Search Results (59,005)

To address the issues of slow response to preceding vehicles and poor string stability in distributed platoon control of connected and autonomous vehicles (CAVs) under mixed traffic flow, this paper proposes a sliding mode control method based on LSTM trajectory prediction, denoted as LSTM-SMC, within a multi-agent framework. The LSTM model is trained using the HighD naturalistic driving dataset to achieve high-precision prediction of the leader vehicle’s trajectory over a horizon of 3 s, with root mean square errors (RMSE) of 8.52 m in the X-direction and 0.896 m in the Y-direction. The predicted trajectory information is converted into a preview error and embedded directly into the design of the sliding surface, enabling each following vehicle to anticipate disturbances before they propagate. A diminishing preview gain strategy (${\gamma }_1=0.4$, ${\gamma }_2=0.2$, ${\gamma }_3=0.1$) is employed to suppress error propagation along the platoon, while a saturation function is introduced to eliminate chattering and ensure smooth control inputs. Three simulation scenarios—prescribed leading, HDV (human-driven vehicle) leading, and curved road scenario—are constructed to validate the proposed method against traditional constant time headway (CTH) control, pure sliding mode control (SMC), and LSTM-MPC. Results demonstrate that under extreme conditions, the proposed method reduces the speed RMSE of the 3rd following vehicle by 18.3% compared to CTH and by 39.7% compared to SMC. Under HDV leading conditions, all string stability amplification factors are less than 1, and the position RMSE of the 3rd vehicle is only 5.03 m in the curved road scenario. Compared with LSTM-MPC, the proposed LSTM-SMC achieves comparable tracking accuracy while reducing computational cost by 1.43–3.51×. The proposed method achieves a native integration of prediction and robust control, significantly improving tracking accuracy, string stability, and computational efficiency across diverse operating conditions in mixed traffic flow. Full article

This study aims to identify and prioritize digital economy factors affecting inflation and to determine effective policy strategies for managing digitally driven inflationary pressures in the context of financial systems and risk dynamics. The analysis considers twelve key digital economy indicators, including e-commerce penetration, digital payment systems, internet infrastructure, price transparency, digital advertising, Industry 4.0 technologies, data-driven inventory and demand systems, fintech adoption, cryptocurrency usage, and digital financial access. In parallel, eight policy strategies are evaluated, covering digital price transparency, expansion of digital payments, digital logistics optimization, digital public services, smart manufacturing, intelligent-based demand forecasting, fintech integration, and digital workforce development. The study employs a novel intelligent-supported decision-making framework integrating an attention-based expert weighting approach, generalized fractal fuzzy sets, the MEREC method, and the ARLON technique. The empirical design is based on expert evaluations obtained from ten specialists with at least 12 years of experience in digital economy, finance, and policymaking. Rather than relying on country-specific or time-series inflation datasets, the study examines the structural relationship between digitalization and inflation through a multi-criteria expert-based approach, with data collected in 2025. The findings indicate that e-commerce penetration and the prevalence of digital payment systems are the most influential factors affecting inflation. In addition, digital price transparency and the expansion of digital payment systems emerge as the most effective strategies for mitigating inflationary pressures. These results provide important insights into how digital transformation reshapes inflation dynamics, monetary transmission mechanisms, and inflation-related financial risks. The proposed model offers a robust and systematic framework for analyzing inflation in digitalized economies and supports policymakers and financial decision-makers in managing emerging risks in intelligent-driven economic environments. Full article

