Search Results (17,539)

Background: Technology like ultrasonography (US) has revolutionised the process of measuring cervical dilatation, cervical elasticity, station, and position of the foetal head to assess progress of labour. Ultrasound (US) is deemed non-traumatic, accurate, and user-friendly, offering an objective alternative to traditional vaginal examination (VE). Alternative methods such as position-tracking systems with fingertip sensors have shown limited precision, despite their widespread use. Additionally, US technologies have not been shown to measure effacement, caput, and moulding, as compared to VE. There is a lack of strong evidence supporting US effectiveness in improving outcomes for women and babies. Further research is required to enable respectful care in monitoring of women during labour and birth while eradicating preventable morbidity and mortality by utilising US. Objective: This scoping review aimed to map intrusive and non-intrusive ultrasound technologies and devices used in vaginal examinations during intrapartum. Methods: This scoping review followed Arksey and O’Malley’s five-step framework and the population, concepts, and contexts (PCC) model. A comprehensive search was conducted across seven databases using refined keywords. The protocol for this scoping review has been registered on the open science framework. The data were extracted, charted, synthesised, and summarised. Result: This scoping review included 47 original articles with a combined sample size of over 9000 women in labour or delivery. Most studies focused on ultrasound-based labour monitoring methods such as transabdominal, transperineal, 2D, 3D, and automated approaches—compared to traditional vaginal examination (VE). These ultrasound techniques were consistently praised for their accuracy, reliability, and feasibility in assessing cervical dilation, foetal head station, and angle of progression. Ultrasound was particularly effective in determining foetal head engagement during the second stage of labour, which can influence clinical decision-making and outcomes. Additionally, the review identified emerging intrapartum technologies, including non-invasive purple line observation and low-intensity light imaging probes. These innovations reflect a growing shift toward objective and less invasive labour monitoring methods. Tools like ultrasound and automated tracking algorithms demonstrated superior sensitivity, specificity, and patient comfort compared to VE. However, challenges remain in clinical applicability, standardisation, and implementation, particularly in resource-limited settings. Overall, the findings suggest a transition toward digital and ultrasound-based technologies as central components of modern labour monitoring, with further research needed to validate newer devices and ensure equitable integration into clinical practice. Conclusions: This scoping review highlights a shift toward ultrasound-based technologies transabdominal, transperineal with 2D/3D, and automated as more accurate and patient-friendly alternatives to vaginal examination. These methods improve assessment of cervical dilation, foetal head station, and progression angle. Emerging tools like the purple line and light imaging probes show promise but require further validation. Policymakers should support investment in affordable digital tools; clinical practice must prioritise training and integration; and research should focus on standardisation, long-term outcomes, and feasibility to ensure equitable and effective implementation of these technologies in labour monitoring. Full article

Digital capitalism is often described either as a clean break with the past or as a continuation of older markets. This article takes a third position: digital capitalism is a reorganisation of capitalist accumulation around platforms, data, attention and digital labour, not a departure from capitalism’s basic logic. The study uses a critical narrative review approach, drawing on Marxian value theory and recent work on platforms, datafication and surveillance. It is anchored by a curated set of publicly reported indicators from institutional and market sources, used as context rather than as a causal test. These show a platform environment that reaches most of humanity, highly concentrated advertising and cloud markets, platform labour as a global phenomenon, and a supposedly weightless economy resting on dense physical infrastructure. The article traces four contradictions: the commodification of unpaid user activity, the material basis of immaterial production, the concentration of market power, and the gap between participation and algorithmic control. The contribution is conceptual. It shows that media business models usually treated as separate, including advertising, subscriptions, creator monetisation, in-game spending and platform commissions, share one logic: user activity is captured as attention, measured as data and converted into revenue. New media therefore function as economic infrastructures for value extraction. Full article

Fracture size governs fluid mobility in shale, yet its direct quantification remains challenging. Nuclear Magnetic Resonance (NMR) transverse relaxation time (T₂) offers a unique, non-destructive probe of fracture size distributions; however, a physically grounded conversion from transverse relaxation time to pore radius r (T₂−r) is essential to translate NMR signals into quantitative geometric constraints on fluid mobility. This study introduces a capillary-constrained experimental method for T₂−r transformation into shale fractures. The workflow uses computed tomography (CT) scanning to extract fracture geometry. The gas-displacing-water process is precisely controlled by integrating the pore capillary pressure and back-pressure feedback algorithm. The NMR-CT conversion method performed in this study differs significantly from the T₂−r transformation based on conventional MICP. Differential spectral analysis isolates fracture-specific T₂ responses, and least-squares fitting derives the T₂−r conversion. Constraining displacement pressure and controlling segmental pressure are effective methods for ensuring the accuracy of fracture displacement. By emphasizing the governing role of capillary pressure during displacement, this method achieves accurate fracture-targeted displacement and reliable T₂−r mapping. The results significantly advance the use of NMR for quantifying fracture size and evaluating fluid transport in shale. Full article

