, and fully autonomous agricultural robot operation. Full article

This article aims to explore and analyze current issues and features of environmental protection in managing local tourism destinations based on the principles of sustainable development through the relevant literature and thus to provide an insight into major environmental measures and activities that should be implemented in practice, emphasizing the importance of environmental sustainability as a key factor in the development and success of local tourist destinations in today’s business environment. Qualitative methods were used, with the literature review based on content analysis by keywords. This particularly affects the business process efficiency and the participation of destination stakeholders and in many cases leads to a low level of environmentally sustainable destination practices. In addition to this theoretical approach, this study also has direct managerial implications for destination environmental business operations. An attractive and well-preserved environment is the primary factor of tourism and local tourism destination development and its success, as well as an integrated part of the tourism product. This study addresses a critical gap in the existing literature on environmental sustainability at local destinations, where prior work has often overlooked the integration of actionable, practice-oriented frameworks tailored for both researchers and practitioners. While theoretical insights into sustainable practices abound, there remains a scarcity of holistic analyses that bridge scholarly understanding with implementable strategies for on-the-ground application. To fill this void, our research provides a comprehensive overview and systematic analysis of current practices, with targeted emphasis on co-developing scalable frameworks for improving environmentally sustainable practices at local destinations. Full article

Widely employed in diverse engineering applications, stiffened steel plates are often subjected to biaxial compressive loads. Under these conditions, buckling may occur, initially within the elastic range but potentially progressing into the elasto-plastic domain, which can lead to permanent deformations or structural collapse. To increase the ultimate buckling stress of plates, the implementation of longitudinal and transverse stiffeners is effective; however, this complexity makes analytical stress calculations challenging. As a result, numerical methods like the Finite Element Method (FEM) are attractive alternatives. In this study, the Constructal Design method and the Exhaustive Search technique were employed and associated with the FEM to optimize the geometric configuration of stiffened plates. A steel plate without stiffeners was considered, and 30% of its volume was redistributed into stiffeners, creating multiple configuration scenarios. The objective was to investigate how different arrangements and geometries of stiffeners affect the ultimate buckling stress under biaxial compressive loading. Among the configurations evaluated, the optimal design featured four longitudinal and two transverse stiffeners, with a height-to-thickness ratio of 4.80. This configuration significantly improved the performance, achieving an ultimate buckling stress 472% higher than the unstiffened reference plate. In contrast, the worst stiffened configuration led to a 57% reduction in performance, showing that not all stiffening strategies are beneficial. These results demonstrate that geometric optimization of stiffeners can significantly enhance the structural performance of steel plates under biaxial compression, even without increasing material usage. The approach also revealed that intermediate slenderness values lead to better stress distribution and delayed local buckling. Therefore, the methodology adopted in this work provides a practical and effective tool for the design of more efficient stiffened plates. Full article

Smart safety helmets equipped with vision systems are emerging as powerful tools for industrial infrastructure inspection. This paper presents a comprehensive state-of-the-art review of such VSLAM-enabled (Visual Simultaneous Localization and Mapping) helmets. We surveyed the evolution from basic helmet cameras to intelligent, sensor-fused inspection platforms, highlighting how modern helmets leverage real-time visual SLAM algorithms to map environments and assist inspectors. A systematic literature search was conducted targeting high-impact journals, patents, and industry reports. We classify helmet-integrated camera systems into monocular, stereo, and omnidirectional types and compare their capabilities for infrastructure inspection. We examine core VSLAM algorithms (feature-based, direct, hybrid, and deep-learning-enhanced) and discuss their adaptation to wearable platforms. Multi-sensor fusion approaches integrating inertial, LiDAR, and GNSS data are reviewed, along with edge/cloud processing architectures enabling real-time performance. This paper compiles numerous industrial use cases, from bridges and tunnels to plants and power facilities, demonstrating significant improvements in inspection efficiency, data quality, and worker safety. Key challenges are analyzed, including technical hurdles (battery life, processing limits, and harsh environments), human factors (ergonomics, training, and cognitive load), and regulatory issues (safety certification and data privacy). We also identify emerging trends, such as semantic SLAM, AI-driven defect recognition, hardware miniaturization, and collaborative multi-helmet systems. This review finds that VSLAM-equipped smart helmets offer a transformative approach to infrastructure inspection, enabling real-time mapping, augmented awareness, and safer workflows. We conclude by highlighting current research gaps, notably in standardizing systems and integrating with asset management, and provide recommendations for industry adoption and future research directions. Full article

