Search Results (42,235)

Background: Diabetes distress is a key mental health dimension of type 2 diabetes (T2D) and a growing concern as populations age, yet its determinants in older adults remain insufficiently characterized. The independent contributions of demographic and metabolic factors to severe distress and their implications for multidisciplinary screening across the lifespan remain unclear. Methods: In this cross-sectional study, we assessed 453 adults with T2D using the 17-item Diabetes Distress Scale (DDS-17). We categorized participants as experiencing low (<2), moderate (2–2.9), or severe (≥3) distress. We compared categories using the Kruskal–Wallis H test and chi-square tests. Logistic regression models were constructed with severe distress (DDS ≥ 3) as the outcome, adjusting for sex, educational level, and HbA1c. Results: Severe distress was identified in 19 participants (4.2%). Age differed significantly across DDS categories (Kruskal–Wallis H test, p = 0.003), with the severe group being significantly older (72.95 ± 4.38 years). Emotional burden was the dominant subscale across all groups. In multivariable logistic regression, increasing age (aOR = 1.163 per year; 95% CI: 1.055–1.282; p = 0.002) and lower educational level (high school vs. basic: aOR = 0.187; p = 0.020) were independently associated with severe distress, whereas metabolic variables were not independently associated with severe distress. ROC analysis identified age ≥ 70 years as an exploratory, sample-derived cut-off (AUC = 0.726, 95% CI 0.602–0.827; sensitivity 84%, specificity 55%). Results were robust in sensitivity analyses that excluded patients with diabetes duration < 5 years. Conclusions: Severe diabetes distress is more strongly associated with demographic vulnerability than with metabolic burden. Age ≥ 70 years and lower educational attainment may help identify older adults at increased risk of diabetes-related distress, supporting targeted psychological screening strategies in this lifespan stage. Full article

Axial blood pumps serve as vital auxiliary therapeutic devices for patients with end-stage heart failure, and parameter optimization of the controller is critical to improve system performance. Existing optimization methods cannot satisfy the parameter optimization requirements of variable-speed axial blood pump controllers in terms of optimization accuracy. Therefore, this paper investigates a many-objective optimization method adapted to the operating characteristics of blood pumps. Firstly, a many-objective optimization model is established for the controller. To efficiently solve the proposed model, an optimization algorithm integrating deep reinforcement learning, named DQN-NSGA-CT, is developed. On the basis of the population evolution state, the optimal strategy learned by DQN dynamically adjusts the crossover probability and mutation probability, which adaptively balances population diversity in the early iteration stage and convergence speed in the later iteration stage. Meanwhile, a novel environmental selection strategy is used to reconcile population convergence and diversity. To select the best compromise solution, an entropy weight–Copula–TOPSIS comprehensive evaluation method is proposed, which realizes objective weight assignment, objective correlation correction and multi-attribute ranking. Experimental results verify the efficiency of the DQN-NSGA-CT algorithm in solving the many-objective optimization model of the controller. The research addresses the many-objective optimization problem of variable-speed axial blood pump control. Full article

To address the challenges of cumulative pseudo-label noise and substantial phenological differences across regions in cropping structure mapping, this study develops a phenological knowledge-guided weakly supervised semantic segmentation framework, termed PhenoStruct-WSF. The framework first constructs typical phenological curves for major annual cropping patterns and uses TWDTW to measure the phenological similarity between samples and templates. A two-class margin indicator, ∆_i, is then proposed, and phenological confidence is derived through probability calibration, after which high-confidence samples are selected as anchors under posterior-FDR control. Subsequently, the anchors are embedded into a structure-aware weakly supervised segmentation network, PhenoStruct-Net, forming a two-stage training strategy of “fidelity first, expansion later”, in which robust pseudo-label expansion is achieved through phenological confidence gating and adaptive weighting. Experiments were conducted in three representative study areas, namely Handan in Hebei Province, Jingzhou in Hubei Province, and Siping in Jilin Province. The anchor samples selected using posterior-FDR achieved accuracies above 94% in all three study areas. PhenoStruct-WSF consistently achieved overall accuracy (OA), mean intersection over union (mIoU), and macro F1-score (mF1) above 89%, 81%, and 89%, respectively, across the three regions, with an average OA improvement of 4.52 percentage points over the comparison methods. Sample sensitivity experiments indicate that the proposed method maintains a stable trend of accuracy improvement under different training sample sizes, demonstrating good adaptability to limited samples. The ablation experiments further show that phenological confidence anchors, posterior-FDR control, and the confidence-weighting mechanism can effectively suppress pseudo-label noise propagation and improve the robustness of crop mapping in complex agricultural landscapes. Full article

