Search Results (142)

Volatile organic compounds produced by microbes are increasingly recognized as modulators of microbial interactions and mediators of both intra- and inter-kingdom communication. This study explored the possible ecophysiological roles of nine amides from Streptomyces sp. NP10 in quorum sensing (QS) and biofilm formation in Pseudomonas aeruginosa PAO1. GC-MS profiling, synthesis, spectral validation, and co-injection experiments confirmed compound identities. Notably, N-(3-methyl-2-butenyl)acetamide is reported as a new natural product and N-(2-methylbutyl)acetamide as a new Streptomyces-produced metabolite. At subinhibitory concentrations (250 μg/mL), most of the amides enhanced P. aeruginosa biofilm formation, with N-(2-methylbutyl)acetamide, N-(3-methyl-2-butenyl)acetamide, and 2-phenylacetamide showing the strongest effects. Simultaneously, these compounds suppressed QS by reducing the production of N-acyl homoserine lactones (AHLs) and 2-alkyl-4-quinolones (AHQs). Aliphatic acetamides preferentially inhibited short-chain AHLs, while N-acetyltyramine and 2-phenylacetamide mainly affected quinolone signaling. These opposing effects on QS and biofilm are consistent with the involvement of alternative regulatory circuits. Motility assays showed biofilm stimulation was not correlated with altered swarming or twitching. Cross-species assays revealed limited QS inhibition, with only N-acetyltryptamine reducing violacein production in Chromobacterium violaceum CV026. Most of the amides were non-cytotoxic at 100 μM (10.5–20.2 μg/mL), except for 2-phenylacetamide. Overall, these amides likely serve as microbial signals influencing QS and biofilm formation, offering leads for anti-virulence strategies. Full article

In recent years, with the rapid development of intelligent communication technologies, anti-jamming techniques based on deep learning have been widely adopted in unmanned aerial vehicle (UAV) systems, yielding significant improvements. Most existing studies primarily focus on intelligent anti-jamming decision-making for single UAVs. However, in UAV swarm systems, single-agent decision models often suffer from data isolation and inconsistent frequency usage decisions among nodes within the same task subnet, caused by asynchronous model updates. Although data sharing among UAVs can partially alleviate model update issues, it introduces significant communication overhead and data security challenges. To address these problems, this paper proposes a novel multi-UAV cooperative intelligent anti-jamming decision-making method, termed Federated Learning-Hierarchical Deep Q-Network (FL-HDQN). First, an adaptive model synchronization mechanism is integrated into the federated learning framework. By sharing only local model parameters instead of raw data, UAVs collaboratively train a global model for each task subnet. This approach ensures decision consistency while preserving data privacy and reducing communication costs. Second, to overcome the curse of dimensionality caused by multi-domain interference parameters, a hierarchical deep reinforcement learning model is designed. The model decouples multi-domain optimization into two levels: the first layer performs time–frequency domain decisions, and the second layer conducts power and modulation-coding domain decisions, ensuring both real-time performance and decision effectiveness. Finally, simulation results demonstrate that, compared with state-of-the-art intelligent anti-jamming models, the proposed method achieves 1% higher decision accuracy, validating its superiority and effectiveness. Full article

Dendritic cell (DC) immunotherapy is a promising approach for treating cancers such as melanoma and prostate cancer. Although DC-based vaccines can elicit potent anti-tumor immune responses, dosing schedules in both preclinical and clinical settings are often chosen empirically rather than through quantitative optimization. In this work, we develop an enhanced mathematical model of tumor-immune dynamics that incorporates a more realistic tumor growth law and an estimated immune-response delay, enabling the systematic design of DC vaccination protocols. Tumor-growth and immunotherapy parameters were calibrated using experimental melanoma data and two metaheuristic optimization methods: Genetic Algorithm and Particle Swarm Optimization. Using the calibrated model, we derived vaccination schedules consisting of three injections totaling $2.4 \times {10}^6$ DCs. Despite using the same total dose as the baseline four-injection protocol, the optimized schedules reduced tumor burden by approximately $52\%$ over a 5000-h window, as measured by the area under the tumor-time curve, while also lowering the number of administrations. These results demonstrate that effective tumor control can be achieved without increasing treatment intensity and with substantially fewer vaccinations than previously assumed. Prior optimization studies often required cumulative doses exceeding $1 \times {10}^7$ cells to obtain comparable therapeutic effects. In contrast, our findings show that metaheuristic algorithms can produce dose-efficient and biologically grounded schedules that significantly enhance treatment performance. This work highlights the value of computational optimization as a decision-support tool for designing efficient and clinically meaningful DC immunotherapy protocols. Full article