Accurately predicting the compressive strength of high-performance concrete (HPC) is critical for ensuring structural integrity and promoting sustainable construction practices. However, HPC exhibits highly complex, nonlinear, and multi-factorial interactions among its constituents (such as cement, aggregates, admixtures, and curing conditions), which pose significant challenges to conventional predictive models. Traditional approaches often fail to adequately capture these intricate relationships, resulting in limited prediction accuracy and poor generalization. Moreover, the high dimensionality and noisy nature of HPC mix data increase the risk of model overfitting and convergence to local optima during optimization. To address these challenges, this study proposes a novel bio-inspired hybrid optimization model, AP-IVYPSO-BP, which is specifically designed to handle the nonlinear and complex nature of HPC strength prediction. The model integrates the ivy algorithm (IVYA) with particle swarm optimization (PSO) and incorporates an adaptive probability strategy based on fitness improvement to dynamically balance global exploration and local exploitation. This design effectively mitigates common issues such as premature convergence, slow convergence speed, and weak robustness in traditional metaheuristic algorithms when applied to complex engineering data. The AP-IVYPSO is employed to optimize the weights and biases of a backpropagation neural network (BPNN), thereby enhancing its predictive accuracy and robustness. The model was trained and validated on a dataset comprising 1,030 HPC mix samples. Experimental results show that AP-IVYPSO-BP significantly outperforms traditional BPNN, PSO-BP, GA-BP, and IVY-BP models across multiple evaluation metrics. Specifically, it achieved an R² of 0.9542, MAE of 3.0404, and RMSE of 3.7991 on the test set, demonstrating its high accuracy and reliability. These results confirm the potential of the proposed bio-inspired model in the prediction and optimization of concrete strength, offering practical value in civil engineering and materials design. Full article

Accurate weld seam recognition is essential in automated welding systems, as it directly affects path planning and welding quality. With the rapid advancement of industrial vision, weld seam instance segmentation has emerged as a prominent research focus in both academia and industry. However, existing approaches still face significant challenges in boundary perception and structural representation. Due to the inherently elongated shapes, complex geometries, and blurred edges of weld seams, current segmentation models often struggle to maintain high accuracy in practical applications. To address this issue, a novel structure-aware and boundary-enhanced YOLO (SABE-YOLO) is proposed for weld seam instance segmentation. First, a Structure-Aware Fusion Module (SAFM) is designed to enhance structural feature representation through strip pooling attention and element-wise multiplicative fusion, targeting the difficulty in extracting elongated and complex features. Second, a C2f-based Boundary-Enhanced Aggregation Module (C2f-BEAM) is constructed to improve edge feature sensitivity by integrating multi-scale boundary detail extraction, feature aggregation, and attention mechanisms. Finally, the inner minimum point distance-based intersection over union (Inner-MPDIoU) is introduced to improve localization accuracy for weld seam regions. Experimental results on the self-built weld seam image dataset show that SABE-YOLO outperforms YOLOv8n-Seg by 3 percentage points in the AP(50–95) metric, reaching 46.3%. Meanwhile, it maintains a low computational cost (18.3 GFLOPs) and a small number of parameters (6.6M), while achieving an inference speed of 127 FPS, demonstrating a favorable trade-off between segmentation accuracy and computational efficiency. The proposed method provides an effective solution for high-precision visual perception of complex weld seam structures and demonstrates strong potential for industrial application. Full article

