Search Results (4,434)

Pineapple leaf fibers (PALFs), obtained from abundant yet underutilized pineapple leaf residues, represent a promising feedstock for producing fibrillated cellulose. In this work, cellulosic fibers were isolated and characterized by Fiber Quality Analysis (FQA), showing lengths between 0.33 and 0.47 mm and widths of 12.2 µm after organosolv pulping using ethanol and acetic acid as a catalyst, followed by hydrogen peroxide bleaching with diethylenetriaminepentaacetic acid as a chelating agent. The cellulosic fibers were then subjected to TEMPO-mediated oxidation and subsequently disintegrated by microfluidization to produce micro-/nanofibrillated cellulose (MNFC) with a carboxylate content of 0.85 and 1.00 mmol COO⁻/g, zeta potential of −41 and −53 mV, and average widths of 15 and 12 nm for unbleached and bleached nanofibrils, respectively. The nanofibrillation yields were 73% and 68% for the bleached and unbleached MNFC samples, indicating the presence of some non-fibrillated or partially fibrillated fractions. X-ray diffraction analysis confirmed preservation of cellulose type I crystalline structure, with increased crystallinity, reaching 85% in the bleached MNFC. These findings demonstrate the feasibility of a sequential process, combining organosolv pulping, hydrogen peroxide bleaching, TEMPO-mediated oxidation, and microfluidization, for preparing MNFC from pineapple leaf fibers. Overall, this study highlights pineapple leaf residues as a sustainable source of MNFC, supporting strategies to transform agricultural waste into valuable bio-based materials. Full article

Mucosal drug delivery is gaining attention for its ability to provide localized treatment with reduced systemic side effects. The vaginal route has been proven effective for managing gynecological conditions, though it poses certain limitations. Biopolymers can help overcome these challenges by enhancing therapeutic efficiency and offering beneficial properties. This study aimed to develop and evaluate hydrogels and their freeze-dried forms (wafers) based on collagen, hydroxypropyl methylcellulose, and gellan gum. Initially, a collagen gel was obtained by extraction from calfskin, which was brought to a concentration of 1% and a physiological pH with 1 M sodium hydroxide solution. This gel was combined with either 2% hydroxypropyl methylcellulose gel, 1.2% gellan gum gel, or both, in different proportions. Thus, five mixed hydrogels were obtained, which, along with the three individual gels (controls), were lyophilized to obtain wafers. Furthermore, the hydrogels were assessed for rheological behavior, while the collagen structural integrity in the presence of the other biopolymers was evaluated using circular dichroism and FT-IR spectroscopy. The wafers were characterized for morphology, wettability, swelling capacity, enzymatic degradation resistance, and in vitro biocompatibility. All hydrogels exhibited non-Newtonian, pseudoplastic behavior and showed collagen structure preservation. The wafers’ characterization showed that gellan gum enhanced the hydrophilicity and enzymatic stability of the samples. In addition, the extracts from the tested samples maintained cell viability and did not affect actin cytoskeleton morphology, indicating a lack of cytotoxic effects. This study emphasizes the importance of evaluating both the physicochemical properties and biocompatibility of biopolymeric supports as a key preliminary step in the development of vaginal drug delivery platforms with biomedical applications in the management of gynecological conditions. Full article

Circulating tumor cells (CTCs) are crucial biomarkers for lung cancer metastasis and recurrence, garnering significant clinical attention. Despite this, efficient and cost-effective detection methods remain scarce. Consequently, there is an urgent demand for the development of highly sensitive CTC detection technologies to enhance lung cancer diagnosis and treatment. This study utilized microspheres and A549 cells to model CTCs, assessing the impact of acoustic field forces on cell viability and proliferation and confirming capture efficiency. Subsequently, CTCs from the peripheral blood of patients with lung cancer were captured and identified using fluorescence in situ hybridization, and the results were compared to the immunomagnetic bead method to evaluate the differences between the techniques. Finally, epidermal growth factor receptor (EGFR) mutation analysis was conducted on CTC-positive samples. The findings showed that acoustic microfluidic technology effectively captures microspheres, A549 cells, and CTCs without compromising cell viability or proliferation. Moreover, EGFR mutation analysis successfully identified mutation types in four samples, establishing a basis for personalized targeted therapy. In conclusion, acoustic microfluidic technology preserves cell viability while efficiently capturing CTCs. When integrated with EGFR mutation analysis, it provides robust support for the precise diagnosis and treatment of lung cancer as well as personalized drug therapy. Full article