Climate change has increased the frequency of hotter droughts and forest decline processes, highlighting the need for monitoring methodologies capable of detecting loss of vigor on the individual-tree scale. Pinus pseudostrobus is an economically important species in the temperate forests of the indigenous community of Nuevo San Juan Parangaricutiro, Michoacán. The aim of this study was to evaluate the elevational and thermal factors associated with the spatial and temporal variability of P. pseudostrobus vigor by integrating multispectral, LiDAR, and land surface temperature (LST) data. Five multispectral flights were conducted using unmanned aerial vehicles (UAVs) between June 2023 and June 2024, along with one LiDAR flight in April 2024, across an elevational gradient ranging from 2150 to 2920 m. High-resolution orthomosaics, NDVI and LCI indices, a canopy height model, and an object-based classification using Random Forest were generated. LST was obtained from Landsat 8–9 imagery in Google Earth Engine using the Statistical Mono-Window algorithm. Spectral, thermal, and elevation values were extracted at the individual-tree level. The classification achieved an overall accuracy of 69.6% and enabled the identification of 28,585 P. pseudostrobus individuals. NDVI and LCI varied significantly among periods, elevations, and their interaction. The lowest values of vigor were recorded in lower elevation areas, particularly in June 2023, April 2024, and June 2024. In addition, both indices decreased as LST increased, showing strong negative relationships for NDVI (R² = 0.96) and LCI (R² = 0.86). Individuals located at the lower elevational limit showed greater vulnerability to thermal stress. Full article

Background: Circular RNAs (circRNAs) constitute an emerging research field, as these RNA molecules play a crucial role in cellular functions and the progression of various human pathologies. Little is known about alternative circularization leading to the formation of distinct circRNAs from the same primary transcript, the role of circRNAs with slightly different back-splice junctions (BSJs) resulting in very similar circRNA sequences—called circRNA isoforms—and the extent to which the same primary transcripts produce alternative circRNAs. In this study, we discovered alternative circRNAs produced by two apoptosis-related genes, BAX and BCL2L12, expressed in established human cell lines originating from myelodysplastic syndrome (MDS) and different types of acute myeloid leukemia (AML). Methods: After total RNA extraction from one MDS cell line and five AML cell lines, first-strand cDNA synthesis, and multiple nested PCRs with distinct sets of divergent primers (10 and 16 primer pairs for BAX and BCL2L12 circRNAs, respectively) annealing in each exon of BAX and BCL2L12 genes, amplicon libraries were prepared and sequenced by both nanopore sequencing and NGS. Detailed bioinformatic analysis was then performed, based on existing bioinformatic tools and our own algorithms. Results: Our approach led to the identification of 72 BAX circRNAs and 52 BCL2L12 circRNAs with distinct expression patterns in MDS and AML cell lines. Most of these circRNAs—either merely exonic or exonic–intronic—were detected for the very first time. Furthermore, several BAX circRNA isoforms were detected in a unique cell line. Moreover, the back-splice sites joined together to form the BSJ of each circRNA were non-canonical, in many cases. The identified circRNAs are predicted to sponge distinct sets of miRNAs, some of which are known to regulate the activity of pivotal pathways. Conclusions: Overall, our findings support the notion that alternative splicing and back-splicing lead to the production of tens of distinct circRNAs from the same human gene. Full article

