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Plot combine harvesters are specialized machines used in breeding trials, germplasm evaluation, and small-batch seed harvesting. Compared with conventional field combine harvesters, they have higher requirements for sample independence, grain integrity, seed purity, low residual grain, rapid plot switching, and plot-level data reliability. However, existing studies remain relatively fragmented, and many studies mainly focus on individual components, whereas analyses of whole-machine coordination, residual-grain control, crop adaptability, and data integration remain insufficient. This paper presents a structured review of the research progress in plot combine harvesters from an agricultural-engineering perspective, covering representative international and domestic models, headers, threshing and separation systems, cleaning systems, residual-seed removal devices, simulation methods, intelligent monitoring, and seed-quality sensing. Existing evidence indicates that plot combine harvesters are developing toward whole-machine low-residue design, coordinated threshing–cleaning–conveying optimization, standardized evaluation methods, sample identification, data traceability, and long-term field validation under continuous multi-plot harvesting conditions. Key challenges include coordinating small-batch intermittent material flow, controlling residual grain during frequent plot switching, balancing threshing completeness with seed protection, improving adaptability to different crops and breeding materials, and validating intelligent sensing technologies under field conditions. This paper provides an engineering reference for improving the mechanization, precision, and intelligence of breeding-trial harvesting equipment. Full article

With the large-scale integration of renewable energy and the deepening of electricity market reform, uncertainty in power system operation has increased significantly. This creates new challenges for multiple generation companies when they work together to develop generation schedules that balance economic efficiency and low-carbon goals. Most existing studies assume fixed loads and ignore the active regulation capability of the demand side under price signals and incentive signals. To address this gap, this paper proposes a low-carbon generation schedule optimization method for multiple generation companies. The method considers heterogeneous flexible loads. First, the paper decomposes flexible load adjustability into two components: price elasticity-based load shifting and incentive-based adjustable capacity. Using the price elasticity matrix method, the market clearing price serves as a known input. The load shifting amount under price elasticity regulation is pre-calculated for each park and treated as an exogenous parameter in the generation schedule model. This allows generation companies to directly use demand-side flexibility information during the planning stage. Second, the paper uses the proportion of residential and industrial loads as a core parameter. It characterizes the heterogeneity of four parks along two dimensions: elasticity coefficients and upper limits of adjustable capacity. Parks with a higher proportion of industrial loads have stronger flexible regulation capability. This result is consistent with real physical characteristics. It also provides a quantitative basis for generation companies to utilize flexible resources differently across parks and optimize their output arrangements. Finally, the paper uses the upward and downward adjustable capacity of each park as decision variables. It builds a multi-generator low-carbon generation schedule optimization model with heterogeneous flexible loads. Generator output constraints, power balance constraints, flexible load adjustable capacity constraints, and carbon quota constraints are all integrated into a single-level mixed-integer linear programming framework. This framework can be solved efficiently using commercial solvers. It helps generation companies develop optimal generation schedules that balance economic efficiency and low-carbon targets. Case study results show that combining price elasticity regulation with incentive-based adjustable capacity can effectively improve both the economic performance and low-carbon performance of generation schedules. Full article

