Search Results (8,804)

To satisfy the demands of 5G communication and reliable data connectivity for unmanned aerial vehicles (UAVs), a novel two-element dual-band MIMO antenna with an inherent self-decoupling property based on orthogonal linear polarization diversity is proposed. Distinct from conventional designs relying on extra decoupling components, the antenna realizes isolation enhancement via coupled currents between annular strips and S-shaped strips without additional decoupling structures, representing the core design novelty. Fabricated on a low-cost 1.6 mm thick FR4 substrate, the antenna features compact overall dimensions of 60 mm × 30 mm × 1.6 mm, covering the 2.40–2.73 GHz ISM band and 3.38–3.63 GHz 5G Sub-6 GHz band. Measured results demonstrate that the reflection coefficient remains below −10 dB across the entire operating bands, with port isolation exceeding 27 dB for the 2.4 GHz band and 20 dB for the 3.5 GHz 5G band. The measured realized gain is 0.7–1.5 dB in the lower band and 2.3–2.9 dB in the upper band. The radiation efficiency, which is obtained exclusively from ANSYS HFSS 2025 R1 simulation, is higher than 90% for the lower band and over 80% for the upper band. The calculated envelope correlation coefficient (ECC) is less than 0.15 throughout the working bandwidth, which effectively suppresses inter-channel electromagnetic interference and mitigates channel fading caused by varying UAV attitudes to improve system channel capacity. Further verifications via epoxy encapsulation and co-simulation on an eight-rotor UAV platform prove slight frequency drift after packaging and installation, whereas its bandwidth and isolation still meet practical engineering requirements. Benefiting from a compact layout and omnidirectional radiation performance, the proposed low-cost MIMO antenna is convenient for conformal integration into a UAV fuselage, improving the practicability of UAV-aided emergency communication, equipment inspection and 5G network coverage. Full article

The environmentally sustainable decontamination of chemical warfare agents (CWAs) remains a critical challenge. This study reports the solar-driven photocatalytic degradation of live CWAs—GD, HD, HN1, and HN2—using titanium dioxide (TiO₂) under natural sunlight. Experiments were conducted in an OPCW-designated laboratory to ensure authenticity and practical relevance. TiO₂ exhibited substantial photocatalytic activity, achieving 60% degradation of GD, 63% of HD, 76% of HN1, and 93% of HN2 after 6 h. High-resolution mass spectrometry (HR-MS) analysis suggested plausible degradation pathways for nitrogen mustards consistent with the higher apparent reactivity of HN2; detailed identification of intermediates and reactive oxygen species remains a subject for future investigation. These findings provide mechanistic insights into the photocatalytic behavior of nitrogen-based agents and address a notable gap in studies that have largely focused on sulfur mustards and nerve agents. Beyond military applications, this solar-assisted photocatalytic approach provides mechanistic information relevant to the green remediation of highly toxic organic contaminants and broader chemical hazard mitigation. This work contributes foundational knowledge toward eco-friendly decontamination technologies capable of mitigating diverse CWA threats. Full article

Supercooled large droplets (SLDs), typically defined as droplets with diameters exceeding 100 μm, represent a significant meteorological hazard to aviation safety. Unlike conventional cloud-sized droplets, SLDs have higher inertia and can follow more ballistic trajectories, leading to impingement well aft of leading-edge ice protection systems. SLD icing is further complicated by high-speed splashing, secondary-droplet re-impingement, delayed solidification, and surface water runback. This paper reviews recent progress in understanding SLD impact, splashing, surface water transport, and ice accretion. The review discusses droplet impact on dry and wet surfaces, oblique impingement, ambient-air effects, non-instantaneous solidification, runback dynamics, and downstream ice growth. Emerging ice protection technologies, including superhydrophobic, lubricant-infused, and compliant surfaces, are also evaluated. By synthesizing these developments, this review connects fundamental droplet-impact physics with practical aviation icing challenges and mitigation strategies. Full article