This paper presents a comparative study on the impact of robust hashing in enhancing image-based malware classification. While Convolutional Neural Networks (CNNs) have shown promise when working with image-based malware samples, their performance degrades significantly when obfuscation techniques are taken into consideration to hamper the malware classification or detection. To address this, we apply a robust hashing technique that generates invariant visual representations of malware samples, enabling improved generalization under obfuscation implemented as image salting. Using a custom obfuscation method to simulate polymorphic variants, we evaluate MobileNet, ResNet, and DenseNet architectures across five salting conditions (0% to 40%). The results demonstrate that robust hashing substantially boosts classification accuracy, with DenseNet achieving 89.50% on unsalted data, compared to only 68.00% without hashing. Across all salting levels, models consistently performed better when robust hashing was applied, confirming its effectiveness in preserving structural features and mitigating adversarial noise. These findings position robust hashing as a powerful preprocessing strategy for resilient malware detection. Full article

We explore a plasmonic interface for perovskite solar cells (PSCs) by integrating inkjet-printed TiO₂-AuNP microdot arrays (MDA) into the electron transport layer. This systematic study examines how the TiO₂ blocking layer (BL) surface conditioning, AuNP layer positioning, and nanoparticle loading collectively influence device performance. Pre-annealing the BL increases its hydrophobicity, yielding smaller and denser AuNP microdots with an enhanced localized surface plasmon resonance (LSPR). Positioning the AuNP MDA at the BL/perovskite interface (above the BL) maximizes near-field plasmonic coupling to the absorber, resulting in higher photocurrent and power conversion devices; these trends are corroborated by finite-difference time-domain (FDTD) simulations. Moreover, these devices demonstrate better stability over time compared to those with AuNPs at the transparent electrode (under BL). Although higher AuNP concentrations improve dispersion stability, preserve MAPI crystallinity, and yield more uniform nanoparticle sizes, device measurements showed no performance gains. After annealing, the samples with the Au content of 23 wt% relative to TiO₂ achieved optimal PSC efficiency by balancing plasmonic enhancement and charge transport without the increased resistance and recombination losses seen at higher loadings. Importantly, X-ray diffraction (XRD) confirms that introducing the TiO₂-AuNP MDA at the interface does not disrupt the perovskite’s crystal structure, underscoring the structural compatibility of this plasmonic enhancement. Overall, our findings highlight a scalable strategy to boost PSC efficiency via engineered light-matter interactions at the nanoscale without compromising the perovskite’s structural integrity. Full article

As a global staple ensuring food security, maize incurs 15–20% annual yield loss from pests/diseases. Conventional manual detection is inefficient (>7.5 h/ha) and subjective, while existing YOLO models suffer from >8% missed detections of small targets (e.g., corn armyworm larva) in complex fields due to feature loss and poor multi-scale fusion. We propose YOLO-FDLU, a YOLO11s-based framework: LAD (Light Attention-Downsampling)-Conv preserves small-target features; C3k2_DDC (DilatedReparam–DilatedReparam–Conv) enhances cross-scale fusion; Detect_FCFQ (Feature-Corner Fusion and Quality Estimation) optimizes bounding box localization; UIoU (Unified-IoU) loss reduces high-IoU regression bias. Evaluated on a 25,419-sample dataset (6 categories, 3 public sources + 1200 compliant web images), it achieves 91.12% Precision, 92.70% mAP@0.5, 78.5% mAP@0.5–0.95, and 20.2 GFLOPs/15.3 MB. It outperforms YOLOv5-s to YOLO12-s, supporting precision maize pest/disease monitoring. Full article