Diabetes mellitus (DM) is a complex metabolic disorder characterized by chronic hyperglycemia and represents a major global public health concern due to its rapidly increasing prevalence. Although current pharmacological therapies effectively achieve glycemic control, their long-term use is limited by adverse effects, high costs, patient compliance issues, and increasing interest in safer, multi-targeted therapeutic strategies. In this context, plant-derived bioactive compounds have gained attention as complementary or alternative approaches to metabolic disease management. Gymnema sylvestre (Retz.) R.Br. ex Sm (GS), traditionally known as “gurmar” (“sugar destroyer”), is one of the most extensively studied medicinal plants with significant antidiabetic potential. This review evaluates the antidiabetic effects of G. sylvestre, focusing on its phytochemical composition, molecular mechanisms, and impact on diabetes-related complications. Major bioactive constituents, including triterpenoid saponins (gymnemic acids), gurmarin-like peptides, flavonoids, and sterols, regulate glucose homeostasis, inhibit intestinal glucose absorption, preserve pancreatic β-cell function, stimulate insulin secretion, modulate lipid metabolism, and suppress inflammatory signaling pathways. Experimental and clinical evidence indicates that G. sylvestre modulates oxidative stress and inflammation associated with complications such as nephropathy, neuropathy, retinopathy, vascular dysfunction, and dyslipidemia. This review adopts a mechanism-oriented framework integrating phytochemical structure–molecular target–metabolic outcome relationships and discusses emerging strategies, including nanotechnology-based delivery systems, molecular docking, and multi-component phytotherapy. Overall, G. sylvestre represents a promising multi-target phytotherapeutic agent, highlighting directions for future mechanistic and clinical research. Full article

Human pose estimation (HPE) has advanced rapidly with deep learning, enabling a transition from specialized sensing and multi-view systems toward monocular RGB-based approaches. These developments have expanded applications in healthcare, robotics, sports analytics, and human–computer interaction. However, the growing diversity of deep learning paradigms, ranging from convolutional and recurrent models to graph-based and Transformer-based approaches, has resulted in a fragmented literature, making it difficult to systematically compare methods and guide system design. This paper addresses this challenge by providing a comprehensive survey of deep learning-based monocular HPE methods published over the past decade and introducing a unified modular framework. The proposed framework organizes HPE systems into six modular estimation paradigms, including single-image-based estimation, multi-frame-based estimation, Top-Down and Bottom-Up pose estimation strategies, 2D-to-3D pose reconstruction, and direct 3D estimation. Each module is analyzed in terms of representative approaches, design trade-offs, and practical considerations, supported by algorithmic formulations that outline the computational pipeline at each stage. Unlike prior surveys that primarily catalog methods or report benchmark results in isolation, this work emphasizes how component-level design choices relate to overall system performance. The paper summarizes performance trends on benchmarks including Human3.6M, COCO, and MPII, highlighting persistent challenges such as occlusion and viewpoint variation, and outlines future research directions including interaction-aware modeling, efficient deployment, and improved robustness under real-world conditions. Full article

As core structural components for deep-sea oil and gas exploitation, deep-sea risers are continuously subjected to wind, wave, and current loads, which readily induce vortex-induced vibration (VIV) and further trigger structural fatigue damage. Furthermore, the progressive exploitation of deepwater and ultra-deepwater oil and gas resources has exacerbated the complexity and risk of riser VIV, rendering it a critical engineering problem that urgently requires effective solutions. This paper presents a comprehensive review of numerical studies on deep-sea riser VIV, systematically elaborating the fundamental principles, research advances, and application scenarios of three mainstream numerical approaches: semi-empirical models, computational fluid dynamics (CFD) models, and computational structural dynamics (CSD) models. The respective accuracy advantages and inherent limitations of each numerical method are thoroughly analyzed. Additionally, this review focuses on key research hotspots and challenging issues, including VIV responses of flexible risers, dynamic fluid–structure boundary coupling, internal–external flow coupling effects, wake interference of multi-riser systems, efficient VIV prediction, and vibration suppression optimization. The current technical bottlenecks in existing research are clarified. This study aims to provide a systematic theoretical framework and methodological reference for subsequent numerical investigations and engineering applications of riser VIV, and offer technical support for the optimal structural design and safety risk prevention of deep-sea riser systems. Full article

