Search Results (4,671)

Brown bear populations in Greece face multiple threats, and parasitic infections may pose an additional risk to these vulnerable animals. This study represents the first comprehensive assessment of endoparasite occurrence, prevalence, and seasonality in brown bears in Greece, in relation to geographical location and the animal’s different physiological phases. A total of 918 faecal samples were collected over a three-year period from regions with brown bear presence in Greece. For each sample, the date of collection and the coordinates of the site were recorded. Samples were examined using sedimentation, flotation, and McMaster techniques, while the Baermann method was additionally applied to a subset of 195 samples. Spatial and temporal patterns in parasite occurrence and diversity were analysed using generalised additive models (GAMs). Ten parasitic taxa were identified, with Baylisascaris transfuga being the most prevalent (39.8%), followed by Crenosoma spp. (26%), Uncinaria spp. (18.09%), and Dicrocoelium dendriticum (14.38%). Less prevalent taxa included Eucoleus aerophilus, Sarcocystis spp., Toxascaris leonina, Eimeria spp., Linguatula serrata, and Taeniidae. Μixed infections, involving two or more parasites, were detected in 22% of the samples. The prevalence of B. transfuga was higher in late autumn, with high-risk infection areas identified in both late summer and autumn. In contrast, Uncinaria spp. and D. dendriticum showed no seasonal variation, while D. dendriticum exhibited spatial clustering patterns similar to B. transfuga but without clear seasonal trends. These findings highlight the widespread occurrence and complexity of parasitic infections in Greek brown bears. Continued long-term monitoring is essential to improve understanding of transmission dynamics and the ecological processes shaping parasite distribution in this animal species. Full article

Intelligent cost control in engineering, procurement, and construction (EPC) projects depends on the continuous transmission, updating, warning, correction, and reuse of cost data across multiple project stages. To analyse the resilience of this process, this study constructs an EPC project cost-data transmission network using complex network theory and Monte Carlo simulation. Eighteen core nodes and 27 directed weighted edges are identified according to EPC cost-management logic and expert evaluation. Node importance is analysed using weighted degree centrality, betweenness centrality, and PageRank, while network efficiency is used to evaluate cost-data reachability and transmission-path efficiency. Node failure, edge-weight perturbation, random edge failure, random failure and targeted attack, feedback enhancement, critical-node failure–recovery, and robustness checks are then conducted. The results show that Dynamic cost, Cost deviation warning, and Historical cost database are the three most critical nodes. Their failures reduce network efficiency by 44.54%, 37.43%, and 45.27%, respectively. Random edge failure has a stronger effect on network efficiency than edge-weight perturbation; when the edge failure probability increases from 5% to 20%, the average efficiency loss rate rises from 10.54% to 37.30%. Feedback-link enhancement increases network efficiency from 0.1858 to 0.2009 and produces a larger improvement than forward-link enhancement and random seven-edge enhancement. Robustness checks under alternative network assumptions indicate the relative stability of the critical-node identification results within the proposed network structure. The findings provide a scenario-based network perspective for identifying structurally critical nodes, vulnerable transmission links, and feedback-improvement priorities in EPC cost-data transmission. They also offer a methodological basis for future project-level calibration using BIM/5D BIM records, procurement data, cost-management platform logs, and settlement audit data. Full article

Under the “dual carbon” targets, using green finance to support renewable energy use is an important way to reduce extreme climate risks. This study builds a balanced panel dataset of 271 Chinese cities from 2010 to 2021. We measured the level of Green Finance (GF) and renewable energy utilization (RE). Employing two-way fixed effects, the Spatial Durbin Model (SDM), and the Heterogeneous Spatial Autoregressive (HSAR) model, we systematically examine the promoting effects, transmission mechanisms, spatial heterogeneity, and economic–environmental consequences of GF on RE. The empirical results reveal that GF significantly enhances RE and generates pronounced positive spatial spillovers. Mechanism analysis indicates that R&D investment and environmental regulation serve as the primary transmission channels. The promotion effect is more pronounced in the eastern and central regions, as well as in areas with higher R&D investment and stricter environmental regulation, whereas the spatial spillover effect is particularly evident in coastal regions. Further consequence analysis demonstrates that GF contributes to reducing conventional energy intensity, improving green total factor productivity, and alleviating extreme climate events. Building on these findings, this study proposes spatially differentiated and sustainability-oriented policy strategies to advance China’s energy transition and foster coordinated economic and environmental sustainability. Full article

