Search Results (223)

With the gradual depletion of high-quality iron ore resources, global steel enterprises have shifted their focus to low-grade, high-impurity iron ores. Using low-grade iron ore to produce pellets for blast furnaces is crucial for companies to control production costs and diversify raw material sources. However, producing qualified pellets from limonite and other low-grade iron ores remains highly challenging. This study investigates the mechanism by which basicity affects the consolidation and reduction behavior of high-Al₂O₃ limonite pellets from a thermodynamic perspective. As the binary basicity of the pellets increased from 0.01 under natural conditions to 1.2, the compressive strength of the roasted pellets increased from 1100 N/P to 5200 N/P. The enhancement in basicity led to an increase in the amount of low-melting-point calcium ferrite in the binding phase, which increased the liquid phase in the pellets, thereby strengthening the consolidation. CaO infiltrated into large-sized iron particles and reacted with Al and Si elements, segregating the contiguous large-sized iron particles and encapsulating them with liquid-phase calcium ferrite. Calcium oxide reacts with the Al and Si elements in large hematite particles, segmenting them and forming liquid calcium ferrite that encapsulates the particles. Additionally, this study used thermodynamic analysis to characterize the influence of CaO on aluminum elements in high-aluminum iron ore pellets. Adding CaO boosted the liquid phase’s ability to incorporate aluminum, lessening the inhibition by high-melting-point aluminum elements of hematite recrystallization. During the reduction process, pellets with high basicity exhibited superior reduction performance. Full article

Background/Objectives: Rates of receiving opioid use disorder (OUD) treatment among adolescents and young adults (AYA) aged 16–25 are low. The current study qualitatively analyzed informants’ perspectives regarding the availability of, developmental considerations relevant to, and barriers associated with OUD treatment for AYA. Methods: Thirty key informants involved with OUD treatment in the northeastern United States completed individual, semi-structured interviews, including treatment providers (N = 11) and clinic leaders in programs that provide medication and psychosocial treatments for AYA with OUD (N = 10), as well as opioid-related policymakers (N = 6) and patient advocates (N = 3). Interviews were transcribed and independently double coded. Template-style thematic analysis methods were used and revealed seven themes. Results: The first theme highlighted limited treatment program availability for adolescents (aged < 18 years) with OUD. Four themes related to developmentally optimizing OUD treatment for AYA, describing the importance of caregiver involvement, AYA peer connections, wraparound services, and early intervention. Two themes described barriers to AYA OUD treatment, including stigma and knowledge gaps about medications for OUD as well as deficits in AYA’s access to basic resources (e.g., housing, food security) that prohibit effective participation in treatment. Conclusions: Results highlight concerns from systems-level key informants regarding gaps in OUD treatment options for youth under the age of 18 and a high need for OUD treatment that is developmentally tailored to AYA. Findings point toward potential modifications and additions to existing adult treatment programs to make OUD treatment more accessible, relevant, and engaging for AYA. Full article

This paper aims to compare the perceptions of hotel customers and hotel staff regarding the value of green hotel attributes. By examining both stakeholder groups, the study addresses a gap in sustainable hospitality research, which largely overlooks employee perspectives in favour of customer preferences. An exploratory, cross-sectional survey was conducted using structured questionnaires, administered to hotel guests (n = 307) and hotel staff (n = 89) in Porto, Portugal. Respondents rated 15 green hotel attributes using a five-point Likert scale. Demographic data were also collected to analyse perceptual differences across gender, age, income, and education. The results revealed that both customers and staff exhibited environmental awareness, though their prioritisation of specific green practices differed. Customers tended to value visible environmental measures, such as recycling bins, energy-saving light bulbs, and renewable energy signage, while staff placed greater emphasis on operational sustainability aspects, including low-flow plumbing fixtures, refillable soap dispensers, and durable goods used in service areas. These differences reflect the stakeholders’ distinct roles and experiences within the hotel ecosystem. This study enriches the discourse on sustainable hospitality by providing a dual-stakeholder analysis of green hotel attributes using a shared evaluative framework. The findings offer practical insights for hotel managers aligning sustainability strategies with the expectations of both guests and employees, supporting more effective and inclusive green implementation in the lodging sector. Full article

