Search Results (132)

Drag embedment anchors (DEAs) play a vital role in maintaining the stability and safety of offshore structures, including floating wind turbines, oil rigs, and marine renewable energy systems. Accurate prediction of anchor performance is essential for optimizing mooring system designs, reducing costs, and minimizing risks in challenging marine environments. By leveraging advanced machine learning techniques, this research provides innovative solutions to longstanding challenges in geotechnical engineering, paving the way for more efficient and reliable offshore operations. The findings contribute significantly to developing sustainable marine infrastructure while addressing the growing global demand for renewable energy solutions in coastal and deep-water environments. This current study evaluated tree-based machine learning algorithms, e.g., decision tree regression (DTR) and random forest regression (RFR), to predict the holding capacity and efficiency of DEAs in sand seabed. To train and validate the results of machine learning models, the K-fold cross-validation method, with K = 5, was utilized. Eleven geotechnical and geometric parameters, including sand friction angle (φ), fluke-shank angle (α), and anchor dimensions, were analyzed using 23 model configurations. Results demonstrated that RFR outperformed DTR, achieving the highest accuracy for capacity prediction (R = 0.985, RMSE = 344.577 KN) and for efficiency (R = 0.977, RMSE = 0.821 KN). Key findings revealed that soil strength dominated capacity, while fluke-shank angle critically influenced efficiency. Single-parameter models failed to capture complex soil-anchor interactions, underscoring the necessity of multivariate analysis. The ensemble approach of RFR provided superior generalization across diverse seabed conditions, maintaining errors within ±10% for capacity and ±5% for efficiency. Full article

The electrochemical reduction of carbon monoxide (COER) offers a promising route for generating value-added multi-carbon (C₂₊) products, such as ethanol, but achieving high catalytic performance remains a significant challenge. Herein, we performed comprehensive density functional theory (DFT) computations to evaluate CO-to-ethanol conversion on single metal atoms anchored on graphitic carbon nitride (TM/g–CN). We showed that these metal atoms stably coordinate with edge N sites of g–CN to form active catalytic centers. Screening 20 TM/g–CN candidates, we identified V/g–CN and Zn/g–CN as optimal catalysts: both exhibit low free-energy barriers (<0.50 eV) for the key ^*CO hydrogenation steps and facilitate C–C coupling via an Eley–Rideal mechanism with a negligible kinetic barrier (~0.10 eV) to yield ethanol at low limiting potentials, which explains their superior COER performance. An analysis of d-band centers, charge transfer, and bonding–antibonding orbital distributions revealed the origin of their activity. This work provides theoretical insights and useful guidelines for designing high-performance single-atom COER catalysts. Full article

Kirigami-inspired geometries offer a lightweight, bioinspired strategy for friction enhancement and anchoring in soft robotics. This study presents a bioinspired kirigami structure designed to enhance the anchoring performance of soft robotic systems through systematic geometric and actuation parameter optimization. Drawing inspiration from the anisotropic friction mechanisms observed in reptilian scales, we integrated linear, triangular, trapezoidal, and hybrid kirigami cuts onto flexible plastic sheets. A compact 12 V linear actuator enabled cyclic actuation via a custom firmware loop, generating controlled buckling and directional friction for effective locomotion. Through experimental trials, we quantified anchoring efficiency using crawling distance and stride metrics across multiple cut densities and actuation conditions. Among the tested configurations, the triangular kirigami with a 4 × 20 unit density on 100 µm PET exhibited the most effective performance, achieving a stride efficiency of approximately 63% and an average crawling speed of ~47 cm/min under optimized autonomous operation. A theoretical framework combining buckling mechanics and directional friction validated the observed trends. This study establishes a compact, tunable anchoring mechanism for soft robotics, offering strong potential for autonomous exploration in constrained environments. Full article