This study introduces a Caputo fractional-order version of the SEIRS-V model to investigate the spreading dynamics of worms within wireless sensor networks. Traditional integer-order worm propagation models describe the instantaneous evolution of network states; however, they do not adequately account for memory and hereditary characteristics that may influence the transmission dynamics. Consequently, their ability to represent realistic network behavior can be limited in systems where past states affect current propagation patterns. The framework divides sensor nodes into susceptible, exposed, infectious, recovered, and vaccinated classes, while explicitly incorporating worm transmission rates, temporary loss of immunity, and the impact of preventive security measures under limited resource conditions. A detailed theoretical examination is performed, covering the existence, boundedness, and uniqueness of solutions of the fractional-order system. The coupled nonlinear fractional system is solved semi-analytically by means of the Fractional Reduced Differential Transform (FRDT) technique. To confirm accuracy and robustness, the identical system is also discretized and solved using the finite difference scheme (FDS). Unlike previous studies on worm propagation models in wireless sensor networks, which are mainly limited to equilibrium point analysis and qualitative investigations without deriving explicit solutions, the present work develops an approximate semi-analytical solution for the fractional-order SEIRS-V system using the FRDTM. Comparisons between the two solution sets demonstrate excellent agreement and high precision. Numerical outcomes are presented through a series of 2D graphical profiles that illustrate the time-dependent behavior of each compartment and reveal the sensitivity of worm propagation and suppression to variations in the fractional order and key model parameters. The integrated theoretical and computational findings underscore the strong protective role of vaccination in mitigating worm outbreaks and offer valuable guidelines for strengthening cybersecurity measures in wireless sensor networks. Full article

Insulated concrete sandwich panels (ICSPs) are widely utilized in modern building structures due to their excellent combination of energy efficiency and structural load-bearing capacity. However, compared to their mechanical and thermal properties, the sound insulation characteristics of ICSPs remain insufficiently studied, presenting a scientific deficit. In practical engineering, insufficient consideration of these acoustic properties—particularly the “acoustic bridging” induced by connectors—often leads to unpredictable noise transmission, making it difficult for building envelopes to meet stringent modern acoustic codes. To further investigate their acoustic characteristics, this paper extends existing theories on infinite periodic ICSPs to study the airborne sound insulation performance of finite-sized ICSPs. First, analytical models for ICSPs under simply supported on all edges (SS) and clamped on all edges (CC) boundary conditions are derived, wherein the connectors are equivalently modeled as elastic media and discrete elastic springs, respectively. Subsequently, the accuracy and applicability of the analytical models are verified through finite element (FE) models and an airborne sound insulation experiment. Finally, based on the analytical models, a parametric study is conducted to explore the effects of the stiffness of connectors, boundary conditions, and the thickness of the core layer on the sound insulation performance of the ICSPs. The results indicate that connector stiffness has a non-monotonic influence on the sound insulation performance of ICSPs. As the connector stiffness increases, the R_w first decreases and then increases, and the sound insulation performance gradually stabilizes when the connector stiffness becomes sufficiently high. Boundary conditions have a significant effect on the acoustic response. For the reference ICSPs, changing the boundary condition from SS to CC increases the R_w from 49 dB to 62 dB, corresponding to an increment of 13 dB and an approximately 95.0% reduction in the equivalent sound transmission coefficient. When the total panel thickness is kept constant, reducing the core layer thickness from 80 mm to 40 mm increases the R_w from 49 dB to 55 dB under SS boundary conditions and from 62 dB to 66 dB under CC boundary conditions, corresponding to increments of 6 dB and 4 dB, respectively. These improvements are equivalent to reductions of approximately 74.9% and 60.2% in the sound transmission coefficient, though this must be weighed against the inevitable reduction in thermal insulation capacity. Although the sound insulation performance of ICSPs is inferior to that of solid concrete panels (SCPs) of equivalent thickness, with reasonable parameter optimization, their sound insulation indices can significantly exceed the latest requirements of current building codes. By fully accounting for boundary effects in practical engineering, this study provides an analytical basis for the acoustic performance prediction and engineering-oriented optimization of finite-sized ICSPs. Full article

