Search Results (346)

Nanomedicine has yielded clinically useful platforms, including liposomes, albumin-bound nanoparticles, and lipid nanoparticles; yet, many systems translate poorly because of nonspecific biodistribution, limited target-site accumulation, inefficient cellular uptake and intracellular delivery, immune clearance, and off-target toxicity. These bottlenecks are often shaped at cell membrane interfaces, where therapeutic materials are recognized, retained, internalized, or cleared and may elicit unsafe responses. Here, we frame cell membrane biophysics as a therapeutic interface for nanomedicine. We examine how lipid organization and fluidity, mechanics, electrochemical state, glycocalyx architecture, and membrane protein identity shape recognition, adhesion, endocytosis, fusion, trafficking, immune responses, and drug release. We assess how disease-associated membrane remodeling can create candidate therapeutic entry points and delivery barriers across cancer, neurodegeneration, inflammation, infection, and vascular disease. We then analyze receptor-mediated targeting, lipid-domain-associated uptake, membrane-coated nanocarriers, engineered extracellular vesicles, and hybrid platforms, with explicit context-of-use definitions and design boundaries. Finally, we propose translational qualification through function-linked critical quality attributes, mechanism-relevant potency assays, context-matched models, in vivo pharmacology and immune safety, scalable manufacturing, and regulatory evaluation. Progress will depend less on descriptive membrane mimicry than on measurable, reproducible, and qualified membrane-dependent functions. Full article

Faced with both global climate change and rapid urbanization, understanding how the built environment affects surface runoff is essential for strengthening urban hydrological resilience. However, elucidating the nonlinear and interactive effects of three-dimensional buildings and two-dimensional green spaces on surface runoff potential in urban blocks remains a scientific challenge for precise flood-mitigation spatial planning. Using six typical waterlogging-prone blocks in Shenyang as case studies, this study constructs a morphological index system for urban gray–green spaces and reveals the nonlinear effects of each index on surface runoff potential using an interpretable Random Forest (RF)–SHAP model. The results indicate that the RF model reliably captures the complex spatial patterns of simulated local surface runoff potential (R² = 0.723–0.825). At the block scale, the surface runoff response exhibits a dual character: it is predictively dominated by three-dimensional morphological dominance and two-dimensional base regulation. Three-dimensional building morphology generally demonstrates pronounced unidirectional thresholds and high-value saturation in model prediction. In particular, when the core indicator, building spatial congestion degree (B_SCD), crosses a critical threshold, surface runoff potential rises sharply. The coefficient of variation in building height (B_HVC) shows a “V-shaped” reversal in areas of extreme surface runoff potential, whereas two-dimensional green space indicators display clear asymmetric critical points. Significant reductions in surface runoff potential appear only when green space scale (G_LPI), boundary complexity (G_LSI), or fragmentation (G_PD) exceed specific model-identified thresholds. Furthermore, the study demonstrates marked interactive effects between the morphologies of gray–green spaces. High surface runoff risk from dense, large buildings can be substantially offset by large green space patches (G_LPI) with highly complex boundaries (G_LSI). The advantage of vertically staggered buildings (B_HVC) requires a green space base with low fragmentation (G_PD) to realize a gray–green synergistic mitigating effect. These findings provide theoretical and methodological support for enhancing the hydrological resilience of urban blocks. Full article

Small object detection in UAV remote sensing remains challenging due to fine-grained feature loss during downsampling and unbalanced cross-scale feature aggregation. Conventional feature pyramid fusion may suffer from hard activation truncation or competitive weight suppression when heterogeneous feature scales are fused. To address these issues, we propose PSG-RTDETR, a P2-aware Softplus-Gated RT-DETR framework tailored for UAV-based dense small-object detection. Specifically, we integrate high-resolution P2 features into a bidirectional feature pyramid to preserve critical spatial details. Furthermore, we introduce Softplus normalization for smooth weight optimization and a sample-adaptive gating mechanism to dynamically suppress noisy shallow responses. Extensive experiments on VisDrone-DET demonstrate that, compared with the RT-DETR-ResNet18 baseline under the same ablation setting, PSG-RTDETR improves mAP_50–95 and AP_small by 4.1 and 4.7 percentage points, respectively. Further validation on HIT-UAV confirms its strong generalizability across infrared modalities. These accuracy gains involve a clear computational trade-off: GFLOPs increase from 57.20 to approximately 110.23, while FPS decreases from 126.18 to 80.02 on an RTX 3090. Thus, PSG-RTDETR is better suited to accuracy-oriented off-board or near-real-time GPU analysis than to direct deployment on resource-constrained onboard UAV devices without further compression or acceleration. Full article