Citrus Huanglongbing (HLB) is one of the most devastating diseases in the global citrus industry, but its early detection under complex field conditions remains a major challenge. Existing methods often suffer from insufficient dataset diversity and poor generalization, and struggle to accurately detect subtle early-stage lesions and multiple HLB symptoms in natural backgrounds. To address these issues, we propose an enhanced YOLO11-based framework, DCH-YOLO11. We constructed a multi-symptom HLB leaf dataset (MS-HLBD) containing 9219 annotated images across five classes: Healthy (1862), HLB blotchy mottling (2040), HLB Zinc deficiency (1988), HLB yellowing (1768), and Canker (1561), collected under diverse field conditions. To improve detection performance, the DCH-YOLO11 framework incorporates three novel modules: the C3k2 Dynamic Feature Fusion (C3k2_DFF) module, which enhances early and subtle lesion detection through dynamic feature fusion; the C2PSA Context Anchor Attention (C2PSA_CAA) module, which leverages context anchor attention to strengthen feature extraction in complex vein regions; and the High-efficiency Dynamic Feature Pyramid Network (HDFPN) module, which optimizes multi-scale feature interaction to boost detection accuracy across different object sizes. On the MS-HLBD dataset, DCH-YOLO11 achieved a precision of 91.6%, recall of 87.1%, F1-score of 89.3, and mAP50 of 93.1%, surpassing Faster R-CNN, SSD, RT-DETR, YOLOv7-tiny, YOLOv8n, YOLOv9-tiny, YOLOv10n, YOLO11n, and YOLOv12n by 13.6%, 8.8%, 5.3%, 3.2%, 2.0%, 1.6%, 2.6%, 1.8%, and 1.6% in mAP50, respectively. On a publicly available citrus HLB dataset, DCH-YOLO11 achieved a precision of 82.7%, recall of 81.8%, F1-score of 82.2, and mAP50 of 89.4%, with mAP50 improvements of 8.9%, 4.0%, 3.8%, 3.2%, 4.7%, 3.2%, and 3.4% over RT-DETR, YOLOv7-tiny, YOLOv8n, YOLOv9-tiny, YOLOv10n, YOLO11n, and YOLOv12n, respectively. These results demonstrate that DCH-YOLO11 achieves both state-of-the-art accuracy and excellent generalization, highlighting its strong potential for robust and practical citrus HLB detection in real-world applications. Full article

Vehicle collisions with street lighting poles generate extremely high impact forces, often resulting in serious injuries or fatalities. Therefore, enhancing the structural resilience of pole bases is a critical engineering objective. This study investigates a comprehensive dynamic analysis conducted with respect to base material behavior and energy absorption of GFRP lighting pole structures under impact loads. A finite element (FE) model of a 5 m-tall tapered GFRP pole with a steel base sleeve, base plate, and anchor bolts was developed. A 500 kg drop-weight impact at 400 mm above the base simulated vehicle collision conditions. The model was validated against experimental data, accurately reproducing the observed failure mode and peak force within 6%. Parametric analyses explored variations in pole diameter, wall thickness, base plate size and thickness, sleeve height, and anchor configuration. Results revealed that geometric parameters—particularly wall thickness and base plate dimensions—had the most significant influence on energy absorption. Doubling the wall thickness reduced normalized energy absorption by approximately 76%, while increases in base plate size and thickness reduced it by 35% and 26%, respectively. Material strength and anchor bolt configuration showed minimal impact. These findings underscore the importance of optimizing pole geometry to enhance crashworthiness. Controlled structural deformation improves energy dissipation, making geometry-focused design strategies more effective than simply increasing material strength. This work provides a foundation for designing safer roadside poles and highlights areas for further exploration in base configurations and connection systems. Full article

Tracking unmanned aerial vehicles (UAVs) in infrared video is an essential technology for the anti-UAV task. Given frequent UAV target disappearances caused by occlusion or moving out of view, global trackers, which have the unique ability to recapture targets, are widely used in infrared UAV tracking. However, global trackers perform poorly when dealing with large target scale variation because they cannot maintain approximate consistency between target sizes in the template and the search region. To enhance the scale adaptation of global trackers, we propose a plug-and-play scale adaptation enhancement module (SAEM). This can generate a scale adaptation enhancement kernel according to the target size in the previous frame, and then perform implicit scale adaptation enhancement on the extracted target template features. To optimize training, we introduce an auxiliary branch to supervise the learning of SAEM and add Gaussian noise to the input size to improve its robustness. In addition, we propose a one-stage anchor-free global tracker (OSGT), which has a more concise structure than other global trackers to meet the real-time requirement. Extensive experiments on three Anti-UAV Challenge datasets and the Anti-UAV410 dataset demonstrate the superior performance of our method and verify that our proposed SAEM can effectively enhance the scale adaptation of existing global trackers. Full article

