Search Results (253)

Background/Objectives: Endocrine-disrupting chemicals (EDCs) interfere with hormonal homeostasis and have been implicated in gynecological malignancy pathogenesis. This narrative review synthesizes current evidence regarding EDC exposure and breast, endometrial, ovarian, and cervical cancers, examining molecular mechanisms, epidemiology, and diagnostic and clinical implications. Methods: We conducted a literature review using PubMed/MEDLINE, Embase, Scopus, and Cochrane databases through April 2026, including systematic reviews, meta-analyses, prospective cohorts, case-control studies, and mechanistic investigations examining EDC-cancer associations. Methodological quality was appraised using the Newcastle-Ottawa Scale and AMSTAR-2, with overall certainty of evidence rated using the GRADE framework. Results: Major EDC classes—bisphenol compounds, phthalates, polychlorinated biphenyls, organochlorine pesticides, and per- and polyfluoroalkyl substances—demonstrate carcinogenic potential through estrogen receptor modulation, epigenetic alterations, oxidative stress, and oncogenic signaling disruption. Breast cancer shows the strongest evidence, with prenatal and early-life DDT/DDE exposure associated with up to a 3.7-fold increased risk. Endometrial cancer demonstrates associations with xenoestrogen mixtures exhibiting non-monotonic dose-responses, whereas ovarian and cervical cancers show emerging but limited associations. Common mechanisms include receptor crosstalk, epigenetic dysregulation with transgenerational effects, oxidative genomic instability, metabolic reprogramming, and cancer stem cell enrichment. Conclusions: Evidence supports EDC contributions to gynecological malignancy through convergent pathways, though causal inference remains constrained by observational epidemiology, long latency periods, and challenges in characterizing real-world mixture exposures. Diagnostic and prevention strategies should integrate EDC exposure into risk-prediction models, leverage multi-omics biomarkers for early detection, and emphasize exposure reduction during critical developmental windows alongside regulatory reform. Full article

Blind image watermarking models such as HiDDeN have laid an important foundation for end-to-end watermarking. Nevertheless, they still suffer from three major limitations: single-scale feature extraction, fixed fusion weights, and slow training convergence. To address these issues, this paper proposes an adaptive multi-scale watermarking algorithm based on collaborative dual-attention mechanisms. The algorithm designs an adaptive multi-scale feature fusion module (MA-FFM) with a dynamic gating network in the encoder, which flexibly combines local multi-scale textures with global contextual information, overcoming the limitation of fixed fusion weights. In the decoder, a multi-level channel attention module is embedded to strengthen the extraction of watermark signals. The two attention modules work synergistically: the encoder focuses on adaptive feature fusion while the decoder leverages channel attention to selectively enhance watermark-related features, forming a dual-attention synergy that balances robustness and imperceptibility. Moreover, the dynamic gating network adaptively adjusts the contribution of local versus global features via learnable weights, whose evolution from approximately 0.51 to about 0.89 improves model interpretability. Experiments are conducted on the COCO 2017 dataset. Compared with HiDDeN, the proposed algorithm reduces the bit error rate (BER) from 0.1696 to 0.1538 under no attack with a relative reduction of 9.3%, increases PSNR by 0.61 dB, and improves SSIM from 0.9058 to 0.9077. Under various attacks—including JPEG compression, Gaussian noise, salt-and-pepper noise, and brightness/contrast adjustments—the BER remains consistently lower than that of HiDDeN. Ablation studies confirm the effectiveness of each module. Overall, the proposed algorithm preserves visual quality, improves the accuracy of watermark embedding and extraction, and exhibits strong generalization robustness against common image distortions. Full article

