Search Results (9,147)

Extremely low-frequency (ELF, 3–30 Hz) signals are effective for cross-medium transmission, yet conventional implementations are hindered by their large size and low efficiency. To address these limitations, a compact electromechanical–magnetic antenna (EMA) was developed and experimentally validated for ELF magnetic communication. The basic unit of the antenna, a single-EMA, consists of a stacked magnetostrictive composite beam, piezoelectric ceramic plates, and tip-mounted permanent magnets. The total envelope volume of a single EMA is only 3.3 cm³ with a maximum length of 12 cm, representing a substantial reduction compared with conventional ELF antennas. Building on this compact architecture, two EMAs were operated in parallel to form a parallel-EMA system, which significantly enhanced magnetic radiation through constructive magnetic coupling. Moreover, the optimal separation distance between the two EMAs was identified, ensuring efficient cooperative radiation. When driven at 50.2 mW, the parallel-EMA configuration generated a magnetic flux density of 114 pT at a transmission distance of 20 m in seawater. This performance demonstrates nearly a twofold improvement over a single-EMA unit, validating the scalability of parallel operation for stronger magnetic radiation. The compact form factor of the single EMA combined with the enhanced radiation performance of the parallel-EMA system enables portable ELF magnetic communication across diverse cross-medium scenarios, including air-to-sea and underground-to-air links. Full article

Methicillin-resistant Staphylococcus spp. are microorganisms found in dairy products, bovine mastitis, and human infections. The prevalence of resistant strains from this genus in the food chain is increasing, drawing attention to transmission in the community and highlighting the importance of One Health studies. Thus, the aim of this study was to determine the MIC of oxacillin (OXA) and the sanitizers benzalkonium chloride (BAC) and sodium hypochlorite (HP) against isolates of methicillin-resistant Staphylococcus spp., and to evaluate the possible influence of sub-MIC application of these compounds on bacterial cells, in order to observe possible microbial resistance. Ten isolates of methicillin-resistant Staphylococcus spp. (S. epidermidis and S. chromogenes) were used. Among the sanitizers, BAC showed greater efficiency during the pre-inhibition test. Increased resistance to OXA was found in isolates of S. chromogenes and S. epidermidis after sub-MICs of 50% and 90% of OXA, while sub-inhibition of HP favored resistance to OXA. The application of HP and OXA, even at low concentrations, induced a reduction in biofilm production. This study shows that sub-inhibitory sanitizer exposure in Staphylococcus spp. induces antimicrobial resistance phenotypes linked to mutations in regulatory, mobile, and DNA repair genes. These findings suggest that selective pressure promotes resistant variants through genomic plasticity and regulatory activation, supporting the hypothesis that sanitizer residues may drive multidrug resistance emergence, although further functional validation is required. Full article

Virus-like particles (VLPs) formed as a result of self-assembly of viral capsid proteins are widely used as a platform for antigen presentation in vaccine development. However, since the inclusion of a foreign peptide into the capsid protein can alter its spatial structure and interfere with VLP assembly, such insertions are usually limited to short peptides. In this study, we have demonstrated the potential of capsid protein (CP) of single-stranded RNA phage PQ465 to present long peptides using green fluorescent protein (GFP) as a model. GFP was genetically linked to either the N- or C-terminus of PQ465 CP. Hybrid proteins were expressed in Escherichia coli and Nicotiana benthamiana plants. Spherical virus-like particles (~35 nm according to transmission electron microscopy) were successfully formed by both N- and C-terminal fusions expressed in E. coli, and by plant-produced CP with GFP fused to the C-terminus. ELISA revealed that GFP in VLPs was accessible for specific antibodies suggesting that it is exposed on the surface of PQ465-GFP particles. VLPs carrying GFP were recognized by anti-CP antibodies with less efficiency than VLPs formed by empty CP, which indicates shielding of the CP core in PQ465-GFP particles. Therefore, PQ465 CP can be used as a chimeric VLP platform for the display of relatively large protein antigens, which can operate in bacterial and plant expression systems. Full article

