Multi-cluster query engines are common in production, but routing queries to appropriately-sized clusters remains an unsolved problem. Current approaches like round-robin, hash-based routing, or manual rules waste resources when simple queries land on oversized clusters and cause failures when complex queries hit undersized ones. Production systems experience resource mismatch failures, requiring manual workarounds such as migrating problematic queries to separate engines. We address this with a system for intelligent cluster sizing using learned execution plan embeddings to predict query resource requirements. Instead of analyzing query text, we vectorize execution plan structure and search for similar historical queries, predicting memory, Central Processing Unit (CPU) and runtime based on actual past performance rather than unreliable optimizer estimates. We employ a tiered inference strategy that supports fast, feature-based vectors (10 basic dimensions; 13–23 ms overhead), gradient boosting with enhanced features (24 dimensions; 76.5% accuracy), and graph neural network embeddings for complex queries (78.1% accuracy). An adaptive classifier maps predictions to cluster sizes using thresholds that adjust based on observed utilization patterns. Unfamiliar queries are routed conservatively, and the system automatically improves from observed outcomes. Empirical validation on 2,954 queries from a Trino deployment shows 98.8–99.8% within-one-size accuracy and 74.8–76.5% exact-match routing. In benchmark workloads, our approach reduces out-of-memory failures by 82% and infrastructure costs by 33%, with less than 51 ms of routing overhead. Gradient boosting achieves 76.5% accuracy, while graph neural networks reach 78.1% for complex join structures.
Marcos S. Croci, Marcelo J.A.A. Brañas, Ellen F. Finch
et al.
Abstract Nonsuicidal self-injury (NSSI) in youth is clinically heterogeneous. We aimed to identify distinct psychopathology-based profiles among children and adolescents reporting NSSI and their longitudinal correlates. Participants (N = 1 345) were drawn from the Brazilian High-Risk Cohort Study, which conducted extensive phenotypic assessments at baseline (ages 6–14 years) and across two follow-up waves (ages 9–18 and 13–23 years). First, we applied unsupervised machine-learning algorithms (Self-Organizing Maps and k-means clustering) to identify distinct psychopathology-based profiles among youth reporting NSSI at the second follow-up. We then employed three models to identify longitudinal predictors of these profiles: logistic regression, elastic net, and random forest. Analyses revealed two distinct profiles of youth reporting NSSI, characterized by high and low psychopathology. The high psychopathology profile (n = 117) was associated with factors identifiable earlier in life and characterized by persistent psychiatric symptoms and significant social adversity throughout development (e.g., family problems and bullying). The low psychopathology profile (n = 127) was marked by lower overall psychopathology and experienced mental health problems only later in development, with less severe challenges over time, such as school suspension and milder depressive symptoms. While the logistic regression did not provide overall significant performance, the elastic net (AUC = 0.72 95% CI 0.65–0.77) and random forest (AUC = 0.73 95% CI 0.67–0.78) did. The present study identified two distinct psychopathology-based profiles among youth reporting NSSI and their longitudinal correlates, using machine learning approaches. Early identification of youth in higher-risk profiles can inform early intervention strategies.
Data-driven MPM often overlooks expert knowledge, leading to poor interpretability and overly broad predictions. We convert the semantic information of geological maps into a semantic knowledge graph(Geo-mapSKG). By embedding the Geo-mapSKG using the TransG model and integrating with geochemical data to enhance the knowledge constraints. Given the spatial variability of geological features, we use a window sampling method for data collection to ensure the completeness of geospatial structural features. To improve the model’s ability to learn the complex variations in geospatial features, we employ the Conformer deep learning model for gold prospectivity prediction. This approach combines the local geological feature extraction capability of CNN with the Transformer’s overall geological dependencies. To validate the method effectiveness, a gold prospective exercise was conducted at Ankang in Shaanxi Province (North China). Results show Geo-mapSKG embedding effectively constrains predicted area distribution, yielding a smaller predicted area, and that the geological semantic features of the predicted areas show strong consistency with the ore geological features of known deposits. Compared with the prediction results of the CNN and Transformer models, the accuracy of the Conformer model is 1.38% higher than the CNN model and 2.92% higher than the Transformer model.
