Search Results (935)

Accurate trajectory tracking is challenging for tracked agricultural vehicles in orchards. Uneven terrain, track slip, and vehicle posture variations are the main causes, often leading to model mismatch and degraded control performance. To address these issues, this paper proposes an improved nonlinear model predictive control (NMPC) strategy integrated with curvature feedforward compensation for trajectory tracking of tracked agricultural vehicles under uneven terrain conditions. An enhanced kinematic model based on the instantaneous center of rotation is developed by incorporating vehicle roll and pitch angles, and track slip parameters are estimated online using a Levenberg–Marquardt optimization method to improve prediction accuracy. Furthermore, curvature feedforward information derived from the reference trajectory is embedded into the NMPC objective function to provide anticipatory control inputs and reduce computational burden. Simulation results demonstrate that compared to conventional NMPC, the proposed method reduces the mean and standard deviation of tracking error by 30.28% and 32.46% respectively, while decreasing the mean and standard deviation of heading error by 37.27% and 35.05%. Concurrently, the maximum of optimize solution time is significantly reduced, effectively resolving tracking accuracy degradation caused by system solution timeouts. Field experiments conducted under different load conditions further validate that the proposed control strategy significantly reduces lateral, longitudinal, and heading tracking errors compared with conventional NMPC, confirming its effectiveness and robustness for tracked agricultural vehicle trajectory tracking in complex orchard environments. Full article

Background/Objectives: We aim to investigate whether tracking pan-immune–inflammation value (PIV) dynamics during radiotherapy (RT) can inform real-time prognosis in patients with head and neck cancer (HNC). Methods: We retrospectively reviewed the medical records of patients with HNC who received RT at our institution between 2005 and 2013. Temporal changes in the PIV throughout the RT were evaluated using the Friedman test and Wilcoxon signed-rank test. The PIV dynamics were quantified using PIV ratios, defined as the PIV at three distinct time points (PIV-2, PIV-4, and PIV-6) during treatment divided by the pretreatment PIV (PIV-0). Overall survival (OS) and progression-free survival (PFS) served as the primary and secondary endpoints analyzed. Results: A total of 676 patients with HNC were enrolled, with a median follow-up of 8.1 years. The PIV demonstrated a continuously ascending trend over time, with the most dramatic increase occurring six weeks after the start of RT. Compared with patients with a low PIV ratio at six weeks (PIV-6/PIV-0), those with a high PIV ratio showed more favorable survival outcomes (five-year OS: 58.9% versus 70.8%, p = 0.002; five-year PFS: 62.0% versus 71.1%, p = 0.013). The subgroup analyses yielded consistent results. Notably, the real-time risks of death and recurrence changed in parallel with the PIV dynamics. Multivariate analysis confirmed PIV-6/PIV-0 as an independent prognostic factor for both OS and PFS. Conclusions: Monitoring longitudinal PIV dynamics may assist in forecasting the OS and PFS in patients with HNC being treated with RT, thus enabling individualized, risk-adapted treatment management. Full article

Reliable real-time detection and counting of tuna during distant-water deck operations is critical for automated catch monitoring but remains challenging due to strong illumination variation, background clutter, and frequent occlusion. This study proposes YOLOv10n-EMCNet, an improved lightweight detector based on YOLOv10n, integrating an ESC-based C2f enhancement in the backbone, a Multi-Branch and Scale Modulation-Fusion Feature Pyramid Network (SMFPN) in the neck, and a Convolutional Attention Fusion Module (CAFM) in the head for fine-grained representation and multi-scale feature fusion. An end-to-end detection–tracking–counting pipeline is further constructed by combining the detector with DeepSORT and an ROI-based de-duplication strategy. On the tuna dataset, YOLOv10n-EMCNet achieved 94.84% mAP@0.5, 65.29% mAP@0.5:0.95, and 91.77% recall with 6.5 GFLOPs. In addition, a controlled comparison among DeepSORT, ByteTrack, and OC-SORT on challenging videos showed that DeepSORT provided the best overall balance between counting accuracy, identity stability, and runtime efficiency. In shipboard video validation on four representative videos covering daytime high glare, nighttime low light, dense occlusion, and dense multi-target, the proposed pipeline achieved an average counting accuracy of 91.4%, with an average relative error of 8.62% and an average absolute error of 1.25 fish per video, while operating at approximately 30 FPS on an RTX 4090D platform. These results provide encouraging preliminary evidence that the proposed method can support automated tuna monitoring under representative shipboard conditions. Full article

