Search Results (8,540)

Lightweight Single-Image Super-Resolution (SISR) faces the core challenge of balancing computational efficiency with reconstruction quality, particularly in preserving both high-frequency details and global structures under constrained resources. To address this, we propose the Periodically Enhanced Cascade Network (PECNet). Our main contributions are as follows: 1. Its core component, a novel Multi-scale Adaptive Feature Aggregation (MAFA) module, which employs three functionally complementary branches that work synergistically: one dedicated to extracting local high-frequency details, another to efficiently modeling long-range dependencies and a third to capturing structured contextual information within windows. 2. To seamlessly integrate these branches and enable cross-window information interaction, we introduce the Periodic Boundary Padding Shift (PBPS) mechanism. This mechanism serves as a symmetric preprocessing step that achieves implicit window shifting without introducing any additional computational overhead. Extensive benchmarking shows PECNet achieves better reconstruction quality without a complexity increase. Taking the representative shift-window-based lightweight model, NGswin, as an example, for ×4 SR on the Manga109 dataset, PECNet achieves an average PSNR 0.25 dB higher, while its computational cost (in FLOPs) constitutes merely 40% of NGswin’s. Full article

Rapid urbanization drives significant land use transformations, making the timely detection of newly constructed buildings a critical research focus. This study presents a novel unsupervised framework that integrates pixel-level change detection with object-level, mono-temporal building information to identify new constructions. Within this framework, we propose the Building Line Index (BLI) to capture structural characteristics from building edges. The BLI is then combined with spectral, textural, and the Morphological Building Index (MBI) to extract buildings. The fusion weight (φ) between the BLI and MBI was determined through experimental analysis to optimize performance. Experimental results on a case study in Wuhan, China, demonstrate the method’s effectiveness, achieving a pixel accuracy of 0.974, an average category accuracy of 0.836, and an Intersection over Union (IoU) of 0.515 for new buildings. Critically, at the object-level—which better reflects practical utility—the method achieved high precision of 0.942, recall of 0.881, and an F1-score of 0.91. Comparative experiments show that our approach performs favorably against existing unsupervised methods. While the single-case study design suggests the need for further validation across diverse regions, the proposed strategy offers a robust and promising unsupervised pathway for the automatic monitoring of urban expansion. Full article

Background: Diabetic foot ulcers (DFUs) carry high risks of infection, amputation, and mortality. We systematically reviewed randomized controlled trials (RCTs) of negative-pressure wound therapy (NPWT), including single-use systems, for clinically uninfected DFUs (with sensitivity analyses for mixed/infected cohorts). Methods: We searched PubMed and Scopus (1 January 2004–30 June 2024). Dual reviewers performed screening and extraction; risk of bias was assessed with Cochrane Risk of Bias 2 (RoB 2) and certainty of evidence with GRADE. When ≥2 trials reported comparable outcomes, we used random-effects meta-analysis. The DiaFu cohort reported in two publications was counted once across analyses. Results: Eleven RCT publications (n = 1699; 10 unique cohorts) met criteria; eight trials (n = 1456) informed the primary endpoint. Trials largely excluded severe ischemia; findings therefore apply mainly to neuropathic or mixed-etiology DFUs with adequate perfusion. NPWT increased complete healing at 12–16 weeks (risk ratio [RR] 1.46, 95% CI 1.21–1.76; I² = 48%) and shortened time to healing (mean difference –18 days, 95% CI −28 to −8). Effects were similar for conventional and single-use NPWT. Outcomes did not vary systematically within commonly used pressure ranges (approximately −80 to −125 mmHg). Only two RCTs reported direct cost data (exploratory). Moderate heterogeneity (Higgins’ I² 48–68%) reflected variation in ulcer severity, device type/settings, dressing-change frequency, and off-loading protocols. Conclusions: NPWT probably improves short-term healing of clinically uninfected DFUs compared with standard care and may reduce minor amputations, without increasing adverse events. Certainty is moderate for healing and low for most secondary outcomes. Benefits appear consistent across device classes and may support earlier discharge and community-based care. Evidence gaps include ischemia-dominated ulcers, long-term outcomes (recurrence and limb preservation), adherence mechanisms, and contemporary cost-effectiveness. Full article

