Search Results (7,906)

Citrus diseases severely impact fruit yield and quality. To facilitate in-field, non-destructive, and rapid detection of citrus leaf physiological and pathological conditions, this study proposes a classification method for citrus leaf physiological and pathological statuses that integrates visible/near-infrared multispectral technology with deep learning. First, a handheld spectrometer was employed to acquire spectral images of five sample categories—Healthy, Huanglongbing, Yellow Vein Disease, Magnesium Deficiency and Manganese Deficiency. Mean spectral data were extracted from regions of interest within the 350–2500 nm wavelength range, and various preprocessing techniques were evaluated. The Standard Normal Variate (SNV) transformation, which demonstrated optimal performance, was selected for data preprocessing. Next, we innovatively introduced an adaptive spectral positional encoding mechanism into the Transformer framework. A lightweight, learnable network dynamically optimizes positional biases, yielding the ASTransformer (Adaptive Spectral Transformer) model, which more effectively captures complex dependencies among spectral features and identifies critical wavelength bands, thereby significantly enhancing the model’s adaptive representation of discriminative bands. Finally, the preprocessed spectra were fed into three deep learning architectures (1D-CNN, 1D-ResNet, and ASTransformer) for comparative evaluation. The results indicate that ASTransformer achieves the best classification performance: an overall accuracy of 97.7%, underscoring its excellent global classification capability; a Macro Average of 97.5%, reflecting balanced performance across categories; a Weighted Average of 97.8%, indicating superior performance in classes with larger sample sizes; an average precision of 97.5%, demonstrating high predictive accuracy; an average recall of 97.7%, showing effective detection of most affected samples; and an average F1-score of 97.6%, confirming a well-balanced trade-off between precision and recall. Furthermore, interpretability analysis via Integrated Gradients quantitatively assesses the contribution of each wavelength to the classification decisions. These findings validate the feasibility of combining a handheld spectrometer with the ASTransformer model for effective citrus leaf physiological and pathological detection, enabling efficient classification and feature visualization, and offer a valuable reference for disease detection of physiological and pathological conditions in other fruit crops. Full article

The development and implementation of scientifically substantiated solutions for the improvement and modernization of electromechanical devices, systems, and complexes, including electric drives, is an urgent theoretical and applied task for energetics, industry, transport, and other key areas, both in global and national contexts. The aim of this paper is to identify a rational model of an induction motor that balances computational simplicity and control system performance based on predictive approaches while ensuring maximum energy efficiency and reference tracking during the operation in dynamic modes. Five main mathematical models of an induction machine with different levels of detail have been selected. Three predictive control models have been implemented using GRAMPC (v 2.2), Matlab MPC Toolbox (v 24.1), and fmincon (R2024a) (from Matlab Optimization Toolbox). It has been established that in the dynamic mode of operation, the equivalent induction motor circuit with parameters ,R_fe = const, L_μ = f(I_1d), and T_F = f(ω_Rm)  is the most appropriate in terms of the following criteria: accuracy of control action generation, computation speed, and calculation of energy consumption. Full article

The apparent normalcy of wheat during the incubation period of Fusarium head blight (FHB) makes early diagnosis challenging. This study employed Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS) to conduct layer-by-layer scanning of wheat leaves during the disease outbreak stage and performed a differential spectral analysis. Spectral information was collected from five sites (D0~D4) on diseased leaves at reducing distances from the lesion caused by the Fusarium graminearum pathogen. The results revealed that the disease caused an increase in spectral similarity between deeper and shallower layers. The spectra of leaves, after removing the D0 background, showed a correlation of 83.5% to that of the pathogen, and the similarity increased at sites closer to the lesion, suggesting that the original spectra captured a large amount of hidden information related to the pathogen. With the threshold for the absorption intensity ratio of R_1650/1050 for background-subtracted spectra set at 0.5, the optimal overall accuracy and F1-score were 86.0% and 0.89 for diagnosing outbreak-stage samples, respectively, while for incubation-period samples, they were 82.5% and 0.83. These results elucidate the mechanism of using FTIR-PAS to diagnose FHB during its incubation period, providing a theoretical and technical foundation for detecting disease information in other crops. Full article

