Search Results (668)

A new nearness grey incidence model that not only measures the degree of correlation but also measures the direction is proposed to analyze the distribution characteristics of air pollutants in the Beijing–Tianjin–Hebei region, from 2016 to 2024. To be specific, improvements in this proposed model lie in the following aspects: First, the calculation method of the symbol judgement factor is updated, so that the final nearness grey incidence degree can better reflect the nearness degree and nearness direction between any two sequences, which improves the stable robustness of the grey incidence degree. Second, the anti-fluctuation factor is introduced into the new model, and the relative volatility between series is included in the calculation process of the grey incidence degree. Third, several practical properties of the proposed model are elaborated to further interpret the feasibility and adaptability of the proposed model. In experiments, based on the daily data of the six pollutants in the Beijing–Tianjin–Hebei region from 2016 to 2024, using the new model as a tool, the main pollutants in the Beijing–Tianjin–Hebei region are identified, the temporal and spatial distribution of different pollutants is analyzed, the changes in trends of air pollution processes in the past 9 years are identified, and a comparison with pollution levels of other cities in Beijing–Tianjin–Hebei and Beijing is also detailed. Finally, the superlative performance of the proposed model is confirmed by the model comparison, Monte Carlo analysis and example analysis. Full article

Background/Objectives: Older adults frequently present with emergency ventral hernias, a situation that carries significant physiological risks and often requires challenging clinical decisions. Despite the prevalence of these cases, there is a lack of robust evidence to inform emergency care in this demographic, as most existing research centres on short-term mortality rates and operative variables. Key aspects such as the impact of frailty and the course of recovery following surgery are insufficiently addressed in the literature. This study aimed to describe management strategies, frailty burden and postoperative outcomes in older adults presenting with emergency ventral hernias. Methods: This study retrospectively examined patients aged 65 and older who were admitted to a UK tertiary centre with emergency ventral hernias from February 2016 to July 2024. Data, including patient demographics, comorbid conditions, frailty status (as measured by the Clinical Frailty Scale), management approach, healthcare resource use, and clinical outcomes, were analysed descriptively. Additionally, a structured literature review was conducted in accordance with PRISMA guidelines to identify research on emergency ventral hernia treatment outcomes in adults aged 60 years and older. Results: A total of 67 patients met the inclusion criteria for the cohort. High rates of frailty and multiple coexisting health conditions were observed. While surgical intervention was the predominant management strategy, a subset of patients received conservative or palliative care. Greater degrees of frailty correlated with longer hospital stays and an increased need for critical care, even though six-month mortality remained comparatively low. Traditional risk assessment tools tended to overpredict mortality risk and failed to reflect the true postoperative burden or the recovery process. The systematic review yielded 7 studies, most of which documented mortality and complication rates, but few addressed frailty or provided detailed postoperative recovery data. Conclusions: The management of emergency ventral hernias in older adults is highly variable, with a significant postoperative impact that extends beyond mortality statistics. Assessing frailty appears to provide additional information that may support clinical decision-making and help anticipate recovery after surgery. Integrating frailty evaluation into emergency hernia care could enhance multidisciplinary collaboration and help ensure that treatment plans are better tailored to patient vulnerability and individual care goals. Full article

Hydrogen combustion is known to be fast compared to traditional hydrocarbon fuels. The fast combustion leads to a higher thermal efficiency. In this research a 600 cc single cylinder hydrogen engine was tested at 1250 rpm, lambda = 2 and 3, and three load levels (load was represented by Manifold Absolute Pressure (MAP); MAPs tested were 75, 95 and 120 kPa) and compared to operation with gasoline and propane. The fast burn duration (Mass Fraction Burnt MFB10% to MFB90%) and the MFB 50% were determined and analyzed. The hydrogen MFB50% location for Minimum Timing for Best Torque (MBT) was found to occur at around the typical 8 Crank Angle Degrees (CADs) After Top Dead Center (ATDC). Measurements of ignition delay based on the fast data direct measurement of spark ignition coil current drop to the change in polarity of net heat release are presented. With shifts towards direct injection and higher injection pressures, consideration was given to the hydrogen pressurization penalty, where it was calculated that pressurizing hydrogen to 100 bar at the flow required for lambda = 2 operation is 2.3 bar, i.e., higher than the Friction Mean Effective Pressure (FMEP)! Furthermore, hydrogen is widely cited to have a higher heat loss than typical hydrocarbon fuels. In this paper, detailed analyses at lambda 2 and lambda 3 showed that hydrogen in fact has lower heat losses. Full article

