Search Results (1,439)

Rubus L. species display considerable morphological and genetic variability. Traditional taxonomic methods, which rely primarily on the observation of external characters, are often insufficient to resolve this complexity. Consequently, molecular biology tools are being increasingly employed. This study aimed to identify markers associated with candidate genes responsible for four selected pollen morphological traits—namely, the length of the polar axis, length of the ectoaperture, distance between the apices of two ectocolpi, and equatorial diameter—using association mapping. Based on the available literature, this is the first report of association mapping used to identify candidate genes for pollen morphological traits in Rubus L. Sixteen Rubus species and the complete set of possible markers (65534) were analyzed. Association mapping enabled the identification of 44 markers that are statistically significantly associated with all four morphological traits under consideration. The ten markers with the highest total LOD value for four pollen morphological traits allowed the distinction of six species: Rubus bifrons, Rubus caesius, Rubus idaeus, Rubus radula, Rubus saxatilis, and Rubus scissus. The results demonstrate that the proposed comprehensive approach of analyzing all possible markers may serve as an effective tool for detecting markers linked to genes controlling important traits, not only in Rubus species but potentially in other taxa as well. Full article

In the present paper we use the proximal point method in order to find an approximate common zero of an infinite collection of maximal monotone maps in a real Hilbert space under the presence of computational errors. We prove that the inexact proximal point method generates an approximate solution if these errors are sufficiently small. Full article

Aboveground biomass (AGB) in grasslands is a vital metric for assessing ecosystem functioning and health. Accurate and efficient AGB estimation is essential for the scientific management and sustainable use of grassland resources. However, achieving low-cost, high-efficiency AGB estimation via remote sensing remains a key challenge. This study integrates Sentinel-1 and Sentinel-2 imagery to derive 38 multi-source feature variables, including backscatter coefficients, texture, spectral reflectance, vegetation indices, and topographic factors. These features are combined with AGB data from 112 field plots in the Three Parallel Rivers area. Feature selection was performed using Pearson correlation, Random Forest (RF), and SHAP values to identify optimal variable sets. Genetic Programming (GP) was then applied for nonlinear optimization of the selected features. Three machine learning models—RF, GBRT, and KNN—were used to estimate AGB and generate spatial distribution maps. The results revealed notable differences in model accuracy, with RF performing best overall, outperforming GBRT and KNN. After GP optimization, all models showed improved performance, with the RF model based on RF-selected features achieving the highest accuracy (R² = 0.90, RMSE = 0.31 t/ha, MAE = 0.23 t/ha), improving R² by 0.03 and reducing RMSE and MAE by 0.05 and 0.03 t/ha, respectively. Spatial mapping showed the AGB ranged from 0.41 to 3.59 t/ha, with a mean of 1.39 t/ha, closely aligned with the actual distribution characteristics. This study demonstrates that the RF model, combined with multi-source features and GP optimization, provides an effective approach to grassland AGB estimation and supports ecological monitoring in complex areas. Full article

Context: It has been claimed that recently, in England, the places with the greatest amount of housing built were the places that least needed them. This is an accusation that has echoes in a number of countries around the globe. The lack of construction leads to greater unaffordability and a lower level of economic activity than could have been achieved if labour, particularly those with high human capital, was not so constrained as to where they could afford to live. The recent National Planning Policy Framework for England imposes mandatory targets on housing planning authorities. As such, the following question is raised: will the targets result in additional residential homes being located in places of greater need than the prevailing pattern? Research Questions: The paper sets out to consider the spatial mismatch between housing additions and national benefit in terms of unaffordability and productivity. Specifically, do the concentrations of high and/or low rates of the prevailing rates of additional dwellings and the target rates of adding dwellings correspond with the clusters of high and/or low unaffordability and productivity? A further question considered is: does the spatial distribution of additional dwellings match the clusters of population growth? Method: The values of the variables are transformed at the first stage into Anselin’s LISA categories. LISA maps can reveal unusually high spatial concentrations of values, or clusters. The second stage entails comparing sets of the transformed data for agreement of the classifications. An agreement coefficient is provided by Fleiss’s kappa. Data: The data used is of additional dwellings, the total number of dwellings, population estimates, gross value added per hour worked (productivity data), and house price–earnings ratios. The period of study covers the eight years prior to 2020 and the two years after, omitting 2020 itself due to the unusual impact on economic activity. All the data is at local authority district level. Findings: The hot and cold spots of additional dwellings do not correspond those of house price–earnings ratios or productivity. However, population growth hot spots show moderate agreement with those of where additional dwellings are concentrated. This is in line with findings from elsewhere, suggesting that population follows housing supply. Concentrations of districts with relatively high targets per unit of existing stocks are found correspond (agree strongly) with clusters of house price–earnings ratios. Links between productivity and housing are much weaker. Conclusions: The strong link between targets and affordability suggests that if the targets are met, the claim that the places that build the most housing are the places that least need them can be challenged. That said, house-price–earnings ratios present a view of unaffordability that will favour greater building in the countryside rather than cities outside of London, which runs against concentrating new housing in urban areas consistent with fostering clusters/agglomerations implicit in the new modern industrial strategy. Full article