To ensure reliable lane change behavior in-wheel motor-driven electric vehicles (IWM-EVs) under steer-by-wire (SBW) failure, this paper presents an integrated lateral–longitudinal lane change control strategy based on differential steering. The control framework and relevant models are first established. An upper-layer model predictive control (MPC) controller is then designed to simultaneously achieve lateral path tracking and longitudinal speed regulation, outputting the desired front-wheel steering angle and acceleration. Finally, a model-free adaptive control (MFAC)-based lower-layer lateral controller transforms the desired steering angle into differential driving torques for the front wheels, while a feedforward–feedback lower-layer longitudinal controller (incorporating drive/brake switching and PI control) computes the required driving torque or braking pressure. Co-simulation in Matlab/Simulink R2022b and CarSim R2020 reveals that the MPC controller designed in this study outperforms the LQR-PID controller, reducing the maximum absolute values of lateral error, heading error, front-wheel steering angle, yaw rate and sideslip angle by 42.9%, 50.0%, 7.8%, 2.8% and 10.3%. The proposed hierarchical control strategy outperforms the compared hierarchical controller, reducing the maximum absolute values of the lateral displacement error, heading error and yaw rate by 17.9%, 6.7%, and 33.3%. These results verify that the strategy can improve trajectory tracking accuracy and achieve basic differential steering functionality in specific scenarios. Full article

Train track occupancy detection is essential for railway operation safety and dispatching, yet GNSS-based positioning and track matching can degrade or fail in turnouts and station yards due to multipath, interference, and dense track layouts. This paper presents an IMU-only method to discriminate track-switching events during turnout passage by exploiting the transient change in heading angular velocity. The Z-axis gyroscope measurement (approximately aligned with the track-plane normal) is used as a heading-rate proxy, and a lightweight indicator is constructed from the difference between a short-window moving average and the full-run mean. The full-run mean further serves as an in situ approximation of the gyroscope zero bias, alleviating the need for pre-calibration and improving robustness to systematic drift. A fixed discrimination threshold is determined from stationary gyroscope noise statistics, and the minimum effective operating speed is derived by combining gyro noise characteristics with the kinematic relationship among train speed, turnout curvature radius, and heading rate. Field experiments conducted from January to April 2025 on three railway sections covering 27 turnouts (300 turnout-passage events) show that, using a constant threshold $T_0=0.002\mathrm{rad}/\mathrm{s}$, the proposed method achieves 100% track-switching discrimination accuracy within 5–40 km/h, without requiring track maps, GNSS, or prior databases. Full article

Head-Up Display (HUD) systems are widely used in vehicles to overlay navigation prompts in the driver’s field of view, thereby reducing eyes-off-road time. However, suboptimal information presentation may impose extra cognitive demands and lead to driver distraction. To quantify the effects of key HUD navigation design factors on navigation task performance and cognitive workload, a 2 × 2 within-subjects experiment was conducted, manipulating display position (upper vs. lower visual field) and the presence of textual navigation information (with vs. Without text). Thirty university students with driving experience completed navigation tasks under four conditions in a single-lane urban driving simulation. Each task lasted 2–4 min and included six turning prompts. Task performance (accuracy, mean reaction time, and total driving time), subjective workload (PAAS), and eye-tracking measures (mean fixation duration, mean pupil diameter, fixation count, and fixation count proportion) were collected and analyzed using repeated-measures ANOVA. Results showed that display position significantly affected driving efficiency and subjective workload: lower-field displays produced shorter reaction times and lower PAAS scores, while accuracy and total driving time showed no significant differences. Eye-tracking results indicated higher fixation counts and fixation ratios for lower displays. A significant interaction between display position and text was observed for mean fixation duration, whereas mean pupil diameter showed no significant effects. These findings indicate that display position is a critical factor in HUD navigation design, while textual information primarily influences visual inspection patterns rather than overall navigation task performance. Full article

