Search Results (36,323)

Unmanned aerial vehicle (UAV) images have increasingly become important data for accurate mapping of tidal marsh vegetation. They are particularly important for determining what spatial resolution is needed because UAV imaging requires a trade-off between spatial resolution and imaging extent. However, there are still insufficient studies for assessing the effects of spatial resolution on the classification accuracy of tidal marsh vegetation. This study utilized UAV images with spatial resolutions of 2 cm, 5 cm, and 10 cm, respectively, to classify seven tidal marsh plots with different vegetation complexities in the Yellow River Delta (YRD), China, using the object-oriented example-based feature extraction with support vector machine approach and the pixel-based random forest classifier, and compared the differences in vegetation classification accuracy. This study indicated the following: (1) Vegetation classification varied at different spatial resolutions, with a difference of 0.95–8.76% between the highest and lowest classification accuracy for different plots. (2) Vegetation complexity influenced classification accuracy. Classification accuracy was lower when the relative dominance and proportional abundance of P. australis and T. chinensis were higher in the plots. (3) Considering the trade-off between classification accuracy and imaging extent, UAV data with 5 cm spatial resolution were recommended for tidal marsh vegetation classification in the YRD or similar vegetation complexity regions. Full article

This study proposes a complete data-processing framework for Geiger-mode avalanche photodiode (GM-APD) light detection and ranging (LiDAR) echo signals. It investigates the feasibility of classifying target and background noise using machine learning. Four feature processing schemes were first compared, among which the PNT strategy (Principal Component Analysis without tail features) was identified as the most effective and adopted for subsequent analysis. Based on this framework, nine models derived from six baseline algorithms—Decision Trees (DTs), Support Vector Machines (SVMs), Backpropagation Neural Networks (NN-BPs), Linear Discriminant Analysis (LDA), Logistic Regression (LR), and k-Nearest Neighbors (KNN)—were systematically assessed under Monte Carlo simulations with varying echo signal-to-noise ratio (ESNR) and statistical frame number (SFN) conditions. Model performance was evaluated using eight metrics: accuracy, precision, recall, FPR, FNR, F1-score, Kappa coefficient, and relative change percentage (RCP). Monte Carlo simulations were employed to generate datasets, and Principal Component Analysis (PCA) was applied for feature extraction in the machine learning training process. The results show that LDA achieves the shortest training time (0.38 s at SFN = 20,000), DT maintains stable accuracy (0.7171–0.8247) across different SFNs, and NN-BP models perform optimally under low-SNR conditions. Specifically, NN-BP-3 achieves the highest test accuracy of 0.9213 at SFN = 20,000, while NN-BP-2 records the highest training accuracy of 0.9137. Regarding stability, NN-BP-3 exhibits the smallest RCP value (0.0111), whereas SVM-3 yields the largest (0.1937) at the same frame count. In conclusion, NN-BP-based models demonstrate clear advantages in classifying sky-background noise. Building on this, we design a ResNet based on NN-BP, which achieves further accuracy gains over the best baseline at 400, 2000, and 20,000 frames—12.5% (400), 9.16% (2000), and 2.79% (20,000)—clearly demonstrating the advantage of NN-BP for GM-APD LiDAR signal classification. This research thus establishes a novel framework for GM-APD LiDAR signal classification, provides the first systematic comparison of multiple machine learning models, and highlights the trade-off between accuracy and computational efficiency. The findings confirm the feasibility of applying machine learning to GM-APD data and offer practical guidance for balancing detection performance with real-time requirements in field applications. Full article

Xylooligosaccharides (XOS) are functional oligosaccharides with recognized prebiotic properties and growing industrial relevance, typically obtained through enzymatic depolymerization of xylan-rich lignocellulosic substrates. In this study, a recombinant endo-β-1,4-xylanase (XynA) from Clostridium acetobutylicum was employed for XOS production. The xynA gene was cloned into the expression vector pET-21a(+) and heterologously expressed in Escherichia coli BL21(DE3) under induction with isopropyl β-D-1-thiogalactopyranoside (IPTG). The recombinant protein, with an estimated molecular mass of 37.5 kDa, was verified by SDS-PAGE and Western blot analysis. Functional characterization via thin-layer chromatography revealed that XynA efficiently hydrolyzed beechwood xylan and rye arabinoxylan, predominantly yielding xylobiose. Additionally, the enzyme catalyzed the conversion of xylotriose into xylobiose and trace amounts of xylose. Notably, XynA demonstrated hydrolytic activity against autohydrolysed and alkali-pretreated coconut husk biomass, facilitating the release of XOS. These results underscore the potential of C. acetobutylicum XynA as a biocatalyst for the valorization of lignocellulosic residues into high-value oligosaccharides. Full article