Background: Image-based dietary assessment provides a more intuitive approach for nutritional monitoring and health management. However, in multi-category bowl-based meals, food boundary adhesion, spatial stacking, and staple-food occlusion by upper-layer dishes still affect the accuracy of volume, weight, and nutritional composition prediction. Methods: This study proposes a nutrition prediction method for bowl-based foods by integrating semantic segmentation, multi-view three-dimensional reconstruction, and occlusion compensation. The improved DBP-FDSNet was used to extract food-category masks from top-view RGB images, while detail enhancement, boundary-assisted supervision, and spatial position encoding were incorporated to improve the segmentation quality of food boundaries and adhesion regions. The visible food surface inside the bowl was reconstructed using a bowl instance model and RGB-TSDF-based multi-view fusion, and the two-dimensional semantic results were mapped into the height-field parameter domain for category-level volume integration. For partially occluded, severely occluded, or completely invisible staple foods, a layered compensation strategy was introduced to reduce staple-food volume prediction errors and the erroneous assignment of upper-layer food volume. Food weight and whole-bowl Calories, Fat, Carbohydrate, and Protein were finally predicted using food density and a nutritional composition database. Results: DBP-FDSNet achieved a meanIntersectionoverUnion ($mIoU$) of 80.51% and a BoundaryF1 Score ($bF1$) of 85.73%. At the whole-bowl level, the MeanAbsolutePercentageError ($MAPE$) values for Calories, Fat, Carbohydrate, Protein, and total food mass were 13.23%, 18.51%, 14.18%, 13.35%, and 10.85%, respectively. Conclusions: The method improves the stability of category-level volume and nutritional composition prediction in complex bowl-based meal scenarios, providing a feasible solution for image-based dietary assessment and intelligent nutrition management. Full article

Bioanalytical methods to quantitate conjugated payloads are essential for assessing antibody-drug conjugate (ADC) stability and pharmacokinetics (PK). Dual-payload ADCs present analytical challenges; different linker chemistries can require complex digestion conditions to perform the cleavage. Developing separate methods for each linker combination can be time and resource demanding. Rat tritosomes—purified lysosomal fractions from Triton-treated rat liver—provide a comprehensive enzymatic mixture that mimics the lysosomal environment. The presented bioanalytical method combines immunoaffinity purification with tritosome-mediated digestion for simultaneous quantitation of dual-conjugated payloads. The method was applied to a model dual-payload ADC containing two different cytotoxic payloads, conjugated using different enzymatically cleavable linkers, with an unrelated DAR (drug-to-antibody ratio). Method validation in mouse plasma demonstrated excellent accuracy (bias ± 20%, LLOQ and ULOQ ± 25%) and precision (coefficient of variation CV% ≤ 20%, LLOQ and ULOQ ± 25%) across all concentration levels (lower to upper limit of quantitation, LLOQ to ULOQ) for both payloads, with 100% of quality control samples (QCs) meeting acceptance criteria for hybrid LC-MS/MS quantitation methods. This tritosome-based approach provides a unified, efficient platform for multi-payload ADC bioanalysis, eliminates linker-specific method optimization, and enables robust support for preclinical studies. The method has been tested for accuracy and precision on 4 different model ADCs and employed to quantify the conjugated payloads in in vivo samples from a homozygous hFcRn transgenic mouse model (Tg32) PK study, resulting in reliable data in accordance with total antibody measurements. Full article

Reliable and rapid forecasting of wind turbine output power is vital for operators, particularly day-ahead and intraday market scheduling and reserve allocation. However, the inherent unpredictability, intermittency, and volatility of wind turbine output make forecasting processes difficult. To address this challenge, this study proposes a Sinusoidal Representation Network (SIREN)-based forecasting model for high-accuracy, rapid direct multi-horizon forecasting of wind turbine output power. SIREN is selected due to the periodic and symmetrical mathematical structure of its sinusoidal activation function, which allows the model to represent both low-frequency trends and high-frequency sudden changes in wind energy data. To improve data quality, compensate for asymmetric fluctuations in wind data, and provide more suitable inputs for SIREN training. Several preprocessing steps are utilized before feeding the data into the model. The proposed preprocessing step includes a moving median filter, robust scaling based on median and interquartile range, Winsorizing clipping, and a Hampel filter to reduce the effects of instantaneous noise, outliers, and local peaks without disrupting temporal continuity. Subsequently, a Savitzky–Golay smoothing is applied to attenuate high-frequency measurement noise while preserving curvature, local peaks, and physically meaningful short-term dynamics in the data. The sliding-window approach is used to formulate the multi-horizon forecasting problem directly, and a direct h-step-ahead forecasting architecture is designed, preserving structural symmetry in the time series. The SIREN is trained and tested using MATLAB with the help of two different datasets: Dataset-1 has a 10 min resolution for 1 year, and Dataset-2 has a 1 h resolution for 15 years. The forecast horizon parameter h is considered separately for each step, and the proposed SIREN is independently trained, validated, and tested for each target horizon while maintaining chronological order. The results demonstrate that the proposed model is able to yield high forecast performance for a wide spectrum of horizons ranging from 10 min to 15 days. The accuracy of the proposed model for Dataset-1 is R² of 99.6%, MSE of 0.085%, MAE of 1.7%, and MAPE of 12%, while for Dataset-2, the accuracy is R² of 98.8%, MSE of 0.3%, MAE of 3.6%, and MAPE of 23%. Ablation and sensitivity analyses are conducted to evaluate the impact of the basic components used in the proposed model on forecasting performance. In addition, combative experiments are performed using traditional time series, ML, and DL forecasting techniques to better assess the contribution of the model. The obtained results show that the SIREN-based direct forecasting approach provides strong learning capability, as well as high forecasting accuracy, for both high-resolution and low-resolution wind power data. Overall, its ability to capture the symmetric and periodic characteristics inherent in wind turbine power data makes it a promising alternative for multi-horizon wind power forecasting applications. Full article