Neurotransmitters such as serotonin regulate key physiological and cognitive functions, yet real-time detection remains challenging due to the limitations of conventional techniques like amperometry and microdialysis. Fluorescent molecularly imprinted polymer nanoparticles (fMIP-NPs) offer a promising alternative and are typically synthesized via solid-phase synthesis, in which template molecules are covalently immobilized on a solid support to enable site-specific imprinting. However, strong template–template interactions during this process can compromise selectivity. To overcome this, we incorporated a poly(methacrolein-co-methacrylamide)-based template anchoring strategy to minimize undesired template interactions and enhance imprinting efficiency. We optimized the synthesis of poly(methacrolein-co-methacrylamide) under three different conditions by varying the monomer compositions and reaction parameters. The poly(methacrolein-co-methacrylamide) synthesized under Condition 3 (5:1 methacrolein-to-methacrylamide molar ratio, 1:150 initiator-to-total monomer ratio, and 4.59 M total monomer concentration) yielded the most selective fMIP-NPs, whose fluorescence intensity increased with an increase in serotonin concentration, rising by up to 37% upon serotonin binding. This improvement is attributed to higher aldehyde functionality in the poly(methacrolein-co-methacrylamide) which enhances template immobilization and generates a rigid imprinted cavity to interact with serotonin. These findings suggest that the developed fMIP-NPs hold significant potential as imaging probes for neurotransmitter detection, contributing to advanced studies in neural network analysis. Full article

Adequate and diverse diets are essential for children’s physical and cognitive development, yet food insecurity and malnutrition continue to threaten this fundamental right, which remains a pressing concern in many resource-poor settings. This study investigated food and nutrition security in Early Childhood Development (ECD) centres in Makhanda, South Africa, through a community-based participatory research approach. Using a mixed-methods approach combining questionnaire interviews, focus group discussions, direct observations, and community asset mapping across eight ECD centres enrolling 307 children aged 0–5 years, the study engaged ECD facilitators and analysed dietary practices across these centres. Results indicated that financial constraints severely affect the quality and diversity of food provided at the centres, thus undermining the ability to provide nutritionally adequate meals. The average amount spent on food per child per month at the centres was R90 ± R25 (South African Rand). Although three meals were generally offered daily, cost-driven dietary substitutions with cheaper, less diverse alternatives, often at the expense of nutritional value, were common. Despite guidance from Department of Health dieticians, financial limitations contributed to suboptimal feeding practices, with diets dominated by grains and starchy foods, with limited access to and rare consumption of protein-rich foods, dairy, and vitamin A-rich fruits and vegetables. ECD facilitators noted insufficient parental contributions and low engagement in supporting centre operations and child nutrition provision, indicating a gap in awareness and limited nutrition knowledge regarding optimal infant and young child feeding (IYCF) practices. The findings emphasise the need for sustainable, multi-level and community-led interventions, including food gardening, creating ECD centre food banks, parental nutrition education programmes, and enhanced financial literacy among ECD facilitators. Strengthening local food systems and establishing collaborative partnerships with communities and policymakers are essential to improve the nutritional environment in ECD settings. Similarly, enhanced government support mechanisms and policy-level reforms are critical to ensure that children in resource-poor areas receive adequate nutrition. Future research should focus on scalable, locally anchored models for sustainable child nutrition interventions that are contextually grounded, community-driven, and should strengthen the resilience of ECD centres in South Africa. Full article

Background/Objectives: Operating rooms represent valuable and pivotal units of any hospital. Therefore, their management affects healthcare service delivery through rescheduling, staff shortage/overtime, cost inefficiency, and patient dissatisfaction, among others. To optimize scheduling, we aim to assess preoperative evaluation criteria that influence the prediction of surgery duration for gastric cancer (GC) patients. In GC, radical surgery with curative intent is the ideal treatment. Nevertheless, the intervention sometimes must be palliative if the patient’s status and tumor staging prove too advanced. Methods: A 6-year retrospective cohort study was performed in a tertiary care hospital, including all cases diagnosed with GC (ICD-10 code C16), confirmed through histopathology, and undergoing surgical treatment (N = 108). Results: The results of our study confirm male predominance (63.89%) among GC surgery candidates while bringing new perspectives on patient evaluation criteria and choice of surgical intervention (curative—Group 1, palliative—Group 2). Surgery duration, including anesthesiology (175.19 [95% CI (157.60–192.77)] min), shows a direct correlation with the number of lymph nodes dissected (Surgical duration [min] = 10.67 × No. of lymph nodes removed − 32.25). Interestingly, the aggressiveness of the tumor based on histological grade (highly differentiated being generally less aggressive than poorly differentiated) shows differential correlation with surgery duration among curative and palliative surgery candidates. Similarly, TNM staging indicates the need for a longer surgical duration (pTNM stage IIA, IIB, and IIIA) for curative interventions in patients with less advanced stages, as opposed to shorter surgery duration for palliative interventions (pTNM stage IIIC and IV). Conclusions: The study quantitatively presents the resources needed for the optimal surgical treatment of different groups of GC patients, as the disease coding systems in use regard the treatment of each pathology as “standard” in terms of patient management. The results obtained are anchored in the global perspectives of surgical outcomes and aim to improve the management of operating room scheduling, staff, and resources. Full article

