Search Results (180)

In this work, the polarity balance of a novel two-dimensional hyperchaotic map is considered, and thus the corresponding manifold of conditional symmetry is coined. The unique map has a simple structure but provides direct 2-D offset boosting, which brings the possibility for the construction of conditional symmetry by introducing an absolute value function. The corresponding evolution of the discrete sequences from the system is verified by the circuit implementation based on the microcontroller of CH32V307. The pseudorandom data from the map increases its adaptability for applications in information security. The hyperchaotic sequence-injected Ant Colony Optimization (ACO), Grey Wolf Optimizer (GWO), and Sparrow Search Algorithm (SSA) show their improved performance in the optimization algorithm. Robot path planning experiments confirm that all three algorithms exhibit superior convergence performance, global search capability, and path smoothness compared with the original algorithms. Full article

Accurate and interpretable staging of Alzheimer’s disease (AD) remains challenging due to the heterogeneous progression of neurodegeneration and the complementary nature of imaging and clinical biomarkers. This study implements and evaluates an optimized Hierarchical Multi-Modal Fusion Framework (HMFF) that systematically integrates 3D structural MRI with clinical assessment scales for robust three-class classification of cognitively normal (CN), mild cognitive impairment (MCI), and AD subjects. A standardized preprocessing pipeline, including N4 bias field correction, nonlinear registration to MNI space, ANTsNet-based skull stripping, voxel normalization, and spatial resampling, was employed to ensure anatomically consistent and high-quality MRI inputs. Within the proposed framework, volumetric imaging features were extracted using a 3D DenseNet-121 architecture, while structured clinical information was modeled via an XGBoost classifier to capture nonlinear clinical priors. These heterogeneous representations were hierarchically fused through a lightweight multilayer perceptron, enabling effective cross-modal interaction. To further enhance discriminative capability and model efficiency, a hierarchical feature selection strategy was incorporated to progressively refine high-dimensional imaging features. Experimental results demonstrated that performance consistently improved with feature refinement and reached an optimal balance at approximately 90 selected features. Under this configuration, the proposed HMFF achieved an accuracy of 0.94 (95% Confidence Interval: [0.918, 0.951]), a recall of 0.91, a precision of 0.94, and an F1-score of 0.92, outperforming unimodal and conventional multimodal baselines under comparable settings. Moreover, Grad-CAM visualization confirmed that the model focused on clinically relevant neuroanatomical regions, including the hippocampus and medial temporal lobe, enhancing interpretability and clinical plausibility. These findings indicate that hierarchical multimodal fusion with interpretable feature refinement offers a promising and extensible solution for reliable and explainable automated AD staging. Full article

Background/Objectives: In all types of diabetes, elevated blood glucose levels cause pathological changes in skeletal muscle, primarily due to oxidative stress, mitochondrial dysfunction, and excessive production of reactive oxygen species (ROS). Regular exercise can help mitigate these effects; however, the underlying mechanisms, particularly those involving the mitochondrial permeability transition pore (mPTP), remain incompletely understood. This study aimed to explore the effects of aerobic exercise training (AET) on oxidative stress and the expression of mPTP components in the skeletal muscle of streptozotocin-induced diabetic rats. Methods: Male Wistar rats were randomly divided into three groups: Healthy Sedentary (H-SED), Diabetic Sedentary (D-SED), and Diabetic Exercise-trained (D-EXER); n = 6 per group. The D-EXER group performed AET (0° slope) 5 days/week for 8 weeks. After the intervention period, body weight and fasting blood glucose (FBG) levels were measured, and soleus muscles were collected and analyzed for oxidative stress biomarkers, Western blotting, and gene expression using qRT-PCR. Results: Following an 8-week intervention, AET reduced FBG concentrations. Accordingly, in the soleus muscles of the D-EXER group, ROS levels decreased, and redox balance was improved compared to the D-SED group. Exercise training reduced CypD and Casp9 mRNA expression and increased Bcl-2 mRNA expression, whereas Ant1 mRNA expression was only slightly altered. CypD protein expression was decreased in exercised diabetic rats, while VDAC1 protein and mRNA levels remained unchanged. In the D-EXER group, there were significant inverse correlations between CypD and Casp9 mRNA expression levels and glutathione redox state. Conclusions: The current study suggests that 8 weeks of AET, in addition to reducing hyperglycemia, may favorably influence oxidative balance and the expression of mPTP-related molecular components in diabetic skeletal muscle. Full article

