Search Results (16,921)

Methane (CH₄) emissions in urban areas remain a major source of uncertainty in greenhouse gas inventories, particularly in Eastern European cities, where observational studies are limited. This study presents a comprehensive, system-based assessment of CH₄ sources in Cluj-Napoca, Romania, based on high-resolution in situ measurements across five representative urban systems: aquatic environments (AQs), natural gas distribution end-use points (NG), sewer infrastructure (SE), building basements (BSs), and traffic emissions (TEs). Elevated CH₄ concentrations were consistently detected across all investigated systems, confirming the coexistence of both diffuse and point sources within the urban environment. Dissolved methane (dCH₄) in aquatic systems showed strong and persistent oversaturation relative to atmospheric equilibrium, reaching up to 3 × 10⁵% of air–water equilibrium, indicating active microbial methanogenesis enhanced by urban inputs of organic matter and nutrients. Measurements at natural gas end-use points revealed highly localized leaks with concentrations up to 482 ppmv. Sewer infrastructure exhibited extreme variability (up to 1222 ppmv), likely controlled by a combination of microbial production, hydraulic conditions, and potential interactions with adjacent gas distribution networks. Basement environments showed CH₄ accumulation up to 12 ppmv, reflecting the combined effects of gas leakage and limited ventilation. Measurements at vehicle exhausts identified transient CH₄ peaks reaching 162 ppmv during vehicle engine acceleration, with distinct ethane-to-methane ratios, indicative of pyrogenic sources. Overall, these results demonstrate that urban CH₄ emissions are spatially heterogeneous, temporally variable, and derived from multiple coexisting sources. The urban area should, therefore, be understood as a hybrid environment, with natural and anthropogenic CH₄ contributions. Full article

Power density and power efficiency are crucial for the design of high-performance computing servers. Buck converters exist due to their simplicity, but achieving a solution that combines high efficiency and high power density remains an ongoing research area in buck converter design. High-frequency switching, which reduces inductor size in buck converters, is a common method for achieving high power density; however, high-frequency switching introduces higher switching losses, hence the frequent use of GaN HEMTs, which have low switching losses. To achieve both high efficiency and high power density, this study proposes a compact buck converter design that pairs a D-type GaN HEMT with a low-voltage PMOS, termed a P-cascode GaN HEMT. We analyze different current switching modes and find that boundary conduction mode (BCM) can minimize inductor size while maintaining high power efficiency. This paper explores the theoretical basis of BCM and the P-cascode GaN HEMT, derives the operating conditions of BCM, estimates power efficiency, and proposes a high-power density buck converter solution. Simulation and experimental results show that the proposed design achieves 95% power efficiency in applications from 12 V to 3.3 V, while reducing the inductor size by a factor of 10 compared to continuous conduction mode (CCM) designs. Full article

The presence of pharmaceutical residues, such as ibuprofen, in aquatic environments poses a growing environmental challenge due to their persistence and potential ecotoxicological effects. In this study, a novel biohybrid composite based on pyrolysed rice husk (biochar) modified with Bacillus cereus cells was developed for the efficient removal of ibuprofen from aqueous solutions. The material was comprehensively characterised using SEM, BET, TGA, CHN analysis, and FTIR spectroscopy. Pyrolysis significantly increased the surface area (up to 300 m² g⁻¹) and porosity compared to raw rice husk, while bacterial immobilisation introduced additional functional groups, enhancing surface heterogeneity. Batch adsorption experiments demonstrated a clear improvement in adsorption capacity in the order of rice husk < biochar < composite. The maximum Langmuir adsorption capacities were 4.86, 11.68, and 13.73 mg g⁻¹ for rice husk, biochar, and the composite, respectively. Isotherm modelling indicated that ibuprofen adsorption was best described by the Langmuir and the Freundlich models, suggesting a combination of monolayer adsorption and heterogeneous surface interactions. Isotherm analyses (D–R energy values < 9 kJ mol⁻¹) indicate that ibuprofen removal occurs predominantly through physisorption, governed by π–π interactions, hydrogen bonding, and surface heterogeneity rather than chemisorption. Kinetic studies revealed rapid adsorption behaviour, with pseudo-first-order and pseudo-second-order models providing the best fit (R² up to 0.997). The Weber–Morris model confirmed that intraparticle diffusion contributed to the process but was not the sole rate-limiting step. The enhanced performance of the composite is attributed to synergistic effects between physicochemical adsorption on the porous carbon matrix and interactions with bacterial cell wall functional groups. The developed composite represents a low-cost, sustainable, and highly effective material for ibuprofen removal from contaminated water. Full article

