Search Results (9,499)

How to economically and effectively mobilize remaining oil and achieve carbon sequestration after water flooding in low-permeability, high-water-cut reservoirs is an urgent challenge. This study, focusing on Block Y of the Daqing Oilfield, employs numerical simulation to systematically reveal the synergistic influencing mechanisms of CO₂ flooding and geological storage. A three-dimensional compositional model characterizing this reservoir was constructed, with a focus on analyzing the controlling effects of key geological (depth, heterogeneity, physical properties) and engineering (gas injection rate, gas injection volume, bottom-hole flowing pressure) parameters on the displacement and storage processes. Simulation results indicate that the low-permeability characteristics of Block Y effectively suppress gas channeling, enabling a CO₂ flooding enhanced oil recovery (EOR) increment of 15.65%. Increasing reservoir depth significantly improves both oil recovery and storage efficiency by improving the mobility ratio and enhancing gravity segregation. Parameter optimization is key to achieving synergistic benefits: the optimal gas injection rate is 700–900 m³/d, the economically reasonable gas injection volume is 0.4–0.5 PV, and the optimal bottom-hole flowing pressure is 9–10 MPa. This study confirms that for Block Y and similar high-water-cut, low-permeability reservoirs, CO₂ flooding is a highly promising replacement technology; through optimized design, it can simultaneously achieve significant crude oil production increase and efficient CO₂ storage. Full article

In the conventional agricultural production process, the harvesting of mature fruits is frequently dependent on the observation and labor of workers, a process that is often time-consuming and labor-intensive. This study proposes an enhanced YOLOv7 detection and recognition model that incorporates a cross-spatial-channel 3D attention mechanism, a prediction head, and a weighted bidirectional feature pyramid neck optimization. The motivation for this study is to address the issues of uneven target distribution, mutual occlusion of fruits, and uneven light distribution that are prevalent in harvesting operations within orchards. The experimental findings demonstrate that the proposed model achieves an mAP@0.5–0.95 of 89.3%, representing an enhancement of 8.9% in comparison to the initial network. This method has resolved the issue of detecting and positioning the harvesting manipulator in complex orchard scenarios, thereby providing technical support for unmanned agricultural operations. Full article

Directional frequency analysis and recording (DIFAR) is a widely used sonobuoy in modern underwater acoustic monitoring and surveillance. The sonobuoy is installed in the area of interest, collects underwater data, and transmits the data to nearby aircraft for data analysis. In this process, transmission of a large volume of raw data poses significant challenges due to limited communication bandwidth. To address this problem, existing studies on autoencoder-based methods have drastically reduced amounts of information to be transmitted with moderate data reconstruction errors. However, the information bottleneck inherent in these autoencoder-based methods often leads to significant fidelity degradation. To overcome these limitations, this paper proposes a novel autoencoder method focused on the reconstruction fidelity. The proposed method operates with two key components: Gated Fusion (GF), proven critical for effectively fusing multi-scale features, and Squeeze and Excitation (SE), an adaptive Channel Attention for feature refinement. Quantitative evaluations on a realistic simulated sonobuoy dataset demonstrate that the proposed model achieves up to a 90.36% reduction in spectral mean squared error for linear frequency modulation signals compared to the baseline. Full article

The proposed work introduces a sizing algorithm to achieve a desired linear transconductance in the optimization of operational transconductance amplifiers (OTAs) by applying the $g_m/I_D$ method to find the initial width (W) and length (L) sizes of the transistors. These size values are used to run the non-dominated sorting genetic algorithm (NSGA-II) to perform a multi-objective optimization of three OTA topologies. The $g_m/I_D$ method begins with transistor characterization using MATLAB R2024a generated look-up tables (LUTs), which map normalized transconductance of the transistor channel dimensions, and key performance metrics of a complementary metal–oxide–semiconductor (CMOS) technology. The LUTs guide the initial population generation within NSGA-II during the optimization of OTAs to achieve not only a desired transconductance but also accuracy alongside linearity, high DC gain, low power consumption, and stability. The feasible W/L size solutions provided by NSGA-II are used to enhance the CMOS design of a memristor emulator, where the OTA with the desired transconductance is adapted to tune the behavior of the memristor, demonstrating improved pinched hysteresis loop characteristics. In addition, process, voltage and temperature (PVT) variations are performed by using TSMC 180 nm CMOS technology. The memristor-based on optimized OTAs is used to design a Leaky Integrate-and-Fire (LIF) neuron, which produces identical spike counts (seven spikes) under the same input conditions, though the time period varied with a CMOS inverter scaling. It is shown that increasing transistor widths by 100 in the inverter stage, the spike quantity is altered while changing the spiking period. This highlights the role of device sizing in modulating LIF neuron dynamics, and in addition, these findings provide valuable insights for energy-efficient neuromorphic hardware design. Full article

