Search Results (3,147)

The clinical durability of SARS-CoV-2 main protease (M^pro) inhibitors depends on their resilience to emerging resistance mutations. Recent genomic surveillance and functional reports have highlighted substitutions at positions 49, 165, and 301, raising questions about the robustness of the noncovalent inhibitor WU-04 in variant backgrounds. Here, we combined μs-scale, triplicate molecular dynamics simulations with end-state binding free energy estimates and a network-rewiring inference (NRI) framework that maps long-range dynamical communication across the full protease dimer. We evaluated wild type (WT), single mutants M49K, M165V, S301P, and selected double mutants (M49K & M165V, M49K & S301P). Relative to WT, single substitutions produced reductions in computed binding affinity of up to ~12kcal/mol, accompanied by loss or reshaping of the S2 subsite and altered ligand burial. Notably, the M49K/S301P double mutant partially restored WU-04 engagement, narrowing the ΔΔG_restore gap to within ΔΔG_restore of WT and re-establishing key hydrophobic and hydrogen-bond contacts. NRI analysis revealed that distal residue 301 participates in a communication corridor linking the C-terminal helical domain to the active-site cleft; its substitution rewires inter-domain coupling that can compensate for local disruptions at residue 49. Together, these results identify structural hotspots and network pathways that may inform the design of next-generation M^pro inhibitors with improved mutation tolerance—specifically by strengthening interactions that do not rely solely on the mutable S2 pocket and by engaging conserved backbone features near the 165–166 region. Full article

Background/Objectives: Urinary incontinence (UI) is a prevalent health condition with a negative impact on quality of life (QoL). Exercise therapy (ET), specifically, pelvic floor muscle training (PFMT), is recommended as a first-line conservative treatment for UI during pregnancy, childbirth, and the postpartum period. This study evaluated the effects of ET on the management of postpartum UI. Methods: A systematic search was conducted to identify clinical trials and randomized controlled trials including women over 18 years with postpartum UI. All included studies used ET as the main intervention. Studies were excluded if UI symptoms were attributable to factors outside the urinary tract or if participants had concomitant pathologies. Results: From 298 records screened, four trials were included. Three trials reported statistically significant improvements in UI outcomes, while findings for pelvic floor function and QoL showed greater heterogeneity. One trial found that supervised PFMT was associated with greater improvements in urinary symptoms (BFLUTS), vaginal pressure (18.96 mmHg (SD: 9.08)), and endurance (11.32 s (SD: 3.17)) compared to unsupervised training. Another trial using electromyographic biofeedback with electrical stimulation reported a continence rate exceeding 70% on the 20 min pad test, with improvements in perceived burden (VAS), symptoms (UDI), and QoL (IIQ). A third trial combining PFMT with infrared physiotherapy showed improvements in pelvic floor function (PFIQ-7, PFDI-20), urodynamic parameters, urine loss, and QoL (GQOLI-74). In the remaining trial, within-group improvements were observed, with no statistically significant between-group differences. Conclusions: ET appears to be beneficial for postpartum UI, with a moderate certainty of evidence. While the greatest benefits are observed with supervised PFMT, the diversity of comparators, and the risk of performance bias limit definitive conclusions regarding its superiority. Given the short-term follow-up, it remains unclear whether the results are influenced by the spontaneous recovery trajectory in the postpartum period and if these effects are sustained in the long term. Full article