This study investigates jet plume deflection in underexpanded supersonic rectangular nozzles with aft-decks. To determine the underlying mechanism, 117 two-dimensional, Reynolds-averaged Navier–Stokes simulations were performed across a nozzle pressure ratio (NPR) range of $1.9\le \mathrm{NPR}\le 5.0$ and aft-deck length ($L_{\mathrm{aft}}/D_h$) range of $1.36\le L_{\mathrm{aft}}/D_h\le 3.37$. For each simulation, the first shock reflection $S_1$, the wall-pressure field, the vertical force $F_y$, and the presence of any separation bubble were recorded to characterize the relationships among $\mathrm{NPR}$, $L_{\mathrm{aft}}$, and $\theta$. Accordingly, a cause-and-effect path was delineated as $(\mathrm{NPR},L_{\mathrm{aft}})\to S_1\to F_y\to \theta$. A weighted regression captured 96% of the variance in the deflection angle and revealed that shifts in shock position set the wall-pressure imbalance. The imbalance fixes the vertical force and the force ultimately rotates the jet plume. Downward deflection arises when the shock reflects near the deck edge, whereas upstream reflection initiates a shock–boundary-layer interaction that forms a separation bubble and drives the jet plume upward. Between these extremes, a narrow operating band allows either outcome, explaining the divergent trends reported in prior work. The quantitative model assumes steady, two-dimensional flow and the regression prioritises illuminating the underlying physics over exact prediction of $\theta$. Nevertheless, under these assumptions, the analysis establishes a physics-based framework that reconciles earlier observations and offers a basis for understanding how nozzle pressure ratio and aft-deck length govern jet plume deflection. Full article

Mitigating ground vibrations from sources like vehicles and construction operations poses significant challenges, often relying on computationally intensive numerical methods such as Finite Element Methods (FEM) or Boundary Element Methods (BEM) for analysis. This study addresses these limitations by developing and evaluating Machine Learning (ML) methodologies for the rapid and accurate prediction of Insertion Loss (IL), a critical parameter for assessing the effectiveness of open trenches as vibration barriers. A comprehensive database was systematically generated through high-fidelity numerical simulations, capturing a wide range of geometric, elastic, and physical configurations of a stratified geotechnical system. Three distinct ML strategies—Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forests (RF)—were initially assessed for their predictive capabilities. Subsequently, a Meta-RF stacking ensemble model was developed, integrating the predictions of these base methods. Model performance was rigorously evaluated using complementary statistical metrics (RMSE, MAE, NMAE, R), substantiated by in-depth statistical analyses (normality tests, Bootstrap confidence intervals, Wilcoxon tests) and an analysis of input parameter sensitivity. The results clearly demonstrate the high efficacy of Machine Learning (ML) in accurately predicting IL across diverse, realistic scenarios. While all models performed strongly, the RF and the Meta-RF stacking ensemble models consistently emerged as the most robust and accurate predictors. They exhibited superior generalization capabilities and effectively mitigated the inherent biases found in the ANN and SVM models. This work is intended to function as a proof-of-concept and offers promising avenues for overcoming the significant computational costs associated with traditional simulation methods, thereby enabling rapid design optimization and real-time assessment of vibration mitigation measures in geotechnical engineering. Full article