Traditional frameworks of innovation diffusion, such as epidemic and Lotka–Volterra–Gause models, treat technological substitution primarily as a population-driven or social communication process, frequently overlooking the critical constraints imposed by external factor scarcities. To address this fundamental economic gap, this study performs a qualitative analysis of exploitative competition between two distinct technologies sharing two complementary resources, modeled via a non-linear system of chemostat-type consumer–resource ordinary differential equations. Technologies are represented as homogeneous populations of elemental firms, where individual output is governed by a ratio-dependent, fixed-proportions Leontief production function integrated with a hyperbolic clearing response. Operating within an open industrial system, the model accounts for resource supply rates and firm exit dynamics. We analytically derive the coordinates of both boundary and interior fixed points within the non-negative orthant of the phase space. By investigating the eigenvalues of the associated Jacobian matrix, we establish necessary and sufficient conditions for local asymptotic stability, competitive exclusion, and technological coexistence, demonstrating that efficiency is determined by a break-even resource availability threshold. Our results reveal that structural reconfigurations of the industry supply plane trigger bifurcations between local dominance and multistability. The latter manifests as a path-dependent, Quastlerian selection of initial conditions rather than inherent technological superiority. Finally, we establish the geometric boundaries of the stable assemblage niche, proving that technological diversity is regulated by resource supply rates. By explicitly incorporating resource scarcity into a dynamical predator–prey framework, the proposed model offers a more robust economic and mathematical foundation for innovation diffusion, providing policymakers with structural insights into the resource allocation strategy and the long-term management of industrial diversity. Full article

Virus-specific antibody responses, including binding antibodies and neutralizing antibodies (nAbs), are important indicators of antiviral immune status after infection or immunization. They provide complementary information on antiviral humoral immunity after infection or vaccination. Antigen-binding antibodies indicate previous exposure and the magnitude of the immune response, whereas receptor-blocking and functional neutralization assays assess whether antibodies interfere with viral entry or infection. Conventional neutralization assays, such as plaque reduction neutralization tests and pseudovirus neutralization tests, provide functional information but are labor-intensive, time-consuming, biosafety-restricted, and difficult to deploy for large-scale or decentralized monitoring. Paper-based biosensors, including lateral flow assays (LFAs), microfluidic paper-based analytical devices (μPADs), and paper-based ELISA, have emerged as promising point-of-care tools owing to their low cost, portability, simple operation, and compatibility with visual or digital readouts. This review critically evaluates these platforms according to whether they measure antigen-binding antibodies, receptor-blocking activity, surrogate neutralization, or functional neutralization and summarizes the applications of these three platforms for monitoring antibody responses against SARS-CoV-2, influenza, dengue, Zika, and monkeypox viruses. Unlike previous reviews that mainly focus on general paper-based biosensor design or conventional nAb assays, this review emphasizes the distinction between antigen-binding, receptor-blocking, and surrogate neutralization readouts, and critically discusses how paper-based signals should be interpreted in relation to functional immunity. We further analyze key translational challenges, including quantitative accuracy, antigen cross-reactivity, standardization, clinical validation, regulatory positioning, and real-world implementation. Future development should combine multiplex detection, standardized calibration, digital and AI-assisted interpretation, and clinically validated assay formats. Paper-based biosensors have considerable potential for decentralized antibody monitoring and public health surveillance, but their clinical utility depends on clear assay positioning and validation against appropriate functional or reference methods. Full article

