Search Results (1,027)

Background/Objectives: Wound infections caused by multidrug-resistant Gram-negative bacteria, such as Escherichia coli, Pseudomonas aeruginosa, and Acinetobacter baumannii, pose a major clinical challenge. This study evaluated the interactions between gamma-polyglutamic acid (γ-PGA), cefiderocol, and silver nanoparticles (AgNPs) within multilayer wound dressing configurations. The primary goal was to clarify the dual role of γ-PGA as a healing promoter and a potential protector of bacterial cells against antimicrobial agents. Methods: Multilayer dressing models were assembled in 96-well plates to simulate vertical stratification of antimicrobial layers4. Bacterial viability was assessed through relative OD₆₀₀ measurements following incubation with varying concentrations and spatial arrangements of cefiderocol, AgNPs, and γ-PGA. Data were analyzed using generalized linear modeling (GLM) with a gamma distribution and random forest regression to determine the relative importance of each factor in modulating bacterial survival. Results: γ-PGA concentration emerged as the dominant factor influencing bacterial viability, accounting for nearly 100% of variable importance in random forest analysis. Despite high antimicrobial pressure from cefiderocol and AgNPs, bacterial viability stabilized at approximately 40% in the presence of γ-PGA. The vertical positioning of γ-PGA significantly impacted survival; direct physical contact between the polymer and bacteria, particularly at high concentrations, enhanced bacterial persistence in P. aeruginosa and E. coli. Cefiderocol showed strain-specific potency, while AgNPs provided consistent growth inhibition. Conclusions: γ-PGA plays a paradoxical role in wound care by providing moisture retention while simultaneously acting as a cytoprotective agent that reduces antimicrobial efficacy, likely by facilitating biofilm formation. These findings underscore the necessity of optimizing the spatial layering and concentration of biopolymers in advanced dressings. Strategic design is crucial to balance regenerative benefits with maximal antimicrobial control to improve clinical outcomes in chronic wound management. Full article

Industrial gateways serve as critical data aggregation points within the Industrial Internet of Things (IIoT), enabling seamless data interoperability that empowers enterprises to extract value from equipment data more efficiently. However, their role exposes a fundamental trade-off between computational efficiency and prediction accuracy—a contradiction yet to be fully resolved by existing approaches. The rapid proliferation of IoT devices has led to a corresponding surge in network traffic, posing significant challenges for traffic forecasting methods, while deep learning models like Transformers and GNNs demonstrate high accuracy in traffic prediction, their substantial computational and memory demands hinder effective deployment on resource-constrained industrial gateways, while simple linear models offer relative simplicity, they struggle to effectively capture the complex characteristics of IIoT traffic—which often exhibits high nonlinearity, significant burstiness, and a wide distribution of time scales. The inherent time-varying nature of traffic data further complicates achieving high prediction accuracy. To address these interrelated challenges, we propose the lightweight and theoretically grounded DOA-MSDI-CrossLinear framework, redefining traffic forecasting as a hierarchical decomposition–interaction problem. Unlike existing approaches that simply combine components, we recognize that industrial traffic inherently exhibits scale-dependent temporal correlations requiring explicit decomposition prior to interaction modeling. The Multi-Scale Decomposable Mixing (MDM) module implements this concept through adaptive sequence decomposition, while the Dual Dependency Interaction (DDI) module simultaneously captures dependencies across time and channels. Ultimately, decomposed patterns are fed into an enhanced CrossLinear model to predict flow values for specific future time periods. The Dream Optimization Algorithm (DOA) provides bio-inspired hyperparameter tuning that balances exploration and exploitation—particularly suited for the non-convex optimization scenarios typical in industrial forecasting tasks. Extensive experiments on real industrial IoT datasets thoroughly validate the effectiveness of this approach. Full article

In terminal flight trajectory, significant dispersion poses a challenge for accurate localization, as the velocity vector of a supersonic flying object increasingly deviates from the normal vector of the measurement plane under gravitational and aerodynamic effects. Therefore, in this study, an impact point localization method, utilizing a dual-rectangular-ring linear optical microphone array based on apparent shock-wave velocity, was developed. A shock-wave measurement array was developed using a dual rectangular ring composed of linear optical microphone arrays. A time-equivalent model, derived from shock-wave propagation, was introduced to analyze the apparent velocity of the shock-wave within the measurement plane. The time difference in the shock-wave arrivals at the dual rectangular ring, combined with the distances between the inner and outer rectangular rings, was used to calculate the non-uniform apparent shock-wave velocity, thereby enabling the localization of supersonic flying objects. The method’s constraints were examined, and its measurement errors were evaluated. The simulation and experimental results showed that the error was less than 0.5 mm. The proposed novel and cost-effective method for impact point localization aids in the effective dispersion assessment of flying objects. Full article