High-value patents are a key indicator of new product development, the emergence of innovative technology, and a source of innovation incentives. Multiple studies have shown that patent value exhibits a significantly skewed distribution, with only about 10% of patents having high value. Identifying high-value patents from a large volume of patent data in advance has become a crucial problem that needs to be addressed urgently. However, current machine learning methods often rely on manual hyperparameter tuning, which is time-consuming and prone to suboptimal results. Existing optimization algorithms also suffer from slow convergence and local optima issues, limiting their effectiveness on complex patent datasets. In this paper, machine learning and intelligent optimization algorithms are combined to process and analyze the patent data. The Fire Hawk Optimization Algorithm (FHO) is a novel intelligence algorithm suggested in recent years, inspired by the process in nature where Fire Hawks capture prey by setting fires. This paper firstly proposes the Enhanced Fire Hawk Optimizer (EFHO), which combines four strategies, namely adaptive tent chaotic mapping, hunting prey, adding the inertial weight, and enhanced flee strategy to address the weakness of FHO development. Benchmark tests demonstrate EFHO’s superior convergence speed, accuracy, and robustness across standard optimization benchmarks. As a representative real-world application, EFHO is employed to optimize Random Forest hyperparameters for high-value patent recognition. While other intelligent optimizers could be applied, EFHO effectively overcomes common issues like slow convergence and local optima trapping. Compared to other classification methods, the EFHO-optimized Random Forest achieves superior accuracy and classification stability. This study fills a research gap in effective hyperparameter tuning for patent recognition and demonstrates EFHO’s practical value on real-world patent datasets. Full article

This paper investigates the structural properties of solutions of vector equilibrium systems and mixed variational inequalities in topological vector spaces. Based on Himmelberg-type fixed point theorem, combined with the analysis of set-valued mapping and quasi-monotone conditions, the existence criteria of solutions for two classes of generalized equilibrium problems with weak compactness constraints are constructed. This work introduces an innovative application of the measurable selection theorem of semi-continuous function space to eliminate the traditional compactness constraints, and provides a more universal theoretical framework for game theory and the economic equilibrium model. In the analysis of mixed variational problems, the topological stability of the solution set under the action of generalized monotone mappings is revealed by constructing a new KKM class of mappings and introducing the theory of pseudomonotone operators. In particular, by replacing the classical compactness assumption with pseudo-compactness, this study successfully extends the research boundary of scholars on variational inequalities, and its innovations are mainly reflected in the following aspects: (1) constructing a weak convergence analysis framework applicable to locally convex topological vector spaces, (2) optimizing the monotonicity constraint of mappings by introducing a semi-continuous asymmetric condition, and (3) in the proof of the nonemptiness of the solution set, the approximation technique based on the family of relatively nearest neighbor fields is developed. The results not only improve the theoretical system of variational analysis, but also provide a new mathematical tool for the non-compact parameter space analysis of economic equilibrium models and engineering optimization problems. This work presents a novel combination of measurable selection theory and pseudomonotone operator theory to handle non-compact constraints, advancing the theoretical framework for economic equilibrium analysis. Full article

