Search Results (17,161)

The design of an Active Reflective Surface Control System (ARCS) is a complex engineering task involving multidimensional and multi-criteria constraints. This paper proposes a novel methodological approach for ARCS design and optimization by integrating Axiomatic Design (AD) and Multi-Criteria Decision Making (MCDM) techniques. Initially, a structured design plan is formulated within the axiomatic design framework. Subsequently, four MCDM methods—Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Entropy Weight Method (EWM), Multi-Criteria Optimization and Compromise Solution (VIKOR), and the integrated TOPSIS–Grey Relational Analysis (GRA) approach—are used to evaluate and compare the alternative solutions. Additionally, fuzzy information axioms are used to calculate the total information content for each alternative to identify the optimal design. A case study is conducted, selecting the optimal actuator for a 5 m diameter scaled model of the Five-hundred-meter Aperture Spherical radio Telescope (FAST), followed by digital control experiments on the chosen actuator. Based on the optimal design scheme, an ARCS prototype is constructed, which accelerates project completion and substantially reduces trial-and-error costs. Full article

Background/Objectives: Falls and fractures are emerging as a near-pandemic and major global health concern, placing an enormous burden on ageing patients and public health economies. Despite the high risk of polypharmacy in the elderly patients, falls are usually attributed to age-related changes. For the “Individual Pharmacotherapy Management (IPM)” established at the University Hospital Halle, the IPM medication adjustments and their association with in-hospital fall prevention were analysed. Methods: On the basis of the most updated digital overall patient view via his inpatient electronic health record (EHR), IPM adapts each drug’s Summary of Product Characteristics to the patient’s condition. A retrospective pre-post intervention study in geriatric traumatology on ≥70 years old patients compared 200 patients before IPM implementation (CG) with 204 patients from the IPM intervention period (IG) for the entire medication list, organ, cardiovascular and vital functions and fall risk parameters. Results: Statistically similar baseline data allowed a comparison of the average 80-year-old patient with a mean of 11.1 ± 4.9 (CG) versus 10.4 ± 3.6 (IG) medications. The IPM adjusted for drug-drug interactions, drug-disease interactions, overdoses, anticholinergic burden, adverse drug reactions, esp. from opioids inducing increased intrasynaptic serotonin, psychotropic drugs and benzodiazepines. IPM was associated with a significant reduction in in-hospital falls from 18 (9%) in CG to 3 (1.5%) in IG, a number needed to treat of 14, relative risk reduction 83%, OR 0.17 [95% CI 0.04; 0.76], p = 0.021 in multivariable regression analysis. Factors associated with falls were antipsychotics, digitoxin, corticosteroids, Würzburg pain drip (combination of tramadol, metamizole, metoclopramide), head injury, cognitive impairment and aspects of the Huhn Fall Risk Scale including urinary catheter. Conclusion: The results indicate medication risks constitute a major iatrogenic cause of falls in this population and support the use of EHR-based IPM in standard care for the prevention of falls in the elderly and for patient and drug safety. In terms of global efforts, IPM contributes to the running WHO and United Nations Decade of Healthy Ageing (2021–2030). Full article

Convection-permitting models (CPMs) are receiving growing scientific interest for their capability to accurately simulate extreme weather events at a kilometer-scale spatial resolution, offering valuable information for local climate change adaptation. This study employs both qualitative and quantitative bibliometric analysis techniques to examine research trends in CPM, utilizing data from 3,508 articles published between 2000 and 2023. The annual number of publications exhibits a linear increase, rising from fewer than 50 in 2000 to over 250 after 2020, with the majority of research originating from the US, China, the UK, and Germany. The most productive institutes include the National Oceanic Atmospheric Administration (NOAA) and the National Center for Atmospheric Research (NCAR) in the US, each contributing over 10% of total publications. Title and abstract terms in publications related to keywords such as ”scenario”, ”climate simulation”, etc., dominate publications from 2018 to 2023, coinciding with advances in computing power. Notably, terms associated with CPM physical processes received the highest citations from 2000 to 2023, underscoring the importance of such these research topics. Given the computational expense of running CPMs and the increasing demand for future predictions using CPMs, novel methods for generating long-term simulations are imperative. Full article