Slow lateral wandering and trochoidal-like motion are commonly observed in intense atmospheric vortices, yet most reduced-order vortex models assume a fixed axis or represent centre motion as purely advective. In this work, we propose a minimal reduced-order framework in which slow gyroscopic precession is introduced as an explicit degree of freedom superimposed on a rapidly rotating vortex core. The vortex is represented by a Burgers–Rott-type velocity field with time-dependent stretching rate and circulation, while the vortex centre undergoes a slow precessional motion governed by a time-dependent rate ${\Omega }_p\left(t\right)$. The evolution of the vortex parameters is coupled to environmental variability through simple relaxation laws driven by standard large-scale diagnostics, including convective available potential energy, vertical shear, and background vorticity. A tracker-only analysis of tropical cyclone best-track data is used to constrain the appropriate dynamical regime at the track scale, indicating that observed centre wandering typically occurs in a slow-precession limit P = ${\Omega }_p/{\omega }_c\ll 1$. Numerical demonstrations in cyclone-like configurations show that, despite the smallness of the precession number, cumulative lateral displacement and enhanced Lagrangian dispersion can develop over the vortex lifetime. The proposed framework is intended as a proof-of-concept reduced-order model that isolates the role of weak, environmentally forced precession in modulating vortex wandering and transport, and complements more detailed numerical and observational studies. Full article

Background: The heterogeneous nature of cancer with varying degrees of fat, necrosis, fibrosis, and varying degrees of tissue repair severely impacts the success of acquiring adequate tissue samples during percutaneous image-guided biopsy. Although ultrasound or CT fluoroscopy are used to identify tumor location and thus to guide biopsy needle insertion, these technologies do not provide the necessary resolution to determine tissue composition and enable the selection of the most appropriate location for biopsy specimen extraction. As a result, biopsy must be repeated, leading to significant cost to the health care system. Methods: In this study, we introduce a combined optical imaging/artificial intelligence (OI/AI) methodology for the real-time assessment of tissue morphology at the tip of the biopsy needle, prior to the collection of a biopsy specimen. Addressing a significant clinical challenge, this approach aims to reduce the proportion of biopsy cores—currently as high as 40%—that yield low diagnostic value due to elevated adipose or low tumor content. Our methodology employs micron-scale optical coherence tomography (OCT) imaging to obtain detailed structural tissue information using a minimally invasive needle probe. The OCT images are automatically analyzed using a convolutional neural network (CNN)-driven AI software developed by our team. A U-net style architecture was used to segment regions of tumor from the OCT scans. U-Net is a specialized convolutional neural network (CNN) architecture designed for fast, precise image segmentation, which involves classifying each pixel in an image to outline objects. This streamlined approach shows promise to provide clinicians with real-time results, supporting more accurate and informed decisions regarding biopsy site selection. To evaluate this technology, we conducted a clinical study using a custom-made OCT imager and recorded OCT images from patients diagnosed with liver cancers. Expert OCT interpreters supplied annotated reference images that were used to train a custom AI algorithm. Results: OCT imaging with ~10 mm axial and 20 mm lateral resolution enabled the collection of high-quality images of the tissue. The AI analysis was performed offline. UNet achieved an AUC of ~0.877 on the validation dataset, indicating promising performance for the relatively small data set used to train the model. The AI model matched human interpretations approximately 90% of the time, highlighting its promise for making biopsy procedures both more accurate and more efficient. Conclusions: A novel OCT instrument and AI software were evaluated for assessing tissue composition at the tip of biopsy needle. The OCT instrument produced micron-scale resolution images of the tissue, enabling AI analysis and accurate real-time discrimination of tissue type. This preliminary study demonstrated the clinical potential of this technology for improving biopsy success. Full article