Transient receptor potential vanilloid 1 (TRPV1) is an ion channel expressed in sensory neurons, immune cells, pancreatic islets, and vascular tissues. Initially recognized for its role in thermosensation and nociception, TRPV1 has emerged as a key regulator of immune modulation, β-cell physiology, vascular integrity, and neuroimmune signaling—processes central to the pathogenesis and progression of Type 1 Diabetes (T1D). Experimental evidence demonstrates that TRPV1 exerts opposing effects on β-cell physiology—enhancing insulin release during short-term activation, yet accelerating stress and cell loss under chronic stimulation. In the vascular and renal systems, TRPV1 contributes to hallmark T1D complications, including endothelial dysfunction, nephropathy, and impaired cardiovascular protection, while in the central nervous system it drives neuroinflammation, cognitive decline, and emotional dysregulation. TRPV1 sensitization also accelerates the onset and severity of diabetic neuropathy by amplifying pain and inflammatory signaling pathways. Genetic and epigenetic regulation further links TRPV1 to individual susceptibility and disease progression. Collectively, these findings position TRPV1 as both a disease-modifying factor and a determinant of T1D outcomes, underscoring its potential as a biomarker and therapeutic target in autoimmune diabetes. Full article

To address the issues of hydrophobicity, easy fouling, and limited application of polyvinylidene fluoride (PVDF) membranes in water treatment processes, this study prepared Cu-MnO₂/GO/PVDF catalytic membranes via the immersion precipitation phase inversion method. Graphene oxide (GO) was incorporated to facilitate the construction of good water channels, while copper-doped manganese dioxide (Cu-MnO₂) was added to enhance catalytic activity. The structure, morphology, and performance of the membranes were characterized comprehensively. Results showed that Cu-MnO₂ was well interspersed between GO sheets, thereby increasing membrane surface roughness, effective filtration area, and hydrophilicity. The best catalytic membrane CM-5 exhibited the highest pure water flux (1391.20 L·m⁻²·h⁻¹) and methyl blue (MBE) rejection rate (98.06%), and it also displayed excellent reusability and stability. EPR tests confirmed the generation of HO· and HOO· in the Fenton-like system, which mediated dye degradation. The Cu-MnO₂/GO/PVDF catalytic membrane demonstrated excellent hydrophilicity, antifouling properties, and catalytic efficiency, thus providing a viable solution for dye wastewater treatment. Full article

This study investigates the influence of solidification conditions on the high-cycle fatigue (HCF) behavior of a second-generation DD6 single-crystal superalloy. Single-crystal bars with a [⁰⁰¹] orientation were prepared using the high-rate solidification (HRS) and liquid-metal cooling (LMC) techniques under various pouring temperatures. The HCF performance of the heat-treated alloy was subsequently evaluated at 800 °C using rotary bending fatigue tests. The results demonstrate that increasing the pouring temperature effectively reduced the content and size of microporosity in the HRS alloys. At an identical pouring temperature, the LMC alloy exhibited a significant reduction in microporosity, with its content and maximum pore size being only 44.4% and 45.8% of those in the HRS alloy, respectively. Consequently, the HCF performance was enhanced with increasing pouring temperature for the HRS alloys. The LMC alloy outperformed its HRS counterpart processed at the same temperature, showing a 9.4% increase in the conditional fatigue limit (at 10⁷ cycles). Microporosity was identified as the dominant site for HCF crack initiation at 800 °C. The role of γ/γ′ eutectic in crack initiation diminished or even vanished as the solidification conditions were optimized. Fractographic analysis revealed that the HCF fracture mechanism was quasi-cleavage, independent of the solidification conditions. Under a typical stress amplitude of 550 MPa, the deformation mechanism was characterized by the slip of a/2<011> dislocations within the γ matrix channels, which was also unaffected by the solidification conditions. In conclusion, optimizing solidification conditions, such as by increasing the pouring temperature or employing the LMC process, enhances the HCF performance of the DD6 alloy primarily by refining microporosity, which in turn prolongs the fatigue crack initiation life. Full article