Séverine Chevalier, Olivier Beauchard, Luc Vandenbulcke
et al.
Abstract Benthic biodiversity is of global significance for the provision of ecosystem services and the mediation of global biogeochemical cycles. The lack of detailed spatial distributions of the functions and vulnerabilities of the benthos critically prevents us from protecting benthic biodiversity and its functioning in the context of increasing human perturbations and climate change. Here, we propose a multidisciplinary approach to bridging in situ benthic data to the maps of macrobenthic functions and vulnerabilities at the scale of the northwestern shelf of the Black Sea. Our findings show that oxygen availability is a key driver of the functional trait composition of macrozoobenthic communities. Shallower well-oxygenated areas support high biomixing and bioirrigation on muddier-sandier substrata and high biodeposition on coarser substrata associated with mussel reef communities. In contrast, at depleted oxygen areas at the edge of the shelf, macrobenthic communities are functionally impoverished with only a combination of a few typical opportunistic traits, and those communities have a negligible impact on ecosystem functions. Mapping of vulnerabilities and functions of benthic communities can support marine management strategies aligned with the United Nations Sustainable Development Goal 14: Life Below Water.
Jean‐Nicolas Pradervand, Florian Zellweger, Jérémy Gremion
et al.
Human‐induced alterations in natural water flow have seriously impaired the integrity of riverine ecosystems. Nonetheless, even in human‐altered riverine and adjacent terrestrial habitats, there is considerable potential for the protection of rare species if management practices prioritize biodiversity conservation. However, the management of such areas often presents complex challenges. On the one hand, efforts to mitigate natural hazards frequently overshadow biodiversity conservation objectives. On the other hand, high‐resolution maps of forest structures are often lacking but could be very useful for spatial prioritization of conservation efforts, especially as vegetation structure can be directly managed through local restoration activities. Here, we used an airborne LiDAR‐derived vegetation structure along an 80 km stretch of the Rhône River (Valais, Switzerland) to assess the habitat characteristics that best explain the presence of a flagship species, the common nightingale Luscinia megarhynchos, a species that historically thrived along this river system but has experienced a drastic population decline over the past decades. Nightingales showed a preference for dense vegetation in the lower strata above ground (3–6 m), as opposed to an open and sparsely vegetated ground level (0–1 m). The preferred habitats were predominantly located within forested regions, as indicated by a preference for taller canopies. These findings align surprisingly well with prior field research on the species, demonstrating the capability of high‐resolution LiDAR to upscale locally derived habitat preferences across very large areas. Based on LiDAR outputs, we proposed management recommendations for the whole river. Such spatially detailed information furthers our understanding of local habitat preferences of endangered species, thus facilitating the formulation of conservation recommendations at the scale of entire populations.
Biology (General), General. Including nature conservation, geographical distribution
Alaa Ali Hameed, Akhtar Jamil, Esraa Mohammed Alazzawi
et al.
In this paper, we introduce a novel approach to enhance the accuracy and convergence behavior of Self-Organizing Maps (SOM) by incorporating a reweighted zero-attracting term into the loss function. We evaluated two SOM versions: conventional SOM and robust adaptive SOM (RASOM). The enhanced versions, reweighted zero-attracting SOM (RZA-SOM) and reweighted zero-attracting RASOM (RZA-RASOM), include an l1 norm in the error function to add a zero-attractor term, which improves weight coefficient adjustments while preserving topology. The models were assessed for convergence speed and misadjustment under sparsity assumptions of the true coefficient matrix, and their robustness was tested under conditions of increased non-zero taps. Using six different datasets, we compared the performance of RZA-SOM and RZA-RASOM against conventional SOM and RA-SOM in terms of accuracy, quantization error, and topology preservation. Experimental results consistently demonstrated that RZA-SOM and RZA-RASOM surpassed the performance of conventional SOM and RA-SOM.
Nallappan Gunasekaran, Murugesan Manigandan, Seralan Vinoth
et al.