The flower thrips, Frankliniella intonsa, is a major pest threatening citrus production. However, chemical control remains the primary management measure, which poses significant risks on ecosystems. Hence, it is urgent to prioritize more eco-friendly measures to efficiently control thrips. The ladybird, Cheilomenes sexmaculata, is a predominant natural enemy in the local citrus agroecosystem and could play a key role in suppressing thrips in agricultural landscapes. Although some ladybirds are known to be attracted to herbivore-induced plant volatiles (HIPVs), little is known about the specific attractive compounds and the effect of F. intonsa-infested lemon plants on the predatory response of C. sexmaculata. Here, we studied the chemical interaction between F. intonsa, C. sexmaculata, and lemon plants. In dual-choice behavioral assays, C. sexmaculata adults significantly preferred volatiles from F. intonsa-infested plants over those from healthy plants. Volatile collection and analysis identified six monoterpenes, five of which (α-pinene, β-pinene, sabinene, myrcene, and eucalyptol) individually attracted C. sexmaculata at specific concentrations. Moreover, a blend of these five compounds, formulated at their optimal attractive concentrations, elicited a stronger attraction in C. sexmaculata than individual compounds, indicating a synergistic interaction. This attractive blend can thus be used to develop a kairomone-based lure to enhance biological control and to complement existing integrated pest management approaches against thrips in lemon agroecosystems. Full article

Severe convective weather is marked by abrupt onset, rapid evolution, and substantial destructive potential, posing major threats to economic activities and human safety. To address this challenge, this study proposes MRKAN, a multi-parameter prediction algorithm for dual-polarization radar that integrates Mamba, radial basis functions (RBFs), and the Kolmogorov–Arnold Network (KAN). The method predicts radar reflectivity, differential reflectivity, and the specific differential phase, enabling a refined depiction of the dynamic structure of severe convective systems. MRKAN incorporates four key innovations. First, a Cross-Scan Mamba module is designed to enhance global spatiotemporal dependencies through point-wise modeling across multiple complementary scans. Second, a Multi-Order KAN module is developed that employs multi-order β-spline functions to overcome the linear limitations of convolution kernels and to achieve high-order representations of nonlinear local features. Third, a Gaussian and Inverse Multiquadratic RBF module is constructed to extract mesoscale features using a combination of Gaussian radial basis functions and Inverse Multiquadratic radial basis functions. Finally, a Multi-Scale Feature Fusion module is designed to integrate global, local, and mesoscale information, thereby enhancing multi-scale adaptive modeling capability. Experimental results show that MRKAN significantly outperforms mainstream methods across multiple key metrics and yields a more accurate depiction of the spatiotemporal evolution of severe convective weather. Full article

The extensive consumption of freshwater resources and the continuous discharge of pharmaceutical residues pose serious risks to aquatic ecosystems and public health. In this study, pristine ZnO, TiO₂, Zn@TiO₂, and Ti@ZnO nanocomposites were synthesized via a precipitation-assisted solid–liquid interference method and systematically evaluated for the photocatalytic degradation of the antibiotic levofloxacin under UV and visible light irradiation. The structural, optical, and surface properties of the synthesized materials were characterized using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), UV–visible diffuse reflectance spectroscopy (UV–DRS), and X-ray photoelectron spectroscopy (XPS). XRD analysis confirmed the crystalline nature of all samples, while SEM images revealed spherical and agglomerated morphologies. Photocatalytic experiments were conducted using a 50-ppm levofloxacin solution with a catalyst dosage of 1 g L⁻¹. Pristine ZnO exhibited limited visible-light activity (33.81%) but high UV-driven degradation (92.98%), whereas TiO₂ showed comparable degradation efficiencies under UV (78.6%) and visible light (78.9%). Notably, Zn@TiO₂ nanocomposites demonstrated superior photocatalytic performance, achieving over 90% and near 70% degradation under both UV and visible light, respectively, while Ti@ZnO composites exhibited less than 60% degradation. The enhanced activity of Zn@TiO₂ is attributed to improved interfacial charge transfer, suppressed electron–hole recombination, and extended light absorption. These findings highlight Zn@TiO₂ nanocomposites as promising photocatalysts for efficient treatment of pharmaceutical wastewater under dual-light irradiation. Full article

