Search Results (182)

We propose a simulation-based design for a flexible color filter (FCF) using a lithium niobate metamaterial (LNM) to investigate its color filtering potential. The FCF is composed of three periodically arranged half-ellipse LN arrays on a polydimethylsiloxane (PDMS) substrate, denoted as LNM-1, LNM-2, and LNM-3. The electromagnetic responses of the FCF can be controlled by adjusting the periods of the LNMs. Our simulations predict high-quality (Q) factors in transmission spectra, ranging from 100 to 200 for LNM-1, 290 to 360 for LNM-2, and 140 to 300 for LNM-3. When the FCF is exposed to the surrounding environments with different refractive indexes, it exhibits a theoretical figure of merit (FOM) up to 900 RIU⁻¹ and a sensitivity reaching 130 nm/RIU. The electromagnetic field distributions reveal strong confinement within the LNM nanostructures, confirming an efficient light–matter interaction. These results indicate that the proposed LNM-based FCF presents a promising design concept for high-performance color sensing and filtering applications. Full article

The inter-shaft bearing is an important component of aero-engine rotor systems. It works between a high-pressure rotor and a low-pressure rotor. Effective fault diagnosis of it is significant for an aero-engine. The casing vibration signals can promptly and intuitively reflect changes in the operational health status of an aero-engine’s support system. However, affected by a complex vibration transmission path and vibration of the dual-rotor, the intrinsic vibration information of the inter-shaft bearing is faced with strong noise and a dual-frequency excitation problem. This excitation is caused by the wide span of vibration source frequency distribution that results from the quite different rotational speeds of the high-pressure rotor and low-pressure rotor. Consequently, most existing fault diagnosis methods cannot effectively extract inter-shaft bearing characteristic frequency information from the casing signal. To solve this problem, this paper proposed the denoised improved envelope spectrum (DIES) method. First, an improved envelope spectrum generated by a spectrum subtraction method is proposed. This method is applied to solve the multi-source interference with wide-band distribution problem under dual-frequency excitation. Then, an improved adaptive-thresholding approach is subsequently applied to the resultant subtracted spectrum, so as to eliminate the influence of random noise in the spectrum. An experiment on a public run-to-failure bearing dataset validates that the proposed method can effectively extract an incipient bearing fault characteristic frequency (FCF) from strong background noise. Furthermore, the experiment on the inter-shaft bearing of an aero-engine test platform validates the effectiveness and superiority of the proposed DIES method. The experimental results demonstrate that this proposed method can clearly extract fault-related information from dual-frequency excitation interference. Even amid strong background noise, it precisely reveals the inter-shaft bearing’s fault-related spectral components. Full article

Real-time train rescheduling is a widely used strategy to minimize knock-on delays in railway networks. While recent research has introduced intelligent solutions to railway traffic management, the tight interdependence of train timetables and the intrinsic complexity of railway networks have hindered the scalability of these approaches to large-scale systems. This paper proposes a multi-agent system (MAS) that addresses these challenges by decomposing the network into single-junction levels, significantly reducing the search space for real-time rescheduling. The MAS employs a Condorcet voting-based collaborative approach to ensure global feasibility and prevent overly localized optimization by individual junction agents. This decentralized approach enhances both the quality and scalability of train rescheduling solutions. We tested the MAS on a railway network in the UK and compared its performance with the First-Come-First-Served (FCFS) and Timetable Order Enforced (TTOE) routing methods. The computational results show that the MAS significantly outperforms FCFS and TTOE in the tested scenarios, yielding up to a 34.11% increase in network capacity as measured by the defined objective function, thus improving network line capacity. Full article