Water supply pipe systems are typically buried in the ground, leakage has always been a significant problem for urban water supply systems. Although leakage detection can be performed using in-pipe inspection devices with hydrophone modules, the accuracy is low and depends on staff experience, and long-term work can harm health. Therefore, leakage detection and classification of various leakage levels are crucial for pipelines. This study presents a one-dimensional convolutional neural network and bidirectional long short-term memory network fusion model (1D-CNN-Bi-LSTM) for leakage detection, with enhanced particle swarm optimization (EPSO) algorithm optimized hyperparameters and multi-feature fusion for data enhancement. Ablation experiments show the key roles of EPSO and Bi-LSTM modules, and full-scale experiments confirm the method’s effectiveness. Compared to other models, this one reaches 98.33% in both leakage detection and severity classification accuracy, with strong anti-noise ability and stable recognition. In conclusion, the proposed method reduces reliance on in pipe devices, offering a more accurate and effective solution for pipeline leakage detection and severity assessment. Full article

Air-to-ground visual target persistent tracking technology for swarm drones, as a crucial interdisciplinary research area integrating computer vision, autonomous systems, and swarm collaboration, has gained increasing prominence in anti-terrorism operations, disaster relief, and other emergency response applications. While recent advancements have predominantly concentrated on improving long-term visual tracking through image algorithmic optimizations, insufficient exploration has been conducted on developing system-level persistent tracking architectures, leading to a high target loss rate and limited tracking endurance in complex scenarios. This paper designs an asynchronous multi-task parallel architecture for drone-based long-term tracking in air-to-ground scenarios, and improves the persistent tracking capability from three levels. At the image algorithm level, a long-term tracking system is constructed by integrating existing object detection YOLOv10, multi-object tracking DeepSort, and single-object tracking ECO algorithms. By leveraging their complementary strengths, the system enhances the performance of the detection and multi-object tracking while mitigating model drift in single-object tracking. At the drone system level, ground target absolute localization and geolocation-based drone spiral tracking strategies are conducted to improve target reacquisition rates after tracking loss. At the swarm collaboration level, an autonomous task allocation algorithm and relay tracking handover protocol are proposed, further enhancing the long-term tracking capability of swarm drones while boosting their autonomy. Finally, a practical swarm drone system for persistent air-to-ground visual tracking is developed and validated through extensive flight experiments under diverse scenarios. Results demonstrate the feasibility and robustness of the proposed persistent tracking framework and its adaptability to wild real-world applications. Full article

Traditional pedestrian evacuation models struggle to balance global exit guidance with local, individual decision making under hazards. We address this by decomposing long-term objectives into Particle Swarm Optimization (PSO)-based micro-goals and proposing a hybrid Cellular Automaton (CA) and PSO model. The hybrid design reduces the decoupling between spatial discretization and individual choices and more tightly couples hazard and density fields with movement decisions. Two contributions are central. First, we develop an autonomous following pathfinding mechanism (AFPM) that linearly blends the direction toward a PSO micro-goal with a herd following direction and adds a small reward for directional consistency. This mitigates path chaos from purely autonomous moves and congestion aggregation from purely herding moves. Second, we build a multi-dimensional interpretability and robustness framework that combines the empirical Cumulative Distribution Function (CDF) and a kernel-smoothed Probability Density Function (PDF) of key evacuation times (T_clear, T_95%_alive) together with vulnerability curves, to analyze the data and assess robustness. It combines Shapley Sobol analysis to quantify parameter effects on clearance time T_clear and the 95% survival evacuation time T_95%_alive, with CDF/PDF summaries and vulnerability curves to assess anti-interference performance. Experiments use a simulated underground shopping mall. In a 60 pedestrian case, a geometry-only baseline yields T_clear $\approx 33 \mathrm{s}$; hazard- and density-aware strategies produce slightly longer T_clear but reduce peak bottleneck congestion by 20–30%. When one exit is closed, the exceedance probability at $\tau =70 \mathrm{s}$ drops from 0.44 to 0.36, reducing long tail risk. Compared with geometry-based Dijkstra, the proposed model slightly increases clearance time while lowering peak congestion by 20–30%, achieving a balance between efficiency and safety. The model and evaluation protocol provide technical support for evacuation policy, facility layout, and emergency system design in large complex buildings. Full article

