Search Results (967)

This study introduces an energy-aware hybrid security framework that safeguards embedded systems against code theft, closing a critical gap. The approach integrates bitstream encryption, dynamic key generation, and Dynamic Function eXchange (DFX)-based memory obfuscation, yielding a layered hardware–software countermeasure to Read-Only Memory (ROM) scraping, side-channel attacks, and Man-in-the-Middle (MITM) intrusions by eavesdropping on communications on pins, cables, or Printed Circuit Board (PCB) routes. Prototyped on a Xilinx Zynq-7020 System-on-Chip (SoC) and applicable to MicroBlaze-based designs, it derives a fresh Authenticated Encryption with Associated Data (AEAD) key for each record via an Ascon-eXtendable-Output Function (XOF)–based Key Derivation Function (KDF) bound to a device identifier and a rotating slice from a secret pool, while relocating both the pool and selected Block RAM (BRAM)-resident code pages via Dynamic Function eXchange (DFX). This moving-target strategy frustrates ROM scraping, probing, and communication-line eavesdropping, while cryptographic confidentiality and integrity are provided by a lightweight AEAD (Ascon). Hardware evaluation reports cycles/byte, end-to-end latency, and per-packet energy under identical conditions across lightweight AEAD baselines; the framework’s key-derivation and DFX layers are orthogonal to the chosen AEAD. The threat model, field layouts (Nonce/AAD), receiver-side acceptance checks, and quantitative bounds are specified to enable reproducibility. By avoiding online key exchange and keeping long-lived secrets off Programmable Logic (PL)-based external memories while continuously relocating their physical locus, the framework provides a deployable, energy-aware defense in depth against code-theft vectors in FPGA-based systems. Overall, the work provides an original and deployable solution for strengthening the security of commercial products against code theft in embedded environments. Full article

This paper addresses the automatic detection of Armillaria disease in cherry trees, a high-impact phytosanitary threat to agriculture. As a solution, a computer application is developed based on RGB images of cherry trees and the exploitation of machine learning (ML) models, using the optimal variant among different Extreme Learning Machine (ELM) models. This tool represents a concrete contribution to the use of artificial intelligence in smart agriculture, enabling more efficient and accessible management of cherry tree crops. The overall goal is to evaluate machine learning-based strategies that enable efficient and low-computational-cost detection of the disease, facilitating its implementation on devices with limited resources. The ERICA database is used by following a proper methodology in order to learning and validation stages are completely independent. Preprocessing includes renaming, cropping, scaling, grayscale conversion, vectorization, and normalization. Subsequently, the impact of reducing image resolution is studied, identifying that a size of 63 × 23 pixels maintains a good balance between visual detail and computational efficiency. Six ELM variants are trained: standard, regularized (R-ELM), class-weighted (W1-ELM and W2-ELM), and multilayer (ML2-ELM and ML3-ELM), and classical machine learning approaches are optimized and compared with classical ML approaches. The results indicate that W1-ELM achieves the best performance among tested variants, reaching an accuracy of 0.77 and a geometric mean of 0.45 with a training time in order of seconds. Full article

West Nile Virus (WNV), a mosquito-borne flavivirus first identified in Uganda in 1937, has emerged over the past quarter century as a major global public health threat. Since its introduction into North America in 1999, WNV has become the leading cause of arboviral neuroinvasive disease, with recurrent outbreaks continuing across Europe, Africa, and the Americas. This review provides a comprehensive overview of the microbiology, epidemiology, and clinical impact of WNV. We discuss the molecular biology of the virus, highlighting its genomic organization, replication strategies, and the structural and non-structural proteins that underpin viral pathogenesis and immune evasion. The complex enzootic transmission cycle, involving Culex mosquitoes and diverse avian reservoir hosts, is examined alongside ecological and climatic determinants of viral amplification and spillover into humans and equines. The clinical spectrum of WNV infection is outlined, ranging from asymptomatic seroconversion to West Nile fever and life-threatening neuroinvasive disease, with particular emphasis on risk factors for severe outcomes in older and immunocompromised individuals. Current approaches to diagnosis, supportive management, and vector control are critically reviewed, while challenges in vaccine development and the absence of effective antiviral therapy are underscored. Finally, we address future research priorities, including therapeutic innovation, predictive outbreak modeling, and genomic surveillance of viral evolution. WNV exemplifies the dynamics of emerging zoonotic diseases, and its persistence underscores the necessity of a coordinated One Health approach integrating human, animal, and environmental health. Continued scientific advances and public health commitment remain essential to mitigate its enduring global impact. Full article