This paper introduces a novel hybrid framework called Interval-Valued Picture Fuzzy Soft Rough Sets (IVPFSRS) designed to address complex uncertainty in multi-criteria group decision-making (MCGDM) problems. The model achieves a synergistic integration of three powerful mathematical theories: interval-valued picture fuzzy sets (IVPFS) for representing nuanced, interval-valued degrees of membership, neutrality, and non-membership; soft sets for parameterized problem formulation; and rough sets for handling data granularity and approximation under incompleteness. We formally define the IVPFSRS framework, investigate its fundamental properties and algebraic operations, and develop a comprehensive MCGDM algorithm with explicit weight incorporation to address the critical role of criterion importance. The effectiveness and robustness of the proposed approach are demonstrated through a detailed illustrative example of administrative position selection and a systematic comparative analysis with existing models. Results show that the IVPFSRS framework provides a more powerful, flexible, and logically coherent tool for robust decision making in highly uncertain and information-deficient environments. The proposed framework complements recent advancements in cloud-rough integration for large group decision making while offering unique advantages in parameterized three-way uncertainty representation and structured multi-criteria evaluation. Full article

Ungulate browsing is a major driver of forest regeneration dynamics and habitat structure in managed temperate forests, influencing species composition, regeneration success, and long-term stand development. Traditional assessments of browsing impacts often rely on field-based indicators such as regeneration density or visual cover, but these metrics provide limited insight into three-dimensional habitat structure. Mobile handheld LiDAR offers highly detailed measurements of forest structure, enabling objective and reproducible quantification of structural complexity that complements and extends conventional field-based methods. In this study, we applied handheld LiDAR as an innovative indicator for habitat structure within the ungulate browsing zone (<2 m height) to evaluate structural development across sites differing in management context. Paired fenced and unfenced plots (12 × 12 m) were surveyed within the WiWaldI project framework in 2019 and 2023 and compared across three hunting regimes representing different degrees of ungulate population management. Structural complexity was quantified by deriving box-counting dimensions from LiDAR point clouds, providing a measure of spatial arrangement and density relevant to ungulate–vegetation interactions. To support interpretation and ecological context, we complemented LiDAR indicators with streamlined field assessments. Based on this framework, we assessed whether forest structural complexity and visual cover differ among regions and over time, and whether ungulate browsing induces detectable structural differences between fenced whether structural differences between fenced and unfenced plots are detectable. We further examined the relative importance of tree species composition, plant architecture, and hunting regime as drivers of three-dimensional habitat structure. A simplified octant method characterized the spatial distribution of woody regeneration, while a silhouette-based approach quantified visual cover from the perspective of a standard ungulate profile. These auxiliary measures contextualize visual and spatial aspects of structure that LiDAR metrics capture with minimal observer bias. LiDAR studies have previously demonstrated potential for linking high-resolution structural data to ungulate habitat use, and our approach extends this by focusing on structural complexity as a habitat indicator. Results show a consistent increase in LiDAR-derived structural complexity between 2019 and 2023 across all regions. This increase occurred across management contexts and was not consistently explained by fencing or hunting regime effects, suggesting that site conditions, forest composition, and successional processes were dominant drivers during the observation period. Hunting regime showed no statistically significant and no consistent effect on structural complexity across regions or years. Visual cover metrics varied strongly among regions and species and declined over time. These findings suggest that three-dimensional habitat structure information has the potential to enhance the evaluation of ungulate impacts and may support evidence-based forest and wildlife management, particularly when interpreted in the context of site conditions and successional dynamics. Beyond ungulate impact assessment, the presented handheld LiDAR approach provides a scalable remote sensing framework for precision forestry by capturing three-dimensional structural attributes that are directly linked to forest stability, resilience, growth dynamics, and stand-level species mixing, thereby supporting evidence-based forest management recommendations. Full article

