Search Results (893)

This paper develops a reliability analysis model for a class of computer systems composed of hardware and software in series, considering a repairman taking multiple vacations. The system follows a series failure rule: hardware can be repaired to be as good as new after failure; software undergoes minor repairs to maintain operability after the first $N-1$ failures with an increasing failure rate, and is overhauled to be as good as new with cycle reset after the N-th failure. Based on the principle of probability conservation and the supplementary variable method, the state probability evolution equations of the system are derived. A Banach space is constructed, and a linear operator is defined, whose denseness, dissipativity, and closedness are verified. It has been proven that the operator generates a positive contractive $C_0$-semigroup, thus rigorously establishing the well-posedness of the model and the existence of a unique positive dynamic solution. Further spectral analysis verifies that zero belongs to the continuous spectrum rather than the point spectrum of the system operator.This indicates that the investigated system admits no time-invariant constant steady-state probability distribution,and only presents slowly decaying quasi-stationary dynamic behavior. The results can provide theoretical support for the reliability design and maintenance strategy optimization of hardware–software series repairable systems. Full article

Amid escalating global crises, tourism sustainability is threatened by heightened industry vulnerability, yet the intrinsic coupling of tourism industry vulnerability (TIV) and resilience (TIR) remains underexplored via systemic theoretical frameworks. This study aimed to define TIV/TIR as industry-specific constructs and develop an integrated analytical model grounded in dissipative structure theory to characterize tourism systems’ crisis responses. We selected Southwest China’s ethnic minority regions (Guizhou, Guangxi, Yunnan) as cases, using 2015–2024 prefecture-level panel data to explores the spatio-temporal differentiation characteristics of TIV/TIR. Results revealed severe COVID-19-induced TIV surges in 2020–2021, followed by rapid TIR rebounds; TIV and TIR exhibited a significant negative correlation with regional heterogeneity. Most cities showed high TIV–low TIR, with Guizhou displaying negative TIV-TIR spatial autocorrelation and Guangxi–Yunnan showing TIR clustering; inter-city TIV disparities widened while TIR levels converged, leading to a low-vulnerability, balanced-resilience tourism system by 2024. This research introduces the novel sensitivity-adaptive capacity-recovery (SACR) framework, advancing understanding of TIV-TIR dynamics and providing targeted empirical insights for tourism resilience building and sustainable development in resource-dependent destinations. Full article

We introduce a minimal relational network model in which superconducting-like behavior emerges as a collective phase of constrained connectivity and phase coherence, without assuming microscopic electrons, phonons, or material-specific interactions. The model is formulated as a concrete instantiation of a previously introduced axiomatic relational–informational framework for emergent geometry and effective spacetime, in which geometry and effective forces arise from constrained information flow rather than from a background manifold. Mathematically, this construction is realized on a finite weighted graph with binary edge-activation variables and compact vertex phase variables, sampled through a Gibbs ensemble generated by an additive informational action. The system is represented as a finite weighted graph with weighted edges encoding transport or informational costs, augmented by dynamically activated low-cost channels and compact phase degrees of freedom defined at vertices. The effective edge costs induce a weighted shortest-path metric, providing an operational notion of emergent relational geometry. Using Monte Carlo simulations on two-dimensional periodic lattices, we show that the same informational action supports three distinct emergent regimes: a normal resistive phase, a fragile low-temperature–like superconducting phase characterized by noise-sensitive coherence, and a noise-robust high-temperature–like superconducting phase in which global phase coherence persists under substantial fluctuations. These regimes are identified using purely relational observables with direct graph-theoretic and statistical-mechanical interpretation, including percolation of low-cost channels, phase correlation functions, an operational phase stiffness (helicity modulus), and a geometric diagnostic based on relational ball growth. In particular, we extract an effective geometric dimension from the scaling of low-cost accessibility balls, using a ball-growth relation of the form $⟨B\left(r\right)⟩~r^{d_{eff}}$, revealing a clear monotonic hierarchy between normal, fragile superconducting, and noise-robust superconducting—like regimes. This demonstrates that superconducting-like behaviour in the present framework corresponds not only to percolation and phase alignment, but also to a qualitative reorganization of relational geometry. Robustness is tested via finite-size comparison between 8 × 8, 12 × 12 and 16 × 16 lattice realizations. Within this framework, normal and superconducting-like behavior arise from the same underlying relational mechanism and differ only in the structural stability of connectivity, coherence, and geometric accessibility under fluctuations. The aim of this work is structural rather than material-specific: we do not reproduce detailed experimental phase diagrams or microscopic pairing mechanisms, but identify minimal relational conditions under which low-dissipation, phase-coherent transport can emerge as a generic organizational regime of constrained relational systems. Full article

