Search Results (609)

Edge-intelligent sensing systems demand ultra-low-power wireless connectivity to sustainably support massive sensor deployments. Ambient backscatter communication (AmBC) meets this demand by harvesting and modulating existing radio-frequency (RF) signals, eliminating dedicated carriers. However, conventional on–off keying (OOK) demodulation in AmBC is highly susceptible to noise, while existing frequency-shift keying (FSK) alternatives relying on first-order derivatives perform poorly at low signal-to-noise ratios (SNRs), compromising the reliability of edge sensing data. In this paper, we propose a signal detection method that exploits high-order derivatives to enhance the demodulation of FSK-modulated ambient backscatter signals. By analytically evaluating the power of interference and noise after high-order differentiation, we reveal that the interference power is minimized at the second order while the noise power increases monotonically with the derivative order, leading to a favorable trade-off in typical AmBC regimes where the modulation frequency is much smaller than the sampling rate and comparable to the ambient signal bandwidth. We then design a phase-preserving frequency amplitude comparison detection (FACD) rule to recover the embedded information. Simulation results show that the proposed second-order derivative-based FACD achieves the lowest bit error rate among all compared schemes, particularly at low SNR. Full article

People who benefit from valuable information do not always pass it on. We develop a delayed IHSCR information propagation model in which a beneficiary can either continue spreading the information or hold it after a behavioral decision lag. The key modeling distinction is that information acquisition and the subsequent sharing-or-holding decision are treated as separate behavioral stages, while information holders can also suppress active spreaders. The delayed transitions are written as outflow rates, so arbitrary nonnegative histories do not automatically preserve positivity. We therefore work with nonnegative-feasible histories and show that such histories exist near each positive equilibrium on any fixed finite interval. For zero delay, we derive the basic reproduction number, prove global asymptotic stability of the information-free equilibrium when $R_0<1$, and give Routh–Hurwitz conditions for local stability of the positive equilibrium. With the delay as a bifurcation parameter, the linearized system gives a transcendental characteristic equation and a quartic frequency equation. The critical delay is recovered from an atan2-based phase condition, and the transversality condition identifies the first spectral stability switch. For the stability-switching parameter set, an independent characteristic-root computation verifies a unique simple crossing, and a characteristic-matrix normal-form calculation gives a negative first Lyapunov coefficient, classifying the local Hopf bifurcation as supercritical with a locally orbitally stable periodic branch. We also prove the existence of a delayed optimal control on the nonnegative-feasible set and derive the optimality system, including advanced adjoint terms. At the baseline cost weights, the computed dynamic control gives a modestly higher net objective than a numerically optimized constant-control benchmark, and this ordering persists when the relative cost ratio $c_1/c_2$ is varied from 1 to 100. A two-parameter sensitivity analysis shows how the beneficiary-to-spreader and beneficiary-to-holder transition rates shift the first critical delay. Full article

Cross-condition bearing fault diagnosis suffers from severe performance degradation due to domain shift across different operating conditions, especially when only a few labeled target-domain samples are available. To address this challenge, this paper proposes a progressive attention-guided two-stage transfer learning framework for few-shot bearing fault diagnosis across different fixed operating points. First, the raw time-domain vibration signals are fused with frequency-domain representations extracted by short-time Fourier transform (STFT) to enhance fault feature representation. Then, a progressive attention-guided feature learning strategy is developed by integrating dual efficient channel attention (ECA) modules into a deep one-dimensional convolutional neural network (1D-CNN), enabling the network to adaptively emphasize fault-sensitive features while suppressing redundant information. Subsequently, a two-stage transfer learning strategy is designed, consisting of transferable feature learning from the source domain and few-shot adaptation to the target domain. During target-domain adaptation, key feature extraction layers are frozen, and a sample-balanced optimization mechanism is introduced to alleviate the dominance of source-domain samples during joint training. Experimental results on the Case Western Reserve University (CWRU) bearing dataset demonstrate that the proposed method achieves an average accuracy of 99.96% across three cross-condition transfer tasks. Furthermore, experiments conducted on a self-built shaft system dataset show that the proposed method achieves an average accuracy of 87.11% under three representative transfer scenarios. The results verify that the proposed framework effectively mitigates domain shift and enables accurate bearing fault diagnosis with limited labeled target-domain samples. Full article

