Search Results (5,437)

Nowadays, the demand for food packaging that maintains the safety and quality of products has become one of the leading challenges. It can be solved by developing functional materials based on biodegradable polymers, such as poly(lactic acid) (PLA). In order to develop PLA-based functional materials with antibacterial activity, silver nanoparticles (AgNPs) were introduced. In the present study, AgNPs stabilized by a copolymer of ethylene and maleic acid were used. Under the joint action of shear deformations and high temperature, the biocomposites of PLA with poly(ethylene glycol) and AgNPs were produced. Their mechanical and thermal characteristics, water absorption, and structure were investigated using modern methods (DSC, FTIR, Raman spectroscopy, SEM). The effect of AgNP concentration on the characteristics of PLA-based biocomposites was detected. Based on the results of antibacterial activity tests (against Gram-positive and Gram-negative bacteria, along with yeast) it is assumed that these systems have potential as materials for extending the storage of food products. At the same time, PLA–PEG biocomposites with AgNPs possess biodegradability. Full article

Foamed concrete is increasingly utilized in protection engineering because it offers a high energy absorption ratio and a relatively low construction cost. To investigate the dynamic properties of foamed concrete, a series of dynamic compression tests are carried out on high-density foamed concrete with densities of 800 kg/m³, 1000 kg/m³, and 1100 kg/m³ under a strain rate range of 59.05 s⁻¹~302.17 s⁻¹ by using a Φ-100 mm split Hopkinson pressure bar (SHPB) device. The effects of strain rate on the stress–strain relationship, dynamic compressive strength, and dynamic increase factor of foamed concrete are discussed in detail. The results show that the dynamic mechanical characteristics of foamed concrete with different densities exhibit a significant strain rate enhancement effect. Additionally, the energy absorption characteristics of foamed concrete are investigated, demonstrating that it can effectively prevent the transmission of incident energy and that its energy absorption efficiency declines as the strain rate increases. A high-speed camera was also employed to capture the failure process of foamed concrete. The results exhibit that fracture production and development induce the failure of foamed concrete, the failure process of foamed concrete advances as the strain rate increases, and the failure mode becomes increasingly severe. Full article

With the continuous advancement of cloud computing and satellite communication technology, the cloud-network-integrated satellite network has emerged as a novel network architecture. This architecture harnesses the benefits of cloud computing and satellite communication to achieve global coverage, high reliability, and flexible information services. However, as business types and user demands grow, addressing differentiated Quality of Service (QoS) requirements has become a crucial challenge for cloud-network-integrated satellite networks. Effective resource allocation algorithms are essential to meet these differentiated QoS requirements. Currently, research on resource allocation algorithms for differentiated QoS requirements in cloud-network-integrated satellite networks is still in its early stages. While some research results have been achieved, there persist issues such as high algorithm complexity, limited practicality, and a lack of effective evaluation and adjustment mechanisms. The first part of this study examines the state of research on network virtual mapping methods that are currently in use. A reinforcement-learning-based virtual network mapping approach that considers quality of service is then suggested. This algorithm aims to improve user QoS and request acceptance ratio by introducing QoS satisfaction parameters. With the same computational complexity, QoS is significantly improved. Additionally, there has been a noticeable improvement in the request acceptance ratio and resource utilization efficiency. The proposed algorithm solves existing challenges and takes a step towards more practical and efficient resource allocation in cloud-network-integrated satellite networks. Experiments have proven the practicality of the proposed virtual network embedding algorithm of Satellite Network (SN-VNE) based on Reinforcement Learning (RL) in meeting QoS and improving utilization of limited heterogeneous resources. We contrast the performance of the SN-VNE algorithm with DDRL-VNE, CDRL, and DSCD-VNE. Our algorithm improve the acceptance ratio of VNEs, long-term average revenue and delay by an average of 7.9%, 15.87%, and 63.21%, respectively. Full article