Fascia is increasingly recognized as a dynamic functional system. It can actively sense, transmit, and regulate mechanical, sensory, and metabolic signals. Why does fascia play such a critical role in chronic pain and movement disorders? Researchers are now rethinking the pathophysiological mechanisms underlying this role. Previous systematic reviews have typically focused primarily on specific mechanisms or interventions. In contrast, this study takes a holistic view of fascial function. It integrates multiple physiological functions of the fascia: mechanical integration, sensory modulation, cellular and matrix remodeling, as well as metabolic and immune regulation. From the perspective of functional imbalance, we further explore the pathological mechanisms associated with the fascia. Building on this, we then focus on how to assess fascial function from multiple dimensions and on specific targeted interventions. For assessment, we have systematically compiled a set of multi-stage quantitative techniques. These include clinical palpation, ultrasound, and elastography, tissue mechanics testing, microdialysis, omics approaches, electrophysiological testing, and digital modeling. For interventions, we have listed a range of modulating approaches, such as manual therapy, exercise rehabilitation, dry needling and acupuncture, fascial injections, targeted drugs, and biotechnological materials derived from tissue engineering. This review summarizes a clinical decision-making framework guided by the assessment of fascial functional status. It emphasizes a systematic approach and links quantitative diagnosis with precise interventions. Additionally, it provides a literature synthesis for understanding fascial mechanisms and related disorders and offers a reference foundation for the field’s transition from empirical treatment to measurable, reproducible, and individualized practice. Full article

In recent decades, humanoids have become more popular in various applications. However, their applications in human life are more than those in industry. In this paper, a humanoid is used to capture the sets of point clouds of an object for three-dimensional reconstruction. The structured light camera is widely used across diverse 3D scanning applications due to its high resolution, rapid acquisition capability, and adaptability to various material surfaces. Therefore, the humanoid developed by our team holds a structured light camera which captures the point clouds of an object put on a platform for the reconstruction of its 3D digital model. The platform is rotated so that the structured light camera can capture the image of all view angles on the object. Meanwhile, the structured light camera captures point clouds, and the camera of the humanoid recognizes the QR code on the platform so that the sets of point clouds can be distinguished by view angles on the object. Then, the automated registration process of the point cloud sets for a 3D model based on the point-to-plane iterative closest point (ICP) algorithm is proposed. The process incorporates preprocessing techniques, such as downsampling and normal vector estimated from plane, and utilizes the ICP algorithm for registration, ultimately achieving markerless and precision automatic merging of multi-view point cloud data. Experimental results demonstrate that the proposed method with the humanoid can effectively improve the completeness and accuracy of 3D reconstruction models, significantly reduce manual intervention, and enhance the system’s versatility and practical feasibility. Key parameters adjusted for more efficient computation of the ICP algorithm are revealed. In addition, the experimental results of the proposed ICP compared with G-ICP are also included. Full article