The high prevalence of cardiovascular diseases and the extensive application wearable electrocardiogram (ECG) devices for long-term monitoring have posed significant challenges for the transmission, storage, and real-time processing of massive amounts of ECG data. Consequently, efficient ECG compression and reconstruction have become a research priority in remote ECG monitoring. Traditional compressed sensing is complex and has high computational overhead, while single deep learning models cannot simultaneously extract local waveforms and model temporal dependencies. To address these shortcomings in the reconstruction process, this paper presents a CNN-LSTM-Attention hybrid model. This model utilizes a convolutional neural network (CNN) to capture local ECG waveform features, employs a long short-term memory (LSTM) network to learn long-term temporal dependencies, and introduces an attention mechanism to weight and fuse key diagnostic features, enabling accurate focus on key components including the QRS complex and ST segment. Experimental results on the MIT-BIH Arrhythmia dataset demonstrate that across the full compression range of 0.1–0.9, the proposed model achieves favorable comprehensive performance. Its PRD is stabilized at 10–12%, the SNR stays above 20 dB, and the RMSE is mostly lower than 0.25 mV. In terms of reconstruction accuracy and stability, our model outperforms the single CNN and CNN-LSTM models by a large margin. Full article

Acoustic propagation in stratified shallow seas is governed by finite-depth waveguiding, impedance contrasts at the seawater–seabed interface, and coupled space–time wave dynamics. Conventional numerical solvers are accurate but often require detailed environmental priors, mesh generation, and explicit time marching, increasing the cost of simulations involving complex boundaries or repeated evaluations. This study proposes a physics-informed residual network (ResNet-PINN) for continuous simulation of two-dimensional acoustic fields in shallow-sea stratified media. The framework embeds a variable-density, variable-sound-speed acoustic pressure wave equation, initial and boundary constraints, and interface-focused collocation into network training. A Gaussian initial wave packet and temporal gating are incorporated through the output transformation to improve early-time physical consistency. The model is validated against SPECFEM2D simulations and a stratified semi-analytical modal benchmark. The results show that it captures source-region spreading, main wavefront evolution, and transmission–reflection structures near the seawater–seabed interface at an equivalent frequency of approximately 477 Hz. Supplementary tests with sloping and arched interfaces and modified boundary conditions indicate adaptability to smooth interface variations. Overall, the framework provides a physically consistent neural network strategy for continuous shallow-sea acoustic field simulation and a complementary basis for future extensions to higher-frequency propagation, more complex environments, and dynamically varying ocean conditions. Full article

One of the bottlenecks in realizing all-optical computing is the lack of on-chip all-optical logic devices that combine compactness, low loss, and high robustness. Valley photonic crystals (VPCs) have become an important solution for realizing such devices, relying on the excellent transmission characteristics of topological valley states. However, existing structures still face issues such as limited design flexibility. In this paper, a high-performance topological all-optical logic device based on VPCs consisting of circular ring dielectric columns is designed and demonstrated. By introducing the inner radius as an independent design parameter, we construct a new type of VPC and systematically investigate its influence on the photonic band gap. Based on this, we design a beam splitter with high operational bandwidth and low insertion loss (<0.5 dB) and then realize fundamental OR and XOR logic gates, achieving extinction ratios of 18.9 dB for the OR gate and up to 44 dB for the XOR gate at an operating frequency of 193.5 THz. The platform also supports the NOT gate and, through cascading, can implement more logic functions such as the AND gate. Full article

Quantum Secure Multi-party Summation (QSMS) is a fundamental primitive of Quantum Secure Multi-party Computation (QSMC), enabling multiple participants to jointly compute the sum of their private inputs without disclosing individual data. However, most existing QSMS protocols require all participants to possess full quantum capabilities and often rely on pre-shared keys, auxiliary mask transmission, or multiple trusted third parties, resulting in high communication overhead and limited practicality. To address these limitations, we propose an efficient Semi-Quantum Secure Multi-party Summation (SQSMS) protocol based on d-dimensional n-particle entangled states. By exploiting the global correlation properties of high-dimensional entangled states, the proposed protocol generates correlated random masks directly from quantum measurement outcomes. These masks cancel automatically during the aggregation process, eliminating the need for additional mask distribution and transmission. Compared with existing QSMS schemes, the proposed protocol reduces communication overhead, improves quantum efficiency, and avoids reliance on pre-shared keys or multiple trusted third parties. Moreover, only simple measurement operations are required from classical participants, making the protocol more practical for semi-quantum environments. We further provide formal correctness and security analyses of the proposed protocol and conduct quantum circuit simulations using the IBM Qiskit platform to demonstrate its feasibility. Moreover, based on the proposed summation protocol, we design several extended application protocols, including anonymous voting, anonymous auction, and anonymous ranking, which further illustrate the scalability and practical applicability of the proposed scheme. Full article