At present, ionic liquids (ILs) are increasingly being used to extract natural products as green solvents, but their residues can lead to risks in terms of further use for the extracted herbal materials. Therefore, it is necessary to remove them with simple and effective methods. For example, after the toxic anthraquinones in Polygonum multiflorum are removed by extraction with the IL of [C₄Bim][PTSA], it needs to be recovered and reused, and the useful stilbene glycosides should not suffer from obvious loss as they are the main functional components. In this study, an ultrasonic method with n-propanol was used to remove the residual [C₄Bim][PTSA] in the solid powders of Polygonum multiflorum that had been extracted for anthraquinones. After single-factor optimization, the removal conditions were as follows: the removal temperature was 303.15 K, the solid–liquid ratio was 1:200 (w (1 g):v (200 mL)), the ultrasonic time was 40 min, and there were four operations. Under these conditions, ILs could be completely removed with almost no loss of stilbene glycosides in solid powders. After that, the IL in the extracting solution and scrubbing solution was recovered by the back-extraction method, and an IL with high purity could be obtained for reuse. The total recovery efficiency of the IL reached more than 98%. Then gas chromatography (GC) was conducted for the determination of residual ethanol and n-propanol in the solid powders of Polygonum multiflorum, which could be used to quickly detect the contents of two organic solvents within three minutes. Besides that, the method could also be applied to the determination of residual organic solvents in the raw materials of Polygonum multiflorum, and the results showed that the residue of ethanol and n-propanol in the solid powders were in accordance with the general provisions of the current Chinese Pharmacopoeia. According to the developed procedures and optimized conditions, the recovered IL could be reused in five runs at least. General applicability and greenness assessment for the developed process also proved that it is an ideal method, which has potential in large-scale application. Full article

Predicting the electric field distribution inside microfluidic devices featuring an embedded array of electrical insulating pillars is critical for applications that require the electrokinetic manipulation of particles (e.g., bacteria, exosomes, microalgae, etc.). Regularly, these predictions are obtained from finite element method (FEM)-based software. This approach is costly, time-consuming, and cannot effortlessly reveal the dependency between the electric field distribution and the microchannel design. An alternative approach consists of analytically solving Laplace’s equation subject to specific boundary conditions. This path, although precise, is limited by the availability of suitable coordinate systems and can only solve for the simplest case of a single pair of pillars and not for a rectangular array of pillars. Herein, we propose and test the hypothesis that the electric field across a longitudinal path within the microchannel can be estimated from an electric circuit model of the microfluidic device. We demonstrate that this approach allows estimating the electric field for whatever pillar shape and array size. Estimations of the electric field extracted from a commercial FEM-based software were used to validate the model. Moreover, the circuit model effortlessly illustrates the relationships between the electric field and the geometrical parameters that define the microchannel design. Full article

This study explores the use of a homemade mung bean protein extract solution (MP) as the moisture source in high-moisture extrusion to produce pea–mung bean composite textured protein (PMP). Single-factor experiments assessed the effects of MP addition amount (30–70%), screw speed (140–220 rpm), and extrusion temperature (140–180 °C) on the textural, physicochemical, and structural properties, followed by optimization using response surface methodology (RSM). MP addition amounts between 50% and 60% promoted higher surface hydrophobicity, a higher disulfide bond content, more ordered secondary structures, and a higher intrinsic fluorescence, accompanied by improved water- and oil-holding capacities, bulk density, and texturization degree (p < 0.05). Screw speeds of 160–180 rpm enhanced texturization and texture via increased shear and reduced residence time, whereas higher extrusion temperatures darkened the color (Maillard browning) and reduced texturization and the bulk density. RSM found that the optimal conditions were 53% MP, 160 rpm, and 150 °C, yielding a theoretical maximum texturization degree of 1.55, which was experimentally validated (1.53 ± 0.02). These findings support MP as an effective green moisture source to tailor the structure and functionality of pea-based high-moisture extrudates. Future work will integrate calibrated SME, sensory evaluation, and application testing in meat-analog formats. Full article

