Search Results (673)

As a perceptual representation of ecosystem structure and function, the soundscape has become an important indicator for evaluating ecological health and assessing the impacts of human disturbances. Understanding the spatiotemporal heterogeneity of soundscapes is essential for revealing ecological processes and human impacts in protected areas. This study investigates such heterogeneity in Zhangjiajie National Forest Park using deep learning approaches. To this end, we constructed a dataset comprising eight representative sound source categories by integrating field recordings with online audio (BBC Sound Effects Archive and Freesound), and trained a classification model to accurately identify biophony, geophony, and anthrophony, which enabled the subsequent analysis of spatiotemporal distribution patterns. Our results indicate that temporal variations in the soundscape are closely associated with circadian rhythms and tourist activities, while spatial patterns are strongly shaped by topography, vegetation, and human interference. Biophony is primarily concentrated in areas with minimal ecological disturbance, geophony is regulated by landforms and microclimatic conditions, and anthrophony tends to mask natural sound sources. Overall, the study highlights how deep learning-based soundscape classification can reveal the mechanisms by which natural and anthropogenic factors structure acoustic environments, offering methodological references and practical insights for ecological management and soundscape conservation. Full article

Unmanned Surface Vehicles (USVs) play a critical role in maritime monitoring, environmental protection, and emergency response, necessitating accurate scene understanding in complex aquatic environments. Conventional semantic segmentation methods often fail to capture global context and lack physical boundary consistency, limiting real-world performance. This paper proposes USV-Seg, a unified segmentation framework integrating a vision-language model, the Segment Anything Model (SAM), DINOv2-based visual features, and a physically constrained refinement module. We design a task-specific <Describe> Token to enable fine-grained semantic reasoning of navigation scenes, considering USV-to-shore distance, landform complexity, and water surface texture. A mask selection algorithm based on multi-layer Intersection-over-Prediction (IoP) heads improves segmentation precision across sky, water, and obstacle regions. A boundary-aware correction module refines outputs using estimated sky-water and land-water boundaries, enhancing robustness and realism. Unlike prior works that simply apply vision-language or geometric post-processing in isolation, USV-Seg integrates structured scene reasoning and scene-aware boundary constraints into a unified and physically consistent framework. Experiments on a real-world USV dataset demonstrate that USV-Seg outperforms state-of-the-art methods, achieving 96.30% mIoU in challenging near-shore scenarios. Full article

In the context of the information age, a land administration system must be technologically driven to manage land information and data transparently. This ensures the registration and protection of land rights for people. In this study, we present a Blockchain Land Registration system designed as a tool for enhancing land administration in South Saharan Africa (SSA). Drawing inspiration from Namibia, we have developed a user interface comprising a homepage/landing page, a users’ registration form, a login form that incorporates MetaMask authentication prompts, and an authenticated dashboard for landowners and purchasers. Design Science was employed as the methodology for this proposal. Being technical design research for solving a land administration problem (that of inefficient land registration), the technical solution identified involves system design, the development of blockchain integration and testing, and development aspects. Based on this approach, blockchain was conceptualised as an “artefact” that could be investigated as a technical solution to address the challenges posed by inefficient land registration. This study provides a comprehensive roadmap for the conceptualisation, development, validation, and deployment of a blockchain-based land titles registry suitable for SSA countries. It also explores a discussion on the practical and policy implications of blockchain in land administration in SSA countries. Full article