Many continuous industrial processes are non-linear, multi-variable, subject to transport or reaction delays, and described by operating-point-dependent parameters. These characteristics reduce the reliability of fixed models used in model predictive control (MPC), particularly when slow disturbances, regime changes and operational constraints are dominant. This paper proposes an output-bias reference-correction framework based on Long Short-Term Memory (LSTM) networks for predictive control of industrial processes with delays and variable parameters. The dominant dynamics are represented by a fixed compact linear nominal model in deviation coordinates; this model drives a standard constrained MPC that remains structurally unchanged throughout operation. The persistent output bias between the actual process and the nominal model is learned from closed-loop data by an LSTM network. At each sampling step, the predicted bias is used to correct the future reference trajectory fed to the nominal MPC, so that the controller compensates for model–process mismatch without modifying its internal model, constraint set or solver. The final implementation uses a one-step bias predictor, selected by ablation, and it extends this one-step estimate across the MPC horizon by exponentially decayed persistence. A closed-loop bias-error bound links the LSTM identification error, the adaptive correction gain and the resulting tracking deviation. The framework is illustrated using a mining thickener, a representative process characterised by slow dynamics, delays, variable parameters and stringent safety constraints. A three-controller Monte Carlo study compares the nominal MPC, a classical offset-free MPC and the proposed LSTM-MPC, and it highlights the resulting tracking–actuation–constraint trade-off. Applied to a mining thickener, the LSTM corrector reduces the first-step output-prediction RMSE by 96.6 % (FIT from $-14.8\%$ to $96.1\%$). In a 50-scenario Monte Carlo closed-loop evaluation, the LSTM-MPC outperforms the nominal MPC in 92 % of scenarios on RMSE while using substantially less actuator activity than the offset-free baseline (mean input total variation: 67.0 vs. 119.4). Full article

Dysregulation of ribosome biogenesis is increasingly recognized as a hallmark of tumor malignancy, yet its prognostic implications in lung adenocarcinoma (LUAD) remain incompletely characterized. This study aimed to construct a ribosome biogenesis-related prognostic model for LUAD and explore its potential relevance to the tumor immune microenvironment. Single-cell and bulk RNA sequencing data were integrated to identify ribosome biogenesis-related genes (RBRGs), from which a prognostic risk score was established via Cox regression, LASSO regression, and multivariate Cox analyses and validated in two independent GEO cohorts. Associations between the risk score and tumor mutation burden, immune infiltration, and computationally inferred immunotherapy response were systematically evaluated. In vitro experiments were performed to characterize the biological function of RPS19BP1, a key gene in the model. A total of 262 RBRGs were identified, and the derived 14-gene risk score demonstrated prognostic value across three cohorts (TCGA: 1-, 2-, 3-year AUC = 73.08, 72.44, 72.20; GSE68571: 1-, 2-, 3-year AUC = 67.93, 73.24, 77.59; GSE8894: 1-, 2-, 3-year AUC = 75.56, 72.99, 71.77). The low-risk group exhibited a more immunocompetent tumor microenvironment, whereas the high-risk group was associated with an immunosuppressive phenotype. Knockdown of RPS19BP1 significantly attenuated the proliferation, migration, and invasion of LUAD cells. This multi-omics-derived prognostic model showed prognostic potential in retrospective LUAD cohorts, is associated with distinct immune infiltration patterns, and identifies RPS19BP1 as a pro-oncogenic regulator in LUAD. Full article