The traditional tank model-based landslip early warning system (LEWS) calculates the soil water index (SWI) as a single time series driven by basin-averaged rainfall, which cannot capture spatial heterogeneity along linear highway infrastructures. To overcome this limitation, this study proposes an integrated model that couples the tank model with an ordinary differential equation (ODE) form stormwater runoff simulation model: namely, the distributed runoff model (DRM). The DRM-computed distributed surface water depth replaces the first-layer water height of the tank model to generate spatially varying SWI values. The proposed framework is validated against the 2016 Typhoon No. 10 event that triggered five landslides (L1–L5) along Highway 274 in Hokkaido, Japan. Quantitative results show the following: (1) at all five landslide locations, the peak SWI values exceed 225 mm, while at a non-landslide reference point (L0) the peak SWI is only 158 mm, demonstrating clear spatial differentiation; (2) the predicted landslide initiation times from the integrated model deviate by less than 1.5 h from the actual occurrence times, whereas the shallow water equations (SWEs) and tank-coupled model advances predictions by over 7 h (L3, L4 and L5); (3) after revising the critical line (CL) based on the event data, the proposed model demonstrates a 100% identification rate for the five landslide sites with zero false alarms at L0 in this case study, indicating its potential for practical application. Compared with the tank + SWEs, the proposed tank + DRM approach maintains comparable spatial resolution but significantly improves temporal accuracy and computational efficiency, making it practical for real-time early warning along elongated highway projects. This study provides a spatially differentiated and temporally reliable decision-support tool for rainfall-induced landslide risk assessment along transportation corridors. Full article

Remaining Useful Life prediction is a core challenge in the field of Prognostics and Health Management. Traditional point prediction methods only provide a single estimate and cannot quantify prediction uncertainty, limiting their application in critical decision-making. This paper proposes a probabilistic prediction model integrating an LSTM–attention mechanism and quantile regression, aiming to achieve interval prediction and uncertainty quantification for RUL. The model employs a bidirectional LSTM network to capture temporal dependencies, focuses on key degradation features through a six-head self-attention mechanism, and outputs predictions for three quantiles (10%, 50%, 90%) simultaneously based on the quantile regression framework, constructing an 80% confidence interval with clear physical meaning. Experimental validation on a public dataset shows that the proposed model performs excellently in both point prediction accuracy and interval prediction quality: RMSE reaches 11.245, NASA Score is 166.414, while the interval coverage remains at 86.7%, and the interval width is 30.398. Ablation experiments further confirm the importance of each component, where removing the attention mechanism causes a 24.4% increase in RMSE, and removing the LSTM module worsens the NASA Score by 164.8%. The research results provide an effective solution for probabilistic remaining life prediction of complex equipment. Full article

Colorectal cancer (CRC) kills approximately 850,000 people annually and is the second leading cause of cancer mortality worldwide. After curative-intent surgical resection, recurrence rates of 15–20% in stage II and 30–40% in stage III diseases highlight the fundamental limitations of pathological staging as the sole basis for adjuvant treatment decision-making. Circulating tumor DNA (ctDNA), which comprises tumor-derived cell-free DNA fragments shed into the bloodstream by dying cancer cells, offers a direct and dynamic readout of molecular residual disease (MRD) in the postoperative period. Because ctDNA clears within hours of macroscopically complete surgery, any persistently detectable postoperative signal reflects viable microscopic disease rather than surgical contamination. Over the past decade, evidence has matured rapidly from proof-of-concept observational studies to completed randomized clinical trials, culminating in the recent publication of DYNAMIC-III and ALTAIR in 2025–2026. The DYNAMIC trial established that ctDNA-guided management reduced adjuvant chemotherapy use from 27.9% to 15.3% in stage II colon cancer while maintaining an equivalent five-year recurrence-free survival (88% vs. 87%; difference 1.1%, 95% CI 5.8% to 8.0%). The GALAXY study confirmed ctDNA as the most powerful prognostic biomarker in resected CRC, with a disease-free survival hazard ratio of 11.99. Critically, DYNAMIC-III demonstrated that escalating chemotherapy intensity in ctDNA-positive stage III patients did not improve recurrence-free survival (HR 1.11, p = 0.6), and the phase III ALTAIR trial showed that trifluridine/tipiracil failed to significantly improve disease-free survival at the point of molecular recurrence (HR 0.79, p = 0.107). This review critically synthesizes the current evidence for ctDNA-guided treatment decisions in resected CRC across three therapeutic strategies: de-escalation in ctDNA-negative patients, escalation in ctDNA-positive patients, and post-adjuvant therapy at the point of molecular recurrence. Future progress requires biomarker-matched escalation strategies, adaptive trial designs, and formal validation of ctDNA clearance as a surrogate endpoint for predicting patient outcomes. Full article