Faults and fractures play a critical role in subsurface systems; they may act as hydrocarbon traps, compartmentalize reservoirs, or serve as conduits for fluid migration across stratigraphic intervals. Consequently, fault delineation from seismic data plays a key role in reservoir characterization. This study presents a workflow for generating ant-tracking attribute volumes using multiple structural attributes to enhance fault/fracture delineation. Our results were thereafter validated with formation microimager (FMI) data. The workflow involves a sequential process comprising seismic data conditioning, structural attribute computation, and ant-tracking volume generation. Variance, curvature, and amplitude contrast attributes were calculated on conditioned 3D seismic data and subsequently used as input for the ant-tracking process. Parameter optimization was conducted through an iterative process of varying individual parameters and qualitatively assessing the results against key seismic features in both vertical sections and time slices. The ant-tracking volumes generated from individual attribute volumes were integrated to produce a composite volume, which served as input for automatic fault extraction. The resultant fault patch orientations were consistent with the formation microimager (FMI) log orientations. The integration of multiple structural attributes within the ant-tracking workflow significantly enhanced fault and fracture delineation by leveraging the complementary strengths of each attribute. Full article

In this study we employed nanopore whole genome sequencing to analyze the resistance genes, genomic islands and prophage DNA in two multidrug resistant E. rhusiopathiae strains, i.e., 670 and 147, isolated from domestic geese. MLST profiles and core-genome phylogeny were determined to assess strain relatedness. In strain 670 (serotype 8, ST 113), a novel 53 kb prophage φEr670 carrying the lnuB and lsaE resistance genes was identified. Regions highly homologous to the φEr670 prophage were detected in 36 of 586 (6.14%) publicly available E. rhusiopathiae genomes, as well as in some other Gram-positive bacteria, and usually contained resistance genes. E. rhusiopathiae strain 147 (serotype 5, ST 243) was found to contain a composite 98 kb genomic island (GI_Er147) carrying the ant(6)-Ia and spw genes, as well as gene encoding a putative lincosamide nucleotidyltransferase designated lnu(J) and a vat family gene encoding a putative streptogramin A O-acetyltransferase. The lnu(J) gene exhibited 83.6% homology to the lnu(D) gene, and lnu(J)-positive E. rhusiopathiae strains displayed intermediate susceptibility to lincomycin. Vat-positive strain 147 and vat-negative E. rhusiopathiae strains showed similar susceptibility to quinupristin/dalfopristin. The presence of the Tn916 transposon carrying the tetM gene was confirmed in the genomes of both E. rhusiopathiae strains; in strain 147, however, Tn916 was located within ICEEr1012. Based on analyses of additional E. rhusiopathiae genomes, the integration sites of Tn916, ICEEr1012, and GI_Er147 were identified as genomic “hot spots,” contributing to the genome plasticity of E. rhusiopathiae. Prophage φEr670 and GI_Er147 as well as the Tn916 transposon and ICEEr1012 are most likely responsible for the dissemination of resistance genes in E. rhusiopathiae. Prophages highly homologous to φEr670 act as carriers of resistance genes in various Gram-positive bacteria. However, the transferability of the identified genetic elements and the functional role of the lnu(J) gene require further investigation. This study provides new insights into the diversity of MGEs in E. rhusiopathiae and advances understanding of the genomic mechanisms driving antimicrobial resistance in Gram-positive bacteria. Full article