With the increasing prevalence of uncertainties and variability in modern energy systems, model predictive control (MPC) often faces the challenge of predictive model mismatch. This paper proposes a desired-dynamics-based predictive control (DDPC) framework, in which an inner shaping layer is introduced to transform the raw plant into a desired dynamic model for the outer MPC. A unified design methodology is developed, including equivalent-model construction, desired-dynamics selection, and two inner-layer realizations based on desired dynamic equation (DDE)-PID and active disturbance rejection control (ADRC). In this way, the prediction model used by MPC is no longer the original uncertain plant but an explicitly shaped equivalent model determined by inner-layer controller parameters. The proposed method is validated on linear and nonlinear benchmark plants, together with frequency-domain and Monte Carlo robustness analyses. Results show that DDPC improves disturbance-rejection ability and enhances robustness against model mismatch and parameter perturbations. Further evaluation on the superheated steam temperature loop of a high-fidelity 660 MW coal-fired boiler hardware-in-the-loop simulator shows that DDPC reduces the peak-to-peak temperature fluctuation from 22.88 °C to 11.39 °C in the deep peak shaving scenario, corresponding to a 50.2% reduction relative to standard MPC. Full article

Vertical axis wind turbines (VAWTs) have regained attention for distributed, urban, and floating offshore applications, yet the literature remains fragmented across competing rotor concepts and modelling traditions. This review consolidates the principal archetypes—Savonius, H-Darrieus, troposkein Darrieus, helical Darrieus, and Savonius–Darrieus hybrids—through five governing parameters: drag-versus-lift-driven operating principle, tip speed ratio $\mathsf{\lambda }=\mathsf{\omega }\mathrm{R}/{\mathrm{V}}_{\infty }$ (0.6–1.2 for Savonius; 2.5–5.0 for Darrieus), solidity $\mathsf{\sigma }=\mathrm{N}\mathrm{c}/\mathrm{R}$ (0.1–0.4), chord-based Reynolds number ${\mathrm{R}}_{\mathrm{e}\_\mathrm{c}}$ (${10}^5–{10}^6$), and peak power coefficient ${\mathrm{C}}_{\mathrm{p}\_\mathrm{m}\mathrm{a}\mathrm{x}}$ (0.15–0.25 for Savonius; 0.35–0.45 for optimized H-Darrieus). Off-design performance is dominated by unsteady mechanisms that quasi-steady streamtube models cannot resolve—leading edge vortex shedding, dynamic stall hysteresis, blade–wake interaction, and flow-curvature-induced virtual camber—each examined for its contribution to the instantaneous torque ${\mathrm{C}}_{\mathrm{T}}\left(\mathsf{\theta }\right)$ and the cycle-averaged ${\mathrm{C}}_{\mathrm{p}}$. Turbulence closures are benchmarked against phase-locked PIV and torque measurements: $\mathrm{k}–\mathsf{\omega }\mathrm{S}\mathrm{S}\mathrm{T}$ URANS captures peak-region ${\mathrm{C}}_{\mathrm{p}}$ to within $\pm 5–10\%$ but over-predicts torque below ${\mathsf{\lambda }}_{\mathrm{o}\mathrm{p}\mathrm{t}}$; the $\mathsf{\gamma }–{\mathrm{R}}_{\mathrm{e}\_\mathsf{\theta }}$ transition SST model reduces this error to $\pm 3–5\%$; DES, DDES, and LES reach $\pm 2–3\%$ at one to two orders of magnitude higher cost. Best practice computational fluid dynamics (CFD) guidelines are consolidated: domain extents of 15D upstream, 10D downstream, and 20D lateral; rotating sub-domain ${\mathrm{D}}_{\mathrm{r}\mathrm{o}\mathrm{t}}$ $\approx 1.5\mathrm{D}$; ${\mathrm{y}}^{+}\le 1$; $\Delta \mathsf{\theta }\le 0.1°$; and 20–30 revolutions before sampling. Performance enhancement strategies (variable pitch, guide vanes, helical twist, and hybridization) are reviewed quantitatively, with reported ${\mathrm{C}}_{\mathrm{p}}$ gains of $5–30\%$. Four research priorities are identified: (i) transition-sensitive turbulence closures validated below ${\mathrm{R}}_{\mathrm{e}\_\mathrm{c}}$ = $5\times {10}^5$; (ii) coupled aero-hydro-servo-elastic models for floating offshore VAWTs; (iii) machine-learning-augmented turbulence modelling—including physics-informed neural networks (PINNs) and neural-network-corrected RANS closures—to improve unsteady flow prediction at sub-LES cost; and (iv) integrated aeroacoustic–aeroelastic frameworks for urban and building-integrated deployment. Full article