Trees are vital to both environmental health and human well-being. They purify the air we breathe, support biodiversity by providing habitats for wildlife, prevent soil erosion to maintain fertile land, and supply wood for construction, fuel, and a multitude of essential products such as fruits, to name a few. Therefore, it is important to monitor and preserve them to protect the natural environment for future generations and ensure the sustainability of our planet. Remote sensing is the rapidly advancing and powerful tool that enables us to monitor and manage trees and forests efficiently and at large scale. Statistical methods, machine learning, and more recently deep learning are essential for analyzing the vast amounts of data collected, making data the fundamental component of these methodologies. The advancement of these methods goes hand in hand with the availability of sample data; therefore, a review study on available high-resolution aerial datasets of trees can help pave the way for further development of analytical methods in this field. This study aims to shed light on publicly available datasets by conducting a systematic search and filter and an in-depth analysis of them, including their alignment with the FAIR—findable, accessible, interoperable, and reusable—principles and the latest trends concerning applications for such datasets. Full article

Graph neural networks (GNNs) are deep learning models that process structured graph data. By leveraging their graphs/node classification and link prediction capabilities, they have been effectively applied in multiple domains such as community detection, location sharing services, and drug discovery. These powerful applications and the vast availability of graphs in diverse fields have facilitated the adoption of GNNs in privacy-sensitive contexts (e.g., banking systems and healthcare). Unfortunately, GNNs are vulnerable to the leakage of sensitive information through well-defined attacks. Our main focus is on membership inference attacks (MIAs) that allow the attacker to infer whether a given sample belongs to the training dataset. To prevent this, we introduce three LLM-guided defense mechanisms applied at the posterior level: posterior encoding with noise, knowledge distillation, and secure aggregation. Our proposed approaches not only successfully reduce MIA accuracy but also maintain the model’s performance on the node classification task. Our findings, validated through extensive experiments on widely used GNN architectures, offer insights into balancing privacy preservation with predictive performance. Full article

Dermal tissue samples are a rich source of germplasm because they can be readily collected, frozen as part of a genebank collection, digested and cultured, and used for a variety of purposes such as genotyping or other forms of genetic research. Derived fibroblasts can also be used for somatic cell nuclear transfer, and the remaining cells can be frozen for future use. However, collection of tissues with ear notchers, scalpels, or biopsy punches can be problematic because tissue handling and the tool surfaces can contaminate the samples. Therefore, the modification of the Allflex Tissue Sampling Unit (TSU) system was explored to determine if the technology can empower rapid collection of clean samples that are easily identifiable and portable. Results indicate that the TSU system was efficient, and samples that were collected and processed for tissue culture resulted in successful derivations of fibroblasts from 7 of 11 animals. Thus, the TSU system appears to be a viable option for collecting and preserving dermal tissue for genebanking and other applications where simple, rapid collection of large quantities of samples is required. Full article

In recent years, convolutional neural network-based object detectors have achieved extensive applications in remote sensing (RS) image interpretation. While multi-scale feature modeling optimization remains a persistent research focus, existing methods frequently overlook the symmetrical balance between feature granularity and morphological diversity, particularly when handling high-aspect-ratio RS targets with anisotropic geometries. This oversight leads to suboptimal feature representations characterized by spatial sparsity and directional bias. To address this challenge, we propose the Parallel Interleaved Convolutional Kernel Network (PICK-Net), a rotation-aware detection framework that embodies symmetry principles through dual-path feature modulation and geometrically balanced operator design. The core innovation lies in the synergistic integration of cascaded dynamic sparse sampling and symmetrically decoupled feature modulation, enabling adaptive morphological modeling of RS targets. Specifically, the Parallel Interleaved Convolution (PIC) module establishes symmetric computation patterns through mirrored kernel arrangements, effectively reducing computational redundancy while preserving directional completeness through rotational symmetry-enhanced receptive field optimization. Complementing this, the Global Complementary Attention Mechanism (GCAM) introduces bidirectional symmetry in feature recalibration, decoupling channel-wise and spatial-wise adaptations through orthogonal attention pathways that maintain equilibrium in gradient propagation. Extensive experiments on RSOD and NWPU-VHR-10 datasets demonstrate our superior performance, achieving 92.2% and 84.90% mAP, respectively, outperforming state-of-the-art methods including EfficientNet and YOLOv8. With only 12.5 M parameters, the framework achieves symmetrical optimization of accuracy-efficiency trade-offs. Ablation studies confirm that the symmetric interaction between PIC and GCAM enhances detection performance by 2.75%, particularly excelling in scenarios requiring geometric symmetry preservation, such as dense target clusters and extreme scale variations. Cross-domain validation on agricultural pest datasets further verifies its rotational symmetry generalization capability, demonstrating 84.90% accuracy in fine-grained orientation-sensitive detection tasks. Full article