The carbonate reservoir of the NT oilfield in the Precaspian Basin is a fracture-pore type with an extremely complex fracture network, comprising both high-angle structural fractures and abundant low-angle bedding-parallel fractures. Both fracture types significantly impact waterflood development, making effective prediction and characterization of the complex fracture network crucial for optimizing waterflooding and development plans. Using core, imaging logging, conventional logging, seismic, and production performance data, we predicted the distribution of high-angle structural and low-angle bedding-parallel fractures. A discrete fracture network (DFN) was constructed by grouping fractures based on strike and dip angles, and the influences of fractures with different dip angles on the initial production of individual wells and production decline rates were analyzed. Results show that high-angle fracture distribution is effectively predicted by combining imaging logging data with seismic volumes processed via ant-tracking technology, while low-angle fractures are well predicted using conventional logging, imaging logging, and seismic data processed by dip deviation. High-angle fractures are predominantly developed near and parallel to faults; low-angle fractures are mainly distributed in fold limbs. Fractures were grouped into northeast, southeast, southwest, northwest high-angle fractures, and low-angle fractures. Fracture modeling indicates a reservoir fracture porosity of 0~0.27% and permeability of 10~100 mD. With increasing fracture density, single-well initial productivity and production decline rates are higher in high-angle fracture zones than in low-angle fracture zones. Low-angle fractures contribute to ~56.45% of high-angle fractures’ production and affect production decline at ~82.5% of high-angle fractures’ level. This method is significant for predicting and modeling complex fracture networks in other reservoirs. Full article

This study presents an integrated geophysical and hydrogeological characterization of fault systems in the sandstone-hosted uranium deposit within the K₁b² Ore Horizon of the Bayin Gobi Basin. Employing 3D seismic exploration with 64-fold coverage and advanced attribute analysis techniques (including coherence volumes, ant-tracking algorithms, and LOW_FRQ spectral attenuation), the research identified 18 normal faults with vertical displacements up to 21 m, demonstrating a predominant NE-oriented structural pattern consistent with regional tectonic features. The fracture network analysis reveals anisotropic permeability distributions (31.6:1–41.4:1 ratios) with microfracture densities reaching 3.2 fractures/km² in the central and northwestern sectors, significantly influencing lixiviant flow paths as validated by tracer tests showing 22° NE flow deviations. Hydrogeological assessments indicate that fault zones such as F11 exhibit 3.1 times higher transmissivity (5.3 m²/d) compared to non-fault areas, directly impacting in situ leaching (ISL) efficiency through preferential fluid pathways. The study establishes a technical framework for fracture system monitoring and hydraulic performance evaluation, addressing critical challenges in ISL operations, including undetected fault extensions that caused lixiviant leakage incidents in field cases. These findings provide essential geological foundations for optimizing well placement and leaching zone design in structurally complex sandstone-hosted uranium deposits. The methodology combines seismic attribute analysis with hydrogeological validation, demonstrating how fault systems control fluid flow dynamics in ISL operations. The results highlight the importance of integrated geophysical approaches for accurate structural characterization and operational risk mitigation in uranium mining. Full article

Aiming at the shortcomings of traditional ACO algorithms in indoor localization applications, a high-performance improved ant colony algorithm (HIPACO) based on dynamic hybrid pheromone strategy is proposed. The algorithm divides the ant colony into worker ants (local exploitation) and soldier ants (global exploration) through a division of labor mechanism, in which the worker ants use a pheromone-weighted learning strategy for refined search, and the soldier ants perform Gaussian perturbation-guided global exploration. At the same time, an adaptive pheromone attenuation model (elite particle enhancement, ordinary particle attenuation) and a dimensional balance strategy (sinusoidal modulation function) are designed to dynamically optimize the searching process; moreover, a hybrid guidance mechanism is introduced to apply adaptive Gaussian perturbation guidance on successive failed particles to dynamically optimize the searching process. A hybrid guidance mechanism is introduced to enhance the robustness of the algorithm by applying adaptive Gaussian perturbation to successive failed particles. The experimental results show that in the 3D localization scenario with four beacon nodes, the average localization error of HIPACO is 0.82 ± 0.35 m, which is 42.3% lower than that of the traditional ACO algorithm, the convergence speed is improved by 2.1 times, and the optimal performance is maintained under different numbers of anchor nodes and spatial scales. This study provides an efficient solution to the indoor localization problem in the presence of multipath effect and non-line-of-sight propagation. Full article

The optimization capabilities of Kolmogorov–Arnold Networks (KANs) remain largely unexplored, which has limited their practical use in video anomaly recognition compared to conventional 3D-CNNs. To address this gap, we introduce a novel hybrid optimization framework that combines a Minimax Ant System (MMAS) for hyperparameter selection with a modified DARTS strategy for adaptive tuning of the 3D KAN architecture. Unlike existing approaches, our method simultaneously optimizes both learning dynamics and architectural configurations, enabling KANs to better exploit their expressive power in spatiotemporal feature extraction. Applied to a three-class video dataset, the proposed approach improved model accuracy to 87%, surpassing the performance of a standard 3D-CNN by 6%. Full article