In complex UAV aerial scenes, small object detection tasks face challenges such as extremely low pixel occupancy, strong background interference, and sparse effective features, which are further compounded by environmental factors like low illumination. Consequently, single-modality detection algorithms are prone to severe target feature loss and missed detections. Multi-modal image fusion, which complements the texture details of visible light with the thermal radiation characteristics of infrared, is considered an effective approach to overcome the limitations of single physical imaging. However, conventional fusion mechanisms often suffer from semantic gaps when processing heterogeneous data, easily introducing redundant noise and background false alarms. To further improve the accuracy and robustness of small object detection in UAV aerial scenes, this paper proposes a multi-modal detection network that integrates direction-aware multi-granularity and asymmetric context guidance, termed DMAC-Net. Specifically, a Direction-Aware Granularity Enhancement (DAGE) module is first constructed for unified backbone feature extraction, which captures local directions and contour edges of small objects in UAV aerial images with high sensitivity, and expands the receptive field through a multi-granularity mechanism, effectively suppressing false positives induced by complex backgrounds while enhancing the recall of occluded and weakly featured targets. Additionally, the Asymmetric Context Guided Fusion (ACGF) module builds a spatial mechanism via asymmetric receptive fields and performs semantic soft alignment of cross-modal features with dynamic weight assignment, effectively filtering out artifacts and clutter from cross-modal interaction. Experimental results on multiple aerial datasets, including RGBTDronePerson, AVMS and LLVIP demonstrate that the proposed method outperforms existing mainstream models in terms of overall detection accuracy and missed-detection suppression, while exhibiting strong generalization capability and stability under complex lighting transitions and multi-scale variations in UAV monitoring environments. Full article

Flexible job shop scheduling with multiple automated guided vehicles (FJSP-AGV) is a challenging production scheduling problem in intelligent manufacturing, where operation sequencing, machine assignment, AGV allocation, and transportation decisions are tightly coupled. Existing exact and meta-heuristic methods can obtain high-quality solutions, but they usually require considerable computational time for large-scale instances. Meanwhile, conventional dispatching rules can make fast decisions but often fail to capture the complex interactions among operations, machines, and AGVs. To address these challenges, this paper proposes an end-to-end deep reinforcement learning framework based on heterogeneous graph neural networks for solving FJSP-AGV. Specifically, a heterogeneous graph is constructed to represent the scheduling state, where operations, machines, and AGVs are modeled as different types of nodes, and their relationships are described by operation–machine and operation–AGV arcs. Based on this representation, a heterogeneous graph neural network is developed to extract scheduling information from different production resources. In particular, a meta-path aggregation mechanism is introduced to capture the complex interaction patterns among operations, machines, and AGVs. The proximal policy optimization algorithm is then employed to train the scheduling policy in an end-to-end manner. Experimental results on public benchmark instances and real-world cases demonstrate that the proposed method outperforms composite heuristic rules and achieves a favorable balance between solution quality and computational efficiency compared with existing state-of-the-art methods. These results indicate that the proposed HGNN-DRL framework is effective for fast and intelligent scheduling decision-making in FJSP-AGV environments. Full article

Rice bacterial leaf blight (BLB), caused by the bacterium Xanthomonas oryzae pv. oryzae, severely damages leaves during rice growth, leading to reduced yield or even death. This study aimed to develop an automated identification and assessment method for rice BLB based on multispectral UAV imagery to overcome the limitations of in-field inspection methods. By obtaining multispectral image data of rice fields and extracting color features (CFs), texture features (TFs), and vegetation indices (VIs) of rice canopy using image processing techniques, three algorithms, namely, Support Vector Machine (SVM), Random Forest (RF), and Back Propagation Neural Network (BPNN), were utilized to establish a monitoring model for the severity levels of rice BLB. The classification results of several models are compared, with the overall Correct Identification Rate (CIR) of the three-feature fusion classification algorithm generally higher than the other two. Among the three algorithms, the RF algorithm performs the best, with a CIR reaching 93.4% and a Kappa coefficient of 0.91. The BPNN algorithm follows, with a CIR of 82.1% and a Kappa coefficient of 0.76, showing moderate effectiveness. Lastly, the SVM algorithm performs the poorest, with a CIR of 65.1% and a Kappa coefficient of 0.54. A rice BLB large-scale detection framework based on unmanned aerial vehicle (UAV) images was designed, and a graphical user interface (GUI) was developed using Python language to achieve automated processing from image input to final recognition results, achieving good results. Full article