Pine wilt disease (PWD), caused by the invasive nematode Bursaphelenchus xylophilus, poses a growing threat to East Asian coniferous forests, which is further exacerbated by climate change. While studies have successfully applied Maximum Entropy (MaxEnt) models to map the potential spread of PWD, they have primarily focused on broad spatial scales and climatic factors. This highlights the need for fine-scale, integrative modeling approaches that also account for environmental and anthropogenic factors. Therefore, we applied the MaxEnt model combined with change vector analysis to evaluate the spatial risk and potential future spread of PWD in Andong-si, Republic of Korea, under the SSP1-2.6 climate scenario. We integrated forest structure, soil conditions, topography, climate variables, and anthropogenic factors to generate high-resolution risk maps and identify the most influential environmental drivers. Notably, we demonstrated that historical infection proximity and isothermality strongly influence habitat suitability. We also, for the first time, projected an eastward shift of high-risk areas in Andong-si under future climate conditions. These findings provide timely insights for designing proactive surveillance networks, implementing risk-based monitoring, and developing climate-resilient management strategies. Our integrative modeling framework offers decision-support tools that can enhance early detection and targeted interventions against invasive forest pests under environmental change. Full article

Maize, a globally important crop, is highly susceptible to aflatoxin contamination, posing a serious threat. Therefore, accurate detection of aflatoxin levels in maize is of critical importance. In this study, the Multi-Scale Feature Network with Efficient Channel Attention (MSFNet-ECA) model, based on near-infrared hyperspectral imaging combined with deep learning techniques was developed to analyze the content of aflatoxin B₁ (AFB₁) in maize. Three data augmentation methods—multiplicative random scaling, bootstrap resampling, and Wasserstein generative adversarial networks (WGAN)—were compared with various preprocessing strategies to assess their impact on model performance. Multiplicative random scaling combined with second derivative (D2) preprocessing yielded the best predictive performance for the MSFNet-ECA model. Using this augmentation, the MSFNet-ECA model outperformed four conventional models (partial least squares regression (PLSR), support vector regression (SVR), extreme learning machine (ELM), and one-dimensional convolutional neural network (1D-CNN)), achieving a root mean square error of prediction (RMSEP) of 2.3 μg·kg⁻¹, coefficient of determination for prediction ($R_p^2$) of 0.99, and the residual predictive deviation (RPD) of 9, with accuracy improvements of 86.4%, 79.1%, 71.3%, and 42.5%, respectively. This finding demonstrates that applying data augmentation methods substantially improves the predictive performance of hyperspectral chemometric models driven by deep learning. Moreover, when combined with data augmentation techniques, the proposed MSFNet-ECA model can accurately predict AFB₁ content in maize, offering an efficient and reliable tool for hyperspectral applications in food quality and safety monitoring. Full article

Mosquito-borne viruses like West Nile virus (WNV) and Usutu virus (USUV) present growing public health concerns, especially with climate change and expanding vector ranges. This study describes the development and validation of a duplex Real-Time RT-PCR assay targeting β-actin (ACTB) mRNA as an endogenous control and a conserved 92 bp region shared by WNV and USUV genomes. Degenerate primers for ACTB ensure RNA extraction quality and PCR performance while enabling simultaneous detection of both viruses. A total of 1002 mosquito pools collected in Piedmont, Italy, during the 2024 vector season under the National Surveillance Plan for Arboviruses (PNA), were tested. The assay showed 100% accuracy—ACTB mRNA was detected in all pools, and six pools tested positive for WNV or USUV (three each). Diagnostic specificity was confirmed on 40 horse and bovine serum samples. Sanger sequencing confirmed ACTB identity across multiple mosquito species. The assay also demonstrated reproducibility across different operators and thermocyclers. The limit of detection (LOD) evaluation showed that the assay is capable of detecting viral RNA at very low concentrations, confirming its high analytical sensitivity. The duplex RT-PCR here developed is a reliable, sensitive, and specific tool for arbovirus surveillance, combining pathogen detection with internal quality control of RNA extraction and amplification, thus improving early warning and rapid response to mosquito-borne disease threats. Full article