Growing interest in sustainable hydrogen production has brought renewed attention to photoelectrochemical (PEC) water splitting as a promising route for direct solar-to-chemical energy conversion. This study explores how integrating hematite (α-Fe₂O₃) and cupric oxide (CuO) photoelectrodes with a series of nickel-based co-catalysts can improve photoelectrochemical activity. Photoanodic (NiO_x, NiFeO_x, NiWO₄) and photocathodic (Ni, NiCu, NiMo) co-catalysts were synthesized via co-precipitation and mechanochemical methods and characterized through X-ray Diffraction (XRD), X-ray Fluorescence (XRF), Transmission Electron Microscopy–Energy Dispersive X-ray Spectroscopy (TEM-EDX), Scanning Electron Microscopy–Energy Dispersive X-ray Spectroscopy (SEM-EDX), X-ray photoelectron spectroscopy (XPS) and Brunauer–Emmett–Teller (BET) gas-adsorption analyses to clarify their crystallographic, morphological, and compositional properties, as well as their surface chemistry and textural properties (surface area and porosity). Electrochemical tests under 1 SUN illumination showed that NiO_x significantly improves the photocurrent of hematite photoanodes. Among the cathodic co-catalysts, NiMo demonstrated the best performance when combined with CuO photocathodes. For both photoelectrodes, an optimal co-catalyst loading was identified, beyond which performance declined due to potential charge transfer limitations and light attenuation. These findings highlight the critical role of co-catalyst composition and loading in optimizing the efficiency of PEC systems based on earth-abundant materials, offering a pathway toward scalable and cost-effective hydrogen production. Full article

To address the technical challenges in bisphenol A (BPA) pollution control, this research introduced a novel synthetic approach combining co-precipitation with subsequent thermal treatment to engineer layered double hydroxides (LDHs) with a spinel-structured CoMn-LDO catalyst. Systematic material characterizations such as a scanning electron microscope (SEM), an X-ray diffractometer (XRD), a transmission electron microscope (TEM), and X-ray photoelectron spectroscopy (XPS) were employed to analyze the structural and chemical properties of the synthesized CoMn-LDO calcined at 500 °C. The catalytic performance was evaluated under optimized conditions (35 °C, pH 7.0, 2.0 mM PMS, 0.3 g/L catalyst), and mechanistic studies were conducted to identify the dominant reactive oxygen species. The CoMn-LDO exhibited exceptional peroxymonosulfate (PMS) activation performance, achieving 96.75% BPA degradation within 90 min and 58.22% TOC removal. The synergistic redox cycling between Co²⁺/Co³⁺ and Mn³⁺/Mn⁴⁺ promoted the generation of ·OH (72.3% contribution) and SO₄·⁻. The catalyst demonstrated superior stability, maintaining 89% degradation efficiency after five cycles. These results provide theoretical and practical insights for developing high-efficiency persulfate-activating catalysts. Full article

This paper presents a modified Orthogonal Frequency Division Multiplexing (OFDM) system that combines Discrete Wavelet Transform (DWT) with Discrete Sine Transform (DST) to enhance data rate capacity over traditional Discrete Fourier Transform (DFT)-based OFDM systems. By applying Inverse Discrete Wavelet Transform (IDWT) to the modulated Binary Phase Shift Keying (BPSK) bits, the constellation diagram reveals that half of the time-domain samples after single-level Haar IDWT are zeros, while the other half are real. The proposed system utilizes these $0.5N$ zero values, modulating them with the DST (IDST) and assigning them as the imaginary part of the signal. Performance comparisons demonstrate that the Bit-Error-Rate (BER) of this hybrid DWT-DST configuration lies between that of BPSK and Quadrature Phase Shift Keying (QPSK) in a DWT-based system, while also achieving data rate improvement of $0.5N$. Additionally, simulation results indicate that the proposed approach demonstrates stable performance even in the presence of estimation errors, with less than 3.4% BER degradation for moderate errors, and consistently better robustness than QPSK-based systems while offering improved data rate efficiency over BPSK. This novel configuration highlights the potential for more efficient and reliable data transmission in OFDM systems, making it a promising alternative to conventional DWT or DFT-based methods. Full article