Quantifying riverscape topography is challenging because riverscapes comprise of both wet and dry surfaces. Advances have been made in demonstrating the capability of mounting topo-bathymetric LiDAR (Light Detection and Ranging) sensors on crewed, occupied aircraft to quantify riverscape topography. However, only recently has miniaturisation of electronic components enabled topo-bathymetric LiDAR to be mounted on consumer-grade Unoccupied Aerial Vehicles (UAVs). We evaluate the capability of a demonstration YellowScan Navigator topo-bathymetric, full waveform LiDAR sensor, mounted on a DJI Matrice 600 UAV, to survey a 1 km long reach of the braided River Feshie, Scotland. Ground-truth data, with centimetre accuracy, were collected across wet areas using an echo-sounder, and in wet and dry areas using RTK-GNSS (Real-Time Kinematic Global Navigation Satellite System). The processed point cloud had a density of 62 points/m². Ground-truth mean errors (and standard deviation) across dry gravel bars were 0.06 ± 0.04 m, along shallow channel beds were −0.03 ± 0.12 m and for deep channels were −0.08 m ± 0.23 m. Geomorphic units with a concave three-dimensional shape (pools, troughs), associated with deeper water, had larger negative errors and wider ranges of residuals than planar or convex units. The case study demonstrates the potential of using UAV topo-bathymetric LiDAR to enhance survey efficiency but a need to evaluate spatial error distribution. Full article

Lithium minerals are essential raw materials for new energy storage systems, and accurate instance segmentation of their microscopic images is crucial for efficient resource exploration and utilization. However, existing segmentation methods face challenges when processing lithium mineral images, including complex texture overlaps, missed detection of small particles, and deployment difficulties on edge devices, making it hard to balance segmentation accuracy with inference speed. To address these challenges, this paper proposes an efficient instance segmentation method based on YOLO11-seg, named EMS-YOLO-seg. First, we designed Multi-Scale Partial Convolution (MSPConv) and integrated it into the C3k2 module. The modified C3k2-MSP module optimizes the model’s receptive field and enhances its multi-scale feature extraction capability. We replaced the PSABlock module with the CBAM attention mechanism, introducing the C2PSA-CBAM module, which strengthens the model’s channel focus and feature extraction abilities. The redesigned Segment-LSCDMSP segmentation head reduces computational complexity and improves detection efficiency. Experimental results on our custom-built lithium mineral microscopic image dataset show that compared to the baseline YOLO11n-seg model, the EMS-YOLO-seg model achieved a 0.8% and 0.8% improvement in $\mathrm{m}\mathrm{A}{\mathrm{P}}_{50}^{\mathrm{b}\mathrm{o}\mathrm{x}}$ and $\mathrm{m}\mathrm{A}{\mathrm{P}}_{50:95}^{\mathrm{b}\mathrm{o}\mathrm{x}}$, respectively, and a 1% and 0.7% improvement in $\mathrm{m}\mathrm{A}{\mathrm{P}}_{50}^{\mathrm{m}\mathrm{a}\mathrm{s}\mathrm{k}}$ and $\mathrm{m}\mathrm{A}{\mathrm{P}}_{50:95}^{\mathrm{m}\mathrm{a}\mathrm{s}\mathrm{k}}$. Additionally, the model reduced the number of parameters by 52.1%, FLOPs by 18.6%, model size by 49.4%, and increased FPS by 12.7%. This study provides reliable technical support for accurate instance segmentation of lithium mineral microscopic images and demonstrates strong scene adaptability and promising potential for real-time deployment under industrial environments and resource-constrained scenarios. Full article