Background: Kölliker’s organ (KO) support cells undergo orderly, time-dependent degeneration that is essential for auditory development and is accompanied by precisely regulated autophagic activity; however, the molecular hierarchy linking autophagy to this remodeling remains obscure. This study aimed to elucidate the regulatory mechanisms connecting autophagic flux to lysosomal biogenesis and auditory function during cochlear development. Method: We established an Atg5^flox/flox; Sox2Cre+ mouse model with deletion of the autophagy gene Atg5 in cochlear-supporting cells. Auditory function was assessed via Auditory Brainstem Response (ABR) testing. Transcriptomic profiling of the neonatal basilar membrane was performed to screen for downstream targets. Mechanistic validation included spatiotemporal immunofluorescence mapping (E18–P30) and in vitro functional assays using siRNA-mediated knockdown and lysosomal tracking. Results: At 2 months of age, Atg5^flox/flox; Sox2Cre+ mice exhibited moderate-to-severe sensorineural hearing loss accompanied by significant outer hair cell loss. Bulk RNA-seq of the basilar membrane identified fork-head box A3 (Foxa3) as a significantly downregulated transcription factor within the lysosomal–autophagy network. Spatiotemporal immunolabelling from embryonic day 18 to postnatal day 30 revealed that FOXA3 expression becomes progressively restricted to KO cells during postnatal development, with ATG5 loss reducing FOXA3 protein levels by 62.4%. In vitro, deficiency of either Atg5 or Foxa3 in primary KO cells resulted in comparable reductions in LAMP1-positive puncta. Conclusions: These findings support a model wherein the ATG5-FOXA3 axis contributes to lysosomal biogenesis in developing KO cells, with implications for understanding mechanisms of congenital sensorineural hearing loss. Full article

Current 360-degree video streaming systems consider viewport prediction, adaptive bitrate allocation, tile selection, and quality-of-experience (QoE) estimation as independent activities, yielding fragmented pipelines that do not scale well across content type and network conditions and do not scale well to individual users. We propose ImmerseFM-3D, a foundation model that jointly solves all four sub-tasks through a single shared representation. Seven input modalities, namely video frames, network traces, head-motion trajectories, ambisonics audio, depth maps, eye-tracking signals, and CLIP scene semantics, are fused by four-layer cross-modal attention and compressed into a 256-dimensional bottleneck latent via a variational information bottleneck. Four task-specific decoders operate on this shared latent simultaneously. A model-agnostic meta-learning adapter augmented with episodic memory and a hypernetwork personalizes the model from as little as 1 s of user interaction data. An extended branch supports six-degrees-of-freedom volumetric content through spherical harmonic viewport decoding and depth-aware tile importance weighting. Trained and evaluated on the IMMERSE-1M combined dataset (1000 h of 360° and volumetric video, 524 users, and over 50,000 mean opinion scores), ImmerseFM-3D reduces the mean angular viewport error by 34%, lowers the bandwidth violation rate from 8.3% to 3.1%, and achieves a QoE Pearson correlation of 0.891. The personalization adapter reaches 90% of peak performance in 22 s, while zero-shot cross-format transfer attains 72% of full in-domain accuracy. Full article