Surface pretreatment significantly influences the hygroscopic behavior of wood, which in turn affects surface stability when exposed to variable climatic conditions. This study focuses on how different surface pretreatment methods impact the performance of protective coating applied on spruce wood (Picea abies (L.) Karst.) during one year of natural weathering. Samples were prepared using various surface treatments: milling and sanding with grit sizes P40, P80, and P120, respectively. Two types of coatings were applied: a solventborne coating (ADLER Pullex Plus-Lasur) and a waterborne coating (DColor FK 47 UV Protect). The samples were exposed for 12 months at an outdoor testing site in Suchdol, Czech. Surface properties were assessed through color changes in the CIE Lab* space, gloss measurements (ISO 2813), contact angle analysis, and visual inspection. The results showed that exposure to UV radiation and microbial activities led to the gradual degradation of the optical properties and aesthetic appearance of the wood. Surfaces with greater roughness preserved their aesthetic properties more effectively, indicating a higher absorption of the coating. Untreated wood exhibited low water repellency, while the coated surface demonstrated enhanced hydrophobicity. Notably, the waterborne coating showed a temporary increase in contact angle around the sixth month, indicating surface clogging by dust particles. In contrast, the solventborne coating had a rapid decrease in wettability during the first nine months. These findings suggested the importance of surface pretreatment and coating type in maintaining the long-term performance and aesthetic appearance for wood used in exterior conditions. Full article

Background/Objectives: Biological aging phenotypes in coronary artery disease (CAD) include coronary atherosclerosis, vascular aging, and endothelial dysfunction. The aim of the present study was to investigate the potential links between aging phenotypes, regulatory immune cells, and features of the thymus in patients with CAD. Methods: A single-center, cross-sectional, comparative study was conducted. Patients were stratified according to the severity of coronary atherosclerosis: patients with a Gensini score ≥ 65 points and patients with a Gensini score < 65 points. Peripheral blood and thymus biopsy were obtained. Imaging flow cytometry, ELISA, and immunohistochemical analysis were used for analysis. Results: Thymic morphology ranged from total fatty involution to a preserved structure of the thymus (20–80% area in 31% of obtained samples) but was not associated with the severity of atherosclerosis. Meanwhile, patients with a Gensini score ≥ 65 had impaired thymus cellular composition compared to patients with a Gensini score < 65 points; increased frequency of CD8+ T lymphocytes and NK cells; and decreased frequency of CD4 + CD8+ T lymphocytes. In peripheral blood, the main determinants of a Gensini score ≥ 65 points were low absolute counts of eMDSCs and CD25^low Tregs with FoxP3 nuclear translocation, while advanced vascular aging was associated with elevated eMDSC absolute counts. Advanced coronary atherosclerosis was also associated with decreased numbers of endothelial progenitor cells in circulation. Conclusions: Thymus dysfunction accompanies CAD progression and is manifested in changes in cellular composition rather than morphology. In CAD patients, MDSC and Treg lymphocytes are equally involved in the progression of coronary atherosclerosis, which is aggravated by the decreased regulatory potential of the endothelium. Vascular aging represents a distinct phenotype of biological aging in CAD patients, characterized by the expansion of eMDSCs. Full article