Cotton leaf mites are pests that cause irreparable damage to cotton and pose a severe threat to the cotton yield, and the application of unmanned aerial vehicles (UAVs) to monitor the incidence of cotton leaf mites across a vast region is important for cotton leaf mite prevention. In this work, 52 vegetation indices were calculated based on the original five bands of spliced UAV multispectral images, and six featured indices were screened using Shapley value theory. To classify and identify cotton leaf mite infestation classes, seven machine learning classification models were used: random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), K-Nearest Neighbors (KNN), decision tree (DT), and gradient boosting decision tree (GBDT) models. The base model and metamodel used in stacked models were built based on a combination of four models, namely, the XGB, GBDT, KNN, and DT models, which were selected in accordance with the heterogeneity principle. The experimental results showed that the stacked classification models based on the XGB, KNN base model, and DT metamodel were the best performers, outperforming other integrated and single individual models, with an overall accuracy of 85.7% (precision: 93.3%, recall: 72.6%, and F1-score: 78.2% in the macro_avg case; precision: 88.6%, recall: 85.7%, and F1 score: 84.7% in the weighted_avg case). This approach provides support for using UAVs to monitor the cotton leaf mite prevalence over vast regions. Full article

Tea Anthracnose (TA), caused by fungi of the genus Colletotrichum, is one of the major threats to global tea production. UAV remote sensing has been explored for non-destructive and high-efficiency monitoring of diseases in tea plantations. However, variations in illumination, background, and meteorological factors undermine the stability of cross-temporal data. Data processing and modeling complexity further limits model generalizability and practical application. This study introduced a cross-temporal, generalizable disease monitoring approach based on UAV multimodal data coupled with relative-difference standardization. In an experimental tea garden, we collected multispectral, thermal infrared, and RGB images and extracted four classes of features: spectral (Sp), thermal (Th), texture (Te), and color (Co). The Normalized Difference Vegetation Index (NDVI) was used to identify reference areas and standardize features, which significantly reduced the relative differences in cross-temporal features. Additionally, we developed a vegetation–soil relative temperature (VSRT) index, which exhibits higher temporal-phase consistency than the conventional normalized relative canopy temperature (NRCT). A multimodal optimal feature set was constructed through sensitivity analysis based on the four feature categories. For different modality combinations (single and fused), three machine learning algorithms, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP), were selected to evaluate disease classification performance due to their low computational burden and ease of deployment. Results indicate that the “Sp + Th” combination achieved the highest accuracy (95.51%), with KNN (95.51%) outperforming SVM (94.23%) and MLP (92.95%). Moreover, under the optimal feature combination and KNN algorithm, the model achieved high generalizability (86.41%) on independent temporal data. This study demonstrates that fusing spectral and thermal features with temporal standardization, combined with the simple and effective KNN algorithm, achieves accurate and robust tea anthracnose monitoring, providing a practical solution for efficient and generalizable disease management in tea plantations. Full article