A barrier to the adoption of floating offshore wind turbines is their high cost relative to conventional fixed-bottom wind turbines. The largest contributor to this cost disparity is generally the floating substructure, due to its large size and complexity. Typically, a primary driver of the geometry and size of a floating substructure is the extreme environmental load case of Region 4, where platform loads are the greatest due to the impact of extreme wind and waves. To address this cost issue, a new concept for a floating offshore wind turbine’s substructure, its moorings, and anchors was optimized for a reference 10-MW turbine under extreme load conditions using OpenFAST. The levelized cost of energy was minimized by fixing the above-water turbine design and minimizing the equivalent substructure mass, which is based on the mass of all substructure components (stem, legs, buoyancy cans, mooring, and anchoring system) and associated costs of their materials, manufacturing, and installation. A stepped optimization scheme was used to allow an understanding of their influence on both the system cost and system dynamic responses for the extreme parked load case. The design variables investigated include the length and tautness ratio of the mooring lines, length and draft of the cans, and lengths of the legs and the stem. The dynamic responses investigated include the platform pitch, platform roll, nacelle horizontal acceleration, and can submergence. Some constraints were imposed on the dynamic responses of interest, and the metacentric height of the floating system was used to ensure static stability. The results offer insight into the parametric influence on turbine motion and on the potential savings that can be achieved through optimization of individual substructure components. A 36% reduction in substructure costs was achieved while slightly improving the hydrodynamic stability in pitch and yielding a somewhat large surge motion and slight roll increase. Full article

Electrocatalytic nitrate reduction to ammonia (ENRA) presents a promising strategy for simultaneous environmental remediation and sustainable ammonia synthesis. In this work, a Cu–Co bimetallic catalyst supported on functionalized reduced graphene oxide (RGO) was systematically designed to achieve efficient and selective ammonia production. Surface oxygen functional groups on graphene oxide (GO) were optimized through alkaline hydrothermal treatments, enhancing the anchoring capacity for metal active sites. Characterization indicated the successful formation of uniform Cu–Co bimetallic heterointerfaces comprising metallic and oxide phases, which significantly improved catalyst stability and performance. Among the studied compositions, Cu₆Co₄/RGO exhibited superior catalytic activity, achieving a remarkable ammonia selectivity of 99.86% and a Faradaic efficiency of 96.54% at −0.6 V (vs. RHE). Long-term electrocatalysis demonstrated excellent durability, with over 90% Faradaic efficiency maintained for ammonia production after 20 h of operation. In situ FTIR analysis revealed that introducing Co effectively promoted water dissociation, facilitating hydrogen generation (*H) and accelerating the transformation of nitrate intermediates. This work offers valuable mechanistic insights and paves the way for the design of highly efficient bimetallic electrocatalysts for nitrate reduction and ammonia electrosynthesis. Full article

This study evaluates the safety aspects of a prototype electric vehicle designed to enable wheelchair users to independently perform simple farm tasks in rural settings, like sample collection and crop monitoring. The vehicle, built at CREA, features four in-wheel electric motors, a pneumatic suspension system, and a secure wheelchair anchoring system. Tests at the CREA experimental farm assessed the vehicle’s whole-body vibrations on different surfaces (asphalt, headland, dirt road) using two tyre models and multiple speeds. A triaxial accelerometer on the wheelchair seat measured vibrations, which were analysed in accordance with ISO standards. Frequency analysis revealed significant vibrations in the 2–40 Hz range, with the Z-axis consistently showing the highest accelerations, which increased with the speed. Tyre A generally induced higher vibrations than Tyre B, likely due to the tread design. At high speeds, the effective accelerations exceeded safety thresholds on asphalt and headland. Statistical analysis confirmed speed as the dominant factor, with the surface type also playing a key role—headland generated the highest vibrations, followed by dirt road and asphalt. The results of these first tests highlighted the high potential of the vehicle to improve the agricultural mobility of disabled people, granting safety conditions and low vibration levels on all terrains at speeds up to 10 km h⁻¹. At higher speeds, however, the vibration levels may exceed the exposure limits, depending on the irregularities of the terrain and the tyre model. Overcoming these limitations is achievable through the optimization of the suspensions and tyres and will be the subject of the next step of this study. This technology could also support wheelchair users in construction, natural parks, and urban mobility. Full article