Dampers are key energy-dissipating components in structural seismic systems. They can effectively dissipate seismic energy, control structural dynamic responses, and mitigate damage to primary structural members. Thus, they play an important role in improving structural seismic resilience and mitigating seismic hazards. By integrating multiple units with different yield thresholds or energy-dissipating mechanisms, multi-stage energy-dissipating dampers realize sequentially activated energy dissipation under varying seismic intensities and spectral characteristics. They broaden the energy dissipation range under varying seismic intensities and enhance cyclic stability and fatigue resistance. They provide an effective technical approach to overcome the inherent limitations of traditional single-stage dampers, such as insufficient energy dissipation capacity and poor cyclic fatigue performance. This study systematically reviews the recent research progress on multi-stage energy-dissipating dampers, focusing on the structural configurations and seismic performance studies of four typical types: stage-yielding metallic dampers, stage-friction dampers, metal-friction hybrid dampers, and metal-viscoelastic hybrid dampers. Relevant numerical simulation and experimental research results are summarized, and the key issues that require further in-depth exploration in this field are prospected. Full article

As mining operations progressively advance to greater depths to meet increasing mineral demand, there is a growing need to develop new or improved rockbolts capable of effectively dissipating energy under dynamic loading conditions. Impact laboratory tests provide valuable insights into the dynamic performance of rockbolts; however, such tests require considerable time and cost associated with specimen preparation and experimental validation. Numerical modeling represents a robust alternative which, when properly calibrated with laboratory results, can accurately simulate the deformation process and energy dissipation mechanisms of support elements. This paper presents the implementation and results of a numerical model developed to simulate the dynamic behavior of a threadbar subjected to impact loading. The model explicitly represents all components of a full-scale impact test configuration, including the impact mass, reaction frame, threadbar geometry, grout, and steel tube. The numerical model enables real-time analysis of the dynamic response and interaction among the test components (steel tube, grout, and bolt). The implemented numerical codes were calibrated and validated against published laboratory results of threadbar dynamic behavior. Subsequently, a comprehensive parametric analysis was conducted to evaluate the response of each component in terms of load, displacement, and dissipated energy. The results allowed identification of the primary factors governing the dynamic response of the rockbolt system. The proposed methodology can be extended to other reinforcement systems and provides relevant insights into the design of bolts under dynamic loading conditions. Full article

Flapping-wing drones (FWDs), owing to their compact size and operation in cluttered and unsteady airflow environments, encounter significant aerodynamic and stability challenges. Studies of avian flight reveal that falcons and other raptors actively deflect their covert feathers to mitigate gusts and maintain stable flight. Drawing inspiration from this mechanism, this study presents a peregrine falcon-inspired Active Flow Control Unit (AFCU) integrated with a Deep Deterministic Policy Gradient (DDPG)-based deep reinforcement learning (DRL) controller for real-time disturbance attenuation. The AFCU employs mechanical covert feathers (MCFs) that actuate to dissipate gust loads during high wind conditions. A reduced-order bond graph model that encapsulates the nonlinear interaction between the primary wing and the feather-based active flow control surfaces is created which is used as a dynamic training environment for the DDPG agent. Utilizing closed-loop interactions, the successfully obtained learned policy produces optimal actuator forces to reduce feather-displacement error and aerodynamic load variations. The designed controller stabilizes the internally unstable open-loop AFCU, attaining near-zero steady-state error and settling times under 1.6 s for gust magnitudes ranging from 12.5 to 20 m/s. Simulations further illustrate a reduction of up to 50% in gust-induced loads compared to traditional approaches. This integration of bio-inspired design with learning-based active flow control offers a viable avenue for the development of highly adaptive and gust-resilient flapping-wing aerial systems. Full article

Deep-sea mining systems are a critical pathway for acquiring key strategic resources such as nickel and cobalt. The core conveying component, the mining rigid pipe, is susceptible to transverse vibrations under complex wave excitation, which threaten system safety, necessitating the development of efficient and reliable vibration control solutions. This paper proposes an improved Triple-spring nonlinear energy sink (TS-NES). An integrated dynamic model coupling the mining rigid pipe and the TS-NES is established using the vector form intrinsic finite element method and solved via the central difference method. The effectiveness and superiority of the TS-NES are verified through displacement, time–frequency, energy, and phase analyses. Subsequently, a systematic parameter sensitivity study is conducted. The results indicate that under both single-frequency and multi-frequency wave excitations, the TS-NES exhibits broadband, high-efficiency vibration suppression performance superior to that of the conventional tuned mass damper (TMD). It can substantially and uniformly dissipate vibration energy and maintain an approximately 90° phase lag with the primary structure. Parameter studies reveal that installing the TS-NES in the upper section of the pipe yields significant vibration reduction. The device is insensitive to stiffness variations, and appropriately increasing its mass, damping, and inclination angle can further enhance the vibration suppression effect. Full article