Agile satellites leverage rapid and flexible maneuvering to image more targets per orbital cycle, which is essential for time-sensitive emergency operations, particularly disaster assessment. Correspondingly, the increasing observation data volumes necessitate the use of on-orbit computing to bypass storage and transmission limitations. However, coordinating precedence-dependent observation, computation, and downlink operations within limited time windows presents key challenges for agile satellite service optimization. Therefore, this paper proposes a deep reinforcement learning (DRL) approach to solve the joint observation and on-orbit computation scheduling (JOOCS) problem for agile satellite constellations. First, the infrastructure under study consists of observation satellites, a GEO satellite (dedicated to computing), ground stations, and communication links interconnecting them. Next, the JOOCS problem is described using mathematical formulations, and then a partially observable Markov decision process model is established with the objective of maximizing task completion profits. Finally, we design a joint scheduling decision algorithm based on multiagent proximal policy optimization (JS-MAPPO). Concerning the policy network of agents, a problem-specific encoder–decoder architecture is developed to improve the learning efficiency of JS-MAPPO. Simulation results show that JS-MAPPO surpasses the genetic algorithm and state-of-the-art DRL methods across various problem scales while incurring lower computational costs. Compared to random scheduling, JOOCS achieves up to 82.67% higher average task profit, demonstrating enhanced operational performance in agile satellite constellations. Full article

The Internet of Vehicles (IoV) relies on Vehicle-to-Everything (V2X) communications to enable cooperative perception among vehicles, infrastructures, and devices, where Vehicle-to-Infrastructure (V2I) links are crucial for reliable transmission. However, the openness of wireless channels exposes IoV to eavesdropping, threatening privacy and security. This paper investigates an Intelligent Reflecting Surface (IRS)-assisted interweave Cognitive IoV (CIoV) network to enhance physical layer security in V2I communications. A non-convex joint optimization problem involving spectrum allocation, transmit power for Vehicle Users (VUs), and IRS phase shifts is formulated. To address this challenge, a heterogeneous multi-agent (HMA) Mamba RainbowDQN algorithm is proposed, where homogeneous VUs and a heterogeneous secondary base station (SBS) act as distinct agents to simplify decision-making. Simulation results show that the proposed method significantly outperform benchmark schemes, achieving a 13.29% improvement in secrecy rate and a 54.2% reduction in secrecy outage probability (SOP). These results confirm the effectiveness of integrating IRS and deep reinforcement learning (DRL) for secure and efficient V2I communications in CIoV networks. Full article

Resveratrol (RES), a naturally occurring polyphenolic compound with well-documented anticancer potential, is limited in clinical application due to its poor aqueous solubility and low bioavailability. This study aimed to develop RES-loaded liposomes coated sequentially with chitosan (CS) and hyaluronic acid-chitosan (HA) (RES-HA-CS-Lip) to enhance RES stability, delivery, and anticancer efficacy in breast cancer cells. HA-CS-coated liposomes were prepared using a thin-film hydration technique. Their physicochemical characteristics were thoroughly investigated through dynamic light scattering, transmission electron microscopy, Fourier transform infrared spectroscopy, and differential scanning calorimetry. The optimized RES-HA-CS-Lip exhibited spherical morphology with an average particle size of 212 nm, a narrow polydispersity index (<0.4), a zeta potential of +9.04 ± 1.0 mV, and high entrapment efficiency of 82.16%. Stability studies demonstrated superior retention of size, surface charge, and encapsulation efficiency over 28 days at both 4 °C and 25 °C. In vitro release profiles at physiological and acidic pH revealed sustained drug release, with enhanced release under acidic conditions mimicking the tumor microenvironment. Antioxidant activity, assessed via DPPH and ABTS radical-scavenging assays, indicated that RES retained its radical-scavenging potential upon encapsulation. Cytotoxicity assays demonstrated markedly improved anticancer activity against MCF-7 breast cancer cells, with an IC₅₀ of 13.08 μg/mL at 48 h, while maintaining high biocompatibility toward normal HaCaT keratinocytes. RES-HA-CS-Lip demonstrated excellent stability against degradation and aggregation. Overall, these findings highlight HA-CS-coated liposomes as a promising polysaccharide-based nanocarrier that enhances stability, bioactivity, and therapeutic efficacy of RES, representing a potential strategy for targeted breast cancer therapy. Full article