Background/Objectives: Biomimetic biomaterials should be evaluated not only through their initial properties but also through their interaction with biological environments over time. This study investigated a human cell-based approach integrating flavonoid-induced cellular responses with the temporal evolution of 3D-printed polylactic acid (PLA) scaffolds under physiological-like conditions. Methods: Human Wharton’s Jelly mesenchymal stem cells (WJ-MSCs) were exposed to naringin and hesperidin (250 µg/mL) and evaluated after 3, 7, and 10 days using ALP, TP, OPN, and OC. In parallel, PLA scaffolds’ biodegradation was studied under static and dynamic conditions. Results: Naringin produced a more pronounced early/intermediate osteogenic-related response, whereas hesperidin showed comparatively more sustained ALP- and OPN-related activity at later stages. Dynamic scaffolds exhibited progressive temporal changes, with weight variations ranging from −0.384% to +0.127% at days 1, 3, 7, and 10, respectively, accompanied by progressive morphological modification. Initial AFM analysis showed an average RMS roughness of 22.1 nm. Conclusions: The distinct temporal profiles of the flavonoids, together with the evolving scaffold–medium interface, support a biomimetic strategy based on temporal coordination of biochemical cues. These findings provide a rationale for future experimentally validated sequential delivery systems designed to promote early osteogenic activation followed by sustained cellular and matrix-associated activity. Full article

Synthesis of multilayer graphene oxide (GO) was achieved through a Hummers method with meticulous control over the addition of the oxidizing agent. Precursors were immersed in an ice bath during the synthesis to maintain a temperature of approximately 275 K in oxidation stages I and II, temperature was measured with a thermocouple inside the cooling bath, in direct contact with the vacuum filter flask at the level of the reactants. In contrast to the variety of Hummers synthesis methods—where the system is maintained at certain temperature during the initial oxidation stage and heat continues to be supplied during the second stage—our setup operates at low temperature from the beginning to the conclusion of the process. A detailed analysis of the compositional, structural, and optical properties of the fabricated GO was conducted, revealing a notable photoluminescent band in the 380–480 nm region. This property is of interest for a potential future application in the field of optoelectronics. X-ray diffraction (XRD) and high-resolution transmission electron microscopy (HRTEM) revealed that the structure of the material consists of a sp³ matrix with sp² domains. Raman spectroscopy revealed an I_D/I_G ratio of 2.7, which could indicate a high density of structural defects or extremely small sp² graphitic domains. Furthermore, deconvolution of the 2D band suggests the presence of multilayers in the GO. Fourier transform infrared spectroscopy (FTIR) analysis revealed the presence of all functional groups associated with the GO. Upon observation of the photoluminescence exhibited by the material, a broadband centered on the violet-blue emission, with a possible mechanism consistent with the literature associated with the transitions between sp² domains and localized states in the basal plane defects or it might be caused by quantum confinement effects. The diminished contribution from the green emission could be caused by the transitions between localized states sp² and the functional groups associated with oxygen. Full article