Open-channel irrigation systems often face constraints due to water supply uncertainty and insufficient gate control precision. This study proposes an integrated framework for canal water allocation and gate control that combines interval-based uncertainty analysis with intelligent optimization to address these challenges. First, we predict the inflow process using an Auto-Regressive Integrated Moving Average (ARIMA) model and quantify the range of water supply uncertainty through Maximum Likelihood Estimation (MLE). Based on these results, we formulate a bi-objective optimization model to minimize both main canal flow fluctuations and canal network seepage losses. We solve the model using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to obtain Pareto-optimal water allocation schemes under uncertain inflow conditions. This study also designs a Fuzzy Proportional–Integral–Derivative (Fuzzy PID) controller. We adaptively tune its parameters using the Particle Swarm Optimization (PSO) algorithm, which enhances the dynamic response and operational stability of open-channel gate control. We apply this framework to the Chahayang irrigation district. The results show that total canal seepage decreases by 1.21 × 10⁷ m³, accounting for 3.9% of the district’s annual water supply, and the irrigation cycle is shortened from 45 days to 40.54 days, improving efficiency by 9.91%. Compared with conventional PID control, the PSO-optimized Fuzzy PID controller reduces overshoot by 4.84%, and shortens regulation time by 39.51%. These findings indicate that the proposed method can significantly improve irrigation water allocation efficiency and gate control performance under uncertain and variable water supply conditions. Full article

Unmanned Aerial Vehicles (UAVs) are promising nodes for Integrated Sensing and Communication (ISAC), but accurate Direction-of-Arrival (DoA) estimation on a small airframe is challenged by platform loading, motion, attitude, and multipath. Traditionally, DoA algorithms have been developed and evaluated for stationary, ground-based (or otherwise mechanically stable) antenna arrays. Extending them to UAVs violates these assumptions. This work designs a six-element Uniform Circular Array (UCA) at 2.4 GHz (radius $\approx 0.5\lambda$) for a quadrotor and introduces a Pose-Aware MUSIC (MUltiple SIgnal Classification) estimator for DoA. The novelty is a MUSIC formulation that (i) applies pose correction using the drone’s instantaneous roll–pitch–yaw (pose correction) and (ii) applies a Doppler correction that accounts for platform velocity. Performance is assessed using data synthesized from embedded-element patterns obtained by electromagnetic characterization of the installed array, with additional channel/hardware effects modeled in post-processing (Rician LOS/NLOS mixing, mutual coupling, per-element gain/phase errors, and element–position jitter). Results with the six-element UCA show that pose and Doppler compensation preserve high-resolution DoA estimates and reduce bias under realistic flight and platform conditions while also revealing how coupling and jitter set practical error floors. The contribution is a practical PA-MUSIC approach for UAV ISAC, combining UCA design with motion-aware signal processing, and an evaluation that quantifies accuracy and offers clear guidance for calibration and field deployment in GNSS-denied scenarios. The results show that, across 0–25 dB SNR, the proposed hybrid DoA estimator achieves <$0.5^{\circ }$ RMSE in azimuth and elevation for ideal conditions and ≈$5^{\circ }$–$6^{\circ }$ RMSE when full platform coupling is considered, demonstrating robust performance for UAV ISAC tracking. Full article

Earthquake prediction remains one of the central unsolved problems in geophysics, and ionospheric variability offers a promising yet debated window into the earthquake preparation process through lithosphere–atmosphere–ionosphere coupling. Progress has been hindered by methodological limitations in prior studies, including the use of inappropriate performance metrics for highly imbalanced seismic data, the reliance on geographically and temporally narrow data, and inclusion of inherent spatial or temporal features that artificially inflate model performance while preventing the discovery of genuine ionospheric precursors. To address these challenges, we introduce a global, temporally validated machine learning framework grounded in thirty-eight years of ionospheric observations from more than a hundred ionosonde stations. We eliminate lookahead bias through strict temporal partitioning, prevent overlapping precursor windows across samples to eliminate autocorrelation artifacts and apply sophisticated feature selection to exclude spatial and temporal identifiers, enabling prevention of data leakage and coincidence effects. We investigate whether spatiotemporally invariant ionospheric precursors exist across diverse seismic regions, addressing the field’s reliance on geographically isolated case studies. Cross-regional validation shows that our models yield modest classification skill above chance levels, with our best-performing model achieving a weighted F1 score of 71% though performance exhibits pronounced sensitivity to temporal validation configuration, suggesting these results represent an upper bound on operational accuracy. While multimodal fusion with complementary precursor channels could possibly improve performance, our focus remains on establishing whether ionospheric observations alone contain learnable, region-independent seismic signatures. These findings suggest that ionospheric precursors, if they exist as universal phenomena, exhibit weaker cross-regional consistency than previously reported in case studies, raising questions about their standalone utility for earthquake prediction while indicating potential value as one component within multimodal observation systems. Full article