Polychlorinated biphenyls (PCBs) remain a major concern in marine ecosystems, where bioaccumulation in fish occurs as complex congener mixtures whose dynamics challenge conventional indicator approaches. This study develops and evaluates a data-driven framework for refining mixture-based indicators of PCB contamination by integrating ensemble machine learning with explainable artificial intelligence. Focusing on PCB-138 as a target indicator of cumulative PCB burden, we analyse concentrations of 24 organochlorines together with biological covariates in four Mediterranean edible pelagic fish species (sardine, anchovy, horse mackerel, and chub mackerel). Comparative evaluation of indicator performance shows that alternative congener combinations, including i4 PCBs (-138, -153, -170, -180), i6 PCBs (-138, -153, -170, -180, -118, -123), and mixtures incorporating DDD and DDE, more effectively represent total PCB burden than traditional indicator groups. Clustering identifies two distinct bioaccumulation settings, characterized by high-concentration coherent congener effects and low-concentration heterogeneous responses, demonstrating that indicator performance depends on concentration range and mixture context. The study illustrates how interpretable machine learning approaches can serve as formal tools for indicator evaluation and optimisation, strengthening long-term monitoring and management of legacy contaminants in marine ecosystems, particularly under conditions of persistent exposure and renewed inputs from sediment remobilization and riverine transport. Full article

Soil contamination by complex pesticide mixtures poses a systemic threat to ecosystem health, yet the mechanisms of microbial community assembly under the coexistence of legacy and modern pollutants remain insufficiently understood. This study evaluated the influence of legacy organochlorine pesticides (OCPs) versus current-use agrochemicals on the structure and inferred functional potential of soil bacterial communities in the Almaty Region, Kazakhstan, using high-throughput 16S rRNA gene sequencing and GC–MS/MS analysis of 217 compounds. Results revealed a clear contrast between contamination regimes: modern organophosphate insecticides and herbicides, such as simazine (up to 32.3 mg kg⁻¹ at the Amangeldy site), were associated with lower alpha diversity (Shannon ≈ 3.03) and enrichment of copiotrophic taxa such as Pseudomonas and Sphingobium. In contrast, persistent OCP residues, such as p,p′-DDE (up to 1.43 mg kg⁻¹ at the Kyzylkairat site), were associated with higher diversity (Shannon ≈ 5.46) and enrichment of more stress-tolerant oligotrophic lineages, including Acidobacteria and Vicinamibacteraceae. Procrustes analysis supported significant concordance between pesticide profiles and taxonomic structure (M² = 0.286, p < 0.001), indicating that pesticide residue composition was strongly associated with bacterial community structure across the studied soils. The observed shift in community balance, particularly the relative increase in Pseudomonas versus Acidobacteria, is proposed as a candidate compositional indicator of ecosystem instability in semi-arid agricultural soils and may inform future remediation-oriented studies. Full article

Trans,trans-2,4-decadienal (tt-DDE), the primary aldehyde component found in cooking oil fumes, is a prevalent environmental pollutant. However, its potential adverse effects on ocular development remain largely unexplored. This study evaluated its toxicity on ocular development and angiogenesis in zebrafish larvae, as well as on human retinal vascular endothelial cells (HRECs). Zebrafish (Danio rerio) larvae at 48 h post-fertilization were microinjected intraocularly with various doses of tt-DDE (65.87–521.3 mM) for 24 h. We observed dose-dependent impairments in ocular development following tt-DDE exposure. It significantly reduced eye size and inhibited the intraocular vascular area at concentrations of 128.9 mM and above. Histopathological analysis revealed retinal structural disorganization, eye shrinkage, and a clear dose-dependent increase in acridine orange (AO) fluorescence intensity. Apoptosis assays confirmed a significant escalation in ocular cell death at higher exposure doses. Additionally, our results demonstrated that tt-DDE (5–100 μM) significantly reduced the viability of HRECs in vitro. These findings suggest that early-life exposure to tt-DDE impairs ocular development in zebrafish by inducing histopathological damage, inhibiting angiogenesis, and promoting apoptosis, and also exerts direct cytotoxicity to human retinal cells. This study underscores the potential risk of tt-DDE exposure as an environmental factor contributing to ocular developmental toxicity. Full article