Email continues to serve as a primary vector for cyber-attacks, with phishing, spoofing, and polymorphic malware evolving rapidly to evade traditional defences. Conventional email security systems, often reliant on static, signature-based detection struggle to identify zero-day exploits and protect user privacy in increasingly data-driven environments. This paper introduces TwinGuard, a privacy-preserving framework that leverages digital twin technology to enable adaptive, personalised email threat detection. TwinGuard constructs dynamic behavioural models tailored to individual email ecosystems, facilitating proactive threat simulation and anomaly detection without accessing raw message content. The system integrates a BERT–LSTM hybrid for semantic and temporal profiling, alongside federated learning, secure multi-party computation (SMPC), and differential privacy to enable collaborative intelligence while preserving confidentiality. Empirical evaluations were conducted using both synthetic AI-generated email datasets and real-world datasets sourced from Hugging Face and Kaggle. TwinGuard achieved 98% accuracy, 97% precision, and a false positive rate of 3%, outperforming conventional detection methods. The framework offers a scalable, regulation-compliant solution that balances security efficacy with strong privacy protection in modern email ecosystems. Full article

West Nile Virus (WNV) is an emerging arboviral threat in Europe, with rising incidence in Spain since 2004. In 2024, Spain experienced its largest outbreak, primarily in small urban areas of south-western regions. We report a subset of an emergency integrated vector management program, focusing on six municipalities accounting for one-third of all human WNV cases nationwide. Over four months, 725 potential larval sites were inspected during 4026 visits. Adult mosquitoes (n = 2553) were collected with suction traps, and immature stages (n = 4457) with dipper techniques, yielding 11 species. Culex pipiens s.l. was predominant, while Cx. perexiguus, though less abundant, was epidemiologically significant. Cytochrome Oxidase I (COI) gene phylogenetic analysis confirmed Cx. perexiguus, forming a distinct clade from Cx. univittatus. Immature mosquitoes were found in 18.6% of sites, especially irrigation canals, ditches, and backwaters near urban areas. Habitat differences in larval abundance were analyzed using generalized linear mixed models. Targeted larviciding with Bacillus thuringiensis var. israelensis (Bti) and focal adulticiding with cypermethrin totaled 259 interventions (70.4% larviciding, 29.6% adulticiding). A significant 63.9% reduction in larval abundance was observed after five consecutive Bti treatments, with some variation among treatment cycles (52.2–75.5%). Adult activity persisted into late autumn. This study provides the first comprehensive characterization of larval mosquitoes in Spain’s main WNV hotspot, highlighting the need for rapid, coordinated expert interventions and extended seasonal control to prevent future outbreaks. Full article

African swine fever (ASF), caused by the African swine fever virus (ASFV), is an acute, febrile, highly contagious, and lethal disease that poses a severe threat to the global pig farming industry. Currently, no globally recognized, safe, and effective commercial ASF vaccine has been developed, making vaccination a crucial strategy for outbreak control. The ASFV structural proteins p72, p30, and p54 are key targets for vaccine development. In this study, we developed a novel baculovirus vector-based system for surface display of a recombinant protein comprising epitopes from p72, p30, and p54. Upon infection, the recombinant protein was expressed and anchored on the plasma membrane of Sf-9 cells. Purified virus analysis revealed that the Bac-recombinant protein enhanced gene delivery and transgene expression in mammalian cells compared to the Bac-Wild Type (Bac-WT). In a murine model, the Bac-recombinant protein induced significantly higher IFN-γ and IL-4 levels than Bac-p30 and the negative control. However, further evaluation in swine models is required to confirm its protective potential against ASFV. Furthermore, it also elicited a robust antibody response, generating high-titer Bac-recombinant protein-specific antibodies. Therefore, these findings suggest that the ASFV Bac-recombinant protein is a promising candidate for a vector-based vaccine. Full article

Oropouche virus (OROV), an emerging arbovirus of the Peribunyaviridae family, is responsible for acute febrile illness and, in some cases, neurological or hemorrhagic complications. Although traditionally confined to tropical areas of Central and South America, the 2024–2025 epidemic has signaled a major shift in its geographic and clinical profile, with sustained transmission in the Caribbean, over 15,000 confirmed cases, and the first imported infections reported in Europe and the United States. New clinical observations include fatalities in previously healthy adults, suspected vertical transmission with adverse fetal outcomes, and potential sexual transmission. Despite entomological data indicating low competence of European mosquito species and the absence of the main vector Culicoides paraensis, the increasing frequency of imported cases underscores the need for continued vigilance. Diagnostic limitations and clinical overlap with other arboviruses further complicate early detection. This review summarizes current knowledge on OROV’s epidemiology, transmission dynamics, and clinical features, and highlights the urgent need for integrated surveillance, diagnostic readiness, and coordinated research efforts. Emphasis is placed on Europe’s preparedness strategies, with Italy’s Jubilee 2025 offering a real-world case study for managing arboviral threats during mass gatherings. Full article