This paper delves into a novel category of nonlocal boundary value problems concerning nonlinear sequential fractional differential equations, coupled with a unique form of generalized Riemann–Liouville fractional differential integral boundary conditions. For single-valued maps, we employ a transformation technique to convert the provided system into an equivalent fixed-point problem, which we then address using standard fixed-point theorems. Following this, we evaluate the stability of these solutions utilizing the Ulam–Hyres stability method. To elucidate the derived findings, we present constructed examples.
Over the past two decades voting by mail has increased. As the COVID-19 pandemic disrupted the 2020 political season, absentee voting increased to over 43% of all votes cast in the November Presidential Election. In recent years, many states have passed laws to limit the scope of absentee voting as necessary to curtail voter fraud. Though no evidence exists that fraud is more likely with mail-in voting, such laws have been found to have a disproportionate impact on some groups of voters more than others. This is true of many Native American communities that often rely on absentee voting. This study will utilize geospatial methods to examine postal realities for Native American on reservations in Montana and South Dakota. By illustrating where postal services are or are not located, a fuller contextual analysis of the issue can be provided. Such methods can enhance current methodologies utilized by political geographers.
Purpose: To quantify interobserver variation (IOV) in target volume and organs-at-risk (OAR) contouring across 31 institutions in breast cancer cases and to explore the clinical utility of deep learning (DL)-based auto-contouring in reducing potential IOV. Methods and materials: In phase 1, two breast cancer cases were randomly selected and distributed to multiple institutions for contouring six clinical target volumes (CTVs) and eight OAR. In Phase 2, auto-contour sets were generated using a previously published DL Breast segmentation model and were made available for all participants. The difference in IOV of submitted contours in phases 1 and 2 was investigated quantitatively using the Dice similarity coefficient (DSC) and Hausdorff distance (HD). The qualitative analysis involved using contour heat maps to visualize the extent and location of these variations and the required modification. Results: Over 800 pairwise comparisons were analysed for each structure in each case. Quantitative phase 2 metrics showed significant improvement in the mean DSC (from 0.69 to 0.77) and HD (from 34.9 to 17.9 mm). Quantitative analysis showed increased interobserver agreement in phase 2, specifically for CTV structures (5–19 %), leading to fewer manual adjustments. Underlying IOV differences causes were reported using a questionnaire and hierarchical clustering analysis based on the volume of CTVs. Conclusion: DL-based auto-contours improved the contour agreement for OARs and CTVs significantly, both qualitatively and quantitatively, suggesting its potential role in minimizing radiation therapy protocol deviation.
Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Bolts are critical components of tunnel linings, essential for ensuring safe tunnel operation. Due to their susceptibility to corrosion and detachment, it is vital to closely monitor them during maintenance. However, identifying a large number of corroded bolts presents challenges, particularly due to interference from complex background noise and the inherent limitations of CNNs in extracting local features. To address these issues, we introduces a novel multi-scale feature extraction detection network (MSDNet) designed to improve tunnel bolt maintenance by reducing false positives and missed detections. We incorporate a pyramid structure within the visual transformer framework to overcome CNNs’ limitations, enabling the generation of multi-scale corroded bolt feature maps that emphasize global information. Additionally, we develop a feature enhancement module (FEM) to capture more detailed features in small or localized areas during the search stage. Lastly, we design an efficient feature aggregation modules (FAM) to fuse coarse-level semantic corroded bolt information with fine-level features in a top-down pathway. We collected and organized a dataset of corroded bolts, and conducted comparative and ablation experiments to demonstrate the effectiveness of our proposed model. The results show that our model performs better in handling the challenges posed by this dataset.
