Search Results (134)

Secure autonomous secret key distillation (SKD) systems traditionally depend on external common randomness (CR) sources, which often suffer from instability and limited reliability over long-term operation. In this work, we propose a novel SKD architecture that synthesizes CR by combining a keystream of a shared-key keystream generator $KSG\left(K_G\right)$ with locally generated binary Bernoulli noise. This construction emulates the statistical properties of the classical Maurer satellite scenario while enabling deterministic control over key parameters such as bit error rate, entropy, and leakage rate (LR). We derive a closed-form lower bound on the equivocation of the shared-secret key $K_G$ from the viewpoint of an adversary with access to public reconciliation data. This allows us to define an admissible operational region in which the system guarantees long-term secrecy through periodic key refreshes, without relying on advantage distillation. We integrate the Winnow protocol as the information reconciliation mechanism, optimized for short block lengths ($N=8$), and analyze its performance in terms of efficiency, LR, and final key disagreement rate (KDR). The proposed system operates in two modes: ideal secrecy, achieving secret key rates up to 22% under stringent constraints (KDR < 10⁻⁵, LR < 10⁻¹⁰), and perfect secrecy mode, which approximately halves the key rate. Notably, these security guarantees are achieved autonomously, without reliance on advantage distillation or external CR sources. Theoretical findings are further supported by experimental verification demonstrating the practical viability of the proposed system under realistic conditions. This study introduces, for the first time, an autonomous CR-based SKD system with provable security performance independent of communication channels or external randomness, thus enhancing the practical viability of secure key distribution schemes. Full article

The use of high-resolution sea level anomaly (SLA) data in climate change research and ocean forecasting has become increasingly important. However, existing datasets often lack the fine spatial resolution required for capturing mesoscale ocean processes accurately. This has led to the development of conventional deep learning models for SLA spatial downscaling, but these models often overlook spatial disparities between land and ocean regions and do not adequately address the spatial structures of SLA data. As a result, their accuracy and structural consistency are suboptimal. To address these issues, we propose a Deep Separable Distillation Network (DSDN) that integrates Depthwise Separable Distillation Blocks (DSDB) and a Landmask Contextual Attention Mechanism (M_CAMB) to achieve efficient and accurate spatial downscaling. The M_CAMB employs geographically-informed land masks to enhance the attention mechanism, prioritizing ocean regions. Additionally, we introduce a novel Pixel-Structure Loss (PSLoss) to enforce spatial structure constraints, significantly improving the structural fidelity of the SLA downscaling results. Experimental results demonstrate that DSDN achieves a root mean square error (RMSE) of 0.062 cm, a peak signal-to-noise ratio (PSNR) of 42.22 dB, and a structural similarity index (SSIM) of 0.976 in SLA downscaling. These results surpass those of baseline models and highlight the superior precision and structural consistency of DSDN. Full article

In recent years, remote sensing imagery has become indispensable for applications such as environmental monitoring, land use classification, and urban planning. However, the physical constraints of satellite imaging systems frequently limit the spatial resolution of these images, impeding the extraction of fine-grained information critical to downstream tasks. Super-resolution (SR) techniques thus emerge as a pivotal solution to enhance the spatial fidelity of remote sensing images via computational approaches. While deep learning-based SR methods have advanced reconstruction accuracy, their high computational complexity and large parameter counts restrict practical deployment in real-world remote sensing scenarios—particularly on edge or low-power devices. To address this gap, we propose LSANet, a lightweight SR network customized for remote sensing imagery. The core of LSANet is the large separable kernel attention mechanism, which efficiently expands the receptive field while retaining low computational overhead. By integrating this mechanism into an enhanced residual feature distillation module, the network captures long-range dependencies more effectively than traditional shallow residual blocks. Additionally, a residual feature enhancement module, leveraging contrast-aware channel attention and hierarchical skip connections, strengthens the extraction and integration of multi-level discriminative features. This design preserves fine textures and ensures smooth information propagation across the network. Extensive experiments on public datasets such as UC Merced Land Use and NWPU-RESISC45 demonstrate LSANet’s competitive or superior performance compared to state-of-the-art methods. On the UC Merced Land Use dataset, LSANet achieves a PSNR of 34.33, outperforming the best-baseline HSENet with its PSNR of 34.23 by 0.1. For SSIM, LSANet reaches 0.9328, closely matching HSENet’s 0.9332 while demonstrating excellent metric-balancing performance. On the NWPU-RESISC45 dataset, LSANet attains a PSNR of 35.02, marking a significant improvement over prior methods, and an SSIM of 0.9305, maintaining strong competitiveness. These results, combined with the notable reduction in parameters and floating-point operations, highlight the superiority of LSANet in remote sensing image super-resolution tasks. Full article

