Search Results (274)

Underwater wireless sensor networks (UWSNs) support critical applications such as environmental monitoring, offshore exploration, and surveillance; however, their performance is constrained by high propagation delay, limited energy resources, and node mobility caused by ocean dynamics. Many clustering approaches assume static nodes and use fixed-weight objective aggregation, which may reduce adaptability and lead to premature convergence. This paper proposes a cluster-head selection and cluster formation method for UWSNs based on a binary multi-objective Dragonfly Algorithm (BMDA-UWSN). The method considers energy consumption, acoustic latency, and load balance within a Pareto-based optimization framework, thereby reducing dependence on fixed-weight aggregation during the search stage. In addition, the Dragonfly-based optimization process uses dynamically adjusted coefficients to regulate the balance between exploration and exploitation while preserving solution diversity. To represent underwater node displacement, a semicircular mobility model with angular variation of ±45° is incorporated into the simulation scenario. Results obtained for a 100-node network show that BMDA-UWSN achieved better performance than Direct Transmission, LEACH, LEACH-C, SS-GSO, and CDFO-UWSN in terms of network lifetime, packet delivery, latency, and residual energy under the evaluated conditions. In particular, the first node dies at iteration 126 with BMDA-UWSN, compared with iteration 95 for CDFO-UWSN, while packet delivery increases by approximately 20% and latency decreases by about 5%. These findings suggest that BMDA-UWSN is a competitive clustering approach for underwater monitoring scenarios when evaluated under controlled node mobility conditions. Full article

Most of the dam structures around the world are approaching the end of their economic life of 50 to 70 years, especially due to sediment accumulation in reservoir areas. This situation necessitates the development of proactive infrastructure management strategies. This study presents an original framework for the process of renewal of aging dams that blends remote sensing techniques and meta-intuitive optimization methods. Within the scope of the study, the Hasanlar Dam located in Düzce was selected as a sample, and a new dam axis was determined in the upper part of the basin. A detailed volume–height curve was created using 12.5 m resolution ALOS PALSAR numerical height models (DEM) and GIS-based spatial data curation to calculate the reservoir storage capacity in precise increments of 2 m. To maximize the structural efficiency of the proposed “New Hasanlar Dam”, the cross-sectional area has been minimized through seven current algorithms such as Genetic Algorithm (GA), Arithmetic Optimization Algorithm (AOA), Gray Wolf Optimizer (GWO), Dragonfly Algorithm (DA), Particle Swarm Optimization (PSO), Crayfish Optimization Algorithm (CAO), and Cheetah Optimizer (CO). The findings obtained prove that the PSO and CAOs achieved a significant reduction in cross-sectional area by 29.36% and successfully approached the global optimum. The replacement of the 55.5 million m³ capacity of the existing Hasanlar Dam with a new structure with a height of 78 m will guarantee sustainability and structural safety in water management. As a result, this study reveals that the integration of high-resolution remote sensing data and advanced heuristic methods is a cost-effective and powerful tool in the strategic renovation of aging hydraulic infrastructures. Full article

Municipal sewage sludge is an environmental liability but also an energy-rich biomass that can support circular economy resource recovery. Here, we benchmark thin-layer drying of dewatered municipal sewage sludge (sludge cake) (40 g; layer thickness ≤ 5 mm) under open-air, convective hot air (40–150 °C), and microwave (70–1200 W) conditions to quantify drying kinetics, hygienisation indicators, and a screening-level energy-use proxy. High-power microwave drying reduced the time to constant mass from 32 h (open air) and 25 h 05 min (40 °C convection) to 20 min (900 W) and 14 min 05 s (1200 W). Faecal indicators (total/thermotolerant coliforms and presumptive Escherichia coli) were below detection after ≥100 °C convection or ≥300 W microwave treatment, while mesophilic aerobes and sulfite-reducing Clostridium spp. decreased by ~3–4 log10 with increasing exposure. A dragonfly-optimised ε-support vector regression model (DA–SVR) predicted drying trajectories across modes (overall RMSE ≈ 0.79 percentage points; held-out RMSE ≈ 1.47; R² ≥ 0.99). Overall, microwave thin-layer drying coupled with DA–SVR decision support enables constraint-based screening of sewage–sludge conditioning windows for logistics and thermal valorisation pathways; the framework can be extended to incorporate additional analytical endpoints where available. Full article