Accurate reservoir dam detection in arid regions is challenging because of spectral similarity between dams and surrounding backgrounds, indistinct boundaries, and substantial target-scale variation. To address these issues, this study proposes a deep learning object detection with multi-conditional constraint assistance (DLOD&MCCA) framework that combines a dual deep enhancement YOLO network (DDE-YOLO) with a multi-conditional constraint assistance (MCCA) strategy. In DDE-YOLO, visible (VIS) and near-infrared (NIR) imagery are fused to enhance cross-spectral discrimination, while task-oriented architectural refinements improve the representation of dam targets with diverse scales and structural characteristics. Meanwhile, the MCCA strategy constrains the search space to geographically plausible candidate regions, thereby reducing background interference and improving detection efficiency. Experiments conducted on the self-constructed S2-Dam dataset and the public DIOR dataset show that DDE-YOLO achieves mAP50 values of 92.8% and 76.2%, respectively, outperforming existing state-of-the-art (SOTA) methods. Furthermore, regional-scale dam mapping in Xinjiang achieved an accuracy of over 95%, demonstrating the effectiveness and practical applicability of the proposed framework for large-scale reservoir dam detection in arid environments. Full article

As a reliable and high-potential wireless communication technology for the Internet of Things (IoT), LoRa excels in long-distance and low-power transmission. The star topology adopted by traditional LoRaWAN suffers from poor deployment flexibility and insufficient scalability in scenarios with complex terrain or harsh environments. LoRa-Mesh networks can effectively solve coverage challenges through characteristics such as multi-hop and self-organization; however, the relay and forwarding requirements of nodes also introduce new challenges in energy consumption management. To address the energy consumption management challenges of LoRa-Mesh, this paper proposes a Data-Driven Energy Saving (DDES) protocol. It flexibly sets and dynamically fine-tunes node sleep durations based on data changes, constructs an efficient energy-saving framework through uplink data streams, and implements precise control over nodes via downlink post-analysis messages to achieve on-demand energy saving. Simulation results in the smart agriculture scenario of soil moisture monitoring and irrigation show that compared with protocols without a sleep mechanism, the battery life of the LoRa-Mesh network using the DDES protocol is extended by approximately 20 times. The proposed protocol breaks through the limitations of fixed sleep schemes, realizes refined and flexible division of sleep regions, and exhibits significant advantages in LoRa network energy saving. Full article

In recent years, the recording of fish with vertebral malformations has attracted growing interest worldwide, as these malformations may be associated with exposure to xenobiotics. This study aimed to determine the presence and concentrations of pesticide residues (organochlorines and organophosphates) in coastal fish in Nayarit, Mexico, and to assess their potential association with vertebral malformations. From November 2013 to September 2021, 32 fish, with visible malformations were conveniently collected, an equal number of healthy specimens per species was selected for comparative analysis. The fish exhibited vertebral malformations of the following types: kyphosis, lordosis, and scoliosis in 9, 8 and 6 species, respectively, while the total number of malformations was higher across the sampled organisms. Furthermore, pesticide residues were detected in both healthy and malformed fish using gas chromatography coupled with tandem mass spectrometry (GC-MS/MS). The samples analyzed contained at least one pesticide, with the group of healthy fish showing greater diversity of organochlorine compounds. The most frequent of these were p,p’-DDE, followed by p,p’-DDT (41%), p,p’-DDD (25%), and endrin (25%). The species C. raredonae and A. seemanni exhibited the greatest number of pesticides. The pesticides observed in malformed fish samples were p,p’-DDE (100%), p,p’-DDD (97%), p,p’-DDT (97%), endrin (50%), and BHC delta (31%). Chlorpyrifos was present (56% and 100% in healthy and malformed fish), with the highest frequency observed in A. seemanni and B. panamensis. The highest concentrations were observed in the DDT group. An association was also observed between vertebral malformations and concentrations of p,p’-DDE and chlorpyrifos in the species C. raredonae and A. guatemalensis. Multivariate analysis revealed a clear separation between malformed and healthy fish based on contaminant profiles. Full article

