Search Results (40,696)

This paper provides a solution to the new fractional-order Lorenz–Stenflo model using the adaptive predictor–corrector approach and the $\rho$-Laplace New Iterative Method ($L_{\rho }NIM$), representing an extensive comparison between both techniques with RK4 related to accuracy and error analysis. The results show that the suggested approaches allow one to be more accurate in analyzing the dynamics of the system. These techniques also produce results that are comparable to the results of other approximate techniques. The techniques can, thus, be used on a wider class of systems in order to provide more accurate results. These techniques also appropriately identify chaotic attractors in the system. These techniques can be applied to solve various numerical problems arising in science and engineering in the future. Full article

Recently, we developed a spatial phage-assisted continuous evolution (SPACE) system. This system utilizes chemotaxis coupled with the growth of motile bacteria during their spatial range expansion in soft agar to provide fresh host cells for iterative phage infection and selection pressure for preserving evolved genes of interest carried by phage mutants. Controllable mutagenesis activated only in a subpopulation of the migrating cells is essential in this system to efficiently generate mutated progeny phages from which desired individuals are selected during the directed evolution process. But, the widely adopted small molecule-dependent inducible system could hardly fulfill this purpose because it always affects all cells homogeneously. In this study, we developed a phage infection-induced gene expression system using modified Escherichia coli (E. coli) phage shock protein operon or sigma factors from Bacillus subtilis. Results showed that this system enabled efficient control of gene expression upon phage infection with dynamic output ranges from small to large using combinations of different engineered phages and corresponding promoters. This system was incorporated into SPACE to function as a phage infection-induced mutagenesis module and successfully facilitated the evolution of T7 RNA polymerase, which generated diverse mutants with altered promoter recognition specificity. We expect that phage infection-induced gene expression system could be further extended to more applications involving partial induction in a portion of a population and targeted induction in specific strains among a mixed bacterial community, which provides an important complement to small molecule-dependent inducible systems. Full article

Nowadays, traffic management challenges in the era of digital transport are rising, as the interactions of various stakeholders providing such technologies play a pivotal role in shaping traffic dynamics. The objective of this paper was to present a game-theory-based framework for modeling and optimizing urban traffic in road networks, considering the co-existence and interactions of different players composed of drivers of conventional vehicles, central governing authorities with traffic management capabilities, and competitive or cooperative connected mobility private service providers. The scope of this work was to explore and present the outcomes of diverse mixed equilibrium conditions in the road network of the city of Thessaloniki (Greece), integrating the principles of user equilibrium, system optimum, and Cournot oligopoly. The impacts of varying network attributes were systematically analyzed to provide quantitative indicators representing the overall network performance. Analysis of the results provided insights into the sensitivity and the resilience of the road network under various prevalence schemes of drivers of conventional vehicles, representing the user equilibrium characteristics, or drivers relying on traffic guidance provided by a central governing authority, representing the system optimum principles as well as the cooperation and competition schemes of private connected mobility providers with certain market shares in the network. Full article

This study presents an optimized system for ship route planning. Computational fluid dynamics simulations were used to modify Kwon’s semi-empirical speed loss estimation method, enabling efficient route planning under variable sea conditions. The study focused on improving the prediction of speed loss in irregular waves for container ships and further applying this to ship-optimized voyage planning. Dynamic programming was used for optimized voyage planning by modifying the ship course in response to meteorological data; this approach could balance both energy efficiency and safety. The modified speed loss predictions aligned closely with the simulation results, enhancing the reliability of weather routing decisions. Case studies for trans-Pacific and trans-Atlantic voyages demonstrated that the proposed system could significantly reduce the voyage time. These findings highlight the potential of real-time updates in voyage planning. The proposed system is a valuable tool for captains and fleet managers. The applicability of this system can be further broadened by validating it on different ship types. Full article