Gellan gum (Gellan), a versatile polysaccharide applied in gel formation and prebiotic formulations, is often processed to tailor its molecular properties. Previous studies employed gamma irradiation and chemical hydrolysis, though without addressing systematic scaling behavior. This study investigates the structural and conformational modifications of Gellan in dilute aqueous salt solutions using a safer and eco-friendly approach: atmospheric low-dose electron beam (e-beam) degradation coupled with viscosity analysis. Native and E-beam-treated Gellan samples (0.05 g/cm³ in 0.1 M KCl) were examined by relative viscosity at varying temperatures, with intrinsic viscosity and molar mass determined via Solomon–Ciuta and Mark–Houwink relations. Molar mass degradation followed first-order kinetics, yielding rate constants and degradation lifetimes. Structural parameters, including radius of gyration and second virial coefficient, produced scaling coefficients of 0.62 and 0.15, consistent with perturbed coil conformations in a good solvent. The shape factor confirmed preservation of an ideal random coil structure despite irradiation. Conformational flexibility was further analyzed using theoretical models. Transition state theory (TST) revealed that e-beam radiation lowered molar mass and activation energy but raised activation entropy, implying reduced flexibility alongside enhanced solvent interactions. The freely rotating chain (FRC) model estimated end-to-end distance (R_θ) and characteristic ratio (C_∞), while the worm-like chain (WLC) model quantified persistence length (l_p). Results indicated decreased R_θ, increased l_p, and largely unchanged C_∞, suggesting diminished chain flexibility without significant deviation from ideal coil behavior. Overall, this work provides new insights into Gellan’s scaling laws and flexibility under aerobic low-dose E-beam irradiation, with relevance for bioactive polysaccharide applications. Full article

Coffee silverskin (CS), the principal solid by-product from coffee roasting, is a promising raw material for sustainable food applications aligned with circular economy principles. Due to its high flammability at roasting temperatures, effective management of CS is not only an environmental but also a safety concern in coffee processing facilities. To the best of our knowledge, this is the first study evaluating the chemical composition, bioactivity, safety, and environmental impact of torrefacto (CT) and natural (CN) coffee silverskin. CT (from Arabica–Robusta blends subjected to sugar-glazing) and CN (from 100% Arabica) were characterized in terms of composition and function. Oven-dried CT showed higher levels of caffeine (13.2 ± 0.6 mg/g vs. 8.7 ± 0.7 mg/g for CN), chlorogenic acid (1.34 ± 0.08 mg/g vs. 0.92 ± 0.06 mg/g), protein (18.1 ± 0.2% vs. 16.7 ± 0.2%), and melanoidins (14.9 ± 0.3 mg/g vs. 9.6 ± 0.2 mg/g), but CN yielded more total phenolics (13.8 ± 0.6 mg GAE/g). Both types exhibited strong antioxidant capacity (ABTS: 48.9–59.2 µmol TE/g), and all oven-dried samples met food safety criteria (microbial loads below 10² CFU/g, moisture 7.9%). Oven drying was identified as the most industrially viable, ensuring preservation of bioactives and resulting in a 19% lower greenhouse gas emissions impact compared to freeze-drying. Sun drying was less reliable microbiologically. The valorization of oven-dried CT as a clean-label, antioxidant-rich colorant offers clear potential for food reformulation and waste reduction. Renewable energy use during drying is recommended to further enhance sustainability. This study provides scientific evidence to support the safe use of coffee silverskin as a novel food, contributing to regulatory assessment and sustainable food innovation aligned with SDGs 9, 12, and 13. Full article

Mountain settlements represent culturally rich but environmentally fragile landscapes, shaped by enduring processes of ecological adaptation and human resilience. In western Sichuan, Jiarong Tibetan villages, with their distinctive integration of defensive stone towers and settlements, embody this coupling of culture and the environment. We hypothesize that settlement cores in these villages were shaped by natural environmental factors, with subsequent expansion reinforced by the cultural significance of towers. To test this, we applied a micro-scale spatial–environmental framework to four sample villages in Suopo Township, Danba County. High-resolution World Imagery (Esri, 0.5–1 m, 2022–2023) was classified via a Random Forest algorithm to generate detailed land-use maps, and a 100 × 100 m fishnet grid extracted topographic metrics (elevation, slope, aspect) and accessibility measures (distances to streams, roads, towers). Geographically weighted regression (GWR) was then used to examine how slope, elevation, aspect, proximity to water and roads, and tower distribution affect settlement patterns. The results show built-up density peaks on southeast-facing slopes of 15–30°, at altitudes of 2600–2800 m, and within 50–500 m of streams, co-locating with historic watchtower sites. Based on these findings, we propose four zoning strategies—a Core Protected Zone, a Construction And Development Zone, an Ecological Conservation Zone, and an Industry Development Zone—to balance preservation with growth. The resulting policy recommendations offer actionable guidance for sustaining traditional settlements in complex mountain environments. Full article