Traditional fault diagnosis methods for gas-insulated switchgear (GIS) equipment primarily rely on offline detection and periodic maintenance, which suffer from limitations such as poor real-time performance and localization difficulties, thereby compromising the safe and stable operation of ultra-high-voltage power grids. To enhance the accurate identification and localization capabilities of defects within GIS equipment, this study first establishes a multi-physics coupled simulation model integrating temperature field, flow field, and concentration field to analyze gas diffusion characteristics under varying conditions of fault source locations, decomposition product types, and initial concentrations. Subsequently, a GIS busbar gas chamber experimental platform is constructed to validate the simulation model. Finally, a response time matrix, a peak concentration matrix, and a fault coverage index are developed, and a weighted comprehensive evaluation method is employed to optimize sensor placement schemes. The findings reveal that fault source location significantly influences concentration response speed and spatial distribution patterns; SO₂, HF, H₂S, and SOF₂ exhibit distinct diffusion characteristics due to their differing physical properties; and initial concentration primarily affects the non-uniformity during the early diffusion stage. The simulation results demonstrate good agreement with experimental data, with a maximum root-mean-square error of 3.936 × 10⁻⁴. Monitoring point M4 achieves the highest comprehensive score, making it the preferred location for single-sensor deployment. These results provide a theoretical foundation and technical guidance for GIS online monitoring and fault diagnosis. Full article

“Shetaicui” is a distinctive quartzite jade from the Dashetai area of Inner Mongolia. Its green variety has attracted considerable academic attention because of its characteristic color and relatively high economic value, yet its color genesis and formation process remain poorly understood. Here, green “Shetaicui” was examined by petrographic observation, infrared and Raman spectroscopy, ultraviolet–visible (UV–Vis) absorption spectroscopy, colorimetric measurements, X-ray fluorescence spectroscopy (XRF), and electron probe microanalysis (EPMA). These methods were used to characterize its mineral composition and textures and investigate its color genesis and formation process. The results show that the jade is a fine-grained muscovite quartzite, whereas the wall rock is a fine-grained chlorite–quartz–muscovite schist. Coloration is mainly associated with Cr-bearing muscovite. Cr is the principal chromogenic element, whereas Fe contributes to coloration to a lesser extent. Pearson correlation analysis and multiple linear regression show that Cr₂O₃ is most strongly correlated with a* and C*, whereas FeO^T is most strongly correlated with L* and shows a weaker positive correlation with b*. Petrographic textures and mineral assemblages indicate that the jade and its wall rock underwent regional metamorphism broadly consistent with greenschist-facies conditions. Formation proceeded through three stages: sedimentary protolith formation, regional metamorphism, and jade formation during late fluid alteration. During regional metamorphism, quartz-rich sandy horizons formed a Qtz I-dominated pre-jade quartzite, whereas muddy horizons formed the chlorite–quartz–muscovite schist wall rock. Subsequent fluid alteration led to the formation of Cr-bearing muscovite and Qtz II within the pre-jade quartzite. Cr-bearing muscovite imparted the characteristic green coloration, completing the transformation of the pre-jade quartzite into “Shetaicui” jade. These results clarify the correlations between chromogenic elements and colorimetric parameters in green “Shetaicui” and provide petrographic and mineralogical evidence for this three-stage formation process. Full article

Background: Racial and socioeconomic differences in prostate cancer outcomes are often described as survival disparities, but observed differences may also reflect healthcare access, treatment delivery, and resource allocation. Methods: NCDB and SEER–Medicare were treated as independent, complementary retrospective data sources without individual-level linkage. The analytic material comprised 111,396 database records from 2010 to 2020 across two independent source files; this is a cross-source record count, not a deduplicated count of unique persons. Harmonized descriptive analyses characterized stage, treatment, and treatment timing, while multivariable Cox proportional hazards modeling evaluated prostate cancer-specific survival (CSS) in outcome-eligible SEER–Medicare records. The CSS model denominator is therefore substantially smaller than the total record count, and the two should not be confused. Results: Records for non-Hispanic Black (NHB) men more often showed stage III–IV disease than records for non-Hispanic White (NHW) men (26.9% vs. 18.7%) and lower receipt of guideline-concordant first-course management initiated within 90 days, particularly in the low-SES subgroup (51.4% vs. 84.3% among high-SES NHW men). In the adjusted CSS model, NHB race was associated with higher mortality (HR = 1.32; 95% CI: 1.24–1.41). Uninsured status (HR = 1.47; 95% CI: 1.33–1.62), low income (HR = 1.28; 95% CI: 1.19–1.37), and rural residence (HR = 1.14; 95% CI: 1.06–1.22) were also associated with higher mortality. Conclusions: The findings support a healthcare-system interpretation of prostate cancer disparities and identify measurable targets for prospective evaluation, including EHR-enabled monitoring, nurse navigation, telehealth-supported follow-up, and facility-level treatment-concordance review. Full article

