Search Results (400)

To address the challenges faced by loquat detection algorithms in orchard settings—including complex backgrounds, severe branch and leaf occlusion, and inaccurate identification of densely clustered fruits—which lead to high computational complexity, insufficient real-time performance, and limited recognition accuracy, this study proposed a lightweight detection model based on the YOLO-MCS architecture. First, to address fruit occlusion by branches and leaves, the backbone network adopts the lightweight EfficientNet-b0 architecture. Leveraging its composite model scaling feature, this significantly reduces computational costs while balancing speed and accuracy. Second, to deal with inaccurate recognition of densely clustered fruits, the C2f module is enhanced. Spatial Channel Reconstruction Convolution (SCConv) optimizes and reconstructs the bottleneck structure of the C2f module, accelerating inference while improving the model’s multi-scale feature extraction capabilities. Finally, to overcome interference from complex natural backgrounds in loquat fruit detection, this study introduces the SimAm module during the initial detection phase. Its feature recalibration strategy enhances the model’s ability to focus on target regions. According to the experimental results, the improved YOLO-MCS model outperformed the original YOLOv8 model in terms of Precision (P) and mean Average Precision (mAP) by 1.3% and 2.2%, respectively. Additionally, the model reduced GFLOPs computation by 34.1% and Params by 43.3%. Furthermore, in tests under complex weather conditions and with interference factors such as leaf occlusion, branch occlusion, and fruit mutual occlusion, the YOLO-MCS model demonstrated significant robustness, achieving mAP of 89.9% in the loquat recognition task. The exceptional performance serves as a robust technical base on the development and research of intelligent systems for harvesting loquats. Full article

Quantum communication presents new opportunities for overcoming the limitations of classical wireless systems, particularly those associated with noise, fading, and interference. Building upon the principles of classical orthogonal frequency division multi-plexing (OFDM), this work proposes a quantum OFDM architecture tailored for video transmission. In the proposed system, video sequences are first compressed using the versatile video coding (VVC) standard with different group of pictures (GOP) sizes. Each GOP size is processed through a channel encoder and mapped to multi-qubit states with various qubit configurations. The quantum-encoded data is converted from serial-to-parallel form and passed through the quantum Fourier transform (QFT) to generate mutually orthogonal quantum subcarriers. Following reserialization, a cyclic prefix is appended to mitigate inter-symbol interference within the quantum channel. At the receiver, the cyclic prefix is removed, and the signal is restored to parallel before the inverse QFT (IQFT) recovers the original quantum subcarriers. Quantum decoding, classical channel decoding, and VVC reconstruction are then employed to recover the videos. Experimental evaluations across different GOP sizes and channel conditions demonstrate that quantum OFDM provides superior resilience to channel noise and improved perceptual quality compared to classical OFDM, achieving peak signal-to-noise ratio (PSNR) up to 47.60 dB, structural similarity index measure (SSIM) up to 0.9987, and video multi-method assessment fusion (VMAF) up to 96.40. Notably, the eight-qubit encoding scheme consistently achieves the highest SNR gains across all channels, underscoring the potential of quantum OFDM as a foundation for future high-quality video transmission. Full article

When considering the adsorption effect, traditional experimental methods have faced significant challenges in obtaining the solute transport parameters for composite pollutants. Based on the adsorption test data of three types of composite pollutants collected from the Web of Science and China National Knowledge Infrastructure databases from 2014 to 2024, this study employed four commonly used machine learning models, that is, Random Forest (RF), Support Vector Machine (SVM), Back Propagation Neural Network (BPNN), and Decision Tree (DT) models, to establish adsorption isotherms of pollutants with liquid-phase equilibrium concentration as the horizontal coordinate and solid-phase adsorption capacity as the vertical coordinate, and systematically investigated the adsorption characteristics of combined pollutants in the porous aquifer. Subsequently, the Mean Square Errors (MSEs) and coefficients of determination, two commonly used evaluation metrics for regression models in machine learning, were chosen to estimate the prediction effect of datasets. Combined with the convection–diffusion equation, the adsorption kinetic parameters under the mutual interference of composite pollutants, namely, the retardation factor, were solved. The results show that for the adsorption isotherms of heavy metal composite pollutants, organic composite pollutants, and heavy metal and organic combined composite pollutants, SVM, BPNN, and RF models have the best prediction effect, respectively, and their MSEs are 0.032, 0.001, and 0.018. The adsorption isotherm fitting results indicate that the heavy metal composite pollutants and organic composite pollutants conform to the Freundlich model. The retardation factor of organic composite pollutants is significantly higher than that of heavy metal composite pollutants. Full article