Background/Objectives: The levonorgestrel-releasing intrauterine device (LNG-IUD) is widely used as a long-acting reversible contraceptive and as a therapeutic option for heavy menstrual bleeding, endometriosis, adenomyosis, and endometrial hyperplasia. Although its predominant effect is local, systemic levonorgestrel exposure has raised questions regarding its potential influence on cancer risk. This review aims to summarise and critically appraise the available evidence on the association between LNG-IUD use and gynaecologic, breast, and selected non-gynaecologic malignancies (including colorectal, lung, pancreatic, gastric, thyroid, skin [melanoma], and haematological cancers). Methods: A structured narrative review was developed by a multidisciplinary expert group affiliated with the European Society of Gynaecological Oncology (ESGO) and the European Society for Gynaecological Endoscopy (ESGE). PubMed/MEDLINE, Embase, and Scopus were searched in December 2024 without publication-date restrictions. We critically appraised epidemiological and clinical evidence on LNG-IUD use and breast, endometrial, ovarian, cervical, and selected non-gynaecologic cancer risks, prioritising population-based cohorts, registry-based studies, case–control studies, systematic reviews, and meta-analyses. Results: The strongest and most consistent evidence supports a substantial reduction in endometrial cancer risk among LNG-IUD users, with a possible protective association also suggested for ovarian cancer. Available data do not indicate a clear increase in cervical cancer, CIN3+, or invasive cervical disease. In contrast, evidence for breast cancer remains heterogeneous, with some registry-based studies suggesting a modest relative increase and other cohort or case–control studies showing no significant association. Evidence regarding non-gynaecologic cancers remains limited and inconclusive. Conclusions: Overall, current evidence suggests that the LNG-IUD has a favourable oncological safety profile, with the strongest evidence supporting a significant reduction in endometrial cancer risk and a potential protective effect against ovarian cancer. There is no evidence pointing to increased risk of cervical cancer. However, results related to breast cancer are mixed, and a slight risk increase cannot be ruled out; thus, counselling should be personalised based on the indication, menopausal status, duration of use, and individual breast cancer risk. Full article

Dynamic uplink power allocation is a critical challenge in cell-free massive MIMO (CF-mMIMO) networks, where distributed access points (APs) jointly serve multiple user equipment (UEs) under mobility, time-varying propagation conditions, and strong inter-user interference. Conventional optimization-based methods can improve fairness or spectral efficiency, but they often require repeated numerical solving and are usually designed for a specific objective. Learning-based approaches can reduce online decision time after training; however, their effectiveness depends strongly on the reward design and the selected operating objective. In response to these challenges, we propose a Deep Hybrid Intelligent (DHI) architecture designed to evaluate dynamic uplink power management within cell-free massive MIMO environments. The framework uses Soft Actor-Critic (SAC) learning to generate continuous uplink transmit-power decisions and evaluates objective-specific configurations for fairness, signal-to-interference-plus-noise ratio (SINR) improvement, and spectral-efficiency enhancement. In addition, three optimization-based strategies, namely max-min fairness, max-product SINR optimization, and max-sum-rate maximization, are incorporated to analyze the trade-off among fairness, signal quality, throughput, and computational cost. Limited-memory Broyden-Fletcher-Goldfarb-Shanno with bound constraints (L-BFGS-B) optimization is employed for the max-product and max-sum-rate objectives, while the max-min strategy is evaluated through a fairness-oriented feasibility procedure. Simulation results show that the fairness-oriented configuration achieves the highest Jain’s fairness index, reaching 0.989 at 120 access points, whereas the sum-rate-oriented configuration provides stronger SINR and user-rate performance. The results also indicate execution-time reductions of 51.6%, 83.7%, and 85.0% for the evaluated max-min, max-product, and max-sum-rate strategies, respectively, compared with conventional optimization-based implementations. These execution-time gains are accompanied by a clear performance trade-off: the max-min strategy provides the strongest fairness behavior, the max-sum-rate strategy improves total spectral efficiency and user-rate performance, and the max-product strategy offers a balanced operating point between collective SINR improvement and user-service balance. Therefore, the proposed framework does not optimize only computational speed, but also clarifies the trade-off among execution time, SINR, spectral efficiency, and fairness under dynamic uplink CF-mMIMO conditions. These results indicate that this architecture serves as an adaptable platform to evaluate dynamic uplink power distribution across CF-mMIMO networks. Full article