Accurate extraction of single-diode photovoltaic (PV) model parameters is essential for reliable performance prediction and diagnostics, yet five-parameter identification from I-V data is ill-posed and computationally expensive. To develop and validate a hybrid analytical–metaheuristic approach that derives the diode ideality factor, saturation current, and photocurrent analytically while optimizing only series and shunt resistances, thereby reducing computational cost without sacrificing accuracy. I-V datasets were collected from a 9.54 kW grid-connected PV installation in Algiers, Algeria (15 operating points; 747–815 W m⁻²; 25.4–28.4 °C). Nine metaheuristics—Stellar Oscillation Optimizer, Enzyme Action Optimization, Grey Wolf Optimizer, Whale Optimization Algorithm, Cuckoo Search, Owl Search Algorithm, Improved War Strategy Optimization, Rüppell’s Fox Optimizer, and Artificial Bee Colony—were benchmarked against full five-parameter optimization and a Newton–Raphson baseline, using root-mean-squared error (RMSE) as the objective and wall-time as the efficiency metric. The hybrid scheme reduced the decision space from five to two parameters and lowered computational cost by ≈60–70% relative to full-parameter optimization while closely reproducing measured I-V/P-V curves. Across datasets, algorithms achieved RMSE ≈ 2.49 × 10⁻² − 2.78 × 10⁻². Rüppell’s Fox Optimizer offered the best overall trade-off (lowest average RMSE and fastest runtime), with Whale Optimization Algorithm a strong alternative (typical runtimes ≈ 107–112 s). Partitioning identification between closed-form physics and light-weight optimization yields robust, accurate, and efficient PV parameter estimation suitable for time-sensitive or embedded applications. Dynamic validation using 1498 real-world measurements across clear-sky and cloudy conditions demonstrates excellent performance: current prediction $R^2=0.9882$, power estimation $R^2=0.9730$, and voltage tracking $R^2=0.9613$. Comprehensive environmental analysis across a 39.2 °C temperature range and diverse irradiance conditions ($0-1014W/m^2$) validates the method’s robustness for practical PV monitoring applications. Full article

Glycyrrhiza glabra L. is a species widely spread all over the world, with a long tradition of use in folk medicine. Here, raw and hydrolyzed extracts obtained from roots collected in different geographical areas belonging to the Mediterranean basin were standardized as regards the amount of three main compounds: glycyrrhizin, the most abundant triterpene saponin of licorice, the 18β-glycyrrhetinic acid and the chalcone isoliquiritigenin. Raw and hydrolyzed extracts, as well as their pure single compounds, were investigated for their potential anti-inflammatory properties. The hydrolyzed extracts significantly reduced the production of pro-inflammatory cytokines such as TNF-α, IL-6, NO mediator in LPS-stimulated RAW 264.7 cells. Moreover, they were able to inhibit JAK2 and STAT3 phosphorylated proteins more than pure single standards tested at the same final concentrations, displaying a strength synergism of action. These findings suggest that G. glabra extracts and, more specifically, the hydrolyzed ones could represent interesting sources of potential anti-inflammatory agents able to inhibit the JAK/STAT signaling pathway. Full article

Phoenix Dancong tea essential oil possesses unique aroma characteristics and bioactivities, offering broad application potential in the food, pharmaceutical, and daily chemical fields. To achieve efficient extraction and expand its use in functional textiles, supercritical CO₂ (SC-CO₂) extraction was employed to optimize the extraction process of Phoenix Dancong tea essential oil. Based on single-factor experiments, the optimal extraction conditions were determined as follows: pressure of 25 MPa, temperature of 50 °C, CO₂ flow rate of 8 L/h, and extraction time of 3 h, resulting in an essential oil yield of 1.12%. Response surface methodology (RSM) revealed that the experimental data fit the regression model well (R² = 95.49%, R²Adj = 89.69%). Furthermore, the extracted essential oil was blade-coating to cotton, nylon, polyester, and wool fabrics to evaluate its aroma retention performance. Results indicated that cotton fibers exhibited the best absorption and sustained fragrance retention, maintaining a high odor grade even after 8 weeks. This study provides a theoretical basis and practical reference for the green extraction of Phoenix Dancong tea essential oil and its application in smart aromatic textiles. Full article