The autonomous positioning of drone-based remote sensing plays an important role in navigation in urban environments. Due to GNSS (Global Navigation Satellite System) signal occlusion, obtaining precise drone locations is still a challenging issue. Inspired by vision-based positioning methods, we proposed an autonomous positioning method based on multi-view reference images rendered from the scene’s 3D geometric mesh and apply a bag-of-words (BoW) image retrieval pipeline to achieve efficient and scalable positioning, without utilizing deep learning-based retrieval or 3D point cloud registration. To minimize the number of reference images, scene coverage quantification and optimization are employed to generate the optimal viewpoints. The proposed method jointly exploits a visual-bag-of-words tree to accelerate reference image retrieval and improve retrieval accuracy, and the Perspective-n-Point (PnP) algorithm is utilized to obtain the drone’s pose. Experiments are conducted in urban real-word scenarios and the results show that positioning errors are decreased, with accuracy ranging from sub-meter to 5 m and an average latency of 0.7–1.3 s; this indicates that our method significantly improves accuracy and latency, offering robust, real-time performance over extensive areas without relying on GNSS or dense point clouds. Full article

In visual Simultaneous Localization and Mapping (SLAM) systems, dynamic elements in the environment pose significant challenges that complicate reliable feature matching and accurate pose estimation. To address the issue of unstable feature points within dynamic regions, this study proposes a robust dual-mask filtering strategy that synergistically integrates semantic segmentation information with geometric outlier detection techniques. The proposed method first identifies outlier feature points through rigorous geometric consistency checks, then employs morphological dilation to expand the initially detected dynamic regions. Subsequently, the expanded mask is intersected with instance-level semantic segmentation results to precisely delineate dynamic areas, effectively constraining the search space for feature matching and reducing interference caused by dynamic objects. A key innovation of this approach is the incorporation of a Perspective-n-Point (PnP)-based optimization module. This module dynamically updates the outlier set on a per-frame basis, enabling continuous monitoring and selective removal of dynamic features. Extensive experiments conducted on benchmark datasets demonstrate that the proposed method achieves average accuracy improvements of 3.43% and 11.42% on the KITTI dataset and 24% and 8.27% on the TUM dataset. Compared to traditional methods, this dual-mask collaborative filtering strategy improves the accuracy of dynamic feature removal and enhances the reliability of dynamic object detection, validating its robustness and applicability in complex dynamic environments. Full article

Relocalization technology is an important part of autonomous vehicle navigation. It allows the vehicle to find its position on the map after a reboot. This paper presents a relocalization algorithm framework that uses image retrieval techniques. An integrated matching algorithm is applied during the feature matching process. This improves the accuracy of the vehicle’s relocalization. We use image retrieval to select the most relevant image from the map database. The integrated matching algorithm then finds precise feature correspondences. Using these correspondences and depth information, we calculate the vehicle’s global pose with the Perspective-n-Point (PnP) and Levenberg–Marquardt (L-M) algorithms. This process helps the vehicle determine its position on the map. Experimental results on public datasets show that the proposed framework outperforms existing methods like LightGlue and LoFTR in terms of matching accuracy. Full article

Considering the cost, efficiency, power density, and other issues of the power electronic system, many papers have mixed the wide-bandgap (WBG) power devices, mainly SiC MOSFET and GaN FET/HEMT, with Si IGBT/MOSFET in the three-level active neutral-point clamped (T-ANPC) topology, forming the hybrid T-ANPC (HT-ANPC) topology. This paper reviews these latest HT-ANPC topologies from the perspective of the material types of switching devices and compares the advantages and disadvantages of various topologies. The potential challenges of HT-ANPC inverters in several mainstream applications are reviewed, and their improvements are compared and discussed in detail. Next, a brief topology selection and design process are provided based on analyzing various typical topologies. In addition, some future research trends on this topic are discussed. The paper will help researchers to select appropriate HT-ANPC topologies in different applications and have a better understanding of the critical issues to be considered during system design. Full article