This study presents a four-year (2021–2024) radar-based analysis of warm cloud (non-glaciated) dynamics across northern Thailand, specifically characterizing their properties, kinematics, and occurrence. Utilizing high-resolution S-band dual-polarization weather radar data, a total of 20,493 warm cloud events were tracked and analyzed, with identification based on a maximum reflectivity (≥35 dBZ) and a cloud top height below the seasonal 0 °C isotherm. Occurrence exhibited a profound seasonal disparity, with the rainy season (82.68% of events) dominating due to the influence of the moist Southwest Monsoon (SWM), while the spatial distribution confirmed that convective initiation is exclusively concentrated over mountainous terrain, underscoring orographic lifting as the essential mechanical trigger. Regarding properties, while vertical development and mass are greater in the warm seasons, microphysical intensity and Duration (mean ~26 min) remain highly uniform, suggesting a constrained, efficient warm rain process. In kinematics, clouds move fastest in winter (mean WSPD ~18.38 km/h), yet pervasive directional chaos (SD > 112°) highlights the strong influence of terrain-induced local circulations. In conclusion, while topography dictates where warm clouds form, the monsoon dictates when and how robustly they develop, creating intense, short-lived events that pose significant operational constraints for localized precipitation enhancement strategies. Full article

In recent years, harmful algal blooms have led to frequent occurrences of shellfish toxin contamination, posing a significant threat to the safety of aquatic products and public health. As a potent neurotoxin, domoic acid (DA) can accumulate in shellfish, highlighting the urgent need for rapid and highly sensitive detection methods. In this study, we developed a fluorescent aptasensor based on a dual-signal amplification system by combining G-quadruplex (G4) dimers with multi-walled carbon nanotubes (CNTs). The sensor is designed with a hairpin-structured aptamer as the recognition probe, where short multi-walled CNTs serve as both a fluorescence quencher and platform, and G4 dimers are incorporated into the sensing interface to enhance signal output. In the absence of the target, the hairpin-structured aptamer remains closed, keeping the fluorescence signal “off”. Upon binding to DA, the aptamer undergoes a specific conformational change that exposes the G4-dimer sequence. The exposed sequence then binds to thioflavin T (ThT), which in turn generates a greatly enhanced fluorescence signal, leading to a substantial fluorescence enhancement and completing the second stage of the cascade amplification. Under optimal conditions, the constructed sensor achieves rapid detection of DA within 5 min, with a low detection limit of 1.1 ng/mL. This work presents a valuable tool for the rapid and sensitive detection of DA in shellfish, with promising applications in marine environmental monitoring and food safety regulation. Full article

In recent years, numerous researchers have investigated various preventive strategies against respiratory viruses that pose a threat to human health. This study aims to develop a nasal spray formulation based on the natural polysaccharide xanthan gum (XG) and camostat, and to evaluate its dual protective mechanism at the nasal mucosa, the primary entry point for respiratory viral infections. The efficacy of the formulation was assessed through physicochemical characterization, cell-based assays, and animal experiments. Initially, muco-adhesiveness was evaluated by monitoring the drying dispersion area of the test formulation over time on a Petri dish. The combination of XG and camostat exhibited a dispersion area more than ten times larger than that of each component used alone. The antiviral efficacy was demonstrated in both human nasal epithelial cells (HNEc) and an influenza-infected mouse model. The cell-based experiment demonstrated a significant inhibition of viral penetration and replication. Furthermore, suppression of transmembrane protease, serine 2 (TMPRSS2) expression, a key factor in influenza virus entry, was observed in mouse lung tissues. These findings suggest that the Camostat–Polysaccharide Dual-Action Nasal Spray (CPNS), currently under development, holds promise as a non-invasive, first-line barrier to prevent the initial infection and replication of respiratory viruses. Full article