Generative image steganography is a key technology for secure information transmission, but existing deep learning-based generative steganographic methods suffer from an extremely high bit error rate (BER) and degraded steganographic image quality in low-bit-rate embedding tasks in which secret information needs duplication or padding to match the model input size. In addition, it is difficult to balance BER reduction and imperceptibility of stego-images. To address these issues, this paper proposes a novel generative image steganography algorithm based on flip watermarking, with the core novelty of designing a mirror flipping preprocessing mechanism to achieve a redundant watermark and eliminate information errors caused by duplication or padding, and constructing an end-to-end Mini-Hide steganographic framework to integrate flip watermarking with generative steganography for the first time. Specifically, the proposed method first converts the binary bitstream of secret information into a square matrix, and performs vertical, horizontal and vertical–horizontal mirror flipping on the matrix to form a redundant basic watermark, which is then expanded to a secret image with the same size as the cover image. After that, the secret image is preprocessed by a preparation network and then input into an encoding network together with the cover image to generate a stego-image. Finally, the generated stego-image is input into the decoding network to extract the secret image. Subsequently, the inverse operation of flip watermarking is performed on the extracted secret image to recover the original binary bitstream. Extensive experiments are conducted on the public COCO dataset ($256\times 256$ pixels) with BER, PSNR, and SSIM, and the proposed method is compared with state-of-the-art generative steganographic methods. Quantitative results show that the proposed method achieves a $0\%$ BER for secret information of $8\times 8$ to $64\times 64$ bits, and the BER is only $0.00002\%$ for $256\times 256$-bit secret information; the PSNR of stego-images reaches 37.75 dB, and the SSIM hits 0.96, which are 7.07 dB and 0.02 higher than those of the classic HiDDeN method ($64\times 64$ bit) respectively. We also validated the flip watermark module by integrating into other methods; the results also show that the PSNR of FNNS-D is improved by 13.12 dB ($256\times 256$), and the BER of SteganoGAN is reduced by $99.99\%$ ($256\times 256$ bit). In addition, the proposed method breaks the embedding size limit of HiDDeN (≤$64\times 64$ bit) and supports up to $256\times 256$-bit secret information embedding with stable performance. This work significantly reduces the BER of generative image steganography while improving the visual quality of stego-images, provides a new preprocessing and optimization scheme for low-BER generative steganographic algorithm design, and also offers a universal lightweight module for performance improvement of existing steganographic methods, which has important theoretical and practical significance for enhancing the security and reliability of covert information transmission in the field of information security. Full article

Accurate prediction of separated flows remains a critical challenge for Reynolds-Averaged Navier–Stokes (RANS) simulations, primarily due to the tendency of standard turbulence models to overpredict separation. To address this limitation, this study develops and validates a helicity-augmented variant of Menter’s Shear Stress Transport (SST) model within a high-fidelity, data-guided framework. First, a scale-resolving database, capturing the physics of corner separation, is established via an improved Delayed Detached Eddy Simulation (DDES) of a linear compressor cascade. Insights from this database directly inform the integration of a normalized helicity parameter into the SST formulation, enabling dynamic modulation of the turbulent eddy viscosity to account for non-equilibrium turbulence and energy backscatter in three-dimensional (3D) vortical flows. The enhanced SST model is subsequently validated against experimental data for two benchmark aerodynamic configurations: ARA M100 wing–fuselage and DLR-F6 aircraft models. Results demonstrate that the proposed correction significantly improves the prediction of separation topology and aerodynamic coefficients, delays the predicted onset of stall, and achieves closer agreement with measurements. These findings confirm the DDES-guided helicity correction as an effective strategy for enhancing the predictive fidelity of RANS models in simulating the complex separated flows encountered in practical aeronautical applications. Full article