Cosmetic wipes are made for multiple functions, baby care, hand washing, feminine and personal cleansing, removing makeup, and applying products such as deodorants and sunless tanners among other uses. Despite the presence of preservatives, cosmetic wipes can become contaminated during processing steps and usage, which may lead to skin infections and other health issues for consumers. No validated method exists for the microbiological testing of cosmetic wipes. The goal of this study was to develop and validate a specific sample preparation method for the quantitative detection of microorganisms in cosmetic wipes for inclusion in the FDA Biological Analytical Manual (BAM). Ten wipe types differing in their composition and preservative combinations were inoculated with Bacillus cereus spore suspensions at three concentration levels and aged for 14 days. Three extraction methods were compared: mBAM1g (reference method using 1 g samples), mBAMww (whole wipe method based on BAM Chapter 23), and ISOww (whole wipe method based on ISO method without Tween 80). For commercial wipes, mBAMww and ISOww, using whole wipes, performed similarly (p ≥ 0.05) or significantly better (p < 0.05) than mBAM1g. For laboratory-made wipes, 1 g samples showed higher recovery rates than whole wipes, likely due to cell loss during aging. Inoculation method and preservatives affect microbial distribution, survival, and recovery rates. T80 may have a positive effect on the recovery of B. cereus from wipes. This study recommends mBAMww for the microbiological analysis of cosmetic wipes. Full article

Three-dimensional Gaussian splatting has emerged as a mainstream method in the field of new viewpoint synthesis due to its outstanding performance. However, its generation quality typically degrades significantly when input viewpoints are sparse. The introduction of InstantSplat further improved new viewpoint generation in sparse viewpoint scenarios. Nevertheless, these methods produce suboptimal results in sparse viewpoint scenes with noise and no camera prior. To address this issue, we propose Denoise-GS, a two-round optimization framework combining N2V-UNet denoising with InstantSplat rendering. First, Noise2Void performs self-supervised denoising on the input image. Next, pose grouping is conducted based on InstantSplat rendered results. Finally, a second round of refinement is applied to the UNet through a joint loss function. The final denoised result is then re-rendered to achieve a higher-quality output image. To simulate a real noisy environment, we added Gaussian noise to the input images. Tests on multiple datasets show that, compared with other mainstream methods, our approach produces images with higher PSNR and SSIM. The method performs well in novel view generation when the input images are sparse and noisy, providing an innovative and practical solution for three-dimensional reconstruction. Full article

In this study, dried biomass of the alga Chlorella vulgaris was ground into a powder as an eco-friendly, low-cost inhibitor to mitigate the corrosion of carbon steel in acidic solutions. Electrochemical and weight loss measurements, surface morphology observations, adsorption isotherms, activation energy, and potential of zero charge calculations were applied to evaluate the inhibition performance. Electrochemical results indicate that C. vulgaris powder can simultaneously inhibit both the anodic and cathodic corrosion processes of carbon steel, demonstrating good inhibition performance and classifying it as a mixed-type inhibitor with both anodic and cathodic characteristics. Weight loss data further confirm that at a concentration of 300 mg/L, the corrosion inhibition efficiency reaches 88%. The fitted adsorption isotherm reveals that the adsorption of Chlorella vulgaris powder on the carbon steel surface follows the Langmuir model. Density functional theory (DFT) and molecular dynamics simulations indicate that the excellent inhibition performance is attributed to the combined effects of physisorption and chemisorption of constituents such as amino acids and cellulose present in C. vulgaris. Full article

Tomatoes are an important economic crop in China, and crop diseases often lead to a decline in their yield. Deep learning-based visual recognition methods have become an approach for disease identification; however, challenges remain due to complex background interference in the field and the diversity of disease manifestations. To address these issues, this paper proposes the SXA-YOLO (an improvement based on YOLO, where S stands for the SAAPAN architecture, X represents the XIoU loss function, and A denotes the AsDDet module) symmetric perception recognition model. First, a comprehensive symmetry architecture system is established. The backbone network creates a hierarchical feature foundation through C3k2 (Cross-stage Partial Concatenated Bottleneck Convolution with Dual-kernel Design) and SPPF (the Fast Pyramid Pooling module) modules; the neck employs a SAAPAN (Symmetry-Aware Adaptive Path Aggregation Architecture) bidirectional feature pyramid architecture, utilizing multiple modules to achieve equal fusion of multi-scale features; and the detection head is based on the AsDDet (Adaptive Symmetry-aware Decoupled Detection Head) module for functional decoupling, combining dynamic label assignment and the XIoU (Extended Intersection over Union) loss function to collaboratively optimize classification, regression, and confidence prediction. Ultimately, a complete recognition framework is formed through triple symmetric optimization of “feature hierarchy, fusion path, and task functionality.” Experimental results indicate that this method effectively enhances the model’s recognition performance, achieving a P (Precision) value of 0.992 and an mAP50 (mean Average Precision at 50% IoU threshold) of 0.993. Furthermore, for ten categories of diseases, the SXA-YOLO symmetric perception recognition model outperforms other comparative models in both p value and mAP50. The improved algorithm enhances the recognition of foliar diseases in tomatoes, achieving a high level of accuracy. Full article