Achieving the co-combustion of biomass and coal in utility boilers while reducing carbon dioxide emissions poses significant challenges owing to the divergent physicochemical properties of the fuels. These differences can induce high-temperature corrosion and erosion of heating surfaces, threatening boiler safety. Despite this, integrated CFD-based assessments of sulfidic corrosion and particle erosion risks remain insufficiently addressed under realistic biomass–coal cofiring conditions. In this study, an integrated CFD-based risk assessment framework was established for biomass–coal cofiring boilers. The main novelty lies in the combined evaluation of high-temperature sulfidic corrosion and particle erosion risks under different biomass injection strategies. Specifically, user-defined functions were developed to classify high-temperature sulfidic corrosion risks based on local O₂, CO, and H₂S concentrations; the effects of biomass injection layers were quantitatively compared; the Oka erosion model was coupled with CFD particle tracking to predict wall wear; and an entropy-weighted multi-indicator method was used to rank the overall safety of different cofiring strategies. This study found that sufficiently high near-wall H₂S concentrations in the main combustion zone indicate an increased risk of sulfidic corrosion under reducing-atmosphere conditions. Compared with pure coal combustion, biomass injection through layer A exacerbates wall corrosion, whereas biomass injection through layer AB mitigates it. Erosion is primarily localized near burner nozzles. Notably, biomass cofiring reduces the average erosion rate by 7.9–30.2% but increases the local maximum erosion rate by 7.1–25.1%. The comprehensive evaluation indicates that the condition with 30% RS injected from layer AB, mixed with coal, yields the best overall performance. The corrosion assessment is limited to sulfidic corrosion risks associated with reducing atmospheres and does not explicitly model alkali- or chlorine-induced corrosion. This study provides a theoretical foundation for biomass cofiring optimization and offers practical guidance for boiler operational safety and maintenance. Full article

Voltage and reactive power control in active distribution networks is a safety-critical and highly dynamic problem, where traditional optimization methods often struggle to balance efficiency and robustness under complex operating conditions. Recently, large language models (LLMs) have shown promise in sequential decision-making tasks, but their direct application to power system control remains limited by the lack of physical grounding and safety guarantees. In this paper, we propose SafeVolt, a closed-loop LLM-based framework that integrates multi-candidate action generation, simulator-in-the-loop evaluation, and a fine-tuned expert judge for safety-aware decision making. In addition, a high-level rule distillation mechanism that converts successful control experiences into reusable operational axioms is introduced to enable iterative self-improvement. Experiments on a standard distribution network scenario demonstrate that the proposed method outperforms representative baselines, achieving substantial improvements in average reward, voltage violation rate, reactive power loss, and system stability. In particular, voltage violations and extreme events are substantially reduced, indicating enhanced operational safety. These results suggest that combining LLM reasoning with physical simulation and structured feedback provides a promising direction for reliable and adaptive power system control. Full article

Stick–slip vibration, reversal, axial impact, and dynamic instability are major challenges in deep drilling operations and are closely associated with nonlinear bit–rock interaction. To investigate these phenomena, this study develops a nonlinear axial–torsional coupled dynamic model of a drill-string system by integrating rock surface morphology evolution with a Stribeck dry friction model. The drill string is discretized into a distributed lumped-parameter model with coupled axial and torsional degrees of freedom. A surface morphology matrix is introduced to simulate the rock-cutting process, while the Stribeck friction model is employed to characterise the nonlinear frictional behaviour at the bit–rock interface. Time-domain simulations, bifurcation analysis, and frequency spectrum analysis are performed to investigate the dynamic responses of the system. The results indicate that rock surface morphology evolution significantly influences the contact conditions and frictional behaviour at the bit–rock interface, and together with dry friction induces transitions among steady-state, multi-periodic, and chaotic motions. Stick–slip vibration is accompanied by axial impact, bit bounce, and a reduction in the dominant torsional vibration frequency. In addition, variations in both driving and frictional parameters can trigger dynamic instability and state transitions. The proposed model provides an effective framework for analysing nonlinear drilling dynamics and offers theoretical guidance for drill-string vibration suppression, drilling parameter optimisation, and efficient drilling in complex formations. Full article