Background/Objectives: As COVID-19 policies shift from government mandates to individual responsibility, understanding how voluntary protective behaviors shape social interactions remains a public health priority. This study examines the association between voluntary mask wearing and interpersonal distance (IPD) preferences in a post-mandate context, focusing on Taiwan, where mask wearing continues to be culturally prevalent. Methods: One hundred university students (50 males, 50 females) in Taiwan completed an online IPD simulation task. Participants adjusted the distance of a virtual avatar in response to targets that varied by gender and mask status. Mask-wearing status upon arrival was recorded naturally, without manipulation. A four-way ANOVA tested the effects of participant gender, participant mask wearing, target gender, and target mask wearing on the preferred IPD. Results: Voluntary mask wearing was more common among female participants (72%) than males (44%). Mask-wearing individuals maintained significantly greater IPDs, suggesting heightened risk perception, whereas masked targets elicited smaller IPDs, possibly due to social signaling of safety. Gender differences emerged in both protective behavior and spatial preferences, with females showing stronger associations between mask use and distancing behavior. Conclusions: These findings offer actionable insights into how voluntary behavioral adaptations continue to shape spatial interaction norms after mandates are lifted. The integration of real-time simulation and statistical modeling highlights the potential of digital behavioral tools to support culturally adaptive, person-centered public health strategies. Full article

Mount Etna, located on the eastern coast of Sicily, is Europe’s most active volcano. Over the past five years, it has experienced numerous significant eruptive episodes, with the most recent occurring in August 2024. During this event, substantial amounts of volcanic ash were dispersed over densely populated areas, particularly in the province of Catania. Environmental factors, such as volcanic eruptions, are known to influence inflammatory skin conditions, including atopic dermatitis. We analyzed a cohort of patients with atopic dermatitis who were exposed to volcanic ash during the Mount Etna eruption in August 2024, aiming to evaluate the impact of the eruption on respiratory and cutaneous symptoms, treatment response, use of protective equipment, and changes in EASI scores over an eight-week period. A total of 67 Caucasian atopic dermatitis patients (mean age 41.2) were assessed after a volcanic eruption. Symptom worsening occurred in 58.9% (respiratory) and 26.9% (skin) of patients. EASI scores significantly increased (p < 0.05). No clinical difference was found between treatment types or mask use, which did not prevent symptom exacerbation. Volcanic ash exposure significantly worsened respiratory and skin symptoms in atopic dermatitis patients, underscoring the need for improved protective measures and further research on environmental triggers of chronic inflammatory conditions. Full article

Background/Objectives: The COVID-19 pandemic caused pronounced disorder in healthcare delivery globally, including ophthalmology. Our study explores how ophthalmologists in Puerto Rico (PR) altered their practices during the pandemic, confronting obstacles such as resource shortages, evolving public health mandates, and unique socio-economic and geographic constraints. The study aims to enhance preparedness for future public health crises. Methods: We conducted descriptive analyses on four online surveys distributed at crucial time points of the pandemic (March 2020, May 2020, August 2020, August 2021) to all practicing ophthalmologists in PR (N ≈ 200), capturing data on closures, patient volume, personal protective equipment (PPE) access, telemedicine use, and financial relief. Results: Survey responses ranged from 41% (n = 81) to 56% (n = 111). By March 2020, 22% (24/111) of respondents closed their offices. By May 2020, 20% (19/93) of respondents maintained a closed office, while 89% (64/72) of open offices reported seeing less than 25% of their usual patient volume. Access to PPE was a challenge, with 59% (65/111) reporting difficulty obtaining N95 masks in March 2020. Telemedicine usage increased initially, peaking in May 2020 and declining in July 2020. By August 2021, all respondents were fully vaccinated and most practices returned to pre-pandemic levels. Overall, 86% (70/81) of respondents found the surveys to be useful for navigating practice changes during the pandemic. Conclusions: PR ophthalmologists showed adaptability during the COVID-19 pandemic to maintain care given limited resources. Guidelines from professional organizations and real time surveys play an important role in future crisis preparedness. Full article