Grounded in a human–land coupling framework, this study takes Purple Mountain, a representative composite urban mountain heritage site, as the research object. It integrates historical archives, field survey data, and multi-source geospatial data, and adopts a set of GIS-based spatial statistical methods, including the nearest neighbour index, kernel density estimation, standard deviational ellipse, coupling coordination degree model, and Geodetector. This paper systematically explores the spatial differentiation, spatiotemporal evolution, human–land coupling patterns, and multidimensional driving mechanisms of four heritage types: geomorphological relics, ritual architecture, modern commemorative heritage, and eco-scenic heritage. The results show that: (1) Heritage resources across Purple Mountain display statistically significant clustering, with ritual architectural heritage exhibiting the highest agglomeration degree; heritage sites form an east–west high-density corridor along the southern foothills, presenting a consistent northeast–southwest spatial orientation. (2) Purple Mountain heritage has undergone multi-stage diachronic evolution. Jointly driven by topographic constraints and socio-cultural forces, its heritage quantity, spatial coverage, and functions fluctuated across dynasties, with an overall expanding trend. (3) A total of 63.07% of the study area’s grid units are in a near-dissonant human–land coupling state, while highly coordinated units are concentrated in the southern core corridor of the Ming Xiaoling Mausoleum, Sun Yat-sen Mausoleum, and Linggu Temple, with remarkable disparities among heritage types in coupling patterns among the four heritage categories. (4) Historical and cultural aggregation density dominates heritage spatial differentiation, while topographic factors show weak explanatory power, and all influencing factor interactions present prominent non-linear enhancement effects. This study establishes a three-level quantitative framework of “spatial pattern—coupling coordination—driving mechanism”, enriching the theoretical framework of human–land coupling for urban mountain heritage, and provides scientific support for refined coordinated governance of mountain heritage embedded in high-density urban environments. Full article

Background: Cancer remains a growing public health challenge globally, with increasing demand for oncology services, particularly in resource-limited settings. In South Africa’s Eastern Cape Province, tertiary hospitals face rising patient loads amid constrained infrastructure and late-stage presentation. This study assessed temporal trends, gender differences, and cancer-type distribution in oncology service utilisation at Nelson Mandela Academic Hospital (NMAH) from April 2023 to February 2025. Methods: A retrospective quantitative observational time-series design was used. Monthly aggregated oncology service data were extracted from hospital records, including total cancer attendances, gender distribution, cancer types, and service utilisation indicators (new cases, follow-ups, chemotherapy, hormone therapy, palliative care, admissions, referrals, and mortality). Descriptive statistics, Pearson correlation, and simple linear regression were applied to assess temporal trends. Time (months) was the independent variable. Results: A total of 16,368 cancer-related encounters were recorded, with a mean of 706.4 cases per month. Females accounted for 11,816 cases, significantly exceeding males (4552). Breast cancer was the most prevalent malignancy (5763 cases), followed by other cancers (4316) and cervical cancer (3616). Cervical cancer demonstrated a strong and statistically significant increasing trend (r = 0.619, p = 0.002), while lung cancer also showed a significant upward trend (r = 0.549, p = 0.007). Breast cancer showed a positive but non-significant trend (p = 0.163), whereas prostate and oesophageal cancers showed no significant temporal changes. Service utilisation was dominated by follow-up visits (14,761) and palliative care (16,911), indicating a high burden of advanced disease. Overall attendance showed an increasing trend, peaking at 1039 cases in April 2024, with seasonal declines in December–January. Conclusions: Oncology service utilisation at NMAH is increasing, with marked gender disparities and rising trends in cervical and lung cancers. The high reliance on palliative and follow-up care highlights late presentation and advanced disease burden. Strengthening early detection, screening programmes, and decentralised oncology service capacity is essential to address the growing cancer burden in this setting. Full article