I-motifs are non-canonical, cytosine-rich DNA structures stabilized by hemiprotonated C•C⁺ base pairs, whose formation is highly pH-dependent. While certain chemical modifications can enhance i-motif stability, modifications at the sugar moiety often disrupt essential inter-strand contacts. In this study, we examine the structural and thermodynamic impact of incorporating 2′-fluoro-ribocytidine (2′F-riboC) into i-motif-forming sequences derived from d(TCCCCC). Using a combination of UV, ¹H NMR, and ¹⁹F NMR spectroscopy, we demonstrate that full substitution with 2′F-riboC strongly destabilizes i-motif, whereas partial substitutions (one or two substitutions per strand) support well-folded structures at acidic pH (pH 5). High-resolution NMR structures reveal well-defined i-motif architectures with conserved C•C⁺ pairing and characteristic interstrand NOEs. Sugar conformational analysis reveals a predominant North pucker for cytosines, which directs the fluorine substituent toward the minor groove of the i-motif. ¹⁹F NMR further confirms slow exchange between folded and unfolded species, enabling the simultaneous detection of both under identical experimental conditions and, consequently, highlighting the utility of fluorine at the 2′ sugar position as a spectroscopic probe. These findings provide insights into fluorine-mediated modulation of i-motif stability and further extend the utility of ¹⁹F NMR in nucleic acid research. Full article

Background/Objectives: Fungal endocarditis (FE), is a rare yet life-threatening disease, which predominantly affects immunocompromised individuals, prosthetic valve recipients, and injection drug users. The purpose of this review is to summarize the evolving epidemiological trends, diagnostic challenges, and treatment strategies, by identifying evidence that supports the optimal clinical approach. Methods: A literature search was performed, drawing from sources such as PubMed and Google Scholar and included articles published between January 2015 and March 2025. Clinical studies, case series, and meta-analyses reporting on FE epidemiology, diagnostics, or treatment were included. Results: The majority of FE cases is caused by Candida species, predominantly C. albicans, while Aspergillus accounted for a lesser percentage of cases. While blood cultures showed limited sensitivity, adjunctive diagnostic tools such as serum biomarkers (β-D-glucan, galactomannan) and advanced imaging modalities (18F-FDG PET/CT) are increasingly used to guide the diagnostic process. Early surgical intervention combined with antifungals improved survival, particularly for Aspergillus, although comprehensive data regarding this approach remains limited due to the rarity of the disease. Conclusions: Fungal endocarditis requires an aggressive treatment strategy, integrating early surgery, targeted antifungals, and long-term suppression, especially for prosthetic valves. Despite advances, the complexity of the condition and the variety of the pathogens involved, continue to impede progress towards effective management of FE. Future research must prioritize rapid diagnostics, standardized treatment protocols, and novel antifungals to address this critical condition. Full article

This article presents the results of a performance comparison of four forecasting methods for prediction of electric power quality parameters (PQPs) in small-scale off-grid environments. Forecasting PQPs is crucial in supporting smart grid control and planning strategies by enabling better management, enhancing system reliability, and optimizing the integration of distributed energy resources. The following methods were compared: Bagging Decision Tree (BGDT), Boosting Decision Tree (BODT), and the K-Nearest Neighbor (KNN) algorithm with $k-5$ and $k-10$ nearest neighbors considered by the algorithm when making a prediction. The main goal of this study is to find a relation between the input variables (weather conditions, first and second back steps of PQPs, and consumed power of home appliances) and the power quality parameters as target outputs. The studied PQPs are the amplitude of power voltage (U), Voltage Total Harmonic Distortion ($TH{D}_u$), Current Total Harmonic Distortion ($TH{D}_i$), Power Factor ($PF$), and Power Load ($PL$). The Root Mean Square Error (RMSE) was used to evaluate the forecasting results. BGDT accomplished better forecasting results for $TH{D}_u$, $TH{D}_i$, and $PF$. Only BODT obtained a good forecasting result for $PL$. The KNN (k = 5) algorithm obtained a good result for $PF$ prediction. The KNN (k = 10) algorithm predicted acceptable results for U and $PF$. The computation time was considered, and the KNN algorithm took a shorter time than ensemble decision trees. Full article