Aiming at the problems of a fragile ecological environment, water shortage and system uncertainty in inland arid irrigation districts in Xinjiang, this study takes sustainable development as the guide, selects the Tailan River Irrigation District in Xinjiang as an example, and constructs a multi-objective optimal allocation model of agricultural water and soil resources in irrigation districts driven by water–carbon–economy synergy. The model aims to minimise irrigation water shortage, maximise crop carbon absorption and maximise economic benefits. By comparing six multi-objective algorithms such as APSEA, CMEGL, DCNSGA-III, DRLOS-EMCMO, MOEA/D-CMT and θ-DEA-CPBI, the optimal is selected based on the hypervolume (HV) index. The surface water, groundwater and crop-planting structure of five decision-making units in the irrigation district from 2021 to 2024 were optimised. Further, combined with the entropy weight–TOPSIS coupling-coordination comprehensive-evaluation model, the scheme evaluation system is constructed to screen the optimal configuration scheme of each year and unit. The results show that the MOEA/D-CMT algorithm has the highest HV value in each unit model over the years, which is the best solution algorithm for the model in this paper. The comprehensive evaluation value and coupling coordination degree of the optimal scheme of each unit fluctuate between years, and the difference between units is significant. Compared with the original planting and water source allocation scheme of the irrigation district from 2021 to 2024, the overall planting area of the optimised irrigation district is moderately reduced, forming an optimised pattern of ‘cotton pressure, grain expansion, economic increase and strong forest’; after optimization, the overall water shortage in the irrigation district is reduced by 1.4~11 million m³; the total amount of crop carbon absorption increased by 90.3~128.8 million kg; the net economic benefits increased by CNY 21.5~68.2 million. The research can provide decision support for the optimisation of the water and soil resource system in arid irrigation districts and has a scientific reference value for promoting the sustainable development and modernisation of agriculture in the inland irrigation districts of Northwest China. Full article

In recent decades, forest disturbances have increased in both frequency and intensity, driven by global warming and urbanization. Remote sensing, together with forest disturbance algorithms, offers broad opportunities for forest disturbance monitoring due to its high temporal and spatial resolution. However, operational methods capable of predicting and classifying disturbances while providing official area estimates suitable for national statistics remain scarce. The Three Indices Three Dimensions (3I3D) algorithm has proven effective in identifying forest changes and providing area estimates in Mediterranean ecosystems using Sentinel-2 imagery. Yet, while suitable for change detection, it does not distinguish among disturbance types. Here, we propose a two-step framework for forest disturbance detection and classification, tested in inland Spain for 2018. First, a binary forest change map is produced through an enhanced version of the 3I3D approach. This step incorporates Receiver Operating Characteristic (ROC) analysis to calibrate the algorithm through data-driven threshold selection, allowing adaptation to specific regional conditions. Second, detected changes are classified into four disturbance types: wildfire, clear-cut, thinning, and non-stand replacing disturbance, using Sentinel-2 spectral bands, 3I3D-derived metrics, and geometric descriptors of disturbance patches. Three machine-learning classifiers were compared: Support Vector Machine, Random Forest, and Neural Network. The detection step reached an overall accuracy of 82%, estimating that 1.43% of Spanish forests (264,900 ha) were disturbed in 2018. In the classification step, Random Forest achieved the best performance, with an overall accuracy of 72%. Of the detected disturbed area, 69% corresponded to non-stand replacing disturbances, while the remaining area was classified as thinnings (19%), wildfires (26%), and clear-cuts (55%). By integrating freely available Sentinel-2 imagery, remote sensing algorithms, and photo-interpreted reference datasets, this study provides a scalable and operational approach capable of producing annual disturbance maps that combine both detection and classification of high- and low-intensity disturbances, supporting official national-scale estimates of forest disturbance areas. Full article