Dissolved Gas Analysis (DGA) of transformer oil is a critical technique for transformer fault diagnosis that involves analyzing the concentration of gases to detect potential transformer faults in a timely manner. Given the issues of large model parameters and high computational resource demands in transformer DGA diagnostics, this study proposes a lightweight convolutional neural network (CNN) model for improving gas ratio methods, combining Knowledge Distillation (KD) and recursive plots. The approach begins by extracting features from DGA data using the ratio method and Multiscale sample entropy (MSE), then reconstructs the state space of the feature data using recursive plots to generate interpretable two-dimensional image features. A deep feature extraction process is performed using the ResNet50 model, integrated with the Convolutional Block Attention Module (CBAM). Subsequently, the Sparrow Optimization Algorithm (SSA) is applied to optimize the hyperparameters of the ResNet50 model, which is trained on DGA data as the teacher model. Finally, a dual-path distillation mechanism is introduced to transfer the efficient features and knowledge from the teacher model to the student model, MobileNetV3-Large. The experimental results show that the distilled model reduces memory usage by 83.5% and computation time by 73.2%, significantly lowering computational complexity while achieving favorable performance across various evaluation metrics. This provides a novel technical solution for the improvement of gas ratio methods. Full article

Seismic signal processing often relies on general convolutional neural network (CNN)-based models, which typically focus on features in the time domain while neglecting frequency characteristics. Moreover, down-sampling operations in these models tend to cause the loss of critical high-frequency details. To this end, we propose a feedback information distillation network (FID-N) in the non-subsampled contourlet transform (NSCT) domain to remarkably suppress seismic noise. The method aims to enhance denoising performance by preserving the fine-grained details and frequency characteristics of seismic data. The FID-N mainly consists of a two-path information distillation block used in a recurrent manner to form a feedback mechanism, carrying an output to correct previous states, which fully exploits competitive features from seismic signals and effectively realizes the signal restoration step by step across time. Additionally, the NSCT has an excellent high-frequency response and powerful curve and surface description capabilities. We suggest converting the noise suppression problem into NSCT coefficient prediction, which maintains more detailed high-frequency information and promotes the FID-N to further suppress noise. Extensive experiments on both synthetic and real seismic datasets demonstrated that our method significantly outperformed the SOTA methods, particularly in scenarios with low signal-to-noise ratios and in recovering high-frequency components. Full article

Agricultural practices significantly influence agroecosystem biodiversity, driving a growing focus on the development of environmentally sustainable management strategies. Olive (Olea europaea L.) is one of the most widely cultivated tree crops in the Mediterranean basin and other regions with a Mediterranean climate. In this study, we employed a split-plot design with whole plots arranged as a randomized complete block design (RCBD) to evaluate the effects of minimum tillage and the application of wood distillate to olive canopies on wild vascular plant and soil-dwelling springtail communities in a conventionally managed olive grove in central Italy. Biotic communities were sampled twice, in November and April. Tillage caused a marginally significant decrease in springtail species richness in April and significantly influenced the composition of both plant and springtail communities in April. All the plant species showed a decrease in abundance under tillage, whereas the abundance of springtail species responded to tillage in a species-specific way. Wood distillate had no effect on any community attribute in either season. Springtail total abundance was not affected by any treatment in either season. Our findings confirm that tillage practices affect the diversity of plant and springtail communities. Moreover, we had evidence that spring tillage may have more negative impacts on the studied communities with respect to autumn tillage. Moreover, we suggest that the application of low-concentration wood distillate to olive canopies can be considered, in the short-term, a sustainable agricultural practice that does not negatively affect agroecosystem biodiversity. Full article