Objectives: An integrated methodological framework was developed for modeling and multiobjective optimization of sustained-release drug delivery systems. Methods: The cumulative release percentage was fitted as a function curve, and functional principal component analysis was subsequently used to transform the function curves into functional principal component scores (FPCs). FPCs were then treated as dependent variables, while the proportions of the formulation factors were used as independent variables to construct Scheffé polynomial regression models. Finally, Non-dominated Sorting Genetic Algorithm III (NSGA-III) was applied to perform multi-objective optimization. Results: FPC1, FPC2, and FPC3 captured 95.18%, 4.39%, and 0.32% of the total variation, respectively. Corresponding Scheffé polynomial regression models were established, including quadratic models for FPC1 (adjusted R² = 0.751, AIC = 168.557) and FPC2 (adjusted R² = 0.592, AIC = 119.302), and a special cubic model for FPC3 (adjusted R² = 0.597, AIC = 64.574). The NSGA-III algorithm generated a Pareto optimal set, yielding stable formulation compositions with mean (SD) values of X₁ = 0.123 (0.015), X₂ = 0.821 (0.032), X₃ = 0.012 (0.017), and X₄ = 0.045 (0.015). The corresponding FPCs were −41.787 (2.544), 10.009 (0.168), and 8.264 (0.010) for FPCs1–FPCs3, respectively. The reconstructed cumulative release percentages were 42.471 (1.661), 52.623 (2.868), 69.942 (1.200), 84.275 (1.010), and 93.330 (0.832), demonstrating good agreement with the target release profiles. Conclusions: The integrated FDA–Scheffé–NSGA-III framework provides a robust and effective approach for accurately modeling release behavior and optimizing sustained-release formulations. Full article

Purpose-aware AI systems are increasingly deployed in safety-critical, multi-agent, and human-facing environments, where they must transform heterogeneous data into timely, explainable, and goal-aligned decisions under uncertainty. Existing architectures often couple perception, reasoning, communication, and security only at the pipeline level. This creates a research gap in unified semantic transformation, purpose-oriented judgment, bounded imperfection handling, and semantic self-protection. To address this gap, this paper proposes a DIKWP+BUG semantic–cognitive reference architecture for artificial-consciousness-oriented computing, without claiming definitive artificial consciousness. The architecture represents cognition through the Data–Information–Knowledge–Wisdom–Purpose (DIKWP) model and uses BUG theory to model bounded approximation, incomplete evidence, and confidence miscalibration in cross-dimensional reasoning. The model is mapped to an Artificial Consciousness Processing Unit (ACPU) reference substrate, an Artificial Consciousness Operating System (ACOS), a DIKWP semantic communication subsystem, and a concept–semantic fused security subsystem. The components are implemented through runtime emulation and evaluated in smart-city governance, autonomous-driving, and medical-triage simulations. Compared with selected baselines, the prototype increased cognitive throughput from $4.5\mathrm{k}$ to $7.8\mathrm{k}$ logged events, reduced perception–action latency from $340\mathrm{ms}$ to $120\mathrm{ms}$, reduced CPU utilization from $95\%$ to $68\%$, lowered smart-city congestion duration by $30\%$, improved emergency response time by approximately $40\%$, achieved 0 collisions versus approximately $2/10$ baseline IoV runs, and improved medical-triage accuracy from $85\%$ to $92\%$. These online-runtime results provide initial feasibility evidence under controlled simulation conditions; they do not include offline model-preparation costs and therefore should not be interpreted as end-to-end lifecycle speedups. Matched-compute ablation, statistical benchmarking, hardware prototyping, and real-world validation remain future work. Full article