Recent advancements in deep-learning and computer vision technologies, coupled with the availability of large-scale remote-sensing image datasets, have accelerated the progress of remote-sensing object detection. However, large-scale remote-sensing images typically feature extensive and complex backgrounds with small and sparsely distributed objects, which pose significant challenges to detection performance. To address this, we propose a novel framework for accurate object detection, termed transparent mask background optimization for accurate object detection (TMBO-AOD), which incorporates a clear focus module and an adaptive filtering framework. The clear focus module constructs an empirical background pool using a Gaussian distribution and introduces transparent masks to prepare for subsequent optimization stages. The adaptive filtering framework can be applied to anchor-based or anchor-free models. It dynamically adjusts the number of candidates generated based on background flags, thereby optimizing the label assignment process. This approach not only alleviates the imbalance between positive and negative samples but also enhances the efficiency of candidate generation. Furthermore, we introduce a novel separated loss function that strengthens both foreground and background consistencies. Specifically, it focuses the model’s attention on foreground objects while enabling it to learn the consistency of background features, thus improving its ability to distinguish objects from the background. We employ YOLOv8 combined with our proposed optimizations to evaluate our model in many datasets, demonstrating improvements in both accuracy and efficiency. Additionally, we validate the effectiveness of our adaptive filtering framework in both anchor-based and anchor-free methods. When implemented with YOLOv5 (anchor based), the framework reduces the candidate generation time by 48.36%, while the YOLOv8 (anchor-free) implementation achieves a 46.81% reduction, both with maintained detection accuracy. Full article

The field of remote sensing target detection has experienced rapid development in recent years, demonstrating significant value in various applications. However, general detection algorithms still face many key challenges when dealing with directional target detection: firstly, conventional networks struggle to accurately represent features of rotated targets, particularly in modeling the slender shape characteristics of high-aspect-ratio targets; secondly, the mismatch between the static label allocation strategy and the feature space of dynamic rotating targets leads to bias in training sample selection under extreme-aspect-ratio scenarios. To address these issues, this paper proposes a single-stage Shape-Aware Dynamic Alignment Network (SADA-Net) that collaboratively enhances detection accuracy through feature representation optimization and adaptive label matching. The network’s design philosophy demonstrates greater flexibility and complementarity than that of previous models. Specifically, a Dynamic Refined Rotated Convolution Module (DRRCM) is designed to achieve rotation-adaptive feature alignment. An Anchor-Refined Feature Alignment Module (ARFAM) is further constructed to correct feature-to-spatial misalignment. In addition, a Shape-Aware Quality Assessment (SAQA) strategy is proposed to optimize sample matching quality based on target shape information. Experiment results demonstrate that SADA-Net achieves excellent performance comparable to state-of-the-art methods on three widely used remote sensing datasets (i.e., HRSC2016, DOTA, and UCAS-AOD). Full article

Cracks and dents in concrete structures are core defects that threaten building safety, but the existing YOLO series algorithms face a huge bottleneck in complex engineering scenarios. Tiny cracks are susceptible to background texture interference, leading to misjudgment. The traditional detection frame has difficulty in accurately characterizing the dent geometry, which affects the quantitative damage assessment. In this paper, we propose a Multi-level Defect Fusion Segmentation Network (MDFNet) to break through the single-task limitation through the detection segmentation synergy framework. We improve the anchor frame strategy of YOLOv11 and enhance the recall of small targets by combining Copy–Pasting, and then enhance the pixel-level characterization of crack edges and dent contours by embedding the Head Attention-Expanded Convolutional Fusion Module (HAEConv) in U-Net with squeeze-and-excitation (SE) channel attention. Joint detection loss and segmentation loss are used for task co-optimization. On our self-constructed concrete defect dataset, MDFNet significantly outperforms the baseline model. In terms of accuracy, the MDFNet Dice coefficient is 92.4%, an improvement of 4.1 percentage points compared to YOLOv11-Seg. Our mean Intersection over Union (mIoU) reaches 81.6%, with strong generalization ability under complex background interference. In terms of engineering efficacy, the model achieves a processing speed of 45 frames per second (FPS) for 640 × 640 images, which is able to meet real-time monitoring requirements. The experimental results verify the feasibility of the model in the research field of crack and dent detection in concrete structures. Full article