Proteus mirabilis (P. mirabilis) serves as a multi-host–pathogen regarded as an alarming foodborne infectious disease, causing illnesses of variable severity in both livestock and human beings. The present study aimed to estimate the prevalence, antibiotic susceptibility profiles, and associated antimicrobial resistance genes (ARGs) of P. mirabilis isolates obtained from diseased broiler chickens and native Egyptian buffaloes in Kafr El-Sheikh and Dakahlia governorates, Egypt. In addition, this study investigated the antibacterial activity of chitosan (CS) and chitosan nanoparticles (CSNPs), including the estimation of the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of CS at concentrations of 1% and 2%, as well as CSNPs. Furthermore, the sub-MIC values were utilized to assess the inhibitory effects of CS and CSNPs on swarming motility. P. mirabilis was detected in 68% (34/50) of broiler chickens and 40.74% (11/27) of buffaloes. Interestingly, all P. mirabilis isolates were tested against 21 antimicrobial drugs and showed high resistance against either critical, highly important, or important antimicrobial drugs. For chicken-originated P. mirabilis, 50% (17/34) of isolates were revealed to be extensively drug-resistant (XDR) and 50% (17/34) of isolates were revealed to be pan-drug-resistant (PDR). Meanwhile, 9.09% (1/11) of buffalo-originated P. mirabilis isolates were revealed to be XDR and 90.91% (10/11) of the isolates were revealed to be PDR. Among P. mirabilis isolates from broiler chickens, the prevalence of resistance genes was as follows: int1 (97.06%), dfrA1 (100%), sul2 (97.06%), catA1 (44.12%), aadA1 (97.06%), tet(M) (81.82%), ermB (23.53%), msrA (0%), qnrA (47.06%), qnrS (0%), gyrA (0%), mcr-1 (11.76%), bla_TEM (97.06%), bla_CTX-M (26.47%), bla_OXA-10 (2.94%), bla_CMY-2 (41.18%), and bla_SHV (0%). The corresponding detection rates in buffalo-derived isolates were 100%, 100%, 90.91%, 63.64%, 100%, 70.59%, 18.18%, 0%, 9.09%, 0%, 0%, 18.18%, 81.82%, 18.18%, 18.18%, 63.64%, and 0%, respectively. Carbapenemase genes were found in none of the isolates from either species. CSNPs demonstrated superior antibacterial and anti-virulence activity against resistant P. mirabilis. CSNPs exhibited significantly lower MIC (0.067–0.081 mg/mL) and MBC (0.167–0.177 mg/mL) values compared with conventional CS formulations (MIC: 3.25–4.5 mg/mL; MBC: 6.67–9.08 mg/mL) in both broiler and buffalo isolates. In inhibition zone assays, the CSNPs + ciprofloxacin (CIP) combination showed the highest efficacy with a 50–58% increase in the inhibition area. Both CSNPs and CS 2% substantially reduced swarming motility by 45–52%, with CSNPs showing the strongest inhibitory effect. These outcomes highlight how P. mirabilis carries and disseminates antibiotic resistance, presenting serious threats to health policy and livestock. Also, CS or CSNPs, either alone or enhanced with CIP, are effective in vitro against resistant P. mirabilis, which promotes the treatment of Proteus infections to guarantee a bactericidal impact. Full article

