Search Results (1,023)

Triply Periodic Minimal Surface (TPMS) structures, with their zero average curvature, excellent energy absorption properties, high specific strength and high surface-to-volume ratio, could be used in a wide range of applications, such as the creation of lightweight and durable structures, grafts and implants. In this study, an internal TPMS structure inspiring trabecular bone and an external TPMS structure inspiring cortical bone were combined with infill density and topologically functionally graded to obtain hybrid structures. The aim of the study was to investigate the effects of functional grading on mechanical properties, energy absorption capacity and surface/volume (S/V) ratio and to determine the best combination. The novelty of the study is to obtain hybrid structures close to bone structures with a functional grading approach. The experimental design of the study was performed using the Design of Experiment (DoE) approach and the Taguchi method. Specimens were created according to the established experimental design and fabricated using a Masked Stereolithography (mSLA)-type 3D printer with bio-resin. The fabricated structures were subjected to compression tests; the results were examined in terms of deformation behavior, first peak, maximum force, energy absorption, specific energy absorption and S/V ratio. The optimal structures for defined input parameters were determined using signal-to-noise (S/N) ratios and ANOVA results. Deformations for diamond and primitive specimens began as shear band formation. Deformations for Neovius structures were mostly as brittle fracture. The highest first peak of 18.96 kN was obtained with the DN specimens, while the highest maximum force of 23.77 kN was obtained with the ND specimens. The best energy absorption property was also obtained with ND. The highest S/V ratio was 5.65 in the GP specimens. The statistical analyses were in accordance with the experimental results. Infill density increases decreased the S/V ratio while increasing all other parameters. This demonstrated the importance of mechanical-strength/porosity optimization for bone scaffold surrogate applications in this study. Full article

The merging of the Internet of Things (IoT) and Artificial Intelligence (AI) advances has intensified challenges related to data authenticity and security. These advancements necessitate a multi-layered security approach to ensure the security, reliability, and integrity of critical infrastructure and intelligent surveillance systems. This paper proposes a two-layered security approach that combines a discrete cosine transform least significant bit 2 (DCT-LSB-2) with artificial neural networks (ANNs) for data forensic validation and mitigating deepfakes. The proposed model encodes validation codes within the LSBs of cover images captured by an IoT camera on the sender side, leveraging the DCT approach to enhance the resilience against steganalysis. On the receiver side, a reverse DCT-LSB-2 process decodes the embedded validation code, which is subjected to authenticity verification by a pre-trained ANN model. The ANN validates the integrity of the decoded code and ensures that only device-originated, untampered images are accepted. The proposed framework achieved an average SSIM of 0.9927 across the entire investigated embedding capacity, ranging from 0 to 1.988 bpp. DCT-LSB-2 showed a stable Peak Signal-to-Noise Ratio (average 42.44 dB) under various evaluated payloads ranging from 0 to 100 kB. The proposed model achieved a resilient and robust multi-layered data forensic validation system. Full article

For long horizontal-section drilling in reservoirs and complex formations, efficient and robust trajectory planning with real-time closed-loop control must be achieved under curvature and mechanical constraints. This study systematically investigates the application of the Dubins curve, a shortest-path model satisfying a minimum curvature constraint, in closed-loop wellbore trajectory control. Six canonical configurations (LSL, RSR, LSR, RSL, LRL, and RLR) are analyzed, and a standardized procedure for path solution and coordinate reconstruction is established. Parametric analyses reveal the effects of curvature limit, target direction, and target distance on trajectory feasibility and path length. Case studies show that unoptimized Dubins trajectories can achieve a high reservoir-contact ratio (99.69%) but exhibit curvature discontinuities at segment junctions, which induce torque and friction peaks. By introducing a multi-objective optimization strategy combining minimum turning-radius expansion and adaptive target adjustment, these curvature discontinuities are effectively mitigated: the maximum curvature was reduced to 11.15°/30 m, the average curvature to 2.57°/30 m, the average friction to 1118.7 N, and the cumulative torque to 31,468 Nm, while maintaining nearly unchanged reservoir contact. The proposed method effectively improves trajectory smoothness and mechanical drillability while preserving real-time computational efficiency, offering a practical approach for closed-loop trajectory optimization in complex geological settings. Full article