Background/Objectives: The global dissemination of tet(X) variants critically threatens tigecycline efficacy as a last-resort antibiotic. The aim of this study was to characterize a tet(X6)-carrying integrative and conjugative element (ICE) in a multidrug-resistant Chryseobacterium lecithinasegens strain, SHZ29, isolated from Shanghai, China. Methods: Minimum inhibitory concentrations (MICs) were determined by broth microdilution for SHZ29. Whole genomic sequencing and bioinformatic analysis were performed to depict the structure of the novel tet(X6)-carrying ICE. Inverse PCR and conjugation experiments were conducted to investigate the transfer ability of the ICE. Results: We depicted a novel tet(X6)-carrying ICE, named ICECleSHZ29, which is 74,906 bp in size and inserted into the 3′ end of tRNA-Met-CAT gene of the C. lecithinasegens strain SHZ29, with 17 bp direct repeats (5′-tcccgtcttcgctacaa-3′). This ICE possesses a 38 kb conserved backbone and four variable regions (VR1-4), with VR3 aggregating multiple resistance genes, including tet(X6), tet(X2), erm(F), ere(D), floR, catB, sul2, ant(6)-I and bla_OXA-1327. NCBI database searching identified 13 additional ICEs sharing a similar backbone to ICECleSHZ29. These ICECleSHZ29-like ICEs could be classified into two types based on their distinct insertion sites: Type I, inserted at the tRNA-Met-CAT gene; and Type II, inserted at the tRNA-Glu-TTC gene. Phylogenetic analysis indicated that differences in integrases may result in differences in the insertion site among these ICEs. A circular intermediate form of ICECleSHZ29 was detected by inverse PCR. However, the conjugation experiments using Escherichia coli EC600 as recipients failed. Conclusions: To our knowledge, this study provides the first report of tet(X6) in C. lecithinasegens and characterizes its carrier, a novel ICE: ICECleSHZ29. Full article

Exploration of coalbed methane (CBM) has long been plagued by critical technical challenges, including a low signal-to-noise (S/N) ratio in seismic data, difficulty identifying thin coal seams, and inadequate accuracy in interpreting complex structures. This study presents an innovative methodological framework that integrates artificial intelligence (AI) with advanced seismic processing and interpretation techniques. Its effectiveness is verified through a case study in the North Bowen Basin, Australia. A multi-scale seismic data enhancement approach combining dynamic balancing and blue filtering significantly improved data quality, increasing the S/N ratio by 53%. Using deep learning-driven, multi-attribute fusion analysis, we achieved a prediction error of less than ±1 m for the thickness of thin coal seams (4–7 m thick). Integrating 3D coherence and ant-tracking techniques improved the accuracy of fault identification, increasing the fault recognition rate by 30% and reducing the spatial localization error to below 3%. Additionally, a finely tuned, spatially variable velocity model limited the depth conversion error to 0.5%. Validation using horizontal well trajectories revealed that the rate of reservoir encounters exceeded 95%, with initial gas production in the predicted sweet spots zone being 25–30% higher than with traditional methods. Notably, this study established a quantitative model linking structural curvature to fracture intensity, providing a robust scientific basis for accurately predicting CBM sweet spots. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in environmental monitoring, logistics, and precision agriculture. Efficient and reliable path planning is particularly critical for UAV systems operating in 3D continuous environments with multiple obstacles. However, single-UAV systems are often inadequate for such environments due to limited payload capacity, restricted mission coverage, and the inability to execute multiple tasks simultaneously. To overcome these limitations, multi-UAV collaborative systems have emerged as a promising solution, yet coordinating multiple UAVs in high-dimensional 3D continuous spaces with complex obstacles remains a significant challenge for path planning. To address these challenges, this paper proposes a reinforcement-learning-driven multi-strategy continuous ant colony optimization algorithm, QMSR-ACO_R, which incorporates a Q-learning-based mechanism to dynamically select from eight strategy combinations, generated by pairing four constructor selection strategies with two walk strategies. Additionally, an elite waypoint repair mechanism is introduced to improve path feasibility and search efficiency. Experimental results demonstrate that QMSR-ACO_R outperforms seven baseline algorithms, reducing average path cost by 10–60% and maintaining a success rate of at least 33% even in the most complex environments, whereas most baseline algorithms fail completely with a success rate of 0%. These results highlight the algorithm’s robustness, adaptability, and efficiency, making it a promising solution for complex multi-UAV path planning tasks in obstacle-rich 3D environments. Full article

