Search Results (7,669)

The rapid digitization of library resources has intensified the need for robust digital-rights management (DRM) mechanisms to safeguard copyright, control access, and preserve user privacy. Conventional DRM approaches are often centralized, prone to single-point-of-failure, and are limited in transparency and interoperability. To address these challenges, this article puts forward a decentralized DRM framework for library systems by leveraging blockchain technology and decentralized DRM-key mechanisms. An integrative review of the available research literature provides an analysis of current blockchain-based DRM library systems, their limitations, and associated challenges. To address these issues, a controlled experiment is set up to implement and evaluate a possible solution. In the proposed model, digital content is encrypted and stored in the Inter-Planetary File System (IPFS), while blockchain smart contracts manage the generation, distribution, and validation of DRM-keys that regulate user-access rights. This approach ensures immutability, transparency, and fine-grained access control without reliance on centralized authorities. Security is enhanced through cryptographic techniques for authentication. The model not only mitigates issues of piracy, unauthorized redistribution, and vendor lock-in, but also provides a scalable and interoperable solution for modern digital libraries. The findings demonstrate how blockchain-enabled DRM-keys can enhance trust, accountability, and efficiency through the development of secure, decentralized, and user-centric digital library systems, which will be of interest to practitioners charged with library IT technology management and to researchers in the wider field of blockchain applications in organizations. Full article

The vehicle-mounted flywheel battery is a complex assembly of multiple components that is subject to intense multi-physical field coupling and external disturbances, which lead to real-time changes in system parameters and reduce control performance. The aim of this study is to enhance the robustness and dynamic stability of the system under emergency avoidance conditions. Its internal multiphysics field coupling is intricate, and external disturbances further intensify the cross-coupling. Building upon this method, a highly robust control strategy with real-time coupling characteristic parameters is designed in this study. First, a bidirectional coupling method combining electromagnetism, heat, and structure fields was proposed. This method captured the dynamic interactions among the magnetic, thermal, and structural fields. Based on this analysis, a coupling characteristic function was extracted to quantify the real-time coupling strength. Then, this function was mapped into the parameters of the sliding mode controller. Adaptive gain adjustment can be achieved without relying on an accurate system model. The key assumptions include linear material properties within the operational temperature range and negligible unsteady turbulence effects in airflow. Full article

The exponential growth and complexity of legal agreements pose significant Big Data challenges and strategic risks for modern organizations, often overwhelming traditional, manual contract management workflows. While AI has enhanced legal research, most current applications require extensive domain-specific fine-tuning or substantial annotated data, and Large Language Models (LLMs) remain susceptible to hallucination risk. This paper presents an AI-based Agreement Management System that addresses this methodological gap and scale. The system integrates a Python 3.1.2/MySQL 9.4.0-backed centralized repository for multi-format document ingestion, a role-based Collaboration and Access Control module, and a core AI Functions module. The core contribution lies in the AI module, which leverages zero-shot learning with OpenAI’s GPT-4o and structured prompt chaining to perform advanced contractual analysis without domain-specific fine-tuning. Key functions include automated metadata extraction, executive summarization, red-flag clause detection, and a novel feature for natural-language contract modification. This approach overcomes the cost and complexity of training proprietary models, democratizing legal insight and significantly reducing operational overhead. The system was validated through real-world testing at a leading industry partner, demonstrating its effectiveness as a scalable and secure foundation for managing the high volume of legal data. This work establishes a robust proof-of-concept for future enterprise-grade enhancements, including workflow automation and predictive analytics. Full article