For hot-rolled ferritic stainless steels with preferential α-fiber texture, the strong α-fiber texture is retained after annealing, greatly affecting the texture and plastic formability during the subsequent cold-rolling process. For optimizing the texture of hot-rolled steels toward the favorable γ-fiber type, it is essential to control the annealing temperature in the annealing process. To investigate the evolution of the microstructure, texture, and properties of hot-rolled ferritic stainless steel with preferential α-fiber orientation, a series of annealing tests was performed at the lab scale at 800, 840, 880, 910, 930, and 950 °C for 3 min. The microstructure, texture, and grain boundary characteristics of the tested samples were analyzed using optical microscopy (OM) and electron back-scattered diffraction (EBSD). The mechanical properties and plastic strain ratio (r-value) were determined through universal tensile testing. The results show that at temperatures above 840 °C, more than 93% of recrystallization occurs, leading to significant microstructural refinement. The α-fiber texture intensity typically diminishes with rising temperature, whereas the γ-fiber texture initially weakens during the early stages of recrystallization (below 840 °C) and subsequently exhibits a slight increase at higher temperatures. The improved formability of the material is mainly attributed to microstructural refinement and texture refinement, as reflected by the I(γ)/I(α) texture intensity ratio. At an annealing temperature of 930 °C, the I(γ)/I(α) ratio peaks at 0.85, static toughness is maximized, the strain-hardening exponent (n) reaches a high value of 0.28, and the maximum average plastic strain ratio ($\overline{r}$) is 0.96. This result represents the optimum balance between mechanical properties and formability, making it suitable for subsequent cold-rolling. Full article

To enhance the accuracy and timeliness of field testing for grounding impulse impedance in complex soil environments, this paper addresses the limitations of traditional peak-ratio methods—such as susceptibility to noise interference and the inability to reflect dynamic impedance variations—by proposing an identification method that combines an improved Hanning window with recursive least squares (RLS). During signal preprocessing, an improved Hanning window with adjustable parameters and energy normalization is employed to enhance the main-lobe energy concentration of impulse voltage and current signals while effectively suppressing high-frequency sidelobe leakage. In the parameter estimation stage, a low-order discrete linear model is established and an RLS algorithm with a forgetting factor is introduced to achieve full-time adaptive estimation of impulse impedance. Using a simulated surge test circuit, 18 sets of typical operating conditions with varying inductance and resistance parameters are designed. The same voltage and current data are processed using three processing methods: no windowing, standard Hanning windowing, and improved Hanning windowing. Results show that the average relative error of surge impedance is 9.16% without windowing, the standard Hanning window reduced the error to 3.78%, and the modified Hanning window further decreased the error to approximately 1.51%. Comparative analysis of different forgetting factor settings indicates that a value of approximately λ = 0.98 achieves an optimal trade-off between dynamic tracking capability and steady-state smoothness. The research results demonstrate that the proposed method achieves high identification accuracy for impact impedance and exhibits satisfactory parameter robustness under strong noise and multiple operating conditions, providing a reference for grounding impact characteristic testing and lightning protection design. Full article