As part of an investigation into downfall impacts from violent overtopping waves, experimental data are presented for the impact pressures and forces generated by regular and focused waves breaking onto a vertical wall and impacting a landward horizontal deck at a scale of 1:38. Particular attention is given to the wave-by-wave uprush and impact downfall events. By selecting regular and focused wave conditions that produce impacts, new trends are identified for violent downfall phenomena that could easily be underestimated in current practice. The characteristics of the downfall impacts are investigated and three different types of downfall impact are identified and discussed. Using a Wavelet Filter to denoise the signal from pressure probes without losing the peak impact pressures or introducing a phase shift, the distinctive features and dynamic behaviours of the white-water impacts are considered, and it is shown that downfall pressure magnitudes of $30–40 \rho gH$ are regularly achieved. Dynamic impulse times of the events are also presented with higher-impact events generally relating to shorter impulse times, highlighting the dynamic character of these impacts. The largest downfall pressures are found to occur further from the vertical wall than previously measured. Importantly, the spray travelling furthest from the point of the initial wave impact on the vertical wall causes some of the largest downfall pressures on the deck. The paper concludes that, while the dataset is small, there are strong indications that the effects of these types of impacts are structurally significant and present a risk to infrastructure located landward of seawalls. Full article

Knowledge graph embedding has been identified as an effective method for node-level classification tasks in directed graphs, the objective of which is to ensure that nodes of different categories are embedded as far apart as possible in the feature space. The directed graph is a general representation of unstructured knowledge graphs. However, existing methods lack the ability to simultaneously approximate high-order filters and globally pay attention to the task-related connectivity between distant nodes for directed graphs. To address this limitation, a directed spectral graph transformer (DSGT), a hybrid architecture model, is constructed by integrating the graph transformer and directed spectral graph convolution networks. The graph transformer leverages multi-head attention mechanisms to capture the global connectivity of the feature graph from different perspectives in the spatial domain, which bridges the gap between frequency responses and, further, naturally couples the graph transformer and directed graph convolutional neural networks (GCNs). In addition to the inherent hard inductive bias of DSGT, we introduce directed node positional and structure-aware edge embedding to provide topological prior knowledge. Extensive experiments demonstrate that the DSGT exhibits state-of-the-art (SOTA) or competitive node-level representation capabilities across datasets of varying attributes and scales. Furthermore, the experimental results indicate that the homophily and degree of correlation of the nodes significantly influence the classification performance of the model. This finding opens significant avenues for future research. Full article

The accurate prediction of mine water inflow is very important for mine design and safe production. The existing forecasting methods based on single factors are often less accurate and stable. Multi-factor data-driven models play a key role in predicting water inflow without taking physical changes into account. Therefore, a multi-factor prediction method based on an improved SSA-RG-MHA model is introduced in this study. The model uses two sets of data related to water inflow as the input to improve prediction accuracy and stability. The model first applies a residual network (ResNet) to mitigate the problems of disappearing gradients and explosions. Gated Recurrent Units (GRUs) are then used to learn the characteristics of long-term sequence data. The model combines ResNet and GRU into a new network architecture and incorporates a multiple attention (MHA) mechanism to focus on information at different time scales. Finally, the optimized sparrow search algorithm (SSA) is used to optimize the network parameters to improve the global search ability and avoid local optimization. The mine water inflow is affected by many factors, among which the water level and microseismic energy data are particularly important. Therefore, these data types are selected as the key variables of mine water inflow prediction. The experimental results show that the improved SSA-RG-MHA model significantly reduces the prediction error: the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were reduced to 4.42 m³/h, 7.17 m³/h, and 5%, respectively. The multi-factor water inflow prediction method is more stable and reliable than single-factor models as it comprehensively considers the factors affecting the water inflow of the working face. Compared with other multi-factor models, this model exhibits higher prediction accuracy and robustness, providing a basis for mine water hazard monitoring and early warning. Full article