The efficient design of biohybrid materials requires controlling the interaction between the cell and the material for a wide range of possible combinations. Single cell force spectroscopy (SCFS), an atomic force microscopy (AFM) experimental procedure based on the binding of an individual cell to an AFM cantilever and the assessment of the adhesion force between the cell and a target substrate, represents one of the most promising alternatives to characterize the interaction between cell and material. However, SCFS relies on the efficient binding of the cell to the AFM in order to avoid drawbacks, such as the detachment of the cell. In this work, three different versatile and robust procedures are presented that allow for the binding of either non-adherent (CD4⁺ T-lymphocytes) or adherent (mesenchymal stem cells, MSC) cells to the AFM probe. The three crosslinking strategies comprise (1) the streptavidin/biotin system, (2) sulfhydryl group-based crosslinkers, and (3) “click” (bioorthogonal) chemistry. Additionally, three decoration schemes of the functionalized AFM probes are explored: a specific antibody, concanavalin A, and direct binding of the cell through azide-derivatized membrane proteins. Differences are observed between these alternatives and it is found that the strength of the interaction (in decreasing order) is as follows: specific antibody, concanavalin A, and binding through azide-derivatized proteins. Full article

The increasing penetration of distributed generation in active distribution networks (ADNs) introduces significant voltage regulation challenges due to the intermittent nature of renewable energy sources. Electric springs (ESs) have emerged as a cost-effective alternative to conventional FACTS devices for voltage regulation, requiring minimal energy storage while providing fast, flexible reactive power compensation. This paper proposes a deep reinforcement learning (DRL)-based approach for voltage regulation in balanced active distribution networks with distributed generation. Electric springs are deployed at selected buses in series with noncritical loads to provide flexible voltage support. The main contributions of this work are: (1) a novel region-based penalized reward function that effectively guides the DRL agent to minimize voltage deviations; (2) a coordinated control strategy for multiple ESs using the Deep Deterministic Policy Gradient (DDPG) algorithm, representing the first application of DRL to ES-based voltage regulation; (3) a systematic hyperparameter tuning methodology that significantly improves controller performance; and (4) comprehensive validation demonstrating an approximately 40% reduction in mean voltage deviation relative to the no-control baseline. Three well-known continuous-control DRL algorithms, Twin Delayed Deep Deterministic Policy Gradient (TD3), Proximal Policy Optimization (PPO), and DDPG, are first evaluated using the default hyperparameter configurations provided by MATLAB R2022b.Based on this baseline comparison, a dedicated hyperparameter-tuning procedure is then applied to DDPG to improve the robustness and performance of the resulting controller. The proposed approach is evaluated through simulation studies on the IEEE 33-bus and IEEE 69-bus test systems with time-varying load profiles and fluctuating renewable generation scenarios. Full article

This paper presents a torque oscillation attenuation method for permanent magnet synchronous motors (PMSMs) based on the combination of sliding-mode control (SMC) and the equivalent input disturbance (EID) approach. To deal with the changes in PMSM parameters, we explored a continuous-domain ant colony optimization (CDACO) method to design a control system for such a plant. This is the first application of SMC-EID to uncertain PMSM plants, with CDACO enabling robust parameter tuning in continuous spaces. First, we designed an EID estimator to estimate the disturbance caused by torque oscillation. Next, we added the estimated disturbance to the sliding-mode controller to improve disturbance attenuation performance. Then, we extended an ant colony optimization (ACO) algorithm to the continuous domain to optimize controller parameters for an uncertain plant. Finally, a speed control experiment was carried out on a two-mass experimental system for PMSMs to demonstrate the validity of the method. The experimental results show that our method yields better control performance than the SMC. Full article

To address the increasing demands for faster response and higher control accuracy in the braking systems of electric and intelligent vehicles, a novel brake-by-wire actuation unit and its braking force control methods are proposed. The braking unit employs a permanent-magnet linear motor as the driving actuator and utilizes the lever-based force-amplification mechanism to directly generate the caliper force. Compared with the “rotary motor and motion conversion mechanism” configuration in other electromechanical braking systems, the proposed scheme significantly simplifies the force-transmission path, reduces friction and structural complexity, thereby enhancing the overall dynamic response and control accuracy. Due to the strong nonlinearity, time-varying parameters, and significant thermal effects of the linear motor, the braking force is prone to drift. As a result, achieving accurate force control becomes challenging. This paper proposes a model-free adaptive control method based on compact-form dynamic linearization. This method does not require an accurate mathematical model. It achieves dynamic linearization and direct control of complex nonlinear systems by online estimation of pseudo partial derivatives. Finally, the proposed control method is validated through comparative simulations and experiments against the fuzzy PID controller. The results show that the model-free adaptive control method exhibits significantly faster braking force response, smaller steady-state error, and stronger robustness against external disturbances. It enables faster dynamic response and higher braking force tracking accuracy. The study demonstrates that the proposed brake-by-wire scheme and its control method provide a potentially new approach for next-generation high-performance brake-by-wire systems. Full article