Magnetic Resonance Imaging (MRI) services in high-complexity hospitals often suffer from operational inefficiencies, including suboptimal MRI machine utilization, prolonged patient waiting times, and inequitable service delivery across clinical priority levels. Addressing these challenges requires intelligent scheduling strategies capable of dynamically managing patient waitlists based on clinical urgency while optimizing resource allocation. In this study, we propose a novel framework that integrates a digital twin (DT) of the MRI operational environment with a reinforcement learning (RL) agent trained via Deep Q-Networks (DQN). The digital twin simulates realistic hospital dynamics using parameters extracted from a MRI publicly available dataset, modeling patient arrivals, examination durations, MRI machine reliability, and clinical priority stratifications. Our strategy learns policies that maximize MRI machine utilization, minimize average waiting times, and ensure fairness by prioritizing urgent cases in the patient waitlist. Our approach outperforms traditional baselines, achieving a 14.5% increase in MRI machine utilization, a 44.8% reduction in average patient waiting time, and substantial improvements in priority-weighted fairness compared to First-Come-First-Served (FCFS) and static priority heuristics. Our strategy is designed to support hospital deployment, offering scalability, adaptability to dynamic operational conditions, and seamless integration with existing healthcare information systems. By advancing the use of digital twins and reinforcement learning in healthcare operations, our work provides a promising pathway toward optimizing MRI services, improving patient satisfaction, and enhancing clinical outcomes in complex hospital environments. Full article

The increasing adoption of electric vehicles (EVs) is driving the need for more efficient, scalable, and flexible charging infrastructures. Among the most promising solutions are reconfigurable multi-point multi-power (MPMP) charging stations, which enable dynamic power allocation across multiple charging points operating at discrete power levels. This paper introduces a novel power management strategy for MPMP stations based on Pareto optimization, aiming to minimize the average charging time while ensuring fairness and efficiency. The method dynamically allocates power among charging points to minimize the average charging time across all connected EVs, while adhering to system constraints and the varying charging profiles required to preserve battery health. The proposed approach was validated through simulations in a dynamic scenario involving six EVs with heterogeneous battery capacities and charging profiles. Results demonstrated that the Pareto-based strategy achieved a significantly lower expected average charging time when compared to the first-come first-served strategy (FCFS). Full article

Recently, electromagnetic wireless power transfer (WPT) has emerged as a promising technology for supplying power to multiple terminals. Previous studies have devised packet transmission methods, commonly used in telecommunication, for power analysis. This study develops a simulator that calculates the received power by integrating a power–packet conversion method, based on previous research. The simulator incorporates several scheduling functions to facilitate the investigation of the efficiency of the power-feeding methods. This study analyzes the efficacy of a first-come–first-served (FCFS) method, a round-robin (RR) method, and a multilevel feedback queue (MFQ) scheme for wireless power transfer, all of which were devised based on existing scheduling methods used in operating systems. Simulation results show that, although the FCFS method is simple, it may lead to battery depletion due to delayed power supply, particularly in terminals with lower initial battery levels. The RR method improves fairness by allocating the power supply in time slices; however, its performance is sensitive to the slice duration. The MFQ method, which incorporates a promotion mechanism based on battery status and power demand, exhibits higher adaptability, achieving efficient and balanced power distribution even when terminals differ in distance from the transmitter or in power consumption. These evaluations were conducted using a proposed power–packet conversion method that discretizes continuous power into packet units, allowing for the application of communication network-inspired scheduling and control techniques. The capacity to construct such models enables the simulator to analyze the flow and distribution of power, predict potential issues that may arise in real systems in advance, and devise optimal control methodologies. Moreover, the model can be employed to enhance the efficiency of power management systems and construct smart grids, and it is anticipated to be utilized for the integration of power and communication systems. Full article