To address the multi-target detection problem in the automatic seedling-feeding procedure of vegetable-grafting robots from dual perspectives (top-view and side-view), this paper proposes an improved YOLOv8-SDC detection segmentation model based on YOLOv8n-seg. The model improves rootstock seedlings’ detection and segmentation accuracy by SAConv replacing the original Conv c2f_DWRSeg module, replacing the c2f module, and adding the CA mechanism. Specifically, the SAConv module dynamically adjusts the receptive field of convolutional kernels to enhance the model’s capability in extracting seedling shape features. Additionally, the DWR module enables the network to more flexibly adapt to the perception accuracy of different cotyledons, growth points, stem edges, and contours. Furthermore, the incorporated CA mechanism helps the model eliminate background interference for better localization and identification of seedling grafting characteristics. The improved model was trained and validated using preprocessed data. The experimental results show that YOLOv8-SDC achieves significant accuracy improvements over the original YOLOv8n-seg model, YOLACT, Mask R-CNN, YOLOv5, and YOLOv11 in both object detection and instance segmentation tasks under top-view and side-view conditions. The mAP of Box and Mask for cotyledon (leaf1, leaf2, leaf), growing point (pot), and seedling stem (stem) assays reached 98.6% and 99.1%, respectively. The processing speed reached 200 FPS. The feasibility of the proposed method was further validated through grafting features, such as cotyledon deflection angles and stem–cotyledon separation points. These findings provide robust technical support for developing an automatic seedling-feeding mechanism in grafting robotics. Full article

Plant protection products are necessary to guarantee food security, but their drift into the environment, usually in the form of aerosol, poses a threat to the health of bystanders and surrounding ecosystems. Appropriate testing of plant protection equipment and of its possible configurations is key to reducing drift while guaranteeing treatment efficacy. A key role in drift generation and treatment quality is played by the drop size distribution (DSD) of the employed spray nozzles. The DSD of nozzles can and should be tested before being employed by various methods. This paper recounts the recent experience in testing the DSD generated by two types of agricultural nozzles by an Oxford Lasers N60V Particle/Droplet Image Analysis (PDIA) system. The analyses put in place aimed at identifying the optimal instrument settings and adapting the methodology to the relevant ISO 25358:2018 standard. The cumulated DSD of the two nozzle types have then been fitted with a logistic function, with the aim to obtain nozzle performance models. The fitting has proven highly reliable, with correlation coefficients $R^2\ge 0.98$. These models are a satisfactory starting point to compare the performance of different PPEs. In perspective, the fitted nozzle models can help bridge the mathematical gap with other aspects of PPE performance, such as aerosol generation and downwind transport. Full article