National parks, such as Domogled-Valea Cernei and Cozia, alongside protected areas like the Mehedinți Plateau Geopark or the Field of Lapiezuri in Ponoare, represent key assets for attracting tourists interested in nature and sustainability. This study investigates how tourists perceive sustainable ecotourism in Romania’s South-West Oltenia Region and identifies the primary determinants of their travel preferences. Specifically, it examines shifts in destination choices, the orientation toward safer and more sustainable locations, and changes in trip frequency and duration. Data were collected between June and December 2025 via a structured questionnaire administered to 500 visitors to these protected areas. To test the research hypotheses, the data were analyzed using multiple statistical methods, including regression analysis, the Chi-square test, an independent-samples t-test, Pearson correlation, and Analysis of Variance (ANOVA). The statistical results largely confirmed the proposed hypotheses. Notably, a significant relationship between tourist age and visit frequency was identified, indicating distinct behavioral patterns across age groups. Furthermore, perceptions of service quality varied significantly by gender, highlighting divergent experiences and expectations between male and female visitors. Full article

Electric control valves (ECVs) are core control components in process industries such as petrochemicals and power generation, and their operational reliability directly affects system safety and energy efficiency. However, frequent changes in the working conditions of ECVs cause vibration signals to exhibit strong nonlinearity and non-stationarity, which leads to the loss of high-frequency transient features, difficulty in extracting weak faults, and cross-condition domain shifts. These issues severely limit the generalization ability of existing fault diagnosis methods. To address this, this study proposes a collaborative fault diagnosis framework that combines a miniaturized high-frequency data acquisition system with a multi-scale attention-conditioned domain adversarial network (MS-ACDAN). First, a miniaturized high-speed data acquisition system is developed based on a field-programmable gate array (FPGA) to enable lossless acquisition of high-frequency transient signals. Subsequently, the original vibration signals are decomposed, filtered, and reconstructed using Adaptive Noise-Complete Empirical Mode Decomposition (CEEMDAN) and the Comprehensive Sensitivity Index (CSI) to generate feature-enhanced signals with high signal-to-noise ratios. Next, a feature extractor combining a one-dimensional convolutional neural network (1D-CNN) with a channel attention mechanism is constructed to automatically focus on key fault frequency band features while suppressing redundant information. Finally, a Conditional Adversarial Network (CDAN) is introduced for transfer learning. By establishing a conditional dependency between class prediction and feature representation. This approach achieves domain alignment while preserving the discriminative features of the data, thereby overcoming the limitation of traditional domain adaptation methods that ignore category information. The experimental results show that the proposed fault diagnosis framework demonstrates high recognition performance in various transfer tasks. Furthermore, even under extreme industrial noise conditions of 0 dB, the framework exhibits good diagnostic robustness. This research provides a theoretical basis and technical solution for addressing the fault diagnosis of critical control equipment under complex and variable working conditions. Full article

Vehicle-side weight is a key constraint in wireless power transfer (WPT) systems for electric vehicles, as it directly affects cost, installation, and vehicle integration. This paper presents a lightweight Vehicle Assembly (VA) based on a conventional Series–Series (SS) compensation topology and a phase-shift-controlled active rectifier, designed within the SAE J2954 framework. The architecture reduces vehicle-side passive components while enabling load adaptation through the rectifier conduction angle. A fixed-output-power time-domain methodology is used to compare two operating strategies. In the exact 2-ZVS mode, only two rectifier commutations satisfy the charge-based ZVS condition, whereas in the robust 4-ZVS mode all four commutations are constrained to achieve ZVS through joint optimization of the rectifier control parameters and switching frequency. In both cases, the primary DC voltage is adjusted to maintain a constant output power of 7.7 kW. Measured coupler parameters are used in the circuit model. Across the aligned position and two measured misalignment conditions, exact 2-ZVS achieves an estimated AC–AC resonant-link efficiency of 98.01–98.41% and a modeled DC–DC efficiency of 96.16–96.90%. Robust 4-ZVS remains feasible, but its higher circulating-current requirement reduces the corresponding efficiencies to 93.26–94.96%, respectively. Therefore, for the investigated system, exact 2-ZVS provides the best efficiency–soft-switching trade-off. The AC–AC metric includes only winding and capacitor-ESR losses. The DC–DC metric additionally includes the modeled conduction and output-capacitance transition losses of the primary inverter and active rectifier; gate-drive, control, and auxiliary losses are excluded. Full article