In this present study, sodium- and strontium-modified bismuth titanate—Bi_0.5Na_0.5TiO₃ (BNT) and Bi_0.5Sr_0.5TiO₃ (BST)—were synthesized using the auto-combustion technique with citric acid (C₆H₈O₇) and glycine (C₂H₅NO₂) as fuels in an optimized ratio of 1.5:1. The resulting powders were characterized using X-ray diffraction (XRD), energy-dispersive X-ray (EDX) spectroscopy, UV–Visible diffuse reflectance spectroscopy (DRS), and Fourier-transform infrared (FT-IR) spectroscopy. The electrical behavior of the samples was studied using an LCR meter. XRD analysis confirmed the formation of a single-phase perovskite structure with average crystallite sizes of 18.60 nm for BNT and 22.03 nm for BST, attributed to the difference in ionic radii between Na⁺ and Sr²⁺. An increase in crystallite size was accompanied by a corresponding increase in lattice parameters and unit-cell volume. The Williamson–Hall analysis further validated the strain-size contributions. EDX (Energy-Dispersive X-ray analysis) results confirmed successful incorporation of Na⁺ and Sr²⁺ without detectable impurity phases. Optical studies revealed distinct absorption peaks at 341 nm for BNT and 374 nm for BST, and the optical bandgap (E_g), calculated using Tauc’s relation, was found to be 2.6 eV and 2.0 eV, respectively. FT-IR spectra exhibited characteristic Ti-O vibrational bands in the range of 420–720 cm⁻¹, consistent with the perovskite structure. For electrical characterization, the powders were pelletized under 3-ton pressure and sintered at 1000 °C for 3 h. The dielectric constant (ε_r), dielectric loss (tan δ), and ac conductivity (σ) of both samples increased with frequency. The combined structural, optical, and electrical results indicate that the optimized compositions of BNT and BST possess properties suitable for use in capacitors and other energy-storage applications. Full article

Flexible piezoelectric sensors based on electrospun poly(vinylidene fluoride) (PVDF)/polyacrylonitrile (PAN)/graphene nanofibers were fabricated and evaluated for passive human body motion detection. Optimized electrospinning yielded smooth, continuous fibers with diameters of 200–250 nm and uniform films with thicknesses of 20–25 µm. Fourier transform infrared (FTIR) spectroscopy confirmed a high fraction of the piezoelectrically active β-phase in PVDF, which was further enhanced by post-deposition thermal treatment. Graphene and lithium phosphate were incorporated to improve electrical conductivity, β-phase nucleation, and piezoelectric response, while PAN provided mechanical reinforcement and flexibility. Custom test platforms were developed to simulate low-amplitude mechanical stimuli, including finger bending and pulsatile pressure. Under applied pressures of 40, 80, and 120 mmHg, the sensors generated stable millivolt-level outputs with average peak voltages of 25–30 mV, 53–60 mV, and 80–90 mV, respectively, with good repeatability and an adequate signal-to-noise ratio. These results demonstrate that PVDF/PAN/graphene nanofiber films are promising candidates for flexible, wearable piezoelectric sensors capable of detecting subtle physiological signals, and highlight the critical roles of electrospinning conditions, functional additives, and post-processing treatments in tuning their electromechanical performance. Full article

Fuel cell commercial vehicles are widely used in commercial transport for their high efficiency and long range. However, in mixed operating scenarios, their energy economy and fuel cell operational stability cannot be fully balanced. Traditional strategies lack adaptability in mixed operating scenarios. Therefore, based on the equivalent factor regulation formula of the Adaptive Equivalent Hydrogen Consumption Minimization Strategy (A-ECMS) and the improved Sparrow Search Algorithm-Long Short-Term Memory (SSA-LSTM) hybrid model, short-term speed prediction and three-stage speed interval division are embedded into the equivalent factor regulation logic. A dynamic equivalent factor regulation strategy integrating SOC deviation is constructed, and an improved Predictive Equivalent Hydrogen Consumption Minimization Strategy (P-ECMS) is finally derived. The SSA-LSTM algorithm is optimized via constrained hyperparameter tuning for short-term speed prediction. A time-decay weighting mechanism enhances recent speed data weight, with weighted results as inputs to boost accuracy. Moving Average Residual Correction (MARC) is used to verify the speed prediction model accuracy and correct residuals. Multi-scenario tests show that the SSA-LSTM model outperforms the Gated Recurrent Unit (GRU) model in prediction accuracy and generalization ability, providing reliable data support for segmented regulation. With battery SOC deviation and the SSA-LSTM-predicted speed trend as core inputs, combined with three-stage speed interval division, A-ECMS’s equivalent factor regulation formula is improved. The model adopts a segmented dynamic regulation logic to integrate dual factors into equivalent factor adjustment, and it reasonably adjusts the energy output ratio of fuel cells and power batteries according to speed intervals and operating condition changes. In scenarios with significant speed fluctuations and frequent operating condition transitions, power shocks are mitigated by the power battery’s peak-shaving and valley-filling function. Simulation results for C-WTVC and NREL2VAIL show that, compared with traditional A-ECMS, the improved P-ECMS has notable energy benefits, with equivalent hydrogen consumption reduced by 3.41% and 5.48%, respectively. The fuel cell’s state is significantly improved, with its high-efficiency share reaching 63%. The output power curve is smoother, start–stop losses are reduced, and the fuel cell’s service life is extended, balancing the energy economy and component durability. Full article