The formation of polycyclic aromatic hydrocarbons (PAHs) during catagenesis does not exclusively lead to planar structures. The inclusion of five-ring elements increases the curvature of PAHs and yields bent molecules. These bowl-like configurations may end in the formation of spherical carbon allotropes as fullerenes or nanotubes, as recently shown. The presence of fullerenes in crude oil raises the question of why the reaction is feasible under catagenic conditions although the laboratory synthesis of fullerenes commonly requires high-energy environments. This study focuses on the feasibility of the simulation of catagenesis under laboratory conditions and the question of which building blocks may lead to spherical structures. Possible educts, reaction mechanisms, and conditions such as temperature are discussed and related to experimental outcomes. For the simulation under laboratory conditions, a light gas condensate was fractionated by distillation in order to reduce the number of compounds per fraction and make them distinguishable. The characterization of the resulting fractions was performed through GC-MS and GC-FID measurements before heat application in a closed reactor. High-resolution mass spectrometry (HRMS) measurements of the products indicated PAH growth and, more importantly, the formation of fullerenes. Interestingly, the characterized fullerenes mostly comprised the range of non-IPR (isolated pentagon rule) fullerenes. Full article

This study was the first to analyze the chemical compositions and bioactivities of Serissa japonica leaf oil. The oil, obtained via hydro-distillation with a 0.1% yield, contained 64 compounds, predominantly non-terpenic compounds (39.0%), oxygenated sesquiterpenes (31.4%), and oxygenated monoterpenes (25.6%). Major constituents included 1,8-cineole, (E)-nerolidol, and iso-longifolol. The oil showed good antioxidant activity (IC₅₀ ≈ 62.79 ± 0.77 µg/mL for DPPH and 57.82 ± 1.12 µg/mL for ABTS) and a good anti-tyrosinase effect (IC₅₀ ≈ 195.6 ± 3.82 µg/mL). The trend was similar to anti-inflammatory activity, with an IC₅₀ value of 63.03 ± 3.22, for NO inhibition without cytotoxicity at 100 µg/mL. The bovine serum albumin (BSA) blocking assay demonstrated an IC₅₀ value of 59.31 ± 0.71 µg/mL, indicating a good interaction regarding enzyme inhibition. Moreover, the computational modeling of the possible association between tyrosinase and cyclooxygenase-2 highlighted their antioxidant and anti-inflammatory properties. The results pointed out the usefulness of S. japonica essential oil as a natural candidate for managing oxidative stress and inflammation. Full article

Accurate detection of tea shoots in field conditions is a challenging task for production management and harvesting in tea plantations. Deep learning is well-suited for performing complex tasks due to its robust feature extraction capabilities. However, low-complexity models often suffer from poor detection performance, while high-complexity models are hindered by large size and high computational cost, making them unsuitable for deployment on resource-limited mobile devices. To address this issue, a lightweight and high-performance model was developed based on YOLOv5 for detecting tea shoots in field conditions. Initially, a dataset was constructed based on 1862 images of the tea canopy shoots acquired in field conditions, and the “one bud and one leaf” region in the images was labeled. Then, YOLOv5 was modified with a parallel-branch fusion downsampling block and a lightweight feature extraction block. The modified model was then further compressed using model pruning and knowledge distillation, which led to additional improvements in detection performance. Ultimately, the proposed lightweight and high-performance model for tea shoot detection achieved precision, recall, and average precision of 81.5%, 81.3%, and 87.8%, respectively, which were 0.4%, 0.6%, and 2.0% higher than the original YOLOv5. Additionally, the model size, number of parameters, and FLOPs were reduced to 8.9 MB, 4.2 M, and 15.8 G, representing decreases of 90.6%, 90.9%, and 85.3% compared to YOLOv5. Compared to other state-of-the-art detection models, the proposed model outperforms YOLOv3-SPP, YOLOv7, YOLOv8-X, and YOLOv9-E in detection performance while maintaining minimal dependency on computational and storage resources. The proposed model demonstrates the best performance in detecting tea shoots under field conditions, offering a key technology for intelligent tea production management. Full article