Background: The widespread use of cardiac magnetic resonance imaging (MRI) in clinical practice has enabled the identification of numerous patients with evident damage from previous myocarditis, whether known or unknown. For years, myocardial fibrosis has been a topic of interest due to its established correlation with arrhythmic events in various clinical settings, including ischemic heart disease, dilated cardiomyopathy, and hypertrophic cardiomyopathy. MIBG scintigraphy is a method widely used in patients who are candidates for defibrillator implantation or have experienced heart failure. This examination evaluates the sympathetic innervation of the myocardium. Objective: To assess the real arrhythmogenic risk of non-ischemic scars identified in symptomatic or asymptomatic patients through the use of MIBG. Methods: Patients were retrospectively selected based on the presence of non-ischemic myocardial fibrosis detected by cardiac MRI, consistent with a myocarditis outcome (even in the absence of a clear history of myocarditis). These patients underwent myocardial scintigraphy with MIBG using a tomographic technique. Results: A total of 50 patients (41 males, mean age 51 ± 16 years) who underwent MRI from 2019 to June 2024 were selected. The primary indication for MRI was ventricular ectopic extrasystoles detected on Holter ECG (n = 12, 54%), while five patients underwent MRI following a known acute infectious event (23%, including three cases of COVID-19 infection). All symptomatic patients presented with chest pain in the acute phase, accompanied by elevated hsTNI levels (mean value: 437 pg/mL). The MRI findings showed normal ventricular volumes (LV: 80 mL/m², RV: 81 mL/m²) and normal ejection fractions (56% and 53%, respectively). The mean native T1 mapping value was 1013 ms (normal range: 950–1050). T2 mapping values were altered in the 5 patients who underwent MRI during the acute phase (mean value: 57 ms), without segmentation. Additionally, three patients had non-tamponade pericardial effusion. All patients exhibited LGE (nine subepicardial, seven midwall, six patchy). All patients underwent myocardial scintigraphy with MIBG at least 6 months after the acute event, with only one case yielding a positive result. This patient, a 57-year-old male, had the most severe clinical presentation, including more than 65,000 premature ventricular beats (PVBs) and multiple episodes of paroxysmal supraventricular tachycardia (PSVT) recorded on Holter ECG. MRI findings showed severe left ventricular dysfunction, a slightly dilated LV, and midwall LGE at the septum, coinciding with hypokinetic areas. Conclusions: MIBG scintigraphy could be a useful tool in assessing arrhythmic risk in patients with previous myocarditis. It could help reduce the clinical burden of incidental findings of non-ischemic LGE, which does not appear to be independently associated with an increased risk profile. Full article

Landslides are among the most frequent and dangerous natural hazards, posing serious threats to life and infrastructure. To mitigate their impacts, landslide susceptibility mapping (LSM) plays a crucial role by identifying areas prone to future landslide occurrences. This study aimed to assess how the choice of feature selection methods influences the performance of LSM models based on the eXtreme Gradient Boosting (XGBoost) algorithm when an extended set of input variables is used. Two study areas located in Southern Poland, called Biały Dunajec and Rożnów, were selected for analysis. These regions differ in terrain, elevation, and environmental characteristics and are situated approximately 65 km apart. Three widely used feature selection techniques were applied: the Pearson correlation coefficient (PCC), symmetrical uncertainty (SU), and analysis of variance (ANOVA). For each method, XGBoost models were trained and evaluated using multiple performance metrics, including the area under the curve (AUC), overall accuracy, precision, recall, and F1-score. The highest AUC values were achieved using the PCC method: 0.985 for Biały Dunajec and 0.983 for Rożnów. The best overall performance (accuracy of 0.93, recall of 0.94, and F1-score of 0.79) was obtained for the Rożnów case study using PCC features. These findings highlight that, when a comprehensive set of input variables is used, the exclusion of less informative features has little effect on model accuracy, as their information is largely preserved within the retained features. Full article

In this paper, we present the notion of fuzzy $\mathcal{R}-F-$contractive mappings and provide some fuzzy fixed point results in the setting of fuzzy metric spaces, which are endowed with binary relations. Furthermore, we apply our newly established fuzzy fixed point results to solve certain boundary value problems for nonlinear fractional differential equations involving the Caputo fractional derivatives. Also, we provide some examples to show the utility of our new results. Full article