The limited availability of in-situ images of the lunar surface significantly hinders the performance improvement of intelligent algorithms, such as scientific target point-of-interest recognition. To address the low diversity of images generated by traditional data augmentation methods under small-sample conditions, we propose a single-image generative adversarial method based on a blending mechanism of effective channel attention and spatial attention (ECSA-SinGAN). First, an effective channel attention module is introduced to assign different weights to each channel, enhancing the feature representation of important channels. Second, a spatial attention module is employed to assign varying weights to different spatial locations within the image, thereby improving the representation of target regions. Finally, based on a blending mechanism, lunar surface in-situ images are generated step by step, following a pyramidal hierarchy for multi-scale feature extraction. Experimental results show that the proposed method reduces MS-SSIM by 41% compared with SinGAN under identical image quality conditions in the lunar surface in-situ image augmentation task. The method preserves the original image style while significantly improving data diversity, making it effective for small-sample lunar surface in-situ image augmentation. Full article

Global supply chains are increasingly exposed to systemic disruptions driven by environmental pressures, geopolitical instability, and social unrest. Although Green Supply Chain Management (GSCM) is a strategic approach balancing sustainability and competitiveness, current research remains fragmented and regionally focused. Prior research has identified critical chokepoints and conceptualized disruption propagation through simulation and event system theory, yet few studies have operationalized large-scale empirical datasets to quantify cross-domain resilience. Addressing this gap, we collected and analyzed over 1.8 million news articles from more than 705 global portals spanning October 2023 to September 2025. Using GPT-based autonomous classification, approximately 67,434 disruption events directly related to GSCM were extracted and categorized by event type, geography, and significance. A system-of-systems framework was employed, linking seven domains: environment and climate, energy and resources, manufacturing and production, logistics and transportation, trade and commerce, agri-food systems, and labor and social systems. The results demonstrate that disruptions are unevenly distributed. The United States (8945 events), China (7822), and India (5311) emerged as global hubs, while Saudi Arabia acted as a single-domain chokepoint in energy. Energy and resources accounted for 22 percent of all events, followed by logistics (19 percent) and manufacturing (17 percent). Temporal analysis revealed major spikes in February 2024 (56,595 weighted intensity units) and June 2024 (10,861 units). Correlation analysis confirmed strong interdependencies across domains with average values greater than 0.7. This study contributes a globally scalable, data-driven framework to quantify disruption intensity, frequency, and interdependence in GSCM. It advances resilience research and offers actionable insights for policymakers and industry leaders. Full article

In this study, we constructed an extraction process for bovine lung peptide-1 (BLP-1) derived from bovine lung tissue utilizing single-factor optimization combined with response surface methodology. We systematically analyzed its antioxidant activity, biological safety, and therapeutic mechanisms against alcoholic liver disease (ALD). In vitro experiments demonstrated that BLP-1 exhibits excellent scavenging activity against various free radicals, while exhibiting no significant cytotoxicity or hemolytic activity. In a model of H₂O₂-induced oxidative damage in HepG2 cells, BLP-1 significantly alleviated oxidative stress injury by upregulating the activities of intracellular antioxidant enzymes. Animal experiments further confirmed that BLP-1 significantly reduced serum levels of transaminase, inhibited the release of inflammatory factors, enhanced antioxidant enzyme activity, and ameliorated lipid peroxidation and pathological injury in ALD mice. By combining liquid chromatography-tandem mass spectrometry (LC-MS/MS) with bioinformatics, we screened 12 novel antioxidant peptides. Among these, the binding energies of GP9, FG6, and WG6 to Keap1 were −10.2, −9.7, and −8.7 kcal/mol, respectively, indicating their potential to modulate the antioxidant defense system through competitive inhibition of Keap1-Nrf2 interactions. This study provides a novel approach for the high-value utilization of bovine lung and the treatment of ALD, as well as a new source for the extraction of natural antioxidant peptides. Full article