In response to the challenge of low recognition rates for similar phenotypic symptoms of tea diseases in low-light environments and the difficulty in detecting small lesions, a novel adaptive method for tea disease severity detection is proposed. This method integrates an image enhancement algorithm based on an improved EnlightenGAN network and an enhanced version of YOLO v8. The approach involves first enhancing the EnlightenGAN network through non-paired training on low-light-intensity images of various tea diseases, guiding the generation of high-quality disease images. This step aims to expand the dataset and improve lesion characteristics and texture details in low-light conditions. Subsequently, the YOLO v8 network incorporates ResNet50 as its backbone, integrating channel and spatial attention modules to extract key features from disease feature maps effectively. The introduction of adaptive spatial feature fusion in the Neck part of the YOLOv8 module further enhances detection accuracy, particularly for small disease targets in complex backgrounds. Additionally, the model architecture is optimized by replacing traditional Conv blocks with ODConv blocks and introducing a new ODC2f block to reduce parameters, improve performance, and switch the loss function from CIOU to EIOU for a faster and more accurate recognition of small targets. Experimental results demonstrate that YOLOv8-ASFF achieves a tea disease detection accuracy of 87.47% and a mean average precision (mAP) of 95.26%. These results show a 2.47 percentage point improvement over YOLOv8, and a significant lead of 9.11, 9.55, and 7.08 percentage points over CornerNet, SSD, YOLOv5, and other models, respectively. The ability to swiftly and accurately detect tea diseases can offer robust theoretical support for assessing tea disease severity and managing tea growth. Moreover, its compatibility with edge computing devices and practical application in agriculture further enhance its value.
Recently, a bit-level image encryption algorithm based on chaotic maps (BCIEA) has been presented. BCIEA consists of diffusion and confusion, and its security performance mainly relies on the dynamic mechanisms introduced during diffusion and confusion, respectively. However, after a careful cryptanalysis, we found that BCIEA has fatal security issues. Although the bit-level diffusion formally employs complex chaining operations, there is a defect that the chaotic sequences can be used as an equivalent key. In addition, by analyzing its confusion, it is found that the dynamic mechanism adopted has obvious statistical characteristics, and it is especially difficult to resist all zero ciphertext attack. In addition, we also found that the BCIEA description is not rigorous, resulting in algorithm details that cannot be decrypted. On the basis of slightly reasonable modification, we propose a chosen-ciphertext attack method for cracking BCIEA. The method first uses the all-zero ciphertext to degrade it into a diffusion-only algorithm, and then chooses a cipher image with the same sum value as that of the target cipher image to break the confusion module possessing a dynamic mechanism. Theoretical analyses and experimental results verify the effectiveness and efficiency of the proposed attack method. This work can provide a reference for improving the security of image encryption schemes based on bit-level techniques.
Boniface K. Ngarega, John M. Nzei, Josphat K. Saina
et al.
Understanding the influence of environmental covariates on plant distribution is critical, especially for aquatic plant species. Climate change is likely to alter the distribution of aquatic species. However, knowledge of this change on the burden of aquatic macroorganisms is often fraught with difficulty. Ottelia, a model genus for studying the evolution of the aquatic family Hydrocharitaceae, is mainly distributed in slow-flowing creeks, rivers, or lakes throughout pantropical regions in the world. Due to recent rapid climate changes, natural Ottelia populations have declined significantly. By modeling the effects of climate change on the distribution of Ottelia species and assessing the degree of niche similarity, we sought to identify high suitability regions and help formulate conservation strategies. The models use known background points to determine how environmental covariates vary spatially and produce continental maps of the distribution of the Ottelia species in Africa. Additionally, we estimated the possible influences of the optimistic and extreme pessimistic representative concentration pathways scenarios RCP 4.5 and RCP 8.5 for the 2050s. Our results show that the distinct distribution patterns of studied Ottelia species were influenced by topography (elevation) and climate (e.g., mean temperature of driest quarter, annual precipitation, and precipitation of the driest month). While there is a lack of accord in defining the limiting factors for the distribution of Ottelia species, it is clear that water-temperature conditions have promising effects when kept within optimal ranges. We also note that climate change will impact Ottelia by accelerating fragmentation and habitat loss. The assessment of niche overlap revealed that Ottelia cylindrica and O. verdickii had slightly more similar niches than the other Ottelia species. The present findings identify the need to enhance conservation efforts to safeguard natural Ottelia populations and provide a theoretical basis for the distribution of various Ottelia species in Africa.
I. D. Zarva, L. D. Shchuchinova, S. A. Chalchikov
et al.