In high-throughput, high-concurrency, low-latency cryptographic scenarios, the throughput of public-key cryptography is a decisive performance metric. As China’s national elliptic curve cryptography standard, the SM2 signature algorithm has been widely adopted, yet scalar multiplication—the core primitive of SM2 signature—constitutes the dominant latency bottleneck. This paper proposes a high-throughput ASIC architecture that integrates precomputation with parallel task scheduling to accelerate SM2 signature generation. First, we design a Comb-algorithm-based precomputation hardware scheme for fixed-base scalar multiplication. The 256-bit scalar is partitioned into 32 segments, and 32 dedicated lookup tables are precomputed in on-chip SRAMs, which reduces fixed-base scalar multiplication to at most 31 elliptic curve point additions. Second, a six-arithmetic-unit parallel scheduling framework is developed for variable-base scalar multiplication. Equipped with two three-stage pipelined Montgomery multipliers and four modular adders, the design overlaps point addition and point doubling across pipeline stages to boost hardware resource utilization. Moreover, we build a 16-core parallel computing platform integrated with hardware task queues and DMA automatic scheduling, achieving efficient throughput scalability with the increase in core count. The proposed design has been taped out in the TSMC 28 nm CMOS process with completed physical design, including placement, clock tree synthesis, and routing. Post-layout simulation results, with full parasitic extraction (RC) and static timing analysis (STA), demonstrate that a single core achieves 89,593 signatures per second at a post-layout maximum frequency of 600 MHz. Its normalized area efficiency, measured as signatures per kilo gate equivalent (KGE), reaches 33.31 sig/s/KGE under the TSMC 28 nm process at 600 MHz. It should be noted that this metric is significantly influenced by the advanced process node and higher operating frequency; therefore, to enable a fairer assessment of intrinsic microarchitectural efficiency independent of process scaling, the frequency-normalized metric (sig/s/MHz/KGE) is adopted as the primary cross-design benchmark. Under this metric, our design achieves 55.5 × 10⁻³ sig/s/MHz/KGE, which is comparable to the 55.0 × 10⁻³ sig/s/MHz/KGE of the most area-efficient referenced design, with a marginal improvement of approximately 0.9%. The area efficiency comparison is presented only as a supplementary reference within a limited and clearly defined scope, acknowledging that the compared designs differ in functionality, process technology, and evaluation methodology. Furthermore, our design achieves the lowest Area–Time (AT) product of 30.03 KGE·ms among the compared works, indicating that our architectural innovation achieves a favorable AT trade-off for high-throughput applications rather than a fundamental shift in circuit efficiency. The 16-core parallel computing platform reaches an overall throughput of 1.03 million signatures per second. In addition, first-order arithmetic masking and key blinding are integrated into the scalar-multiplication data path to resist first-order side-channel attacks, and simulation-based TVLA evaluation indicates its leakage suppression capability under simulated conditions. Full article