Dragonflies exhibit remarkable flight stability in unsteady environments, largely due to aerodynamic interaction between their forewings and hindwings. This study investigates gust response in dragonfly-inspired micro-aerial vehicles (MAVs) from a system dynamics perspective, with emphasis on the aerodynamic role of tandem-wing interaction rather than control compensation. A six-degree-of-freedom (6DOF) rigid-body framework is developed and coupled with a quasi-steady aerodynamic model that includes explicit phase-dependent interaction between forewing and hindwing forces. Gusts are introduced as time-varying inflow perturbations, allowing physically consistent analysis of how disturbances propagate through aerodynamic loading into vehicle motion. Simulations are performed for representative flight conditions, including gliding, hovering, and gust-perturbed ascent. The results show bounded trajectory, velocity, and attitude responses under sustained gust excitation, even with conservative baseline control. Force and energy analyses indicate that wing–wake interaction redistributes aerodynamic loads in time and reduces peak force and moment fluctuations before they reach the rigid-body dynamics. This behavior is interpreted as passive aerodynamic filtering of gust disturbances inherent to the tandem-wing configuration. Comparative simulations using backstepping control and Active Disturbance Rejection Control (ADRC) further show that the dominant gust attenuation arises from aerodynamic configuration rather than from control action. Although the aerodynamic model is quasi-steady, the framework reproduces key trends reported in biological and CFD-based studies and provides a numerical foundation for future wind-tunnel and free-flight experiments on configuration-level gust attenuation. Full article

Inspired by the wing-opening morphology of dragonflies, a series of bio-inspired dragonfly-shaped bluff bodies are designed and investigated, and further integrated into a piezoelectric wind energy harvester. The energy-harvesting performance and aerodynamic responses of bluff-body configurations with different wing-opening angles (0°, 15°, 30°, 45°, and 60°) are comparatively analyzed through a combination of numerical simulations and wind tunnel experiments. Experimental results demonstrate pronounced differences among the configurations in the low wind speed regime. Specifically, the prototype with α = 0° achieves relatively higher output under very low wind speeds, whereas the α = 15° configuration exhibits the best overall performance across the entire tested wind speed range. Taking the α = 15° case as an example, the cut-in wind speed is reduced to 1.7 m/s, while the maximum RMS voltage and output power are increased by 20.16% and 44.39% compared with the cuboid bluff body, and by 50.95% and 127.84% compared with the cylinder bluff body, respectively. Further CFD results reveal that, at specific wing-opening angles, the dragonfly-shaped bluff body undergoes a coupled vortex-induced vibration (VIV) and galloping response, enabling certain configurations to sustain stable oscillations with large amplitudes over a relatively wide wind speed range. Within the investigated parameter range, an appropriate selection of the wing-opening angle effectively balances the cut-in capability and output stability under low wind speed conditions. These findings provide useful design guidelines for flow-induced vibration-based wind energy harvesters operating in low wind speed environments. Full article