Background: Cancer survivors represent a growing proportion of the UK population and often experience higher multimorbidity and healthcare needs. However, limited research in the UK has explored ethnic and socioeconomic disparities in healthcare resource use among long-term cancer survivors. Methods: Using linked primary care (Clinical Practice Research Data) and secondary care (Hospital Episode Statistics–Admitted Patient Care) data between 2010 and 2020, this population-based cohort study compared healthcare utilisation among 170,352 cancer survivors and 415,975 matched controls without a cancer diagnosis. Outcomes included primary care consultations and hospital admissions (planned and emergency). Analyses adjusted for age, sex, body mass index, smoking, ethnicity, and the Index of Multiple Deprivation. Negative binomial models were used to estimate incidence rate ratios (IRRs). Results: Cancer survivors averaged 33 more primary-care consultations over ten years than controls, with Pakistani, Indian, and White survivors recording the higher rates. Hospital admissions were consistently higher among survivors across all age groups, peaking in those aged 60–75 years. Planned admissions were highest among Black Caribbean (IRR 1.80 (95% CI 1.73–1.87)), Pakistani (IRR 1.71 (1.63–1.78)), and Bangladeshi (IRR 1.66 (1.53–1.80)) groups. Emergency admissions followed a similar trend, remaining statistically significant only for Pakistani survivors (IRR 1.23 (1.16–1.30)). A strong socioeconomic gradient was observed, with healthcare utilisation increasing as deprivation worsened. Conclusions: Cancer survivors experience substantially greater healthcare use than matched controls, with persistent ethnic and socioeconomic disparities. Strategies to reduce disparities should focus on earlier diagnosis, enhanced long-term care coordination, and culturally informed interventions addressing both cancer survivorship and multimorbidity. Full article

Visible Light Communication (VLC) systems commonly employ optical orthogonal frequency division multiplexing (O-OFDM) to achieve high data rates, benefiting from its robustness against multipath effects and intersymbol interference (ISI). However, a key limitation of asymmetrically clipped direct current biased optical–OFDM (ACO-OFDM) systems lies in their inherently high peak-to-average power ratio (PAPR), which significantly affects signal quality and system performance. This paper proposes a joint chaotic encryption and modified μ-non-linear logarithmic companding (μ-MLCT) scheme for ACO-OFDM–based VLC systems to simultaneously enhance security and reduce PAPR. First, image data is encrypted at the upper layer using a hybrid chaotic system (HCS) combined with Arnold’s cat map (ACM), mapped to quadrature amplitude modulation (QAM) symbols and further encrypted through chaos-based symbol scrambling to strengthen security. A μ-MLCT transformation is then applied to mitigate PAPR and enhance both peak signal-to-noise ratio (PSNR) and bit-error-ratio (BER) performance. A mathematical model of the proposed secured ACO-OFDM system is developed, and the corresponding BER expression is derived and validated through simulation. Simulation results and security analyses confirm the effectiveness of the proposed solution, showing gains of approximately 13 dB improvement in PSNR, 2 dB in BER performance, and a PAPR reduction of about 9.2 dB. The secured μ-MLCT-ACO-OFDM not only enhances transmission security but also effectively reduces PAPR without degrading PSNR and BER. As a result, it offers a robust and efficient solution for secure image transmission with low PAPR, making it well-suitable for emerging wireless networks such as cognitive and 5G/6G systems. Full article