With the continuous progress of intelligent transportation systems and automated driving technologies, complex urban road environments put forward higher requirements on the real-time characteristic and accuracy of path planning algorithms. Traditional single-layer path planning methods struggle to effectively handle the complexity of road and lane networks, leading to high computational complexity and suboptimal planning outcomes. To address this issue, we propose a dual-layer path planning model. First, at the road level, we employ the A* algorithm to efficiently determine the optimal macroscopic route, reducing computational overhead. At the lane level, we introduce a Proportional–Integral–Derivative Q-network (PIDQN) based on deep reinforcement learning, which leverages PID control mechanisms to enhance lane selection accuracy and adaptability. By incorporating proportional, integral, and derivative control, PIDQN effectively handles dynamic environments and avoids local optima, ensuring stable and faster convergence. Compared with traditional Deep Q-Network (DQN) and Q-learning algorithms, PIDQN demonstrates significant improvements in success rate and convergence speed in path planning tasks. Using high-precision maps in real-world environments and Python for simulation experiments, we verify the superiority of this approach in complex urban road networks, and we compare the performance of traditional A* algorithms and two-layer planning algorithms. The results show that the two-layer planning algorithm outperforms the traditional A* algorithm and provides a more robust and efficient solution for self-driving car navigation. Full article

Human activity recognition (HAR) systems are essential in healthcare, surveillance, and sports analytics, enabling automated movement analysis. This research presents a novel HAR system combining graph structures with deep neural networks to capture both spatial and temporal patterns in activities. While CNN-based models excel at spatial feature extraction, they struggle with temporal dynamics, limiting their ability to classify complex actions. To address this, we applied the Firefly Optimization Algorithm to fine-tune the hyperparameters of both the graph-based model and a CNN baseline for comparison. The optimized graph-based system, evaluated on the UCF101 and Kinetics-400 datasets, achieved 88.9% accuracy with balanced precision, recall, and F1-scores, outperforming the baseline. It demonstrated robustness across diverse activities, including sports, household routines, and musical performances. This study highlights the potential of graph-based HAR systems for real-world applications, with future work focused on multi-modal data integration and improved handling of occlusions to enhance adaptability and performance. Full article

In many soil processes, including solute and gas dynamics, the architecture of intra-aggregate pores is a crucial component. Soil management practices and wetting-drying (W-D) cycles, the latter having a significant impact on pore aggregation, are two key factors that shape pore structure. This study examines the effects of W-D cycles on the architecture of intra-aggregate pores under three different soil management systems: no-tillage (NT), minimum tillage (MT), and conventional tillage (CT). The soil samples were subjected to 0 and 12 W-D cycles, and the resulting pore structures were scanned using X-ray micro-computed tomography, generating reconstructed 3D volumetric data. The data analyses were conducted in terms of multifractal spectra, normalized Shannon entropy, lacunarity, porosity, anisotropy, connectivity, and tortuosity. The multifractal parameters of capacity, correlation, and information dimensions showed mean values of approximately 2.77, 2.75, and 2.75 when considering the different management practices and W-D cycles; 3D lacunarity decreased mainly for the smallest boxes between 0 and 12 W-D cycles for CT and NT, with the opposite behavior for MT. The normalized 3D Shannon entropy showed differences of less than 2% before and after the W-D cycles for MT and NT, with differences of 5% for CT. The imaged porosity showed reductions of approximately 50% after 12 W-D cycles for CT and NT. Generally, the largest pores (>0.1 mm³) contributed the most to porosity for all management practices before and after W-D cycles. Anisotropy increased by 9% and 2% for MT and CT after the cycles and decreased by 23% for NT. Pore connectivity showed a downward trend after 12 W-D cycles for CT and NT. Regarding the pore shape, the greatest contribution to porosity and number of pores was due to triaxial-shaped pores for both 0 and 12 W-D cycles for all management practices. The results demonstrate that, within the resolution limits of the microtomography analysis, pore architecture remained resilient to changes, despite some observable trends in specific parameters. Full article