Background—Pancreatic ductal adenocarcinoma (PDAC) is among the deadliest malignancies, with a dismal 5-year survival rate. Despite continuous efforts to study its molecular signatures, the high degree of tumor-associated cellular heterogeneity in PDAC introduces extraneous microenvironmental components that complicate analysis. In recent years, multi-omics approaches have shown promise in deconvoluting cellular composition and enabling more specific, comprehensive cancer profiling. Method—To better characterize PDAC, we analyzed transcriptomic and proteomic data from 140 tumor tissues with 67 paired normal adjacent tissues and single-cell RNA sequencing data from 73 tumor tissues. Results—Using this approach, we successfully attributed molecular signatures to distinct cell-type populations. Overall, we found 59 tumor-cell-derived PDAC molecular signatures and evaluated them for functional relevance, prognostic value, and potential therapeutic implications. Among these, we identified molecular features associated with increased tumorigenic activity and immunosuppression. Moreover, survival analysis of protein phosphorylation and overall expression informed prognostic significance for potential therapeutic targets. Notably, we found that several phosphorylation changes correlate with poor patient survival, suggesting potential paths for therapeutic intervention by targeting protein post-translational modifications. Conclusion—Our study provides a detailed understanding of PDAC by characterizing key tumor-specific signatures that could serve as potential targets to improve clinical outcomes for this disease. Full article

Rapeseed stalks (Brassica napus), an abundant agricultural residue, represent a promising non-woody raw material for the pulp and paper industry. This study focuses on the chemical and structural characterization of alkali lignins isolated from black liquors generated by two common delignification methods: Kraft and Soda-Anthraquinone Pulping of rapeseed stalks. The objective is to understand how the chemical environment of each process influences the final structure, fragmentation degree, and reactivity of the isolated lignin. In practice, lignin samples are recovered from black liquors produced under varying conditions (alkali charge, time, and temperature) to achieve defined levels of delignification. Detailed characterization was performed using advanced analytical techniques, including Gel Permeation Chromatography, Solid-State Cross-Polarization/Magic-Angle-Spinning Nuclear Magnetic Resonance, and FT-IR and UV-Vis Spectroscopy. The findings provide essential data on the structural differences, confirming the suitability of the resulting materials for potential high-value applications. Furthermore, the structural similarities and performance indicators suggest that the Soda-AQ process enables successful delignification of rapeseed stalks without the sulfur emission issues associated with the Kraft method, thus validating the former as an environmentally cleaner alternative for non-wood biomass utilization supporting the complete valorization of rapeseed agricultural waste. Full article

This Data Descriptor provides an open, anonymized dataset describing anthropometric and physical fitness outcomes in undergraduate students enrolled in a Physical Activity and Sport Sciences degree program. The dataset included 156 participants (28 females and 128 males) and reported sex, age, body mass, stature, and body mass index, alongside standardized field-based tests covering flexibility, muscular endurance, strength, and jump performance. Hip flexibility was assessed using the Thomas test on both sides. Trunk extensor endurance was measured using the Biering–Sørensen test, and upper-body strength–endurance was assessed using a dead-hang test. Upper limb strength was recorded as elbow flexion strength. Lower limb power was evaluated using vertical jump tests, including Abalakov, squat jump, and countermovement jump, and a derived indicator (IE) was provided to facilitate comparisons across jump modalities. The data are distributed as a machine-readable CSV file accompanied by a detailed data dictionary describing the variables, units, and missingness. The dataset is intended to support the reproducible reporting of normative fitness profiles in sports science students, facilitate teaching and benchmarking in exercise science contexts, and enable secondary analyses exploring associations between anthropometry and physical performance. For reproducible inferential comparisons, users may apply Welch’s two-sample t-test for sex-based differences. Full article