High-precision detection of hazardous gases with low refractive indices ranging from 1.000 to 1.100, specifically including methane, carbon monoxide, and sulfur dioxide, is critical for industrial safety, yet conventional sensors often suffer from limited sensitivity and severe thermal cross-sensitivity. This work presents a Magneto-Optical Differential Photonic Crystals Sensor (MO-DPCS) utilizing indium antimonide (InSb) to address these constraints. Employing the Multi-Objective Dragonfly Algorithm (MODA), the system was inversely optimized to maximize magneto-optical polarization splitting while rigorously maintaining an ultra-high transmission efficiency. Crucially, an angular interrogation architecture operating under oblique incidence was established to maximize the magneto-optical non-reciprocity, where the detection was realized by fixing the terahertz source frequency and monitoring the precise angular displacements of the steep spectral edges. A differential detection technique was employed to utilize the non-reciprocal phase changes wherein Transverse Electric (TE) and Transverse Magnetic (TM) modes display contrasting kinematic characteristics in the presence of an external magnetic field. The findings indicate that with an adjusted magnetic field of 0.033 T, the MO-DPCS attains an exceptional differential sensitivity of 30.8°/RIU, much above the 0.8°/RIU seen in the unmagnetized condition. The differential approach efficiently eliminates common-mode thermal noise, minimizing temperature-induced drift to below 0.35° across a 1 K range. The suggested MO-DPCS offers a robust, self-referencing solution for stable and high-sensitivity gas sensing applications with a detection limit of 4.18 × 10⁻⁴ RIU. Full article

The valorization of agro-industrial by-products is a sustainable approach to recovering high-value bioactive compounds. In this study, Opuntia ficus-indica (L.) Mill. seed press residues were investigated as a source of phenolic and flavonoid compounds using microwave-assisted extraction (MAE). A multi-step optimization strategy was implemented, combining preliminary single-factor experiments (OVAT), response surface methodology based on a Box–Behnken design (BBD), and machine learning modeling using K-nearest neighbors coupled with the dragonfly algorithm (KNN_DA), followed by desirability-based validation. The effects of ethanol concentration (50–100%), microwave power (400–800 W), extraction time (2–4 min), and liquid-to-solid ratio (30–50 mL/g) were evaluated on Folin–Ciocalteu reducing capacity (FCRC), AlCl₃ complexation response, and antioxidant activity assessed by DPPH radical scavenging and reducing power assays. Optimal conditions were identified at 50% ethanol, 800 W microwave power, 4 min extraction time, and a liquid-to-solid ratio of 47.28 mL/g. Under these conditions, FCRC reached 376.85 ± 0.23 mg GAE/100 g DW and 49.16 ± 0.33 mg QE/100 g DW for AlCl₃ complexation response, with prediction errors of 2.80% and 0.82%, respectively. The optimized extracts exhibited enhanced antioxidant activity. These findings confirm MAE as a rapid and environmentally friendly technique and highlight the predictive performance of the KNN_DA model for process optimization. Full article

Optimizing the control parameters of an islanded microgrid with active load integration presents a challenging operational research problem since current methodologies frequently fail to reach the ideal balance or symmetry between transient response, stability, and efficiency. The conventional methods, such as the canonical Particle Swarm Optimization (PSO), have settling time and voltage ripple minimization constraints, indicating possible improvement scopes. This research addresses this gap by employing advanced metaheuristic algorithms such as Accelerated Particle Swarm Optimization (APSO), Accelerated Particle Swarm Optimization with variable α (APSO α), Accelerated Particle Swarm Optimization with Normal Distribution (APSO_G), Rayleigh Distribution Accelerated Particle Swarm Optimization (RDAPSO), Rayleigh Distribution Accelerated Particle Swarm Optimization with variable α (RDAPSO α), and the Dragonfly Algorithm (DA). The algorithms were tested for their performance by using CEC Standard Benchmark functions from 2017, 2019, and 2022, providing a basis for rigorous and symmetrical testing and validation. The optimized RDAPSO α algorithm showed a significant reduction in voltage ripple, which was reduced from 4 V to 0.47 V, with an 88.25% reduction. It also showed a 46.32% improvement in settling time, which was reduced from 184.2 ms to 98.9 ms compared to PSO. A detailed statistical analysis was conducted to enhance the reliability and symmetry of the outcomes using Multivariate Analysis of Variance (MANOVA), the Mann–Whitney U test, the Friedman test, and the Bonferroni test. The results show that RDAPSO α offers a significant edge over the rest of the algorithms, with improvements that can be declared statistically superior in optimizing microgrids with improved symmetry in performance. Full article