Relevance. The experience of combating rabies in Europe has shown that the tactics of preventive measures in mountains must be modified. At the beginning of the 21st century, the spread of fox rabies into the previously rabies-free mountain areas in southern Siberia was noted. The aim is to trace the spread of rabies in the Altai Mountains after the introduction of the virus. Materials and methods. A retrospective descriptive study using GIS was carried out. For mapping, information on 55 laboratory confirmed cases of rabies in the Altai Republic, QGIS 3.16.0, ArcMap 10.8.1, ArcScene 10.8.1 programs and an electronic landscape-geographical maps "Natural Earth" and "Open street map" were used. The spatial-temporal distribution of rabies was compared with changes in postexposure prophylaxis (PEP). Results. In 2007, rabies was detected for the first time since 1948 in the Altai Mountains. Wild animals (fox, wolf, badger) accounted for 52.7% (95% CI 39.5–65.9). Most of the cases were found in the foothills and river valleys at an altitude of less than 1,000 meters above sea level and only 16.4% (0.0–26.2) – in areas with heights from 1,000 to 2,000 m. Rabies was not recorded in the mountains above 2,000 m. Two different directions of the virus introduction are assumed: from the forest-steppe plains of the Altai Territory (Russia) and from the mountainous steppes of Mongolia. In 2007–2019 the annual number of patients seeking medical attention after animal bites increased by 86%. A correlation between the animal case number in different areas and the average annual PEP was noted (r = 0.649, p = 0.03). 4. Conclusions. Features of the fox rabies spread in the Altai Mountains allows to use the experience of fighting this disease in the mountainous regions of Central Europe.
Rahmawati Laila, Saputra Daniel, Sahim Kaprawi
et al.
The previous research showed that the duku’s peel which dried using infrared radiation could extend the shelf life up to 25 days. The aims of this study to illustrate using 3D visual analysis on microstructural of dried duku’s peel that had dried using infrared radiation. Scanning Electron Microscopy (SEM) technique with magnifications of x100, x500, and x2500, resolution of 10 μm, 50 μm, and 100 μm in dried duku’s peel using infrared radiation at a distance of infrared emitter (IRE) 6 cm and 10 cm with an exposure temperature of 300°C for 60 s. The 3D visual illustration using Mountain Maps Program shows the porosity value on 6 cm distance of IRE, with 300°C of IRE temperature and 60 s of exposure time has 90,91%, while the 10 cm distances of IRE, 300°C of IRE temperature and 60 s of exposure time has 146,95%. It could conclude that from 3D illustrative of SEM by reconstructing a single image into pseudo-color view and a profile curve produced at drying distance of 6 cm, 300°C, and 60 s has lower porosity value, and more stable contour when compared to drying with a distance of 10 cm, 300°C, 6 s, and control treatment. This condition could confirm the previous research. The duku’s peel microtexture condition which was exposed by IRE could create a dry condition as shell-likeness that could maintain the fruit quality and prolong the shelf life.
The dolostone of the Cambrian Longwangmiao Formation has been a significant gas exploration area in Sichuan Basin. In Gaoshiti-Moxi regions, a giant gas pool with thousands of billion cubic meters' reserve has been discovered. However, the origin of the reservoir and the distribution patterns are still disputed, eventually constraining the dolostone exploration of the Longwangmiao Formation. This paper focuses on the characteristics, origin, and distribution patterns of the dolostone reservoir in the Longwangmiao Formation based on: the outcrop geological survey, cores, thin-sections observation, reservoir geochemical characteristics study, and reservoir simulation experiments. As a result, two realizations were acquired: (1) The Cambrian Longwangmiao Formation could be divided into upper and lower part in Sichuan Basin. Based on the two parts of the Longwangmiao Formation, two lithofacies paleogeographic maps were generated. In addition, the carbonate slope sedimentary models were established. The grainstone shoals are mainly distributed in the shallow slope of the upper part in the Longwangmiao Formation. (2) The grainstone shoals are the developing basis of the dolostone reservoir in the Longwangmiao Formation. Moreover, the contemporaneous dissolution was a critical factor of grainstone shoal reservoir development in the Longwangmiao Formation. Controlled by the exposure surface, the dissolution vugs are not only extensively distributed, but also successively developed along the contemporaneous pore zones. Hence, the distribution patterns could be predicted. The geological understandings of the origin of dolostone reservoir in the Longwangmiao Formation show that the reservoir distributed in the areas of karstification in the Gaoshiti-Moxi regions, as well as the widespread grainstone shoals in the whole basin, are the potential exploration targets. Keywords: Sichuan Basin, Longwangmiao Formation, Carbonate slope, Dolograinstone shoal reservoir, Genesis and distribution of reservoir