Agricultural pest and disease monitoring has recently become a crucial aspect of modern agriculture. Toward this end, this study investigates methodologies for implementing low-power, high-performance convolutional neural networks (CNNs) on agricultural edge detection devices. Recognizing the potential of field-programmable gate arrays (FPGAs) to enhance inference parallelism, we leveraged their computational capabilities and intensive storage to propose an embedded FPGA-based CNN accelerator design aimed at optimizing rice leaf disease image classification. Additionally, we trained the MobileNetV2 network using multimodal image data and employed knowledge distillation from a stronger teacher (DIST) as the hardware benchmark. The solution was deployed on the ZYNQ-AC7Z020 hardware platform using High-Level Synthesis (HLS) design tools. Through a combination of fine-grained pipelining, matrix blocking, and linear buffering optimizations, the proposed system achieved a power consumption of 3.21 W, an accuracy of 97.41%, and an inference speed of 43 ms per frame, making it a practical solution for edge-based rice leaf disease classification. Full article

This study assessed the repair shear bond strength (SBS; MPa) and surface roughness (Ra; μm) of aged hybrid ceramic (Cerasmart270, GC) and nano-hybrid ceramic (Grandio Blocs, Voco) CAD/CAM blocks after different surface pretreatment methods. In this study, 2 mm thick Cerasmart270 and Grandio Blocs were cut into slabs (Isomet; n = 80 per group). Following aging for six months, the specimens in each CAD/CAM material were randomly divided into four groups (n: 20 each) according to the surface pretreatments: control (no pretreatment), Er:YAG laser, sandblasting, and bur grinding. A total of 10 specimens in each CAD/CAM material pretreatment group were used for Ra evaluation (Perthometer Mahr), while the other 10 were for SBS. After the application of a silane primer (G-Multi Primer, GC) and universal adhesive (G2-Bond, GC), composite build-ups (Filtek Z250; 3MESPE) were performed for the SBS evaluation. After storage in distilled water for 24 h, SBS was evaluated with a universal testing machine (Instron). SBS and Ra data were analyzed with two-way ANOVA and Tukey’s post hoc tests (p < 0.05). SBS was significantly affected by the surface pretreatment methods (p = 0.0001) and by the types of CAD/CAM material (p = 0.005). Bur grinding showed the highest SBS for both CAD/CAM materials, while the control groups yielded significantly lower SBS than bur grinding and sandblasting (p < 0.05). Er:YAG lasers did not significantly enhance the SBS compared to the control group. Sandblasting presented significantly higher SBS than lasers only in Grandio Blocs (p < 0.05). The surface pretreatment methods significantly influenced Ra (p = 0.0001); however, no significant interaction was found between the types of CAD/CAM material and the surface pretreatments (p > 0.05). Control groups exhibited, significantly, the lowest Ra for both materials (p = 0.0001), while no significant differences were observed between the other pretreatment methods. Bur grinding was identified as the most effective pretreatment method for repairing hybrid ceramic CAD/CAM materials. Full article