Electronic equipment brings great convenience to daily life but also causes a lot of electromagnetic radiation pollution. Therefore, there is an urgent demand for electromagnetic wave-absorbing materials with a low thickness, wide bandwidth, and strong absorption. This work obtained a high-performance electromagnetic wave absorption system by adding conductive carbon spheres (CSs) to the CH₃NH₃PbI₃ (MAPbI₃) absorber. In this system, MAPbI₃, with strong dipole and relaxation polarization, acts dominant to the wave absorber. The carbon spheres provide a free electron transport channel between MAPbI₃ lattices and constructs interfacial polarization loss in MAPbI₃/CS. By regulating the content of CSs, we speculate that this increased effective absorption bandwidth and reflection loss intensity are attributed to the conductive channel of the carbon sphere and the interfacial polarization. As a result, when the mass ratio of the carbon sphere is 7.7%, the reflection loss intensity of MAPbI₃/CS reaches −54 dB at 12 GHz, the corresponding effective absorption bandwidth is 4 GHz (10.24–14.24 GHz), and the absorber thickness is 2.96 mm. This work proves that enhancing conduction loss and interfacial polarization loss is an effective strategy for regulating the properties of dielectric loss-type absorbing materials. It also indicates that organic-inorganic hybrid perovskites have great potential in the field of electromagnetic wave absorption. Full article

This study explores the fabrication and characterisation of 3D-printed polylactic acid (PLA) scaffolds reinforced with calcium hydroxyapatite (cHAP) for bone tissue engineering applications. By varying the cHAP content, we aimed to enhance PLA scaffolds’ mechanical and thermal properties, making them suitable for load-bearing biomedical applications. The results indicate that increasing cHAP content improves the tensile and compressive strength of the scaffolds, although it also increases brittleness. Notably, incorporating cHAP at 7.5% and 10% significantly enhances thermal stability and mechanical performance, with properties comparable to or exceeding those of human cancellous bone. Furthermore, this study integrates machine learning techniques to predict the mechanical properties of these composites, employing algorithms such as XGBoost and AdaBoost. The models demonstrated high predictive accuracy, with R² scores of 0.9173 and 0.8772 for compressive and tensile strength, respectively. These findings highlight the potential of using data-driven approaches to optimise material properties autonomously, offering significant implications for developing custom-tailored scaffolds in bone tissue engineering and regenerative medicine. The study underscores the promise of PLA/cHAP composites as viable candidates for advanced biomedical applications, particularly in creating patient-specific implants with improved mechanical and thermal characteristics. Full article

This study investigates the development and application of electrospun wound dressings based on polylactic acid (PLA) nanofibers, chitosan, and copper nanoparticles (CuNPs) for the treatment of purulent skin wounds. The materials were evaluated for their structural, antibacterial, and wound healing properties using an animal model. PLA/Ch-CuNPs demonstrated the most significant antibacterial activity against Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa, surpassing the other tested materials. The integration of CuNPs into the nanofiber matrices not only enhanced the antimicrobial efficacy but also maintained the structural integrity and biocompatibility of the dressings. In vivo experiments using a rat model showed that PLA/Ch-CuNPs facilitated faster wound healing with reduced exudative and inflammatory responses compared to PLA alone or PLA-CuNPs. Histological and immunohistochemical assessments revealed that the combination of PLA, chitosan, and CuNPs mitigated the inflammatory processes and promoted tissue regeneration more effectively. However, this study identified potential toxicity related to copper ions, emphasizing the need for careful optimization of CuNP concentrations. These findings suggest that PLA/Ch-CuNPs could serve as a potent, cost-effective wound dressing with broad-spectrum antibacterial properties, addressing the challenge of antibiotic-resistant infections and enhancing wound healing outcomes. Full article