The accuracy of apple fruit recognition in orchard environments is significantly affected by factors such as occlusion and lighting variations, leading to issues such as missed and false detections. To address these challenges, particularly related to occluded apples, this study proposes an improved apple-detection model, MSOAR-YOLOv10, based on YOLOv10. Firstly, a multi-scale feature fusion network is enhanced by adding a 160 × 160 feature scale layer to the backbone network, which increases the model’s sensitivity to small local features, particularly for occluded fruits. Secondly, the Squeeze-and-Excitation (SE) attention mechanism is integrated into the C2fCIB convolution module of the backbone network to improve the network’s focus on the regions of interest in the input images. Additionally, a Diverse Branch Block (DBB) module is introduced to enhance the performance of the convolutional neural network. Furthermore, a Normalized Wasserstein Distance (NWD) loss function is proposed to effectively reduce missed detections of densely packed and overlapping targets. Experimental results in orchards indicate that the proposed improved YOLOv10 model achieves precision, recall, and mean average precision rates of 89.3%, 89.8%, and 92.8%, respectively, representing increases of 3.1%, 2.2%, and 3.0% compared to the original YOLOv10 model. These results validate that the proposed network significantly enhances apple recognition accuracy in complex orchard environments, particularly improving the operational precision of harvesting robots in real-world conditions. Full article

With the growing number of electronic devices loaded and increasing influence from electromagnetic interference, large-scale systems or platforms are confronted with increasingly severe electromagnetic compatibility challenges. Due to the vast size of these systems and the multitude of electronic devices they contain, standard laboratory environments are often inadequate for meeting test requirements. This paper reviews the state-of-art in the area of field measurement techniques related to the checking of electromagnetic compatibility, and the key technologies of electromagnetic interference filtering and wide-bandwidth, large-dynamic, and rapidly transient signal extraction in the measurement field are analyzed. The research status of electromagnetic interference suppression, transient and broadband measurement, and environmental interference suppression combined with time-domain fast measurement and other technologies are summarized and analyzed. Based on a comparative analysis of the aforementioned technologies, the future development trends of field measurement technology are also discussed. Full article

Sport-based youth development programs, delivered through campus–community partnerships, can create impactful experiences for college students, meet university objectives, and improve the health of children in under-resourced rural communities. This study aimed to pilot test intervention mapping (IM), a systematic approach to intervention development and implementation, to refine the Hoosier Sport intervention, which is a local public health initiative that utilizes the Obesity-Related Behavioral Intervention Trials (ORBITs) model to improve physical activity in middle school children. The IM process, which included a diverse IM planning and advisory group of university representatives and local schools, was guided by self-determination theory (SDT) and social cognitive theory (SCT) and followed four steps: Logic Model of the Problem, Logic Model of Change, Program Design, and Program Production. Using SDT and SCT, we identified our personal determinants as autonomy, competence, and relatedness, while our environmental determinants were role-modeling and sports equipment access. We then created change methods and practical applications for refining and implementing our intervention and gathered pilot test data to assess the feasibility of the intervention. The IM process provided a more robust and evidence-based approach to intervention design and production, while involving stakeholders to foster meaningful collaboration and increase program success. By using IM in program development, public health interventions that promote youth development through sport will likely be more easily scaled up. Full article

Objectives: Blood pressure (BP) management is challenging in patients with acute ischemic supratentorial stroke undergoing recanalization therapy due to the lack of established guidelines. Assessing dynamic cerebral autoregulation (dCA) may address this need, as it is a bedside technique that evaluates the transfer function phase in the very low-frequency (VLF) range (0.02–0.07 Hz) between BP and cerebral blood flow velocity (CBFV) in the middle cerebral artery. This phase is a prognostically relevant parameter, with lower values associated with poorer outcomes. This study aimed to evaluate whether early cranial computed tomography perfusion (CTP) can predict this parameter. Methods: In this retrospective study, 165 consecutive patients with hemispheric strokes who underwent recanalizing therapy were included (median age: 73 years; interquartile range (IQR) 60–80; women: 43 (26%)). The cohort comprised 91 patients treated with intravenous thrombolysis (IV-lysis) alone (median National Institute of Health Stroke Scale (NIHSS) score: 5; IQR 3–7) and 74 patients treated with mechanical thrombectomy (median NIHSS: 15; IQR 9–18). Regression analysis was performed to assess the relationship between pretreatment CTP-derived ischemic penumbra and core stroke volumes and the dCA VLF phase, as well as CBFV assessed within the first 72 h post-stroke event. Results: Pretreatment penumbra volume was a significant predictor of the VLF phase (adjusted r² = 0.040; β = −0.001, 95% confidence interval (CI): −0.0018 to −0.0002, p = 0.02). Core infarct volume was a stronger predictor of CBFV (adjusted r² = 0.082; β = 0.205, 95% CI: 0.0968–0.3198; p = 0.0003) compared to penumbra volume (p = 0.01). Additionally, in the low-frequency range (0.07–0.20 Hz), CBFV and BP were inversely related to the gain, an index of vascular tone. Conclusion: CTP metrics appear to correlate with the outcome-relevant VLF phase and reactive hyperemic CBFV, which interact with BP to influence vascular tone and gain. These aspects of dCA could potentially guide BP management in patients with acute stroke undergoing recanalization therapy. However, further validation is required. Full article