Horizontally buried rectangular hollow pipe barriers are investigated as a potential solution for mitigating ground-borne vibrations in densely built environments. This study combines high-fidelity three-dimensional finite-element analyses, a computationally efficient two-dimensional plane-strain modeling strategy, and a Python-based automated optimization framework to evaluate the effects of barrier geometry and material properties on vibration isolation performance. The results show that vertical vibration attenuation is consistently better than horizontal attenuation. Among the geometric variables, burial depth and barrier width are the dominant factors, with isolation benefits becoming marginal when the burial depth exceeds approximately 3 m and with barrier widths smaller than about 0.5 m leading to poor performance. The material parametric study indicates a threshold behavior for stiffness contrast: the improvement in isolation gradually saturates when the Young’s modulus ratio of barrier to soil exceeds about 5.12, suggesting that reinforced concrete provides a practical balance between structural reliability and engineering applicability. A comparison between the three-dimensional and two-dimensional models shows that the plane-strain approximation can reproduce the three-dimensional results with acceptable accuracy while substantially reducing the computational demand. The automated optimization further identifies high-performing design configurations for practical application. Overall, the study offers numerical insight and computational guidance for the preliminary design and evaluation of rectangular hollow pipe barriers for ground vibration mitigation. Full article

Practical socioeconomic systems commonly contain imprecise and subjective data, while existing uncertain regression methods perform poorly for highly multicollinear variables. Uncertain least squares is susceptible to multicollinearity and outliers, and uncertain LASSO fails to stably select correlated variables. To address these issues, this paper proposes an uncertain elastic net regression model targeting multicollinear uncertain data. Based on the minimum uncertain expectation framework, the model adopts combined regularization to realize sparse variable screening and grouping effect, which mitigates multicollinearity and enhances estimation stability. We verify the model via numerical examples and an empirical study on Shandong’s domestic tourism data, taking two classic uncertain regression methods as benchmarks. The results show that our model outperforms competitors in fitting accuracy, coefficient stability and variable selection. This method provides a reliable, interpretable tool for regression modeling under uncertainty and multicollinearity, and can be applied to tourism and socioeconomic research. Full article

Jellyfish stings are the most common type of marine life injuries. However, at present, the treatment measures against jellyfish stings are mostly empirical and supportive, with uncertain therapeutic outcomes, and there is a lack of specific antidotes based on the toxic mechanism of jellyfish venom in clinical practice. In our previous study, polyphenol epigallocatechin-3-gallate (EGCG) was found to neutralize the toxicity of jellyfish Nemopilema nomurai venom (NnV) in vivo and in vitro. Herein we further demonstrated that EGCG exerted its antagonistic effect against NnV through inhibiting the oxidative stress, pro-apoptotic proteins, and systemic inflammatory responses. Subsequently, we constructed a polyethylene glycol (PEG)-EGCG/tetracycline hydrochloride (HTC) co-loaded micellar nanocomplex in order to enhance the stability and bioavailability of EGCG in vivo, which successfully integrated the membrane-repair function of PEG, the enzyme inhibitory effect of HTC and the antioxidant properties of EGCG. Notably, this micellar nanocomplex demonstrated significant protective effects against both functional damage and pathological alterations in a non-lethal NnV-envenomed mouse model. When administered 1 h after NnV envenomation, EGCG (40 mg/kg), HTC and PEG-EGCG (containing 40 mg/kg EGCG) only partially improved abnormal blood biochemical indicators and moderately alleviated histopathologic damage, and PEG-EGCG/HTC containing merely 8 mg/kg EGCG completely mitigated the toxic reactions in envenomed mice. In the preventive regimen, the administration of EGCG, HTC or PEG-EGCG 30 min before exposure showed no significant improvement in abnormal blood biochemical indicators and histopathologic damage, while PEG-EGCG/HTC could still significantly improve the functional impairments and histopathologic damage of the heart and liver in NnV-envenomed mice. These findings suggest the clinical translational potential of PEG-EGCG/HTC against jellyfish envenomation. Full article