Bidens pilosa L. extract (BPE), a traditional medicine known for its antimicrobial properties, has not been thoroughly investigated for its potential against avian pathogenic Escherichia coli (APEC), a major pathogen responsible for severe economic losses and high mortality in poultry. This study aimed to comprehensively assess the antibacterial activity of BPE against APEC through both in vivo and in vitro experiments and to explore its underlying mechanisms. In a chicken infection model, BPE treatment led to an 80% cure rate and 20% mortality, in contrast to the 90% diarrhea and 70% mortality observed in the untreated model group. BPE also significantly alleviated intestinal tissue damage and reduced serum levels of inflammatory cytokines IL-6 and IL-1β (p < 0.01). In vitro analyses revealed a minimum inhibitory concentration (MIC) of 625 mg/mL. BPE dose-dependently suppressed bacterial motility, swarming, and biofilm formation (p < 0.01) and markedly increased membrane permeability, indicated by elevated release of nucleic acids, proteins, and alkaline phosphatase (p < 0.001). Moreover, PCR results showed that treatment with BPE at 1/2 MIC for 24 h significantly downregulated multiple virulence-associated genes, including aatA, papC, ibeB, vat, ompA, iss, fyuA, and irp2 (p < 0.01). These results demonstrate that BPE exerts its anti-APEC effects by damaging cell membrane integrity, inhibiting biofilm formation and motility, and suppressing virulence gene expression. Our findings support the potential of BPE as a natural alternative for controlling APEC infections and contribute a scientific basis for the use of traditional herbal medicine in combating bacterial diseases. Full article

Addressing the problems of wind power’s anti-peak regulation characteristics, increasing system peak regulation difficulty, and wind power uncertainty causing frequency deviation leading to power imbalance, this paper considers the peak shaving and valley filling function and frequency regulation characteristics of energy storage, establishing a day-ahead and intraday coordinated two-stage optimization scheduling model for research. Stage 1 establishes a deterministic wind power prediction model based on time series Autoregressive Integrated Moving Average (ARIMA), adopts dynamic peak-valley identification method to divide energy storage operation periods, designs energy storage peak regulation working interval and reserves frequency regulation capacity, and establishes a day-ahead 24 h optimization model with minimum cost as the objective to determine the basic output of each power source and the charging and discharging plan of energy storage participating in peak regulation. Stage 2 still takes the minimum cost as the objective, based on the output of each power source determined in Stage 1, adopts Monte Carlo scenario generation and improved scenario reduction technology to model wind power uncertainty. On one hand, it considers how energy storage improves wind power system inertia support to ensure the initial rate of change of frequency meets requirements. On the other hand, considering energy storage reserve capacity responding to frequency deviation, it introduces dynamic power flow theory, where wind, thermal, load, and storage resources share unbalanced power proportionally based on their frequency characteristic coefficients, establishing an intraday real-time scheduling scheme that satisfies the initial rate of change of frequency and steady-state frequency deviation constraints. The study employs improved chaotic mapping and an adaptive weight Particle Swarm Optimization (PSO) algorithm to solve the two-stage optimization model and finally takes the improved IEEE 14-node system as an example to verify the proposed scheme through simulation. Results demonstrate that the proposed method improves the system net load peak-valley difference by 35.9%, controls frequency deviation within ±0.2 Hz range, and reduces generation cost by 7.2%. The proposed optimization scheduling model has high engineering application value. Full article