Millimeter-wave (mmWave) technology is essential for meeting the reliable connectivity and high-capacity demands of autonomous driving applications. Vehicle-to-infrastructure (V2I) networks have been modeled and analyzed based on stochastic geometry (SG) in many studies. However, most studies focus only on two-dimensional (2D) antenna models and disregard a key characteristic of V2I networks, i.e., the rapid mobility of vehicles. In this work, a three-dimensional (3D) coverage and connectivity analysis framework is proposed for mmWave V2I downlink transmission based on SG. First, a realistic 3D system model is developed, which includes 3D transmission channel, blockage, and antenna array models. Then, exact expressions for the coverage probability, connectivity probability, and effective throughput of a typical vehicle are derived. Finally, the theoretical analysis is validated through simulation results, which also reveal that an optimal density of roadside units (RSUs) that maximizes spectral efficiency exists and that disregarding the effect of the vertical beam of a 3D antenna array can lead to inaccurate evaluations. Moreover, appropriately setting system parameters can mitigate the negative impact of high vehicular mobility on connectivity performance. Full article

Electromagnetic shielding has become a practical concern in buildings and structures exposed to persistent interference. This paper reports experimental measurements of the frequency-dependent shielding properties of a square-resonator-integrated double-concrete structure, using a free-space S-parameter setup built around WR229 waveguide adaptors and horn antennas. Three variables were tested: concrete thickness D, relative permittivity ε_r, and relative magnetic permeability μ_r. Both ε_r and μ_r were characterized experimentally from carbon-fibre- and copper-slag-modified concrete rather than taken from standard tables. The novelty of the study lies in combining experimentally characterized concrete electromagnetic properties, an embedded square-resonator geometry, and explainability-driven machine learning analysis within a single experimental framework for cement-based EMI shielding design. A total of 96 parameter combinations were evaluated using calibrated S₁₁ and reference-corrected S₂₁ responses across 3.3–4.9 GHz. Thickness and electromagnetic material properties interacted—neither governed shielding performance on its own. The strongest transmission attenuation occurred at D = 5, ε_r = 7, and μ_r = 1.2, where minimum S₂₁ reached approximately −62.98 dB at 3.6392 GHz. S₁₁ varied considerably less than S₂₁ across the tested combinations, suggesting transmission suppression is the dominant mechanism rather than reflection enhancement. A machine learning analysis confirmed that nonlinear ensemble models outperformed the linear baseline and identified thickness as the most influential predictor of minimum S₂₁. Full article

This paper proposes and experimentally demonstrates a fiber ring laser (FRL) salinity sensing system based on a Sagnac loop (SL) formed by a tapered polarization-maintaining fiber (TPMF). The operating principle is that salinity modulates the birefringence of the polarization-maintaining fiber (PMF), causing a shift in the interference wavelength of the SL transmission spectrum, while the FRL narrows the optical spectrum and enhances the signal-to-noise ratio (SNR). In the experiment, the SL consists of a 20-cm-long PMF with a tapered waist diameter of 10.86 μm. Over the salinity range of 0‰ to 30‰, the sensitivity of the laser-based sensing system is 97 pm/‰, which agrees well with the 93 pm/‰ sensitivity obtained using a broadband light source (BBS), and the salinity exhibits a good linear relationship with the wavelength shift, with a coefficient of determination (R²) of 0.997. Meanwhile, the ring laser cavity improves the SNR of the sensing system from 22 dB to approximately 54 dB, and compresses the 3-dB bandwidth from 1.75 nm to 0.06 nm. Further adopting the support vector regression (SVR) algorithm for linear regression modeling of the spectral data, the results show that the mean absolute error (MAE) decreases from 0.50‰ to 0.04‰, the root mean square error (RMSE) decreases from 0.54‰ to 0.11‰, and R² reaches as high as 0.99988. To the best of our knowledge, this is the first work that combines salinity laser sensing with an artificial intelligence algorithm. The proposed sensor leverages the narrow linewidth and high SNR advantages of the FRL together with the high-precision linear fitting capability of the SVR algorithm, achieving significantly improved accuracy for salinity measurement compared to conventional spectral demodulation. Full article