Contemporary deep learning-based image steganography techniques for embedding images within images are hindered by inadequate utilization of frequency-domain features and limited steganographic security, restricting their effectiveness in practical privacy protection contexts. To mitigate these limitations, we introduce a frequency–spatial dual-path fusion-based deep steganography approach, termed FS-Stego. This method incorporates a frequency–spatial dual-path architecture within the generator network. Specifically, the frequency-domain processing module facilitates feature embedding in the complex domain, while the spatial-domain processing module maintains the image’s structural integrity, thereby enabling the co-optimization of multi-dimensional features. Second, an adaptive fusion module is developed to dynamically adjust the weights of the two paths, while residual connections and attention mechanisms are utilized to mitigate feature loss. Third, a multi-objective loss function is implemented to simultaneously optimize the quality of the stego images and the reconstruction accuracy of the secret images. The proposed method utilizes three open-source datasets as cover images and the LFW dataset as the secret images. Experimental results demonstrate that, compared to existing deep steganographic techniques, the stego and recovered images achieve superior peak signal-to-noise ratios (PSNR) and structural similarity (SSIM). Regarding model efficiency, the number of parameters is reduced to below 0.98 million, significantly enhancing practical performance. The proposed method ensures high-quality image recovery while maintaining steganographic security, thereby offering an effective solution for privacy protection. Full article

Analog in-memory computing (AIMC) has emerged as a promising approach to mitigate the Von Neumann bottleneck in matrix operations, which are common in deep learning applications. However, the practical implementation of resistive crossbar arrays is limited by challenges in signed weight representation, conductance quantization, and device nonlinearity. This paper presents a differential mixed-signal architecture for accurate signed matrix–vector multiplication (MVM), integrated with a RISC-V microcontroller for edge inference applications. A structured digital-to-analog mapping framework encodes quantized neural network weights into programmable conductance values while preserving arithmetic correctness. The design employs voltage-mode input encoding, differential current summation, and transimpedance-based readout followed by analog-to-digital conversion, enabling single-cycle signed accumulation without duplicating crossbar resources. A 32 × 16 dual-layer prototype crossbar was fabricated and experimentally characterized. Measurements demonstrate a mean absolute percentage error (MAPE) below 1% within the linear operating region and below 4% over the full-scale conductance range. These results validate the robustness of the proposed mapping methodology and confirm the feasibility of hybrid analog–digital acceleration for edge AI systems. Consequently, this discrete prototype serves as a physical verification platform for the AIMC approach, providing valuable insights for more efficient mixed-signal computing integrated circuit (IC) designs. Full article

Background/Objectives: Chronic diseases account for 74% of global deaths, with multimorbidity (existence of more than one chronic condition) increasing disability risk and treatment burden, leading to poor adherence, disease progression, and reduced quality of life. Nursing-led proactive care models that focus on patient engagement, education, and self-care can help mitigate these challenges. The study aims to evaluate the effectiveness of a nurse-led proactive health intervention in improving care for individuals with chronic diseases in general practice. Methods: A quasi-experimental pre–post study will be conducted in a Community Health Home in Northern Italy. Family and community nurses will deliver the intervention, which includes assessments, educational sessions, and follow-ups for patients aged 65+ with at least one chronic condition. Recruitment will occur over three months. Results: Primary outcomes include emergency department visits and hospitalizations, while secondary outcomes focus on medication adherence, self-care, and service utilization. Data will be collected at 6 and 12 months, and statistical analysis will use descriptive methods and generalized estimating equations (GEEs). Conclusions: This study will improve the understanding of the value of nurse-led proactive intervention, filling the gap in the literature by testing evidence-based approaches on a realistic frail population. Moreover, delivering a complex but structured intervention will provide evidence for future interventions to reduce treatment burden and improve health outcomes. Full article