The dynamic response of ancient multi-drum columns, commonly found in historical monuments, is characterized by complex nonlinear mechanisms including rocking, sliding, and wobbling. Unlike modern monolithic columns, these structures consist of large, unbonded stone drums that rotate and interact dynamically during ground motion, resulting in highly nonlinear behavior due to intermittent impacts and evolving contact surfaces. The objective of this study is to evaluate the influence of the friction coefficient at the interfaces on the dynamic response of multi-drum columns. Two structural configurations are considered: (i) simple free-standing multi-drum columns, and (ii) multi-drum columns connected with iron dowels, replicating ancient Greek construction techniques. The columns analyzed are representative of the colonnade system of the Gymnasium of Ancient Messene, Greece. Sinusoidal base excitations with varying characteristics are applied, and parametric study is conducted by varying the interfacial friction coefficient. The results indicate that in the first configuration, low friction promotes interfacial sliding, leading to enhanced energy dissipation, a softened rocking response, and a reduced overturning frequency range. In the second configuration, variations in friction have a limited effect on the collapse frequency range, because at lower friction levels strong excitations lead to dowel reinsertion failure over a wide frequency range. Full article

Due to advancements in thermal engineering and nanotechnology, nanofluids—base fluids containing dispersed nanoparticles (1–100 nm)—have emerged as promising high-performance coolants. Their enhanced thermal properties make them attractive for application in hybrid-electric aircraft, which require efficient Thermal Management Systems (TMS) to dissipate significant heat loads. This study employs a dynamic TMS model to assess the influence of key nanofluid features, including nanoparticle type, volume fraction, particle diameter, and base fluid. Metal nanoparticles provided the greatest thermal improvement (up to 19%). Increasing concentration enhanced cooling efficiency, with 0.5%, 1%, and 2% volume fractions reducing mean temperature by 14%, 19%, and 24%, respectively. Smaller particles performed better, as 20 nm nanoparticles achieved a 21.3% temperature reduction compared to 17.5% for 60 nm. Water-based nanofluids exhibited the best overall thermal behaviour, although they remain unsuitable for aeronautical applications. Full article

Microfluidic electrokinetic flows play a central role in applications such as lab-on-a-chip diagnostics, microelectronics cooling, and biomedical sample manipulation. These systems involve intricate heat transfer processes, including Joule heating from ionic currents, temperature-driven flow instabilities, and coupled thermal–fluid interactions, that crucially affect device performance, reliability, and scalability. Current challenges include non-equilibrium charge dynamics, incomplete thermophysical property data for complex fluids, and thermal crosstalk in integrated platforms. This review summarizes the literature published over the past 20 years, with occasional reference to earlier work, covering the fundamental mechanisms of heat generation and dissipation in electrokinetic microflows; analytical, numerical, and experimental approaches for characterizing thermal effects; and discussion on the limitations and application-driven opportunities. It also highlights open questions and future research directions and offers a comprehensive view of design principles and guidelines for developing robust, thermally optimized electrokinetic microfluidic technologies. Full article

The mechanism by which fish enhance hydrodynamic performance through collective swimming is a research hotspot in the field of underwater bionic robots. This study employs the Immersed Boundary-Lattice Boltzmann Method (IB-LBM) to conduct numerical simulations on a two-dimensional, single-degree-of-freedom (1-DOF) autonomous propulsion bionic fish swarm. It systematically investigates the effects of swarm size and inter-individual spacing on swimming speed and cost of transport (CoT) under two typical configurations: series and parallel arrangements. Findings reveal that hydrodynamic benefits are highly dependent on the spatiotemporal evolution of flow field structures. In the series configuration, an optimal spacing range of 1.5 L to 2.0 L exists within the school, where the “wake capture” effect is pronounced. Trailing fish achieve a maximum speed increase of approximately 41.1% while significantly reducing energy consumption. However, as spacing increases to 2.5 L, the cooperative gain for front and middle-row individuals rapidly diminishes, and the lead fish even experiences significant performance loss. Uniquely, the trailing fish in the four-fish formation exhibits distinct flow field reorganization and performance recovery at the 4.5 L trailing position. In the parallel formation, the “channel effect” and “blocking effect” of the fluid dominate. The study identifies 0.4 L laterally as the critical instability spacing under the investigated kinematic regime, where strong destructive interference causes a sharp deterioration in individual swimming performance. Additionally, the parallel formation exhibits pronounced positional differentiation. Central individuals, constrained by dual lateral flow fields, experience restricted lateral wake expansion and accelerated energy dissipation, resulting in significantly weaker escape capabilities from low-speed conditions compared to marginal individuals. The vortex-dynamic mechanism revealed herein provides theoretical foundations for formation control in multi-fish biomimetic cooperative systems. Full article