Abstract Background The health literacy skills required by individuals to interact effectively with health services depends on the complexity of those services, and the demands they place on people. Public health and social service organisations have a responsibility to provide services and information in ways that promote equitable access and engagement, that are responsive to diverse needs and preferences, and support people to participate in decisions regarding their health and wellbeing. The aim of this study was to develop a conceptual framework describing the characteristics of health literacy responsive organisations. Methods Concept mapping (CM) workshops with six groups of professionals (total N = 42) from across health and social services sectors were undertaken. An online concept mapping consultation with 153 professionals was also conducted. In these CM activities, participants responded to the seeding statement “Thinking broadly from your experiences of working in the health system, what does an organisation need to have or do in order to enable communities and community members to fully engage with information and services to promote and maintain health and wellbeing”. The CM data were analysed using multidimensional scaling and hierarchical cluster analyses to derive concept maps and cluster tree diagrams. Clusters from the CM processes were then integrated by identifying themes and subthemes across tree diagrams. Results Across the workshops, 373 statements were generated in response to the seeding statement. An additional 1206 statements were generated in the online consultation. 84 clusters were derived within the workshops and 20 from the online consultation. Seven domains of health literacy responsiveness were identified; i) External policy and funding environment; ii) Leadership and culture; iii) Systems, processes and policies; iv) Access to services and programs; v) Community engagement and partnerships; vi) Communication practices and standards; and vii) Workforce. Each domain included 1 to 5 sub-domains (24 sub-domains in total). Conclusions Using participatory research processes, a conceptual framework describing the characteristics, values, practices and capabilities of organisational health literacy responsiveness was derived. The framework may guide the planning and monitoring of health service and health system improvements, and has the potential to guide effective public health policy and health system reforms.
Air temperature is an essential component of the factors used in landscape planning. At similar topographic conditions, vegetation may show considerable differences depending on air temperature and precipitation. In large areas, measuring temperature is a cost and time-consuming work. Therefore, prediction of climate variables at unmeasured sites at an acceptable accuracy is very important in regional resource planning. In addition, use a more proper prediction method is crucial since many different prediction techniques yield different performance in different landscape and geographical conditions. We compared inverse distance weighted (IDW), ordinary kriging (OK), and ordinary cokriging (OCK) to predict air temperature at unmeasured sites in Malatya region (East Central Anatolia) of Turkey. Malatya region is the most important apricot production area of Turkey and air temperature is the most important factor determining the apricot growing zones in this region. We used mean monthly temperatures from 1975 to 2010 measured at 28 sites in the study area and predicted temperature with IDW, OC, and OCK techniques, mapped temperature in the region, and tested the reliability of these maps. The OCK with elevation as an auxiliary variable occurred the best procedure to predict temperature against the criteria of model efficiency and relative root mean squared error.
Lívia Ramazzoti Silva, Karina Gomes Angilelli, Hágata Cremasco
et al.
<p class="orbitalabstract"><span>Artificial neural networks self-organizing map type (SOM) was used to classify samples of automotive gasoline C marketed in the eastern and northern regions of the state of Paraná, Brazil. The input order of parameters in the network were the values of temperature of the first drop, the 10, 50 and 90% distilled bulk, the final boiling point, density, residue content and alcohol content. A network with a topology of 25x25 and 5000 training epochs was used. The weight maps of input parameters for the trained network identified that the most important parameters for classifying samples were the temperature of the first drop and the temperature of the 10% and 50% of the distilled fuel.</span></p><p class="orbitalabstract"> </p><p class="orbitalabstract"><span>DOI: <a href="http://dx.doi.org/10.17807/orbital.v7i2.732">http://dx.doi.org/10.17807/orbital.v7i2.732</a><br /></span></p><p class="orbitalabstract"> </p>