Accurate tropical cyclone (TC) track prediction remains challenging, as numerical models suffer from high computational cost, substantial storage requirements, and physical parameterization uncertainties, while data-driven large AI models depend heavily on training data volume and high-resolution inputs, resulting in prohibitive computational overhead. To address these issues, this paper proposes TCN-GAN-DM, a three-stage deep learning framework based on the China Meteorological Administration (CMA) Tropical Cyclone Best Track Dataset. Specifically, a dual-stream temporal convolutional network (TCN) first extracts temporal features from track and meteorological sequences, respectively. A generative adversarial network (GAN) then takes these features and produces multiple physically plausible candidate tracks via noise injection. Finally, a conditional diffusion model (DM) refines the predicted positions through progressive denoising. Experimental results for TCs in 2024 show that under the fair deterministic comparison using a single fixed candidate, the model achieves a 6 h track error of 49.10 km, which is comparable to CMA-GFS (49.75 km) and HWRF (44.34 km), and substantially lower than the large AI model FuXi (120.44 km). When evaluating the oracle metric (best-of-K, K = 6) as an upper bound of coverage, the model achieves the smallest errors among all models at 6 h (24.04 km) and 12 h (55.81 km). In addition, the proposed model has advantages over CMA-GFS, HWRF, and FuXi in terms of computational resource consumption and hardware deployment cost. However, its mean track error increases more rapidly beyond 12 h, and at lead times of 18 h and 24 h the model is outperformed by HWRF, FuXi, and CMA-GFS, indicating that its current strength lies primarily in short-term prediction. Consequently, the practical utility of TCN-GAN-DM is currently demonstrated for 6–12 h TC track prediction, offering a new solution for disaster prevention and mitigation that balances accuracy and deployment cost at these specific time scales. Full article

Rapid and reliable flood segmentation utilizing optical remote-sensing imagery is critical for effective flood disaster response and risk assessment. Nevertheless, current models frequently struggle with imprecise boundary delineation and fragmented predictions in complex environments, especially where floodwater displays high spectral variability and closely resembles shadows, dark pavements, or wet soil. To overcome these challenges, we introduce FloodSeg, an innovative Mamba-CNN encoder–decoder network incorporating two lightweight yet highly effective components: a Shift module and a sequence-shuffle module. The spatial Shift module leverages spatially shifted feature aggregation to fortify boundary-aware representations, thereby ensuring the continuity of inundation contours even under varying illumination and cluttered backgrounds. Meanwhile, the sequence-shuffle module reorganizes multi-scale features via sequence-wise mixing and cross-regional interaction, significantly enhancing long-range dependency modeling. This facilitates the generation of globally consistent flood masks while mitigating local overfitting to dataset-specific textures. Evaluated on the Kaggle and FloodNet benchmark datasets, FloodSeg achieves outstanding mIoU scores of 81.85% and 91.21%, respectively. By outperforming various state-of-the-art CNN-, Transformer-, and Mamba-based baselines, our model demonstrates a superior accuracy-efficiency trade-off. These results substantiate that FloodSeg significantly advances boundary recognition and overall segmentation completeness, establishing it as a robust and practical solution for real-world remote-sensing flood mapping applications. Full article

Deep-mining operations are increasingly challenged by severe thermal hazards, which have become a critical bottleneck for achieving safe, efficient, and sustainable mineral extraction. While research on deep-mine cooling and heat hazard mitigation has proliferated, the field lacks a systematic, critical review that explicitly examines these advances through the lens of sustainability science. To address this gap, this study conducted a comprehensive bibliometric analysis of 432 publications (1994–2024) retrieved from the Web of Science Core Collection. The methodology employs Bibliometrix, Vosviewer, and CiteSpace to map the intellectual landscape, research hotspots, and evolving frontiers of the field. The results reveal a clear three-stage development trajectory and identify China, the USA, South Africa, and Canada as leading contributors, with national research emphases on ventilation, energy conservation, and refrigeration, respectively. Crucially, keyword clustering and burst detection uncover a notable paradigm shift: the focus has moved from isolated cooling techniques toward integrated, multi-objective strategies—including geothermal energy co-exploitation, phase-change material applications, and system-level energy optimization—signaling a growing alignment with resource efficiency and low-carbon mining principles. However, a critical finding is that the literature remains predominantly techno-centric, overwhelmingly evaluating performance through operational energy savings while largely neglecting life-cycle environmental impacts, holistic sustainability assessment metrics, and the influence of policy drivers. This review thus not only provides a structured overview of the domain, but, more importantly, exposes these critical knowledge gaps. We argue that future research must pivot toward a multi-dimensional sustainability framework that integrates technical, economic, and environmental dimensions, thereby guiding the next generation of research toward truly sustainable deep-mining practices. Full article