Background/Objectives: Nutrition during school age is a key public health issue, as eating habits formed in this period may affect growth, cognitive development, and future health. This study aimed to assess food consumption frequency and age, gender, and socio-demographic differences in dietary patterns among schoolchildren aged 7–17 years in Karaganda, Kazakhstan. Methods: A cross-sectional study was conducted among schoolchildren aged 7–17 years attending state secondary schools in Karaganda, Kazakhstan, from September 2025 to February 2026. Food consumption frequency was assessed using a bilingual Russian–Kazakh questionnaire developed by the authors based on food frequency questionnaire approaches and adapted to the Kazakhstani context; the questionnaire included 41 food items, including national meat products and traditional fermented milk drinks, and Principal Component Analysis was applied to identify dietary patterns. Results: A total of 864 schoolchildren aged 7–17 years were included in the study, with comparable distribution by sex and age groups. Frequent consumption of fast food, coffee, processed and canned meat products increased markedly with age, while consumption of milk, fermented dairy products, juices, fresh fish and berries was lower, especially among older adolescents. Principal Component Analysis identified three dietary patterns—“processed and fast-food”, “traditional meat-based”, and “mixed dairy-based pattern”—with the processed and fast-food pattern more strongly associated with older age, non-Kazakh ethnicity, and school location in the South-East district. Conclusions: The findings indicate age-related shifts toward less balanced dietary choices among Karaganda schoolchildren, particularly among adolescents, supporting the need for targeted school- and family-based interventions to reduce fast food, sugary drink, coffee, and processed meat consumption while promoting healthier food choices. Full article

Targeting the rock burst hazard induced by the hard roof key stratum during deep mining at the Mengcun Coal Mine in the Binchang mining area, this study takes the No. 403109 working face as the engineering background and systematically investigates the rockburst prevention mechanism and effectiveness of ground hydraulic fracturing through theoretical analysis, UDEC numerical simulation, and surface microseismic monitoring. The results indicate that fracturing pre-weakens the overlying key stratum, transforming its load-bearing mode from a long-beam rigid support to a segmented flexible support. This significantly reduces the cantilever length, lowers the accumulation of elastic strain energy, and enables flexible load transfer and stress redistribution in the overburden. Numerical simulations reveal that after fracturing, the breakage timing of the key stratum advances, the fragmentation size decreases, and the over-burden movement shifts from stepwise fracturing to sequential caving, with the stress concentration zone substantially narrowed. In the field, a total of 44 fracturing stages were implemented in wells MC-05L and MC-06L, creating a fracture network with an average fracture length of 317 m and an average fracture height of 55 m, achieving an effective stimulated volume ratio of 86.7%. During the mining period, microseismic events exhibited a median energy of only 868.14 J, characterized by high frequency and low energy. The average weighting interval was 13.69 m, the peak coal stress was controlled within 5.0–6.7 MPa, and the loads on roadway bolts and cables remained within safe limits. This study validates the source-control effect of ground hydraulic fracturing on working faces with strong rock burst risks in deep mining, providing a theoretical basis and engineering reference for mines with analogous conditions. Full article

This study presents a comprehensive thermodynamic evaluation of several CO₂ conversion pathways, both non-catalytic and catalyst-assisted, using density functional theory (DFT) calculations in Gaussian 16 (B3LYP/6-311++G(d,p)). In the non-catalyzed section, three key routes are examined: hydrogenation (CO₂ + H₂ → CO + H₂O), dry methane reforming, and the reverse water–gas shift (RWGS). For the hydrogenation reaction, the Gibbs free energy change (ΔG) decreases from +0.018 to +0.005 Hartree as the temperature increases from 298.15 K to 1173.15 K, indicating a slight improvement in feasibility but still a high activation barrier of 0.326 Hartree, underscoring the need for catalysis. Dry methane reforming is both exothermic and spontaneous, with ΔG ≈ = −0.049 Hartree at 298.15 K and −0.030 Hartree at 593.15 K; however, operating under harsh conditions may accelerate degradation of reactor materials. In the catalyst-assisted section, copper surfaces and Cu₃M clusters (M = Sc, V, Ni, Cu, Co and Fe) are evaluated alongside two bimetallic catalysts, Fe₂ and Ni₂, under electrochemical CO₂ reduction (eCO₂RR) conditions. Scandium- and vanadium-doped clusters exhibit significant CO₂ adsorption, as evidenced by shifted vibrational frequencies between 800 and 1800 cm⁻¹ that signal C=O bond weakening. Under the evaluated thermobarometric conditions, Ni₂-containing systems displayed lower Gibbs energy values within their own optimized intermediate set and higher entropy values than the corresponding Fe₂-containing set, suggesting greater configurational flexibility and favorable stabilization trends. However, because Fe₂ and Ni₂ systems are chemically different, absolute total energies were not used as a standalone criterion for intrinsic catalytic superiority. Overall, while some non-catalytic routes become thermodynamically more favorable only at high temperature, the explicit inclusion of catalytic models, particularly doped Cu₃M clusters and Ni-containing systems, indicates enhanced CO₂ activation through stronger catalyst–adsorbate interactions, vibrational weakening of C=O bonds, and favorable electronic descriptors. These results suggest that catalytic systems may enable CO₂ conversion under milder conditions, although full kinetic confirmation requires comparative transition state calculations for each elementary catalytic step. Full article