Unmanned aerial vehicle (UAV) acquired imagery provides a flexible non-contact visual sensing modality for monitoring inland waterway locks, yet reliable perception remains challenging due to significant scale variation among targets, as well as partial visibility, low contrast, water-surface texture variation, and complex backgrounds. To address these issues, this study proposes BISRF-Net, a baseline-preserving scale-guided residual feature routing network built upon the CBv2 Faster R-CNN framework. The method retains the original backbone, feature pyramid network, and region proposal network, while introducing scale-guided residual routing at the region-of-interest (ROI) refinement stage. Through identity-preserved residual addition, the baseline ROI representation is preserved and enhanced with complementary scale-sensitive information without altering the original feature pathway. Experiments conducted on the UAV image subset of the TROUT lock-monitoring dataset demonstrate that BISRF-Net maintains comparable overall detection performance while improving medium-scale target representation. Repeated trials with different random seeds further assess the robustness of the proposed method and indicate that its main benefit lies in medium-scale target refinement. Ablation and computational analyses further show that the proposed design enables targeted ROI-level refinement with additional computational cost. These findings highlight the potential of scale-guided ROI feature refinement for robust UAV-based visual sensing in complex inland waterway environments. Full article

Fruit thinning concentrates nutrients and serves as an essential procedure for cultivating high-quality strawberries with exceptional palatability. It is established that controlling the number of fruits per peduncle to three to five significantly increases the proportion of large fruits and enhances sweetness while effectively reducing the risks of disease infection. Consequently, pesticide application is minimized, thereby ensuring food safety and improving overall product quality. Although precise execution baselines are a prerequisite for automated thinning, current research is largely restricted to isolated fruit recognition, leaving the direct detection of peduncle–fruit topological associations under complex occlusions unaddressed. To address this gap, a lightweight, three-stage (“coarse-to-fine”) detection method tailored for automated thinning is proposed. The pipeline comprises global localization, local cropping with background suppression, and fine secondary inference, which effectively mitigates environmental interference. To support this investigation, a dedicated dataset comprising 1131 high-quality images was constructed. For efficient edge deployment, YOLOv11n was adopted as the baseline architecture, integrated with structural Re-parameterized Convolution (RepConv), Coordinate Attention (CoordAtt), and a dynamic re-weighting loss. This configuration ensures robust feature extraction of slender peduncles with an extremely low parameter overhead. The experimental results demonstrate that with only 2.77 M parameters, the proposed model achieves an mAP@0.5 of 75.73% and an F1-score of 73.85%. Notably, the average false positive (FP) detections per image in complex scenarios were significantly reduced from 1.53 to 0.23. Following deployment on a Jetson Orin Nano Super edge device utilizing TensorRT and FP16 quantization, the end-to-end system inference speed stabilized at 15.58 frames per second (FPS) with near-lossless precision. Ultimately, this method provides a reliable technical foundation for automated thinning decisions, facilitates sustainable greenhouse management, and secures the supply of high-quality food from the source. Full article

To reduce the communication and centralized-computation burden associated with frequent intraday upper-level updates under renewable-energy uncertainty, this paper proposes an edge-autonomous multi-objective energy management strategy with an offline–online architecture. In the day-ahead stage, Gaussian Copula modeling and trajectory screening generate temporally correlated photovoltaic (PV) scenarios. The conventional Strength Pareto Evolutionary Algorithm 2 (SPEA2) first constructs a base library C0. A dual-space coordinated selection mechanism then retains every C0 strategy exactly and adds complementary trajectories according to their objective responses and differences in energy storage system power trajectories. In the intraday stage, the edge controller combines current local measurements with preloaded PV, load, and price profiles, propagates every stored candidate over a receding horizon, and applies the first action of the strict minimum-composite-cost candidate. The case-study results show that, without relying on intraday upper-level communication, the proposed method achieves a composite-objective value close to that of the perfect-information centralized reference. Full article