Background: Vitamin D plays an important role in overall health. This study aimed to conduct a pilot screening of serum vitamin D levels in a Malagasy cohort and to compare vitamin D status groups with selected health indicators. Methods: A cross-sectional observational pilot study was performed in two geographically distinct regions of Madagascar—a coastal area and an inland area. In total, 150 individuals underwent a single health screening, including semi-quantitative assessment of serum 25-hydroxyvitamin D, as well as evaluation of glycemic and cholesterol levels, blood pressure, anthropometric parameters, and a brief personal and lifestyle questionnaire. Results: A total of 148 participants (aged 18–88 years) were analyzed. 45.9% of participants had low serum vitamin D levels (<75 nmol/L). Lower vitamin D levels and higher total cholesterol were observed in the coastal group compared to the inland group (p < 0.05). No significant differences were found for most other examined health indicators. In multivariable analysis, age was identified as an important determinant of several outcomes. Vitamin D status did not remain an independent predictor; however, a trend toward an independent association with hypercholesterolemia was observed (p = 0.07), while the association with hyperglycemia was less pronounced (p = 0.11). Conclusions: A substantial proportion of participants exhibited low vitamin D levels despite favorable geographic conditions. The results suggest a potential relationship between vitamin D status and lipid metabolism, although this association did not reach statistical significance after adjustment. These findings provide initial insight into vitamin D status and its potential associations in this setting and may inform future research and public health monitoring. Full article

To address over-extrusion and forming defects at path corners caused by path overlap in additive manufacturing, this paper proposes an angle-adaptive look-ahead compensation algorithm based on a Sech attenuation curve. This method establishes a mapping model between the path angle and the adaptive look-ahead distance of the overlapping area, aiming to eliminate the material accumulation at the corner by precisely identifying the overlapping area and modulating the flow rate. By building a Beckhoff five-axis 3D-printing device and relying on the TwinCAT control platform, the compensation triggering logic based on PLC real-time Euclidean distance calculation was realized, and a slicing software with dynamic bias compensation was also developed. Experiments were conducted on triangular samples with extreme acute angles of 5°, universal acute angles of 85°, and 90° straight angles for printing verification. The results show that this algorithm can effectively suppress the material over-extrusion and accumulation at the path overlap in multiple angles, achieving a smooth transition of the sharp corners in the printed contour. The research confirms that the algorithm proposed in this study, together with the integrated software and hardware system, can ensure the forming accuracy of extreme and conventional geometric features while also guaranteeing the printing efficiency, providing an important reference for ensuring the quality coordination control of the formation process of extreme geometric features in additive manufacturing. Full article

In complex sedimentary environments, the identification of thin sandbodies and the accurate prediction of their thickness remain challenging, particularly when relying on a single analytical approach. Taking the lower sub-member of the fourth member of the Shahejie Formation (Es₄^L) in the Chenguanzhuang area of the Dongying Depression as a case study, this study proposes a quantitative prediction method that integrates sedimentary facies constraints with machine learning-based seismic multi-attribute fusion. Based on core observations, well log data, and 3D seismic datasets, the study area is subdivided into two zones: Zone I (shallow-water delta front) and Zone II (shore–shallow lake). Sensitive attributes for each zone are optimized using Pearson correlation analysis and hierarchical clustering, and five machine learning models—SVR, Random Forest, MLP, Ridge Regression, and Lasso Regression—are systematically evaluated. The MLP model is selected for Zone I, achieving R² values of 0.856 and 0.936 for the training and test sets, respectively, whereas Ridge Regression combined with leave-one-out cross-validation (LOOCV) is adopted for Zone II to mitigate overfitting caused by limited well data, yielding R² values of 0.864 and 0.779. Compared with conventional linear regression (R² = 0.45), the proposed approach significantly improves the accuracy of quantitative sandbody prediction, providing a reliable geological basis for hydrocarbon exploration and an effective technical framework for similar complex sedimentary environments. Full article