To address the issue of low efficiency in recovering low-frequency vibration energy during vehicle operation, this paper proposes a piezoelectric energy capture harvester based on wheel vibration. The device employs a parallel configuration of dual cantilever beam piezoelectric transducers in its mechanical structure, with additional mass blocks to optimize its resonant characteristics in the low-frequency range. A synchronous switch energy harvesting circuit was designed. By actively synchronizing the switch with the peak output voltage of the piezoelectric element, it effectively circumvents the turn-on voltage threshold limitations of diodes in bridge rectifier circuits, thereby enhancing energy conversion efficiency. A dynamic model of this device was established, and multiphysics simulation analysis was conducted using COMSOL-Multiphysics to investigate the modal characteristics, stress distribution, and output performance of the energy harvester. This revealed the influence of the piezoelectric vibrator’s thickness ratio and the mass block’s weight on its power generation capabilities. Experimental results indicate that under 20 Hz, 12 V sinusoidal excitation, the system achieves an average output power of 3.019 mW with an average open-circuit voltage reaching 16.70 V. Under simulated road test conditions at 70 km/h, the output voltage remained stable at 6.86 V, validating its feasibility in real-world applications. This study presents an efficient and reliable solution for self-powering in-vehicle wireless sensors and low-power electronic devices through mechatronic co-design. Full article

To address the limitations of traditional non-local mean (NLM) denoising algorithms in terms of neighborhood similarity metrics, weight calculation, and computational efficiency, this paper proposed a structural-tensor-driven and dynamic window-based fast non-local mean denoising algorithm with dual kernel weighting. First, a Gaussian–Tukey dual-kernel weighting function was designed to optimize similarity metrics. Then, spatial neighborhood features were adopted. By measuring both grayscale similarity and spatial correlation, the weight distribution rationality was further enhanced. Second, structural tensor eigenvalues were used to quantify regional structural properties. A dynamic window allocation function was designed to adaptively match search window sizes to different image regions. Finally, an integral image acceleration mechanism was proposed, significantly improving algorithm execution efficiency. Experimental results demonstrated that the proposed algorithm achieved both excellent denoising performance and edge/texture preservation capabilities. In high-noise environments, its Peak Signal-to-Noise Ratio (PSNR) outperformed the Gauss kernel non-local mean algorithm by an average of 1.96 dB, while Structural Similarity (SSIM) improved by an average of 5.7%. Moreover, the algorithm’s execution efficiency increased by approximately 7–11 times, indicating strong potential for real-time application in digital image processing. Full article