Background/Objectives: Advanced endometrial cancer (FIGO III–IV) is characterised by poor prognosis and a heterogeneous biological profile, and molecular classification enables treatment personalisation by identifying subtypes with distinct therapeutic targets. We aimed to characterise the molecular and histopathological features of FIGO III–IV cases in a Polish multicentre cohort and to discuss the resulting treatment implications. Methods: This retrospective multicentre study included 915 consecutive patients with endometrial cancer operated on between April 2022 and May 2025 at three oncology centres in south-eastern Poland. Molecular subtyping (POLEmut, p53abn, dMMR/MSI-H, NSMP) was performed using immunohistochemistry (IHC) and next-generation sequencing (NGS). FIGO stage was assigned according to the FIGO 2009 classification. Results: Among 888 patients with a known molecular subtype, FIGO III–IV cases accounted for 15.9% (n = 141). The p53abn subtype predominated (35.5%), followed by dMMR/MSI-H (26.2%), NSMP (24.1%), and POLEmut (5.7%). The proportion of p53abn increased with stage (I–II vs. III–IV, p < 0.001), whereas dMMR/MSI-H remained stable regardless of stage (p = 0.83). POLEmut was absent in FIGO IV (0/16; 95% CI 0.0–19.4%), which should be regarded as an exploratory observation requiring prospective validation. Conclusions: The molecular profile of advanced endometrial cancer may inform treatment strategy; the therapeutic implications presented here are descriptive and hypothesis-generating, as the study did not include survival data. The dMMR/MSI-H subtype identifies patients who may benefit from immunotherapy in accordance with current clinical indications, supporting routine MMR testing regardless of disease stage. Conversely, p53abn tumours point to the need for a more intensive treatment strategy, in line with current guidelines. The absence of POLEmut in FIGO IV is an exploratory observation requiring prospective validation. Treatment de-escalation in POLEmut FIGO IIIC remains subject to further clinical validation and requires individualised assessment after complete staging. Full article

Seed dormancy is an important adaptive mechanism in plants. However, some seeds, such as those of Xanthoceras sorbifolium (a valuable economic and medicinal species), exhibit strong dormancy, resulting in a very low natural germination rate. This work investigated the mechanism by which low temperatures (LT: −20 °C storage for 60 days) release seed dormancy and promote germination. This was achieved by examining seed germination conditions, applying scanning electron microscopy (SEM), and measuring physiological indicators of seeds and the hormone levels. Targeted metabolomic analysis of sugar and fatty acid metabolism was also performed. The results were as follows: (1) A high germination rate of 47.3% was observed under LT condition, compared to 32.7% at room temperature (RT: 25 °C). Of four germination methods, direct GMS (germination in moist sand) was the most effective. (2) The two stages of VI (after storage) and VII (on the 7th day of germination) were key stages to breaking the seed dormancy and triggering germination. At both stages, the high integrity of the seed shells and kernels—particularly, the kernels—was observed using SEM. The activities of SOD, CAT, and POD, as well as the levels of IAA and IPA, and the ratios of IAA/ABA and (IAA + GA3 + ZR + IPA)/ABA (tHor/ABA) were found to be higher, especially in stage VII, where tHor/ABA increased by 62.7%, while ABA decreased by 23.2% in the LT treatment compared to the RT treatment. An optimal germination condition (GMS) created a suitable microenvironment, and this could have maintained highly active antioxidant enzymes and kept H₂O₂ (one of reactive oxygen species, or ROS) within signal transduction levels, and cross-talk to hormones. These changes (enzymes, hormones, ROS, and microenvironment) ensured the seeds reaching an optimal state for dormancy release in the VI stage, and facilitated the seed germination in the VII stage. (3) LT treatment promoted the degradation of starch and fats in the seeds. Significant accumulations of eight soluble sugars, such as glucose, D-fructose, and trehalose, as well as three fatty acids, such as Cis-11,14,17-eicosatrienoic acid (C20-3n3) and γ-linolenic acid (C18-3n6), were observed, while two soluble sugars and one fatty acid decreased under LT conditions. In conclusion, low temperature, as an external signal, together with ROS and GA-ABA-IAA co-regulated the seed dormancy release and germination. The moist-sand microenvironment was also a key factor in awakening the embryos of X. sorbifolium seeds. Full article