As the prevalence of respiratory diseases continues to rise, inhalation therapy has emerged as a crucial method for their treatment. The effective transmission of medications within the respiratory tract is vital to achieve therapeutic outcomes. Given that most inhaled particles carry electrostatic charges, understanding the electrostatic effect on particle behavior in bifurcated tubes is of significant importance. This work combined Large Eddy Simulation-Lagrangian particle tracking (LES-LPT) technology to simulate particle behavior with three particle sizes (10, 20, and 50 μm) from G2 to G3 (“G” stands for generation) in bifurcated tubes, either with or without electrostatics, under typical human physiological conditions (Re = 1036). The results indicate that the electrostatic force has a significant effect on particle behavior in bifurcated tubes, which increases with particle size. Within the bifurcated tubes, the electrostatic force enhances particle movement in alignment with the secondary flow as well as intensifies the interaction of particles with local turbulent vortices and promotes particle dispersion rather than agglomeration. On the other hand, the distribution of the electrostatic field is influenced by particle behavior. Higher particle concentration presents stronger electrostatic strength, which increases with particle size. Therefore, it can be concluded that the electrostatic interactions among particles can prevent particles from aggregating and enhance the efficiency of inhalation therapy. Full article

In open-network environments of smart buildings and urban infrastructure, abnormal traffic from security and energy monitoring systems is critical for operational safety and decision reliability. We can develop malware that exploits building automation protocols to simulate attacks involving the falsification or modification of chiller controller commands, thereby endangering the entire network infrastructure. Intrusion detection systems rely on abundant labeled abnormal traffic data to detect attack patterns, improving network system reliability. However, transmitting such data faces two major challenges: single-feature representations fail to capture comprehensive traffic features, limiting the information representation for artificial intelligence (AI)-based detection models, and unconcealed abnormal traffic is easily intercepted by firewalls or intrusion detection systems, hindering cross-departmental sharing. Existing methods struggle to balance feature integrity and transmission stealth, often sacrificing one for the other or relying on easily detectable spatial-domain steganography. To address these gaps, we propose a multi-channel imaging hiding method that reconstructs abnormal traffic into multi-channel images by combining three mappings to generate grayscale images that depict traffic state transitions, dynamic trends, and internal similarity, respectively. These images are combined to enhance feature representation and embedded into frequency-domain adversarial examples, enabling evasion of security devices while preserving traffic integrity. Experimental results demonstrate that our method captures richer information than single-representation approaches, achieving a PSNR of 44.5 dB (a 6.0 dB improvement over existing methods) and an SSIM of 0.97. The high-fidelity reconstructions enabled by these gains facilitate the secure and efficient sharing of abnormal traffic data, thereby enhancing AI-driven security in smart buildings. Full article

The dead-time effect seriously affects the soft-switching performance and operating efficiency of the dual-active-bridge converter, and also causes problems such as reduced duty cycle, distortion of voltage and current waveforms, and narrowed transmission power range. The proposal of the five-degree-of-freedom modulation strategy transforms the working voltage waveforms of the primary and secondary sides as well as the inductor current waveform of the DAB converter from symmetric to asymmetric, while the dead-time issue still persists. Based on the five-degree-of-freedom modulation strategy, this paper analyzes the electrical characteristics of the converter before and after the introduction of dead time, designs switch drive pulses to avoid the dead time, and proposes a dead-time compensation modulation strategy based on five-degree-of-freedom phase shift. The results show that the proposed dead-time compensation control strategy can avoid problems such as voltage and current waveform distortion and reduction in the soft-switching power range caused by dead time, realizing dead-time compensation in the full power range. Experimental measurements show that, for different voltage transmission ratios, the maximum efficiency improvement is approximately 3.8–4% and the current stress is reduced by 2.11% to 3.13% under low-power operating conditions. The maximum efficiency improvement is approximately about 1.4–2.8% and the current stress is reduced by 1.84% to 2.53% under high-power operating conditions. Full article