Sliding mode control (SMC) is a robust nonlinear control framework that enforces system trajectories onto predefined manifolds, providing strong robustness guarantees against uncertainties. However, SMC inherently introduces unwanted transients or chattering in system state trajectories, which may cause issues especially for sensitive applications such as regulation of drug administration. This paper proposes a multi-input smooth sliding mode control (MISSMC) strategy that combines radiotherapy and chemotherapy for a nonlinear tumor–immune dynamical system described by ordinary differential equations. The closed-loop system is first analyzed to establish key qualitative properties: all state variables remain positive and bounded, the sliding surfaces exhibit asymptotic convergence, and explicit analytical upper bounds on the cumulative therapy doses are derived under clinically motivated constraints. On this basis, a smooth hyperbolic-tangent sliding manifold and associated control law are designed to regulate the radiation and drug infusion rates. While the use of a hyperbolic-tangent smoothing function effectively suppresses chattering, it introduces a small steady-state error due to the presence of a boundary layer. To address this limitation, integral action is incorporated into the sliding surfaces, ensuring asymptotic convergence of tumor state and reducing residual steady-state error, while enhancing robustness against model uncertainties and parameter variations. Numerical simulations, based on a brain-tumor case study, show that the proposed smooth SMC markedly suppresses transient overshoots in both states and control inputs, while preserving effective tumor reduction. Compared with a conventional (non-smooth) SMC scheme, the MISSMC controller reduces baseline radiation and chemotherapy intensities on average by roughly 70%. Similarly, MISSMC lowers the overall cumulative doses on average by about 40%, without degrading the therapeutic outcome. The resulting integral smooth SMC framework therefore offers a rigorous nonlinear-systems approach to designing combined radio-chemotherapy protocols with guaranteed positivity, boundedness, and asymptotic stabilization of the closed-loop system, together with explicit bounds on the control inputs. Full article

Digital chaotic systems suffer severe dynamical degradation under finite computational precision, compromising their randomness and unpredictability in security-critical applications. To address this challenge, we introduce the High-Dimensional Delayed Cyclic-Coupled Chaotic Model (HD-DCCCM), a unified framework that integrates high-dimensional coupling, delayed feedback, and time-varying parameter control. In this synergistic design, dynamic perturbations from delays and time-varying signals continuously excite the high-dimensional structure, effectively preventing the collapse into short periodic orbits typical of low-precision environments. Systematic numerical analyses confirm that the HD-DCCCM generates stable hyperchaos with significantly extended periods, consistently outperforming classical maps and representative anti-degradation methods. Moreover, the framework demonstrates strong robustness and flexibility across both homogeneous (identical maps) and heterogeneous (hybrid maps) configurations. These results position the HD-DCCCM as a general and powerful paradigm for constructing degradation-resilient chaotic systems, with broad potential for next-generation secure communications and cryptographic applications. Full article