Air travel induces substantial landside traffic through the movement of passengers, employees, suppliers, and cargo between airports and their surrounding regions. While this airport-induced landside traffic has received growing attention within airport sustainability research, its associated external costs remain insufficiently quantified. This study develops a modular model to estimate traffic volumes and associated external costs of airport-induced landside traffic. It accounts for key behavioral and operational parameters, including modal split, trip distances, occupancy rates, and trip frequencies, differentiated across user groups and transport modes. The model is applied to Germany as a case study using national mobility statistics, airport data, and external cost factors from European transport studies. The assessment covers greenhouse gas emissions, air pollution, accidents, noise, habitat damage, and upstream fuel supply impacts. Results indicate that airport-induced landside traffic generated external costs of approximately EUR 1.43 billion in Germany in 2019, with passengers and airport employees accounting for the largest shares. Accident costs and greenhouse gas emissions dominate the overall impacts. Sensitivity analyses further show that moderate behavioral changes, such as modal shifts toward public transport and increased vehicle occupancy, can significantly reduce external costs. The findings highlight the importance of integrating landside access into environmental assessments and sustainable airport planning. Full article

This study conceptualizes forest experiences into multidimensional values rather than a single metric and demonstrates that a statistically non-significant linear path does not imply theoretical irrelevance. Comprehensive evaluations of the structural pathways through which accumulated forest tourism experiences relate to quality-of-life value perceptions from a transformative perspective are lacking. We investigated the linear and nonlinear structural relationships of forest tourism activities on the quality-of-life value perceptions and overall satisfaction of the exchange population. Survey data from 1163 forest tourism-experienced respondents in South Korea were analyzed using a hybrid model combining partial least-squares structural equation model, Random Forest, and Shapley additive explanations (SHAP). Increased exchange frequency was linearly associated with social, growth-need, and intellectual values. Social, esthetic, emotional-health, and intellectual value perceptions were positively associated with overall satisfaction. Random Forest and SHAP analyses exploratorily supported Herzberg’s Motivation–Hygiene Theory’s applicability; maintaining Close Relationship was the dominant hygiene factor for the dissatisfied group, whereas Emotional Comfort and Discovery of Natural Beauty were key motivators for the satisfied group. These findings suggest shifting from linear satisfaction management to a systemic approach: using emotional-esthetic factors for initial influx and social-intellectual values for potential long-term engagement. Full article

Motivated by the increasing security requirements of next-generation satellite-terrestrial communication systems and the emergence of Open Radio Access Network (O-RAN) architectures, this paper presents a secrecy analysis of a novel reconfigurable intelligent surface (RIS)-assisted mixed free-space optical (FSO) and radio frequency (RF) satellite downlink transmission system within an O-RAN-enabled non-terrestrial network (NTN) framework. The inherent broadcast nature of RF transmissions presents significant eavesdropping risks, which serves as the primary impetus for this study. We analyze the combined effects of imperfect channel state information (CSI) and random link blockage within such integrated networks. The impact of discrete phase shift constraints at the RIS is also investigated. Closed-form expressions are derived for three key performance metrics: connection outage probability (COP), secrecy outage probability (SOP), and the probability of positive secrecy capacity (PPSC). Through high signal-to-noise ratio (SNR) asymptotic analysis, corresponding asymptotic expressions are obtained, and all analytical results are validated via extensive Monte Carlo simulations. Our findings demonstrate that: (i) Link blockage probability and channel estimation accuracy jointly govern the secrecy performance floor. (ii) Increasing the number of RIS elements enhances physical-layer security by driving both the COP and SOP toward their theoretical lower bounds. (iii) Improving channel estimation accuracy diminishes the eavesdropper’s channel advantage and improves the overall system security. These results offer valuable insights for designing secure mixed FSO/RF satellite-terrestrial systems within O-RAN-enabled NTN architectures that effectively balance connectivity and confidentiality. Full article