Construction site layout planning (CSLP) is an optimization issue that has been studied for decades. However, risk factors are still open to exploration, and risk is often not addressed comprehensively in the state-of-the-art literature; moreover, only a few studies have investigated safety as an optimization component in construction sites. This research aims to obtain optimal layout solutions that minimize site risk. In this study, the components of the risk factor were defined as interaction flows between facilities, closeness factors, and the influence of tower cranes. The Dragonfly Algorithm (DA) was selected to solve the CSLP problem due to its strong exploration and exploitation capacity. A DA-based model was developed that integrates the relationships between facilities into a single objective function. This integration extends existing CSLP optimization frameworks by explicitly incorporating multiple risk factors, which constitutes the novelty of the proposed approach. The model was implemented in an actual construction site as a case study. Also, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) were employed for comparison purposes. The acquired layout plans showed that the DA provided lower site risk values with feasible solutions for CSLP optimization problems. To validate the results, structured feedback was obtained from 10 experienced project managers and Occupational Health and Safety (OHS) experts, confirming the practical and safety relevance of the optimized layouts. Overall, the proposed DA-based model in this study not only provides feasible solutions for CSLP problems but also integrates comprehensive safety considerations that enable more efficient and safer construction site layouts. Full article

Parametric simulation is an effective engineering tool for addressing sustainability challenges, yet small-scale thermal comfort assessment remains limited by plugin-hybridizing complexities and workflow inefficiencies. To address these limitations, here we propose a novel comparative workflow that integrates Lands Design and Dragonfly with the assistance of Ladybug-only (LB) and Honeybee (LB&HB) in the Grasshopper model to predict the Universal Thermal Climate Index (UTCI) as the primary indicator. A playground was selected as a sample site to provide a comprehensive training dataset for the extremely hot summer period. Sensitivity analysis was conducted to assess the impact of input uncertainties on model predictions, and the simulation model’s performance was validated against urban–rural microclimate parameters and the calculated UTCI. Among the microclimate results tested, the wind speed and air temperature predictions achieved the highest accuracy (STDE: 0.10 m/s, 0.20 °C). The UTCI simulation of the LB workflow exhibited a strong correlation between calculated UTCI values (R² = 0.90; p = 0.03). Moreover, the agreement between the LB and LB&HB workflows was strong, with simulated UTCI showing good consistency (R² = 0.70–0.80; r = 0.85–0.88). This framework successfully enables real-time UTCI heatmap analysis in simplified cubic neighborhoods. Additionally, it improves the temporal and spatial resolution of thermal predictions, providing designers with critical insights into the algorithms implemented in new workflows to facilitate urban simulation and parametric sustainability. Full article

While bionic sawtooth and wave structures effectively reduce aerodynamic noise on fixed airfoils, their efficacy on rotating fans is often limited. Inspired by the protrusion structures of dragonfly wings and the gentle circular arches of manta rays, this study proposes a novel bionic circular arch structure to suppress aeroacoustic noise in axial flow fans. Numerical simulations were validated against experimental data from a standard fan, showing a sound pressure level (SPL) deviation within 3 dB at the first blade passing frequency (BPF), confirming calculation accuracy. The results indicate that the bionic design reduces the total SPL by approximately 2.5 dB. Notably, in the human-sensitive frequency range of 1000–3000 Hz, noise reduction reaches up to 6.6 dB at the upstream monitoring point. Analysis of Root Mean Square (RMS) fluctuating pressure and Fourier transforms reveals that the bionic structure significantly mitigates noise source intensity at the blade tip. This design effectively reduces pressure disturbances at the first BPF and shrinks the high-intensity disturbance region of the boundary layer compared to the prototype. Full article