Caking and powder adhesion are widespread challenges in dry powder processes. The influence of process parameters such as humidity and temperature on the adhesion behavior of dry powders has been extensively studied in numerous studies. Besides that, the impact of other process characteristics, such as additional process parameters or wall materials, has received little attention so far. In addition, existing methods to characterize caking behavior do not account for powders in a fluidized state. To address phenomena based on process and material behavior, a test rig was specifically designed to investigate the adhesion of dry particles to different metal walls at varying speeds at a 90° angle, representing the main novelty of this study. The deposition area, deposition mass, and maximum deposition thickness were evaluated, and the correlations were discussed. The investigations revealed that at low velocities (<12 m/s) and for smooth surfaces (Sq < 0.3–0.4 µm), wall materials with a high ratio of dispersive to polar surface energy components (D/P: 13–15.8) exhibit minimal powder adhesion. The test rig has demonstrated its effectiveness as a straightforward method for measuring adhesion across various powder–wall material pairs and could serve as a valuable preliminary test for industrial applications. Full article

Extreme precipitation constitutes one of the most devastating climatic resulting from global climate change. Jilin Province, a significant commodities grain base in China by a temperate monsoon climate, is particularly susceptible to flood disasters caused by extreme precipitation, usually occurring from late July to early August. The 2010 flood impacted moreover 5.12 million individuals and resulted in direct economic damages amounting to 45.1 billion CNY. However, research on the spatiotemporal characteristics and future trends of extreme precipitation in Jilin Province is still quite inadequate. This study examined the spatiotemporal distribution and future forecasts of extreme precipitation utilizing daily meteorological data from 31 stations (1960–2019) and three CMIP6 models (CanESM5, MPI-ESM1-2-HR, FGOALS-g3) under SSP2-4.5 and SSP5-8.5 scenarios. Eleven extreme precipitation indices, as specified by the WMO, were analyzed utilizing linear regression, the Mann–Kendall test, wavelet analysis, and inverse distance weighting interpolation. The findings indicated that from 1960 to 2019, extreme precipitation demonstrated traits of “increased frequency and total amount, decreased intensity”, with a significant decline in CDD (−2.184 d·(10a)⁻¹, p < 0.001), a notable increase in PRCPTOT (1.493 mm·(10a)⁻¹, p < 0.05), and a significant reduction in SD II (−0.016 mm·d⁻¹·(10a)⁻¹, p < 0.01). The majority of indicators had a predominant cycle of 30 to 50 years. A significant northwest-to-southeast gradient characterized most indicators, with PRCPTOT varying from 327.5 mm in Baicheng to 824.3 mm in Tonghua. Future projections (2025–2100) suggested scenario-dependent intensification. Under SSP5-8.5, all three models forecast substantial increases in precipitation amount indices (PRCPTOT: 2.071–2.457 mm·(10a)⁻¹) and SD II (0.010–0.013 mm·d⁻¹·(10a)⁻¹), reversing the past downward trend in intensity. The anticipated alterations exhibited a northwest-to-southeast gradient, with PRCPTOT increases above 230 mm in the central and southeastern regions. These findings offer a scientific basis for flood management and climate change adaptation in Jilin Province and analogous areas. Full article

Sepsis is a life-threatening complication among patients with liver cirrhosis and is associated with high morbidity and mortality. Early diagnosis is challenging due to immune dysfunction, chronic systemic inflammation, and overlap between clinical and laboratory findings during infection and hepatic decompensation. Therefore, there is a need to identify routinely available predictors that may enable the stratifying of patients at risk of developing sepsis in this population and facilitate intensive monitoring, antibiotic treatment, and potentially reduce mortality. The aim of this study is to evaluate the association between routine laboratory parameters and the development of sepsis among cirrhotic patients. A total of 171 cirrhotic patients met the inclusion criteria and were followed at a tertiary liver clinic between February 2015 and February 2022. Sepsis was defined according to Sepsis-3 criteria. Univariate analyses were performed to compare sepsis patients versus non-sepsis patients. Multivariable logistic regression was conducted to identify independent predictors of sepsis. Among 171 patients, 41 (24%) developed sepsis and 130 (76%) did not. Baseline characteristics were similar between groups: patients with sepsis were slightly older (67.5 ± 10.9 vs. 64.5 ± 12.3 years, p = 0.172), with no significant differences in sex (53.7% vs. 56.2%, p = 0.78) or ethnicity (Arab ethnicity 56.1% vs. 39.1%, p = 0.055). Ascites was more frequent in the sepsis group (53.7% vs. 26.2%, p = 0.001), whereas esophageal varices were less common (12.2% vs. 35.4%, p = 0.006). Rates of hepatic encephalopathy and acute kidney injury did not differ significantly. Higher creatinine (1.35 (0.80–3.35) vs. 0.80 (0.70–1.49) mg/dL, p < 0.001), INR (1.50 (1.20–1.80) vs. 1.30 (1.10–1.50), p = 0.011), and total bilirubin (1.90 (0.61–2.85) vs. 0.90 (0.59–1.70) mg/dL, p = 0.049) was observed in the sepsis group. In the multivariable model including age, sex, ethnicity, ascites, esophageal varices, INR, creatinine, neutrophil-to-lymphocyte ratio, and CRP, baseline serum creatinine was the only independent predictor of sepsis (adjusted OR 1.58 per 1 mg/dL increase, 95% CI 1.08–2.33, p = 0.01). Receiver operating characteristic (ROC) analysis demonstrated that the multivariable model had acceptable discriminative ability for prediction of sepsis, with an area under the curve (AUC) of 0.741 (95% CI 0.647–0.835). Among ambulatory patients with liver cirrhosis, baseline serum creatinine was independently associated with the development of sepsis. These findings highlight the need for dedicated risk-stratification tools in the outpatient setting. Further external validation in independent cohorts is required. Full article