Fringe projection profilometry (FPP) is widely used for 3D reconstruction, but conventional single-view FPP systems suffer from inherent occlusions and shadow regions, leading to incomplete surface recovery. In this study, we propose CVA-Net, an end-to-end deep learning framework with cross-view attention (CVA) that directly reconstructs dense depth maps from multi-view fringe patterns. CVA-Net simultaneously processes four fringe images acquired from orthogonal projection directions and leverages a CVA module to explicitly model inter-view dependencies, enabling adaptive fusion of complementary information. A 3D U-Net backbone with attention gates, atrous spatial pyramid pooling (ASPP), and an auxiliary parameter estimation branch further enhances reconstruction accuracy and structural consistency via multitask learning. To support Sim2Real network training, we build a Blender-based digital twin of a multi-view FPP system and generate a large-scale synthetic dataset with perfect ground truth. Extensive experiments on both synthetic and real-world objects demonstrate that CVA-Net significantly outperforms state-of-the-art single-view methods. With a symmetric four-view configuration and fringe period of 8, CVA-Net achieves an MAE of 0.0359 mm, an MSE of 0.0379 mm² and an RMSE of 0.1947 mm, reducing the MAE, MSE, and RMSE by 32.8%, 54.1%, and 32.2%, respectively, compared to the best single-view competitor. Ablation studies validate the contribution of each architectural component, while real-system experiments demonstrate the feasibility of transferring a network trained purely on synthetic data to practical FPP measurements without domain adaptation. Although further improvements are required to enhance reconstruction accuracy under real imaging conditions, the proposed framework provides an effective initial step toward bridging the gap between digital-twin-based training and real-world multi-view FPP applications. CVA-Net provides a robust, occlusion-aware solution for multi-view FPP reconstruction. Full article

Pharmacogenomics AI offers significant potential for individualized drug therapy; however, its clinical benefits remain unevenly distributed. Models trained predominantly on European-ancestry data consistently underperform in non-European populations, with polygenic risk scores (PRS) showing an estimated 39–73% reduction in predictive accuracy in African-ancestry cohorts across complex traits. These disparities have driven increased interest in moving beyond single-layer genomic approaches. Multi-omics frameworks integrating genomic, transcriptomic, proteomic, and metabolomic data have emerged as a promising strategy to improve prediction across heterogeneous clinical populations, as each molecular layer provides distinct and complementary biological information. Among these layers, metabolomics may represent a particularly transferable component across populations. Metabolite profiles capture the downstream functional output of biological systems influenced by genetic, environmental, dietary, and microbiome-related factors, and may therefore be less reliant on ancestry-stratified allele frequency structures that underlie performance disparities in genomic models. This review synthesizes evidence regarding the mechanistic basis of genomic bias in pharmacogenomics AI, the emerging role of multi-omics integration, especially metabolomics, in improving predictive performance, and the current landscape of computational strategies for bias mitigation, including federated learning, transfer learning, domain adaptation, and synthetic data generation. Collectively, current evidence supports metabolomics-inclusive multi-omics frameworks as a biologically plausible, hypothesis-generating strategy to reduce reliance on ancestry-linked genomic features. However, direct evidence that such frameworks reduce ancestry-related bias in clinical AI outputs remains limited, underscoring the need for globally diverse datasets and prospective multi-population validation. Full article

Cast iron components are indispensable in aerospace and automotive systems, yet their milling operations are severely affected by regenerative chatter, which degrades machining quality and damages equipment. Although various chatter prediction methods have been reported, the optimal interpolation strategy of full-discretization methods (FDMs) for multi-factor coupled dynamic systems remains unclear. This study proposes an enhanced FDM to fill this research gap. A dynamic milling model accounting for regenerative effects, modal coupling and process damping is established, and an improved FDM based on Lagrange interpolation is further developed. A systematic single-factor analysis is carried out to examine the performance of 1st–4th-order interpolation for state, delay and periodic terms. Counter-intuitively, convergence analysis and stability lobe diagram (SLD) verification reveal that higher-order interpolation does not guarantee better performance. The optimal orders are identified as 2nd/3rd for state terms, 3rd for delay terms and 1st for periodic terms. Accordingly, the proposed 321-FDM (3rd-order state, 2nd-order delay, 1st-order periodic) exhibits higher accuracy and computational efficiency compared with benchmark methods, namely the semi-discretization method and Hermite-based 3rd-order FDM. Milling experiments on cast iron workpieces validate the established model and the 321-FDM, and the experimental stability thresholds agree well with numerical predictions. This work presents a validated, high-performance stability prediction tool for chatter avoidance in cast iron machining. Full article