Groundwater represents a critical global resource, increasingly threatened by overexploitation and pollution from contaminants such as arsenic (As), fluoride (F), nitrates (NO₃⁻), and heavy metals in arid to semi-arid regions like Mexico. Traditional Water Quality Indices (WQIs), while useful, suffer from subjectivity in assigning weights, which can lead to misinterpretations. This study addresses these limitations by developing a novel, objective Groundwater Quality Index (GWQI) through the seamless integration of Machine Learning (ML) models. Utilizing a database of 775 wells from the Mexican National Water Commission (CONAGUA), Principal Component Analysis (PCA) was applied to achieve significant dimensionality reduction. We successfully reduced the required monitoring parameters from 13 to only three key indicators: total dissolved solids (TDSs), chromium (Cr), and manganese (Mn). This reduction allows for an 87% decrease in the number of indicators, maximizing efficiency and generating potential savings in monitoring resources without compromising water quality prediction accuracy. Six WQI methods and six ML models were evaluated for quality prediction. The Unified Water Quality Index (WQIu) demonstrated the best performance among the WQIs evaluated and exhibited the highest correlation (R² = 0.85) with the traditional WQI based on WHO criteria. Furthermore, the ML Support Vector Machine with polynomial kernel (svmPoly) model achieved the maximum predictive accuracy for WQIu (R² = 0.822). This robust GWQI-ML approach establishes an accurate, objective, and efficient tool for large-scale groundwater quality monitoring across Mexico, facilitating informed decision-making for sustainable water management and enhanced public health protection. Full article

Rolling is a critical step in the processing of black tea, marking the beginning of fermentation. At this stage, the formation of tea pigments causes significant changes in the color of the processed leaves, laying the essential groundwork for the development of color and flavor quality components in subsequent fermentation processes. However, the rapid and non-destructive sensing of tea pigments during black tea rolling remains challenging. This study focused on black tea products undergoing rolling as its research subject, utilizing electrical characteristic detection technology to collect time-series electrical parameters of rolling leaves at various testing frequencies. The original electrical parameters were preprocessed using multiplicative scatter correction (MSC), min-max normalization (Min-Max), and smoothing (Smooth). Various selection methods, including the competitive adaptive reweighting algorithm (CARS), uninformative variable elimination (UVE), and the variable combination population analysis and iterative retained information variable algorithm (VCPA-IRIV), were employed to identify electrical parameters relevant to the targeted attributes. Quantitative prediction models for the content of tea pigments were established using partial least squares regression (PLSR) and support vector machine regression (SVR). The results demonstrated that the Smooth-VCPA-IRIV-SVR model exhibited superior performance in predicting the contents of theaflavins (TFs), thearubigins (TRs), and theabrownins (TBs). Correlation coefficients of prediction (R_p) all exceeded 0.99, and Relative prediction deviation (RPD) values were all above 6.5, indicating that the model enables rapid and non-destructive detection of tea pigment content during black tea rolling. These findings provide preliminary technical support and reference for the digital production of black tea. Full article

The integration of Reconfigurable Intelligent Surfaces (RISs) into wireless networks is a promising paradigm for enhancing spectral efficiency and coverage in beyond-5G systems. However, in multiuser downlink scenarios, the joint optimization of discrete RIS phase shifts and user scheduling presents a high-dimensional combinatorial challenge due to their tight coupling, which is often intractable with conventional methods. Furthermore, conventional RISs are limited by their unidirectional signal reflection, creating coverage blind spots. To address these issues, this paper first investigates a multi-user scheduling system assisted by a conventional RIS. We employed a vector projection relaxation method to transform the complex joint optimization problem, and then used an algorithm based on particle swarm optimization to jointly optimize the discrete phase shift and user scheduling. Simulation results demonstrate that this proposed algorithm significantly improves the system’s achievable data rate compared to existing benchmarks. Subsequently, to overcome the fundamental coverage limitation of conventional RISs, we extend our framework to two advanced systems: double-RIS and Simultaneously Transmitting and Reflecting RIS (STAR-RIS). For the STAR-RIS system, leveraging its energy-splitting protocol, we develop a novel joint optimization algorithm for phase shifts, amplitudes, and user scheduling based on an alternating optimization framework. This constitutes another significant contribution, as it effectively manages the added complexity of simultaneous transmission and reflection control. Simulations confirm that the STAR-RIS-assisted system, optimized by our algorithm, not only eliminates coverage blind spots but also surpasses the performance of traditional RIS, offering new perspectives for optimizing next-generation wireless communication networks. Full article

This paper addresses the joint design of transmit waveforms and receive filters for multiple-input multiple-output (MIMO) radar systems in the presence of signal-dependent clutter and steering vector mismatch. A low-complexity peak searching algorithm is developed to maximize the output signal-to-clutter-plus-noise ratio (SCNR) under a constant-modulus constraint. Different from existing approaches, this paper decomposes the receive filter into a spatial beamformer and a temporal filter to reduce the dimensionality of matrix inversion. The angular uncertainty of the target direction is discretized, and a peak searching strategy identifies the optimal error angle, which is then used to optimize the initial phases of the transmit waveform subcarriers. Based on the optimized initial phases, the estimates of the target angle and steering vector are updated, and the receive filter coefficients are further modified, thereby improving the output SCNR. Numerical simulations are provided to evaluate the performance of the proposed approach compared with existing mismatch-robust methods. The results show that the proposed method preserves inter-subcarrier orthogonality, achieves near-ideal output SCNR with reduced computational complexity, and enables real-time acquisition of more accurate target angles. Full article