Background: The U.S. Medicare Annual Wellness Visit (AWV) offers a structured opportunity for cognitive screening and personalized prevention planning among older adults. Yet, implementation of AWVs, particularly for individuals with cognitive impairment, remains inconsistent across primary care or other diverse care settings. Methods: We conducted a scoping review using the Consolidated Framework for Implementation Research (CFIR) to explore multilevel factors influencing the implementation of the Medicare AWV’s cognitive screening component, with a focus on how these processes support the detection and management of cognitive impairment among older adults. We searched four databases and screened peer-reviewed studies published between 2011 and March 2025. Searches were conducted in Ovid MEDLINE, PubMed, EBSCOhost, and CINAHL databases. The initial search was completed on 3 January 2024 and updated monthly through 30 March 2025. All retrieved citations were imported into EndNote 21, where duplicates were removed. We screened titles and abstracts for relevance using the predefined inclusion criteria. Full-text articles were then reviewed and scored as either relevant (1) or not relevant (0). Discrepancies were resolved through consensus discussions. To assess the methodological quality of the included studies, we used the Joanna Briggs Institute critical appraisal tools appropriate to each study design. These tools evaluate rigor, trustworthiness, relevance, and risk of bias. We extracted the following data from each included study: Author(s), year, title, and journal; Study type and design; Data collection methods and setting; Sample size and population characteristics; Outcome measures; Intervention details (AWV delivery context); and Reported facilitators, barriers, and outcomes related to AWV implementation. The first two authors independently coded and synthesized all relevant data using a table created in Microsoft Excel. The CFIR guided our data analysis, thematizing our findings into facilitators and barriers across its five domains, viz: (1) Intervention Characteristics, (2) Outer Setting, (3) Inner Setting, (4) Characteristics of Individuals, and (5) Implementation Process. Results: Among 19 included studies, most used quantitative designs and secondary data. Our CFIR-based synthesis revealed that AWV implementation is shaped by interdependent factors across five domains. Key facilitators included AWV adaptability, Electronic Health Record (EHR) integration, team-based workflows, policy alignment (e.g., Accountable Care Organization participation), and provider confidence. Barriers included vague Centers for Medicare and Medicaid Services (CMS) guidance, limited reimbursement, staffing shortages, workflow misalignment, and provider discomfort with cognitive screening. Implementation strategies were often poorly defined or inconsistently applied. Conclusions: Effective AWV delivery for older adults with cognitive impairment requires more than sound policy and intervention design; it demands organizational readiness, structured implementation, and engaged providers. Tailored training, leadership support, and integrated infrastructure are essential. These insights are relevant not only for U.S. Medicare but also for global efforts to integrate dementia-sensitive care into primary health systems. Our study has a few limitations that should be acknowledged. First, our scoping review synthesized findings predominantly from quantitative studies, with only two mixed-method studies and no studies using strictly qualitative methodologies. Second, few studies disaggregated findings by race, ethnicity, or geography, reducing our ability to assess equity-related outcomes. Moreover, few studies provided sufficient detail on the specific cognitive screening instruments used or on the scope and delivery of educational materials for patients and caregivers, limiting generalizability and implementation insights. Third, grey literature and non-peer-reviewed sources were not included. Fourth, although CFIR provided a comprehensive analytic structure, some studies did not explicitly fit in with our implementation frameworks, which required subjective mapping of findings to CFIR domains and may have introduced classification bias. Additionally, although our review did not quantitatively stratify findings by year, we observed that studies from more recent years were more likely to emphasize implementation facilitators (e.g., use of templates, workflow integration), whereas earlier studies often highlighted systemic barriers such as time constraints and provider unfamiliarity with AWV components. Finally, while our review focused specifically on AWV implementation in the United States, we recognize the value of comparative analysis with international contexts. This work was supported by a grant from the National Institute on Aging, National Institutes of Health (Grant No. 1R01AG083102-01; PIs: Tzeng, Kuo, & Raji). Full article