Mediterranean biogeography is characterized as a global “hotspot” for climate change; understanding the impacts of these changes on local agricultural systems through high-resolution analyses has thus become a critical need. This study addresses this gap by evaluating the holistic effects of climate change on site-specific agriculture systems, focusing on the Eğirdir–Karacaören (EKB) and Beyşehir (BB) lake basins in the Lakes Region of Türkiye. This study employed machine learning modeling techniques to forecast changes in the yields of key crops, such as wheat, maize, apple, alfalfa, and sugar beet. Detailed spatial analyses of changes in agro-climatic conditions (heat stress, chilling requirement, frost days, and growing degree days for key crops) between the reference period (1995–2014) and two decadal periods projected for 2040–2049 and 2070–2079 were conducted under the Shared Socioeconomic Pathways (SSP3-7.0). Daily temperature, precipitation, relative humidity, and solar radiation data, derived from high-resolution climate simulations, were aggregated into annual summaries. These datasets were then spatially matched with district-level yield statistics obtained from the official data providers to construct crop-specific data matrices. For each crop, Random Forest (RF) regression models were fitted, and a Leave-One-Site-Out (LOSOCV) cross-validation method was used to evaluate model performance during the reference period. Yield prediction models were evaluated using the mean absolute error (MAE). The models achieved low MAE values for wheat (33.95 kg da⁻¹ in EKB and 75.04 kg da⁻¹ in BB), whereas the MAE values for maize and alfalfa were considerably higher, ranging from 658 to 986 kg da⁻¹. Projections for future periods indicate declines in relative yield across both basins. For 2070–2079, wheat and maize yields are projected to decrease by 10–20%, accompanied by wide uncertainty intervals. Both basins are expected to experience a substantial increase in heat stress days (>35 °C), a reduction in frost days, and an overall acceleration of plant phenology. Results provided insights to inform region-specific, evidence-based adaptation options, such as selecting heat-tolerant varieties, optimizing planting calendars, and integrating precision agriculture practices to improve resource efficiency under changing climatic conditions. Overall, this study establishes a scientific basis for enhancing the resilience of agricultural systems to climate change in two lake basins within the Mediterranean biogeography. Full article

This study presents a machine learning framework to automate the mapping of the Integrated Relative Anthropization Index (INRA, by its Spanish acronym). A predictive model was developed to estimate the degree of anthropization in the basin of Lake Tota, Colombia, using the XGBoost machine learning algorithm and remote sensing data. This research, part of a broader wetland monitoring project, aimed to identify the optimal spatial scale for analysis and the most influential predictor variables. Methodologically, models were tested at resolutions from 20 m to 500 m. The results indicate that a 50 m spatial scale provides the optimal balance between predictive accuracy and computational efficiency, achieving robust performance in identifying highly anthropized areas (sensitivity: 0.83, balanced accuracy: 0.91). SHAP analysis identified proximity to infrastructure and specific Sentinel-2 spectral bands as the most influential predictors in the INRA emulation model. The main result is a robust, replicable model that produces a detailed anthropization map, serving as a practical tool for monitoring human impact and supporting sustainable management strategies in threatened high-Andean ecosystems. Rather than a simple classification exercise, this approach serves to deconstruct the INRA methodology, using SHAP analysis to reveal the latent non-linear relationships between spectral variables and human impact, providing a transferable and explainable monitoring tool. Full article

Pumpkin seedlings serve as rootstocks for watermelon grafting, and the partial leaf trimming operation performed approximately two days before grafting is crucial for the survival rate of grafted watermelon seedlings. Extracting the position of the main veins of the leaf is a prerequisite for achieving automated partial pruning. The existing methods have problems such as low segmentation accuracy and misclassification between primary and branch veins in the pumpkin seedling segmentation task. This study proposes a three-classification segmentation model Dynamic Region Enhancement Transformer (DRE-Former) of main vein, branch vein and background, as well as a post-processing system. The encoder of DRE-Former consists of two modules. The former is Dynamic Frequency Conv and Normalized Efficient Conv (DN Block), which can enhance the feature extraction ability for small targets. The latter is the Region Transformer Block, which enhances the ability to distinguish between the main vein and the branch vein. In addition, in the skip connection part of the model, a Skip Connection Fusion Block (SCF Block) has been added, which can reduce the dilution degree of detailed features. The post-processing section outputs the cutting position and cutting Angle through rule-based methods and geometric analysis. The experimental results show that the proposed model achieves mean Intersection-over-Union (mIoU) and Overall Accuracy (OA) of 90.80% and 95.88%, respectively, outperforming the comparative models. In stability and error testing, the average standard deviation is 0.60, and the average relative error is 11.90%. Compared with the primary mIoU data in the dataset, the average relative error differs by only 2.11%. The post-processing system enables the accurate determination of cutting positions and angles, but it has a strong dependence on the segmentation model. The research can provide reliable technical support for the subsequent automatic cutting equipment for pumpkin seedlings. Full article