This review paper systematically investigates the surface modification technology of wooden table tennis blades, with a particular focus on the inherent conflict between coating-induced protection and the preservation of acoustic performance—a critical yet underexplored aspect of blade design. While protective coatings are essential for enhancing durability against moisture, wear, and impact, they inevitably alter the blade’s vibrational characteristics and acoustic feedback, compromising the tactile–auditory perception that elite players rely upon. The current literature predominantly treats protection and acoustics as separate design objectives, lacking an integrated framework to resolve their inherent trade-off. To address this gap, this review establishes a material–structure–function integrated design paradigm that elucidates the synergistic optimization of coating protection and acoustic response. We systematically analyze the regulatory mechanisms of key coating parameters—specifically elastic modulus, density, and damping coefficient—on blade vibration modes and impact sound characteristics, demonstrating that conventional singular optimization inevitably leads to undesirable frequency shifts and diminished tactile feedback. Our synthesis of materials science, acoustic analysis, and biomechanics reveals that the key to synergy lies in constructing a mechanical impedance-matching transition system through material selection and thickness gradient design. Notably, we show that a multi-layer gradient coating architecture, guided by finite element simulation, can enhance protective performance by 40% while restricting acoustic deviation to within 5%, validating a rational “design–simulation–verification” closed-loop methodology. Furthermore, this review identifies critical research frontiers, including smart adaptive coatings and sustainable bio-based materials, and proposes a multi-objective optimization framework to bridge the gap between laboratory innovation and manufacturable, high-performance sporting equipment. This work provides a foundational theoretical roadmap for the next-generation design of competition-grade table tennis blades, balancing durability with the nuanced sensory demands of elite athletes. Full article

Power transformers are core equipment in power grids, and polar substances in their insulating oil—such as furfural, methanol, water, and formic acid—serve as key biomarkers for assessing insulation condition. Traditional detection methods are time-consuming and operationally complex, making it difficult to meet the demand for rapid on-site testing. Terahertz spectroscopy, with its high sensitivity to polar molecules and non-destructive testing capabilities, shows great potential for assessing insulation oil aging. However, existing research has largely focused on the detection of single substances or overall condition assessment, and faces challenges such as limited spectral sample data and insufficient model generalization ability. In this study, a transmission-type terahertz time-domain spectroscopy detection platform was established, and insulating oil samples containing different volume concentrations of polar substances were prepared to obtain their absorption spectra. To address the challenge of training with a small sample size, we proposed a multi-strategy spectral data augmentation method that integrates Gaussian noise addition, baseline shifting and intensity scaling, and minor frequency-axis shifts, thereby expanding the trainable data volume to four times that of the original data. Based on this, we used principal component analysis to extract spectral features and established a support vector machine classification model for pattern recognition of the four polar substances mentioned above. The results show that the model without data augmentation achieved only 86.0% accuracy on the test set, indicating poor generalization ability; however, after applying data augmentation, the model’s recognition accuracy on the test set improved to 96.0%, with both recall and precision for each substance remaining above 90.0%, effectively overcoming the issue of overfitting. This study demonstrates that terahertz spectroscopy, combined with data augmentation and machine learning algorithms, enables rapid, high-precision, and non-destructive identification of polar substances in insulating oil, thereby offering a potential new technical pathway for transformer insulation condition assessment. Full article