In complex urban environments, GNSS satellite signals suffer from severe multipath errors caused by building occlusion and reflection, which significantly degrades the accuracy of precise point positioning (PPP). This paper proposes a deep-learning-based multipath hemispherical grid correction model (DL-MHM) that integrates combined filtering and satellite embedding mechanisms. The model adopts the multi-system interoperable MHM framework to achieve effective multipath error correction. First, pseudorange and carrier phase observation residuals are calculated using the ionosphere-free combination for PPP. Then, a joint median and Kalman filtering scheme is applied to suppress noise in multi-day continuous residual sequences. A transformer-based time-series learning model is constructed, which introduces satellite-specific embedding vectors to characterize the differences between individual satellites and deeply fuse temporal features. This enables the model to adaptively fit the residual variation patterns of different satellites and accurately extract multipath errors. Finally, the multipath components predicted by the deep learning model are incorporated into the multi-system interoperable MHM model to generate the final multipath corrections. Test results show that in heavily obstructed urban scenarios, the root mean square (RMS) values of the east (E), north (N), and up (U) coordinate residuals are improved by 49.27%, 1.80%, and 3.35%, respectively, after DL-MHM correction compared to the uncorrected data. In open-sky environments, the corresponding improvements are 7.70%, 5.48%, and 34.28%. In all experimental scenarios, the proposed method outperforms both the conventional multipath hemispherical map (MHM) model and the convolutional neural network-long short-term memory (CNN-LSTM)-based MHM model in terms of overall multipath correction performance. The experimental results demonstrate that the proposed DL-MHM model can effectively mitigate multipath errors in complex urban scenarios and significantly improve the accuracy of GNSS precise positioning. Full article

As the core actuation component of intelligent precision spraying systems, the variable-rate nozzle is essential for achieving on-demand agricultural spraying; improving the use efficiency of water, fertilizers and pesticides; and reducing environmental pollution. This paper systematically reviews the development of agricultural variable-rate nozzles, from early mechanical profiling structures to modern intelligent control technologies based on Pulse Width Modulation (PWM). First, the existing variable-rate nozzles are classified into three major categories: electromagnetic-integrated type, centrifugal type, and variable-diameter type. A comparative analysis is conducted from three dimensions of working principle, performance characteristics and application scenarios, to delineate the respective advantages and limitations of each nozzle category. Second, the paper examines key technological advances in three areas: high-frequency solenoid valves, PWM control, and pressure and flow stabilization. It identifies the nonlinear response of solenoid valves, flow distortion under low duty cycles, and water hammer pressure fluctuation induced by high-speed switching as the three core technical bottlenecks at the current stage. Subsequently, the latest achievements and typical methodologies of variable-rate nozzles in structural design, simulation and experimental analysis are systematically reviewed, and their application performance in scenarios including field crops, orchards, protected agriculture and beyond are summarized. Finally, the remaining open issues in this field are put forward. It is suggested that future research should focus on key breakthroughs in the development of corrosion and wear-resistant high-frequency solenoid valves, the formation mechanism and suppression methods of pressure fluctuation, as well as adaptive algorithms based on machine learning or Model Predictive Control (MPC), to promote the leapfrog development of agricultural variable-rate nozzle technology from single variable control to multi-factor coupling optimization. All references cited in this paper are from articles published after the year 2000. Among them, the literature published in the last decade accounts for 86.6%, and literature published in the last five years accounts for 58.9%. Full article