Reliable state-of-health (SOH) estimation is important for the safe operation and management of lithium-ion batteries. This study proposes an SOH estimation framework that combines six health factors extracted from incremental-capacity curves, a transformer encoder, a state space model (SSM) decoder, and the Rime Optimization Algorithm (RIME). The transformer extracts relationships across different cycle positions, while the SSM describes the continuous change in battery health. RIME jointly selects the attention-head number and SSM state dimension. The model was evaluated using four NASA batteries through cross-battery four-fold validation and prediction experiments starting at 30%, 50%, and 70% of the cycle sequence. Ten models were compared under the same data partitions and preprocessing procedure. In four-fold validation, RIME–SSM–Transformer achieved an average MAE of 0.537% and an average RMSE of 0.740%, giving the lowest errors among all models. At the 30%, 50%, and 70% prediction starting points, its average MAE values were 1.194%, 0.810%, and 0.576%, while the corresponding RMSE values were 1.428%, 0.987%, and 0.654%. Parameter sensitivity analysis showed that six attention heads and an SSM state dimension of 64 gave the lowest validation error within the tested range. External validation on four additional batteries produced an average MAE of 0.629% and an average RMSE of 0.827%. These results show that the proposed framework provides accurate and stable SOH estimates under different degradation paths and amounts of available battery history. Full article

This study addresses the safety and efficiency challenges of manual inspection in complex open-pit mining environments, where terrain steepness, limited coverage, and personnel exposure to hazards render conventional methods inadequate. We propose an integrated UAV inspection framework that combines 3D environmental modeling with a hierarchical optimization paradigm. The framework operates in three sequential stages. First, a high-fidelity 3D terrain model is constructed from point cloud data via skeletal feature extraction, which reduces computational complexity while preserving topographic structure. Second, an upper-layer Traveling Salesman Problem (TSP) solver determines the optimal inspection sequence across mandatory points (loading sites, dump sites, and crushing stations). Third, a lower-layer Chaotic Adaptive Population-based Grey Wolf Optimizer (CAP-GWO) refines the 3D path between consecutive TSP-ordered points, augmented by B-spline smoothing to ensure kinematic feasibility. Key inputs include: (i) raw LiDAR point cloud data of the mining site, (ii) facility coordinates and operational constraints (safety margins, maximum pitch angle, minimum turn radius), and (iii) UAV kinematic parameters. Outputs comprise a smooth, collision-free 3D trajectory with verified constraint satisfaction. Comparative experiments against eight metaheuristic algorithms (PSO, GA, ACO, BA, COA, GWO, SRA, SFOA) demonstrate that the proposed method reduces total path length by 15–20% on synthetic benchmark scenarios while maintaining zero constraint violations. Statistical validation via the Sign Test confirms the significance of these improvements (p < 0.05) across repeated independent trials. The framework is further validated on measured airborne LiDAR data of the Bingham Canyon open-pit copper mine (Utah, USA; USGS 3D Elevation Program), one of the largest operating open-pit mines in the world: on this real terrain, CAP-GWO achieves the best performance among the GWO-family algorithms, with a statistically significant 12.5% improvement over SRA (Wilcoxon p < 0.001) and 24% lower variance than the standard GWO, and all 210 experimental runs produce collision-free trajectories. Notably, the proposed hierarchical optimization framework achieves structural symmetry between the upper-layer sequencing task and the lower-layer path refinement task. This symmetric decomposition significantly reduces computational complexity while preserving solution quality, aligning with the principles of symmetry in engineering optimization. The framework offers a practical solution for autonomous, adaptive inspection scheduling in dynamic mining environments. Full article

Optical sensing arrays have emerged as transformative tools for disease diagnosis, offering low-cost, rapid, and multiplexed fingerprint detection capabilities. However, the high-dimensional and complex data generated by these arrays pose significant challenges for conventional analytical methods. The integration of machine learning (ML) has advanced this field by enabling automated feature extraction, robust pattern recognition, and accurate disease classification. This review provides a systematic overview of ML-reinforced optical sensing arrays, with a particular focus on three major modalities: colorimetric, fluorescent, and surface-enhanced Raman scattering (SERS) sensor arrays. We critically evaluate how ML algorithms—encompassing unsupervised, supervised, and deep learning paradigms—synergistically enhance the diagnostic performance of each sensor modality. Representative applications are highlighted, demonstrating high accuracy in detecting cancers and infectious diseases. Finally, we discuss the pressing challenges related to data standardization, model interpretability, and clinical translation, while outlining future directions toward intelligent, point-of-care, and personalized diagnostic systems. Full article