DNA sequence context plays a critical role in modulating the mutational effects of DNA damage. Here, we investigated how base identity influences the replication bypass and mutagenicity of a site-specific dG-AAF (2-acetylaminofluorene) bulky adduct in the well-defined AG*N and TG*N sequence contexts. By selecting adenine and thymine as 5′-flanking bases and systematically varying the 3′-base, we established a controlled system to examine sequence-dependent lesion replication. We found that the 5′-flanking base strongly affects the bypass profile, with AG*N sequences exhibiting uniformly low bypass (≤9.7%) and TG*N sequences showing markedly elevated bypass (23.6–50.4%) with strong sequence dependence. These differences may arise from the structure of the dG-AAF, whose conformation heterogeneities are sensitive to the flanking sequence context. In contrast, mutagenicity remains consistently low across all sequences examined, with a low frequency of point mutations and no detectable frameshift events. These results reveal a clear decoupling between lesion bypass efficiency and replication fidelity, where sequence context strongly controls lesion tolerance but has limited impact on mutagenicity. In total, our findings demonstrate that DNA sequence affects lesion processing, providing insights into how local sequence context shapes genome stability and mutational processes. Full article

The reliability of electric vehicle fast-charging infrastructure is becoming increasingly critical as deployment accelerates and the number of unavailable charging points grows. This work presents an unsupervised anomaly detection framework aimed at supporting predictive maintenance in DC fast-charging stations. The approach uses real minute-resolution operational data from a real charging station, including active, reactive and apparent power, power factor, phase power measurements and charger-side power measurements. Three complementary anomaly detection models were designed to capture different abnormal operating conditions: deviations in consumption patterns, efficiency losses between charger and grid analyser measurements, and phase imbalance in three-phase operation. Local Outlier Factor and Isolation Forest algorithms were integrated into an automated monitoring pipeline. Since labelled fault data were not available, validation was based on controlled injection of synthetic anomalies into real test signals, including physically coherent power disturbances, sensor or communication inconsistencies, progressive efficiency degradation and phase imbalance events. The results show that the framework is effective for detecting anomaly families that produce clear or sustained deviations, while more subtle temporal behaviours remain more challenging. Overall, the proposed framework provides a practical condition monitoring and early-warning approach that can support predictive maintenance decisions in DC charging infrastructure, while further temporal modelling is required for explicit degradation forecasting. Full article

Large-scale thick curved components made of high-strength steel are critical to deep-sea pressure hulls and large structural components of engineering machinery. During forming, these components experience reverse loading upon unloading, and the pronounced Bauschinger effect of high-strength steel significantly compromises springback prediction accuracy, leading to costly die iterations. Existing cyclic tension–compression and shear tests are limited to uniaxial stress states and fail to capture the mechanical behavior under in-plane biaxial cyclic loading. Herein, a cyclic four-point bending method is proposed to characterize the Bauschinger effect of Q890 steel under biaxial cyclic loading. By tailoring the width-to-thickness ratio of the specimens, a series of plane stress states with different initial plastic stress ratios were obtained, covering the dominant stress conditions encountered in forming typical large-scale double-curvature thick plates. Full-field strain evolution during cyclic bending was captured in real time via digital image correlation (DIC), enabling systematic acquisition of equivalent stress–strain curves under various biaxial stress ratios over multiple cycles. As the width-to-thickness ratio increases, both forward and reverse yielding progressively degrade: the equivalent yield strength, forward peak flow stress, and reverse yield strength drop from 1098, 1193, and 742 MPa to 934, 1065, and 685 MPa, respectively. Accordingly, the Bauschinger ratio B, Bauschinger hardening parameter BHP, and Bauschinger energy parameter BEP decrease from 0.479, 0.789, and 4.747 to 0.363, 0.655, and 2.900, respectively, revealing a strong stress-ratio dependence of the Bauschinger effect. Notably, the springback ratio also shows clear dependence on the biaxial stress ratio, loading direction, and cyclic history, indicating that in-plane biaxial stress-state effects should be considered when characterizing the Bauschinger effect and springback behavior of Q890 high-strength steel. Full article