Targeting the thinly bedded and strongly heterogeneous tight sandstone gas reservoirs of the Shaximiao Formation in the Yingshan area of the Sichuan Basin, this study establishes an integrated workflow that combines high-fidelity 3D seismic processing with quantitative interpretation to address key challenges such as insufficient resolution of conventional seismic data under complex near-surface conditions and difficulty in depicting sand-body geometries. On the processing side, a 2D-3D integrated amplitude-preserving high-resolution strategy is applied. In contrast to conventional workflows that treat 2D and 3D datasets independently and often sacrifice true-amplitude characteristics during static correction and noise suppression, the proposed approach unifies first-break picking and static-correction parameters across 2D and 3D data while preserving relative amplitude fidelity. Techniques such as true-surface velocity modeling, coherent-noise suppression, and wavelet compression are introduced. As a result, the effective frequency bandwidth of the newly processed data is broadened by approximately 10–16 Hz relative to the legacy dataset, and the imaging of small faults and narrow river-channel boundaries is significantly enhanced. On the interpretation side, ten sublayers within the first member of the Shaximiao Formation are correlated with high precision, yielding the identification of 41 fourth-order local structural units and 122 stratigraphic traps. Through seismic forward modeling and attribute optimization, a set of sensitive attributes suitable for thin-sandstone detection is established. These attributes enable fine-scale characterization of sand-body distributions within the shallow-water delta system, where fluvial control is pronounced, leading to the identification of 364 multi-phase superimposed channels. Based on attribute fusion, rock-physics-constrained inversion, and integrated hydrocarbon-indicator analysis, 147 favorable “sweet spots” are predicted, and six well locations are proposed. The study builds a reservoir-forming model of “deep hydrocarbon generation–upward migration, fault-controlled charging, structural trapping, and microfacies-controlled enrichment,” achieving high-fidelity imaging and quantitative prediction of tight sandstone reservoirs in the Shaximiao Formation. The results provide robust technical support for favorable-zone evaluation and subsequent exploration deployment in the Yingshan area. Full article

This paper presents an efficient parameter optimization approach to the plastic injection molding process to achieve high productivity. In collaboration with a company specializing in plastic injection-mold-based production, real process data was collected and used in this research. The result is an integrated framework, combining a genetic algorithm (GA) with a CatBoost-based surrogate model for multi-objective optimization of the injection molding machine parameters. The aim of the optimization is to minimize the cycle time and cycle energy while maintaining the product quality. Ten process parameters were optimized, which are machine-specific. An evolutionary optimization using the NSGA-II algorithm is used to generate the recommended parameter set. The proposed GA-surrogate hybrid approach produces the optimal set of parameters that reduced the cycle time by 4.5%, for this specific product, while maintaining product quality. Cycle energy was evaluated on an hourly basis; its variation across candidate solutions was limited, but it was retained as an optimization objective to support energy-based process optimization. A total of 95% of the generated solutions satisfied industrial quality constraints, demonstrating the robustness of the proposed optimization framework. While classical Design of Experiment (DOE) approaches require sequential physical trials, the proposed GA-surrogate framework achieves convergence in computational iterations, which significantly reduces machine usage for optimization. This approach demonstrates a practical way to automate data-driven process optimization in an injection mold machine for an industrial application, and it can be extended to other manufacturing systems that require adaptive control parameters. Full article

To address trajectory tracking of cable-driven continuum robots (CDCRs) in the presence of obstacles, this paper proposes an integrated control framework that combines online trajectory replanning, obstacle avoidance, and tracking control. The control system consists of two modules. The first is a trajectory replanning controller developed on an improved model predictive control (IMPC) framework. The second is a trajectory-tracking controller that integrates an adaptive disturbance observer with a fast non-singular terminal sliding mode control (ADO-FNTSMC) strategy. The IMPC trajectory replanning controller updates the trajectory of the CDCRs to avoid collisions with obstacles. In the ADO-FNTSMC strategy, the adaptive disturbance observer (ADO) compensates for uncertain dynamic factors, including parametric uncertainties, unmodeled dynamics, and external disturbances, thereby enhancing the system’s robustness and improving trajectory tracking accuracy. Meanwhile, the fast non-singular terminal sliding mode control (FNTSMC) guarantees fast, stable, and accurate trajectory tracking. The average tracking errors for IMPC-ADO-FNTSMC, MPC-FNTSMC, and MPC-SMC are 1.185 cm, 1.540 cm, and 1.855 cm, with corresponding standard deviations of 0.035 cm, 0.057 cm, and 0.078 cm in the experimental results. Compared with MPC-FNTSMC and MPC-SMC, the IMPC-ADO-FNTSMC controller reduces average tracking errors by 29.96% and 56.54%. Simulation and experimental results demonstrate that the designed two-module controller (IMPC-ADO-FNTSMC) achieves fast, stable, and accurate trajectory tracking in the presence of obstacles and uncertain dynamic conditions. Full article