To improve the operational efficiency of electric vehicle (EV) charging infrastructure, this paper proposes a multi-stage hybrid planning method for charging stations (CSs) based on graph auto-encoder (GAE). First, the network topology and dynamic interaction process of the coupled “Vehicle-Station-Network” system are characterized as a graph-structured model. Second, in the first stage, a GAE-based deep neural network is used to learn the graph-structured model and identify and classify different charging station (CS) types for the network nodes of the coupled system topology. The candidate CS set is screened out, including fast-charging stations (FCSs), fast-medium-charging stations, medium-charging stations, and slow-charging stations. Then, in the second stage, the candidate CS set is re-optimized using a traditional swarm intelligence algorithm, considering the interests of multiple parties in CS construction. The optimal CS locations and charging pile configurations are determined. Finally, case studies are conducted within a practical traffic zone in Hong Kong, China. The existing CS planning methods rely on simulation topology, which makes it difficult to realize efficient collaboration of charging networks. However, the proposed scheme is based on the realistic geographical space and large-scale traffic topology. The scheme determines the station and pile configuration through multi-stage planning. With the help of an artificial intelligence (AI) algorithm, the user behavior characteristics are captured adaptively, and the distribution rule of established CSs is extracted to provide support for the planning of new CSs. The research results will help the power and transportation departments to reasonably plan charging facilities and promote the coordinated development of EV industry, energy, and transportation systems. Full article

The mechanical properties of faecal sludge (FS) influence its moisture retention characteristics to a greater extent than other properties. A comprehensive fundamental characterisation of the mechanical properties is scarcely discussed in the literature. This research focused on bulk and true densities, porosity, particle size distribution and zeta-potential, extracellular polymeric substances, rheology and dilatancy, microstructure analysis, and compactibility in the context of using the FS as a substitute for soil in land reclamation and bioremediation processes. FSs from different on-site sanitation systems were collected from around Durban, South Africa. The porosity of the FSs varied between 42% and 63%, with the zeta-potential being negative, below 10 mV. Over 95% of the particles were <1000 µm. With its presence in the inner part of the solid particles, tightly bound extra-cellular polymeric substances (TB-EPSs) influenced the stability of the sludge by tightly attaching to the cell walls, with the highest being in the septic tank with the greywater sample. More proteins than carbohydrates also confirmed characterised the anaerobic nature of the sludge. The results of the textural properties using a penetrometer showed that the initial slope of the positive part of the penetration curve was related to the stiffness of the sludge sample and similar to that of sewage sludge. The dynamic oscillatory measurements exhibited a firm gel-like behaviour with a linear viscoelastic behaviour of the sludges due to the change in EPSs because of anaerobicity. The high-TS samples exhibited the role of moisture as a lubricating agent on the motion of solid particles, leading to dilatancy with reduced moisture, where the yield stress was no longer associated with the viscous forces but with the frictional contacts of solid–solid particle interactions. The filtration–compression cell test showed good compactibility, but the presence of unbound moisture even at a high pressure of 300 kPa meant that not all unbound moisture was easily removable. The moisture retention behaviour of FS was influenced by its mechanical properties, and any interventional changes to these properties can result in the release of the bound moisture of FS. Full article