This work proposes a design technique to facilitate the design and optimization of a highperformance power amplifier (PA) in an automated manner. The proposed optimizationoriented strategy consists of the implementation of four deep neural networks (DNNs), sequentially. Firstly, a bidirectional long short-term memory (BiLSTM)-based DNN is trained based on the X-parameters for which the hyperparameters are optimized through the multi-objective ant lion optimizer (MOALO) algorithm. This step is significant since it conforms to the hidden-layer construction of DNNs that will be trained in the following steps. Afterward, a generative adversarial network (GAN) is employed for forecasting the load–pull contours on the Smith chart, such as gate and drain impedances that are employed for the topology construction of the PA. In the third phase, the classification the BiLSTM-based DNN is trained for the employed high-electron-mobility transistor (HEMT), leading to the selection of the optimal configuration of the PA. Finally, a regression BiLSTMbased DNN is executed, leading to optimizing the PA in terms of power gain, efficiency, and output power by predicting the optimal design parameters. The proposed method is fully automated and leads to generating a valid PA configuration for the determined transistor model with much more precision in comparison with long short-term memory (LSTM)-based networks. To validate the effectiveness of the proposed method, it is employed for designing and optimizing a PA operating from 1.8 GHz up to 2.2 GHz at 40 dBm output power. Full article

Carbonate fracture–cavity reservoirs are significant oil and gas reservoirs globally, and their efficient development is influenced by the connectivity between fracture–cavity units within the reservoir. These reservoirs primarily consist of large caves, dissolution holes, and natural fractures, which serve as the primary storage and flow spaces. The S91 unit of the Tarim Oilfield is a karstic fracture–cavity reservoir with shallow coverage. It exhibits significant heterogeneity in the fracture–cavity reservoirs and presents complex connectivity between the fracture–cavity bodies. The integration of static and dynamic data, including geology, well logging, seismic, and production dynamics, resulted in the development of a set of static and dynamic connectivity evaluation processes designed for highly heterogeneous fracture–cavity reservoirs. Methods include using structural gradient tensors and stratigraphic continuity attributes to delineate the boundaries of caves and holes; performing RGB fusion analysis of coherence, curvature, and variance attributes to characterize large-scale fault development features; applying ant-tracking algorithms and fracture simulation techniques to identify the distribution and density characteristics of fracture zones; utilizing 3D visualization technology to describe the spatial relationship between fracture–cavity units and large-scale faults and fracture development zones; and combining dynamic data to verify interwell connectivity. This process will provide a key geological basis for optimizing well network deployment, improving water and gas injection efficiency, predicting residual oil distribution, and formulating adjustment measures, thereby improving the development efficiency of such complex reservoirs. Full article

Traditional unmanned aerial vehicle (UAV) path planning methods for image-based 3D reconstruction often rely solely on geometric information from initial models, resulting in redundant data acquisition in non-architectural areas. This paper proposes a UAV path planning method via semantic segmentation of 3D reality mesh models to enhance efficiency and accuracy in complex scenarios. The scene is segmented into buildings, vegetation, ground, and water bodies. Lightweight polygonal surfaces are extracted for buildings, while planar segments in non-building regions are fitted and projected into simplified polygonal patches. These photography targets are further decomposed into point, line, and surface primitives. A multi-resolution image acquisition strategy is adopted, featuring high-resolution coverage for buildings and rapid scanning for non-building areas. To ensure flight safety, a Digital Surface Model (DSM)-based shell model is utilized for obstacle avoidance, and sky-view-based Real-Time Kinematic (RTK) signal evaluation is applied to guide viewpoint optimization. Finally, a complete weighted graph is constructed, and ant colony optimization is employed to generate a low-energy-cost flight path. Experimental results demonstrate that, compared with traditional oblique photogrammetry, the proposed method achieves higher reconstruction quality. Compared with the commercial software Metashape, it reduces the number of images by 30.5% and energy consumption by 37.7%, while significantly improving reconstruction results in both architectural and non-architectural areas. Full article