Autonomous navigation is essential for orchard transportation robots to support automated operations and precision orchard management. However, in trellis orchards, dense vegetation and complex canopy structures often degrade the stability of GNSS-based navigation in in-row environments. To address this issue, this study proposes a GNSS–vision integrated navigation framework for orchard transportation robots. The performance of GNSS-based navigation in out-of-row environments and vision-based navigation in in-row environments was experimentally evaluated under representative orchard operating conditions. In out-of-row areas, the robot employs GNSS-based path planning and trajectory tracking to achieve reliable navigation in relatively open, lightly occluded environments. During in-row navigation, a deep learning-based real-time object detection approach is used to detect tree trunks and trellis supporting structures. By integrating corner-point selection with temporal RANSAC-based line fitting, a stable orchard row structure is constructed to generate robust navigation references. The visual perception module serves as the front-end sensing component of the navigation system and is designed to be independent of specific object detection architectures, allowing flexible integration with different real-time detection models. Field experiments were conducted under various orchard layouts and growth stages. The average lateral deviation of GNSS-based navigation in out-of-row scenarios ranged from 0.093 to 0.221 m, while the average heading deviation of in-row visual navigation was approximately 5.23° at a robot speed of 0.6 m/s. These results indicate that the proposed perception and navigation methods can maintain stable navigation performance within their respective applicable scenarios in trellis orchard environments. The experimental findings provide a practical and engineering-oriented basis for future research on automatic navigation mode switching and system-level integration of orchard transportation robots. Full article

Near-miss events, defined as hazardous traffic interactions without actual collisions, provide valuable indicators for proactive traffic safety assessment. However, existing studies mainly focus on collision detection or object-level perception, while near-miss interactions and their severity remain insufficiently explored. This study proposes a video-based framework for real-time near-miss detection and risk evaluation in complex urban intersections. The framework integrates an enhanced YOLOv11 detector with a small-object detection head, BoT-SORT multi-object tracking, and bird’s-eye-view (BEV) transformation to accurately extract trajectories and motion features of heterogeneous road users. A Near-Miss Risk Index (RI) is developed by jointly considering spatial proximity, time-to-collision, and motion intensity to quantify near-miss severity levels. Experimental results on real-world CCTV data demonstrate that the proposed method effectively identifies high-risk interactions among vehicles, motorcycles, and pedestrians, providing interpretable severity assessment and supporting proactive traffic safety analysis for intelligent transportation systems. Full article

Reliable individual identification and minimally invasive tracking are essential for monitoring threatened snake populations. A relict high-altitude population of Vipera ursinii ursinii was studied in the Maiella National Park (Central Apennines, Italy) during two field seasons (2024–2025) to (i) validate dorsal head photo-identification against unequivocal PIT-tag identities and (ii) test a novel, non-invasive telemetry method based on externally attached harmonic diodes detected with a RECCO^® harmonic direction finder (HDF). All analysed snakes were PIT-tagged and photographed under standardised conditions. Manual photo-identification based on dorsal cephalic scale counts was performed independently by four blinded operators. In parallel, software-assisted photo-identification was conducted with two independent programmes (Wild-ID and Hotspotter). Both methods were evaluated exclusively against PIT-tag-confirmed identities. Manual identification achieved moderate-to-high overall accuracy (0.77–0.91) but showed marked inter-operator variability. Software-assisted matching appeared more consistent: Hotspotter identified 75% of true recaptures at first suggestion (85% within the top six suggestions), while Wild-ID identified 56% at first suggestion (88% within the top six). Correct matches were primarily supported by the distinctive pholidosis of the dorsal head region, especially apical, intercanthal and parafrontal scales—which were highly diverse but independent of sex and age class in the studied population. Externally attached HDF diodes enabled repeated short-term relocations with detachments occurring within hours to several days and mostly associated with ecdysis. The method was minimally invasive, supporting its applicability for monitoring small-bodied animals with low-density populations and restricted ranges. Full article

Cooperative pursuit–evasion with heterogeneous agents poses a training challenge that flat multi-agent reinforcement learning methods handle poorly: the pursuer team must coordinate internally while competing against adversarial targets, and the two forms of coupling require different learning signals. We present a potential-game-constrained role-structured tracking framework: a centralized training, decentralized execution algorithm for airship-guided unmanned aerial vehicle teams. It decomposes the multi-agent interaction into an internal potential game among pursuers and an external general-sum game against independently controlled targets, and pairs role-structured critics with multi-head attention over heterogeneous agent tokens and a two-stage task-assignment solver embedded as critic conditioning. The simulation results in a three-dimensional environment show that the proposed framework maintains high capture success in multi-target scenarios where standard baselines degrade substantially. A Gazebo-based visual simulation with full rigid-body dynamics confirms that the learned policy transfers to a higher-fidelity simulator after continuation training with a cascaded PID inner-loop controller. Full article