To address challenges in weld recognition during vacuum electron beam welding caused by dark environments and metal reflections, this study proposes an improved hybrid algorithm combining YOLOv11-seg with adaptive Canny edge detection. By incorporating the UFO-ViT attention mechanism and optimizing the network architecture with the EIoU loss function, along with adaptive threshold setting for the Canny operator using the Otsu method, the recognition performance under complex conditions is significantly enhanced. Experimental results demonstrate that the optimized model achieves an average precision (mAP) of 77.4%, representing a 9-percentage-point improvement over the baseline YOLOv11-seg. The system operates at 20 frames per second (FPS), meeting real-time requirements, with the generated welding trajectories showing an average length deviation of less than 3 mm from actual welds. This approach provides an effective pre-weld visual guidance solution, which is a critical step towards the automation of electron beam welding. Full article

Post-earthquake fires often cause more severe losses than the earthquakes themselves, highlighting the critical role of water distribution networks (WDNs) in mitigating fire risks. This study proposed an improved assessment framework for the post-earthquake firefighting functionality of WDNs. This framework integrates a WDN firefighting simulation model into a cloud model-based assessment method. By combining seismic damage and firefighting scenarios, the simulation model derives sample values of the functional indexes through Monte Carlo simulations. These indexes integrate the spatiotemporal characteristics of the firefighting flow and pressure deficiencies to assess a WDN’s capability to control fire and address fire hazards across three dimensions: average, severe, and prolonged severe deficiencies. The cloud model-based assessment method integrates the sample values of functional indexes with expert opinions, enabling qualitative and quantitative assessments under stochastic–fuzzy conditions. An illustrative study validated the efficacy of this method. The flow- and pressure-based indexes elucidated functionality degradation owing to excessive firefighting flow and the diminished supply capacity of a WDN, respectively. The spatiotemporal characteristics of severe flow and pressure deficiencies demonstrated the capability of firefighting resources to manage concurrent fires while ensuring a sustained water supply to fire sites. This method addressed the limitations of traditional quantitative and qualitative assessment approaches, resulting in more reliable outcomes. Full article

Plasmonics and metasurfaces (MSs) have emerged as two of the most influential platforms for manipulating light at the nanoscale, each offering complementary strengths that challenge the limits of conventional optical design. Plasmonics enables extreme subwavelength field confinement, ultrafast light–matter interaction, and strong optical nonlinearities, while MSs provide versatile and compact control over phase, amplitude, polarization, and dispersion through planar, nanostructured interfaces. Recent advances in materials, nanofabrication, and device engineering are increasingly enabling these technologies to be combined within unified planar and hybrid optical platforms. This review surveys the physical principles, material strategies, and device architectures that underpin plasmonic, MS, and hybrid plasmonic–dielectric systems, with an emphasis on interface-mediated optical functionality rather than long-range guided-wave propagation. Key developments in modulators, detectors, nanolasers, metalenses, beam steering devices, and programmable optical surfaces are discussed, highlighting how hybrid designs can leverage strong field localization alongside low-loss wavefront control. System-level challenges including optical loss, thermal management, dispersion engineering, and large-area fabrication are critically examined. Looking forward, plasmonic and MS technologies are poised to define a new generation of flat, multifunctional, and programmable optical systems. Applications spanning imaging, sensing, communications, augmented and virtual reality, and optical information processing illustrate the transformative potential of these platforms. By consolidating recent progress and outlining future directions, this review provides a coherent perspective on how plasmonics and MSs are reshaping the design space of next-generation planar optical hardware. Full article