Rolling bearing fault features are often weak and easily affected by noise and interference. To improve fault feature extraction performance, this paper proposes an AHBKA-VME-SMHD method. First, the black-winged kite algorithm is improved by opposition-based learning, a Gompertz-based adaptive step size strategy, and an NGO-inspired random displacement strategy. Then, the improved algorithm is used to optimize the penalty factor and desired mode center frequency of VME, guided by a composite fitness function combining Higuchi fractal dimension and energy concentration index. Finally, SMHD is applied to enhance periodic impulsive components, and envelope spectrum analysis is used to identify fault characteristic frequencies. The proposed method is validated using simulated signals and two real-world bearing datasets, namely the CWRU and XJTU-SY datasets. The results show that the proposed method extracts clearer fault-related harmonics than the comparison methods. In addition, it obtains higher kurtosis and Gini index values and lower envelope spectrum entropy values, demonstrating its effectiveness for rolling bearing fault feature extraction. Full article

Aiming at the current SLAM (Simultaneous Localization and Mapping) algorithms in urban scenarios, which have problems such as elevation drift, odometry drift, and the appearance of false loop closures, a tightly coupled SLAM method with LiDAR and inertial guidance is proposed. In the front-end, a raster-based point cloud feature extraction method is introduced, enabling simultaneous segmentation and extraction of line, surface, and ground features. Utilizing the alignment results of line and surface features as the initial value for ground point alignment, interpolation weights are determined based on roll and pitch angle errors, effectively reducing global elevation errors through frame-by-frame constraints. The back-end employs an error state-based Kalman filter (ESKF) for GPS and IMU data fusion, enhancing the validity of true state estimation. A Scan Context loop closure detection method is designed, augmented by GPS detection as an auxiliary loop closure constraint to mitigate false loop closures. A global factor graph optimization model is also proposed. Experimental results demonstrate that, compared to existing open-source algorithms, the proposed method exhibits improved performance in structured urban scenes, reducing the average RMSE APE by 47.4% compared with LiDAR-only methods and by 22.9% compared with tightly coupled LiDAR-inertial methods. This work highlights the potential of multi-sensor fusion SLAM for achieving high-precision 3D localization and mapping in complex urban environments. Full article

Multi-scale entropy is a powerful analytical tool that extends single-scale entropy analysis into a multi-scale feature extraction approach, enabling more reliable fault diagnosis in complex machinery by revealing hidden dynamic information that single-scale metrics neglect. Driven by advances in symbolic dynamics, statistical processing, and robustness enhancement, multi-scale methods have evolved into more than 80 variants. Despite this diversity, a comprehensive and systematic evaluation of these methods is still lacking, particularly with regard to the trade-off between diagnostic accuracy and computational efficiency. This study provides a comprehensive benchmark of 85 multi-scale methods evaluated on the XJTU-Gearbox dataset to establish an evidence-based roadmap that balances diagnostic accuracy and computational cost. Through performance comparison and algorithmic-principle analysis, this study elucidates the mechanisms underlying the performance differences among these variants, highlighting how advanced multi-scale methods preserve richer fault information and enhance feature separability. Furthermore, this work identifies promising development trajectories and offers a basis for future integration of multi-scale methods with deep learning models. To avoid overgeneralization, the results are interpreted as a controlled single-dataset benchmark rather than a universal ranking across all datasets, classifiers, operating conditions, or fault severities. Full article

To address the dynamic target allocation and resource scheduling problems of multiple High-Energy Laser Systems (HELSs) in defending critical infrastructure against large-scale heterogeneous UAV swarm penetration, this paper proposes a soft–hard damage cooperative task allocation and optimization method, termed Attention-based Centralized Training and Decentralized Execution proximal policy optimization (Attn-CTDE-PPO), which integrates an attention mechanism with multi-agent reinforcement learning. First, a cooperative multi-agent sequential decision-making model for multi-HELS engagement against UAV swarms is constructed by considering continuous laser irradiation, system energy consumption, thermal accumulation limits, and soft–hard damage mechanisms. Second, a multi-head attention set encoder based on a Transformer is introduced to extract global situational features at the state level. This design enables the policy network to handle a time-varying number of targets and mitigates the explosion of hybrid action spaces. At the action level, a discrete–continuous dual-head network with a masking mechanism is proposed to achieve target allocation and laser-parameter scheduling. Furthermore, a multi-dimensional threat assessment module is developed, which can prune infeasible actions in the action space via physical rule-based masking, thereby accelerating the decision-making speed of the method. Monte Carlo simulation experiments verified the defense reliability, energy management efficiency and real-time responsiveness of the proposed method. The experimental results demonstrate that, in heterogeneous swarm scenarios with different numbers of UAVs, the proposed method effectively suppresses UAV penetration, improves the defense success rate, and reduces total system energy consumption compared with baseline algorithms. Full article