In this paper, a dual-band bandpass filter with a wide upper stopband is proposed and designed by integrating stepped-impedance resonators (SIRs) and a low-pass filter. The operating center frequencies of the designed dual-band filter are targeted at 2.5 GHz and 5.35 GHz, respectively, to meet the frequency requirements of typical wireless communication scenarios. Notably, the filter achieves a wide upper stopband ranging from 6.1 GHz to 25 GHz, which can effectively suppress unwanted high-frequency interference signals within this frequency range and avoid mutual interference with other high-frequency communication systems. And it exhibits insertion losses of 0.12 dB (2.5 GHz) and 0.6 dB (5.35 GHz) in its two passbands to ensure minimal useful signal attenuation. The simulation results agree well with the measured results. Full article

With the acceleration of urbanization, municipal landscape roads play a crucial role in urban public spaces. This study focuses on the distress detection and aging characteristics of asphalt pavements in municipal landscape roads. Firstly, a novel method is proposed based on the SpA-Former shadow removal network, which effectively addresses the interference caused by tree shadows and significantly improves the accuracy of automated distress identification. Distress detection results indicate that transverse cracks are the most common type of distress, primarily influenced by environmental factors such as asphalt material aging, temperature fluctuations, and freeze-thaw cycles—these factors induce asphalt embrittlement and a substantial decline in crack resistance. Subsequently, accelerated aging experiments were conducted to simulate the aging process of asphalt materials. It was found that as aging time extends, asphalt stiffness increases significantly; while this enhances deformation resistance, it also makes the material more prone to cracking under low-temperature conditions. Low-temperature crack resistance tests reveal that asphalt aged for more than six years exhibits a sharp deterioration in low-temperature crack resistance, showing distinct brittle characteristics. Furthermore, freeze-thaw cycle experiments demonstrate that the coupling effect of asphalt aging and freeze-thaw action significantly impairs its freeze-thaw resistance—particularly for asphalt aged over six years, which nearly loses its freeze-thaw resistance. In summary, the coupling effect of asphalt aging and environmental factors is the primary cause of pavement damage in municipal landscape roads. This study divides 2542 images into three mutually exclusive subsets: a training set of 2123 images, a validation set of 209 images, and a test set of 210 images. The research provides new theoretical references and technical support for the maintenance and management of landscape roads, especially demonstrating practical significance in distress detection and the analysis of material aging mechanisms. Full article

To extend the drilling limit of horizontal sections, the demand for utilizing multiple hydraulic oscillators at intervals has become increasingly prominent. However, current research on the operating range of multiple oscillators, their mutual interference characteristics, and the impact of oscillation on drill string fatigue damage remains scarce. The results indicate that when the vibration ranges of multiple oscillators overlap, the overlapping segment experiences negative effects under identical excitation frequencies. If the oscillators operate at different frequencies, the displacement envelope of the drill string tends to become irregular. Within the normal amplitude range of oscillators, no significant fatigue damage is generally observed in adjacent sections of the drill string. However, when minor initial cracks exist in the drill string, an increase in oscillator amplitude leads to accelerated crack propagation, significantly hastening the fatigue failure process. In field operations, the parameters of multiple oscillators should be optimized according to specific working conditions, and timely non-destructive inspection of the drill string sections within the effective range of the oscillators must be conducted. The study provides novel insights into vibration wave propagation and fatigue damage in drill strings under multi-point excitation. Full article