Using the fly eye as a model system, we previously demonstrated that upregulation of the fly gene mask protects against FUS- and Tau-induced photoreceptor degeneration. Building upon this finding, we investigated whether the protective role of mask is conserved in mammals. To this end, we generated a transgenic mouse line carrying Cre-inducible ANKHD1, the human homolog of mask. Utilizing the TauP301S-PS19 mouse model for Tau-related dementia, we found that expressing ANKHD1 driven by CamK2a-Cre reduced hyperphosphorylated human Tau in 6-month-old mice. Additionally, ANKHD1 expression was associated with a trend toward reduced gliosis and preservation of the presynaptic marker Synaptophysin, suggesting a protective role of ANKHD1 against TauP301S-linked neuropathology. At 9 months of age, novel object recognition (NOR) testing revealed cognitive impairment in female, but not male, PS19 mice. Notably, co-expression of ANKHD1 restored cognitive performance in the affected female mice. Together, this study highlights the novel effect of ANKHD1 in counteracting the adverse effects induced by the mutant human Tau protein. This finding underscores ANKHD1’s potential as a unique therapeutic target for tauopathies. Full article

This paper introduces a robust and imperceptible video watermarking framework designed for blind extraction in dynamic video environments. The proposed method operates in the spatial domain and combines multiscale perceptual analysis, adaptive Just Noticeable Difference (JND)-based quantization, and temporal redundancy via multiframe embedding. Watermark bits are embedded selectively in blocks with high perceptual masking using a QIM strategy, and the corresponding DCT coefficients are estimated directly from the spatial domain to reduce complexity. To enhance resilience, each bit is redundantly inserted across multiple keyframes selected based on scene transitions. Extensive simulations over 21 benchmark videos (CIF, 4CIF, HD) validate that the method achieves superior performance in robustness and perceptual quality, with an average Bit Error Rate (BER) of 1.03%, PSNR of 50.1 dB, SSIM of 0.996, and VMAF of 97.3 under compression, noise, cropping, and temporal desynchronization. The system outperforms several recent state-of-the-art techniques in both quality and speed, requiring no access to the original video during extraction. These results confirm the method’s viability for practical applications such as copyright protection and secure video streaming. Full article

Background: Influenza A viruses (IAV) cause seasonal flu and occasional pandemics. In addition, the potential for the emergence of new strains presents unknown challenges for public health. Face masks and other personal protective equipment (PPE) can act as barriers that prevent the spread of these viruses. Metal ions embedded into PPE have been demonstrated to inactivate respiratory viruses, but the underlying mechanism of inactivation and potential for resistance is presently not well understood. Methods: In this study, we used hemagglutination assays to quantify the effect of zinc ions on IAV sialic acid receptor binding. We varied the zinc concentration, incubation time, incubation temperature, and passaged IAV in the presence of zinc ions to investigate if resistance to zinc ions could evolve. Results: We found that zinc ions impact the ability of IAV particles to hemagglutinate and observed inhibition within 1 min of exposure. Maximum inhibition was achieved within 1 h and sustained for at least 24 h in a concentration-dependent manner. Inhibition was also temperature-dependent, and optimal above room temperature. Serial passaging of IAV in the presence of zinc ions did not result in resistance. Conclusions: e conclude that zinc ions prevent IAV hemagglutination in a concentration and temperature-dependent manner for at least 24 h. Overall, these findings are in line with previous observations indicating that zinc-embedded materials can inactivate the IAV hemagglutinin and SARS-CoV-2 spike proteins, and they support work toward developing robust, passive, self-cleaning antiviral barriers in PPE. Full article

Oxidized phospholipids (OxPLs) are increasingly recognized as biologically active lipids involved in various pathologies. Both exposure to pathogenic factors and the efficacy of protective mechanisms are critical to disease development. In this study, we characterized an immunoassay that quantified the total capacity of the plasma to degrade or mask OxPLs, thereby preventing their interaction with cells and soluble proteins. OxLDL-coated plates were first incubated with human blood plasma or a control vehicle, followed by an ELISA using a monoclonal antibody specific to oxidized phosphatidylethanolamine. Pretreatment with the diluted blood plasma markedly inhibited mAb binding. The masking assay was optimized by evaluating the buffer composition, the compatibility with various anticoagulants, potential interfering compounds, the kinetic parameters, pre-analytical stability, statistical robustness, and intra- and inter-individual variability. We propose that this masking assay provides a simple immunological approach to assessing protective mechanisms against lipid peroxidation products. Establishing this robust and reproducible method is essential for conducting clinical association studies that explore masking activity as a potential biomarker of the predisposition to a broad range of lipid-peroxidation-related diseases. Full article