Sarcopenia is a degenerative condition that imposes a substantial global public health burden, yet safe and effective interventions remain limited. Nutritional support is regarded as an important strategy to mitigate age-related muscle loss and improve physical function in older adults. Due to time and cost constraints, dexamethasone (DEX)-treated models are often used as an alternative to age-related sarcopenia models. This study investigated the effects of pumpkin seed protein peptides (PSPP) and vitamin D on DEX-treated mice. In vitro, PSPP attenuated senescence-associated phenotypes, reduced cellular injury, and partially alleviated DEX-treated myofibrillar atrophy, as evidenced by decreased Atrogin-1 and MuRF1 expression and increased MyoD expression. In vivo, PSPP and vitamin D, particularly in combination, ameliorated DEX-treated declines in muscle mass, grip strength, and endurance. Histological analyses further demonstrated improvements in myofibrillar architecture and muscle fiber cross-sectional area. In addition, each intervention was associated with increased ATP content, elevated interleukin-10 and insulin-like growth factor-1 levels, and reduced tumor necrosis factor-α and malondialdehyde levels. Collectively, these findings suggest that PSPP, either alone or combined with vitamin D, may alleviate DEX-treated sarcopenia, potentially through the modulation of mitochondrial homeostasis, attenuation of oxidative stress and inflammatory responses, and promotion of myogenic regeneration. Full article

Two-dimensional materials have broad application prospects in the field of optoelectronic devices. As a next-generation power electronic device, AlN materials have obvious advantages in power processing, and their monolayer structure has excellent optoelectronic properties, which is of great significance for the study of 2D AlN monolayers. Properties such as electronic and optical properties of metal-adsorbed AlN (M-AlN) systems have been systematically investigated using density functional theory from first principles. The results of the energy bands of the M-AlN system indicate that the adsorption of Al, Li, Ag, Au, Bi, Cr, Mn, Na, Pb, Sn, Ti, and K metals makes the monolayer AlN magnetic, the incorporation of two metals, Al and Li, is the transition of the monolayer AlN from a semiconductor to a semi-metal, and the introduction of K metal makes the monolayer AlN transition from a semiconductor to a metal. The work function of the M-AlN system shows that the introduction of the metal reduces the work function of the monolayer AlN, especially for K-AlN, which is reduced by 56.12% compared to the monolayer AlN. In addition, the results of the optical absorption spectra of the M-AlN system revealed that the introduction of the metals made the monolayer AlN exhibit high absorption peaks in the visible and near-infrared regions; in particular, the intensity of the absorption peaks of the Ti-AlN system at 557.8 nm reached 7.4 × 10⁴ cm⁻¹ and the intensity of the absorption peaks of the K-AlN system at 1109.3 nm reached 1.01 × 10⁵ cm⁻¹. This indicates that the introduction of Ti and K metal atoms enhances the absorption properties of monolayer AlN in the visible and near-infrared regions. Finally, the time-domain finite difference using spherical metal nanoparticles is used to excite the localized surface plasmon resonance, and the results show a small area of strong electric field around the electric field hotspot of Cr and Li particles, and a good concentration of the electric field strength in the x and y directions. In summary, the system of metal atoms adsorbed on AlN will be favorable for the design of spintronics, field-emitting devices and solar photovoltaic devices. Full article