Despite the large amount of successful existing methods and frameworks for planning sets of multiple unmanned aerial systems (UASs), there is still a lack of coordination frameworks that are capable of coping with real-world operational conditions. This paper presents U-Plan, an integrated management framework for the coordination of multi-UAS missions. U-Plan is designed to plan, schedule, monitor, and replan a heterogeneous set of UASs to complete point of interest (PoI) visiting missions while ensuring that all the generated trajectories are safe, feasible, and compliant with the required PoIs’ arrival times, UAS kinematics and energy constraints, and the existing 3D no-fly zones (NFZs). U-Plan is designed as a practical tool for strongly dynamic missions and is built upon three core components: (1) an NFZ-aware route computation method that explicitly accounts for NFZs prior to vehicle routing problem (VRP) optimization, resulting in shorter NFZ-safe routes; (2) a trajectory smoothing module that ensures the generation of kinematically feasible trajectories for fixed-wing UASs; and (3) a mission supervision module for real-time monitoring and replanning in case of changes in the UAS, mission, wind speed, or airspace restrictions. To validate the proposed architecture, we conducted rigorous experiments utilizing the VECTOR-SIL autopilot and Visionair Ground Control Station to realistically replicate the behavior of certified fixed-wing autopilots under various weather conditions using the exact same hardware and flight control software that runs onboard the physical drones. The validation shows U-Plan’s capacity to efficiently satisfy complex mission requirements with strong scalability. Due to its high computational efficiency, U-Plan enables online mission replanning, allowing UAS fleets to seamlessly adapt to changes that are typical of real-world operational scenarios. Full article

Power systems continuously generate large-scale load time series data for forecasting, consumption analysis, and equipment health monitoring. However, real-world load measurements are often contaminated by anomalies caused by sensor faults, communication errors, and abnormal consumption behaviors, which may degrade data quality and affect operational decision-making. To address this issue, this paper proposes an integrated anomaly detection framework named PEL, which combines Prophet-based seasonal-trend decomposition, ensemble empirical mode decomposition (EEMD), and a multilayer long short-term memory (LSTM) network. Prophet is first employed to decompose the original series into trend, seasonal, holiday, and residual components. Sample entropy analysis and white noise tests are then adopted to evaluate whether the residual component still contains complex structured information requiring secondary decomposition. Next, EEMD is applied to the residual component to extract multi-scale intrinsic mode functions. Finally, all decomposed components are normalized and fed into a multilayer LSTM model for anomaly detection. Experiments on a real-world power load dataset demonstrate that the proposed PEL framework achieves an accuracy of 99.92%, a precision of 97.33%, a recall of 100%, an F1-score of 98.65%, and an AUC of 0.9996, outperforming or matching several baseline and hybrid models. Full article

Red clover (Trifolium pratense L.) is an important forage legume that is also a valuable source of bioactive compounds with potential health-promoting properties. This study evaluated the variability among diploid (2n) and tetraploid (4n) red clover cultivars in forage productivity, quality-related parameters, polyphenol and flavonoid content, and antioxidant activity, in order to identify promising ideotypes for dual-purpose breeding. A total of 90 cultivars were assessed under field conditions; green matter yield, dry matter yield, crude protein content, and protein yield were analyzed together with total polyphenols, total flavonoids, and antioxidant activity. Spearman correlation and principal component analysis (PCA) were used to relate the traits and identify cultivars with contrasting characteristics. Cultivar differentiation was pronounced within each ploidy group, whereas diploid and tetraploid cultivars overlapped substantially in the multivariate space, indicating that ploidy alone is not a reliable predictor of forage or medicinal value. At the group level, tetraploids tended toward higher biomass, protein-related traits, and total polyphenol concentration, while total flavonoids and antioxidant activity were broadly comparable between groups. Forage- and medicinal-related traits were only weakly correlated and thus behaved as largely independent selection targets—which is precisely why integrated multi-trait evaluation is required to identify cultivars combining both. Several cultivars did combine favorable agronomic and phytochemical characteristics, supporting within-group selection of red clover germplasm with dual forage and medicinal potential for sustainable agricultural systems. Full article