The study encompasses the application of two different advanced optimization algorithms on asset management and predictive maintenance in Industry 4.0—Grey Wolf Optimization and Jaya-based Sea Lion Optimization (J-SLnO). Using this derivative, the authors showed how these techniques could be combined through resource scheduling techniques to demonstrate drastic improvement in the level of efficiency, cost-effectiveness, and energy consumption, as opposed to the standard MinMin, MaxMin, FCFS, and Round Robin. In this sense, GWO results in an execution time reduction between 13 and 31%, whereas, in J-SLnO, there is an execution time reduction of 16–33%. In terms of cost, GWO shows an advantage of 8.57–9.17% over MaxMin and Round Robin, based on costs, while J-SLnO delivers a better economy for the range of savings achieved, which is between 13.56 and 19.71%. Both algorithms demonstrated tremendous energy efficiency, according to the analysis, which showed 94.1–94.2% less consumption of energy than traditional methods. Moreover, J-SLnO was reported to be more accurate and stable in predictability, making it an excellent choice for accurate and more time-trusted applications. J-SLnO is being increasingly recognized as a powerful yet realistic solution for the application of Industry 4.0 because of efficacy and reliability in predictive modeling. Not only does this research validate these optimization techniques to better use in practical life, but it also extends recommendations for putting the techniques into practice in industrial settings, thus laying the foundation for smarter, more efficient asset management and maintenance processes. Full article

Efficient ship scheduling in inland waterways is critical for maritime transportation safety and economic viability. However, traditional scheduling methods, primarily based on First Come First Served (FCFS) principles, often produce suboptimal results due to their inability to account for complex spatial–temporal dependencies, directional asymmetries, and varying ship characteristics. This paper introduces SSRL (Ship Scheduling through Reinforcement Learning), a novel framework that addresses these limitations by integrating three complementary components: (1) a Q-learning framework that discovers optimal scheduling policies through environmental interaction rather than predefined rules; (2) a clustering mechanism that reduces the high-dimensional state space by grouping similar ship states; and (3) a sliding window approach that decomposes the scheduling problem into manageable subproblems, enabling real-time decision-making. We evaluated SSRL through extensive experiments using both simulated scenarios and real-world data from the Xiaziliang Restricted Waterway in China. Results demonstrate that SSRL reduces total ship waiting time by 90.6% compared with TSRS, 48.4% compared with FAHP-ES, and 32.6% compared with OSS-SW, with an average reduction of 57.2% across these baseline methods. SSRL maintains superior performance across varying traffic densities and uncertainty conditions, with the optimal information window length of 13–14 ships providing the best balance between solution quality and computational efficiency. Beyond performance improvements, SSRL offers significant practical advantages: it requires minimal computation for online implementation, adapts to dynamic maritime environments without manual reconfiguration, and can potentially be extended to other complex transportation scheduling domains. Full article

Pasteurella multocida is a zoonotic pathogen responsible for severe diseases in domestic and wild animals, posing threats to public health and causing substantial economic losses. Here, we describe a naturally attenuated P. multocida strain, FCF147, isolated from a mortality event involving black-necked swans (Cygnus melancoryphus) in a wildlife habitat in Fujian, China. Genomic and phylogenetic analyses revealed that FCF147 is evolutionarily distant from other P. multocida lineages and lacks the entire capsule gene cluster. Morphological observations revealed that the loss of the capsule exposed proteins on the bacterial surface. Phenotypic characterization demonstrated reduced capsule production, enhanced biofilm formation, and increased tolerance to heat stress. In vivo infection models confirmed that FCF147 exhibits markedly attenuated virulence in both mice and poultry. However, immunization with FCF147 did not provide effective protection against the challenge of a virulent capsular type A strain. These findings suggest that while FCF147 is poorly virulent, its ability to form robust biofilms and survive thermal stress may facilitate persistence in wild bird reservoirs and potential transmission routes. These findings offer novel insights into the ecological adaptation and pathogenic potential of naturally capsule-deficient P. multocida in wildlife, highlighting their relevance to wildlife surveillance and disease ecology. Full article