The segmentation of medical images, particularly for brain tumors, is essential for clinical diagnosis and treatment planning. In this study, we proposed MMAformer, a Multiscale Modality-Aware Transformer model, which is designed for segmenting brain tumors by utilizing multimodality magnetic resonance imaging (MRI). Complementary information between different sequences helps the model delineate tumor boundaries and distinguish different tumor tissues. To enable the model to acquire the complementary information between related sequences, MMAformer employs a multistage encoder, which uses a cross-modal downsampling (CMD) block for learning and integrating the complementary information between sequences at different scales. In order to effectively fuse the various information extracted by the encoder, the Multimodal Gated Aggregation (MGA) block combines the dual attention mechanism and multi-gated clustering to effectively fuse the spatial, channel, and modal features of different MRI sequences. In the comparison experiments on the BraTS2020 and BraTS2021 datasets, the average Dice score of MMAformer reached 86.3% and 91.53%, respectively, indicating that MMAformer surpasses the current state-of-the-art approaches. MMAformer’s innovative architecture, which effectively captures and integrates multimodal information at various scales, offers a promising solution for tackling complex medical image segmentation challenges. Full article

Background/Objectives: Non-obstructive azoospermia (NOA) is a severe form of male infertility characterized by the absence of sperm in the ejaculate due to impaired spermatogenesis. Testicular sperm extraction (TESE) combined with intracytoplasmic sperm injection is the primary treatment, but success rates are unpredictable, causing significant emotional and financial burdens. Traditional clinical and hormonal predictors have shown inconsistent reliability. This review aims to evaluate current and emerging non-invasive preoperative predictors of successful sperm retrieval in men with NOA, highlighting promising biomarkers and their potential clinical applications. Methods: A comprehensive literature review was conducted, examining studies on clinical and hormonal factors, imaging techniques, molecular biology biomarkers, and genetic testing related to TESE outcomes in NOA patients. The potential role of artificial intelligence and machine learning in enhancing predictive models was also explored. Results: Traditional predictors such as patient age, body mass index, infertility duration, testicular volume, and serum hormone levels (follicle-stimulating hormone, luteinizing hormone, inhibin B) have limited predictive value for TESE success. Emerging non-invasive biomarkers—including anti-Müllerian hormone levels, inhibin B to anti-Müllerian hormone ratio, specific microRNAs, long non-coding RNAs, circular RNAs, and germ-cell-specific proteins like TEX101—show promise in predicting successful sperm retrieval. Advanced imaging techniques like high-frequency ultrasound and functional magnetic resonance imaging offer potential but require further validation. Integrating molecular biomarkers with artificial intelligence and machine learning algorithms may enhance predictive accuracy. Conclusions: Predicting TESE outcomes in men with NOA remains challenging using conventional clinical and hormonal parameters. Emerging non-invasive biomarkers offer significant potential to improve predictive models but require validation through large-scale studies. Incorporating artificial intelligence and machine learning could further refine predictive accuracy, aiding clinical decision-making and improving patient counseling and treatment strategies in NOA. Full article