The main objective of this paper is to develop a maximum power point tracker (MPPT) for a photovoltaic module array (PVMA) under conditions of partial shading and sudden changes in solar irradiance. PVMAs exhibit nonlinear characteristics with respect to temperature and solar irradiance conditions. Therefore, when some modules in the array are shaded or when there is a sudden change in solar irradiance, the maximum power point (MPP) of the array will also change, and the power–voltage (P-V) characteristic curve may exhibit multiple peaks. Under such conditions, if the tracking algorithm employs a fixed step size, the time required to reach the MPP may be significantly prolonged, potentially causing the tracker to converge on a local maximum power point (LMPP). To address the issues mentioned above, this paper proposes a novel MPPT technique based on the nature-inspired idols algorithm (IA). The technique allows the promotion value (PM) to be adjusted through the anti-fans weight (afw) in the iteration formula, thereby achieving global maximum power point (GMPP) tracking for PVMAs. To verify the effectiveness of the proposed algorithm, a model of a 4-series–3-parallel PVMA was first established using MATLAB (2024b version) software under both non-shading and partial shading conditions. The voltage and current of the PVMAs were fed back, and the IA was then applied for GMPP tracking. The simulation results demonstrate that the IA proposed in this study outperforms existing MPPT techniques, such as particle swarm optimization (PSO), cat swarm optimization (CSO), and the bat algorithm (BA), in terms of tracking speed, dynamic response, and steady-state performance, especially when the array is subjected to varying shading ratios and sudden changes in solar irradiance. Full article

Aiming at the problems that traditional maintenance task scheduling schemes for damaged equipment have, poor adaptability to changes in uncertain factors and difficult-to-deal-with emergency scenarios, this paper proposes a maintenance task scheduling method for battle-damaged equipment based on digital twin (DT) and robust optimization. The purpose is to realize the dynamic synchronization between physical entities and virtual models through DT technology, and to leverage the anti-interference characteristics of robust optimization. The method involves constructing a multi-objective optimization model that maximizes the comprehensive importance of damaged equipment and minimizes maintenance time, and solving the model using the discrete particle swarm optimization (DPSO) algorithm. Simulation results show that this method can improve the efficiency of maintenance scheduling and the anti-interference ability in emergency situations. Through the comparison of three indicators, DT-DPSO performs the best in the maintenance scheduling of battle-damaged equipment: its convergence speed is 33.3% faster than that of DPSO and 20% faster than that of DT-non-dominated sorting genetic algorithm II (DT-NSGAII); its robustness is 16.3% higher than that of DPSO and 10.7% higher than that of DT-NSGAII; its dynamic reallocation speed is more than 40% faster than that of DPSO and more than 30% faster than that of DT-NSGAII. This method is suitable for maintenance scheduling requirements of high speed, stability, and anti-interference. Full article

Background: Antimicrobial resistance is a major global health threat, particularly from pathogens such as Pseudomonas aeruginosa, known for forming biofilms and producing virulence factors that cause persistent infections. Essential oils (EOs) offer promising alternatives to conventional antimicrobial therapy due to their antimicrobial and antibiofilm properties. This study aimed to investigate the modulatory effects of a thymol-rich EO from Satureja montana L. on planktonic growth, biofilm formation, swarming motility, proteolytic activity and pyocyanin production of P. aeruginosa PAO1. Methods: The essential oil, isolated by hydrodistillation from S. montana aerial parts, was analysed by GC-MS. The minimum inhibitory concentration (MIC) of the EO and thymol was determined using the broth microdilution method. Sub-MICs were tested for planktonic growth and biofilm formation. Virulence was assessed by testing swarming motility, proteolytic activity and pyocyanin production. Results: The EO was characterised by a very high content of monoterpenes, with thymol dominating (56.47%). MIC for both EO and thymol was 4 mg/mL. They showed a biphasic effect: higher concentrations significantly inhibited planktonic growth (36–58% reduction; p < 0.05), while lower concentrations promoted it (10–17% increase; p < 0.05). Biofilm biomass varied, but the biofilm index indicated promotion at higher concentrations (0.125–0.5 mg/mL; p < 0.05). Both inhibited swarming at 0.5 mg/mL (thymol was more effective). Thymol decreased proteolytic activity, while EO increased pyocyanin production. Conclusions: S. montana essential oil and thymol show concentration-dependent modulation of P. aeruginosa growth, biofilms and virulence, suggesting their potential as anti-virulence agents, although the biphasic responses require careful dosing. Full article