Effective laser cooling schemes are fundamental for preparing ultracold triatomic molecules. Here, efficient laser cooling strategies for alkaline-earth hydroxides (XOH, X = Ca, Sr, Ba) are proposed using high-precision quantum calculations. By mapping Λ-S- and Ω-state potential energy surfaces, we identified quasi-closed optical cycles with dominant Franck–Condon factors (FCFs) and strong transition dipoles. The scheme utilizes targeted repumping to suppress vibrational leaks, enabling >10⁴ photon scatters per molecule, exceeding Doppler cooling requirements. These results establish XOH molecules, particularly BaOH, as viable candidates for laser cooling experiments, providing key theoretical insights for ultracold triatomic molecule production. Full article

Essential oils (EOs) of three native species Baccharis uncinella, B. retusa and B. calvescens, obtained through hydrodistillation, were analyzed by GC-MS and GC-FID for seasonality, and the antimicrobial and anti-proliferative activities were evaluated. EO of B. calvescens and B. uncinella consisted mainly of oxygenated sesquiterpenes, while in the EO of B. retusa, monoterpene hydrocarbons were predominant. The highest antimicrobial activity was observed for spring B. uncinella EO against S. aureus, C. albicans and summer B. uncinella EO against C. albicans and B. subtilis. Essential oils of B. calvescens showed more effective anti-proliferative activity than B. retusa EO and B. uncinella EO. This is the first study of the EO of B. retusa, and it was demonstrated that the majority composition was different in all seasons of the year, justifying the importance of the seasonal study. Furthermore, the summer and spring EO showed potent cytostatic effects against the K562 and OVCAR-03 cell lines, respectively. For each species, PCA differentiated the EO chemical composition seasonally. PCA of all samples distinguished the three species. This study underscores the importance of assessing seasonal variation in the chemical composition and biological activities of essential oils, highlighting the potential of compounds spathulenol, caryophyllene oxide, limonene and α-pinene for achieving the desired product properties. Full article

The processing of Kappaphycus alvarezii algae to obtain carrageenan (polysaccharide) generates a residue composed mainly of glucans and galactans that can be converted to monosaccharides, making these algae a renewable feedstock that can be used to produce biofuels. This residue was subjected to batch enzyme hydrolysis with different commercial enzymatic cocktails, achieving, after 72 h of reaction time, a complete conversion of glucan to glucose for all the cocktails used. A simple mathematical model, based on a semi-empirical approach, was proposed to describe the behavior of the experimental data. The temporal profile of glucose concentration was obtained by direct analytical integration of the mathematical model, resulting in an explicit equation as a time function. Estimation of the model parameters was carried out by non-linear regression, using the least squares criterion, together with the Levenberg–Marquardt method. The quality of the model fit was evaluated by specific statistical criteria, including Fisher’s F test, the R² value, and the p-value test. The accuracy of the model was considered acceptable (p-value < 0.05 and R² ≥ 0.98), enabling its use in subsequent studies aimed at improving the enzymatic hydrolysis process under similar experimental conditions. Full article

The application of connected automated vehicles (CAVs) provides new opportunities and challenges for optimizing and controlling urban intersections. To avoid collisions of vehicles in conflicting directions at intersections and improve the efficiency of intersections, an optimal scheduling model for CAVs at an unsignalized intersection is proposed. The model develops a linear programming model of intersection vehicle timing with the minimum average vehicle delay within the optimization time window as the optimization objective and the minimum safe time interval for vehicles to pass through the intersection as the constraint. A rolling optimization algorithm is designed to improve the efficiency of the algorithm solution. Finally, the effects of different traffic demand conditions on the results are investigated based on numerical simulation experiments. The results show that both the proposed algorithm and the Gurobi solver can significantly reduce the average vehicle delay compared with the first-come-first-served (FCFS) control method, and the proposed model and algorithm can reduce the average vehicle delay by 76.22% at most. Compared with the Gurobi solver, the proposed model and algorithm can reduce the solution time and ensure the optimization effect to the greatest extent. Therefore, the proposed model and algorithm provide theoretical support for managing CAVs at unsignalized intersections. Full article