The increasing use of renewable energy sources, such as wind and solar energy, presents challenges to the stability and efficiency of other energy sources due to their intermittent and unpredictable surpluses. The unintended consequence of stabilizing the power supply system is an increase in emissions and external costs from the suboptimal use of coal power plants. The rising number of RES curtailments needs to be addressed by either the adjusting energy supply from fossil fuel or the flexible energy consumption. In Poland’s energy mix, coal-fired power plants are a critical component in ensuring energy security for the foreseeable future. Using domestic lignite to generate a total power of 8.5 GW can stabilize the national power supply, as it is currently done in Germany, where 15 GW of lignite-fueled power units provide the power supply base for the country. The leading Belchatów power plant comprises 10 retrofitted units and one new unit, with a total rating of 5.5 GW. Access to the new coal deposit, Zloczew, is necessary to ensure its longer operation. The other domestic lignite power plants are located in Central Poland at Patnów (0.47 GW from the new unit and 0.6 GW from its three retrofitted counterparts) and located in the Lusatian lignite basin at Turów (operating a brand new unit rated at 0.5 GW and retrofitted units with a total rating of 1.5 GW). The use of this fuel is currently being penalized as a result of increasing carbon costs. However, the continuous surface mining technology that is used in lignite mines is fully electrified, and large amounts of electric energy are required to remove and dump overburden and mining coal and its conveying to power units (the transport of coal from the new lignite mine Zloczew to the Belchatów power plant would be a long-distance operation). A possible solution to this problem is to focus on the lignite fuel supply operations of these power plants, with extensive simulations of the entire supply chain. A modern lignite mine is operated by one control room, and it can balance the dynamic consumption of surplus renewable energy sources (RESs) and reduce the need for reduction. When a lignite supply chain is operated this way, a high-capacity power bank can be created with energy storage in the form of an open brown coal seam. This would enable an almost emission-free supply of cheap and domestic fossil fuel, making it insensitive to changes in the world prices of energy resources for power units operating at the base of the system. Furthermore, extending the life of relatively new and efficient lignite-fired units in Poland would facilitate the decommissioning of older and exhausted hard coal-fired units. Full article

The non-Newtonian Carreau fluid model is a suitable model for pseudoplastic fluids and can be used to characterize fluids not so different from biological fluids, such as the blood, and fluids involved in geological processes, such as lava and magma. These fluids are frequently conveyed by complex flow structures, which consist of a network of channels that allow the fluid to flow from one place (source or sink) to a variety of locations or vice versa. These flow networks are not randomly arranged but show self-similarity at different spatial scales. Our work focuses on the design of self-similar branched flow networks that look the same on any scale. The flow is incompressible and stationary with a viscosity following the Carreau model, which is important for the study of complex flow systems. The flow division ratios, the flow resistances at different scales, and the geometric size ratios for maximum flow access are studied, based on Computational Fluid Dynamics (CFD). A special emphasis is placed on investigating the possible incidence of flow asymmetry in these symmetric networks. Our results show that asymmetries may occur for both Newtonian and non-Newtonian fluids and shear-thinning fluids most affect performance results. The lowest flow resistance occurs when the diameters of the parent and daughter ducts are equal, and the more uniform distribution of flow resistance occurs for a ratio between the diameters of the parent and daughter ducts equal to 0.75. Resistances for non-Newtonian fluids are 4.8 to 5.6 times greater than for Newtonian fluids at Reynolds numbers of 100 and 250, respectively. For the design of engineering systems and the assessment of biological systems, it is recommended that the findings presented are taken into account. Full article

As the antibiotic resistance of pathogens becomes increasingly severe, it is becoming more feasible to use methods that suppress the virulence of pathogens rather than exerting selective pressure on their growth. Pseudomonas aeruginosa, a dangerous opportunistic pathogen, infects hosts by producing multiple virulence factors, which are regulated by quorum-sensing (QS) systems, including the las systems, rhl systems, and pqs systems. This study used the chromosome lacZ transcription fusion reporter model to screen the traditional Chinese medicine monomer library and found that bakuchiol can effectively inhibit the pqs system and related virulence phenotypes of P. aeruginosa, including the production of virulence factors (pyocyanin, hydrogen cyanide, elastase, and lectin) and motility (swarming, swimming, and twitching motility) without affecting its growth. Subsequently, through genetic complementation analysis, we found that bakuchiol inhibited the function of the transcriptional activation protein PqsR of the pqs system in P. aeruginosa in a concentration-dependent manner. Furthermore, molecular dynamics simulation study results indicated that bakuchiol can target PqsR of the pqs system, thereby inhibiting the pqs system. Among the amino acids in PqsR, ALA-168 may be a key amino acid residue in the hydrophobic interaction between PqsR protein and bakuchiol. Finally, in vivo experiments demonstrated that bakuchiol attenuated the pathogenicity of P. aeruginosa to Chinese cabbage (Brassica pekinensis) and Caenorhabditis elegans. In summary, this study suggests that bakuchiol is an effective inhibitor that targets the pqs system of P. aeruginosa, providing a new strategy for addressing P. aeruginosa infections. Full article