The Sanjiang Plain hosts the largest freshwater wetland in Northeastern China and plays a critical role in regional climate stability. However, climate change and human activities have degraded the wetland, forming a successional gradient from the original flooded wetland to dry shrub and forest vegetation with a lower ground water level. This degradation has altered soil microbial structure and functions, reducing ecological and socio-economic benefits. Along this successional gradient, we used Biolog-ECO plates combined with soil enzyme assays (catalase, urease, sucrase, and acid phosphatase) to assess the dynamics of microbial carbon metabolic activity, measured by average well color development (AWCD). The results showed a systematic decline in AWCD values with advancing succession, revealing a pronounced reduction in overall microbial metabolic activity during wetland degradation. This trend correlated with loss of soil moisture, organic carbon, and nitrogen nutrients. Microbial communities in early successional wetland stages (i.e., original natural wetland and wetland edge) preferred labile carbon sources (e.g., carbohydrates, amino acids), while forested stages favored relatively more structural (e.g., polymers, phenolic compounds). These findings indicate that vegetation succession regulates microbial carbon metabolism by modifying soil physicochemical properties, providing insights for wetland restoration. Full article

To address the freeze-thaw (F-T) durability of concrete structures in severely cold plateau regions, this study investigates recycled coarse aggregate concrete (RCAC) by designing mixtures with varying replacement ratios of recycled brick aggregate (RBA). Rapid freeze-thaw cycling tests are conducted in combination with macro- and microscale analytical techniques to systematically elucidate the frost resistance and damage mechanisms of mixed recycled coarse aggregate concrete. When the RBA content is 50%, the concrete demonstrates relatively better frost resistance within the mixed recycled aggregate system. This is evidenced by the lowest mass loss rate coupled with the highest retention ratios for both the relative dynamic elastic modulus (RDEM) and the compressive strength. Micro-analysis indicates that an appropriate amount of RBA can optimize the pore structure, exerting a “micro air-cushion” buffering effect. Blending RBA with recycled concrete aggregate (RCA) may create functional complementarity between pores and the skeleton, effectively delaying freeze–thaw damage. A GM (1,1) damage prediction model based on gray system theory is established, which demonstrates high accuracy (R² > 0.92). This study provides a reliable theoretical basis and a predictive tool for the durability design and service life assessment of mixed recycled coarse aggregate concrete engineering in severely cold regions. Full article

Recent advances in satellite observations have expanded the use of Sea Surface Temperature (SST) and Sea Surface Height (SSH) data in climate and oceanography, yet their low spatial resolution limits fine-scale analyses. We propose HiT_DS, a modular hierarchical Transformer framework for high-resolution downscaling of SST and SSH fields. To address challenges in multiscale feature representation and physical consistency, HiT_DS integrates three key modules: (1) Enhanced Dual Feature Extraction (E-DFE), which employs depth-wise separable convolutions to improve local feature modeling efficiently; (2) Gradient-Aware Attention (GA), which emphasizes dynamically important high-gradient structures such as oceanic fronts; and (3) Physics-Informed Loss Functions, which promote physical realism and dynamical consistency in the reconstructed fields. Experiments across two dynamically distinct oceanic regions demonstrate that HiT_DS achieves improved reconstruction accuracy and enhanced physical fidelity, with selective module combinations tailored to regional dynamical conditions. This framework provides an effective and extensible approach for oceanographic data downscaling. Full article