Background: Age-related macular degeneration (AMD) is a major cause of vision loss, yet publicly available Optical Coherence Tomography (OCT) datasets lack demographic diversity, particularly from South Asian populations. Existing datasets largely represent Western cohorts, limiting AI generalizability. Moreover, raw OCT volumes contain redundant spatial information and speckle noise, hindering efficient analysis. Methods: We introduce BanglaOCT2025, a retrospective dataset collected from the National Institute of Ophthalmology and Hospital (NIOH), Bangladesh, using Nidek RS-330 Duo 2 and RS-3000 Advance systems. We propose a novel preprocessing pipeline comprising two stages: (1) A constraint-based centroid minimization algorithm automatically localizes the foveal center and extracts a fixed 33-slice macular sub-volume, robust to retinal tilt and acquisition variability; and (2) A self-supervised volumetric denoising module based on a Flip-Flop Swin Transformer (FFSwin) backbone suppresses speckle noise without requiring paired clean reference data. Results: The dataset comprises 1585 OCT volumes (202,880 B-scans), including 857 expert-annotated cases (54 DryAMD, 61 WetAMD, and 742 NonAMD). Denoising quality was evaluated using reference-free volumetric metrics, paired statistical analysis, and blinded clinical review by a retinal specialist, confirming preservation of pathological biomarkers and absence of hallucination. Under a controlled paired evaluation using the same classifier with frozen weights, downstream AMD classification accuracy improved from 69.08% to 99.88%, interpreted as an upper-bound estimate of diagnostic signal recoverability rather than independent generalization. Conclusions: BanglaOCT2025 is the first clinically validated OCT dataset representing the Bengali population and establishes a reproducible fovea-centric volumetric preprocessing and restoration framework for AMD analysis, with future validation across independent and multi-centre test cohorts. Full article

In the rubidium-87 atomic fountain clock, the laser collimating and beam-expanding telescope plays a key role in atomic cooling and manipulation, as well as in realizing the cold-atom fountain. To address the bulkiness of conventional laser collimating and beam-expanding telescopes, which limits system integration and miniaturization, we design and implement a compact laser collimating and beam-expanding telescope. The design employs a Galilean beam-expanding optical path to shorten the optical path length. Combined with optical modeling and optimization, this approach reduces the mechanical length of the telescope by approximately 50%. We present the mechanical structure of a five-degree-of-freedom (5-DOF) adjustment mechanism for the light source and the associated optical elements and specify the corresponding tolerance ranges to ensure their precise alignment and mounting. Based on this 5-DOF adjustment mechanism, we further propose a method for tuning the output beam characteristics, enabling precise and reproducible control of the emitted beam. The experimental results demonstrate that, after adjustment, the divergence angle of the output beam is better than 0.25 mrad, the coaxiality is better than 0.3 mrad, the centroid offset relative to the mechanical axis is less than 0.1 mm, and the output beam diameter is approximately 35 mm. Furthermore, long-term monitoring over 45 days verified the system’s robustness, maintaining fractional power fluctuations within ±1.2% without manual realignment. Compared with the original telescope, all of these beam characteristics are significantly improved. The proposed telescope therefore has broad application prospects in integrated atomic fountain clocks, atomic gravimeters, and cold-atom interferometric gyroscopes. Full article

This paper presents the design and implementation of a novel autonomous underwater robot with fully vectored propulsion based on model predictive control (MPC) to rapidly respond to the position and attitude required for autonomous operation. Specifically, the mechatronic design of the eight vector-distributed thruster layout for the robot’s fully vectored propulsion is detailed, and the software architecture based on the robot operating system (ROS) is constructed. Then, the corresponding dynamics model is established by adopting the Fossen approach for the prediction and optimization of the control process. To achieve autonomous control, an MPC-based controller is designed and implemented to calculate the control input for the specified control objective. Finally, way-point tracking and trajectory-tracking experiments are carried out in an indoor tank equipped with a motion-capture system to validate the feasibility and effectiveness of the robot’s design and control framework. In addition, the robustness of the robot system is verified by artificially perturbing the robot in the hovering state. Full article