In this work, we present a wideband on-wafer characterization technique for InAlAs/InGaAs/InAs InP-based high-electron mobility transistors (HEMTs) using an optimized multiline Thru-Reflect-Line (mTRL) calibration kit. Our goal is to directly extract transition frequency fT and maximum frequency of oscillation fmax values from S-parameters measurements with frequencies up to 1.1 THz and overcome the limitations of the traditional 20 dB/dec extrapolation method using lower-frequency band measurements. Indeed, as the state-of-the-art transistors now exhibit cutoff frequencies exceeding 1 THz, standard low-frequency extrapolation methods become increasingly inaccurate. Full-wave electromagnetic simulations were used to design low-loss coplanar waveguide (CPW) access structures with stable impedance and minimal parasitic effects. These structures were co-fabricated with HEMTs and calibration standards on the same InP substrate. The 2-finger transistor with a 80 nm gate length exhibits a directly measured fT = 320 GHz and fmax = 800 GHz. The technique showed high consistency across six frequency bands and confirms that direct broadband measurement with mTRL improves accuracy. This work highlights the metrological strength of mTRL-based setups for next-generation THz device characterization. Full article

A para-substituted 1,10-phenanthroline ligand, 2-(4-methylphenoxy)-1,10-phenanthroline (L24), was synthesized and structurally characterized. Complexes with Eu³⁺, Tb³⁺, Sm³⁺, and Dy³⁺ were obtained in a 2:1 ligand-to-metal ratio and analyzed using single-crystal x-ray diffraction, photoluminescence spectroscopy, and TD-DFT calculations. Coordination via the phenanthroline nitrogen atoms, combined with steric asymmetry from the para-methylphenoxy group, induces low-symmetry environments favorable for electric-dipole transitions. Excited-state lifetimes reached 2.12 ms (Eu³⁺) and 1.12 ms (Tb³⁺), with quantum yields of 42% and 68%, respectively. The triplet-state energy of L24 (22 741 cm⁻¹) aligns well with emissive levels of Eu³⁺ and Tb³⁺, consistent with Latva’s criterion. Fluorescence titrations indicated positively cooperative complexation, with association constants ranging from 0.60 to 1.67. Stark splitting and high ⁵D₀→⁷F₂/⁷F₁ intensity ratios (R₂ = 6.25) confirm the asymmetric coordination field. The para-methylphenoxy substituent appears sufficient to lower coordination symmetry and strengthen electric-dipole transitions, offering a controlled route to enhance photoluminescence in Eu³⁺ and Tb³⁺ complexes. Full article

Replication-defective adenoviruses are widely used as vectors for vaccines, but their efficacy may be compromised by the prevalence of pre-existing neutralizing antibodies from natural infections or prior vaccination with adenovirus-based vaccines. To overcome these limitations, less common human adenovirus (HAdV) types and simian adenoviruses (SAdV) have been explored as alternative vectors to the widely prevalent HAdV-C5. Despite their importance, there is limited information on the epidemiology of adenovirus immunity in many countries and geographical regions, including Mexico. In this study, we analyzed 2488 serum samples from healthy adults across all 32 states of Mexico to assess the prevalence of both total and neutralizing antibodies against various HAdV types from species A-F, and three related SAdVs. Our findings indicate a high prevalence of neutralizing antibodies against HAdV-C5 and HAdV-C6, with significant cross-reactivity observed among related adenoviruses. Notably, HAdV-D26 exhibited a lower prevalence of neutralizing antibodies, suggesting its potential suitability as a vector for vaccine development in populations with high pre-existing immunity to more common HAdV types. These results provide critical insights for optimizing adenovirus-based vaccine strategies in Mexico. Full article