Anthropogenic and climatic pressures can transform contiguous forests into smaller, less connected fragments. Forest biodiversity and ecosystem functioning can furthermore be compromised or enhanced. We present a descriptive analysis of forest fragmentation in Bavaria, the largest federal state in Germany. We calculated 22 metrics of fragmentation using forest polygons, aggregated within administrative units and with respect to both elevation and aspect orientation. Using a forest mask from September 2024, we found 2.384 million hectares of forest across Bavaria, distributed amongst 83,253 forest polygons 0.1 hectare and larger. The smallest patch category (XS, <25 ha) outnumbered all other size classes by nearly 13 to 1. Edge zones accounted for more than 1.68 million hectares, leaving less than 703,000 hectares as core forest. Although south-facing slopes dominated the state, the highest forest cover (~36%) was found on the least abundant east-oriented slopes. Most of the area is located at 400–600 m.a.s.l., with around 30% of this area covered by forests; however, XL forest patches (>3594 ha) dominated higher elevations, covering 30–60% of land surface area between 600 and 1400 m.a.s.l. The distribution of the largest patches follows the higher terrain and corresponds well to protected areas. K-Means clustering delineated 3 clusters, which corresponded well with the predominance of patchiness, aggregation, and edginess within districts. Full article

The palm-vein recognition system has garnered attention as a biometric technology due to its resilience to external environmental factors, protection of personal privacy, and low risk of external exposure. However, with recent advancements in deep learning-based generative models for image synthesis, the quality and sophistication of fake images have improved, leading to an increased security threat from counterfeit images. In particular, palm-vein images acquired through near-infrared illumination exhibit low resolution and blurred characteristics, making it even more challenging to detect fake images. Furthermore, spoof detection specifically targeting palm-vein images has not been studied in detail. To address these challenges, this study proposes the Fourier-gated feature-fusion network (FGFNet) as a novel spoof detector for palm-vein recognition systems. The proposed network integrates masked fast Fourier transform, a map-based gated feature fusion block, and a fast Fourier convolution (FFC) attention block with global contrastive loss to effectively detect distortion patterns caused by generative models. These components enable the efficient extraction of critical information required to determine the authenticity of palm-vein images. In addition, fractal dimension estimation (FDE) was employed for two purposes in this study. In the spoof attack procedure, FDE was used to evaluate how closely the generated fake images approximate the structural complexity of real palm-vein images, confirming that the generative model produced highly realistic spoof samples. In the spoof detection procedure, the FDE results further demonstrated that the proposed FGFNet effectively distinguishes between real and fake images, validating its capability to capture subtle structural differences induced by generative manipulation. To evaluate the spoof detection performance of FGFNet, experiments were conducted using real palm-vein images from two publicly available palm-vein datasets—VERA Spoofing PalmVein (VERA dataset) and PLUSVein-contactless (PLUS dataset)—as well as fake palm-vein images generated based on these datasets using a cycle-consistent generative adversarial network. The results showed that, based on the average classification error rate, FGFNet achieved 0.3% and 0.3% on the VERA and PLUS datasets, respectively, demonstrating superior performance compared to existing state-of-the-art spoof detection methods. Full article