River flow has historically been the primary force shaping the morphology of the Yellow River estuary. However, since the Xiaolangdi Reservoir began operating in 2000, the hydrological processes reaching the estuary have been significantly modified. To evaluate the morphological response of the estuary, we examined the evolution of the mouth channel from 1996 to 2023 using remote sensing, cartographic generalization, and hydrological analysis, supported by annual Landsat imagery, daily hydrological records, and field survey data. Our findings indicate that the channel extended slowly between 1996 and 2002, then advanced rapidly from 2003 to 2007, culminating in a natural avulsion between 2004 and 2008. Following the avulsion, the newly formed channel progressively extended (2008–2013) and, after 2014, developed into a multi-branch system. The development of this bifurcating system since 2014 is attributed to the sustained release of low-sediment-concentration flows from the Xiaolangdi Reservoir. In contrast, the earlier avulsion was triggered by the rapid discharge of a high-sediment-concentration flow in 2004. These results demonstrate that releases from the Xiaolangdi Reservoir with varying sediment concentrations at different timescales elicited distinct morphological responses in the Yellow River estuary, underscoring the need for carefully calibrated hydrological regulation. Full article

In open wireless communication channels, the combined effects of random pulse jamming and multipath-induced time-varying fading significantly degrade the reliability and efficiency of information transmission. Particularly in highly dynamic scenarios such as unmanned aerial vehicle (UAV) communications, existing Q-learning-based anti-jamming methods often rely on idealized channel assumptions, leading to mismatched “transmit/silence” decisions under fading conditions. To address this issue, this paper proposes a Q-learning and time-varying fading channel-aware anti-jamming method against random pulse jamming. In the proposed framework, a fading channel model is incorporated into Q-learning, where the state space jointly represents timeslot position, jamming history, and channel sensing results. Furthermore, a reward function is designed by jointly considering jamming power and channel quality, enabling dynamic strategy adaptation under rapidly varying channels. A moving average process is applied to smooth simulation fluctuations. The results demonstrate that the proposed method effectively suppresses jamming collisions, enhances the successful transmission rate, and improves communication robustness in fast-fading environments, showing strong potential for deployment in practical open-channel applications. Full article

Anomaly Detection (AD) in telecommunication networks is critical for maintaining service reliability and performance. However, operational networks present significant challenges: high-dimensional Key Performance Indicator (KPI) data collected from thousands of network elements must be processed in near real time to enable timely responses. This paper presents an unsupervised approach leveraging Mahalanobis Distance (MD) to identify network anomalies. The MD model offers a scalable solution that capitalizes on multivariate relationships among KPIs without requiring labeled data. Our methodology incorporates preprocessing steps to adjust KPI ratios, normalize feature distributions, and account for contextual factors like sample size. Aggregated anomaly scores are calculated across hierarchical network levels—cells, sectors, and sites—to localize issues effectively. Through experimental evaluations, the MD approach demonstrates consistent performance across datasets of varying sizes, achieving competitive Area Under the Receiver Operating Characteristic Curve (AUC) values while significantly reducing computational overhead compared to baseline AD methods: Isolation Forest (IF), Local Outlier Factor (LOF) and One-Class Support Vector Machines (SVM). Case studies illustrate the model’s practical application, pinpointing the Random Access Channel (RACH) success rate as a key anomaly contributor. The analysis highlights the importance of dimensionality reduction and tailored KPI adjustments in enhancing detection accuracy. This unsupervised framework empowers telecom operators to proactively identify and address network issues, optimizing their troubleshooting workflows. By focusing on interpretable metrics and efficient computation, the proposed approach bridges the gap between AD and actionable insights, offering a practical tool for improving network reliability and user experience. Full article