Small multi-rotor UAVs face endurance limitations during long-range missions due to high rotor energy consumption and limited battery capacity. This paper proposes a folding-wing range extender integrating a sliding-rotating two-degree-of-freedom folding wing—which, when deployed, quadruples the fuselage length yet folds within its profile—and a tail-thrust propeller. The device can be rapidly installed on host small multi-rotor UAVs. During cruise, it utilizes wing unloading and incoming horizontal airflow to reduce rotor power consumption, significantly extending range while minimally impacting portability, operational convenience, and maneuverability. To evaluate its performance, a 1-kg-class quadrotor test platform and matching folding-wing extender were developed. An energy consumption model was established using Blade Element Momentum Theory, followed by simulation analysis of three flight conditions. Results show that after installation, the required rotor power decreases substantially with increasing speed, while total system power growth slows noticeably. Although the added weight and drag increase low-speed power consumption, net range extension emerges near 15 m/s and intensifies with speed. Subsequent parametric sensitivity analysis and mission profile analysis indicate that weight reduction and aerodynamic optimization can effectively enhance the device’s performance. Furthermore, computational fluid dynamics (CFD) analysis confirms the effectiveness of the dihedral wing design in mitigating mutual interference between the rotor and the wing. Flight tests covering five conditions validated the extender’s effectiveness, demonstrating at 20 m/s cruise: 20% reduction in total power, 25% improvement in endurance/range, 34% lower specific power, and 52% higher equivalent lift-to-drag ratio compared to the baseline UAV. Full article

Abductive reasoning—the search for plausible explanations—has long been central to human inquiry, from forensics to medicine and scientific discovery. Yet formal approaches in AI have largely reduced abduction to eliminative search: hypotheses are treated as mutually exclusive, evaluated against consistency constraints or probability updates, and pruned until a single “best” explanation remains. This reductionist framing fails on two critical fronts. First, it overlooks how human reasoners naturally sustain multiple explanatory lines in suspension, navigate contradictions, and generate novel syntheses. Second, when applied to complex investigations in legal or scientific domains, it forces destructive competition between hypotheses that later prove compatible or even synergistic, as demonstrated by historical cases in physics, astronomy, and geology. This paper introduces quantum abduction, a non-classical paradigm that models hypotheses in superposition, allowing them to interfere constructively or destructively, and collapses only when coherence with evidence is reached. Grounded in quantum cognition and implemented with modern NLP embeddings and generative AI, the framework supports dynamic synthesis rather than premature elimination. For immediate decisions, it models expert cognitive processes; for extended investigations, it transforms competition into “co-opetition” where competing hypotheses strengthen each other. Case studies span historical mysteries (Ludwig II of Bavaria, the “Monster of Florence”), literary demonstrations (Murder on the Orient Express), medical diagnosis, and scientific theory change. Across these domains, quantum abduction proves more faithful to the constructive and multifaceted nature of human reasoning, while offering a pathway toward expressive and transparent AI reasoning systems. Full article

The task of tracking weak targets in low-altitude UAV scenarios requires high robustness and generalization ability of the model. Against this backdrop, this paper proposes a novel annotation and training mechanism based on SwinTrack. To improve the model’s tracking ability for weak targets, this paper proposes a pseudo-label consistency screening and background enhancement annotation strategy. This strategy enlarges the target box proportionally before training to obtain more effective background information. Furthermore, pseudo-labels are screened using a hybrid gating system of geometric overlap and confidence consistency to reduce the negative transfer interference of noise generated in different domains on the model. Since the data feature distribution varies significantly in tracking tasks, this paper introduces a mutual-teaching pseudo-label iterative training method into the field of weak target tracking. This aims to continuously transfer the model from the source domain to the target domain during iteration, thereby improving the model’s generalization ability. Experiments have shown that, when faced with a completely new dataset of weak target tracking, the proposed method improves upon recent strong baselines in single-target tracking by 0.05 in both P@20 and NP-AUC, and by 0.04 in SUS, demonstrating the enhanced tracking performance and generalization ability of the proposed method in the field of weak target tracking. Full article