Cathepsin L (CatL) is a critical protease involved in cleaving the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), facilitating viral entry into host cells. Inhibition of CatL is essential for preventing SARS-CoV-2 cell entry, making it a potential therapeutic target for drug development. Six QSAR models were established to predict the inhibitory activity (expressed as IC₅₀ values) of candidate compounds against CatL. These models were developed using statistical method heuristic methods (HMs), the evolutionary algorithm gene expression programming (GEP), and the ensemble method random forest (RF), along with the kernel-based machine learning algorithm support vector regression (SVR) configured with various kernels: radial basis function (RBF), linear-RBF hybrid (LMIX2-SVR), and linear-RBF-polynomial hybrid (LMIX3-SVR). The particle swarm optimization algorithm was applied to optimize multi-parameter SVM models, ensuring low complexity and fast convergence. The properties of novel CatL inhibitors were explored through molecular docking analysis. The LMIX3-SVR model exhibited the best performance, with an $R^2$ of 0.9676 and 0.9632 for the training set and test set and RMSE values of 0.0834 and 0.0322. Five-fold cross-validation $R_{5-fold}^2$ = 0.9043 and leave-one-out cross-validation $R_{loo}^2$ = 0.9525 demonstrated the strong prediction ability and robustness of the model, which fully proved the correctness of the five selected descriptors. Based on these results, the IC₅₀ values of 578 newly designed compounds were predicted using the HM model, and the top five candidate compounds with the best physicochemical properties were further verified by Property Explorer Applet (PEA). The LMIX3-SVR model significantly advances QSAR modeling for drug discovery, providing a robust tool for designing and screening new drug molecules. This study contributes to the identification of novel CatL inhibitors, which aids in the development of effective therapeutics for SARS-CoV-2. Full article

IP spoofing is a critical component in many cyberattacks, enabling attackers to evade detection and conceal their identities. This study rigorously compares eight deep learning models—LSTM, GRU, CNN, MLP, DNN, RNN, ResNet1D, and xLSTM—for their efficacy in detecting IP spoofing attacks. Overfitting was mitigated through techniques such as dropout, early stopping, and normalization. Models were trained using binary cross-entropy loss and the Adam optimizer. Performance was assessed via accuracy, precision, recall, F1 score, and inference time, with each model executed a total of 15 times to account for stochastic variability. Results indicate a powerful performance across all models, with LSTM and GRU demonstrating superior detection efficacy. After ONNX conversion, the MLP and DNN models retained their performance while achieving significant reductions in inference time, miniaturized model sizes, and platform independence. These advancements facilitated the effective utilization of the developed systems in real-time network security applications. The comprehensive performance metrics presented are crucial for selecting optimal IP spoofing detection strategies tailored to diverse application requirements, serving as a valuable reference for network anomaly monitoring and targeted attack detection. Full article

Point cloud segmentation underpins various applications in geospatial analysis, such as autonomous navigation, urban planning, and management. Kernel Point Convolution (KPConv) has become a de facto standard for such tasks, yet its fixed geometric kernel limits the modeling of fine-grained contextual relationships—particularly in heterogeneous, real-world point cloud data. In this paper, we introduce the adaptation of a Local Contextual Attention (LCA) mechanism for the KPConv network, with reweighting kernel coefficients based on local feature similarity in the spatial proximity domain. Crucially, our lightweight design preserves KPConv’s distance-based weighting while embedding adaptive context aggregation, improving boundary delineation and small-object recognition without incurring significant computational or memory overhead. Our comprehensive experiments validate the efficacy of the proposed LCA block across multiple challenging benchmarks. Specifically, our method significantly improves segmentation performance by achieving a 20% increase in mean Intersection over Union (mIoU) on the STPLS3D dataset. Furthermore, we observe a 16% enhancement in mean F1 score (mF1) on the Hessigheim3D benchmark and a notable 15% improvement in mIoU on the Toronto3D dataset. These performance gains place LCA-KPConv among the top-performing methods reported in these benchmark evaluations. Trained models, prediction results, and the code of LCA are available in a GitHub opensource repository. Full article