Forecast risk management is central to the financial management process. This study aims to apply Monte Carlo simulation to solve three classic probabilistic paradoxes and discuss their implementation in corporate financial management. The article presents Monte Carlo simulation as an advanced tool for risk management in financial management processes. This method allows for a comprehensive risk analysis of financial forecasts, making it possible to assess potential errors in cash flow forecasts and predict the value of corporate treasury growth under various future scenarios. In the investment decision-making process, Monte Carlo simulation supports the evaluation of the effectiveness of financial projects by calculating the expected net value and identifying the risks associated with investments, allowing more informed decisions to be made in project implementation. The method is used in reducing cash flow volatility, which contributes to lowering the cost of capital and increasing the value of a company. Simulation also enables more accurate liquidity planning, including forecasting cash availability and determining appropriate financial reserves based on probability distributions. Monte Carlo also supports the management of credit and interest rate risk, enabling the simulation of the impact of various economic scenarios on a company’s financial obligations. In the context of strategic planning, the method is an extension of decision tree analysis, where subsequent decisions are made based on the results of earlier ones. Creating probabilistic models based on Monte Carlo simulations makes it possible to take into account random variables and their impact on key financial management indicators, such as free cash flow (FCF). Compared to traditional methods, Monte Carlo simulation offers a more detailed and precise approach to risk analysis and decision-making, providing companies with vital information for financial management under uncertainty. This article emphasizes that the use of Monte Carlo simulation in financial management not only enhances the effectiveness of risk management, but also supports the long-term growth of corporate value. The entire process of financial management is able to move into the future based on predicting future free cash flows discounted at the cost of capital. We used both numerical and analytical methods to solve veridical paradoxes. Veridical paradoxes are a type of paradox in which the result of the analysis is counterintuitive, but turns out to be true after careful examination. This means that although the initial reasoning may lead to a wrong conclusion, a correct mathematical or logical analysis confirms the correctness of the results. An example is Monty Hall’s problem, where the intuitive answer suggests an equal probability of success, while probabilistic analysis shows that changing the decision increases the chances of winning. We used Monte Carlo simulation as the numerical method. The following analytical methods were used: conditional probability, Bayes’ rule and Bayes’ rule with multiple conditions. We solved truth-type paradoxes and discovered why the Monty Hall problem was so widely discussed in the 1990s. We differentiated Monty Hall problems using different numbers of doors and prizes. Full article

Due to external environmental factors, the layout of compound channels in estuarine ports is restricted. Moreover, with the opposing distribution of anchorages and terminals within the port, vessels navigating between these areas must cross the channel, severely affecting channel navigation safety and efficiency. In order to improve the efficiency of vessel scheduling, we analyze the layout characteristics of an estuarine port and its compound channel, summarize vessel navigation modes and traffic flow conflicts, and identify five key conflict areas. On this basis, we develop a multi-objective optimization model aimed at minimizing vessel waiting times and the total channel occupancy time ratio. This model incorporates constraints such as navigation rules, traffic flow conflicts, tidal effects, and traffic control. To solve the model, we propose an adaptive non-dominated sorting genetic algorithm, ANSGA-NS-SA, which integrates neighborhood search (NS) and Simulated Annealing (SA). The entropy-weighted technique for order preference by similarity to ideal solution (TOPSIS) is used to calculate the objective weights of the Pareto frontier and identify the optimal solution. Experimental results show that the proposed model and algorithm yield optimal port entry and exit scheduling solutions. In terms of port scheduling performance, the proposed model and algorithm outperform the traditional First-Come-First-Served (FCFS) strategy and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), reducing total vessel waiting time by 33.8% and improving total channel occupancy ratio by 8.8%. This study provides a practical and effective decision support tool for estuarine port scheduling, enhancing overall port operational efficiency. Full article