Federated learning (FL) enables privacy-preserving collaboration for Network Intrusion Detection Systems (NIDSs), but its effectiveness under heterogeneous traffic, severe class imbalance, and domain shift remains insufficiently characterized. We evaluate FL in two settings: (i) single-domain training on CICIDS-2017, InSDN/OVS, and 5G-NIDD with cross-domain testing, and (ii) multi-domain training that learns a unified model across enterprise and Software-Defined Network (SDN) traffic. Using consistent preprocessing and controlled ablations over balancing strategy, loss function, and client sampling, we find that dataset structure (class separability) largely determines single-domain FL gains. On datasets with lower separability, FL with Per-Client Synthetic Minority Over-sampling Technique (SMOTE) substantially improves Macro-F1 over centralized baselines, while well-separated datasets show limited benefit. However, single-domain models degrade sharply under domain shift, showing substantial degradation in cross-domain transfer. To mitigate this, we combine multi-domain FL with AutoEncoder pretraining and achieve 77% Macro-F1 across environments, demonstrating that FL can learn domain-invariant representations when trained on diverse traffic sources. Overall, our results indicate that Per-Client SMOTE is the preferred balancing strategy for federated NIDS, and that multi-domain training is often necessary when deployment environments differ from training data. Full article

In this work, a substrate-integrated waveguide (SIW) filtering power divider with a modified complementary split-ring resonator (CSRR) is reported. Firstly, by integrating the meander-shaped slots with the conventional CSRR, the proposed inner-meander-slot CSRR (IMSCSRR) can enlarge the total length of the defected slot and increase the width of the split, thus enhancing the equivalent capacitance and inductance. In this way, the fundamental resonant frequency of the IMSCSRR can be effectively decreased without enlarging the circuit size, which can generally help to reduce the physical size by over 35%. Subsequently, to further reduce the circuit size, two IMSCSRRs are separately loaded on the top and bottom metal covers to constitute a broadside-coupled IMSCSRR, which is combined with the SIW. To verify the efficacy of the proposed SIW-IMSCSRR unit cell, a two-way filtering power divider is implemented. It combines the band-selection function of a filter and the power-distribution property of a power divider, thereby enhancing system integration and realizing size compactness. Experimental results show that the proposed filtering power divider achieves a center frequency of 3.53 GHz, a bandwidth of about 320 MHz, an in-band insertion loss of (3 + 1.3) dB, an in-band isolation of over 21 dB, and a size reduction of about 30% compared with the design without broadside-coupling, as well as good magnitude and phase variations. All the results indicate that the proposed filtering power divider achieves a good balance between low loss, high isolation, and compact size, which is suitable for system integration applications in microwave scenarios. Full article

Pest detection in the field is crucial for realizing smart agriculture. Deep learning-based target detection algorithms have become an important pest identification method due to their high detection accuracy, but the existing methods still suffer from misdetection and omission when detecting small-targeted pests and small-targeted pests in more complex backgrounds. For this reason, this study improves on YOLO11 and proposes a new model called MSDS-YOLO for enhanced detection of small-target pests. First, a new dynamic multi-scale feature extraction module (C3k2_DMSFE) is introduced, which can be adaptively adjusted according to different input features and thus effectively capture multi-scale and diverse feature information. Next, a novel Dimensional Selective Feature Pyramid Network (DSFPN) is proposed, which employs adaptive feature selection and multi-dimensional fusion mechanisms to enhance small-target saliency. Finally, the ability to fit small targets was enhanced by adding 160 × 160 detection heads removing 20 × 20 detection heads and using Normalized Gaussian Wasserstein Distance (NWD) combined with CIoU as a position loss function to measure the prediction error. In addition, a real small-target pest dataset, Cottonpest2, is constructed for validating the proposed model. The experimental results showed that a mAP50 of 86.7% was achieved on the self-constructed dataset Cottonpest2, which was improved by 3.0% compared to the baseline. At the same time, MSDS-YOLO has achieved better detection accuracy than other YOLO models on public datasets. Model evaluation on these three datasets shows that the MSDS-YOLO model has excellent robustness and model generalization ability. Full article