Energy-efficient routing is critical for extending the operational lifespan of wireless sensor networks (WSNs). While the Power-Efficient Gathering in Sensor Information Systems (PEGASIS) protocol achieves high efficiency through chain-based data aggregation, its standard round-robin leader selection fails to account for dynamic node factors, such as residual energy and historical reliability. This often leads to premature energy depletion and network instability. To address these limitations, this paper proposes K-NN-PEGASIS, a data-driven machine learning framework that utilises a weighted k-nearest neighbours (K-NN) algorithm for intelligent leader selection. By processing a normalised feature vector comprising residual energy, distance to the base station (BS), node degree, and historical performance, the framework adaptively identifies optimal leaders in each round. Simulations conducted in MATLAB for networks ranging from 100 to 1000 nodes demonstrate that K-NN-PEGASIS improves network lifetime by up to 47.3% and reduces total energy dissipation by 52.8% compared to baseline algorithms. Furthermore, the framework provides passive resilience against routing attacks, reducing the selection of malicious leaders by 96% and maintaining a 32.3% higher packet delivery ratio under attack scenarios. Full article

High-intensity conditioning (HIC) is a common pretreatment process for enhancing the flotation of fine-grained minerals. This study introduces fractal theory into the structural design of pulp conditioning impellers. A fractal blade with multi-scale fractal edge features was proposed, and its separation performance was evaluated in a fine-grained malachite (−20 μm) and quartz flotation system. Computational fluid dynamics simulation revealed that the fractal blade altered the energy dissipation pattern. Compared with conventional rectangular blades, it induced stronger fluid compression and collision effects in localized regions. These hydrodynamic changes improved the suspension homogeneity and dispersion efficiency of fine-grained malachite. Furthermore, the fractal blade reduced the scale of turbulent vortices while increasing local turbulent kinetic energy and shear rates. This optimized turbulent flow field effectively reduced mass-transfer resistance and promoted interfacial interactions between flotation reagents and mineral particles. Adsorption experiments and optical microscopy indicated that after conditioning at 1500 rpm for 3 min, the fractal blade increased sodium oleate adsorption on malachite compared to the conventional blade. This enhanced adsorption promoted the aggregation of fine-grained malachite, increasing its aggregate size by 15.52%, while no significant aggregation was observed for quartz particles. Consequently, the single mineral flotation recovery of fine-grained malachite increased by 4.13%. For artificial mixed minerals, the copper concentrate grade and recovery were improved by 2.28% and 1.04%, respectively. This study provides a theoretical basis for equipment optimization and structural innovation design in HIC processes. Full article

Precise manipulation of two-phase flow in micro-confined electrowetting pixels is limited by contact angle hysteresis (CAH). To elucidate this non-equilibrium process, we establish a high-fidelity electrohydrodynamic (EHD) phase-field simulation framework. The model rigorously couples Navier–Stokes equations with molecular kinetic theory (MKT) to characterize energy dissipation at the three-phase contact line (TCL) and further integrates charge transport kinetics. Numerical results reveal CAH is driven by physical pinning and interfacial charge trapping, with the latter dominating interfacial retreat and causing significant residual displacement. Furthermore, analysis shows alternating current (AC) waveforms mitigate charge accumulation and promote depinning via micro-oscillations, minimizing the hysteresis loop compared to direct current (DC) waveforms. Additionally, an overdrive strategy utilizing a suprathreshold Maxwell stress pulse rapidly overcomes static friction. This strategy significantly improves transient dynamics, substantially reducing the time to reach 90% of the steady-state target from 19.6 ms (under standard DC waveform driving) to 7.4 ms. This work provides a comprehensive theoretical basis and design criteria for optimizing active driving strategies in optofluidic and digital microfluidic systems. Full article

Seismic design methods often involve high construction costs and may lead to severe structural damage during strong earthquakes. Energy dissipation technology represents an efficient approach to improving seismic performance through the integration of devices that absorb and dissipate induced seismic energy. This study investigates the seismic behavior of a five-story mixed-use hotel building with and without viscous fluid dampers through advanced numerical modeling using ETABS software, applying static, dynamic, and time-history analyses and considering representative seismic records from Ica, Peru. The research follows an applied and quantitative approach, in which two structural configurations were modeled to evaluate the efficiency of energy dissipation systems in mitigating seismic effects. The results demonstrate that the incorporation of viscous fluid dampers reduced maximum displacements by 51.12% and interstory drifts by 52.82% along the X–X axis, while absorbing approximately 74% of the induced seismic energy. All structural responses remained within safe performance limits. The findings confirm that viscous dampers substantially enhance structural seismic performance by increasing safety and functionality, and they validate their applicability as an efficient and reliable alternative for mid-rise buildings located in high-seismicity regions. Full article