To address the challenges of difficult feature extraction and insufficient recognition accuracy caused by the small size of chili flowers, occlusion by branches and leaves, and illumination variations in complex field environments, a dual-backbone-based chili flower pose estimation algorithm, termed DCFP-YOLO, is proposed. Built upon the YOLO11n framework, the proposed method performs classification and recognition of five typical upward-oriented chili flower poses. To alleviate the loss of local detail features of small chili flowers under complex backgrounds, a dual-backbone feature extraction network composed of StarNet and ShuffleNetV2 is constructed. Specifically, the StarNet backbone enhances the extraction of fine-grained local features from key floral regions, while the ShuffleNetV2 backbone improves the perception of global spatial structural information. The complementary fusion of dual-backbone features strengthens the representation capability of chili flower pose features in complex environments. To mitigate the attenuation of shallow detail information during multi-scale feature transmission, a Bidirectional Multi-branch Auxiliary Feature Pyramid Network (BiMAFPN) is designed to enhance feature propagation through cross-scale feature interaction, thereby improving pose recognition performance under occlusion and overlapping conditions. Furthermore, a Programmable Gradient Information (PGI)-assisted training mechanism is introduced to optimize gradient propagation paths and alleviate information bottlenecks in deep networks, thereby enhancing the robustness of multi-pose feature extraction under occlusion, blur, and complex illumination conditions. Experimental results demonstrate that DCFP-YOLO achieves recall, mAP50, and mAP50 values of 87.4%, 92.0%, and 66.9%, respectively, representing improvements of 1.7, 1.3, and 3.5 percentage points over the baseline model. Overall performance surpasses that of current mainstream object detection algorithms. After deployment on the NVIDIA Jetson AGX Orin platform, the model achieves an inference speed of 20.9 frames/s, which can basically satisfy the real-time perception requirements of chili flower pose recognition in complex agricultural environments. The proposed method provides an effective visual perception framework for chili flower pose recognition in complex agricultural environments. Rather than constituting a complete robotic pollination solution, the developed model serves as a potential perception component for future intelligent pollination robotic systems, providing reliable flower pose information for subsequent research on target localization, end-effector alignment, and robotic pollination in unstructured greenhouse environments. Full article

The primary control mechanism for synaptic uptake of GABA is through γ-aminobutyric acid transporter 1 (GAT-1, SLC6A1), a known target for anti-epileptic drugs. Although there is a clinically used GAT-1 inhibitor, tiagabine, the development of a new ligand with an advanced pharmacological profile is desirable. For this purpose, a multi-tiered virtual approach to screening has been created, involving pharmacophore-based search; application of the Informational Spectrum Method for Small Molecules, followed by EIIP/AQVN filtering (ISM-SM); molecular docking using an ensemble of several experimentally obtained structures of GAT-1; and ADMET predictions. Pharmacophore-based screening of the ZINC database of natural products, combined with ISM-SM/EIIP filtering, yielded 237 candidate compounds. Structural separation analysis discriminated between the positives and negatives, enabling enrichment-based prioritization. The use of a composite normalized rank score based on docking affinity and structural similarity allowed for the identification of the top candidates: ZINC03643214 and ZINC67840571. Collectively, these refinements establish a more sophisticated computational model for identifying novel GAT-1 inhibitors and highlight promising candidates for future experimental evaluation. Full article

Climate change introduces severe non-stationarity and high-frequency noise into hydro-meteorological data. This noise degrades the predictive accuracy of traditional data-driven streamflow models. We propose a physics-inspired data enhancement framework coupling the CEEMDAN-based Hilbert-Huang Transform (HHT) with Stochastic Resonance (SR). We applied this framework to the Lanzhou section of the upper Yellow River. HHT isolates the dominant characteristic frequency of the basin’s streamflow system at 0.0026 cycles/day. Using this frequency as a target, we constructed a Bayesian-optimized SR system. The system converts the energy of high-frequency meteorological noise into low-frequency periodic components, facilitating frequency alignment between the meteorological inputs and the hydrological response. We evaluated the SR-enhanced meteorological inputs across three machine learning architectures: Random Forest, XGBoost, and LSTM. All algorithms demonstrated an improved performance. The SR-LSTM model achieved a Nash-Sutcliffe Efficiency (NSE) of 0.91 ± 0.03. This represents a 19% improvement over the baseline LSTM score of 0.79 ± 0.02. The SR-LSTM demonstrated robust accuracy during extreme hydrological events; it achieved a high-flow NSE of 0.89 and effectively mitigated the common peak-underestimation issue by constraining relative peak magnitude errors to approximately −5.08%. Overall, this study presents a practical data enhancement approach for streamflow forecasting under complex climatic conditions. Full article