Driven by the intensification of climate change, extreme snow events are becoming more frequent, posing significant risks to the safety of arched-roof structures. The combination of strong winds and heavy snowfall will cause localized snowdrifts that may exceed the safety design values in load codes. Such localized snowdrift phenomena even get worse under the action of mutual interference between buildings. To address this critical safety concern, this study employed a multiphase numerical model to evaluate interference effects on snow accumulation characteristics for grouped arched-roof buildings. Initially, the predictive accuracy of the numerical method was confirmed by comparing the results of a wind tunnel test. On the basis of full validation, a comprehensive numerical investigation was conducted to examine the snow redistribution patterns and aerodynamic interference effects among multiple arch-roofed buildings. Through analysis, the interference effect manifests as additional U-shaped snow accumulation on the windward surface of the disturbed arched roof, and specifically exhibits three distinct evolution stages of snow accumulation in relation to building spacing. Under adverse conditions, the disturbed snow load could increase by up to 1.4~1.5 times the fully exposed load, exceeding values specified in most design codes. Therefore, an amplification factor of 1.4 is recommended for the code-calculated fully exposed snow load to appropriately account for building interference effects. Full article

The renewable power grid is a complex system with multiple symmetries, in which active power and reactive power play different but symmetrical roles. In weak power grid condition, the virtual synchronous generators (VSGs) are prone to large power angles and high impedance ratios in the transmission lines, leading to severe coupling between active and reactive power. This coupling causes mutual interference between the active and reactive power outputs of the VSG, increasing the dynamic oscillations and prolonging the regulation time. To solve this problem, a power decoupling strategy for VSGs based on active disturbance rejection control (ADRC) is proposed in this paper. The ADRC control decouples the VSG, a dual-input, dual-output coupled system, into two single-input, single-output systems by observing and compensating for disturbances. This approach eliminates the coupling between the VSG’s power loops without considering virtual impedance. Compared with the conventional virtual impedance power decoupling method for VSGs, the proposed ADRC strategy can deal with multi-variable systems with different orders without complex and detailed high-order models, effectively decoupling the active power and reactive power of the VSG and improving the stability of the power grid. Full article

Electromagnetic compatibility is the capability of electrical and electronic equipment to function properly around devices radiating electromagnetic energy, without mutual disturbance. Hospital environments contain numerous devices operating simultaneously and sharing resources. Undetected electromagnetic interference can cause medical devices’ malfunctions, exposing patients and staff. Traditional monitoring is time-consuming and relies on expert interpretation. An Internet of Things-enabled embedded system architecture for remote and real-time monitoring of electromagnetic fields from medical devices is proposed. It integrates frequency probes, a Raspberry Pi 4, and a communication module. A three-month study conducted at Muhima District Hospital, Kigali, Rwanda, demonstrated the system’s effectiveness in monitoring electromagnetic field levels and cloud transmission. The signals were benchmarked against International Electrotechnical Commission and Rwanda Standards Board standards. Alerts are triggered when thresholds are exceeded, with results plotted on website and mobile interfaces. Emissions were highest at noon when the equipment was most active and lower after 1:30 PM, indicating reduced activity. The sample recorded statistics of electric fields include mean (1.0028), minimum (0.7228), and maximum (1.3515). Among the five filters evaluated, the Savitzky–Golay performed better, with MSE (0.235) and SNR (9.308). A 412 ms average latency and 24 h operation was achieved, offering a portable solution for hospital safety and equipment optimization. Full article