This study presents an innovative unmanned aerial vehicle (UAV)-based intelligent detection method utilizing an improved Faster Region-based Convolutional Neural Network (Faster R-CNN) architecture to address the inefficiency and inaccuracy inherent in manual wheat spike counting. We systematically collected a high-resolution image dataset (2000 images, 4096 × 3072 pixels) covering key growth stages (heading, grain filling, and maturity) of winter wheat (Triticum aestivum L.) during 2022–2023 using a DJI M300 RTK equipped with multispectral sensors. The dataset encompasses diverse field scenarios under five fertilization treatments (organic-only, organic–inorganic 7:3 and 3:7 ratios, inorganic-only, and no fertilizer) and two irrigation regimes (full and deficit irrigation), ensuring representativeness and generalizability. For model development, we replaced conventional VGG16 with ResNet-50 as the backbone network, incorporating residual connections and channel attention mechanisms to achieve 92.1% mean average precision (mAP) while reducing parameters from 135 M to 77 M (43% decrease). The GFLOPS of the improved model has been reduced from 1.9 to 1.7, an decrease of 10.53%, and the computational efficiency of the model has been improved. Performance tests demonstrated a 15% reduction in missed detection rate compared to YOLOv8 in dense canopies, with spike count regression analysis yielding R² = 0.88 (p < 0.05) against manual measurements and yield prediction errors below 10% for optimal treatments. To validate robustness, we established a dedicated 500-image test set (25% of total data) spanning density gradients (30–80 spikes/m²) and varying illumination conditions, maintaining >85% accuracy even under cloudy weather. Furthermore, by integrating spike recognition with agronomic parameters (e.g., grain weight), we developed a comprehensive yield estimation model achieving 93.5% accuracy under optimal water–fertilizer management (70% ETc irrigation with 3:7 organic–inorganic ratio). This work systematically addresses key technical challenges in automated spike detection through standardized data acquisition, lightweight model design, and field validation, offering significant practical value for smart agriculture development. Full article

Background: Remifentanil, an ultra-short-acting μ-receptor agonist, is used with propofol or thiopental for tracheal intubation without muscle relaxants. While effective with both, its combination with thiopental provides better hemodynamic stability. Thiopental has long been a standard intravenous agent for anaesthesia induction and remains a cost-effective alternative to propofol in resource-limited settings. To date, no study has directly compared the effects of thiopental–remifentanil and propofol–remifentanil combinations on LMA insertion conditions. This study aims to compare the effects of thiopental or propofol with 2 µg·kg⁻¹ remifentanil on laryngeal mask airway (LMA) insertion conditions and success in a prospective, randomised double-blind study. Method: The study included 80 premedicated ASA I-II patients, aged 18–65, randomised into Group P (propofol) and Group T (thiopental). Anaesthesia induction was with 2 μg·kg⁻¹ remifentanil, followed by 5 mg·kg⁻¹ thiopental or 2.5 mg·kg⁻¹ propofol. LMA insertion occurred 90 s post-induction. LMA insertion conditions were evaluated using a six-variable scale. Systolic arterial pressure (SAP), diastolic arterial pressure (DAP), mean arterial pressure (MAP), heart rate (HR), and bispectral index monitor (BIS) values were recorded at baseline, 1 min pre-insertion, and at 1, 2, 3, 4, and 5 min after insertion. Apnoea duration, loss of eyelash reflex duration, insertion duration, number of attempts, and perioperative complications were also documented. Results: Demographic data were similar. Group P showed significantly shorter eyelash reflex loss and LMA insertion durations, longer apnoea duration, and higher rates of full mouth opening, excellent LMA insertion condition, and hypotension or bradycardia compared to Group T (p < 0.05). Group P had significantly lower HR, SAP, DAP, and MAP at various time points (p < 0.05). There were no significant differences in blood presence on LMA, sore throat, or dysphagia (p > 0.05). Conclusions: In our study, administration of 2 μg·kg⁻¹ remifentanil before induction along with thiopental or propofol was shown to provide acceptable LMA insertion conditions at comparable levels. As hemodynamic parameters were less affected, we believe the remifentanil–thiopental combination may be a suitable alternative. Full article