Juniperus is one of the vital genera of the Cupressaceae family; many Juniperus species (juniper) have served as traditional folk medicines. The aims of this study are to analyze its chemical composition and to evaluate the mosquito larvicidal activity of leaf essential oil and its constituents. The constituents of leaf essential oil were analyzed by GC-MS. Leaf essential oil is mainly composed of hydrocarbon monoterpenes and, secondly, oxygenated monoterpenes. Leaf essential oil exhibited good brine shrimp lethality activity, which is highly correlated with larvicidal activity, with the LC₅₀ of 49.89 μg/mL. Leaf essential oil showed a strong mosquito larvicidal activity against two Dengue vector mosquitoes, Aedes aegypti and Ae. albopictus, the LC₅₀ values for both species were lower than 50 μg/mL. Among the major constituents of leaf essential oil, compounds limonene, sabinene, and β-myrcene also exhibited a significant larvicidal effect. Through these investigations, it is expected that leaf essential oil from J. chinensis var. kaizuka and its constituents are of potential use as environmental control chemicals against Dengue vector mosquitoes. Full article

Background/Objectives: Varicellovirus bovinealpha1 and Varicellovirus bovinealpha5 (BoAHV-1 and BoAHV-5), respectively, are widely distributed pathogens that cause distinct clinical conditions in cattle including infectious bovine rhinotracheitis, infectious pustular vulvovaginitis/balanoposthitis, and meningoencephalitis. Due to the establishment of viral latency, controlling these infections is challenging, and vaccination remains the most effective strategy. In this study, vaccine candidates targeting both BoAHV-1 and BoAHV-5 were developed. Methods: A synthetic gene encoding immunodominant epitopes from the gB and gD proteins and tegument phosphoprotein of BoAHV-1 and BoAHV-5 was designed to produce a multi-epitope recombinant antigen, expressed both in a prokaryotic system (RecBoAHV) and by a modified vaccinia Ankara (MVA-BoAHV) viral vector. The binding affinity of MHC-I to bovine leukocyte antigens (BoLA) was predicted using the NetMHCpan tool (version 4.1). The immunogenicity of the vaccine candidates was evaluated in rabbit and mouse models, using prime-boost immunization protocols. Sera from bovines naturally infected with BoAHV-1 and/or BoAHV-5 were used to evaluate the chimeric protein antigenicity. Immune responses were assessed by indirect ELISA and Western blot. Results: The recombinant multi-epitope protein was effectively recognized by IgG and IgM antibodies in sera from cattle naturally infected with BoAHV-1 or BoAHV-5, confirming the antigenic specificity. Both RecBoAHV and MVA-RecBoAHV induced strong and specific humoral immune responses in rabbits following a homologous prime-boost regimen. In mice, both homologous and heterologous prime-boost protocols revealed robust immunogenicity, particularly after the second booster dose. Conclusions: These findings highlight the immunogenic potential of the RecBoAHV multi-epitope vaccine candidates for controlling BoAHV-1 and BoAHV-5 infections. Further characterization of these vaccine formulations is currently underway in bovine, the target specie. Full article