Coastal cities are highly sensitive and vulnerable to climate change risks. A scientifically grounded assessment of urban climate change risk and its driving mechanisms is essential for strengthening urban climate adaptation capacity and supporting sustainable development. Taking Ningbo as the study area, this paper constructs a risk assessment system comprising five categories of extreme climate indicators, namely heat, rainstorm, drought, humidity, and strong wind, based on the China Surface Climate Normals Dataset for 1981–2010 and meteorological observations from the National Centers for Environmental Information (NCEI) for 2015–2024. Using 30 m resolution land-use data for 2023, three land-use sensitivity indicators are extracted: the proportion of built-up land, the proportion of green space and forest land, and the proportion of water area. The CRITIC objective weighting method is then applied to construct an integrated climate change risk index and identify the spatial pattern of climate change risk in Ningbo. On this basis, the high-risk area identification performance of Logistic Regression, Random Forest, and XGBoost is compared. The optimal XGBoost model is selected and combined with the SHAP method to systematically reveal the direction, relative importance, and nonlinear threshold relationships through which land-use characteristics affect the formation of high-risk areas. The results show that urban climate change risk in Ningbo exhibits a pronounced spatial differentiation pattern, with higher risk in the northeastern coastal and central–eastern areas and lower risk in the western and southwestern areas. Insufficient green space and forest land buffering is the most important factor affecting the formation of high-risk areas. All three land-use variables have clear nonlinear thresholds. The critical turning points for identifying high-risk areas are 20.0% built-up land, 2.0% green space and forest land, and whether there is a water body or not. Significant interaction effects are observed among land-use variables, among which the interaction between built-up land and green space/forest land is the most prominent. These findings provide methodological support and empirical evidence for climate change risk assessment and climate-adaptive spatial planning regulation in coastal cities. Full article

To address the major safety hazard that heat released from mining polyurethane (PU) reinforcement materials may induce spontaneous combustion of residual coal in goaf, this study selected No. 3 coal from Wangzhuang Coal Mine, Shanxi Lu’an, as the research object. A self-developed large-capacity, large-scale experimental system was used to conduct programmed heating experiments on 2.0 kg multi-particle-size coal-PU mixed samples. The effects of PU content on characteristic gas release, crossing point temperature (CPT), residue morphology, and TGA-DSC characteristic temperatures were systematically investigated, and the reaction-kinetic evolution was further analyzed using the distributed activation energy model (DAEM). The results show that coal and PU exhibit a significant synergistic enhancement effect during co-heating. As the PU content increased, the release concentrations of CO, C₂H₄, and C₂H₆ increased markedly, and their initial release temperatures decreased, whereas CH₄ generation was inhibited by hydrogen-radical competition; no C₂H₂ was produced below 400 °C. The CPT decreased linearly with an increasing PU content, with an average decrease of approximately 8.5 °C for every 10% increase in PU content. Residue morphology showed clear critical features: glassy agglomerates appeared when the PU content exceeded 16.67%, and dense bulk coking occurred when the PU/coal mass ratio was greater than 1:10. TGA-DSC analysis showed that when the PU/coal ratio was lower than 1:10, the ignition temperature of the mixed sample was higher than that of pure coal, indicating an inhibitory synergistic effect. When the ratio exceeded 1:10, the ignition temperature decreased significantly, and the synergy shifted to promotion; increasing the heating rate shifted the characteristic temperatures to higher values and increased the reaction intensity. DAEM analysis further confirmed that when the PU ratio exceeded 1:10, the apparent activation energy of the mixed samples was lower than that of pure coal. Coal powder also acted as a physical skeleton that effectively dispersed molten PU, eliminated the activation-energy peaks of pure PU in the conversion ranges of 30–50% and 70–90%, and substantially improved combustion stability. Mechanistically, low-temperature PU melting and coating optimized heat and mass transfer, medium-temperature pyrolysis released active radicals and combustible gases that altered coal pyrolysis pathways and the radical reaction environment, and high-temperature hydrogen-radical competition reshaped the gas-product distribution. Together, these processes form a complete chain of synergistic spontaneous combustion. This study identifies key safety threshold parameters for PU reinforcement materials, recommends a PU content of ≤9.10%, and identifies CO and C₂H₄ as priority early-warning gases, providing direct experimental evidence for characteristic-gas-based early warning and mine fire prevention. Full article