Colloidal silica acts as a multifunctional reagent in the froth flotation process of semi-soluble salt-type minerals, enabling the selective depression of calcite. This study investigates its effect on four key minerals—calcite, scheelite, apatite, and fluorite—using a comprehensive suite of techniques to identify the flotation subprocesses modulated by colloidal silica. This work also aims to determine the specific flotation zones affected by colloidal silica, assessing the influence of its dosage, surface modification, and specific surface area on metallurgical outcomes. Atomic force microscopy revealed mineral-specific surface responses to colloidal silica conditioning: calcite exhibited localized nanoparticle adsorption, whereas apatite underwent a dissolution–reprecipitation mechanism. Scheelite and fluorite, in contrast, showed minimal surface modifications. These differences are attributed to variations in surface reactivity, hydration behavior, and crystallographic structure, with calcite offering a uniquely favorable environment for colloidal silica attachment. Mechanistic insights show that colloidal silica—especially the aluminate-modified type with high specific surface area—influences both the pulp and froth zones by producing small, stable bubbles, enhancing fine scheelite recovery, stabilizing froth, and effectively depressing calcite. In contrast, non-functionalized colloidal silica resulted in poor bubble control and unstable froth. These findings elucidate the subprocess-specific mechanisms by which colloidal silica operates and highlight its potential as a tunable, multifunctional reagent for improving selectivity in the flotation of semi-soluble salt-type minerals. Full article

To address the shortcomings of poor convergence and the ease of falling into local optima when using the traditional gold rush optimization (GRO) algorithm to solve the complex scheduling problem of a combined cooling, heating, and power (CCHP) microgrid system, an optimal scheduling model for a microgrid based on the improved gold rush optimization (IGRO) algorithm is proposed. First, the Halton sequence is introduced to initialize the population, ensuring a uniform and diverse distribution of prospectors, which enhances the algorithm’s global exploration capability. Then, a dynamically adaptive weighting factor is applied during the gold mining phase, enabling the algorithm to adjust its strategy across different search stages by balancing global exploration and local exploitation, thereby improving the convergence efficiency of the algorithm. In addition, a weighted global optimal solution update strategy is employed during the cooperation phase, enhancing the algorithm’s global search capability while reducing the risk of falling into local optima by adjusting the balance of influence between the global best solution and local agents. Finally, a t-distribution mutation strategy is introduced to improve the algorithm’s local search capability and convergence speed. The IGRO algorithm is then applied to solve the microgrid scheduling problem, with the objective function incorporating power purchase and sale cost, fuel cost, maintenance cost, and environmental cost. The example results show that, compared with the GRO algorithm, the IGRO algorithm reduces the average total operating cost of the microgrid by 3.29%, and it achieves varying degrees of cost reduction compared to four other algorithms, thereby enhancing the system’s economic benefits. Full article

Background: Pancreatic adenocarcinoma (PDAC) with liver oligometastases (LOM) presents a therapeutic challenge, with optimal management strategies remaining uncertain. This study evaluates the long-term outcomes, patterns of disease progression, and potential factors influencing prognosis in this patient subset. Methods: Patients diagnosed with PDAC and LOM were retrospectively analyzed. Disease progression patterns, causes of death, and predictors of long-term outcomes were assessed. Results: Among 1442 patients diagnosed with metastatic PDAC between November 2009 and July 2024, 129 (9%) presented with LOM, defined as ≤3 liver lesions each measuring <2 cm. Patients with LOM had significantly improved overall survival (OS) compared to those with high-burden disease (p = 0.026). The cause of death (local regional disease vs. systemic disease) could be determined in 74 patients (57%), among whom age at diagnosis, history of smoking, and white blood cell (WBC) count differed significantly between groups. However, no significant difference in OS was observed between the two groups (p = 0.64). Sixteen patients (22%) died from local complications of the primary tumor, including 6 patients (7%) who showed no evidence of new or progressive metastases. In competing risk and multivariable analysis, a history of smoking remained the only factor significantly associated with death due to local complications. Conclusions: Approximately one in five patients with PDAC-LOM died from local tumor-related complications—some without metastatic progression—highlighting a potential role for surgical intervention. Further multicenter studies are warranted to refine diagnostic criteria and better identify patients who may benefit from surgery. Full article

This paper presents the design and implementation of a SLAM-based online mapping and autonomous trajectory execution system for the Nimbulus-e, a concept vehicle designed for agile maneuvering in confined spaces. The Nimbulus-e uses individual steer-by-wire corner modules with in-wheel motors at all four corners. The associated eight joint variables serve as control inputs, allowing precise trajectory following. These control inputs can be derived from the vehicle’s trajectory using nonholonomic constraints. A LiDAR sensor is used to map the environment and detect obstacles. The system processes LiDAR data in real time, continuously updating the environment map and enabling localization within the environment. The inclusion of vehicle odometry data significantly reduces computation time and improves accuracy compared to a purely visual approach. The A* and Hybrid A* algorithms are used for trajectory planning and optimization, ensuring smooth vehicle movement. The implementation is validated through both full vehicle simulations using an ADAMS Car—MATLABco-simulation and a scaled physical prototype, demonstrating the effectiveness of the system in navigating complex environments. This work contributes to the field of autonomous systems by demonstrating the potential of combining advanced sensor technologies with innovative control algorithms to achieve reliable and efficient navigation. Future developments will focus on improving the robustness of the system by implementing a robust closed-loop controller and exploring additional applications in dense urban traffic and agricultural operations. Full article