There are two problems with traditional knowledge distillation methods in industrial anomaly detection: first, traditional methods mostly use feature alignment between the same layers. The second is that similar or even identical structures are usually used to build teacher-student models, thus limiting the ability to represent anomalies in multiple ways. To address these issues, this work proposes a Hierarchical Knowledge Transfer (HKT) framework for detecting industrial surface anomalies. First, HKT utilizes the deep knowledge of the highest feature layer in the teacher’s network to guide student learning at every level, thus enabling cross-layer interactions. Multiple projectors are built inside the model to facilitate the teacher in transferring knowledge to each layer of the student. Second, the teacher-student structural symmetry is decoupled by embedding Convolutional Block Attention Modules (CBAM) in the student network. Finally, based on HKT, a more powerful anomaly detection model, HKT+, is developed. By adding two additional convolutional layers to the teacher and student networks of HKT, HKT+ achieves enhanced detection capabilities at the cost of a relatively small increase in model parameters. Experiments on the MVTec AD and BeanTech AD(BTAD) datasets show that HKT+ achieves state-of-the-art performance with average area under the receiver operating characteristic curve (AUROC) scores of 98.69% and 94.58%, respectively, which outperforms most current state-of-the-art methods. Full article

The research hypothesis assumes that Fusarium culmorum infection affects the baking value of wheat. The aim of the research was to determine the effect of the cultivar on the rheological properties of wheat dough in response to Fusarium culmorum infection of wheat. A two-factor experiment conducted during the 2018–2020 growing seasons in Osiny, Poland, was set up using the completely randomized block design with three replications. The first factor was winter wheat cultivars (six cultivars), while the second factor was inoculation (two levels—Fusarium culmorum and distilled water—control). The immunoenzymatic ELISA method was used to determine the content of deoxynivalenol (DON) in grain. The DON content in the grain varied between cultivars. Fusarium culmorum inoculation resulted in an increase in protein, ash content, and flour water absorption, changes in dough rheological properties, and a decrease in the sedimentation index. Inoculation also caused negative changes in starch properties. The observed interaction between Fusarium culmorum inoculation and cultivars in shaping the qualitative parameters and rheological properties of the dough indicates that there are wheat cultivars less susceptible to Fusarium infection, which do not show any significant changes as a result of infection. Full article

Lightweight remote sensing image super-resolution methods aim to enhance image resolution and recover fine details through lightweight neural networks. However, current lightweight methods still suffer from poor performance and unattractive details. DyLKANet introduces a novel lightweight architecture that utilizes a multi-level feature integration strategy to enhance information exchange between context-aware and large kernel attention mechanisms. The network comprises two core modules: the feature distillation and enhancement block for efficient feature extraction, and the context-aware attention-based feature fusion module for capturing global interdependencies. Experiments conducted on the UCMerced, AID, and DIV2K datasets reveal that DyLKANet achieves comparable performance while maintaining a low parameter count and computational complexity. Taking the 2× upscaling results on the UCMerced dataset as an example, specifically, DyLKANet improves PSNR by 0.212–3.544 dB, SSIM by 0.005–0.038, and reduces parameters by 18.79–95.46%. DyLKANet reduces FLops by 7.25–82.63%, making it a promising solution for remote sensing image super-resolution tasks in resource-constrained environments. Full article

This article contributes to the reimagining of Roman Catholic ethics in the twenty-first century, building on the research of Sweeny Block, who argues that the unconscious dimensions of the moral life play a decisive role in moral agency. By taking account of the work of researchers in moral psychology, the traditional boundaries of moral theology can be reimagined to give a more accurate accounting of moral agency, leading to improved work in moral formation. This interdisciplinary approach engages the experiences of Catholic thinkers from the Global South, whose experiences are not usually countenanced in theorising on the nature of morality. The discussion presents a Caribbean refinement of Bandura’s eight mechanisms of moral disengagement, which are amplified and distilled into culturally relevant attitudes captured in the everyday language or speech events of the Jamaican people. Roman Catholic ethics have not treated with the concept of moral disengagement in any meaningful fashion. The amplification of the mechanisms of moral disengagement points to and reinforces the inadequacy of models of moral agency that disregard unconscious dimensions while centring rationality and free will in the face of human fallibility and social contexts that are distorting and deforming. It points to storytelling, an important part of the Caribbean culture, as one way to improve our moral agency by expanding the moral imagination to better form our moral vision. Full article