Tea Anthracnose (TA), caused by fungi of the genus Colletotrichum, is one of the major threats to global tea production. UAV remote sensing has been explored for non-destructive and high-efficiency monitoring of diseases in tea plantations. However, variations in illumination, background, and meteorological factors undermine the stability of cross-temporal data. Data processing and modeling complexity further limits model generalizability and practical application. This study introduced a cross-temporal, generalizable disease monitoring approach based on UAV multimodal data coupled with relative-difference standardization. In an experimental tea garden, we collected multispectral, thermal infrared, and RGB images and extracted four classes of features: spectral (Sp), thermal (Th), texture (Te), and color (Co). The Normalized Difference Vegetation Index (NDVI) was used to identify reference areas and standardize features, which significantly reduced the relative differences in cross-temporal features. Additionally, we developed a vegetation–soil relative temperature (VSRT) index, which exhibits higher temporal-phase consistency than the conventional normalized relative canopy temperature (NRCT). A multimodal optimal feature set was constructed through sensitivity analysis based on the four feature categories. For different modality combinations (single and fused), three machine learning algorithms, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP), were selected to evaluate disease classification performance due to their low computational burden and ease of deployment. Results indicate that the “Sp + Th” combination achieved the highest accuracy (95.51%), with KNN (95.51%) outperforming SVM (94.23%) and MLP (92.95%). Moreover, under the optimal feature combination and KNN algorithm, the model achieved high generalizability (86.41%) on independent temporal data. This study demonstrates that fusing spectral and thermal features with temporal standardization, combined with the simple and effective KNN algorithm, achieves accurate and robust tea anthracnose monitoring, providing a practical solution for efficient and generalizable disease management in tea plantations. Full article

Monitoring electrocardiogram (ECG) and respiration continuously and non-invasively is essential for managing cardiopulmonary health. An effective wearable device can be used to regularly monitor key vitals, reducing the need for clinical visits. In this work, we propose a custom device for real-time continuous ECG by inkjet printed (IJP) dry electrodes and respiration monitoring by using a novel single 6-axis inertial measurement unit (IMU). The proposed system can extract the heart rate (HR) and respiration rate (RR) during static and dynamic postures. The respiration process implements a quaternion-based update and multiple filtering stages to estimate the signal. The custom device uses Bluetooth protocol to send the raw and processed data to a mobile application. The RR is investigated in stationary, i.e., sitting and standing, and dynamic, i.e., walking, running, and cycling, postures. The proposed device is evaluated with commercial Go Direct^® respiration belt from Vernier^® for RR and offers an overall accuracy of 99.3% and 98.6% for static and dynamic conditions, respectively. The wearable also offers 98.9% and 97.9% accuracy for HR measurements, respectively, in static and active postures when compared with the Kardia^® device. Furthermore, the device is assessed in an ambulatory monitoring setup in both indoor and outdoor environments. The low-power wearable consumes an average of only 7.4 mA of current during data processing. The device performs effectively and efficiently in both stationary and active states, offering a low complexity, portable solution for real-time monitoring. The proposed system can benefit from the continuous monitoring and early detection of pulmonary and cardio-respiratory health issues. Full article

Amid the global energy transition, geothermal energy, as a clean, stable, and renewable energy source, serves as a core direction for energy structure optimization. The development of medium-deep geothermal reservoirs is dominated by thermo–hydro–mechanical (THM) multi-physics coupling effects, yet the quantitative regulation laws of their operational parameters remain unclear. In this study, a numerical model for geothermal extraction considering THM multi-physics coupling was established. Using the single-factor variable method, simulations were conducted within the set parameter ranges of injection–production pressure difference, well spacing, and injection temperature. The spatiotemporal evolution characteristics of the temperature field, the dynamic temperature–pressure responses at the midpoint of injection–production wells and production wells, and efficiency indicators, such as instantaneous heat extraction power and cumulative heat extraction, were analyzed and quantified. The results show that a larger pressure difference accelerates the expansion of the cold zone in the reservoir, which improves short-term heat extraction efficiency but increases the risk of long-term thermal depletion; a smaller well spacing leads to higher initial heat production power but results in lower long-term cumulative heat extraction due to rapid heat consumption; within the normal temperature range of 16–24 °C, the injection temperature has a negligible impact on heat extraction efficiency. This study clarifies the regulatory laws of operational parameters and provides theoretical support for well pattern design and injection–production process optimization in medium-deep geothermal development. Full article