Soil moisture is a key factor in the exchange of energy and matter between the soil and atmosphere, playing a vital role in the hydrological cycle and agricultural management. Traditional monitoring methods are limited in achieving large-scale, real-time observations, while deep learning offers new avenues to model the complex nonlinear relationships between spectral features and soil moisture content. This study focuses on the Wei-Ku Oasis in Xinjiang, using multi-source remote sensing data (Landsat series and Sentinel-1) and in situ multi-layer soil moisture measurements. The BOSS feature selection algorithm was applied to construct 46 feature parameters, including vegetation indices, soil indices, and microwave indices, and to identify optimal variable sets for each depth. Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and their hybrid model (CNN-LSTM) were used to build soil moisture inversion models at various depths. Their performances were systematically compared on both training and testing sets, and the optimal model was used for spatiotemporal mapping. The results show that the CNN-LSTM-based multi-depth soil moisture inversion model achieved superior performance, with the 0–10 cm model showing the highest accuracy and a testing R² of 0.64, outperforming individual models. The testing R² values for the soil moisture inversion models at depths of 10–20 cm, 20–40 cm, and 40–60 cm were 0.59, 0.54, and 0.59, respectively. According to the mapping results, soil moisture in the 0–60 cm profile of the Wei-Ku Oasis exhibited a vertical gradient, increasing with depth. Spatially, soil moisture was higher in the central oasis and lower toward the periphery, forming a “center-high, edge-low” pattern. This study provides a high-accuracy method for multi-layer soil moisture remote sensing in arid regions, offering valuable data support for oasis water resource management and precision irrigation planning. Full article

This study aims to demonstrate how mathematics, especially calculus concepts, can be expanded to include semi-entities and how these can be applied to sampling activities. Here, the multivalued logic uses pseudo-complemented lattices, instead of Boolean algebras. Truth values can express the intensity of a property: for example, the property of being heavy intensifies as weight increases. They can also express the state-of-the-art knowledge of an individual about a certain thing. To express that a number $x$ approaches $a$ is to say that the statement “$x=b$” is not fully true but approaches the full-true value as $b-a$ approaches zero. This approach generalizes the concept of a limit and the concepts derived from it, such as differentiation and integration. A Monte Carlo algorithm replaces one function with another with finite domain, preferably its finite part, by sampling the domain and calculating its map. The discussion extends to integration over an unbounded interval, integral transforms, and differential equations. This study then covers strategies for producing Monte Carlo estimates of respective problems and determining their crucial truth values. In the discussion, a topic related to axiomatizing set theory is also suggested. Full article

Floods stand as one of the most harmful natural disasters, which have become more dangerous because of climate change effects on urban structures and agricultural fields. This research presents a comprehensive flood mapping approach that combines multi-sensor satellite data with a machine learning method to evaluate the July 2021 flood in the Netherlands. The research developed 25 different feature scenarios through the combination of Sentinel-1, Landsat-8, and Radarsat-2 imagery data by using backscattering coefficients together with optical Normalized Difference Water Index (NDWI) and Hue, Saturation, and Value (HSV) images and Synthetic Aperture Radar (SAR)-derived Grey Level Co-occurrence Matrix (GLCM) texture features. The Random Forest (RF) classifier was optimized before its application based on two different flood-prone regions, which included Zutphen’s urban area and Heijen’s agricultural land. Results demonstrated that the multi-sensor fusion scenarios (S18, S20, and S25) achieved the highest classification performance, with overall accuracy reaching 96.4% (Kappa = 0.906–0.949) in Zutphen and 87.5% (Kappa = 0.754–0.833) in Heijen. For the flood class F1 scores of all scenarios, they varied from 0.742 to 0.969 in Zutphen and from 0.626 to 0.969 in Heijen. Eventually, the addition of SAR texture metrics enhanced flood boundary identification throughout both urban and agricultural settings. Radarsat-2 provided limited benefits to the overall results, since Sentinel-1 and Landsat-8 data proved more effective despite being freely available. This study demonstrates that using SAR and optical features together with texture information creates a powerful and expandable flood mapping system, and RF classification performs well in diverse landscape settings. Full article