Since the 2005 discovery of the first enzyme capable of depolymerizing polyethylene terephthalate (PET), an aromatic polyester once thought to be enzymatically inert, extensive research has been undertaken to identify and engineer new biocatalysts for plastic degradation. This effort was directed toward developing efficient enzymatic recycling technologies that could overcome the limitations of mechanical and chemical methods. These enzymes are versatile molecules obtained from microorganisms living in various environments, including soil, compost, surface seawater, and extreme habitats such as hot springs, hydrothermal vents, deep-sea regions, and Antarctic seawater. Among various plastics, PET and polylactic acid (PLA) have been the primary focus of enzymatic depolymerization research, greatly enhancing our knowledge of enzymes that degrade these specific polymers. They often display unique catalytic properties that reflect their particular ecological niches. This review explores recent advancements in marine-derived enzymes that can depolymerize synthetic plastic polymers, emphasizing their structural and functional features that influence the efficiency of these catalysts in biorecycling processes. Current status and future perspectives of enzymatic plastic depolymerization are also discussed, with a focus on the underexplored marine enzymatic resources. Full article

Introduction: Aging is associated with increased reactive oxygen species (ROS) and reduced bioavailability of nitric oxide (NO). Curcumin has been shown to increase NO bioavailability due to its ability to neutralize ROS, preventing oxidative stress. The present study aimed to investigate the effect of curcumin intake on skeletal muscle oxygen parameters and exercise tolerance in response to exercise in older people. Changes in circulating levels of NO metabolites were also investigated. Methods: Older subjects consumed 10 g of turmeric root extract from Curcuma longa L. (containing 95.33% of the total curcuminoids) or placebo in a randomized, double-blind, crossover study. A time of 2 h after ingestion, the participants performed one set of rhythmic handgrip exercise until the limit of tolerance, followed by 5 min of recovery. During exercise and exercise recovery, skeletal muscle oxygen saturation parameters were recorded. Results: During exercise, the amplitude of deoxyhemoglobin was greater after curcumin intake compared to placebo (CUR: 13.11 ± 9.52 vs. PLA: 10.22 ± 8.39 μM, p = 0.030). Furthermore, a faster skeletal muscle oxygen resaturation during exercise recovery was observed after curcumin compared to placebo (CUR: 1.01 ± 0.65 vs. PLA: 0.32 ± 0.20%.s⁻¹, p = 0.004). These results were associated with significant changes in plasma nitrite (CUR: 6.82 ± 11.68 vs. PLA: −4.94 ± 17.28%, p = 0.028). There was no statistical difference in the total hemoglobin, exercise time until fatigue, and plasma nitrate between groups. Conclusions: The present study suggests that curcumin improves muscle oxygenation status at the capillary level in older adults by possibly improving muscle oxygen extraction and/or delivery, with no effect on exercise tolerance. Full article

This study investigates the impact toughness of 3D-printed PLA structures with Hilbert curve and honeycomb infill patterns at various raster angles. Samples were fabricated using Fused Deposition Modeling (FDM) and tested for impact energy absorption using the Charpy test. The results showed that specimens printed at a 90° raster angle exhibited the highest impact absorption. Hilbert curve patterns demonstrated 20.6% less energy absorption than plain samples with 40% infill and 11% higher energy absorption than plain samples with 100% infill, highlighting the significant role of material utilization in enhancing structural integrity. Full article

The development of poly(lactic acid) (PLA) nanocomposites incorporating urchin-like WO₃ particles through a cost-effective solution-casting method has led to significant enhancements in structural, thermal, optical, and rheological properties. The incorporation of these WO₃ particles up to 7 wt% resulted in the formation of an irregular honeycomb-like morphology with broad pore sizes ranging from 14.1 to 24.7 µm, as confirmed by SEM and EDX analysis. The urchin-like WO₃ particles acted as effective nucleating agents, increasing the crystallinity of PLA from 40% to 50% and achieving an impressive overall crystallinity rate of 97%. Differential scanning calorimetry (DSC) revealed an 11 K reduction in the crystalline phase transition temperature while maintaining stable melting (Tm) and glass transition (Tg) temperatures. Thermal analysis indicated a significant decrease in the onset of degradation and maximum thermal stability (T_max), with a reduction of 21 K due to the incorporation of the WO₃ particles. Optical measurements showed enhancement of UV-blocking properties from 9% to 55% with the WO₃ particle loading. Rheological tests demonstrated substantial improvements in viscoelastic properties, including a remarkable 30-fold increase in storage modulus, suggesting enhanced gel formation. Although the nanocomposites showed minimal antibacterial activity against Escherichia coli and Staphylococcus aureus, they exhibited significant antifungal activity against Candida albicans. These results underscore the potential of the PLA/WO₃ nanocomposites for advanced material applications, particularly where enhanced mechanical, thermal, optical, and antifungal performance is required. Full article