This study deciphers the anionic modulation mechanism of halide ions (F⁻/Cl⁻) in cobalt-based hydroxides for oxygen evolution reaction (OER). Phase-pure Co(OH)₂, Co(OH)F, and Co₂(OH)₃Cl were fabricated via substrate-independent hydrothermal synthesis to eliminate conductive support interference. Electrocatalytic evaluation on glassy carbon electrodes demonstrates fluoride’s superior regulatory capability over chloride. X-ray photoelectron spectroscopy (XPS) analyses revealed that F⁻ incorporation induces charge redistribution through Co → F electron transfer, optimizing the electronic configuration via ligand effects. F⁻ incorporation simultaneously guided the anisotropic growth of 1D nanorods and reduced surface energy, thereby enhancing the wettability of Co(OH)F. The engineered Co(OH)F catalyst delivers exceptional OER performance: 318 mV overpotential at 10 mA/cm² in 1 M KOH with 94% current retention over 20 h operation. This study provides a synthetic strategy for preparing pure-phase Co(OH)F and compares halide ions’ effects on enhancing OER activity through electronic structure modulation and morphological control of basic cobalt salts. Full article

Dickeya solani causes soft rot in potato (Solanum tuberosum L.) tubers. We used bulk RNA-seq to compare the early transcriptional responses of the diploid F₁ genotypes from the mapping population that varied in tuber resistance to D. solani. RNA was collected from wounded tubers inoculated with D. solani (B), wounded tubers treated with sterile water (W), and non-treated tubers (NT) at 8, 24, and 48 hours post-inoculation (hpi). The largest transcriptional divergence between resistant (R) and susceptible (S) genotypes occurred at 8 hpi, with R tubers showing stronger induction of phenylpropanoid biosynthesis, phenylalanine and tyrosine metabolism, amino sugar and nucleotide sugar metabolism, isoquinoline alkaloid biosynthesis, and glutathione metabolism. Phenylpropanoid biosynthesis was dominant in R tubers, in 17 differentially expressed genes (DEGs), consistent with rapid suberin and lignin deposition as a physical barrier. RT-qPCR of nine defence-related genes corroborated the RNA-seq trends. The suberisation-associated anionic peroxidase POPA was located within a QTL for D. solani resistance on chromosome II, supporting its role as a candidate for future functional studies. This is the first transcriptome-based comparison of R and S potato genotypes challenged with D. solani, providing candidate pathways and genes that may guide future molecular breeding once their roles are validated. Full article

The Songnen Grassland, a typical saline–alkali ecosystem in Northeast China, is increasingly degraded by soil salinization. Arbuscular mycorrhizal fungi (AMF) are critical for enhancing plant tolerance to saline–alkali stress via root symbiosis. To investigate the species diversity and community structure of AMF in the rhizosphere of salt-tolerant plants in the Songnen Grassland, this study combined morphological identification with high-throughput sequencing (based on virtual taxa, VTs, from the MaarjAM database) to analyze the composition and distribution characteristics of AMF in the rhizosphere of eight salt-tolerant plant species, including Arundinella anomala, Leymus chinensis, Taraxacum mongolicum and others. Morphological identification revealed a total of 22 AMF species belonging to 7 genera. Among these, the genus Glomus was the dominant genus, comprising eight species (accounting for 36.4% of the total species), followed by the genus Acaulospora (five species, 22.7%), the genus Rhizophagus (four species, 18.2%), the genus Ambispora (two species, 9.1%), and the remaining genera each represented by one species (4.5%). High-throughput sequencing analysis identified a total of 40 virtual taxa (VTs) with clear taxonomic assignments belonging to six genera. The genus Glomus accounted for the highest proportion (34 VTs, 85%) with a relative abundance of 89.33%, representing the overwhelmingly dominant group. Rhizosphere soil electrical conductivity (EC) of the eight plant species indicated a significant gradient (high EC group: A–D and G, 2.07–2.61 mS/cm; low EC group: E, F, H, 0.20–0.48 mS/cm). The AMF diversity in the high EC group was significantly higher than that in the low EC group, indicating that AMF in the rhizosphere of salt-tolerant plants enhanced plant tolerance to high-salt environments, and their diversity did not decrease with increasing salinity but instead remained at a high level. Plant-specific AMF community characteristics were evident. Hierarchical clustering analysis further confirmed that the AMF community composition in the rhizosphere of Taraxacum mongolicum and Vicia amoena differed significantly from that of the other plant species, indicating that plant species have a key driving role in AMF community structure. These findings provide critical insights into the plant–AMF symbiotic mechanisms underlying saline–alkali adaptation and offer a theoretical basis for selecting efficient AMF strains to support ecological restoration of saline–alkali lands. Full article