To reveal the varieties and color genesis of emerald-green tourmaline crystals from Tanzania, a systematic study was conducted using conventional gemological tests, X-ray diffraction, Fourier-transform infrared spectroscopy, polarized ultraviolet–visible spectroscopy (UV–vis), X-ray photoelectron spectroscopy (XPS), low-temperature photoluminescence (PL) spectroscopy, and electron probe microanalysis (EPMA). The results indicate that the tourmaline is dravite. Its UV–vis absorption spectrum shows strong broad absorption bands at approximately 436 and 600 nm, with a pronounced transmission at 520 nm, which directly accounts for its emerald green color. Obvious polarized absorption was observed along and perpendicular to the c-axis. XPS and PL results confirm that chromium is present in the samples in the form of Cr³⁺. EPMA compositional analysis indicated a low Cr₂O₃ content of 0.804 wt.%; combined with crystal structural properties and spectral responses, these results suggest that Cr³⁺ preferentially occupies the Y site in the crystal structure and that d–d electronic transitions represent the underlying mechanism of its color formation. This study comprehensively illustrated the mineralogical and spectral properties of Cr-bearing dravite, providing fundamental data for further research on its genesis and gemological application. Full article

In radio-over-fiber (RoF) links, optical single-sideband (OSSB) modulation is an effective method to mitigate power fading caused by chromatic dispersion. However, its low modulation efficiency leads to suboptimal link performance. To address this, we propose a tunable optical carrier-to-sideband ratio (OCSR) OSSB modulation scheme based on a dual-electrode Mach–Zehnder modulator (DEMZM) in a Sagnac loop. Firstly, by adjusting the OCSR, higher radio-frequency (RF) transmission efficiency can be achieved. The experimental results demonstrate that the proposed link provides a 6 dB improvement in received RF power compared to conventional SSB modulation schemes. Furthermore, this approach effectively optimizes nonlinear distortions in the link, achieving a 12.14 dB enhancement in spurious-free dynamic range (SFDR). For tests conducted with a broadband signal featuring a 15 GHz carrier frequency and 500 MHz bandwidth, the optimal error vector magnitude (EVM) reaches 4.88%. Additionally, the link performance can be flexibly improved by adjusting the polarization controller configurations for each channel, making it suitable for multi-user application scenarios. Full article

Intrusion detection serves as a core technology for securing heterogeneous edge networks, including IoT, industrial edges, and 5G networks. However, existing federated learning-based intrusion detection systems suffer from environmental heterogeneity, limited sample availability, and severe class imbalance—issues that result in inefficient resource allocation and compromised detection performance against rare attacks. In this paper, we propose a novel lightweight intrusion detection model for heterogeneous edge networks, named FedNIDS-CNN, which is based on dynamic distillation-aided federated learning with a CNN backbone. In the data preprocessing phase, a two-level class balancing strategy integrating nearest-neighbor interpolation augmentation and adaptive synthetic sampling is employed to ensure distortion-free sample synthesis. For feature and model optimization, principal component analysis (PCA) is used to reduce the dimensionality of traffic features, while a lightweight 1D-CNN is adopted as the base model to alleviate computational overhead on edge devices. During federated training and knowledge aggregation, a dynamic weight distillation loss mechanism is designed to enhance the model’s ability to recognize minority-class attacks. Meanwhile, the federated framework supports client-side local training and server-side weighted soft-label aggregation, enabling effective knowledge fusion across heterogeneous models. Experimental results on the CICIDS2017 dataset demonstrate that the proposed method achieves an accuracy of 98.55% and an F1-score of 98.40%. Benefiting from the soft-label transmission and parameter-free aggregation design, the framework gets rid of the constraint of homogeneous model architecture and natively supports heterogeneous network models and edge devices with different computing capabilities. It also significantly reduces communication traffic and per-round training latency, confirming its excellent real-time performance and applicability in resource-constrained edge environments. Full article