Scaffolds, as temporary structural support systems in civil engineering, play an essential role during construction. Independent steel scaffold systems, typically composed of assembled steel tubes, can be erected and function as standalone supports without mutual interference. This feature offers notable advantages over conventional scaffolding, including easier dismantling and higher reusability efficiency. However, the absence of specific design and construction codes for this type of scaffolding has hindered its broader application, underscoring the need for further research into its structural reliability. This study investigates the stability of basic load-bearing units in independent scaffolding through vertical loading tests on three specimens with varying heights and end conditions. The failure modes of the specimens are systematically compared, and the load-transfer mechanism and mechanical behavior of the scaffold units are analyzed. Experimental results, validated against ABAQUS finite element simulations, reveal that the critical region under axial compression lies at the junction between the inner and outer tubes. As specimen height increases, a plastic hinge develops in this region under load. In shorter specimens, the inner and outer tubes interact in a nearly fixed-end condition, without failure of the connecting pins. All three specimens failed by instability, and reducing the specimen height significantly enhanced the load-bearing capacity. When the top of the specimen is pin-supported, the material’s compressive strength is not fully utilized. To improve the axial stability of independent scaffolding, several structural improvements are proposed: replacing the pinned top with a plate-supported end to enhance compressive stability; integrating transverse bracing at the ends to connect individual units into an integrated system, thereby improving overall stability without compromising spatial flexibility; and applying mechanical reinforcement with external collars at the inner–outer tube interface to increase local bending stiffness and reduce initial imperfection, thus strengthening the global buckling resistance of the independent scaffolding system. Full article

In recent years, with the development of electronic products toward high frequency and high speed, Printed Circuit Board (PCB) routing technology has been continuously evolving to meet the requirements of complex signal transmission. Meanwhile, the increase in circuit frequency and device density has led to a sharp deterioration of simultaneous switching noise (SSN), which has escalated from a minor interference to a core bottleneck. SSN not only impairs signal integrity and increases bit error rate, but also interferes with circuit operation, causes device failure, and even leads to system collapse, becoming a “fatal obstacle” to the performance and reliability of high-frequency products. The SSN problem has become increasingly severe due to the rise in circuit operating frequency and device density, posing a key challenge in high-speed circuit design. To address the challenge of suppressing SSN at the PCB board level in high-speed digital circuits, this paper proposes a collaborative optimization scheme integrating simulation analysis and the Seagull Optimization Algorithm (SOA). In this study, a multi-physical field coupling model of SSN is established to reveal that SSN essentially arises from the electromagnetic interaction between the parasitic inductance of the power distribution network (PDN) and high-speed transient current. Based on the research on frequency-domain impedance analysis, time-domain response prediction, and decoupling capacitor suppression mechanism, the limitations of traditional capacitor placement in suppressing GHz-level high-frequency noise are overcome. This method enables precise power integrity (PI) design via simulation analysis frequency-domain parameter extraction and power–ground noise simulation quantify PDN impedance characteristics and the coprocessor switching current spectrum; resonance analysis locates key frequency points and establishes an SSN–planar resonance correlation model to guide decoupling design; finally, noise coupling analysis optimizes signal–power plane spacing, markedly reducing mutual inductance coupling. On this basis, the SOA is innovatively introduced to construct a multi-objective optimization model, with capacitor frequency, capacitance value, and package size as variables. A spiral search algorithm is used to balance noise-suppression performance and cost constraints. Simulation results show that this scheme can reduce the SSN amplitude by 37.5%, effectively suppressing the signal integrity degradation caused by SSN and providing a feasible solution for SSN suppression. Full article

The changes in water flow caused by hydropower projects and river diversions have had a profound impact on aquatic ecosystems, especially due to artificial structures such as dams and bridge piers. This study investigates the swimming behavior differences between single and dual fish in the wake region behind a D-shaped obstacle, using Percocypris pingi as the experimental species. The results show that single fish efficiently utilize vortex energy through the Kármán gait, improving swimming efficiency, while the dual-fish group failed to maintain a stable Kármán gait, resulting in irregular swimming trajectories. However, the dual-fish group optimized wake utilization by maintaining a fore–aft linear alignment, improving swimming efficiency and resisting vortices. The conclusion indicates that mutual interference in group swimming affects swimming efficiency, with fish adjusting their swimming patterns to adapt to complex hydrodynamic conditions. By altering swimming formations, fish schools can adapt to the flow environment, offering new insights into the swimming behavior of fish and providing theoretical support for ecological conservation and hydropower project design. Full article