Background: Trypanosoma cruzi, the causative agent of Chagas disease, remains a major public health concern, and there is a continued need for new antitrypanosomal agents. Thiosemicarbazone (TSC) derivatives have emerged as a promising class of compounds with potential antiparasitic activity. Objectives: This study aimed to report the synthesis, characterization, and biological profiling of a novel series of thiosemicarbazone derivatives as antitrypanosomal agents against Trypanosoma cruzi. Methods: Fourteen new compounds and six previously described analogues were prepared and characterized by ¹H/¹³C nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). As a preliminary in vitro screen, activity was assessed by direct parasite counting in epimastigote and bloodstream trypomastigote forms, as tractable models of replicative and infective stages sharing core metabolic targets with intracellular amastigotes. Epimastigote potency was quantified as half-maximal effective concentrations (EC₅₀) derived from dose–response curves, whereas trypomastigote response was evaluated as percent viability after treatment at a fixed concentration of 20 µM. Mechanistic profiling included inhibition assays against the cysteine protease cruzipain (CZP) and selected redox defense enzymes, complemented by in silico similarity clustering and binding-pose affinity scoring. Results: A nitro-methoxy-substituted TSC showed potent CZP inhibition but limited trypomastigote efficacy, whereas brominated analogues displayed dual-stage activity independent of CZP inhibition. Tanimoto similarity analysis identified distinct structure–activity clusters, linking nitro-methoxy substitution to epimastigote selectivity and brominated scaffolds to broader antiparasitic profiles, with hydrophobicity and steric complementarity as key determinants. Enzymatic assays revealed no significant inhibition of cytosolic tryparedoxin peroxidase (cTXNPx) or glutathione peroxidase type I (TcGPx-I), suggesting redox disruption is not a primary mode of action. In vitro and in silico analyses showed low or no non-specific cytotoxicity under the tested conditions, supporting further optimization of these derivatives as antitrypanosomal preliminary hits. Key hits included derivative 3e (epimastigote EC₅₀ = 0.36 ± 0.02 µM) and brominated analogues 2c and 2e (epimastigote EC₅₀ = 3.92 ± 0.13 and 4.36 ± 0.10 µM, respectively), while docking supported favorable binding-pose affinity (e.g., ΔG_S-pose = −20.78 ± 2.47 kcal/mol for 3e). Conclusions: These results support further optimization of the identified thiosemicarbazone derivatives as preliminary antitrypanosomal hits and provide insight into structure–activity relationships and potential mechanisms of action. Full article

The proliferation of unmanned aerial vehicles (UAVs) poses escalating security threats across critical infrastructures, necessitating robust real-time detection systems. Existing vision-based methods predominantly rely on single-modality data and exhibit significant performance degradation under challenging scenarios. To address these limitations, we propose DCAM-DETR, a novel multimodal detection framework that fuses RGB and thermal infrared modalities through an enhanced RT-DETR architecture integrated with state space models. Our approach introduces four innovations: (1) a MobileMamba backbone leveraging selective state space models for efficient long-range dependency modeling with linear complexity $\mathcal{O}\left(n\right)$; (2) Cross-Dimensional Attention (CDA) and Cross-Path Attention (CPA) modules capturing intermodal correlations across spatial and channel dimensions; (3) an Adaptive Feature Fusion Module (AFFM) dynamically calibrating multimodal feature contributions; and (4) a Dual-Attention Decoupling Module (DADM) enhancing detection head discrimination for small targets. Experiments on Anti-UAV300 demonstrate state-of-the-art performance with 94.7% mAP@0.5 and 78.3% mAP@0.5:0.95 at 42 FPS. Extended evaluations on FLIR-ADAS and KAIST datasets validate the generalization capacity across diverse scenarios. Full article

Addressing the environmental challenges posed by phosphogypsum (PG) stockpiling, this study investigates the synergistic mechanisms of a dual-functional application strategy where PG serves as both cementitious binder and aggregate. Unlike previous research limited to single-mode utilization, this study focuses on the interfacial compatibility between PG-based binders and PG aggregates (PGA). Through a comparative experimental program, the mechanical performance and microstructure of different binder–aggregate combinations were evaluated. The proposed dual-functional formulation achieved a high PG incorporation rate of 38% by mass. While the compressive strength of 39.3 MPa was lower than that of the reference ordinary concrete, it comfortably surpasses the C30 strength requirement for structural applications, validating its engineering feasibility. Comparative analysis revealed that although natural stone aggregates possess higher intrinsic strength, the PG-binder/PGA system exhibits superior interfacial bonding compared to the PG-binder/stone system. Microstructural observations indicated that this synergistic interaction facilitates the formation of interwoven ettringite and C-S-H gel networks, contributing to a structurally integrated interfacial transition zone (ITZ). These findings suggest that the dual-functional strategy offers a viable pathway for developing low-carbon building materials by balancing high-volume waste utilization with mechanical compliance. Full article