Vision-based forest fire detection systems have significantly advanced through Deep Learning (DL) applications. However, DL-based models typically require large-scale labeled datasets for effective training, where the quality of data annotation is crucial to their performance. To address challenges related to the quality and quantity of labeling, a domain adaptation-based approach called FireDA is proposed for forest fire recognition in scenarios with limited labels. Domain adaptation, a subfield of transfer learning, facilitates the transfer of knowledge from a labeled source domain to an unlabeled target domain. The construction of the source domain FBD is initiated, which includes three common fire scenarios: forest (F), brightness (B), and darkness (D), utilizing publicly available labeled data. Subsequently, a novel algorithm called Neighborhood Aggregation-based 2-Stage Domain Adaptation (NA2SDA) is proposed. This method integrates feature distribution alignment with target domain Proxy Classification Loss (PCL), leveraging a neighborhood aggregation mechanism and a memory bank designed for the unlabeled samples in the target domain. This mechanism calibrates the source classifier and generates more accurate pseudo-labels for the unlabeled sample. Consequently, based on these pseudo-labels, the Local Maximum Mean Discrepancy (LMMD) and the Proxy Classification Loss (PCL) are computed. To validate the efficacy of the proposed method, the publicly available forest fire dataset, FLAME, is employed as the target domain for constructing a transfer learning task. The results demonstrate that our method achieves performance comparable to the supervised Convolutional Neural Network (CNN)-based state-of-the-art (SOTA) method, without requiring access to labels from the FLAME training set. Therefore, our study presents a viable solution for forest fire recognition in scenarios with limited labeling and establishes a high-accuracy benchmark for future research. Full article

Brevetoxins (PbTxs) are very potent marine neurotoxins that can cause an illness clinically described as neurologic shellfish poisoning (NSP). These toxins are cyclic polyether in chemistry and have increased their geographical distribution in the past 2 decades. However, the ethical problems as well as technical difficulties associated with currently employed analysis methods for marine toxins have spurred the quest for suitable alternatives to be applied in a regulatory monitoring regime. In this work, we reported the first instance of concurrent aptamer selection of Brevetoxin-1 (PbTx-1) and Brevetoxin-2 (PbTx-2) and constructed a biolayer interferometry (BLI) biosensor utilizing PbTx-1 aptamer as a specific recognition element. Through an in vitro selection process, we have, for the first time, successfully selected DNA aptamers with high affinity and specificity to PbTx-1 and PbTx-2 from a vast pool of random sequences. Among the selected aptamers, aptamer A5 exhibited the strongest binding affinity to PbTx-1, with an equilibrium dissociation constant (K_D) of 2.56 μM. Subsequently, we optimized aptamer A5 by truncation to obtain the core sequence (A5-S3). Further refinement was achieved through mutations based on the predictions of a QGRS mapper, resulting in aptamer A5-S3G, which showed a significant increase in the K_D value by approximately 100-fold. Utilizing aptamer A5-S3G, we fabricated a label-free, real-time optical BLI aptasensor for the detection of PbTx-1. This aptasensor displayed a broad detection range from 100 nM to 4000 nM PbTx-1, with a linear range between 100 nM and 2000 nM, and a limit of detection (LOD) as low as 4.5 nM. Importantly, the aptasensor showed no cross-reactivity to PbTx-2 or other marine toxins, indicating a high level of specificity for PbTx-1. Moreover, the aptasensor exhibited excellent reproducibility and stability when applied for the detection of PbTx-1 in spiked shellfish samples. We strongly believe that this innovative aptasensor offers a promising alternative to traditional immunological methods for the specific and reliable detection of PbTx-1. Full article