Early plant disease detection is crucial for sustainable crop production and food security. Stem rust, caused by Puccinia graminis f. sp. tritici, poses a major threat to wheat and barley. This study evaluates the feasibility of using hyperspectral imaging and machine learning for early detection of stem rust and examines the cross-crop transferability of diagnostic models. Hyperspectral datasets of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) were collected under controlled conditions, before visible symptoms appeared. Multi-stage preprocessing, including spectral normalization and standardization, was applied to enhance data quality. Feature engineering focused on spectral curve morphology using first-order derivatives, categorical transformations, and extrema-based descriptors. Models based on Support Vector Machines, Logistic Regression, and Light Gradient Boosting Machine were optimized through Bayesian search. The best-performing feature set achieved F1-scores up to 0.962 on wheat and 0.94 on barley. Cross-crop transferability was evaluated using zero-shot cross-domain validation. High model transferability was confirmed, with F1 > 0.94 and minimal false negatives (<2%), indicating the universality of spectral patterns of stem rust. Experiments were conducted under controlled laboratory conditions; therefore, direct field transferability may be limited. These findings demonstrate that hyperspectral imaging with robust preprocessing and feature engineering enables early diagnostics of rust diseases in cereal crops. Full article

Intrusion detection aims to identify the unauthorized activities within computer networks or systems by classifying events into normal or abnormal categories. As modern scenarios often involve multi-source data, multi-view fusion deep learning methods are employed to leverage diverse viewpoints for enhancing security threat detection. This paper introduces a novel intrusion detection approach using multi-view fusion within a federated learning framework, proposing an integrated AE Neural SVM (AE-NSVM) model that combines auto-encoder (AE) multi-view feature extraction and Support Vector Machine (SVM) classification. This approach simultaneously learns representative features from multiple views and classifies network samples into normal or seven attack categories while employing federated learning across clients to ensure adaptability and robustness in diverse network environments. The experimental results obtained from two benchmark datasets validate its superiority: on TON_IoT, the CAE-NSVM model achieves a highest F1-measure of 0.792 (1.4% higher than traditional pipeline systems); on UNSW-NB15, it delivers an F1-score of 0.829 with a 73% reduced training time and an 89% faster inference compared to baseline models. These results demonstrate the advantages of multi-view fusion in federated learning for balancing accuracy and efficiency in distributed intrusion detection systems. Full article

In southern Chile, harmful algal blooms (HABs) pose a threat to public health, artisanal fisheries, and the aquaculture industry (mussels and salmon). However, little is known about the environmental factors contributing to outbreaks of HABs in fjord systems. In summer 2017, an oceanographic cruise was carried out to study the physical processes associated with a bloom of the dinoflagellate Karenia spp. in the Gulf of Penas and Taitao Peninsula, Chilean Patagonia, causing a massive mortality of salmon (approximately 170,000 fish, worth USD 390,000). Satellite images from Sentinel-3 were utilized to distinguish between areas with high and low densities of Karenia cells. Cell densities were highest in the waters of the northern Taitao Peninsula (70 × 10³ cells L⁻¹), and lowest at the Gulf of Penas. Support vector classification (SVC) based on bands 1 (400 nm), 2 (412.5 nm), and 6 (560 nm) from the Sentinel-3 images and the normalized fluorescence line height (FLH) classified bloom presence/absence with an 83% coincidence rate. The SVC model correctly identified non-bloom areas, with limited false positives, and successfully captured bloom zones where Karenia densities were highest. These results demonstrate the importance of incorporating satellite tools in the design and implementation of monitoring programs for the early detection of HABs, particularly in remote, difficult-to-access areas. Full article