With the rapid development in data mining and learning analytics, integrating predictive analytics into educational data has become increasingly critical for supporting students’ learning trajectories. In many schooling systems, the academic tracks (such as Liberal Arts or Science) and the performance of junior high school students can substantially shape their subsequent university pathways and career planning. Despite the long-term impact of these decisions, academic track selections and the evaluation of students’ potential are often made without systematic and evidence-based guidance. Predictive computer applications can assist, but the training of accurate models and the selection of adequate features remain key challenges. This paper details our process of engineering such an application comprising two tasks based on 1357 real-world junior high school academic performance records. The first task applies a classification approach to predict students’ academic track orientation, while the second task employs a multi-output regression model to forecast students’ future academic performance in senior high school. Our approach shows that the stacking ensemble model achieved a classification accuracy of 85.76%, whereas the Bi-LSTM model with multi-head attention attained an overall R² exceeding 82% in performance forecasting; both models demonstrated strong and reliable predictive capability. Moreover, the proposed approach provides inherent interpretability by decomposing predictions at the subject level. Feature importance analysis reveals how different academic subjects contribute variably to both academic track decisions and future academic performance, offering actionable insights for academic counselling and future study planning. By bridging predictive modelling with students’ educational and career planning needs, this study advances the practical application of educational data mining and provides support for evidence-based academic guidance and future career choices in real-world contexts. Full article

This paper presents the development of two adaptive autopilots for the Cessna Citation X business jet aircraft. The two adaptive control strategies, including a dynamic inversion controller and a neural network controller, provide dual adaptation. The control objective consists of tracking the vertical speed, altitude, and heading commands. Dynamic inversion is applied on each output variable, and then the neural network (NN) controller is updated using adaptive law, derived from backpropagation. Dynamic inversion (DI) is achieved locally using a Recursive Least Squares (RLS) algorithm for state estimation. An inner control loop for the pitch, roll and yaw rates is integrated within the autopilots. The longitudinal states were separated from the lateral states in order to differentiate between longitudinal and lateral control. Robustness tests were conducted under turbulence and wind-gust conditions. The autopilot results were compared with flight simulation data from a Cessna Citation X research flight simulator. Results have shown that the autopilots accurately track the vertical speed, altitude and heading reference signals. The flight simulation comparison has shown that the proposed adaptive controllers were better than the one currently on board the Cessna Citation X. Full article

Continuous representations in brain navigation system are manifested as spatially structured patterns of population activity, such as a single-peaked bump moving along a ring manifold in head-direction system and hexagonal lattice patterns underlying spatial representation in grid-cell systems. These phenomena are commonly modelled within the framework of continuous attractor networks (neural dynamical field), yet the mechanisms by which activation-function nonlinearities interact with connectivity structure to determine pattern selection and dynamics remain incompletely understood. This paper separately analyses the interactions between non-resonant and resonant modes using a multiscale unfolding approach. We show that, when the critical modes satisfy a resonance condition, the quadratic nonlinearity of the activation function induces a three-mode coupling that fundamentally alters the structure of the amplitude equations and becomes the dominant mechanism governing spatial pattern selection. Building on this analysis, we introduce a weak asymmetric component in the connectivity and analytically derive the resulting pattern drift velocity, which is subsequently confirmed by numerical simulations. Finally, we apply these dynamical mechanisms to input-driven scenarios, illustrating that similar dynamical mechanisms can account for activity-bump tracking in head-direction models and lattice translations in grid-cell models. Overall, this work provides an analytically tractable framework for studying pattern dynamics in neural field models relevant to spatial representations, and may inform biomimetic approaches to spatial representation and navigation. Full article