This paper presents a hybrid indoor localization framework combining time difference of arrival (TDoA) measurements with a swarm intelligence optimization technique. To address the nonlinear optimization challenges in three-dimensional (3D) indoor localization via TDoA measurements, we systematically evaluate the artificial bee colony (ABC) algorithm and chimpanzee optimization algorithm (ChOA). Through comprehensive Monte Carlo simulations in a cubic 3D environment with eight beacons, our comparative analysis reveals that the ChOA achieves superior localization accuracy while maintaining computational efficiency. Building upon the ChOA framework, we introduce a multi-beacon fusion strategy incorporating a local outlier factor-based linear weighting mechanism to enhance robustness against measurement noise and improve localization accuracy. This approach integrates spatial density estimation with geometrically consistent weighting of distributed beacons, effectively filtering measurement outliers through adaptive sensor fusion. The experimental results show that the proposed algorithm exhibits excellent convergence performance under the condition of a low population size. Its anti-interference capability against Gaussian white noise is significantly improved compared with the baseline algorithms, and its anti-interference performance against multipath noise is consistent with that of the baseline algorithms. However, in terms of dealing with UWB device failures, the performance of the algorithm is slightly inferior. Meanwhile, the algorithm has relatively good time-lag performance and target-tracking performance. The study provides theoretical insights and practical guidelines for deploying reliable localization systems in complex indoor environments. Full article

The Ouarzazate Group in the Anti-Atlas Belt of southern Morocco, part of the West African Craton (WAC), is a significant Proterozoic lithostratigraphic unit formed during the late Ediacaran period. It includes extensive volcanic rocks associated with the early stages of Iapetus Ocean opening. Zircon U-Pb dating and geochemical analyses of the Oued Dar’a Caldera (ODC) volcanic succession in the Saghro Massif reveal two major eruptive cycles corresponding to the lower and upper Ouarzazate Group. The 1st cycle (588–563 Ma) includes pre- and syn-caldera volcanic succession characterized by basaltic andesite to rhyolitic rocks, formed in a volcanic arc setting through lithospheric mantle-derived mafic magmatism and crustal melting. A major caldera-forming eruption occurred approximately 571–562 Ma, with associated rhyolitic dyke swarms indicating a larger caldera extent than previously known. The 2nd cycle (561–543 Ma) features post-caldera bimodal volcanism, with tholeiitic basalts and intraplate felsic magmas, signaling a shift to continental flood basalts and silicic volcanic systems. The entire volcanic activity spans approximately 23–40 million years. This succession is linked to late Ediacaran intracontinental super-eruptions tied to orogenic collapse and continental extension, likely in association with the Central Iapetus Magmatic Province (CIMP), marking a significant transition in the geodynamic evolution of the WAC. Full article

Pseudomonas aeruginosa biofilm formation is critical to antibiotic resistance and persistence. Targeting cyclic di-GMP (c-di-GMP) signaling, a master biofilm formation and virulence regulator, presents a promising strategy to combat resistant bacterial infections. Myricetin, a natural polyphenolic flavonoid with documented antimicrobial and anti-biofilm activities, may enhance antibiotic efficacy against Pseudomonas aeruginosa. This study evaluated the synergistic effects of myricetin combined with azithromycin, ciprofloxacin, or cefdinir against both standard and drug-resistant Pseudomonas aeruginosa strains. Antibacterial activity, biofilm disruption, and motility inhibition were experimentally assessed, while molecular dynamic (MD) simulations elucidated myricetin’s molecular mechanism of action. Our results suggested that myricetin synergistically potentiated all three antibiotics, reducing c-di-GMP synthesis by 28% (azithromycin), 57% (ciprofloxacin), and 30% (cefdinir). It enhanced bactericidal effects, suppressed biofilm formation, and impaired swimming, swarming, and twitching motility. Computational analyses revealed that myricetin binds allosterically to FimX very well, a key regulator in the c-di-GMP signaling pathway. Hence, myricetin may act as a c-di-GMP inhibitor, reversing biofilm-mediated resistance in Pseudomonas aeruginosa and augmenting antibiotic efficacy. This integrated experimental and computational approach provides a framework for developing anti-virulence and antibiotic combination therapies against recalcitrant Gram-negative pathogens. Full article