Background: Uropathogenic Escherichia coli (UPEC) is the primary etiological agent of urinary tract infections (UTIs) worldwide. The emergence of strains combining high virulence with multidrug resistance (MDR) poses a significant challenge to public health. This study aimed to characterize the phylogenetic distribution, virulence profiles, and antimicrobial susceptibility of UPEC isolates recovered from patients in the metropolitan area of Buenos Aires (AMBA), Argentina. Methodology: Phylogenetic groups, the ST131 lineage, and virulence-associated genes were identified using PCR-based assays. Antimicrobial susceptibility testing (AST) was performed using automated methods and extended-spectrum beta-lactamase (ESBL) production was confirmed using the double-disk synergy test. Colistin (COL) resistance was evaluated by Colistin Drop Test and PCR screening for the mcr-1 (mobile colistin resistance gene 1). Biofilm formation was detected by the Tissue Culture Plate (TCP) method, whereas phenotypic virulence factors (VF) were assessed with Congo Red agar, hemagglutination, and hemolysis assays. Results: Phylogenetic groups B2 (43.8%) and D (26.7%), typically associated with extraintestinal infections, were the most frequent. The high-risk clone B2-ST131 was detected in 6.7% of isolates. Biofilm production was observed in 92.4% of the isolates, with curli fimbriae (87.6%) being the most frequently expressed VF. The highest resistance rates were observed for ampicillin (62.1%), ampicillin-sulbactam (39.8%), and trimethoprim-sulfamethoxazole (25.2%). Interestingly, 3.8% of isolates exhibited colistin resistance, despite the absence of the mcr-1 gene. Conclusions: This study highlights the detection of MDR-UPEC isolates that showed strong resistance to fluoroquinolones and were ESBL producers with high virulence in Argentina, justifying future research encompassing genomic and epidemiological monitoring of local UPEC, which is essential for managing infections and developing new therapeutic and preventive measures. Full article

Hyperspectral imagery (HSI) offers rich spectral signatures and fine-grained spatial structures for remote sensing, but practical HSI classification is often constrained by scarce labels and complex geometric disturbances, including translation, rotation, scaling, and shear. Existing deep models are typically developed under Euclidean assumptions and rely on data-hungry training pipelines, which makes them brittle in the few-shot regime. To address this challenge, we propose EMNet, a Lie-group-based Equivariant Manifold Network for few-shot HSI classification that explicitly encodes geometric invariance and improves discriminative accuracy. EMNet couples an SE(2)-based Equivariance-Guided Module (EGM) to enforce equivariance to translations and rotations with an affine Lie-group-based Characteristic Filtering Convolution (CFC) that models scaling and shearing on the feature manifold while adaptively suppressing redundant responses. Extensive experiments on WHU-Hi-HongHu, Houston2013, and Indian Pines demonstrate state-of-the-art performance with competitive complexity, achieving OAs of 95.77% (50 samples/class), 97.37% (50 samples/class), and 96.09% (5% labeled samples), respectively, and yielding up to +3.34% OA, +6.01% AA, and +4.14% Kappa over the strong DGPF-RENet baseline. Under a stricter 25-samples-per-class protocol with 10 repeated random hold-out splits, EMNet consistently improves the mean accuracy while exhibiting lower variance, indicating better stability to sampling uncertainty. On the city-scale Xiongan New Area dataset with extreme long-tail imbalance (1580 × 3750 pixels, 256 bands, and 5.925 M labeled pixels), EMNet further boosts OA from 85.89% to 93.77% under the 1% labeled-sample protocol, highlighting robust generalization for large-area mapping. Beyond point estimates, we report mean ± SD/SE across repeated splits and provide rigorous statistical validation by computing Yule’s Q statistic for class-wise behavior similarity, performing the Friedman test with Nemenyi post hoc comparisons for multi-method ranking significance, and presenting 95% confidence intervals together with Cohen’s d effect sizes to quantify practical improvement. Full article