Edge computing networks represent an emerging technological paradigm that enhances real-time responsiveness for mobile devices by reallocating computational resources from central servers to the network’s edge. This shift enables more efficient computing services for mobile devices. However, deploying computing services on inappropriate edge nodes can result in imbalanced resource utilization within edge computing networks, ultimately compromising service efficiency. Consequently, effectively leveraging the resources of edge computing devices while minimizing the energy consumption of terminal devices has become a critical issue in resource scheduling for edge computing. To tackle these challenges, this paper proposes a resource scheduling algorithm for edge computing networks based on multi-objective optimization. This approach utilizes the entropy weight method to assess both dynamic and static metrics of edge computing nodes, integrating them into a unified computing power metric for each node. This integration facilitates a better alignment between computing power and service demands. By modeling the resource scheduling problem in edge computing networks as a multi-objective Markov decision process (MOMDP), this study employs multi-objective reinforcement learning (MORL) and the proximal policy optimization (PPO) algorithm to concurrently optimize task transmission latency and energy consumption in dynamic environments. Finally, simulation experiments demonstrate that the proposed algorithm outperforms state-of-the-art scheduling algorithms in terms of latency, energy consumption, and overall reward. Additionally, it achieves an optimal hypervolume and Pareto front, effectively balancing the trade-off between task transmission latency and energy consumption in multi-objective optimization scenarios. Full article

Background: Recombinant monoclonal antibodies (mAbs) represent the fastest-growing sector of the biopharmaceutical industry, with their efficient expression being a key technological factor for scalability. Objectives: In this study we compared the performance of two bicistronic vectors, which alternate the positions of the light and heavy chain coding genes, employing a wild-type Encephalomyocarditis virus (EMCV) IRES functional element to drive expression of the second gene. Methods: Using two neutralizing anti-SARS-CoV-2 IgG1 antibodies as model molecules, we conducted transient transfections in the commercially available ExpiCHO^TM platform. Following protein A affinity purification and quantification, vectors positioning the light chain as the first cistron consistently yielded higher expression levels than those with the heavy chain upstream. To confirm the quality attributes of the mAbs, we applied a comprehensive analytical workflow, including SDS-PAGE and Western blot for molecular mass and purity, circular dichroism for secondary structure, intrinsic tryptophan fluorescence for tertiary structure, and SEC-HPLC for quaternary structure and aggregate detection. Additionally, we assessed binding affinity to the target using spot blot and surface plasmon resonance, analyzed N-glycosylation profiles by HILIC-HPLC and mass spectrometry, and examined molecular structure by transmission electron microscopy. Results and Conclusions: Together, these results provide insight into the impact of gene positioning within bicistronic vectors on mAb expression efficiency and quality, supporting optimization strategies for scalable recombinant antibody production. Full article

Planetary gearbox serves as a key transmission component in planetary ball screw actuator systems. Under the action of alternating loads, the stress concentration locations of the planet carrier in actuators with planetary gear trains are prone to fatigue cracks, which can lead to catastrophic system breakdowns. However, due to the complex vibration transmission path and the interference of uninterested vibration components, the characteristic modulation signal is ambiguous, so it is challenging to diagnose this fault. Therefore, this paper proposes a new fault diagnosis method. Firstly, a vibration signal model is established to accurately characterize the amplitude and phase modulation effects caused by cracked carriers, providing theoretical guidance for fault feature identification. Subsequently, three novel sideband evaluators of the modulation signal bispectrum (MSB) and their parameter selection ranges are proposed to efficiently locate the optimal fault-related bifrequency signatures and reduce computational cost, leveraging the effects identified by the model. Finally, a novel health indicator, the mean absolute root value (MARV), is used to monitor the state of the planet carrier. The effectiveness of this method is verified by experiments on the planetary gearbox test rig. The results show that the robustness of the amplitude and phase modulation effect of the cracked carrier in the low-frequency band is significantly higher than that in the high-frequency band, and the initial carrier crack can be accurately identified using this phenomenon under different operating conditions. This study provides a reliable solution for the condition monitoring and health management of the actuation system, which is helpful to improve the safety and reliability of operation. Full article