We engaged with health sector stakeholders and public health professionals within the health system through a participatory modeling approach to support policy-making in the early COVID-19 pandemic in Saskatchewan, Canada. The objective was to use simulation modeling to guide the implementation of public health measures and short-term hospital capacity planning to mitigate the disease burden from March to June 2020. We developed a hybrid simulation model combining System Dynamics (SD), discrete-event simulation (DES), and agent-based modeling (ABM). SD models the population-level transmission of COVID-19, ABM simulates individual-level disease progression and contact tracing intervention, and DES captures COVID-19-related hospital patient flow. We examined the impact of mixed mitigation strategies—physical distancing, testing, conventional and digital contact tracing—on COVID-19 transmission and hospital capacity for a worst-case scenario. Modeling results showed that enhanced contact tracing with mass testing in the early pandemic could significantly reduce transmission, mortality, and the peak census of hospital beds and intensive care beds. Using a participatory modeling approach, we not only directly informed policy-making on contact tracing interventions and hospital surge capacity planning for COVID-19 but also helped validate the effectiveness of the interventions adopted by the provincial government. We conclude with a discussion on lessons learned and the novelty of our hybrid approach. Full article

In mission-critical environments such as industrial and military settings, the use of unmanned vehicles is on the rise. These scenarios typically involve a ground control system (GCS) and nodes such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). The GCS and nodes exchange different types of information, including control data that direct unmanned vehicle movements and sensor data that capture real-world environmental conditions. The GCS and nodes communicate wirelessly, leading to loss or delays in control and sensor data. Minimizing these issues is crucial to ensure nodes operate as intended over wireless links. In dynamic networks, distributed path calculation methods lead to increased network traffic, as each node independently exchanges control messages to discover new routes. This heightened traffic results in internal interference, causing communication delays and data loss. In contrast, software-defined networking (SDN) offers a centralized approach by calculating paths for all nodes from a single point, reducing network traffic. However, shifting from a distributed to a centralized approach with SDN does not inherently guarantee faster route creation. The speed of generating new routes remains independent of whether the approach is centralized, so SDN does not always lead to faster results. Therefore, a key challenge remains: determining how to create new routes as quickly as possible even within an SDN framework. This paper introduces a caching technique for forwarding rules based on predicted link states in SDN, which was named the CRIMSON (Cashing Routing Information in Mobile SDN Network) algorithm. The CRIMSON algorithm detects network link state changes caused by node mobility and caches new forwarding rules based on predicted topology changes. We validated that the CRIMSON algorithm consistently reduces end-to-end latency by an average of 88.96% and 59.49% compared to conventional reactive and proactive modes, respectively. Full article

An automated micro-tweezers system with a flexible workspace would benefit the intelligent sorting of live cells. Such micro-tweezers could employ a forced vortex strong enough to capture a single cell. Furthermore, addressable control of the position to the vortex would constitute a robotic system. In this study, a spherical micro-object composed of super paramagnetic particles tightly packed in a non-magnetic resin is rotated with a combined magnetic field of permanent magnets. The said magnetic field is articulated by an open-kinematic chain controlled with a simple adaptive PI-control scheme. A vortex is formed as the spherical particle, assumed to be submerged under the surface of fluid, and follows the position and orientation of the external magnetic field. This forced vortex induces a radial pressure gradient that captures the live cell orbiting around the spherical object combined with the inertial effects. Here, a comprehensive mathematical model is presented to reflect on the dynamics of such micro-tweezer systems. Numerical results demonstrate that it is theoretically possible to capture and tow a bacterium cell while meeting extreme tracking references for motion control. Magnetic and fluid forces on the spherical particle traverse the vortex and the bacterium cell, with orbiting and sporadic collusion of the bacterium cell around the spherical particle, and the positions of the end-effector, i.e., the magnets, are analyzed. Full article