Laser-based remote sensing (lidar) is a proven technique for detecting atmospheric features such as clouds and aerosols as well as for determining their vertical distribution with high accuracy. Even simple elastic backscatter lidars can distinguish clouds from aerosols, and accurate knowledge of their vertical location is essential for air quality assessment, hazard avoidance, and operational decision-making. However, daytime lidar measurements suffer from reduced signal-to-noise ratio (SNR) due to solar background contamination. Conventional processing approaches mitigate this by applying horizontal and vertical averaging, which improves SNR at the expense of spatial resolution and feature detectability. This work presents a deep learning-based framework that enhances lidar SNR at native resolution and performs fast layer detection and cloud–aerosol discrimination. We apply this approach to ICESat-2 532 nm photon-counting data, using artificially noised nighttime profiles to generate simulated daytime observations for training and evaluation. Relative to the simulated daytime data, our method improves peak SNR by more than a factor of three while preserving structural similarity with true nighttime profiles. After recalibration, the denoised photon counts yield an order-of-magnitude reduction in mean absolute percentage error in calibrated attenuated backscatter compared with the simulated daytime data, when validated against real nighttime measurements. We further apply the trained model to a full month of real daytime ICESat-2 observations (April 2023) and demonstrate effective layer detection and cloud–aerosol discrimination, maintaining high recall for both clouds and aerosols and showing qualitative improvement relative to the standard ATL09 data products. As an alternative to traditional averaging-based workflows, this deep learning approach offers accurate, near real-time data processing at native resolution. A key implication is the potential to enable smaller, lower-power spaceborne lidar systems that perform as well as larger instruments. Full article

Financial time-series labeling remains fundamentally limited by three critical deficiencies: temporal rigidity (fixed horizons regardless of market conditions), scale blindness (single-resolution analysis), and correlation-causation conflation. These limitations cause systematic failure during regime shifts. We introduce Adaptive Event-Driven Labeling (AEDL), integrating three core innovations: (1) multi-scale temporal analysis capturing hierarchical market patterns across five time resolutions, (2) causal inference using Granger causality and transfer entropy to filter spurious correlations, and (3) model-agnostic meta-learning (MAML) for adaptive parameter optimization. The framework outputs calibrated probability distributions enabling uncertainty-aware trading strategies. Evaluation on 16 assets spanning 25 years (2000–2025) with rigorous out-of-sample validation demonstrates substantial improvements: AEDL achieves average Sharpe ratio of 0.48 (across all models and assets) while baseline methods average near-zero or negative (Fixed Horizon: −0.29, Triple Barrier: −0.03, Trend Scanning: 0.00). Systematic ablation experiments on a 12-asset subset reveal that selective innovation deployment outperforms both minimal baselines and maximal integration: removing causal inference improves performance to 0.65 Sharpe while maintaining full asset coverage (12/12), whereas adding attention mechanisms reduces applicability to 2/12 assets due to compound filtering effects. These findings demonstrate that judicious component selection outperforms kitchen-sink approaches, with peak individual asset performance exceeding 3.0 Sharpe. Wilcoxon tests confirm statistically significant improvements over Fixed Horizon baseline (p = 0.0024). Full article

With the increasing functional and geometric complexity of modern steel buildings, irregular-shaped beam-to-column joints are becoming common in engineering practice. However, their seismic behavior remains insufficiently understood, particularly for configurations with geometric asymmetry and complex stress transfer mechanisms. This study experimentally investigates the seismic performance of irregular steel-beam-to-concrete-filled steel tube (CFST) column joints incorporating inclined internal diaphragms (IIDs), taking unequal-depth beam (UDB) and staggered beam (SB) joints as representative cases. Two full-scale joint specimens were designed and tested under cyclic loading to evaluate their failure modes, load-bearing capacity, stiffness/strength degradation, energy dissipation capacity, strain distribution, and panel zone shear behavior. Both joints exhibited satisfactory strength and initial stiffness. Although diaphragm fracture occurred at approximately 3% drift, the joints retained 45–60% of their peak load capacity, based on the average strength of several loading cycles at the same drift level after diaphragm failure, and maintained stable hysteresis with average equivalent damping ratios above 0.20. Final failure was governed by successive diaphragm fracture followed by the tearing of the column wall, indicating that the adopted diaphragm thickness (equal to the beam flange thickness) was insufficient and that welding quality significantly affected joint performance. Refined finite element (FE) models were developed and validated against the test responses, reasonably capturing global strength, initial stiffness, and the stress concentration patterns prior to diaphragm fracture. The findings of this study provide a useful reference for the seismic design and further development of internal-diaphragm irregular steel-beam-to-CFST column joints. Full article