Background: Multiple endocrine neoplasia type 1 (MEN1)-related primary hyperparathyroidism (MPHPT) belongs to genetic PHPT that accounts for 10% of all PHPT cases, being considered the most frequent hereditary PHPT (less than 5% of all PHPT). Objective: We aimed to provide an updated clinical perspective with a double purpose: to highlight the clinical features in MPHPT, particularly, the bone health assessment, as well as the parathyroidectomy (PTx) impact. Methods: A comprehensive review of the latest 5-year, English-published, PubMed-accessed original studies. Results: The sample-based analysis (n = 17 studies) enrolled 2426 subjects (1720 with MPHPT). The study design was retrospective, except for one prospective and one case–control study. The maximum number of patients per study was of 517. Female predominance (an overall female-to-male ratio of 1.139) was confirmed (except for three studies). Age at MPHPT diagnosis (mean/median per study): 28.7 to 43.1 years; age at PTx: 32 to 43.5 years. Asymptomatic PHPT was reported in 38.3% to 67% of MPHPT. Mean total calcium varied between 1.31 and 2.88 mmol/L and highest PTH was of 317.2 pg/mL. Two studies reported similar PTH and calcaemic levels in MPHPT vs. sporadic PHPT, while another found higher values in MPHPT. Symptomatic vs. asymptomatic patients with MPHPT had similar PTH and serum calcium levels (n = 1). Osteoporosis (n = 8, N = 723 with MPHPT) was reported in 10% to 55.5% of cases, osteopenia in 5.88% to 43.9% (per study); overall fracture rate was 10% (of note, one study showed 0%). Lower bone mineral density (BMD) at DXA (n = 4) in MPHPT vs. sporadic PHPT/controls was found by some studies (n = 3, and only a single study provided third distal radius DXA-BMD assessment), but not all (n = 1). Post-PTx DXA (n = 3, N = 190 with MPHPT) showed a BMD increase (e.g., +8.5% for lumbar spine, +2.1% for total hip, +4.3% for femoral neck BMD); however, post-operatory, BMD remains lower than controls. Trabecular bone score (TBS) analysis (n = 2, N = 142 with MPHPT vs. 397 with sporadic PHPT) showed a higher prevalence of reduced TBS (n = 1) or similar (n = 1). PTx analysis in MPHPT (n = 14): rate of subtotal PTx of 39% to 66.7% (per study) or less than subtotal PTx of 46.9% (n = 1). Post-PTx complications: persistent PHPT (5.6% to 25%), recurrent PHPT (16.87% to 30%, with the highest re-operation rate of 71% in one cohort); hypoparathyroidism (12.4% to 41.7%). Genetic analysis pointed out a higher risk of post-PTx recurrence in exon 10 MEN1 pathogenic variant. Post-PTx histological exam showed a multi-glandular disease in 40% to 52.1% of MPHPT, and a parathyroid carcinoma prevalence of 1%. Conclusions: MPHPT remains a challenging ailment amid a multi-layered genetic syndrome. Current data showed a lower age at MPHPT diagnosis and surgery than found in general population, and a rate of female predominance that is lower than seen in sporadic PHPT cases, but higher than known, for instance, in MEN2. The bone involvement showed heterogeneous results, more consistent for a lower BMD, but not necessarily for a lower TBS vs. controls. PTx involves a rather high rate of recurrence, persistence and redo surgery. About one out of ten patients with MPHPT might have a prevalent fracture and PTx improves the overall bone health, but seems not to restore it to the general population level, despite the young age of the subjects. This suggests that non-parathyroid components and potentially menin protein displays negative bone effects in MEN1. Full article

Automated storage and retrieval systems (AS/RS) play a crucial role in modern logistics, yet effectively monitoring cargo stacking patterns remains challenging. While computer vision and deep learning offer promising solutions, existing methods struggle to balance detection accuracy, computational efficiency, and environmental adaptability. This paper proposes a novel machine vision-based detection algorithm that integrates a pallet surface object detection network (STEGNet) with a box edge detection algorithm. STEGNet’s core innovation is the Efficient Gated Pyramid Feature Network (EG-FPN), which integrates a Gated Feature Fusion module and a Lightweight Attention Mechanism to optimize feature extraction and fusion. In addition, we introduce a geometric constraint method for box edge detection and employ a Perspective-n-Point (PnP)-based 2D-to-3D transformation approach for precise pose estimation. Experimental results show that STEGNet achieves 93.49% mAP on our proposed GY Warehouse Box View 4-Dimension (GY-WSBW-4D) dataset and 83.2% mAP on the WSGID-B dataset, surpassing existing benchmarks. The lightweight variant maintains competitive accuracy while reducing the model size by 34% and increasing the inference speed by 68%. In practical applications, the system achieves pose estimation with a Mean Absolute Error within 4 cm and a Rotation Angle Error below 2°, demonstrating robust performance in complex warehouse environments. This research provides a reliable solution for automated cargo stack monitoring in modern logistics systems. Full article