Inherited retinal diseases (IRDs) are a clinically and genetically heterogeneous spectrum of disorders that lead to progressive and irreversible vision loss. Gene therapy is the most promising emerging treatment for IRDs. While gene augmentation strategies have demonstrated clinical benefit and results within the first approved ocular gene therapy, their application is restricted by adeno-associated virus (AAV) packaging capacity and limited efficacy for dominant mutations. Recent breakthroughs in precision genome editing, particularly base editing (BE) and prime editing (PE), have provided alternatives capable of directly correcting pathogenic variants. BE enables targeted single-nucleotide conversions, whereas PE further allows for precise insertions and deletions, both circumventing the double-strand DNA cleavage or repair processes typically induced by conventional CRISPR–Cas editing systems, thereby offering advantages in post-mitotic retinal cells. Preclinical investigations across murine and non-human primate models have demonstrated the feasibility, molecular accuracy, and preliminary safety profiles of these platforms in targeting IRD-associated mutations. However, critical challenges remain before clinical application can be realized, including limited editing efficiency in photoreceptors, interspecies variability in therapeutic response, potential risks of off-target effects, and barriers in large-scale vector manufacturing. Moreover, the delivery of genome editors to the outer retina remains suboptimal, prompting intensive efforts in capsid engineering and the development of non-viral delivery systems. This review synthesizes the current progress in BE and PE optimization, highlights innovations in delivery platforms that encompass viral and emerging non-viral systems and summarizes the major barriers to clinical translation. We further discuss AI-driven strategies for the rational design of BE/PE systems, thereby outlining their future potential and perspectives in the treatment of IRDs. Full article

Leishmaniasis is endemic in 99 countries worldwide, including the USA where it has low endemicity. The disease is emerging but likely underdiagnosed due to its historical absence in the diagnostic differentials of American physicians. Additionally, the public seems to have little knowledge about this disease. Here, a comprehensive literature review was carried out on autochthonous leishmaniases in humans in the USA, including their associated Leishmania spp., capable sand fly vector, transmission route, and risk to the parasitic infection. All 89 cases were cutaneous leishmaniasis reported in Texas, Oklahoma, Arizola, and North Dakota. The collective information should serve to mitigate both autochthonous and imported leishmaniasis by expanding reservoir and vector surveillance and improving physician training in diagnosis in the USA. Full article

The use of fungal entomopathogens, such as Metarhizium anisopliae, is a promising alternative for pest biocontrol but suffers the disadvantage of a relatively slower killing speed when compared with chemical pesticides. Nilaparvata lugens (brown planthopper, BPH) is a destructive sap-sucking pest that seriously threatens rice production worldwide. In the present study, we characterized a key immune-regulating protein, Spätzle (SPZ), encoding gene NlSPZ5 in BPH, and constructed a transgenic strain of M. anisopliae that expressed a specific dsRNA targeting the NlSPZ5 gene for enhancing the fungal virulence. Expression pattern analysis revealed that NlSPZ5 was expressed with the highest levels in the second-instar nymphs and hemolymph and could be largely activated by M. anisopliae infection. Microinjection of dsNlSPZ5 resulted in a markedly decreased survival rate and increased susceptibility to fungal infection in BPH. Notably, a transgenic strain of M. anisopliae expressing dsNlSPZ5 could effectively suppress the target gene expression and promote fungal proliferation in BPH upon fungal challenge. Compared to the wild-type strain, the transgenic fungal strain exhibited significantly enhanced insecticidal efficacy against BPH without compromising mycelial growth and sporulation. Our results demonstrate that fungal entomopathogens used as a delivery vector to express dsRNAs targeting insect immune defense-associated genes can effectively augment their virulence to the host insect, providing clues to develop novel pest management strategies through the combination of RNAi-based biotechnology and entomopathogen-based biocontrol. Full article

Leishmaniasis is a vector-borne disease of global concern, transmitted by sand flies of the genera Lutzomyia and Phlebotomus. Attractive toxic sugar baits (ATSBs) have emerged as a promising alternative for vector control, leveraging the sugar-feeding behavior of adult sand flies. Mandelonitrile, a plant-derived compound with potent anti-Leishmania activity, has shown promise in reducing parasite load and sand fly longevity. However, key aspects such as bait stability and off-target effects remain poorly understood. In this study, we evaluated the stability of 70% sucrose-based sugar baits supplemented with methylparaben or sodium azide, and quantified mandelonitrile degradation over time using a GC-MS protocol adapted for high-sucrose matrices. We also assessed the physiological impact of mandelonitrile on the non-target model organism Drosophila melanogaster. Results demonstrated that methylparaben and sodium azide can stabilize sucrose solutions for up to seven days, although both compounds affected sand fly survival. Mandelonitrile was undetectable after one week, indicating rapid degradation or volatilization. In D. melanogaster, mandelonitrile reduced longevity and inhibited oviposition. This is the first report on the stability and off-target effects of mandelonitrile sugar baits, highlighting the need for improved formulations and thorough evaluation of their ecological safety, with the goal of developing transmission-blocking sugar baits for leishmaniasis control. Full article