Dental caries is an example of an oral infectious disease that affects many people worldwide, but it is not well studied in deterministic mathematical modeling. Therefore, we are interested in studying the dynamics of tooth cavity disease using a deterministic modeling approach. We propose a delay differential equation system (DDEs) to describe the phenomenon. The breakthrough of the constructed model is the formulation of the recovery rate as a saturation function constrained by healthcare capacity and the plausibility of caries reformation. In addition, we consider two controls, such as a health campaign and a post-treatment intervention. The mathematical analysis yields equilibrium solutions and their stability, which is determined by the basic reproduction number $\left({\mathfrak{R}}_0\right)$. Furthermore, backward bifurcation occurs as the medical facility’s capacity decreases, driven by an increasing infectious population. The sensitivity analysis results indicate that both considered controls are the most influential parameters. The optimal control problem is formulated using the Pontryagin Maximum Principle to obtain an optimal solution in suppressing the number of caries formation cases. At the end, a numerical simulation shows that interventions reduce the risk of transmission and suppress the number of infectious individuals. The constructed model has excellent future potential, such as generating a function for relapse cases or other preventive actions into an optimal control problem. Full article

For high-speed compound helicopters, such as the S-97 Raider, the reflection and diffraction effects of vertical/horizontal tails on pusher propeller noise are inevitable. To investigate the noise distortion effect of the rear-mounted pusher propeller, this study first relies on the Chinese Laboratory of Rotorcraft Navier-Stokes (CLORNS) solver, adopting the high-resolution Perturbed polynomial reconstructed Targeted Essentially Non-Oscillatory scheme (TENO-P) combined with the Delayed Detached Eddy Simulation based on the Spalart–Allmaras (SA-DDES) turbulence model to resolve the multi-scale rotor flowfield. Additionally, a continuous and conserved acoustic source extraction method is proposed to eliminate non-physical waves at the one-way Computational Fluid Dynamics and Computational AeroAcoustics (CFD–CAA) coupling interface, addressing the temporal inconsistency between flowfield evolution and acoustic propagation. Finally, numerical investigations are conducted on the instantaneous acoustic wave propagation and acoustic directivity of the pusher propeller under the influence of vertical/horizontal tails. The results show that significant acoustic distortion occurs when pusher propeller-generated noise interacts with vertical/horizontal tails. This interaction not only produces reflected and diffracted acoustic waves but also leads to wavefront discontinuities, the formation of short acoustic waves, and changes in acoustic directivity. The maximum variation in the sound pressure level reaches 10 dB at local azimuths. The distortion effect of tails on pusher propeller noise is closely correlated with the number of propeller blades. The interaction process between the propeller and tails becomes more complex with the increase in blade count, resulting in the generation of shorter acoustic waves. For the six-blade rotor, the originally continuous acoustic wave branch can be split into up to four short waves. This study confirms that the proposed Hybrid Computational AeroAcoustics (HCAA) method holds significant application prospects in the aeroacoustic research of compound helicopters. Full article

This study systematically analyzed the pollution levels, distribution characteristics, and associated health risks of 17 organochlorine pesticides (OCPs) and 9 polychlorinated biphenyls (PCBs) in Oviductus Ranae (Rana dybowskii) from major production areas in Heilongjiang Province, China. OCPs and PCBs were detected in all samples. The total concentration of OCPs ranged from 11.7 to 67.9 ng/g (dry weight), while that of total PCBs ranged from 4.43 to 8.06 ng/g. Endosulfans constituted the predominant OCP group, accounting for 54.5% of ∑OCPs, with an α/β-endosulfan ratio (~2:1) indicative of recent agricultural input. Among DDTs, the dominance of p,p′-DDE and the absence of parent DDT suggested aerobic degradation of historical residues. For HCHs, the isomer profile (β-HCH predominance, α/γ-HCH = 0.27) pointed to weathered lindane sources. The PCB profile was uniquely dominated by lower-chlorinated congeners (PCB1 and PCB29), implying influences from atmospheric transport and/or in situ microbial dechlorination of legacy PCBs. The persistent organic pollutants (POPs) contamination profile in Oviductus Ranae reflects a combined influence of recent pesticide application, weathered historical residues, and long-range transport. Although the concentrations are below current regulatory limits, the cumulative and persistent nature of these POPs, coupled with the product’s medicinal use, justifies a precautionary stance regarding long-term consumption. The distinct congener patterns underscore the necessity for future research to prioritize the environmental behavior and toxicology of dominant transformation products within such specific agro-ecosystems. Full article