The ability of an animal to perceive its visual environment underpins many behaviors essential to survival, including navigation, foraging, predator avoidance, and recognition of conspecific individuals, making vision a critical element of both reproductive success and survival itself. In insects, eyes have evolved widely, shaped by different habitats and lifestyles, with striking examples such as the high-resolution diurnal vision of dragonflies, which enables rapid detection of prey and environmental features, in contrast with the highly sensitive nocturnal optical system of hawkmoths, which specializes in capturing even single photons. At the core of this diversity is a fundamental trade-off: at one extreme lies sensitivity, the ability to perceive visual stimuli, even under poor lighting conditions. At the other extreme, acuity, is the ability to resolve fine spatial details. This review seeks to synthesize current knowledge of insect visual systems, from their evolutionary origins to the developmental processes so far identified, from cellular organization to their role in behavior, to provide insights for designing novel, targeted, and sustainable vision-based technologies for the control of pest insects. Full article

Corrugated insect wings inspire biomimetic aerodynamic design, yet their behaviour at low and transitional Reynolds numbers remains not fully understood. This study presents a three-dimensional computational analysis of flow over an infinite corrugated wing across Reynolds numbers from 10 to 10,000 and angles of attack from −5 to 20°, with emphasis on spanwise effects. An expanded verification and validation procedure ensured numerical reliability. At the lowest Reynolds numbers, the flow is steady and largely two-dimensional, with localised recirculation zones. As Reynolds numbers or angles of attack increase, the flow transitions to periodic vortex shedding, and three-dimensional structures appear. At a Reynolds number of ten thousand, periodic shedding occurs at zero degrees incidence, indicating a shift toward turbulent or bluff body-like behaviour. The examined corrugated profile does not exhibit a lift-to-drag benefit over smooth aerofoils in steady gliding, although root section corrugation helps delay separation in transitional regimes. This behaviour reflects mechanisms used by dragonflies to maintain stable gliding despite textured wings. By extending flow regime classification, the study identifies conditions where two-dimensional assumptions fail and highlights the influence of spanwise flow structures. These findings deepen understanding of insect wing aerodynamics and support biomimetic design of future wings. Full article

Dragonfly networks $D(n,h)$ are a class of interconnection topologies widely used for large-scale high-performance computing (HPC) systems. In such networks, path connectivity serves as a fundamental metric for evaluating fault tolerance and operational reliability. Let G be a connected simple graph with vertex set $V\left(G\right)$. Let $\mathrm{\Omega }$ be a subset of $V\left(G\right)$ with cardinality at least two. A path containing all vertices of $\mathrm{\Omega }$ is said to be an $\mathrm{\Omega }$-path of G. Two paths ($T_1$ and $T_2$) of G are internally disjoint if $V\left(T_1\right)\cap V\left(T_2\right)=\mathrm{\Omega }$ and $E\left(T_1\right)\cap E\left(T_2\right)=\varnothing$. For an integer with $2\le \ell$, the ℓ-path connectivity ${\pi }_{\ell }\left(G\right)$ is defined as ${\pi }_{\ell }\left(G\right)=min\{{\pi }_G\left(\mathrm{\Omega }\right)|\mathrm{\Omega }\subseteq V\left(G\right)\mathrm{and}|\mathrm{\Omega }|=\ell \}$, where ${\pi }_G\left(\mathrm{\Omega }\right)$ represents the maximum number of internally disjoint $\mathrm{\Omega }$-paths. This paper focuses on resolving the exact value of 3-path connectivity of dragonfly networks, ${\pi }_3\left(D(n,h)\right)$, defined as the maximum number of internally disjoint paths among any three distinct vertices in $D(n,h)$. For $D(n,h)$ with $n\ge 5$ and $h\ge 2$, the exact 3-path connectivity is ${\pi }_3\left(D(n,h)\right)=\lfloor {\displaystyle \frac{3h+2n}{4}}\rfloor$ if $h\le n-2$, and ${\pi }_3\left(D(n,h)\right)=\lfloor {\displaystyle \frac{3n+2h-2}{4}}\rfloor$ if $h\ge n-1$. Full article