Trees and their morphology can mitigate the urban heat island (UHI) effect, but the impacts of tree species and their two-dimensional (2D) and three-dimensional (3D) morphological characteristics on the thermal environment of residential spaces at the building scale have not been effectively evaluated. This research extracted the data of trees in the spatial range of a 50 m radius of the sampling sites located in a subtropical humid city’s residential area based on unmanned aerial vehicle (UAV) imagery and field measurements. It included Ficus microcarpa L. f., Cinnamomum camphora (L.) J. Presl, and Alstonia scholaris (L.) R. Br. as three typical evergreen tree species and six quantitative indicators of trees, with the number of trees (N) serving as fundamental indicator and mean canopy width (MCW), mean canopy height (MCH), mean tree height (MTH), canopy biomass (CV), and mean canopy biomass (MCV) as morphological characteristic indicators. We analyzed the impact of the six indicators above on two thermal environment parameters: Air temperature (AT) and relative humidity (RH), by correlation analysis and multiple linear regression analysis. Results showed that: (1) F. microcarpa, as a dominant local species, provided more than 65% of the tree canopy volume within the study area (50 m radius buffer zones), and its contribution to cooling and humidification effects was superior to those of C. camphora and A. scholaris. (2) The MTH and CV of F. microcarpa are the key factors influencing daytime AT and RH, respectively, with temporal fluctuation in impact intensity during the spring (May) daytime. (3) The MTH and N of F. microcarpa show the best cooling effect (adjusted R² = 0.731, p < 0.05) during midday (13:00–14:00 p.m.), while its CV and MTH have the best humidification effect (adjusted R² = 0.748, p < 0.05) during the morning (9:00–10:00 a.m.) among three typical tree species. The 2D and 3D morphological characteristic indicators effectively describe the impact and variation of tree species on the spring microclimate within small-scale residential spaces. This work provides new insights into the thermal benefits brought by the spatial growth features of trees at the building scale and offers reference for urban residential areas in the planning and management related to tree species selection, canopy maintenance, and the improvement of thermal comfort for inhabitants. Full article

Land surface anomalies refer to various activities on the Earth’s surface that consist of short-term and sudden changes due to external disturbances. These anomalies are closely related to the safety of human life and property. Remote sensing offers irreplaceable advantages such as broad coverage, high temporal dynamics, and comprehensive observations, so it is the most effective tool for monitoring land surface anomalies and measuring their intensities. However, existing studies have limitations such as unclear sensitivity features, uncertain applicability, and a lack of quantitative expression at different scales. Therefore, this study develops a quantitative assessment framework for land surface anomaly intensity across four scales: the pixel scale, structure scale, object scale, and scene scale. This framework enables an adaptive and flexible weight determination of the intensity of land surface anomalies from a satellite perspective. Using the Chongqing fire as an example of a land surface anomaly, this study evaluates its land surface anomaly intensity. Moreover, we demonstrate the method’s applicability to other land surface anomaly events, such as floods and earthquakes. The experiments reveal that the land surface anomaly intensity evaluation framework, which is constructed based on pixel-scale, structure-scale, object-scale, and scene-scale features, can quantitatively express the land surface anomaly intensity with an accuracy of 75.25% and more effectively represent severely affected areas. The weights of the features at the four scales sequentially decrease: structure scale (0.2974), pixel scale (0.3225), object scale (0.1867), and scene scale (0.1932). The extensive application of this method to other land surface anomaly events provides accurate quantitative expressions of the land surface anomaly intensity. This remote sensing-based multiscale feature assessment method is adaptable and applicable to various land surface anomalies and offers critical decision support for land surface anomaly intensity warning systems. Full article

Represention and modeling the dynamic landslide scenes is essential for gaining a comprehensive understanding and managing them effectively. Existing models, which focus on a single scale make it difficult to fully express the complex, multi-scale spatiotemporal process within landslide scenes. To address these issues, we proposed a hierarchical spatiotemporal data model, named as HSDM, to enhance the representation for geographic scenes. Specifically, we introduced a spatiotemporal object model that integrates both their structural and process information of objects. Furthermore, we extended the process definition to capture complex spatiotemporal processes. We sorted out the relationships used in HSDM and defined four types of spatiotemporal correlation relations to represent the connections between spatiotemporal objects. Meanwhile, we constructed a three-level graph model of geographic scenes based on these concepts and relationships. Finally, we achieved representation and modeling of a dynamic landslide scene in Heifangtai using HSDM and implemented complex querying and reasoning with Neo4j’s Cypher language. The experimental results demonstrate our model’s capabilities in modeling and reasoning about complex multi-scale information and spatio-temporal processes with landslide scenes. Our work contributes to landslide knowledge representation, inventory and dynamic simulation. Full article