Disturbance events and subsequent management practices significantly shape the ecological legacies of affected sites. This study evaluated the impacts of a 2022 derecho and the subsequent forest management on forest structure and arthropod diversity by comparing affected forests at Fogwell Forest Nature Preserve and Fox Island County Park with control forests at Blue Cast Springs and Hammer Wald Nature Preserves. Arthropod communities were sampled using pitfall traps, while forest structure was assessed through detailed surveys of understory, midstory, and overstory vegetation. Results indicated a decrease in overall arthropod diversity across all sites since 2016, variably attributed to forest maturation, climatic variability, and the 2022 disturbance, with some taxa showing declines, such as Formicidae and Curculionidae. Fogwell exhibited a significant decline in arthropod diversity, likely linked to the derecho, while Fox Island’s diversity aligned more closely with undisturbed control sites. Notable midstory reductions were observed across sites over time, especially at Fox Island, due to harvest and storm impacts. Meanwhile, overstory diversity varied between properties. Regression modeling revealed that forest management practices at Fox Island may have mitigated the disturbance’s effects, aiding arthropod recovery. All in all, these findings highlight the importance of forest management strategies in influencing biodiversity and ecological recovery post-disturbance. Full article

Interferometric Synthetic Aperture Radar (InSAR) is a promising payload for Unmanned Aerial Vehicle (UAV) scene matching navigation due to the rich textures in interferogram images compared to SAR intensity images. However, geometric parameter estimation errors during reference interferogram image generation cause significant textural discrepancies with real-time data. Compounded by inherent non-local similarity of InSAR images, these issues render conventional matching algorithms ineffective, degrading navigation accuracy. To address these challenges, this paper proposes a Morpho-Phase feature-based InSAR image matching method to mitigate the impact of parameter errors. Firstly, a Phase-Robust Keypoint (PRK) detection method is proposed, which overcomes the impact of parameter errors on keypoint detection by introducing a compensated phase and extracting phase extrema. Secondly, a Hierarchical Morphological-Phase Descriptor (HMPD) is constructed to resolve the feature ambiguity caused by the non-local similarity of interferograms by combining morphological features with phase statistics. Experimental results based on real-world InSAR data demonstrate that the proposed matching method effectively mitigates the impact of parameter errors on InSAR image matching, enhances navigation positioning accuracy, and provides stable, high-precision positioning capabilities in practical scene matching navigation tasks. Full article

Multilingual knowledge editing (MKE) remains challenging because language-specific edits interfere with one another, even when locate-then-edit methods succeed in monolingual settings. We study whether vector merging—combining independently computed per-language updates into a single edit—can mitigate this interference. We evaluate six merging variants with two backbone large language models, two base knowledge editing methods, and 12 languages on the MzsRE benchmark under a large-scale batch-editing setting, and we examine how the weight scaling factor and the rank compression ratio affect editing performance. Summation with shared covariance proves the most reliable strategy overall, whereas naive summation without shared covariance performs poorly. Task Singular Vectors for Merging (TSVM) helps only in specific settings, so its ability to reduce multilingual interference is limited. Performance is also sensitive to both weight scale and rank ratio, with larger-than-default scaling and relatively low rank often yielding the best results. When the results are analyzed by language-resource level, the choice of merging method matters most for the relatively low-resource languages, such as Thai and Vietnamese. These findings clarify the practical strengths and limits of current vector merging methods for MKE and provide guidance for future multilingual knowledge editing research. Full article