The classification of cancer based on gene expression profiles is a central challenge in precision oncology due to the high dimensionality and low sample size inherent in microarray datasets. Effective gene selection is crucial for improving classification accuracy while minimizing computational overhead and model complexity. This study introduces Genetic-Embedded Nuclear Reaction Optimization (GNR), a novel hybrid metaheuristic that enhances the conventional Nuclear Reaction Optimization (NRO) algorithm by embedding a genetic uniform crossover mechanism into its fusion phase. The proposed algorithm leverages a two-stage process: an initial F-score filtering step to reduce dimensionality, followed by GNR-driven optimization to identify compact, informative gene subsets. Evaluations were conducted on six widely used microarray cancer datasets, with Support Vector Machines (SVM) employed as classifiers and performance assessed via Leave-One-Out Cross-Validation (LOOCV). Results show that GNR consistently outperforms the original NRO and several benchmark hybrid algorithms, achieving 100% classification accuracy with significantly smaller gene subsets across all datasets. These findings confirm the efficacy of the genetic-embedded fusion strategy in enhancing local exploitation while preserving the global search capabilities of NRO, thereby offering a robust and interpretable approach for gene selection in cancer classification. Full article

Synthetic aperture radar (SAR) images suffer from speckle noise due to their imaging mechanism, which deteriorates image interpretability and hinders subsequent tasks like target detection and recognition. Traditional denoising methods fall short of the demands for high-quality SAR image processing, and deep learning approaches trained on synthetic datasets exhibit poor generalization because noise-free real SAR images are unattainable. To solve this problem and improve the quality of SAR images, a speckle noise suppression method based on subaperture decomposition and non-local low-rank tensor approximation is proposed. Subaperture decomposition yields azimuth-frame subimages with high global structural similarity, which are modeled as low-rank and formed into a 3D tensor. The tensor is decomposed to derive a low-dimensional orthogonal basis and low-rank representation, followed by non-local denoising and iterative regularization in the low-rank subspace for data reconstruction. Experiments on simulated and real SAR images demonstrate that the proposed method outperforms state-of-the-art techniques in speckle suppression, significantly improving SAR image quality. Full article

Background: Papillary thyroid microcarcinoma (PTMC) is associated with certain features that carry an increased risk of local recurrence, underscoring the importance of preoperative risk assessment. This study investigated the clinicopathological factors associated with high-risk lymph node metastasis (HRLNM) and patient outcomes. HRLNM is defined as ≥5 metastatic lymph nodes and/or lateral neck metastasis. Methods: We conducted a retrospective review of 985 patients with PTMC who underwent thyroidectomy at the Kaohsiung Chang Gung Memorial Hospital from 2013 to 2022. Results: Among the 985 patients, 100 (10.2%) had lymph node metastasis (LNM), and 27% of these were classified as having HRLNM. Male sex (OR 3.61, p = 0.04) and extranodal extension (OR 3.76, p = 0.043) were independent predictors of HRLNM. Patients with LNM exhibited lower rates of excellent treatment response (75% vs. 87%, p = 0.001), higher recurrence rates (9.0% vs. 0.6%, p = 0.001), and an increased risk of distant metastasis (2.0% vs. 0%). Recurrence-free survival (RFS) was significantly shorter in patients with LNM (120.9 vs. 198.6 months, p < 0.001). Although HRLNM showed a trend toward reduced RFS (113.5 vs. 124.6 months, p = 0.177), its impact on long-term survival remains uncertain. Conclusions: Male sex and extranodal extension were significant risk factors for HRLNM in patients with PTMC. These findings highlight the need for individualized risk stratification to guide treatment strategies and improve patient outcomes. Full article