Slow-growing and locally invasive, chordoma is a rare malignant bone tumor, with a reported annual worldwide incidence of 0.08 per 100,000 cases. It accounts for about 3 percent of all bone tumors and about 20 percent of primary spinal tumors. The incidence rates vary between countries and races, with white/Caucasian males in the 5th or 6th decade of life having a higher prevalence. Chordoma poses significant challenges because of its high recurrence rate and resistance to several standard treatment techniques. All cancers, including chordomas, have altered energy metabolism processes that contribute to their unchecked growth and survival. The significance of non-coding RNAs, particularly circular RNAs (circRNAs), as key regulators at the intersection of cellular metabolism and immune function has been highlighted by recent discoveries. By focusing on important glycolytic enzymes in tumor cells and altering metabolic reprogramming pathways, CircRNAs can influence cancer metabolic adaptability. Furthermore, via influencing immune cell functions as immunological checkpoint signaling and macrophage polarization, circRNAs influence immune evasion in the tumor microenvironment. These frequently happen via regulating important pathway signals, like PI3K/AKT/mTOR and NRF2, or by processes like miRNA sponging, creating a tumor microenvironment that is immunosuppressive and metabolically friendly. The translational pathway of circRNA-targeted therapeutics is promoted as a developing pharmacological entity in this review, which also highlights recent information on the control of circRNA-mediated immunometabolism in chordoma and examines numerous important molecular axes. There are promising opportunities to develop novel precision treatments for chordoma by considering circRNAs as dual regulators of immunological and metabolic networks. Full article

This study analyzes the competitive asymmetries and trade effects of the proposed EU–Mercosur Trade Agreement on the European Union’s (EU) and Polish agri-food sectors. The comparative analysis reveals that Mercosur holds a significant structural advantage driven by substantially lower labor costs, cheaper agricultural land, and a climate permitting multiple harvests. This cost advantage is further compounded by weaker regulatory standards (e.g., on pesticides and antibiotics). This structural edge is most pronounced in high-volume commodities, leading to Mercosur trade surpluses in products such as soybeans, sugar cane, and wheat, which pose the primary competitive threats to the EU market. Conversely, the EU maintains an intensive advantage through superior yields in intensive farming (e.g., maize) and specialization in high-value, processed products. This creates quantifiable export opportunities for EU/Polish producers in sectors where Mercosur is a consistent net importer, notably other frozen vegetables, preserved tomatoes, and apples. The findings confirm an asymmetric effect of liberalization, which necessitates a dual strategy of internal structural reform (e.g., the EU Protein Strategy) and the implementation of external protective mechanisms, including strategic Common Agricultural Policy (CAP) adaptations and safeguard clauses, to maintain the long-term competitiveness and Supply Chain Resilience of European agriculture. Full article

With the increasing penetration of renewable energy, power systems are facing greater uncertainty and volatility, which poses significant challenges for Virtual Power Plant scheduling. Existing research mainly focuses on optimizing economic efficiency but often overlooks system reliability and the impact of forecasting deviations on scheduling, leading to suboptimal performance. Thus, this paper presents a reliability-cost bi-objective cooperative optimization model based on a dual-swarm particle swarm algorithm: it introduces positive and negative imbalance price penalty factors to explicitly describe the economic costs of forecast deviations, constructs a reliability evaluation system covering PV, EVs, air-conditioning loads, electrolytic aluminum loads, and energy storage, and solves the multi-objective model via algorithm design of “sub-swarms specializing in single objectives + periodic information exchange”. Simulation results show that the method ensures stable intraday operation of VPPs, achieving 6.8% total cost reduction, 12.5% system reliability improvement, and 14.8% power deviation reduction, verifying its practical value and application prospects. Full article