Background: Achieving breast symmetry is an important aesthetic goal following reconstruction post-mastectomy; however, little is known about women’s awareness, attitudes, and barriers regarding Contralateral Breast Symmetry Procedures (CBSP) in Jordan. Objectives: To assess awareness, perceptions, and barriers toward contralateral breast symmetry procedures among breast cancer survivors who underwent reconstruction at King Hussein Cancer Center. Methods: A cross-sectional study was conducted from July to Oct 2025 at KHCC, among 314 women diagnosed with breast cancer who had post-breast reconstruction. Data were collected using a structured Arabic questionnaire, which was developed based on existing literature, validated by an expert panel, and piloted on 10 women for clarity and reliability. The final tool demonstrated acceptable internal-consistency (Cronbach’s α = 0.712). The questionnaire captured sociodemographic and clinical data and detailed knowledge, attitudes, and barriers related to CBSP. Descriptive statistics summarized the data. Results: Participants’ mean age was 45.8 years; the majority were married (83.8%) and held university degrees (65.6%). Most reconstructions used silicone implants (94.6%). Only 6.4% had undergone CBSP, primarily delayed breast augmentation or mastopexy, with 75% reporting satisfaction. Awareness of CBSP was limited (37.9%), and less than one-third had discussed CBSP options with their surgeon or knew about insurance coverage. While 82.5% valued symmetry for body image, 31.5% viewed it as unnecessary after cancer recovery. Main barriers included satisfaction with current appearance (48.1%), fear of additional surgery (32.2%), financial constraints (37.3%), and lack of physician counseling (27.1%). Trust in medical team recommendations was high (89.2%). Conclusions: Contralateral breast symmetry procedures are under-recognized and infrequently pursued, primarily due to limited awareness, financial concerns, and insufficient counseling. Focused education and enhanced surgeon–patient communication are essential to support women’s aesthetic and psychological needs after reconstruction in Jordan. Full article

Optical imagery is often contaminated by clouds to varying degrees, which greatly affects the interpretation and analysis of images. Synthetic Aperture Radar (SAR) possesses the characteristic of penetrating clouds and mist, and a common strategy in SAR-assisted cloud removal involves fusing SAR and optical data and leveraging deep learning networks to reconstruct cloud-free optical imagery. However, these methods do not fully consider the characteristics of the frequency domain when processing feature integration, resulting in blurred edges of the generated cloudless optical images. Therefore, an adaptive frequency domain feature reconstruction-based cloud removal method is proposed to solve the problem. The proposed method comprises four key sequential stages. First, shallow features are extracted by fusing optical and SAR images. Second, a Transformer-based encoder captures multi-scale semantic features. Subsequently, the Frequency Domain Decoupling Module (FDDM) is employed. Utilizing a Dynamic Mask Generation mechanism, it explicitly decomposes features into low-frequency structures and high-frequency details, effectively suppressing cloud interference while preserving surface textures. Finally, robust information interaction is facilitated by the Cross-Frequency Reconstruction Module (CFRM) via transposed cross-attention, ensuring precise fusion and reconstruction. Experimental evaluation on the M3R-CR dataset confirms that the proposed approach achieves the best results on all four evaluated metrics, surpassing the performance of the eight other State-of-the-Art methods. It has demonstrated its effectiveness and advanced capabilities in the task of SAR-optical fusion for cloud removal. Full article