We propose a secure and scalable file-encryption scheme for cloud systems by integrating Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD), and Advanced Encryption Standard (AES) within a distributed architecture. While prior studies have primarily focused on secure key exchange or authentication protocols (e.g., layered PQC-QKD key distribution), our scheme extends beyond key management by implementing a distributed encryption architecture that protects large-scale files through integrated PQC, QKD, and AES. To support high-throughput encryption, our proposed scheme partitions the target file into fixed-size subsets and distributes them across slave nodes, each performing parallel AES encryption using a locally reconstructed key from a PQC ciphertext. Each slave node receives a PQC ciphertext that encapsulates the AES key, along with a PQC secret key masked using QKD based on the BB84 protocol, both of which are centrally generated and managed by the master node for secure coordination. In addition, an encryption and transmission pipeline is designed to overlap I/O, encryption, and communication, thereby reducing idle time and improving resource utilization. The master node performs centralized decryption by collecting encrypted subsets, recovering the AES key, and executing decryption in parallel. Our evaluation using a real-world medical dataset shows that the proposed scheme achieves up to 2.37× speedup in end-to-end runtime and up to 8.11× speedup in encryption time over AES (Original). In addition to performance gains, our proposed scheme maintains low communication cost, stable CPU utilization across distributed nodes, and negligible overhead from quantum key management. Full article

With the advance of Artificial Intelligence, Deep Neural Networks are widely employed in various sensor-based systems to analyze operational conditions. However, due to the inherently nondeterministic and probabilistic natures of neural networks, the assurance of overall system performance could become a challenging task. In particular, soft errors could weaken the robustness of such networks and thereby threaten the system’s safety. Conventional fault-tolerant techniques by means of hardware redundancy and software correction mechanisms often involve a tricky trade-off between effectiveness and scalability in addressing the extensive design space of Deep Neural Networks. In this work, we propose a Reinforcement-Learning-based approach to protect neural networks from soft errors by addressing and identifying the vulnerable bits. The approach consists of three key steps: (1) analyzing layer-wise resiliency of Deep Neural Networks by a fault injection simulation; (2) generating layer-wise bit masks by a Reinforcement-Learning-based agent to reveal the vulnerable bits and to protect against them; and (3) synthesizing and deploying bit masks across the network with guaranteed operation efficiency by adopting transfer learning. As a case study, we select several existing neural networks to test and validate the design. The performance of the proposed approach is compared with the performance of other baseline methods, including Hamming code and the Most Significant Bits protection schemes. The results indicate that the proposed method exhibits a significant improvement. Specifically, we observe that the proposed method achieves a significant performance gain of at least 10% to 15% over on the test network. The results indicate that the proposed method dynamically and efficiently protects the vulnerable bits compared with the baseline methods. Full article

Background/Objectives: Ellagic acid (EA) is a polyphenol found in several fruits and vegetables, including pomegranate, nuts and berries. It exhibits significant health benefits, mainly cardio- and vaso-protective; indeed, EA protects the myocardium against infarction and inhibits cardiac fibrosis. These beneficial effects may be, at least in part, promoted by calcium release from and uptake by the sarcoplasmic reticulum, which are crucial events for cardiac relaxation and contraction. Regardless, the exact mechanism is currently unclear. Methods: A deeper investigation of the role of EA in cardiac contractility and the underlying mechanism has been carried out by using an ex vivo model of isolated and perfused rat heart. Results and Discussion: EA perfusion (100 nM–10 µM) did not influence the coronary flow (CF), suggesting the absence of a vasoactivity, but significantly increased contractility parameters (LVDP and dP/dt). Interestingly, a more marked effect of EA on LVDP and dP/dt values was observed when it was perfused in the presence of AngII. Cyclopiazonic acid (CA) and red ruthenium (RR), specific antagonists of SERCA and RyRs, respectively, were used to explore the contribution of EA when the intracellular calcium handling was altered. In the presence of CA, EA, perfused at increasing concentrations, showed a very modest positive inotropism (significant only at 1 µM). Instead, RR, which significantly compromised all functional parameters, completely masked the effects of EA; furthermore, a marked reduction in CF and a dramatic impact on the positive inotropism occurred. Conclusions: These results demonstrate the positive inotropism of EA on isolated and perfused hearts and suggest that the RyRs may be a main target through which EA plays its effects, since inhibition with RR almost completely blocks the positive inotropism. Full article