With the rapid process of urbanization, water conflicts between different water use industries and areas are increasing. Therefore, China has implemented the three-cordons system of water resources management since 2012, when how to make more reasonable regulation of water resources became an urgent problem in most areas of China. In this study, taking the Yuanhe River Basin as an example, an integrated model for the simulation and regulation of water resources considering water quantity and quality from a river basin perspective was proposed, where the water supply was constrained by requirements of water resources management. First, the water resources system was conceptualized into a topologically hydraulic network in the form of point, line, and area elements, including 80 water use units and 79 water supply units. Then, taking the water quantity and quality as constraint conditions in the water supply for corresponding water use sectors, a management-oriented integrated model was established, which highlights the cordon control of the total water use and the pollution load limits of a basin. Finally, through a model simulation, the total water supply was controlled by regulating the water resources, while the pollutant loads into rivers depended on the discharge of water users. Based on the model, strategies for the utilization of water resources and achieving emission reductions of pollution loads were provided. The results of the proposed model in the Yuanhe River Basin showed that benchmarked against the total water demand of 1.705 billion m³, the water shortage was 212 million m³ with a rate of 13.5%, and the loads of COD (Chemical Oxygen Demand) and NH3-N (Ammonia Nitrogen) were 29,096.7 and 2587.3 tons, respectively. The model can provide support for integrated water resources regulation in other basins or regions through a simulation of the natural–social water resources systems, and help stakeholders and decision-makers establish and implement advantageous strategies for regional efficient utilization of water resources. Full article

Background: From a biopsychosocial perspective, health literacy is a key factor for healthy development and the development of more comprehensive interventions directed at health literacy determinants. The present study had the main goal of studying demographic, individual, social, and contextual variables related to health literacy in adolescents. Methods: The data used in this study are part of the Health Behavior in School-aged Children (HBSC) 2022 survey. The study included 7649 adolescents, 53.9% (n = 3961) female, with an average age of 15.05 years (SD = 2.36), in the 6th, 8th, 10th, and 12th grades, proportionally distributed across the five regions of the Portuguese mainland. Results: Health literacy was explained by factors related to physical, psychological, social, and environmental health. The factors with the higher explanatory value were the psychological variables, followed by social and lifestyle-related variables. Sociodemographic and environmental factors had a more modest explanatory value. These results point to the complexity of adolescents’ health literacy. Conclusions: These results are of the utmost importance for educators, professionals, and policymakers who can use this information to create friendly environments that promote health literacy and health-promoting activities according to a multidisciplinary, continuous, and consistent plan. Full article

This work proposes a novel multi-camera system-based method for relative pose estimation and virtual–physical collision detection for anchor digging equipment. It is dedicated to addressing the critical challenges of achieving accurate relative pose estimation and reliable collision detection between multiple devices during collaborative operations in coal mines. The key innovation is that the multi-camera multi-target system is established to collect images, and the relative pose estimation is completed by the EPNP (Efficient Perspective N-Point) algorithm based on multiple infrared LED targets. At the same time, combined with the characteristics of a roadheader and anchor drilling machine, AABB (Axis Alignment Bounding Box) with a simple structure and convex hull with a strong wrapping are selected to create the mixed hierarchical bounding box, and the collision detection is carried out by combining SAT (Split Axis Theorem) and GJK (Gilbert–Johnson–Keerthi) algorithms. The experimental results show that the relative pose estimation error of the multi-camera system is within 20 mm, with an angular error within 1.002°. The position error in the X-axis direction is within 1.160 mm, and the maximum deviation in the Y-axis direction is within 0.957 mm in the virtual–physical space. Compared with the existing methods, our method integrates digital twin technology, and has a simple system structure, which can meet the requirements of relative attitude estimation and collision detection between equipment in the process of heading face operation, and at the same time improve the system performance. Full article

One of the challenges in calibrating millimeter-wave radar and camera lies in the sparse semantic information of the radar point cloud, making it hard to extract environment features corresponding to the images. To overcome this problem, we propose a track association algorithm for heterogeneous sensors, to achieve targetless calibration between the radar and camera. Our algorithm extracts corresponding points from millimeter-wave radar and image coordinate systems by considering the association of tracks from different sensors, without any explicit target or prior for the extrinsic parameter. Then, perspective-n-point (PnP) and nonlinear optimization algorithms are applied to obtain the extrinsic parameter. In an outdoor experiment, our algorithm achieved a track association accuracy of 96.43% and an average reprojection error of 2.6649 pixels. On the CARRADA dataset, our calibration method yielded a reprojection error of 3.1613 pixels, an average rotation error of 0.8141°, and an average translation error of 0.0754 m. Furthermore, robustness tests demonstrated the effectiveness of our calibration algorithm in the presence of noise. Full article