A deep learning strategy was exploited to learn and predict the deformation of stiffeners, 3D printed onto a pre-stretched soft membrane. The working process reads as follows: the membrane is stretched until a pre-defined level; a specific geometry of stiffeners is printed onto it; the membrane is finally released, and due to the presence of the printed stiffeners, the system undergoes an out-of-plane deformation due to buckling. Fused deposition modeling was specifically calibrated to print PLA (Polylactic acid or polylactide) on a Lycra fabric. To assess how the printed pattern affects the buckled configuration, samples featuring different dimensions and in-plane geometries of the stiffeners were printed and numerically modeled via finite elements (FEs). The calibrated model was next exploited to construct a larger training dataset of stiffener geometries. A pre-trained You Only Look Once (YOLO)-based digital model was finally trained to foresee the link between the in-plane dimensions of the stiffeners before the release and the out-of-plane displacements in the buckled configuration. By handling around 100 different patterns, a precision of 93% in terms of recognition of the in-plane dimensions of the stiffeners and a mean absolute percentage error of 5% at most in terms of an estimate of the features of the buckled configuration were attained. The reported results testify the capability of the proposed approach and its potential efficiency to optimize the shape of the 3D printed geometries. Full article

Quorum sensing (QS) is a mechanism by which bacteria detect and respond to cell density, regulating collective behaviors. Burkholderia plantarii, the causal agent of rice seedling blight, employs the LuxIR-type QS system, common among Gram-negative bacteria, where LuxI-type synthase produces QS signals recognized by LuxR-type regulators to control gene expression. This study aimed to elucidate the QS mechanism in B. plantarii KACC18965. Through whole-genome analysis and autoinducer assays, the plaI gene, responsible for QS signal production, was identified. Motility assays confirmed that C8-homoserine lactone (C8-HSL) serves as the QS signal. Physiological experiments revealed that the QS-defective mutant exhibited reduced virulence, impaired swarming motility, and delayed biofilm formation compared to the wild type. Additionally, the QS mutant demonstrated weakened antibacterial activity against Escherichia coli and decreased phosphate solubilization. These findings indicate that QS in B. plantarii significantly influences various pathogenicity and survival traits, including motility, biofilm formation, antibacterial activity, and nutrient acquisition, highlighting the critical role of QS in pathogen virulence and adaptability. Full article

In actual underground rock engineering, to prevent the deformation and damage of the rock mass, rock bolt reinforcement technology is commonly employed to maintain the stability of the surrounding rock. Therefore, studying the anchoring and crack-stopping effect of rock bolts on fractured granite rock mass is essential. It can provide significant reference and support for the design of underground engineering, engineering safety assessment, the theory of rock mechanics, and resource development. In this study, indoor experiments are combined with numerical simulations to explore the impact of fracture dip angles on the mechanical behavior of unanchored and anchored granite samples from both macroscopic and microscopic perspectives. It also investigates the evolution of the anchoring and crack-stopping effect of rock bolts on granite containing fractures with different dip angles. The results show that the load-displacement trends, displacement fields, and debris fields from indoor experiments and numerical simulations are highly similar. Additionally, it was discovered that, in comparison to the unanchored samples, the anchored samples with fractures at various angles all exhibited a higher degree of tensile failure rather than shear failure that propagates diagonally across the samples from the regions around the fracture tips. This finding verifies the effectiveness of the numerical model parameter calibration. At the same time, it was observed that the internal force chain value level in the anchored samples is higher than in the unanchored samples, indicating that the anchored samples possess greater load-bearing capacity. Furthermore, as the angle α_s increases, the reinforcing and crack-stopping effects of the rock bolts become increasingly less pronounced. Full article